bio3d/0000755000176200001440000000000012632753312011256 5ustar liggesusersbio3d/inst/0000755000176200001440000000000012632664353012241 5ustar liggesusersbio3d/inst/examples/0000755000176200001440000000000012632340443014046 5ustar liggesusersbio3d/inst/examples/hivp.pdb0000644000176200001440000003563112632340443015513 0ustar liggesusersATOM 1 CA PRO A 1 51.842 59.784 -6.815 1.00 0.00 ATOM 2 CA GLN A 2 51.577 59.564 -2.997 1.00 0.00 ATOM 3 CA ILE A 3 48.107 57.932 -2.171 1.00 0.00 ATOM 4 CA THR A 4 46.701 58.033 1.344 1.00 0.00 ATOM 5 CA LEU A 5 44.183 55.442 2.514 1.00 0.00 ATOM 6 CA TRP A 6 41.544 57.701 4.088 1.00 0.00 ATOM 7 CA GLN A 7 39.388 56.510 1.115 1.00 0.00 ATOM 8 CA ARG A 8 39.662 53.213 -0.904 1.00 0.00 ATOM 9 CA PRO A 9 42.708 53.768 -3.078 1.00 0.00 ATOM 10 CA LEU A 10 40.903 53.618 -6.532 1.00 0.00 ATOM 11 CA VAL A 11 42.884 54.589 -9.625 1.00 0.00 ATOM 12 CA THR A 12 42.251 54.709 -13.425 1.00 0.00 ATOM 13 CA ILE A 13 43.809 51.981 -15.445 1.00 0.00 ATOM 14 CA LYS A 14 43.975 51.774 -19.198 1.00 0.00 ATOM 15 CA ILE A 15 43.797 48.245 -20.632 1.00 0.00 ATOM 16 CA GLY A 16 42.521 47.316 -24.076 1.00 0.00 ATOM 17 CA GLY A 17 41.869 50.971 -25.072 1.00 0.00 ATOM 18 CA GLN A 18 39.188 50.443 -22.473 1.00 0.00 ATOM 19 CA LEU A 19 39.687 52.728 -19.521 1.00 0.00 ATOM 20 CA LYS A 20 38.532 51.307 -16.096 1.00 0.00 ATOM 21 CA GLU A 21 39.141 52.167 -12.417 1.00 0.00 ATOM 22 CA ALA A 22 40.640 49.603 -10.016 1.00 0.00 ATOM 23 CA LEU A 23 41.703 49.173 -6.386 1.00 0.00 ATOM 24 CA LEU A 24 45.477 49.089 -5.522 1.00 0.00 ATOM 25 CA ASP A 25 45.318 46.159 -3.128 1.00 0.00 ATOM 26 CA THR A 26 48.582 45.179 -1.430 1.00 0.00 ATOM 27 CA GLY A 27 46.733 42.224 -0.031 1.00 0.00 ATOM 28 CA ALA A 28 45.775 40.761 -3.455 1.00 0.00 ATOM 29 CA ASP A 29 48.501 38.562 -4.998 1.00 0.00 ATOM 30 CA ASP A 30 46.716 38.676 -8.410 1.00 0.00 ATOM 31 CA THR A 31 44.915 41.225 -10.624 1.00 0.00 ATOM 32 CA VAL A 32 41.270 40.373 -11.334 1.00 0.00 ATOM 33 CA LEU A 33 39.440 42.554 -13.719 1.00 0.00 ATOM 34 CA GLU A 34 35.920 42.747 -14.872 1.00 0.00 ATOM 35 CA GLU A 35 34.897 40.976 -18.035 1.00 0.00 ATOM 36 CA MET A 36 36.229 42.597 -21.125 1.00 0.00 ATOM 37 CA SER A 37 37.638 41.297 -24.332 1.00 0.00 ATOM 38 CA LEU A 38 41.219 41.024 -24.431 1.00 0.00 ATOM 39 CA PRO A 39 42.734 39.737 -27.687 1.00 0.00 ATOM 40 CA GLY A 40 44.704 36.743 -26.500 1.00 0.00 ATOM 41 CA ARG A 41 45.060 33.095 -25.730 1.00 0.00 ATOM 42 CA TRP A 42 43.813 32.352 -22.311 1.00 0.00 ATOM 43 CA LYS A 43 44.000 29.437 -19.900 1.00 0.00 ATOM 44 CA PRO A 44 41.164 28.670 -17.317 1.00 0.00 ATOM 45 CA LYS A 45 41.624 29.507 -13.608 1.00 0.00 ATOM 46 CA MET A 46 39.283 29.172 -10.580 1.00 0.00 ATOM 47 CA ILE A 47 39.491 31.925 -7.918 1.00 0.00 ATOM 48 CA GLY A 48 37.788 32.073 -4.568 1.00 0.00 ATOM 49 CA GLY A 49 35.043 34.442 -3.513 1.00 0.00 ATOM 50 CA ILE A 50 32.347 35.074 -0.949 1.00 0.00 ATOM 51 CA GLY A 51 29.854 32.271 -1.556 1.00 0.00 ATOM 52 CA GLY A 52 32.385 30.105 -3.553 1.00 0.00 ATOM 53 CA PHE A 53 34.898 29.701 -6.356 1.00 0.00 ATOM 54 CA ILE A 54 34.222 31.428 -9.650 1.00 0.00 ATOM 55 CA LYS A 55 36.012 30.468 -12.945 1.00 0.00 ATOM 56 CA VAL A 56 37.951 33.331 -14.627 1.00 0.00 ATOM 57 CA ARG A 57 40.195 33.474 -17.711 1.00 0.00 ATOM 58 CA GLN A 58 43.958 34.040 -17.315 1.00 0.00 ATOM 59 CA TYR A 59 45.679 36.355 -19.847 1.00 0.00 ATOM 60 CA ASP A 60 49.549 36.399 -19.405 1.00 0.00 ATOM 61 CA GLN A 61 51.715 39.205 -20.715 1.00 0.00 ATOM 62 CA ILE A 62 48.974 41.895 -20.905 1.00 0.00 ATOM 63 CA LEU A 63 50.169 45.531 -20.985 1.00 0.00 ATOM 64 CA ILE A 64 48.156 47.683 -18.627 1.00 0.00 ATOM 65 CA GLU A 65 48.817 51.312 -17.679 1.00 0.00 ATOM 66 CA ILE A 66 48.259 52.150 -13.988 1.00 0.00 ATOM 67 CA CYS A 67 48.343 55.819 -13.081 1.00 0.00 ATOM 68 CA GLY A 68 50.240 56.475 -16.154 1.00 0.00 ATOM 69 CA HIS A 69 52.828 53.643 -15.720 1.00 0.00 ATOM 70 CA LYS A 70 53.041 50.890 -18.295 1.00 0.00 ATOM 71 CA ALA A 71 53.222 47.514 -16.704 1.00 0.00 ATOM 72 CA ILE A 72 53.255 44.000 -18.230 1.00 0.00 ATOM 73 CA GLY A 73 51.697 41.280 -16.224 1.00 0.00 ATOM 74 CA THR A 74 49.054 38.652 -16.082 1.00 0.00 ATOM 75 CA VAL A 75 45.485 39.767 -15.462 1.00 0.00 ATOM 76 CA LEU A 76 42.659 37.379 -14.574 1.00 0.00 ATOM 77 CA VAL A 77 39.347 38.530 -16.381 1.00 0.00 ATOM 78 CA GLY A 78 36.102 37.372 -14.768 1.00 0.00 ATOM 79 CA PRO A 79 32.810 38.262 -12.975 1.00 0.00 ATOM 80 CA THR A 80 34.467 40.006 -10.125 1.00 0.00 ATOM 81 CA PRO A 81 32.448 42.646 -8.163 1.00 0.00 ATOM 82 CA VAL A 82 35.612 44.765 -8.065 1.00 0.00 ATOM 83 CA ASN A 83 38.639 45.443 -10.272 1.00 0.00 ATOM 84 CA ILE A 84 41.739 44.698 -8.177 1.00 0.00 ATOM 85 CA ILE A 85 45.407 45.630 -9.031 1.00 0.00 ATOM 86 CA GLY A 86 47.282 43.168 -6.872 1.00 0.00 ATOM 87 CA ARG A 87 50.954 42.682 -6.044 1.00 0.00 ATOM 88 CA ASN A 88 51.888 40.981 -9.300 1.00 0.00 ATOM 89 CA LEU A 89 51.485 44.433 -11.016 1.00 0.00 ATOM 90 CA LEU A 90 52.110 46.793 -8.029 1.00 0.00 ATOM 91 CA THR A 91 55.719 45.544 -8.243 1.00 0.00 ATOM 92 CA GLN A 92 56.232 46.651 -11.858 1.00 0.00 ATOM 93 CA ILE A 93 55.001 50.180 -11.313 1.00 0.00 ATOM 94 CA GLY A 94 57.347 50.391 -8.267 1.00 0.00 ATOM 95 CA CYS A 95 54.716 50.613 -5.473 1.00 0.00 ATOM 96 CA THR A 96 55.903 50.713 -1.945 1.00 0.00 ATOM 97 CA LEU A 97 54.052 51.039 1.392 1.00 0.00 ATOM 98 CA ASN A 98 55.612 53.860 3.315 1.00 0.00 ATOM 99 CA PHE A 99 55.232 55.089 6.899 1.00 0.00 ATOM 100 CA PRO B 1 59.784 51.842 6.815 1.00 0.00 ATOM 101 CA GLN B 2 59.564 51.577 2.997 1.00 0.00 ATOM 102 CA ILE B 3 57.932 48.107 2.171 1.00 0.00 ATOM 103 CA THR B 4 58.033 46.701 -1.344 1.00 0.00 ATOM 104 CA LEU B 5 55.442 44.183 -2.514 1.00 0.00 ATOM 105 CA TRP B 6 57.701 41.544 -4.088 1.00 0.00 ATOM 106 CA GLN B 7 56.510 39.388 -1.115 1.00 0.00 ATOM 107 CA ARG B 8 53.213 39.662 0.904 1.00 0.00 ATOM 108 CA PRO B 9 53.768 42.708 3.078 1.00 0.00 ATOM 109 CA LEU B 10 53.618 40.903 6.532 1.00 0.00 ATOM 110 CA VAL B 11 54.589 42.884 9.625 1.00 0.00 ATOM 111 CA THR B 12 54.709 42.251 13.425 1.00 0.00 ATOM 112 CA ILE B 13 51.981 43.809 15.445 1.00 0.00 ATOM 113 CA LYS B 14 51.774 43.975 19.198 1.00 0.00 ATOM 114 CA ILE B 15 48.245 43.797 20.632 1.00 0.00 ATOM 115 CA GLY B 16 47.316 42.521 24.076 1.00 0.00 ATOM 116 CA GLY B 17 50.971 41.869 25.072 1.00 0.00 ATOM 117 CA GLN B 18 50.443 39.188 22.473 1.00 0.00 ATOM 118 CA LEU B 19 52.728 39.687 19.521 1.00 0.00 ATOM 119 CA LYS B 20 51.307 38.532 16.096 1.00 0.00 ATOM 120 CA GLU B 21 52.167 39.141 12.417 1.00 0.00 ATOM 121 CA ALA B 22 49.603 40.640 10.016 1.00 0.00 ATOM 122 CA LEU B 23 49.173 41.703 6.386 1.00 0.00 ATOM 123 CA LEU B 24 49.089 45.477 5.522 1.00 0.00 ATOM 124 CA ASP B 25 46.159 45.318 3.128 1.00 0.00 ATOM 125 CA THR B 26 45.179 48.582 1.430 1.00 0.00 ATOM 126 CA GLY B 27 42.224 46.733 0.031 1.00 0.00 ATOM 127 CA ALA B 28 40.761 45.775 3.455 1.00 0.00 ATOM 128 CA ASP B 29 38.562 48.501 4.998 1.00 0.00 ATOM 129 CA ASP B 30 38.676 46.716 8.410 1.00 0.00 ATOM 130 CA THR B 31 41.225 44.915 10.624 1.00 0.00 ATOM 131 CA VAL B 32 40.373 41.270 11.334 1.00 0.00 ATOM 132 CA LEU B 33 42.554 39.440 13.719 1.00 0.00 ATOM 133 CA GLU B 34 42.747 35.920 14.872 1.00 0.00 ATOM 134 CA GLU B 35 40.976 34.897 18.035 1.00 0.00 ATOM 135 CA MET B 36 42.597 36.229 21.125 1.00 0.00 ATOM 136 CA SER B 37 41.297 37.638 24.332 1.00 0.00 ATOM 137 CA LEU B 38 41.024 41.219 24.431 1.00 0.00 ATOM 138 CA PRO B 39 39.737 42.734 27.687 1.00 0.00 ATOM 139 CA GLY B 40 36.743 44.704 26.500 1.00 0.00 ATOM 140 CA ARG B 41 33.095 45.060 25.730 1.00 0.00 ATOM 141 CA TRP B 42 32.352 43.813 22.311 1.00 0.00 ATOM 142 CA LYS B 43 29.437 44.000 19.900 1.00 0.00 ATOM 143 CA PRO B 44 28.670 41.164 17.317 1.00 0.00 ATOM 144 CA LYS B 45 29.507 41.624 13.608 1.00 0.00 ATOM 145 CA MET B 46 29.172 39.283 10.580 1.00 0.00 ATOM 146 CA ILE B 47 31.925 39.491 7.918 1.00 0.00 ATOM 147 CA GLY B 48 32.073 37.788 4.568 1.00 0.00 ATOM 148 CA GLY B 49 34.442 35.043 3.513 1.00 0.00 ATOM 149 CA ILE B 50 35.074 32.347 0.949 1.00 0.00 ATOM 150 CA GLY B 51 32.271 29.854 1.556 1.00 0.00 ATOM 151 CA GLY B 52 30.105 32.385 3.553 1.00 0.00 ATOM 152 CA PHE B 53 29.701 34.898 6.356 1.00 0.00 ATOM 153 CA ILE B 54 31.428 34.222 9.650 1.00 0.00 ATOM 154 CA LYS B 55 30.468 36.012 12.945 1.00 0.00 ATOM 155 CA VAL B 56 33.331 37.951 14.627 1.00 0.00 ATOM 156 CA ARG B 57 33.474 40.195 17.711 1.00 0.00 ATOM 157 CA GLN B 58 34.040 43.958 17.315 1.00 0.00 ATOM 158 CA TYR B 59 36.355 45.679 19.847 1.00 0.00 ATOM 159 CA ASP B 60 36.399 49.549 19.405 1.00 0.00 ATOM 160 CA GLN B 61 39.205 51.715 20.715 1.00 0.00 ATOM 161 CA ILE B 62 41.895 48.974 20.905 1.00 0.00 ATOM 162 CA LEU B 63 45.531 50.169 20.985 1.00 0.00 ATOM 163 CA ILE B 64 47.683 48.156 18.627 1.00 0.00 ATOM 164 CA GLU B 65 51.312 48.817 17.679 1.00 0.00 ATOM 165 CA ILE B 66 52.150 48.259 13.988 1.00 0.00 ATOM 166 CA CYS B 67 55.819 48.343 13.081 1.00 0.00 ATOM 167 CA GLY B 68 56.475 50.240 16.154 1.00 0.00 ATOM 168 CA HIS B 69 53.643 52.828 15.720 1.00 0.00 ATOM 169 CA LYS B 70 50.890 53.041 18.295 1.00 0.00 ATOM 170 CA ALA B 71 47.514 53.222 16.704 1.00 0.00 ATOM 171 CA ILE B 72 44.000 53.255 18.230 1.00 0.00 ATOM 172 CA GLY B 73 41.280 51.697 16.224 1.00 0.00 ATOM 173 CA THR B 74 38.652 49.054 16.082 1.00 0.00 ATOM 174 CA VAL B 75 39.767 45.485 15.462 1.00 0.00 ATOM 175 CA LEU B 76 37.379 42.659 14.574 1.00 0.00 ATOM 176 CA VAL B 77 38.530 39.347 16.381 1.00 0.00 ATOM 177 CA GLY B 78 37.372 36.102 14.768 1.00 0.00 ATOM 178 CA PRO B 79 38.262 32.810 12.975 1.00 0.00 ATOM 179 CA THR B 80 40.006 34.467 10.125 1.00 0.00 ATOM 180 CA PRO B 81 42.646 32.448 8.163 1.00 0.00 ATOM 181 CA VAL B 82 44.765 35.612 8.065 1.00 0.00 ATOM 182 CA ASN B 83 45.443 38.639 10.272 1.00 0.00 ATOM 183 CA ILE B 84 44.698 41.739 8.177 1.00 0.00 ATOM 184 CA ILE B 85 45.630 45.407 9.031 1.00 0.00 ATOM 185 CA GLY B 86 43.168 47.282 6.872 1.00 0.00 ATOM 186 CA ARG B 87 42.682 50.954 6.044 1.00 0.00 ATOM 187 CA ASN B 88 40.981 51.888 9.300 1.00 0.00 ATOM 188 CA LEU B 89 44.433 51.485 11.016 1.00 0.00 ATOM 189 CA LEU B 90 46.793 52.110 8.029 1.00 0.00 ATOM 190 CA THR B 91 45.544 55.719 8.243 1.00 0.00 ATOM 191 CA GLN B 92 46.651 56.232 11.858 1.00 0.00 ATOM 192 CA ILE B 93 50.180 55.001 11.313 1.00 0.00 ATOM 193 CA GLY B 94 50.391 57.347 8.267 1.00 0.00 ATOM 194 CA CYS B 95 50.613 54.716 5.473 1.00 0.00 ATOM 195 CA THR B 96 50.713 55.903 1.945 1.00 0.00 ATOM 196 CA LEU B 97 51.039 54.052 -1.392 1.00 0.00 ATOM 197 CA ASN B 98 53.860 55.612 -3.315 1.00 0.00 ATOM 198 CA PHE B 99 55.089 55.232 -6.899 1.00 0.00 TER END bio3d/inst/examples/hivp_xray.fa0000644000176200001440000013476012040627421016377 0ustar liggesusers>d1hhp__ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1nh0a_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1nh0b_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1s65a_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPANIIGRNLLTQIGATLNF >d1s65b_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPANIIGRNLLTQIGATLNF >d1kzka_ PQITLWKRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1kzkb_ PQITLWKRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1sdua_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLMTQIGATLNF >d1sdub_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLMTQIGATLNF >d1s6ga_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1s6gb_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1k1ta_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPIMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPSNIIGRNLLTQIGATLNF >d1k1tb_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPIMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPSNIIGRNLLTQIGATLNF >d1sdta_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1sdtb_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1sdva_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPANIIGRNLLTQIGATLNF >d1sdvb_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPANIIGRNLLTQIGATLNF >d1s6sa_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNVIGRNLLTQIGATLNF >d1s6sb_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNVIGRNLLTQIGATLNF >d1k1ua_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPIMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLMTQIGATLNF >d1k1ub_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPIMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLMTQIGATLNF >d1dazc_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPIMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1dazd_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPIMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1difa_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1difb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1d4la_ PQITLWKRPLVTIRIGGQLKEALLDTGADDTVIEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1d4lb_ PQITLWKRPLVTIRIGGQLKEALLDTGADDTVIEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1mtra_ PQITLWKRPLVTIRIGGQLKEALLDTGADDTVIEEMNLPGKWKPKMIGGIIGGGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1mtrb_ PQITLWKRPLVTIRIGGQLKEALLDTGADDTVIEEMNLPGKWKPKMIGGIIGGGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1ffic_ PQITLWKRPLVTIKIGGQLKEALLDTGADNTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1ffid_ PQITLWKRPLVTIKIGGQLKEALLDTGADNTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1k2ba_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRDLMTQIGATLNF >d1k2bb_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRDLMTQIGATLNF >d1ec0a_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1ec0b_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1b6la_ PQITLWKRPLVTIRIGGQLKEALLDTGADDTVIEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1b6lb_ PQITLWKRPLVTIRIGGQLKEALLDTGADDTVIEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1d4ia_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1d4ib_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1d4ha_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1d4hb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1b6ja_ PQITLWQRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1b6jb_ PQITLWQRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1g35a_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1g35b_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1ebwa_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1ebwb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1b6oa_ PQITLWKRPLVTIRIGGQLKEALLDTGADDTVIEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1b6ob_ PQITLWKRPLVTIRIGGQLKEALLDTGADDTVIEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1d4ja_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1d4jb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1proa_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1prob_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1fejc_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLMTQIGATLNF >d1fejd_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLMTQIGATLNF >d1hvia_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hvib_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1izha_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1izhb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1b6na_ PQITLWKRPLVTIRIGGQLKEALLDTGADDTVIEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1b6nb_ PQITLWKRPLVTIRIGGQLKEALLDTGADDTVIEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1hvka_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hvkb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hxwa_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hxwb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1ff0c_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPIMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1ff0d_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPIMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1fg6c_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRDLLTQIGATLNF >d1fg6d_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRDLLTQIGATLNF >d1mt9a_ PQITLWKRPLVTIRIGGQLKEALLNTGADDTVLE-MNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPANI-GRNLLTQIGCTLNF >d1mt9b_ PQITLWKRPLVTIRIGGQLKEALLNTGADDTVLE-MNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPANIIGRNLLTQIGCTLNF >d2aida_ PQITLWKRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d2aidb_ PQITLWKRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1mt7a_ PQITLWKRPLVTI-IGGQLKEAL-NTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPANIIGRNLLTQIGCTLNF >d1mt7b_ PQITLWKRPLVTIRIGGQLKEALLNTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPANI-GRNLLTQIGCTLNF >d1b6ka_ PQITLWKRPLVTIRIGGQLKEALLDTGADDTVIEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1b6kb_ PQITLWKRPLVTIRIGGQLKEALLDTGADDTVIEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1hvja_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hvjb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hpxa_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hpxb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hvla_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hvlb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1bwba_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPFNVIGRNLLTQIGCTLNF >d1bwbb_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPFNVIGRNLLTQIGCTLNF >d1ec3a_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1ec3b_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1npaa_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1npab_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d2bpva_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d2bpvb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1b6ma_ PQITLWKRPLVTIRIGGQLKEALLDTGADDTVIEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1b6mb_ PQITLWKRPLVTIRIGGQLKEALLDTGADDTVIEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1fffc_ PQITLWKRPLVTIKIGGQLKEALLDTGADNTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1fffd_ PQITLWKRPLVTIKIGGQLKEALLDTGADNTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1ebza_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1ebzb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1ajxa_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1ajxb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1qbsa_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1qbsb_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1g2ka_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1g2kb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1kjha_ PQITLWKRPLVTIRIGGQLKEALLNTGADDTVLE-MNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1kjhb_ PQITLWKRPLVTIRIGGQLKEALLNTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YD-IPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1mesa_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNVIGRNLLTQIGCTLNF >d1mesb_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNVIGRNLLTQIGCTLNF >d1dw6c_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLMTQIGATLNF >d1dw6d_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLMTQIGATLNF >d1d4ka_ PQITLWKRPLVTIRIGGQLKEALLDTGADDTVIEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1d4kb_ PQITLWKRPLVTIRIGGQLKEALLDTGADDTVIEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1kjga_ PQITLWKRPLVTIRIGGQLK-ALLNTGADDTVLE-MNLPGKWKPKMIG----GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1kjgb_ PQITLWKRPLVTI-IGGQLK-ALLNTGADDTVLEEMNLPGKWKPKMIG----GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1ajva_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1ajvb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1qbra_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1qbrb_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1mera_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNVIGRNLLTQIGCTLNF >d1merb_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNVIGRNLLTQIGCTLNF >d1fg8c_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRDLLTQIGATLNF >d1fg8d_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRDLLTQIGATLNF >d1hvra_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1hvrb_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1hiva_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hivb_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d2bpya_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d2bpyb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1d4ya_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1d4yb_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1cpia_ PQITLWQRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1cpib_ PQITLWQRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1hiha_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hihb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1a30a_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1a30b_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hsga_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hsgb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1odxa_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKTIGTVLVGPTPANIIGRNLLTQIGCTLNF >d1odxb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKTIGTVLVGPTPANIIGRNLLTQIGCTLNF >d1f7aa_ PQITLWKRPLVTIRIGGQLKEALLNTGADDTVLEEMNLPGKWKPKMIG----GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1f7ab_ PQITLWKRPLVTIRIGGQLKEALLNTGADDTVLEEMNLPGKWKPKMIG----GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1g6la1 PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGMTLNF >d1g6la2 PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hpva_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hpvb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1npwa_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1npwb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1b6pa_ PQITLWKRPLVTIRIGGQLKEALLDTGADDTVIEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1b6pb_ PQITLWKRPLVTIRIGGQLKEALLDTGADDTVIEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1qbua_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1qbub_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1fgcc_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1fgcd_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1kjfa_ PQITLWKRPLVTIRIGGQLKEALLNTGADDTVLE--MLPGKWKPKMIG----GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1kjfb_ PQITLWKRPLVTIRIGGQLKEALLNTGADDTVLEEMNLPGKWKPKMIG----GGFIKVRQ YD-IPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1kj7a_ PQITLWKRPLVTIRIGGQLKEALLNTGADDTVLEEMNLPGKWKPKMIG----GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1kj7b_ PQITLWKRPLVTIRIGGQLKEALLNTGADDTVLEEMNLPGKWKPKMIG----GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1meua_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPFNVIGRNLLTQIGCTLNF >d1meub_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPFNVIGRNLLTQIGCTLNF >d1hvha_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1hvhb_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1ec2a_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1ec2b_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hwra_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1hwrb_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1hsha_ PQFSLWKRPVVTAYIEGQPVEVLLDTGADDSIVAGIELGNNYSPKIVGGI--GGFINTKE YKNVEIEVLNKKVRATIMTGDTPINIFGRNILTALGMSLNL >d1hshb_ PQFSLWKRPVVTAYIEGQPVEVLLDTGADDSIVAGIELGNNYSPKIVGGI--GGFINTKE YKNVEIEVLNKKVRATIMTGDTPINIFGRNILTALGMSLNL >d1hshc_ PQFSLWKRPVVTAYIEGQPVEVLLDTGADDSIVAGIELGNNYSPKIVGGI--GGFINTKE YKNVEIEVLNKKVRATIMTGDTPINIFGRNILTALGMSLNL >d1hshd_ PQFSLWKRPVVTAYIEGQPVEVLLDTGADDSIVAGIELGNNYSPKIVGGI--GGFINTKE YKNVEIEVLNKKVRATIMTGDTPINIFGRNILTALGMSLNL >d1odya_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGAWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1odyb_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGAWKPKAIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1mrwa_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1mrwb_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1msma_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1msmb_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1mrxa_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEICGHKAIGTVLVGPTPFNVIGRNLLTQIGCTLNF >d1mrxb_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEICGHKAIGTVLVGPTPFNVIGRNLLTQIGCTLNF >d1bwaa_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPFNVIGRNLLTQIGCTLNF >d1bwab_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPFNVIGRNLLTQIGCTLNF >d1htga_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1htgb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1npva_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1npvb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1msna_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEICGHKAIGTVLVGPTPFNVIGRNLLTQIGCTLNF >d1msnb_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEICGHKAIGTVLVGPTPFNVIGRNLLTQIGCTLNF >d1ohra_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1ohrb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1aida_ PQITLWQRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1aidb_ PQITLWQRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1lv1a1 PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGMTLNF >d1lv1a2 PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1ec1a_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1ec1b_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1meta_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPFNIIGRNLLTQIGCTLNF >d1metb_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPFNIIGRNLLTQIGCTLNF >d1a94a_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWEPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1a94b_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWEPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1a94d_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWEPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1a94e_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWEPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1odwa_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1odwb_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hvsa_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPANIIGRNLLTQIGCTLNF >d1hvsb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPANIIGRNLLTQIGCTLNF >d1ebkc_ -QITLWKQPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1ebkd_ PQITLWKQPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1ebke_ PQITLWKQPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1ebkf_ PQITLWKQPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d3tlha_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d4phva_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d4phvb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hvc__ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1ebya_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1ebyb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1a8ka_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1a8kb_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1a8kd_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1a8ke_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1mt8a_ PQITLWKRPLVTIRIGGQLKEALLNTGADDTVLEEMNLPGKWKPKMIG----GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPANIIGRNLLTQIGCTLNF >d1mt8b_ PQITLWKRPLVTIRIGGQLKEALLNTGADDTVLE-MNLPGKWKPKMIG----GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPANIIGRNLLTQIGCTLNF >d1bv9a_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNVIGRNLLTQIGCTLNF >d1bv9b_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNVIGRNLLTQIGCTLNF >d1qbta_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1qbtb_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1hxba_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hxbb_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1izia_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKVIGTVLVGPTPTNVIGRNLLTQIGCTLNF >d1izib_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKVIGTVLVGPTPTNVIGRNLLTQIGCTLNF >d1iiqa_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1iiqb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1axaa_ PQITLWQRPLVTIKIGGQLKEALLDTGSDDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1axab_ PQITLWQRPLVTIKIGGQLKEALLDTGSDDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1dmpa_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1dmpb_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1bv7a_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPFNIIGRNLLTQIGCTLNF >d1bv7b_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPFNIIGRNLLTQIGCTLNF >d1lzqa_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKVIGTVLVGPTPTNVIGRNLLTQIGCTLNF >d1lzqb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKVIGTVLVGPTPTNVIGRNLLTQIGCTLNF >d1m0ba_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1m0bb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hefe_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEENSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hosa_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hosb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1a9ma_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIHGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1a9mb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIHGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1sbga_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1sbgb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d4hvpa_ PQITLWQRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d4hvpb_ PQITLWQRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d2bpza_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d2bpzb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hbva_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hbvb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1c70a_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1c70b_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d7hvpa_ PQITLWQRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d7hvpb_ PQITLWQRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1hpoa_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hpob_ PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hege_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEENSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1mtba_ PQITLWQRPLVTIRIGGQLKEALLNTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1mtbb_ PQITLWQRPLVTIRIGGQLKEALLNTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1htfa_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1htfb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1gnoa_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1gnob_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1a8ga_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1a8gb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1c6za_ PQITLWQRPVVTIKIGGQLMEALIDTGADDTVLEEMDLPGRWKPKIIGGI--GGFVKVRQ YDQIPIEICGHKVIGTVLVGPTPTNIIGRNLLTQIGCTLNF >d1c6zb_ PQITLWQRPVVTIKIGGQLMEALIDTGADDTVLEEMDLPGRWKPKIIGGI--GGFVKVRQ YDQIPIEICGHKVIGTVLVGPTPTNIIGRNLLTQIGCTLNF >d1upj__ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1c6ya_ PQITLWQRPVVTIKIGGQLMEALIDTGADDTVLEEMDLPGRWKPKIIGGI--GGFVKVRQ YDQIPIEICGHKVIGTVLVGPTPTNIIGRNLLTQIGCTLNF >d1c6yb_ PQITLWQRPVVTIKIGGQLMEALIDTGADDTVLEEMDLPGRWKPKIIGGI--GGFVKVRQ YDQIPIEICGHKVIGTVLVGPTPTNIIGRNLLTQIGCTLNF >d8hvpa_ PQITLWQRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d8hvpb_ PQITLWQRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1n49a_ PQITLWKRPLVTIRIGGQLKEALLNTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPANIIGRNLLTQIGCTLNF >d1n49b_ PQITLWKRPLVTIRIGGQLKEALLNTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPANIIGRNLLTQIGCTLNF >d1n49c_ PQITLWKRPLVTIRIGGQLKEALLNTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPANIIGRNLLTQIGCTLNF >d1n49d_ PQITLWKRPLVTIRIGGQLKEALLNTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPANIIGRNLLTQIGCTLNF >d1hpsa_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hpsb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1gnma_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPDNIIGRNLLTQIGCTLNF >d1gnmb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPDNIIGRNLLTQIGCTLNF >d5hvpa_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d5hvpb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1aaqa_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1aaqb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d2bpwa_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d2bpwb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1gnna_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPNNIIGRNLLTQIGCTLNF >d1gnnb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPNNIIGRNLLTQIGCTLNF >d1bdqa_ PQITLWQRPLVTIKIGGQLKEALLDTGADDSIVAGIELPGRWKPKMVGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPINIIGRNLLTQIGCTLNF >d1bdqb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDSIVAGIELPGRWKPKMVGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPINIIGRNLLTQIGCTLNF >d1vika_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1vikb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d3aida_ PQITLWKRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d3aidb_ PQITLWKRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1fb7a_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIVGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLMTQIGCTLNF >d2bpxa_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d2bpxb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1c6xa_ PQITLWQRPVVTIKIGGQLMEALIDTGADDTVLEEMDLPGRWKPKIIGGI--GGFVKVRQ YDQIPIEICGHKVIGTVLVGPTPTNIIGRNLLTQIGCTLNF >d1c6xb_ PQITLWQRPVVTIKIGGQLMEALIDTGADDTVLEEMDLPGRWKPKIIGGI--GGFVKVRQ YDQIPIEICGHKVIGTVLVGPTPTNIIGRNLLTQIGCTLNF >d1d4sa_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPFNVIGRNLLTQIGCTLNF >d1d4sb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPFNVIGRNLLTQIGCTLNF >d2upja_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d2upjb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1htea_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1hteb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1vija_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1vijb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1bdra_ PQITLWQRPLVTIKIGGQLKEALLDTGADDSVVAGIELPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1bdrb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDSVVAGIELPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1bdla_ PQITLWQRPLVTIKIGGQLKEALLDTGADDSIVAGIELPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1bdlb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDSIVAGIELPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1k2ca_ PQITLWKRPLVTIKIGGQLKEALLDTGADNTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPSNIIGRNLLTQIGATLNF >d1k2cb_ PQITLWKRPLVTIKIGGQLKEALLDTGADNTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPSNIIGRNLLTQIGATLNF >d1kj4a_ PQITLWKRPLVTIRIGGQLKEALLNTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1kj4b_ PQITLWKRPLVTIRIGGQLKEALLNTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1kj4c_ PQITLWKRPLVTIRIGGQLKEALLNTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1kj4d_ PQITLWKRPLVTIRIGGQLKEALLNTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1fqxa_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1fqxb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d3hvp__ PQITLWQRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1muia_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1muib_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d3phv__ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d9hvpa_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d9hvpb_ PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1bvga_ -QVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1bvgb_ -QVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1bvea_ -QVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d1bveb_ PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d2hvp__ ------QRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1k6c_a PQITLWKRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQIPIEICGHKAIGTVLVGPTPTNVIGRNLLTQIGCTLNF >d1k6c_b PQITLWKRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQIPIEICGHKAIGTVLVGPTPTNVIGRNLLTQIGCTLNF >d1k6p_a PQITLWKRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQIPIEICGHKAIGTVLVGPTPTNVIGRNLLTQIGCTLNF >d1k6p_b PQITLWKRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQIPIEICGHKAIGTVLVGPTPTNVIGRNLLTQIGCTLNF >d1k6t_a PQITLWKRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQIPIEICGHKAIGTVLVGPTPTNVIGRNLLTQIGCTLNF >d1k6t_b PQITLWKRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQIPIEICGHKAIGTVLVGPTPTNVIGRNLLTQIGCTLNF >d1k6v_a PQITLWKRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQIPIEICGHKAIGTVLVGPTPTNVIGRNLLTQIGCTLNF >d1k6v_b PQITLWKRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQIPIEICGHKAIGTVLVGPTPTNVIGRNLLTQIGCTLNF >d1rl8_a PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGAWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPANIIGRNLLTQIGCTLNF >d1rl8_b PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGAWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPANIIGRNLLTQIGCTLNF >d1rpi_a PQITLWQRPIVTIKIGGQLKEALLNTGADDTVLEEVNLPGRWKPKLIGGI--GGFVKVRQ YDQVPIEICGHKVIGTVLVGPTPANVIGRNLMTQIGCTLNF >d1rpi_b PQITLWQRPIVTIKIGGQLKEALLNTGADDTVLEEVNLPGRWKPKLIGGI--GGFVKVRQ YDQVPIEICGHKVIGTVLVGPTPANVIGRNLMTQIGCTLNF >d1rq9_a PQITLWQRPIVTIKIGGQLKEALLNTGADDTVLEEVNLPGRWKPKLIGGI--GGFVKVRQ YDQVPIEICGHKVIGTVLVGPTPANVIGRNLMTQIGCTLNF >d1rq9_b PQITLWQRPIVTIKIGGQLKEALLNTGADDTVLEEVNLPGRWKPKLIGGI--GGFVKVRQ YDQVPIEICGHKVIGTVLVGPTPANVIGRNLMTQIGCTLNF >d1sgu_a PQITLWQRPLVTIKIGGQLREALLDTGADDTIFEEISLPGRWKPKMIGGI--GGFVKVRQ YDQIPIEICGHKVIGTVLVGPTPANVIGRNLMTQIGCTLNF >d1sgu_b PQITLWQRPLVTIKIGGQLREALLDTGADDTIFEEISLPGRWKPKMIGGI--GGFVKVRQ YDQIPIEICGHKVIGTVLVGPTPANVIGRNLMTQIGCTLNF >d1sh9_a PQITLWQRPLVTIKIGGQLREALLDTGADDTIFEEISLPGRWKPKMIGGI--GGFVKVRQ YDQIPIEICGHKVIGTVLVGPTPANVIGRNLMTQIGCTLNF >d1sh9_b PQITLWQRPLVTIKIGGQLREALLDTGADDTIFEEISLPGRWKPKMIGGI--GGFVKVRQ YDQIPIEICGHKVIGTVLVGPTPANVIGRNLMTQIGCTLNF >d1sp5_a PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1sp5_b PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1t3r_a PQITLWKRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1t3r_b PQITLWKRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1t7i_a PQITLWKRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQIPIEICGHKAIGTVLVGPTPTNVIGRNLLTQIGCTLNF >d1t7i_b PQITLWKRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQIPIEICGHKAIGTVLVGPTPTNVIGRNLLTQIGCTLNF >d1t7j_a PQITLWKRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQIPIEICGHKAIGTVLVGPTPTNVIGRNLLTQIGCTLNF >d1t7j_b PQITLWKRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGI--GGFIKVRQ YDQIPIEICGHKAIGTVLVGPTPTNVIGRNLLTQIGCTLNF >d1t7k_a PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1t7k_b PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1tcx_a PQITLWQRPLVTIKIGGQLKEALLDTGADDTILEEMSLPGRWKPKMVGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPINIIGRNLLTQIGCTLNF >d1tcx_b PQITLWQRPLVTIKIGGQLKEALLDTGADDTILEEMSLPGRWKPKMVGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPINIIGRNLLTQIGCTLNF >d1tsq_a PQITLWKRPLVTIRIGGQLKEALLNTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPANIIGRNLLTQIGCTLNF >d1tsq_b PQITLWKRPLVTIRIGGQLKEALLNTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPANIIGRNLLTQIGCTLNF >d1tsu_a PQITLWKRPLVTIRIGGQLKEALLNTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1tsu_b PQITLWKRPLVTIRIGGQLKEALLNTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1tw7_a PQITLWQRPIVTIKIGGQLKEALLNTGADDTVLEEVNLPGRWKPKLIGGI--GGFVKVRQ YDQVPIEICGHKVIGTVLVGPTPANVIGRNLMTQIGCTLNF >d1tw7_b PQITLWQRPIVTIKIGGQLKEALLNTGADDTVLEEVNLPGRWKPKLIGGI--GGFVKVRQ YDQVPIEICGHKVIGTVLVGPTPANVIGRNLMTQIGCTLNF >d1u8g_a PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1u8g_b PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1w5v_a PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1w5v_b PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1w5w_a PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1w5w_b PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1w5x_a PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1w5x_b PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1w5y_a PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1w5y_b PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1wbk_a PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1wbk_b PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1wbm_a PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1wbm_b PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1xl2_a PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1xl2_b PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1xl5_a PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1xl5_b PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1ytg_a PQITLWKRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1ytg_b PQITLWKRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1yth_a PQITLWKRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1yth_b PQITLWKRPLVTIRIGGQLKEALLDTGADDTVLEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1z1h_a PQITLWKRPLVTIRIGGQLKEALLDTGADDTVIEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1z1h_b PQITLWKRPLVTIRIGGQLKEALLDTGADDTVIEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1z1r_a PQITLWKRPLVTIRIGGQLKEALLDTGADDTVIEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1z1r_b PQITLWKRPLVTIRIGGQLKEALLDTGADDTVIEEMNLPGKWKPKMIGGI--GGFIKVRQ YDQIPVEIXGHKAIGTVLVGPTPVNIIGRNLLTQIGXTLNF >d1zp8_a PQITLWKRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1zpa_a PQITLWKRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1ztz_a PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d1ztz_b PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d2a4f_a PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d2a4f_b PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d2aoc_a PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNVIGRNLLTQIGATLNF >d2aoc_b PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNVIGRNLLTQIGATLNF >d2aod_a PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d2aod_b PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d2aoe_a PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPANIIGRNLLTQIGATLNF >d2aoe_b PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPANIIGRNLLTQIGATLNF >d2aof_a PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPANIIGRNLLTQIGATLNF >d2aof_b PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPANIIGRNLLTQIGATLNF >d2aog_a PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPANIIGRNLLTQIGATLNF >d2aog_b PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPANIIGRNLLTQIGATLNF >d2aoh_a PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPANIIGRNLLTQIGATLNF >d2aoh_b PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPANIIGRNLLTQIGATLNF >d2aoi_a PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d2aoi_b PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d2aoj_a PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d2aoj_b PQITLWKRPLVTIKIGGQLKEALLDTGADDTVIEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQIIIEIAGHKAIGTVLVGPTPVNIIGRNLLTQIGATLNF >d2bb9_a PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d2bb9_b PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d2bbb_a PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d2bbb_b PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d2bqv_a PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d2bqv_b PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d2sam_a PQFHLWKRPVVTAHIEGQPVEVLLDTGADDSIVTGIELGPHYTPKIVGGI--GGFINTKE YKNVEIEVLGKRIKGTIMTGDTPINIFGRNLLTALGMSLNF >d7upj_a PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF >d7upj_b PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGI--GGFIKVRQ YDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF bio3d/inst/examples/1dpx.pdb0000644000176200001440000036663012526367343015441 0ustar liggesusersHEADER HYDROLASE 28-DEC-99 1DPX TITLE STRUCTURE OF HEN EGG-WHITE LYSOZYME COMPND MOL_ID: 1; COMPND 2 MOLECULE: LYSOZYME; COMPND 3 CHAIN: A; COMPND 4 EC: 3.2.1.17 SOURCE MOL_ID: 1; SOURCE 2 ORGANISM_SCIENTIFIC: GALLUS GALLUS; SOURCE 3 ORGANISM_COMMON: CHICKEN; SOURCE 4 ORGANISM_TAXID: 9031; SOURCE 5 TISSUE: EGG-WHITE KEYWDS PROTEIN-CHLORIDE COMPLEX, HYDROLASE EXPDTA X-RAY DIFFRACTION AUTHOR M.S.WEISS,G.J.PALM,R.HILGENFELD REVDAT 4 24-FEB-09 1DPX 1 VERSN REVDAT 3 09-OCT-02 1DPX 1 REMARK REVDAT 2 30-AUG-00 1DPX 1 JRNL REVDAT 1 03-JAN-00 1DPX 0 JRNL AUTH M.S.WEISS,G.J.PALM,R.HILGENFELD JRNL TITL CRYSTALLIZATION, STRUCTURE SOLUTION AND REFINEMENT JRNL TITL 2 OF HEN EGG-WHITE LYSOZYME AT PH 8.0 IN THE JRNL TITL 3 PRESENCE OF MPD. JRNL REF ACTA CRYSTALLOGR.,SECT.D V. 56 952 2000 JRNL REFN ISSN 0907-4449 JRNL PMID 10944331 JRNL DOI 10.1107/S0907444900006685 REMARK 1 REMARK 2 REMARK 2 RESOLUTION. 1.65 ANGSTROMS. REMARK 3 REMARK 3 REFINEMENT. REMARK 3 PROGRAM : REFMAC REMARK 3 AUTHORS : MURSHUDOV,VAGIN,DODSON REMARK 3 REMARK 3 DATA USED IN REFINEMENT. REMARK 3 RESOLUTION RANGE HIGH (ANGSTROMS) : 1.65 REMARK 3 RESOLUTION RANGE LOW (ANGSTROMS) : 40.00 REMARK 3 DATA CUTOFF (SIGMA(F)) : 0.000 REMARK 3 COMPLETENESS FOR RANGE (%) : NULL REMARK 3 NUMBER OF REFLECTIONS : 13961 REMARK 3 REMARK 3 FIT TO DATA USED IN REFINEMENT. REMARK 3 CROSS-VALIDATION METHOD : NULL REMARK 3 FREE R VALUE TEST SET SELECTION : RANDOM REMARK 3 R VALUE (WORKING + TEST SET) : NULL REMARK 3 R VALUE (WORKING SET) : 0.187 REMARK 3 FREE R VALUE : 0.246 REMARK 3 FREE R VALUE TEST SET SIZE (%) : NULL REMARK 3 FREE R VALUE TEST SET COUNT : 694 REMARK 3 REMARK 3 NUMBER OF NON-HYDROGEN ATOMS USED IN REFINEMENT. REMARK 3 PROTEIN ATOMS : 1013 REMARK 3 NUCLEIC ACID ATOMS : 0 REMARK 3 HETEROGEN ATOMS : 2 REMARK 3 SOLVENT ATOMS : 177 REMARK 3 REMARK 3 B VALUES. REMARK 3 FROM WILSON PLOT (A**2) : 17.20 REMARK 3 MEAN B VALUE (OVERALL, A**2) : NULL REMARK 3 OVERALL ANISOTROPIC B VALUE. REMARK 3 B11 (A**2) : NULL REMARK 3 B22 (A**2) : NULL REMARK 3 B33 (A**2) : NULL REMARK 3 B12 (A**2) : NULL REMARK 3 B13 (A**2) : NULL REMARK 3 B23 (A**2) : NULL REMARK 3 REMARK 3 ESTIMATED OVERALL COORDINATE ERROR. REMARK 3 ESU BASED ON R VALUE (A): NULL REMARK 3 ESU BASED ON FREE R VALUE (A): NULL REMARK 3 ESU BASED ON MAXIMUM LIKELIHOOD (A): NULL REMARK 3 ESU FOR B VALUES BASED ON MAXIMUM LIKELIHOOD (A**2): NULL REMARK 3 REMARK 3 RMS DEVIATIONS FROM IDEAL VALUES. REMARK 3 DISTANCE RESTRAINTS. RMS SIGMA REMARK 3 BOND LENGTH (A) : NULL ; NULL REMARK 3 ANGLE DISTANCE (A) : NULL ; NULL REMARK 3 INTRAPLANAR 1-4 DISTANCE (A) : NULL ; NULL REMARK 3 H-BOND OR METAL COORDINATION (A) : NULL ; NULL REMARK 3 REMARK 3 PLANE RESTRAINT (A) : NULL ; NULL REMARK 3 CHIRAL-CENTER RESTRAINT (A**3) : NULL ; NULL REMARK 3 REMARK 3 NON-BONDED CONTACT RESTRAINTS. REMARK 3 SINGLE TORSION (A) : NULL ; NULL REMARK 3 MULTIPLE TORSION (A) : NULL ; NULL REMARK 3 H-BOND (X...Y) (A) : NULL ; NULL REMARK 3 H-BOND (X-H...Y) (A) : NULL ; NULL REMARK 3 REMARK 3 CONFORMATIONAL TORSION ANGLE RESTRAINTS. REMARK 3 SPECIFIED (DEGREES) : NULL ; NULL REMARK 3 PLANAR (DEGREES) : NULL ; NULL REMARK 3 STAGGERED (DEGREES) : NULL ; NULL REMARK 3 TRANSVERSE (DEGREES) : NULL ; NULL REMARK 3 REMARK 3 ISOTROPIC THERMAL FACTOR RESTRAINTS. RMS SIGMA REMARK 3 MAIN-CHAIN BOND (A**2) : NULL ; NULL REMARK 3 MAIN-CHAIN ANGLE (A**2) : NULL ; NULL REMARK 3 SIDE-CHAIN BOND (A**2) : NULL ; NULL REMARK 3 SIDE-CHAIN ANGLE (A**2) : NULL ; NULL REMARK 3 REMARK 3 OTHER REFINEMENT REMARKS: NULL REMARK 4 REMARK 4 1DPX COMPLIES WITH FORMAT V. 3.15, 01-DEC-08 REMARK 100 REMARK 100 THIS ENTRY HAS BEEN PROCESSED BY RCSB ON 30-DEC-99. REMARK 100 THE RCSB ID CODE IS RCSB010274. REMARK 200 REMARK 200 EXPERIMENTAL DETAILS REMARK 200 EXPERIMENT TYPE : X-RAY DIFFRACTION REMARK 200 DATE OF DATA COLLECTION : NULL REMARK 200 TEMPERATURE (KELVIN) : 100 REMARK 200 PH : 4.5 REMARK 200 NUMBER OF CRYSTALS USED : 1 REMARK 200 REMARK 200 SYNCHROTRON (Y/N) : N REMARK 200 RADIATION SOURCE : ROTATING ANODE REMARK 200 BEAMLINE : NULL REMARK 200 X-RAY GENERATOR MODEL : ENRAF-NONIUS FR591 REMARK 200 MONOCHROMATIC OR LAUE (M/L) : M REMARK 200 WAVELENGTH OR RANGE (A) : 1.54 REMARK 200 MONOCHROMATOR : NULL REMARK 200 OPTICS : NULL REMARK 200 REMARK 200 DETECTOR TYPE : IMAGE PLATE REMARK 200 DETECTOR MANUFACTURER : MARRESEARCH REMARK 200 INTENSITY-INTEGRATION SOFTWARE : DENZO REMARK 200 DATA SCALING SOFTWARE : SCALEPACK REMARK 200 REMARK 200 NUMBER OF UNIQUE REFLECTIONS : 13962 REMARK 200 RESOLUTION RANGE HIGH (A) : 1.650 REMARK 200 RESOLUTION RANGE LOW (A) : 99.000 REMARK 200 REJECTION CRITERIA (SIGMA(I)) : 0.000 REMARK 200 REMARK 200 OVERALL. REMARK 200 COMPLETENESS FOR RANGE (%) : 99.7 REMARK 200 DATA REDUNDANCY : 12.660 REMARK 200 R MERGE (I) : 0.03800 REMARK 200 R SYM (I) : NULL REMARK 200 FOR THE DATA SET : 51.0000 REMARK 200 REMARK 200 IN THE HIGHEST RESOLUTION SHELL. REMARK 200 HIGHEST RESOLUTION SHELL, RANGE HIGH (A) : 1.65 REMARK 200 HIGHEST RESOLUTION SHELL, RANGE LOW (A) : 1.71 REMARK 200 COMPLETENESS FOR SHELL (%) : 100.0 REMARK 200 DATA REDUNDANCY IN SHELL : NULL REMARK 200 R MERGE FOR SHELL (I) : 0.14300 REMARK 200 R SYM FOR SHELL (I) : NULL REMARK 200 FOR SHELL : NULL REMARK 200 REMARK 200 DIFFRACTION PROTOCOL: SINGLE WAVELENGTH REMARK 200 METHOD USED TO DETERMINE THE STRUCTURE: NULL REMARK 200 SOFTWARE USED: NULL REMARK 200 STARTING MODEL: NULL REMARK 200 REMARK 200 REMARK: NULL REMARK 280 REMARK 280 CRYSTAL REMARK 280 SOLVENT CONTENT, VS (%): 36.15 REMARK 280 MATTHEWS COEFFICIENT, VM (ANGSTROMS**3/DA): 1.93 REMARK 280 REMARK 280 CRYSTALLIZATION CONDITIONS: SODIUM ACETATE, SODIUM CHLORIDE, PH REMARK 280 4.5, VAPOR DIFFUSION, HANGING DROP, TEMPERATURE 293K REMARK 290 REMARK 290 CRYSTALLOGRAPHIC SYMMETRY REMARK 290 SYMMETRY OPERATORS FOR SPACE GROUP: P 43 21 2 REMARK 290 REMARK 290 SYMOP SYMMETRY REMARK 290 NNNMMM OPERATOR REMARK 290 1555 X,Y,Z REMARK 290 2555 -X,-Y,Z+1/2 REMARK 290 3555 -Y+1/2,X+1/2,Z+3/4 REMARK 290 4555 Y+1/2,-X+1/2,Z+1/4 REMARK 290 5555 -X+1/2,Y+1/2,-Z+3/4 REMARK 290 6555 X+1/2,-Y+1/2,-Z+1/4 REMARK 290 7555 Y,X,-Z REMARK 290 8555 -Y,-X,-Z+1/2 REMARK 290 REMARK 290 WHERE NNN -> OPERATOR NUMBER REMARK 290 MMM -> TRANSLATION VECTOR REMARK 290 REMARK 290 CRYSTALLOGRAPHIC SYMMETRY TRANSFORMATIONS REMARK 290 THE FOLLOWING TRANSFORMATIONS OPERATE ON THE ATOM/HETATM REMARK 290 RECORDS IN THIS ENTRY TO PRODUCE CRYSTALLOGRAPHICALLY REMARK 290 RELATED MOLECULES. REMARK 290 SMTRY1 1 1.000000 0.000000 0.000000 0.00000 REMARK 290 SMTRY2 1 0.000000 1.000000 0.000000 0.00000 REMARK 290 SMTRY3 1 0.000000 0.000000 1.000000 0.00000 REMARK 290 SMTRY1 2 -1.000000 0.000000 0.000000 0.00000 REMARK 290 SMTRY2 2 0.000000 -1.000000 0.000000 0.00000 REMARK 290 SMTRY3 2 0.000000 0.000000 1.000000 18.60500 REMARK 290 SMTRY1 3 0.000000 -1.000000 0.000000 38.52500 REMARK 290 SMTRY2 3 1.000000 0.000000 0.000000 38.52500 REMARK 290 SMTRY3 3 0.000000 0.000000 1.000000 27.90750 REMARK 290 SMTRY1 4 0.000000 1.000000 0.000000 38.52500 REMARK 290 SMTRY2 4 -1.000000 0.000000 0.000000 38.52500 REMARK 290 SMTRY3 4 0.000000 0.000000 1.000000 9.30250 REMARK 290 SMTRY1 5 -1.000000 0.000000 0.000000 38.52500 REMARK 290 SMTRY2 5 0.000000 1.000000 0.000000 38.52500 REMARK 290 SMTRY3 5 0.000000 0.000000 -1.000000 27.90750 REMARK 290 SMTRY1 6 1.000000 0.000000 0.000000 38.52500 REMARK 290 SMTRY2 6 0.000000 -1.000000 0.000000 38.52500 REMARK 290 SMTRY3 6 0.000000 0.000000 -1.000000 9.30250 REMARK 290 SMTRY1 7 0.000000 1.000000 0.000000 0.00000 REMARK 290 SMTRY2 7 1.000000 0.000000 0.000000 0.00000 REMARK 290 SMTRY3 7 0.000000 0.000000 -1.000000 0.00000 REMARK 290 SMTRY1 8 0.000000 -1.000000 0.000000 0.00000 REMARK 290 SMTRY2 8 -1.000000 0.000000 0.000000 0.00000 REMARK 290 SMTRY3 8 0.000000 0.000000 -1.000000 18.60500 REMARK 290 REMARK 290 REMARK: NULL REMARK 300 REMARK 300 BIOMOLECULE: 1 REMARK 300 SEE REMARK 350 FOR THE AUTHOR PROVIDED AND/OR PROGRAM REMARK 300 GENERATED ASSEMBLY INFORMATION FOR THE STRUCTURE IN REMARK 300 THIS ENTRY. THE REMARK MAY ALSO PROVIDE INFORMATION ON REMARK 300 BURIED SURFACE AREA. REMARK 350 REMARK 350 COORDINATES FOR A COMPLETE MULTIMER REPRESENTING THE KNOWN REMARK 350 BIOLOGICALLY SIGNIFICANT OLIGOMERIZATION STATE OF THE REMARK 350 MOLECULE CAN BE GENERATED BY APPLYING BIOMT TRANSFORMATIONS REMARK 350 GIVEN BELOW. BOTH NON-CRYSTALLOGRAPHIC AND REMARK 350 CRYSTALLOGRAPHIC OPERATIONS ARE GIVEN. REMARK 350 REMARK 350 BIOMOLECULE: 1 REMARK 350 AUTHOR DETERMINED BIOLOGICAL UNIT: MONOMERIC REMARK 350 APPLY THE FOLLOWING TO CHAINS: A REMARK 350 BIOMT1 1 1.000000 0.000000 0.000000 0.00000 REMARK 350 BIOMT2 1 0.000000 1.000000 0.000000 0.00000 REMARK 350 BIOMT3 1 0.000000 0.000000 1.000000 0.00000 REMARK 375 REMARK 375 SPECIAL POSITION REMARK 375 THE FOLLOWING ATOMS ARE FOUND TO BE WITHIN 0.15 ANGSTROMS REMARK 375 OF A SYMMETRY RELATED ATOM AND ARE ASSUMED TO BE ON SPECIAL REMARK 375 POSITIONS. REMARK 375 REMARK 375 ATOM RES CSSEQI REMARK 375 HOH A 232 LIES ON A SPECIAL POSITION. REMARK 375 HOH A 238 LIES ON A SPECIAL POSITION. REMARK 375 HOH A 240 LIES ON A SPECIAL POSITION. REMARK 375 HOH A 267 LIES ON A SPECIAL POSITION. REMARK 375 HOH A 303 LIES ON A SPECIAL POSITION. REMARK 470 REMARK 470 MISSING ATOM REMARK 470 THE FOLLOWING RESIDUES HAVE MISSING ATOMS(M=MODEL NUMBER; REMARK 470 RES=RESIDUE NAME; C=CHAIN IDENTIFIER; SSEQ=SEQUENCE NUMBER; REMARK 470 I=INSERTION CODE): REMARK 470 M RES CSSEQI ATOMS REMARK 470 LEU A 129 C O REMARK 500 REMARK 500 GEOMETRY AND STEREOCHEMISTRY REMARK 500 SUBTOPIC: CLOSE CONTACTS IN SAME ASYMMETRIC UNIT REMARK 500 REMARK 500 THE FOLLOWING ATOMS ARE IN CLOSE CONTACT. REMARK 500 REMARK 500 ATM1 RES C SSEQI ATM2 RES C SSEQI DISTANCE REMARK 500 O HOH A 237 O HOH A 255 1.69 REMARK 500 O HOH A 290 O HOH A 331 1.95 REMARK 500 O HOH A 298 O HOH A 327 2.02 REMARK 500 O HOH A 330 O HOH A 355 2.13 REMARK 500 O HOH A 229 O HOH A 338 2.14 REMARK 500 O HOH A 214 O HOH A 234 2.16 REMARK 500 REMARK 500 REMARK: NULL REMARK 500 REMARK 500 GEOMETRY AND STEREOCHEMISTRY REMARK 500 SUBTOPIC: CLOSE CONTACTS REMARK 500 REMARK 500 THE FOLLOWING ATOMS THAT ARE RELATED BY CRYSTALLOGRAPHIC REMARK 500 SYMMETRY ARE IN CLOSE CONTACT. AN ATOM LOCATED WITHIN 0.15 REMARK 500 ANGSTROMS OF A SYMMETRY RELATED ATOM IS ASSUMED TO BE ON A REMARK 500 SPECIAL POSITION AND IS, THEREFORE, LISTED IN REMARK 375 REMARK 500 INSTEAD OF REMARK 500. ATOMS WITH NON-BLANK ALTERNATE REMARK 500 LOCATION INDICATORS ARE NOT INCLUDED IN THE CALCULATIONS. REMARK 500 REMARK 500 DISTANCE CUTOFF: REMARK 500 2.2 ANGSTROMS FOR CONTACTS NOT INVOLVING HYDROGEN ATOMS REMARK 500 1.6 ANGSTROMS FOR CONTACTS INVOLVING HYDROGEN ATOMS REMARK 500 REMARK 500 ATM1 RES C SSEQI ATM2 RES C SSEQI SSYMOP DISTANCE REMARK 500 O HOH A 253 O HOH A 331 8555 1.86 REMARK 500 O HOH A 298 O HOH A 298 7556 1.96 REMARK 500 REMARK 500 REMARK: NULL REMARK 500 REMARK 500 GEOMETRY AND STEREOCHEMISTRY REMARK 500 SUBTOPIC: COVALENT BOND ANGLES REMARK 500 REMARK 500 THE STEREOCHEMICAL PARAMETERS OF THE FOLLOWING RESIDUES REMARK 500 HAVE VALUES WHICH DEVIATE FROM EXPECTED VALUES BY MORE REMARK 500 THAN 6*RMSD (M=MODEL NUMBER; RES=RESIDUE NAME; C=CHAIN REMARK 500 IDENTIFIER; SSEQ=SEQUENCE NUMBER; I=INSERTION CODE). REMARK 500 REMARK 500 STANDARD TABLE: REMARK 500 FORMAT: (10X,I3,1X,A3,1X,A1,I4,A1,3(1X,A4,2X),12X,F5.1) REMARK 500 REMARK 500 EXPECTED VALUES PROTEIN: ENGH AND HUBER, 1999 REMARK 500 EXPECTED VALUES NUCLEIC ACID: CLOWNEY ET AL 1996 REMARK 500 REMARK 500 M RES CSSEQI ATM1 ATM2 ATM3 REMARK 500 ARG A 5 NE - CZ - NH2 ANGL. DEV. = -3.3 DEGREES REMARK 500 ARG A 14 NE - CZ - NH1 ANGL. DEV. = 3.3 DEGREES REMARK 500 ARG A 14 NE - CZ - NH2 ANGL. DEV. = -6.4 DEGREES REMARK 500 ARG A 45 NE - CZ - NH1 ANGL. DEV. = -3.6 DEGREES REMARK 500 REMARK 500 REMARK: NULL REMARK 800 REMARK 800 SITE REMARK 800 SITE_IDENTIFIER: AC1 REMARK 800 EVIDENCE_CODE: SOFTWARE REMARK 800 SITE_DESCRIPTION: BINDING SITE FOR RESIDUE CL A 200 REMARK 800 SITE_IDENTIFIER: AC2 REMARK 800 EVIDENCE_CODE: SOFTWARE REMARK 800 SITE_DESCRIPTION: BINDING SITE FOR RESIDUE CL A 201 REMARK 900 REMARK 900 RELATED ENTRIES REMARK 900 RELATED ID: 1DPW RELATED DB: PDB REMARK 900 STRUCTURE OF HEN EGG-WHITE LYSOZYME IN COMPLEX WITH MPD DBREF 1DPX A 1 129 UNP P00698 LYSC_CHICK 19 147 SEQRES 1 A 129 LYS VAL PHE GLY ARG CYS GLU LEU ALA ALA ALA MET LYS SEQRES 2 A 129 ARG HIS GLY LEU ASP ASN TYR ARG GLY TYR SER LEU GLY SEQRES 3 A 129 ASN TRP VAL CYS ALA ALA LYS PHE GLU SER ASN PHE ASN SEQRES 4 A 129 THR GLN ALA THR ASN ARG ASN THR ASP GLY SER THR ASP SEQRES 5 A 129 TYR GLY ILE LEU GLN ILE ASN SER ARG TRP TRP CYS ASN SEQRES 6 A 129 ASP GLY ARG THR PRO GLY SER ARG ASN LEU CYS ASN ILE SEQRES 7 A 129 PRO CYS SER ALA LEU LEU SER SER ASP ILE THR ALA SER SEQRES 8 A 129 VAL ASN CYS ALA LYS LYS ILE VAL SER ASP GLY ASN GLY SEQRES 9 A 129 MET ASN ALA TRP VAL ALA TRP ARG ASN ARG CYS LYS GLY SEQRES 10 A 129 THR ASP VAL GLN ALA TRP ILE ARG GLY CYS ARG LEU HET CL A 200 1 HET CL A 201 1 HETNAM CL CHLORIDE ION FORMUL 2 CL 2(CL 1-) FORMUL 4 HOH *177(H2 O) HELIX 1 1 GLY A 4 HIS A 15 1 12 HELIX 2 2 SER A 24 ASN A 37 1 14 HELIX 3 3 CYS A 80 SER A 85 5 6 HELIX 4 4 ILE A 88 SER A 100 1 13 HELIX 5 5 ASN A 103 ALA A 107 5 5 HELIX 6 6 TRP A 108 CYS A 115 1 8 HELIX 7 7 ASP A 119 ARG A 125 5 7 SHEET 1 A 3 THR A 43 ARG A 45 0 SHEET 2 A 3 THR A 51 TYR A 53 -1 N ASP A 52 O ASN A 44 SHEET 3 A 3 ILE A 58 ASN A 59 -1 O ILE A 58 N TYR A 53 SSBOND 1 CYS A 6 CYS A 127 1555 1555 1.99 SSBOND 2 CYS A 30 CYS A 115 1555 1555 2.04 SSBOND 3 CYS A 64 CYS A 80 1555 1555 2.04 SSBOND 4 CYS A 76 CYS A 94 1555 1555 2.04 SITE 1 AC1 2 TYR A 23 ASN A 113 SITE 1 AC2 4 SER A 24 GLY A 26 GLN A 121 ILE A 124 CRYST1 77.050 77.050 37.210 90.00 90.00 90.00 P 43 21 2 8 ORIGX1 1.000000 0.000000 0.000000 0.00000 ORIGX2 0.000000 1.000000 0.000000 0.00000 ORIGX3 0.000000 0.000000 1.000000 0.00000 SCALE1 0.012980 0.000000 0.000000 0.00000 SCALE2 0.000000 0.012980 0.000000 0.00000 SCALE3 0.000000 0.000000 0.026870 0.00000 ATOM 1 N LYS A 1 1.990 9.126 10.027 1.00 16.50 N ATOM 2 CA LYS A 1 1.067 9.654 8.994 1.00 16.87 C ATOM 3 C LYS A 1 1.167 11.156 8.783 1.00 16.39 C ATOM 4 O LYS A 1 1.155 11.908 9.776 1.00 15.96 O ATOM 5 CB LYS A 1 -0.361 9.267 9.380 1.00 18.93 C ATOM 6 CG LYS A 1 -1.429 9.868 8.467 1.00 19.34 C ATOM 7 CD LYS A 1 -2.781 9.259 8.802 1.00 21.34 C ATOM 8 CE ALYS A 1 -3.899 9.938 8.018 0.50 21.41 C ATOM 9 CE BLYS A 1 -3.839 10.005 7.983 0.50 22.40 C ATOM 10 NZ ALYS A 1 -4.034 9.390 6.642 0.50 21.17 N ATOM 11 NZ BLYS A 1 -5.212 9.563 8.333 0.50 24.73 N ATOM 12 N VAL A 2 1.278 11.558 7.532 1.00 16.01 N ATOM 13 CA VAL A 2 1.246 12.972 7.197 1.00 15.32 C ATOM 14 C VAL A 2 -0.182 13.272 6.760 1.00 15.17 C ATOM 15 O VAL A 2 -0.639 12.820 5.703 1.00 16.01 O ATOM 16 CB VAL A 2 2.284 13.391 6.154 1.00 18.75 C ATOM 17 CG1 VAL A 2 2.168 14.868 5.819 1.00 19.78 C ATOM 18 CG2 VAL A 2 3.681 13.115 6.719 1.00 18.93 C ATOM 19 N PHE A 3 -0.913 13.974 7.612 1.00 13.76 N ATOM 20 CA PHE A 3 -2.285 14.327 7.313 1.00 13.57 C ATOM 21 C PHE A 3 -2.360 15.431 6.261 1.00 14.39 C ATOM 22 O PHE A 3 -1.528 16.329 6.193 1.00 14.83 O ATOM 23 CB PHE A 3 -3.040 14.841 8.530 1.00 16.19 C ATOM 24 CG PHE A 3 -3.673 13.812 9.419 1.00 15.21 C ATOM 25 CD1 PHE A 3 -2.900 13.114 10.329 1.00 15.00 C ATOM 26 CD2 PHE A 3 -5.043 13.579 9.383 1.00 16.87 C ATOM 27 CE1 PHE A 3 -3.461 12.164 11.170 1.00 18.25 C ATOM 28 CE2 PHE A 3 -5.600 12.635 10.219 1.00 15.89 C ATOM 29 CZ PHE A 3 -4.818 11.917 11.104 1.00 15.57 C ATOM 30 N GLY A 4 -3.481 15.362 5.521 1.00 14.57 N ATOM 31 CA GLY A 4 -3.767 16.491 4.630 1.00 14.92 C ATOM 32 C GLY A 4 -4.437 17.545 5.518 1.00 15.36 C ATOM 33 O GLY A 4 -4.936 17.238 6.603 1.00 16.28 O ATOM 34 N ARG A 5 -4.454 18.789 5.074 1.00 15.67 N ATOM 35 CA ARG A 5 -5.049 19.858 5.883 1.00 15.52 C ATOM 36 C ARG A 5 -6.508 19.624 6.233 1.00 16.36 C ATOM 37 O ARG A 5 -6.896 19.671 7.412 1.00 15.56 O ATOM 38 CB ARG A 5 -4.855 21.159 5.106 1.00 17.05 C ATOM 39 CG ARG A 5 -5.422 22.389 5.791 1.00 18.54 C ATOM 40 CD ARG A 5 -5.134 23.622 4.972 1.00 15.70 C ATOM 41 NE ARG A 5 -5.637 23.599 3.603 1.00 19.15 N ATOM 42 CZ ARG A 5 -6.888 23.946 3.282 1.00 16.84 C ATOM 43 NH1 ARG A 5 -7.769 24.339 4.191 1.00 16.94 N ATOM 44 NH2 ARG A 5 -7.219 23.890 1.989 1.00 21.28 N ATOM 45 N CYS A 6 -7.354 19.370 5.237 1.00 16.34 N ATOM 46 CA CYS A 6 -8.777 19.141 5.499 1.00 16.26 C ATOM 47 C CYS A 6 -9.012 17.803 6.197 1.00 14.69 C ATOM 48 O CYS A 6 -9.947 17.658 6.988 1.00 14.93 O ATOM 49 CB CYS A 6 -9.612 19.149 4.208 1.00 17.62 C ATOM 50 SG CYS A 6 -9.692 20.816 3.487 1.00 18.05 S ATOM 51 N GLU A 7 -8.149 16.833 5.920 1.00 14.37 N ATOM 52 CA GLU A 7 -8.178 15.522 6.535 1.00 14.18 C ATOM 53 C GLU A 7 -7.993 15.656 8.053 1.00 14.37 C ATOM 54 O GLU A 7 -8.764 15.069 8.822 1.00 14.87 O ATOM 55 CB GLU A 7 -7.098 14.596 5.961 1.00 15.22 C ATOM 56 CG GLU A 7 -7.183 13.180 6.526 1.00 15.78 C ATOM 57 CD GLU A 7 -6.048 12.315 6.036 1.00 17.87 C ATOM 58 OE1 GLU A 7 -5.017 12.791 5.501 1.00 18.58 O ATOM 59 OE2 GLU A 7 -6.222 11.079 6.172 1.00 20.43 O ATOM 60 N LEU A 8 -7.015 16.490 8.416 1.00 14.34 N ATOM 61 CA LEU A 8 -6.778 16.716 9.847 1.00 13.81 C ATOM 62 C LEU A 8 -7.907 17.524 10.455 1.00 13.61 C ATOM 63 O LEU A 8 -8.369 17.246 11.572 1.00 14.07 O ATOM 64 CB LEU A 8 -5.420 17.412 10.063 1.00 11.51 C ATOM 65 CG LEU A 8 -5.119 17.682 11.541 1.00 12.42 C ATOM 66 CD1 LEU A 8 -5.065 16.387 12.326 1.00 13.65 C ATOM 67 CD2 LEU A 8 -3.793 18.446 11.655 1.00 15.48 C ATOM 68 N ALA A 9 -8.435 18.511 9.730 1.00 13.23 N ATOM 69 CA ALA A 9 -9.522 19.337 10.243 1.00 12.88 C ATOM 70 C ALA A 9 -10.719 18.454 10.567 1.00 14.06 C ATOM 71 O ALA A 9 -11.322 18.567 11.634 1.00 14.68 O ATOM 72 CB ALA A 9 -9.839 20.411 9.207 1.00 16.86 C ATOM 73 N ALA A 10 -11.045 17.507 9.677 1.00 13.71 N ATOM 74 CA ALA A 10 -12.200 16.640 9.937 1.00 15.49 C ATOM 75 C ALA A 10 -11.991 15.727 11.132 1.00 15.60 C ATOM 76 O ALA A 10 -12.933 15.472 11.884 1.00 16.64 O ATOM 77 CB ALA A 10 -12.541 15.763 8.735 1.00 16.77 C ATOM 78 N ALA A 11 -10.760 15.269 11.337 1.00 14.64 N ATOM 79 CA ALA A 11 -10.458 14.389 12.465 1.00 15.70 C ATOM 80 C ALA A 11 -10.572 15.118 13.792 1.00 15.70 C ATOM 81 O ALA A 11 -11.106 14.626 14.780 1.00 15.52 O ATOM 82 CB ALA A 11 -9.073 13.794 12.274 1.00 17.40 C ATOM 83 N MET A 12 -9.984 16.327 13.799 1.00 14.14 N ATOM 84 CA MET A 12 -10.053 17.189 14.978 1.00 14.62 C ATOM 85 C MET A 12 -11.494 17.553 15.336 1.00 15.86 C ATOM 86 O MET A 12 -11.829 17.508 16.505 1.00 16.32 O ATOM 87 CB MET A 12 -9.268 18.475 14.742 1.00 15.01 C ATOM 88 CG MET A 12 -7.763 18.206 14.809 1.00 13.33 C ATOM 89 SD MET A 12 -6.840 19.722 14.431 1.00 12.91 S ATOM 90 CE MET A 12 -5.352 19.364 15.355 1.00 16.71 C ATOM 91 N LYS A 13 -12.298 17.858 14.318 1.00 15.42 N ATOM 92 CA LYS A 13 -13.705 18.186 14.557 1.00 15.76 C ATOM 93 C LYS A 13 -14.460 16.954 15.057 1.00 16.81 C ATOM 94 O LYS A 13 -15.206 17.039 16.042 1.00 17.14 O ATOM 95 CB LYS A 13 -14.333 18.675 13.250 1.00 14.49 C ATOM 96 CG LYS A 13 -15.814 19.057 13.415 1.00 15.69 C ATOM 97 CD LYS A 13 -16.288 19.603 12.064 1.00 19.90 C ATOM 98 CE LYS A 13 -17.760 20.006 12.201 1.00 29.11 C ATOM 99 NZ LYS A 13 -18.275 20.550 10.912 1.00 32.38 N ATOM 100 N ARG A 14 -14.176 15.801 14.446 1.00 17.07 N ATOM 101 CA ARG A 14 -14.889 14.602 14.941 1.00 16.60 C ATOM 102 C ARG A 14 -14.581 14.313 16.402 1.00 17.29 C ATOM 103 O ARG A 14 -15.463 13.906 17.184 1.00 16.75 O ATOM 104 CB AARG A 14 -14.605 13.371 14.082 0.50 16.07 C ATOM 105 CB BARG A 14 -14.553 13.417 14.033 0.50 19.58 C ATOM 106 CG AARG A 14 -15.192 12.096 14.704 0.50 19.04 C ATOM 107 CG BARG A 14 -15.114 12.109 14.566 0.50 26.17 C ATOM 108 CD AARG A 14 -15.005 10.934 13.737 0.50 18.21 C ATOM 109 CD BARG A 14 -14.728 10.947 13.663 0.50 30.80 C ATOM 110 NE AARG A 14 -13.651 10.968 13.222 0.50 10.74 N ATOM 111 NE BARG A 14 -14.363 9.812 14.504 0.50 33.11 N ATOM 112 CZ AARG A 14 -12.569 10.399 13.707 0.50 12.39 C ATOM 113 CZ BARG A 14 -13.122 9.501 14.851 0.50 33.04 C ATOM 114 NH1AARG A 14 -12.566 9.620 14.789 0.50 17.90 N ATOM 115 NH1BARG A 14 -12.093 10.215 14.408 0.50 36.74 N ATOM 116 NH2AARG A 14 -11.479 10.640 13.006 0.50 7.03 N ATOM 117 NH2BARG A 14 -12.884 8.455 15.623 0.50 33.50 N ATOM 118 N HIS A 15 -13.353 14.586 16.819 1.00 16.36 N ATOM 119 CA HIS A 15 -12.914 14.403 18.184 1.00 15.95 C ATOM 120 C HIS A 15 -13.228 15.565 19.114 1.00 17.32 C ATOM 121 O HIS A 15 -12.716 15.564 20.246 1.00 17.87 O ATOM 122 CB HIS A 15 -11.414 14.123 18.193 1.00 18.12 C ATOM 123 CG HIS A 15 -11.023 12.748 17.764 1.00 19.90 C ATOM 124 ND1 HIS A 15 -10.327 12.406 16.647 1.00 24.89 N ATOM 125 CD2 HIS A 15 -11.297 11.583 18.430 1.00 20.42 C ATOM 126 CE1 HIS A 15 -10.156 11.089 16.614 1.00 20.82 C ATOM 127 NE2 HIS A 15 -10.745 10.579 17.674 1.00 24.48 N ATOM 128 N GLY A 16 -14.017 16.535 18.687 1.00 16.73 N ATOM 129 CA GLY A 16 -14.489 17.587 19.538 1.00 18.49 C ATOM 130 C GLY A 16 -13.547 18.729 19.839 1.00 18.36 C ATOM 131 O GLY A 16 -13.769 19.385 20.858 1.00 19.10 O ATOM 132 N LEU A 17 -12.597 19.017 18.948 1.00 17.90 N ATOM 133 CA LEU A 17 -11.750 20.183 19.211 1.00 19.00 C ATOM 134 C LEU A 17 -12.327 21.493 18.706 1.00 19.55 C ATOM 135 O LEU A 17 -11.875 22.550 19.160 1.00 18.56 O ATOM 136 CB LEU A 17 -10.361 20.033 18.589 1.00 17.64 C ATOM 137 CG LEU A 17 -9.332 19.205 19.310 1.00 18.62 C ATOM 138 CD1 LEU A 17 -8.019 19.267 18.531 1.00 15.31 C ATOM 139 CD2 LEU A 17 -9.085 19.671 20.751 1.00 16.33 C ATOM 140 N ASP A 18 -13.239 21.485 17.731 1.00 19.98 N ATOM 141 CA ASP A 18 -13.802 22.728 17.200 1.00 21.21 C ATOM 142 C ASP A 18 -14.550 23.446 18.318 1.00 21.85 C ATOM 143 O ASP A 18 -15.519 22.940 18.867 1.00 23.12 O ATOM 144 CB ASP A 18 -14.740 22.516 16.018 1.00 21.68 C ATOM 145 CG ASP A 18 -15.187 23.744 15.260 1.00 27.36 C ATOM 146 OD1 ASP A 18 -14.740 24.890 15.491 1.00 27.43 O ATOM 147 OD2 ASP A 18 -16.029 23.598 14.321 1.00 27.84 O ATOM 148 N ASN A 19 -14.064 24.621 18.681 1.00 20.27 N ATOM 149 CA ASN A 19 -14.573 25.444 19.745 1.00 19.44 C ATOM 150 C ASN A 19 -14.302 24.888 21.139 1.00 17.76 C ATOM 151 O ASN A 19 -14.917 25.354 22.109 1.00 17.54 O ATOM 152 CB ASN A 19 -16.067 25.777 19.565 1.00 27.45 C ATOM 153 CG ASN A 19 -16.236 26.636 18.314 1.00 38.17 C ATOM 154 OD1 ASN A 19 -15.606 27.696 18.245 1.00 45.03 O ATOM 155 ND2 ASN A 19 -17.045 26.157 17.377 1.00 42.07 N ATOM 156 N TYR A 20 -13.343 23.983 21.279 1.00 16.42 N ATOM 157 CA TYR A 20 -12.977 23.461 22.590 1.00 16.85 C ATOM 158 C TYR A 20 -12.221 24.550 23.355 1.00 16.62 C ATOM 159 O TYR A 20 -11.306 25.193 22.847 1.00 15.80 O ATOM 160 CB TYR A 20 -12.125 22.207 22.508 1.00 16.39 C ATOM 161 CG TYR A 20 -11.974 21.587 23.884 1.00 16.50 C ATOM 162 CD1 TYR A 20 -12.947 20.683 24.321 1.00 16.56 C ATOM 163 CD2 TYR A 20 -10.897 21.865 24.695 1.00 16.61 C ATOM 164 CE1 TYR A 20 -12.830 20.105 25.580 1.00 18.24 C ATOM 165 CE2 TYR A 20 -10.781 21.285 25.949 1.00 17.87 C ATOM 166 CZ TYR A 20 -11.754 20.408 26.376 1.00 19.79 C ATOM 167 OH TYR A 20 -11.630 19.839 27.620 1.00 21.37 O ATOM 168 N ARG A 21 -12.755 24.874 24.537 1.00 15.91 N ATOM 169 CA ARG A 21 -12.219 25.991 25.333 1.00 16.69 C ATOM 170 C ARG A 21 -12.302 27.296 24.546 1.00 15.91 C ATOM 171 O ARG A 21 -11.558 28.240 24.789 1.00 15.76 O ATOM 172 CB ARG A 21 -10.805 25.757 25.837 1.00 19.57 C ATOM 173 CG ARG A 21 -10.632 24.645 26.847 1.00 28.39 C ATOM 174 CD ARG A 21 -11.453 24.827 28.105 1.00 39.22 C ATOM 175 NE ARG A 21 -10.800 25.644 29.118 1.00 42.28 N ATOM 176 CZ ARG A 21 -10.843 25.332 30.417 1.00 45.46 C ATOM 177 NH1 ARG A 21 -10.239 26.099 31.317 1.00 46.44 N ATOM 178 NH2 ARG A 21 -11.467 24.242 30.853 1.00 45.99 N ATOM 179 N GLY A 22 -13.245 27.394 23.609 1.00 15.41 N ATOM 180 CA GLY A 22 -13.517 28.540 22.795 1.00 14.49 C ATOM 181 C GLY A 22 -12.600 28.705 21.595 1.00 14.19 C ATOM 182 O GLY A 22 -12.656 29.762 20.970 1.00 15.22 O ATOM 183 N TYR A 23 -11.769 27.707 21.306 1.00 14.34 N ATOM 184 CA TYR A 23 -10.841 27.843 20.178 1.00 14.12 C ATOM 185 C TYR A 23 -11.398 27.187 18.912 1.00 13.63 C ATOM 186 O TYR A 23 -11.617 25.979 18.891 1.00 14.94 O ATOM 187 CB TYR A 23 -9.497 27.215 20.564 1.00 13.40 C ATOM 188 CG TYR A 23 -8.754 28.070 21.578 1.00 12.80 C ATOM 189 CD1 TYR A 23 -7.970 29.120 21.154 1.00 12.01 C ATOM 190 CD2 TYR A 23 -8.842 27.789 22.936 1.00 13.06 C ATOM 191 CE1 TYR A 23 -7.297 29.926 22.057 1.00 12.42 C ATOM 192 CE2 TYR A 23 -8.167 28.586 23.861 1.00 12.92 C ATOM 193 CZ TYR A 23 -7.415 29.654 23.408 1.00 13.15 C ATOM 194 OH TYR A 23 -6.718 30.463 24.271 1.00 12.84 O ATOM 195 N SER A 24 -11.608 27.999 17.875 1.00 13.73 N ATOM 196 CA SER A 24 -12.187 27.470 16.639 1.00 14.62 C ATOM 197 C SER A 24 -11.267 26.481 15.954 1.00 14.51 C ATOM 198 O SER A 24 -10.041 26.459 16.147 1.00 14.88 O ATOM 199 CB SER A 24 -12.515 28.626 15.682 1.00 14.56 C ATOM 200 OG SER A 24 -11.284 29.222 15.269 1.00 15.71 O ATOM 201 N LEU A 25 -11.858 25.652 15.090 1.00 14.36 N ATOM 202 CA LEU A 25 -11.079 24.612 14.399 1.00 13.88 C ATOM 203 C LEU A 25 -9.836 25.105 13.693 1.00 13.76 C ATOM 204 O LEU A 25 -8.794 24.415 13.751 1.00 12.85 O ATOM 205 CB LEU A 25 -11.992 23.868 13.427 1.00 14.00 C ATOM 206 CG LEU A 25 -11.458 22.596 12.769 1.00 14.02 C ATOM 207 CD1 LEU A 25 -11.122 21.576 13.850 1.00 17.04 C ATOM 208 CD2 LEU A 25 -12.485 22.005 11.804 1.00 14.38 C ATOM 209 N GLY A 26 -9.880 26.275 13.073 1.00 13.52 N ATOM 210 CA GLY A 26 -8.736 26.834 12.357 1.00 13.74 C ATOM 211 C GLY A 26 -7.548 27.066 13.283 1.00 12.49 C ATOM 212 O GLY A 26 -6.398 26.967 12.868 1.00 12.78 O ATOM 213 N ASN A 27 -7.795 27.451 14.531 1.00 12.27 N ATOM 214 CA ASN A 27 -6.702 27.584 15.498 1.00 11.78 C ATOM 215 C ASN A 27 -5.942 26.275 15.715 1.00 13.00 C ATOM 216 O ASN A 27 -4.711 26.313 15.789 1.00 12.58 O ATOM 217 CB ASN A 27 -7.248 28.040 16.859 1.00 11.61 C ATOM 218 CG ASN A 27 -7.491 29.541 16.837 1.00 14.37 C ATOM 219 OD1 ASN A 27 -6.564 30.355 16.873 1.00 13.63 O ATOM 220 ND2 ASN A 27 -8.762 29.922 16.777 1.00 13.86 N ATOM 221 N TRP A 28 -6.679 25.170 15.848 1.00 12.57 N ATOM 222 CA TRP A 28 -6.039 23.870 16.062 1.00 12.05 C ATOM 223 C TRP A 28 -5.310 23.366 14.831 1.00 12.07 C ATOM 224 O TRP A 28 -4.234 22.756 14.919 1.00 12.53 O ATOM 225 CB TRP A 28 -7.118 22.850 16.456 1.00 14.11 C ATOM 226 CG TRP A 28 -7.754 23.223 17.773 1.00 11.68 C ATOM 227 CD1 TRP A 28 -8.971 23.792 17.986 1.00 12.85 C ATOM 228 CD2 TRP A 28 -7.169 22.999 19.062 1.00 14.15 C ATOM 229 NE1 TRP A 28 -9.186 23.948 19.342 1.00 12.86 N ATOM 230 CE2 TRP A 28 -8.082 23.465 20.012 1.00 12.97 C ATOM 231 CE3 TRP A 28 -5.961 22.440 19.481 1.00 14.04 C ATOM 232 CZ2 TRP A 28 -7.841 23.401 21.381 1.00 13.14 C ATOM 233 CZ3 TRP A 28 -5.709 22.383 20.855 1.00 13.88 C ATOM 234 CH2 TRP A 28 -6.650 22.855 21.788 1.00 14.45 C ATOM 235 N VAL A 29 -5.909 23.591 13.647 1.00 11.44 N ATOM 236 CA VAL A 29 -5.220 23.174 12.403 1.00 11.51 C ATOM 237 C VAL A 29 -3.963 24.005 12.219 1.00 11.97 C ATOM 238 O VAL A 29 -2.891 23.493 11.846 1.00 12.00 O ATOM 239 CB VAL A 29 -6.161 23.283 11.196 1.00 12.38 C ATOM 240 CG1 VAL A 29 -5.430 22.951 9.892 1.00 13.79 C ATOM 241 CG2 VAL A 29 -7.316 22.296 11.343 1.00 12.68 C ATOM 242 N CYS A 30 -4.066 25.328 12.467 1.00 11.38 N ATOM 243 CA CYS A 30 -2.893 26.198 12.355 1.00 11.20 C ATOM 244 C CYS A 30 -1.808 25.747 13.333 1.00 11.19 C ATOM 245 O CYS A 30 -0.625 25.665 12.969 1.00 10.49 O ATOM 246 CB CYS A 30 -3.314 27.648 12.667 1.00 12.47 C ATOM 247 SG CYS A 30 -1.972 28.859 12.483 1.00 11.06 S ATOM 248 N ALA A 31 -2.169 25.458 14.578 1.00 12.04 N ATOM 249 CA ALA A 31 -1.143 24.998 15.541 1.00 10.89 C ATOM 250 C ALA A 31 -0.474 23.723 15.055 1.00 11.75 C ATOM 251 O ALA A 31 0.765 23.580 15.117 1.00 12.20 O ATOM 252 CB ALA A 31 -1.829 24.727 16.876 1.00 12.94 C ATOM 253 N ALA A 32 -1.292 22.769 14.573 1.00 11.68 N ATOM 254 CA ALA A 32 -0.693 21.528 14.060 1.00 11.94 C ATOM 255 C ALA A 32 0.209 21.764 12.857 1.00 11.76 C ATOM 256 O ALA A 32 1.283 21.132 12.708 1.00 10.64 O ATOM 257 CB ALA A 32 -1.797 20.522 13.726 1.00 10.36 C ATOM 258 N LYS A 33 -0.149 22.685 11.966 1.00 11.49 N ATOM 259 CA LYS A 33 0.725 22.960 10.821 1.00 11.83 C ATOM 260 C LYS A 33 2.108 23.410 11.294 1.00 12.00 C ATOM 261 O LYS A 33 3.114 22.907 10.788 1.00 12.88 O ATOM 262 CB LYS A 33 0.087 24.057 9.964 1.00 13.47 C ATOM 263 CG LYS A 33 1.056 24.658 8.925 1.00 17.22 C ATOM 264 CD LYS A 33 1.245 23.642 7.826 1.00 17.45 C ATOM 265 CE LYS A 33 2.151 24.131 6.708 1.00 23.72 C ATOM 266 NZ LYS A 33 2.264 23.054 5.675 1.00 23.82 N ATOM 267 N PHE A 34 2.132 24.372 12.228 1.00 12.51 N ATOM 268 CA PHE A 34 3.432 24.902 12.635 1.00 12.30 C ATOM 269 C PHE A 34 4.122 24.074 13.700 1.00 14.82 C ATOM 270 O PHE A 34 5.365 24.183 13.832 1.00 15.60 O ATOM 271 CB PHE A 34 3.326 26.388 13.004 1.00 11.76 C ATOM 272 CG PHE A 34 2.881 27.201 11.807 1.00 12.09 C ATOM 273 CD1 PHE A 34 3.602 27.148 10.633 1.00 11.72 C ATOM 274 CD2 PHE A 34 1.749 28.003 11.912 1.00 13.88 C ATOM 275 CE1 PHE A 34 3.183 27.906 9.547 1.00 12.51 C ATOM 276 CE2 PHE A 34 1.352 28.756 10.802 1.00 16.14 C ATOM 277 CZ PHE A 34 2.071 28.700 9.622 1.00 15.05 C ATOM 278 N GLU A 35 3.381 23.215 14.383 1.00 13.40 N ATOM 279 CA GLU A 35 4.074 22.368 15.373 1.00 13.41 C ATOM 280 C GLU A 35 4.676 21.148 14.682 1.00 13.91 C ATOM 281 O GLU A 35 5.832 20.751 14.982 1.00 14.32 O ATOM 282 CB GLU A 35 3.070 21.947 16.444 1.00 15.49 C ATOM 283 CG GLU A 35 2.661 23.063 17.392 1.00 11.90 C ATOM 284 CD GLU A 35 3.765 23.547 18.292 1.00 13.50 C ATOM 285 OE1 GLU A 35 4.922 23.044 18.283 1.00 13.70 O ATOM 286 OE2 GLU A 35 3.505 24.506 19.059 1.00 13.45 O ATOM 287 N SER A 36 3.941 20.534 13.780 1.00 12.23 N ATOM 288 CA SER A 36 4.390 19.254 13.204 1.00 12.91 C ATOM 289 C SER A 36 4.329 19.098 11.705 1.00 12.45 C ATOM 290 O SER A 36 4.678 18.049 11.134 1.00 13.32 O ATOM 291 CB SER A 36 3.502 18.133 13.804 1.00 13.38 C ATOM 292 OG SER A 36 2.181 18.269 13.290 1.00 10.74 O ATOM 293 N ASN A 37 3.854 20.129 10.988 1.00 12.49 N ATOM 294 CA ASN A 37 3.615 20.082 9.549 1.00 13.68 C ATOM 295 C ASN A 37 2.656 18.941 9.240 1.00 14.00 C ATOM 296 O ASN A 37 2.744 18.236 8.231 1.00 15.39 O ATOM 297 CB ASN A 37 4.930 19.967 8.757 1.00 18.52 C ATOM 298 CG ASN A 37 4.716 20.501 7.350 1.00 22.16 C ATOM 299 OD1 ASN A 37 3.785 21.280 7.100 1.00 21.09 O ATOM 300 ND2 ASN A 37 5.538 20.074 6.398 1.00 26.09 N ATOM 301 N PHE A 38 1.731 18.672 10.155 1.00 13.56 N ATOM 302 CA PHE A 38 0.697 17.656 10.084 1.00 13.00 C ATOM 303 C PHE A 38 1.234 16.225 10.124 1.00 13.56 C ATOM 304 O PHE A 38 0.509 15.288 9.757 1.00 12.49 O ATOM 305 CB PHE A 38 -0.190 17.839 8.843 1.00 12.15 C ATOM 306 CG PHE A 38 -0.837 19.176 8.625 1.00 12.14 C ATOM 307 CD1 PHE A 38 -1.334 19.904 9.707 1.00 12.74 C ATOM 308 CD2 PHE A 38 -1.006 19.707 7.362 1.00 15.83 C ATOM 309 CE1 PHE A 38 -1.971 21.115 9.523 1.00 13.21 C ATOM 310 CE2 PHE A 38 -1.628 20.932 7.167 1.00 13.63 C ATOM 311 CZ PHE A 38 -2.108 21.642 8.244 1.00 11.31 C ATOM 312 N ASN A 39 2.425 16.019 10.647 1.00 12.73 N ATOM 313 CA ASN A 39 3.057 14.703 10.693 1.00 13.15 C ATOM 314 C ASN A 39 2.880 14.089 12.072 1.00 13.12 C ATOM 315 O ASN A 39 3.511 14.608 12.999 1.00 13.52 O ATOM 316 CB ASN A 39 4.560 14.891 10.367 1.00 14.57 C ATOM 317 CG ASN A 39 5.283 13.574 10.147 1.00 12.98 C ATOM 318 OD1 ASN A 39 4.798 12.490 10.526 1.00 14.00 O ATOM 319 ND2 ASN A 39 6.460 13.625 9.527 1.00 18.03 N ATOM 320 N THR A 40 2.119 12.994 12.206 1.00 12.50 N ATOM 321 CA THR A 40 1.926 12.378 13.523 1.00 11.76 C ATOM 322 C THR A 40 3.229 11.877 14.100 1.00 11.93 C ATOM 323 O THR A 40 3.307 11.749 15.328 1.00 12.52 O ATOM 324 CB THR A 40 0.924 11.200 13.545 1.00 12.79 C ATOM 325 OG1 THR A 40 1.454 10.162 12.692 1.00 15.08 O ATOM 326 CG2 THR A 40 -0.403 11.617 12.942 1.00 12.99 C ATOM 327 N GLN A 41 4.250 11.576 13.262 1.00 11.70 N ATOM 328 CA GLN A 41 5.483 11.044 13.837 1.00 12.34 C ATOM 329 C GLN A 41 6.518 12.075 14.245 1.00 11.82 C ATOM 330 O GLN A 41 7.584 11.718 14.754 1.00 13.25 O ATOM 331 CB GLN A 41 6.174 10.063 12.860 1.00 15.60 C ATOM 332 CG GLN A 41 5.230 8.963 12.391 1.00 12.40 C ATOM 333 CD GLN A 41 6.001 7.840 11.713 1.00 17.63 C ATOM 334 OE1 GLN A 41 6.727 7.100 12.379 1.00 17.30 O ATOM 335 NE2 GLN A 41 5.843 7.696 10.400 1.00 13.89 N ATOM 336 N ALA A 42 6.243 13.367 14.167 1.00 11.78 N ATOM 337 CA ALA A 42 7.211 14.384 14.519 1.00 11.40 C ATOM 338 C ALA A 42 7.629 14.331 15.979 1.00 11.97 C ATOM 339 O ALA A 42 6.803 14.286 16.876 1.00 12.76 O ATOM 340 CB ALA A 42 6.578 15.751 14.239 1.00 12.23 C ATOM 341 N THR A 43 8.932 14.400 16.226 1.00 12.73 N ATOM 342 CA THR A 43 9.476 14.479 17.574 1.00 13.87 C ATOM 343 C THR A 43 10.562 15.543 17.560 1.00 13.42 C ATOM 344 O THR A 43 11.305 15.720 16.573 1.00 13.86 O ATOM 345 CB THR A 43 10.100 13.177 18.096 1.00 16.26 C ATOM 346 OG1 THR A 43 11.200 12.864 17.215 1.00 15.72 O ATOM 347 CG2 THR A 43 9.134 12.007 18.135 1.00 15.56 C ATOM 348 N ASN A 44 10.630 16.391 18.589 1.00 13.63 N ATOM 349 CA ASN A 44 11.650 17.442 18.631 1.00 13.86 C ATOM 350 C ASN A 44 12.122 17.624 20.068 1.00 14.83 C ATOM 351 O ASN A 44 11.317 17.774 20.992 1.00 15.66 O ATOM 352 CB ASN A 44 11.120 18.781 18.095 1.00 15.14 C ATOM 353 CG ASN A 44 10.877 18.740 16.597 1.00 17.51 C ATOM 354 OD1 ASN A 44 11.845 18.675 15.819 1.00 20.30 O ATOM 355 ND2 ASN A 44 9.630 18.683 16.131 1.00 16.80 N ATOM 356 N ARG A 45 13.438 17.530 20.284 1.00 15.87 N ATOM 357 CA ARG A 45 14.004 17.690 21.624 1.00 16.06 C ATOM 358 C ARG A 45 14.037 19.160 22.016 1.00 17.54 C ATOM 359 O ARG A 45 14.365 20.027 21.213 1.00 18.09 O ATOM 360 CB ARG A 45 15.444 17.174 21.659 1.00 19.49 C ATOM 361 CG ARG A 45 15.986 16.903 23.059 1.00 15.81 C ATOM 362 CD ARG A 45 15.351 15.624 23.557 1.00 25.18 C ATOM 363 NE ARG A 45 15.791 15.165 24.853 1.00 30.71 N ATOM 364 CZ ARG A 45 16.740 14.288 25.134 1.00 27.82 C ATOM 365 NH1 ARG A 45 16.949 14.025 26.418 1.00 28.93 N ATOM 366 NH2 ARG A 45 17.459 13.698 24.194 1.00 32.48 N ATOM 367 N ASN A 46 13.657 19.431 23.255 1.00 15.69 N ATOM 368 CA ASN A 46 13.691 20.783 23.795 1.00 17.20 C ATOM 369 C ASN A 46 15.029 20.983 24.514 1.00 17.91 C ATOM 370 O ASN A 46 15.658 20.013 24.936 1.00 18.81 O ATOM 371 CB ASN A 46 12.519 20.961 24.750 1.00 16.03 C ATOM 372 CG ASN A 46 11.176 20.724 24.095 1.00 21.74 C ATOM 373 OD1 ASN A 46 10.355 20.003 24.672 1.00 21.91 O ATOM 374 ND2 ASN A 46 10.921 21.249 22.907 1.00 22.89 N ATOM 375 N THR A 47 15.412 22.248 24.690 1.00 19.67 N ATOM 376 CA THR A 47 16.650 22.563 25.388 1.00 20.37 C ATOM 377 C THR A 47 16.661 22.100 26.826 1.00 20.76 C ATOM 378 O THR A 47 17.751 21.780 27.305 1.00 21.54 O ATOM 379 CB THR A 47 16.977 24.075 25.381 1.00 27.12 C ATOM 380 OG1 THR A 47 15.949 24.786 26.076 1.00 29.45 O ATOM 381 CG2 THR A 47 17.076 24.563 23.946 1.00 28.33 C ATOM 382 N ASP A 48 15.521 21.993 27.506 1.00 19.67 N ATOM 383 CA ASP A 48 15.483 21.544 28.883 1.00 19.14 C ATOM 384 C ASP A 48 15.563 20.034 28.998 1.00 19.12 C ATOM 385 O ASP A 48 15.589 19.516 30.124 1.00 19.76 O ATOM 386 CB ASP A 48 14.246 22.078 29.607 1.00 17.48 C ATOM 387 CG ASP A 48 12.955 21.486 29.106 1.00 17.83 C ATOM 388 OD1 ASP A 48 12.996 20.605 28.218 1.00 14.85 O ATOM 389 OD2 ASP A 48 11.863 21.869 29.585 1.00 24.15 O ATOM 390 N GLY A 49 15.711 19.316 27.891 1.00 16.97 N ATOM 391 CA GLY A 49 15.866 17.879 27.811 1.00 18.17 C ATOM 392 C GLY A 49 14.575 17.108 27.667 1.00 16.79 C ATOM 393 O GLY A 49 14.613 15.879 27.552 1.00 17.78 O ATOM 394 N SER A 50 13.456 17.808 27.703 1.00 15.19 N ATOM 395 CA SER A 50 12.179 17.114 27.463 1.00 14.12 C ATOM 396 C SER A 50 12.089 17.004 25.937 1.00 13.24 C ATOM 397 O SER A 50 12.954 17.499 25.220 1.00 12.87 O ATOM 398 CB SER A 50 11.011 17.906 28.039 1.00 13.16 C ATOM 399 OG SER A 50 10.981 19.180 27.409 1.00 14.96 O ATOM 400 N THR A 51 11.073 16.286 25.464 1.00 12.78 N ATOM 401 CA THR A 51 10.840 16.154 24.027 1.00 12.79 C ATOM 402 C THR A 51 9.358 16.419 23.758 1.00 13.79 C ATOM 403 O THR A 51 8.476 16.143 24.560 1.00 13.23 O ATOM 404 CB THR A 51 11.225 14.746 23.565 1.00 12.34 C ATOM 405 OG1 THR A 51 12.596 14.522 23.941 1.00 12.44 O ATOM 406 CG2 THR A 51 11.125 14.569 22.049 1.00 11.50 C ATOM 407 N ASP A 52 9.112 16.974 22.558 1.00 13.36 N ATOM 408 CA ASP A 52 7.751 17.230 22.092 1.00 13.27 C ATOM 409 C ASP A 52 7.365 16.138 21.098 1.00 13.99 C ATOM 410 O ASP A 52 8.178 15.768 20.247 1.00 13.69 O ATOM 411 CB ASP A 52 7.683 18.589 21.393 1.00 12.91 C ATOM 412 CG ASP A 52 7.711 19.765 22.352 1.00 17.41 C ATOM 413 OD1 ASP A 52 7.632 19.600 23.577 1.00 16.32 O ATOM 414 OD2 ASP A 52 7.798 20.897 21.821 1.00 19.54 O ATOM 415 N TYR A 53 6.158 15.613 21.221 1.00 12.70 N ATOM 416 CA TYR A 53 5.712 14.497 20.407 1.00 13.43 C ATOM 417 C TYR A 53 4.401 14.713 19.667 1.00 12.80 C ATOM 418 O TYR A 53 3.406 15.217 20.192 1.00 12.87 O ATOM 419 CB TYR A 53 5.450 13.272 21.328 1.00 12.20 C ATOM 420 CG TYR A 53 6.689 12.771 22.032 1.00 11.53 C ATOM 421 CD1 TYR A 53 7.102 13.340 23.220 1.00 12.21 C ATOM 422 CD2 TYR A 53 7.467 11.756 21.459 1.00 11.27 C ATOM 423 CE1 TYR A 53 8.252 12.936 23.865 1.00 11.70 C ATOM 424 CE2 TYR A 53 8.616 11.324 22.109 1.00 10.80 C ATOM 425 CZ TYR A 53 8.993 11.921 23.294 1.00 11.40 C ATOM 426 OH TYR A 53 10.141 11.462 23.916 1.00 12.14 O ATOM 427 N GLY A 54 4.377 14.261 18.409 1.00 12.80 N ATOM 428 CA GLY A 54 3.196 14.149 17.611 1.00 12.45 C ATOM 429 C GLY A 54 2.718 15.409 16.911 1.00 13.24 C ATOM 430 O GLY A 54 3.387 16.440 16.830 1.00 13.19 O ATOM 431 N ILE A 55 1.486 15.260 16.431 1.00 13.98 N ATOM 432 CA ILE A 55 0.824 16.315 15.662 1.00 13.71 C ATOM 433 C ILE A 55 0.709 17.642 16.380 1.00 13.72 C ATOM 434 O ILE A 55 0.733 18.679 15.723 1.00 13.06 O ATOM 435 CB ILE A 55 -0.576 15.788 15.267 1.00 18.90 C ATOM 436 CG1 ILE A 55 -1.272 16.703 14.258 1.00 28.94 C ATOM 437 CG2 ILE A 55 -1.474 15.532 16.462 1.00 18.47 C ATOM 438 CD1 ILE A 55 -1.143 16.136 12.863 1.00 28.28 C ATOM 439 N LEU A 56 0.579 17.637 17.707 1.00 13.89 N ATOM 440 CA LEU A 56 0.511 18.878 18.464 1.00 13.26 C ATOM 441 C LEU A 56 1.725 19.033 19.378 1.00 13.84 C ATOM 442 O LEU A 56 1.693 19.885 20.266 1.00 13.70 O ATOM 443 CB LEU A 56 -0.827 18.980 19.231 1.00 12.22 C ATOM 444 CG LEU A 56 -2.019 19.308 18.290 1.00 15.11 C ATOM 445 CD1 LEU A 56 -3.361 19.027 18.947 1.00 14.40 C ATOM 446 CD2 LEU A 56 -1.915 20.769 17.881 1.00 15.30 C ATOM 447 N GLN A 57 2.833 18.326 19.115 1.00 12.52 N ATOM 448 CA GLN A 57 4.075 18.517 19.860 1.00 11.86 C ATOM 449 C GLN A 57 3.842 18.671 21.366 1.00 12.75 C ATOM 450 O GLN A 57 4.323 19.608 22.010 1.00 12.81 O ATOM 451 CB GLN A 57 4.851 19.723 19.275 1.00 9.79 C ATOM 452 CG GLN A 57 5.344 19.423 17.837 1.00 10.50 C ATOM 453 CD GLN A 57 6.463 18.397 17.841 1.00 10.63 C ATOM 454 OE1 GLN A 57 7.624 18.760 18.077 1.00 12.76 O ATOM 455 NE2 GLN A 57 6.128 17.130 17.594 1.00 11.89 N ATOM 456 N ILE A 58 3.269 17.611 21.925 1.00 12.44 N ATOM 457 CA ILE A 58 2.960 17.556 23.365 1.00 12.03 C ATOM 458 C ILE A 58 4.248 17.218 24.098 1.00 13.00 C ATOM 459 O ILE A 58 4.979 16.309 23.699 1.00 12.27 O ATOM 460 CB ILE A 58 1.821 16.541 23.558 1.00 14.33 C ATOM 461 CG1 ILE A 58 0.551 17.172 22.943 1.00 15.72 C ATOM 462 CG2 ILE A 58 1.640 16.227 25.037 1.00 16.30 C ATOM 463 CD1 ILE A 58 -0.623 16.207 22.939 1.00 19.58 C ATOM 464 N ASN A 59 4.548 17.977 25.152 1.00 13.56 N ATOM 465 CA ASN A 59 5.821 17.864 25.841 1.00 15.06 C ATOM 466 C ASN A 59 5.887 16.861 26.975 1.00 15.66 C ATOM 467 O ASN A 59 4.961 16.744 27.761 1.00 17.53 O ATOM 468 CB ASN A 59 6.186 19.261 26.397 1.00 16.59 C ATOM 469 CG ASN A 59 7.634 19.275 26.857 1.00 21.97 C ATOM 470 OD1 ASN A 59 7.946 19.352 28.033 1.00 34.80 O ATOM 471 ND2 ASN A 59 8.538 19.171 25.898 1.00 37.21 N ATOM 472 N SER A 60 7.014 16.173 27.092 1.00 14.88 N ATOM 473 CA SER A 60 7.243 15.161 28.096 1.00 15.86 C ATOM 474 C SER A 60 7.554 15.763 29.460 1.00 17.19 C ATOM 475 O SER A 60 7.489 14.998 30.402 1.00 19.11 O ATOM 476 CB SER A 60 8.384 14.251 27.630 1.00 11.55 C ATOM 477 OG SER A 60 9.605 14.993 27.628 1.00 14.76 O ATOM 478 N ARG A 61 7.798 17.058 29.591 1.00 17.46 N ATOM 479 CA ARG A 61 8.065 17.598 30.938 1.00 18.93 C ATOM 480 C ARG A 61 6.808 17.516 31.793 1.00 19.51 C ATOM 481 O ARG A 61 6.933 17.252 32.986 1.00 21.21 O ATOM 482 CB ARG A 61 8.588 19.033 30.897 1.00 25.26 C ATOM 483 CG ARG A 61 8.782 19.586 32.321 1.00 31.29 C ATOM 484 CD ARG A 61 9.980 20.503 32.391 1.00 40.32 C ATOM 485 NE ARG A 61 11.186 19.943 31.814 1.00 46.29 N ATOM 486 CZ ARG A 61 11.997 19.039 32.347 1.00 46.51 C ATOM 487 NH1 ARG A 61 11.764 18.517 33.548 1.00 44.12 N ATOM 488 NH2 ARG A 61 13.064 18.664 31.652 1.00 43.34 N ATOM 489 N TRP A 62 5.631 17.705 31.194 1.00 17.36 N ATOM 490 CA TRP A 62 4.399 17.678 31.968 1.00 17.09 C ATOM 491 C TRP A 62 3.358 16.656 31.599 1.00 15.64 C ATOM 492 O TRP A 62 2.590 16.155 32.442 1.00 15.50 O ATOM 493 CB TRP A 62 3.670 19.047 31.794 1.00 21.49 C ATOM 494 CG TRP A 62 4.497 20.216 32.242 1.00 25.27 C ATOM 495 CD1 TRP A 62 5.185 21.093 31.451 1.00 31.43 C ATOM 496 CD2 TRP A 62 4.759 20.599 33.591 1.00 33.57 C ATOM 497 NE1 TRP A 62 5.848 22.013 32.235 1.00 36.44 N ATOM 498 CE2 TRP A 62 5.601 21.725 33.551 1.00 36.42 C ATOM 499 CE3 TRP A 62 4.349 20.092 34.830 1.00 37.32 C ATOM 500 CZ2 TRP A 62 6.046 22.359 34.713 1.00 40.44 C ATOM 501 CZ3 TRP A 62 4.793 20.720 35.982 1.00 43.37 C ATOM 502 CH2 TRP A 62 5.628 21.843 35.907 1.00 42.07 C ATOM 503 N TRP A 63 3.258 16.334 30.301 1.00 14.66 N ATOM 504 CA TRP A 63 2.100 15.607 29.826 1.00 14.06 C ATOM 505 C TRP A 63 2.159 14.148 29.490 1.00 13.80 C ATOM 506 O TRP A 63 1.127 13.484 29.588 1.00 15.16 O ATOM 507 CB TRP A 63 1.599 16.379 28.555 1.00 14.24 C ATOM 508 CG TRP A 63 1.403 17.834 28.890 1.00 14.30 C ATOM 509 CD1 TRP A 63 2.177 18.876 28.462 1.00 15.36 C ATOM 510 CD2 TRP A 63 0.406 18.376 29.768 1.00 14.38 C ATOM 511 NE1 TRP A 63 1.698 20.050 29.012 1.00 18.28 N ATOM 512 CE2 TRP A 63 0.641 19.763 29.823 1.00 14.74 C ATOM 513 CE3 TRP A 63 -0.642 17.810 30.498 1.00 16.06 C ATOM 514 CZ2 TRP A 63 -0.178 20.617 30.582 1.00 17.10 C ATOM 515 CZ3 TRP A 63 -1.447 18.660 31.251 1.00 16.14 C ATOM 516 CH2 TRP A 63 -1.188 20.033 31.276 1.00 16.87 C ATOM 517 N CYS A 64 3.290 13.629 29.038 1.00 14.24 N ATOM 518 CA CYS A 64 3.373 12.217 28.688 1.00 13.13 C ATOM 519 C CYS A 64 4.663 11.664 29.292 1.00 13.66 C ATOM 520 O CYS A 64 5.556 12.462 29.593 1.00 13.70 O ATOM 521 CB CYS A 64 3.288 11.987 27.168 1.00 12.35 C ATOM 522 SG CYS A 64 4.599 12.784 26.194 1.00 12.29 S ATOM 523 N ASN A 65 4.774 10.330 29.350 1.00 13.27 N ATOM 524 CA ASN A 65 6.002 9.742 29.846 1.00 13.40 C ATOM 525 C ASN A 65 6.882 9.167 28.736 1.00 13.11 C ATOM 526 O ASN A 65 6.375 8.371 27.945 1.00 13.38 O ATOM 527 CB ASN A 65 5.684 8.575 30.794 1.00 14.26 C ATOM 528 CG ASN A 65 7.000 7.953 31.257 1.00 17.69 C ATOM 529 OD1 ASN A 65 7.832 8.680 31.776 1.00 17.85 O ATOM 530 ND2 ASN A 65 7.136 6.661 31.000 1.00 23.06 N ATOM 531 N ASP A 66 8.147 9.621 28.713 1.00 13.15 N ATOM 532 CA ASP A 66 9.083 9.071 27.748 1.00 13.84 C ATOM 533 C ASP A 66 10.268 8.406 28.472 1.00 14.87 C ATOM 534 O ASP A 66 11.179 7.970 27.776 1.00 15.07 O ATOM 535 CB ASP A 66 9.574 10.065 26.715 1.00 13.01 C ATOM 536 CG ASP A 66 10.404 11.218 27.206 1.00 13.24 C ATOM 537 OD1 ASP A 66 10.750 11.247 28.386 1.00 15.15 O ATOM 538 OD2 ASP A 66 10.727 12.102 26.374 1.00 13.59 O ATOM 539 N GLY A 67 10.239 8.383 29.790 1.00 15.39 N ATOM 540 CA GLY A 67 11.305 7.739 30.551 1.00 17.01 C ATOM 541 C GLY A 67 12.650 8.402 30.525 1.00 18.70 C ATOM 542 O GLY A 67 13.612 7.852 31.110 1.00 20.29 O ATOM 543 N ARG A 68 12.809 9.558 29.903 1.00 17.00 N ATOM 544 CA ARG A 68 14.100 10.229 29.865 1.00 18.17 C ATOM 545 C ARG A 68 13.970 11.712 30.156 1.00 18.62 C ATOM 546 O ARG A 68 14.817 12.503 29.738 1.00 20.22 O ATOM 547 CB ARG A 68 14.741 9.969 28.507 1.00 16.63 C ATOM 548 CG ARG A 68 14.005 10.632 27.359 1.00 16.60 C ATOM 549 CD ARG A 68 14.949 10.573 26.145 1.00 15.25 C ATOM 550 NE ARG A 68 14.508 11.546 25.131 1.00 16.84 N ATOM 551 CZ ARG A 68 14.969 11.525 23.872 1.00 15.76 C ATOM 552 NH1 ARG A 68 15.890 10.645 23.489 1.00 19.00 N ATOM 553 NH2 ARG A 68 14.524 12.428 22.999 1.00 14.12 N ATOM 554 N THR A 69 12.930 12.114 30.881 1.00 18.96 N ATOM 555 CA THR A 69 12.768 13.533 31.244 1.00 20.03 C ATOM 556 C THR A 69 12.708 13.637 32.769 1.00 22.06 C ATOM 557 O THR A 69 11.642 13.550 33.361 1.00 20.67 O ATOM 558 CB THR A 69 11.506 14.116 30.601 1.00 17.21 C ATOM 559 OG1 THR A 69 11.494 13.791 29.185 1.00 14.31 O ATOM 560 CG2 THR A 69 11.431 15.633 30.715 1.00 14.59 C ATOM 561 N PRO A 70 13.852 13.725 33.424 1.00 24.14 N ATOM 562 CA PRO A 70 13.925 13.747 34.876 1.00 26.59 C ATOM 563 C PRO A 70 13.092 14.840 35.496 1.00 28.31 C ATOM 564 O PRO A 70 13.218 16.003 35.103 1.00 29.43 O ATOM 565 CB PRO A 70 15.401 14.033 35.176 1.00 27.24 C ATOM 566 CG PRO A 70 16.128 13.494 33.994 1.00 27.21 C ATOM 567 CD PRO A 70 15.212 13.768 32.830 1.00 25.98 C ATOM 568 N GLY A 71 12.286 14.461 36.483 1.00 28.93 N ATOM 569 CA GLY A 71 11.481 15.430 37.208 1.00 31.07 C ATOM 570 C GLY A 71 10.126 15.591 36.537 1.00 31.15 C ATOM 571 O GLY A 71 9.428 16.510 36.929 1.00 32.49 O ATOM 572 N SER A 72 9.815 14.734 35.580 1.00 31.97 N ATOM 573 CA SER A 72 8.623 14.778 34.807 1.00 31.32 C ATOM 574 C SER A 72 7.342 14.229 35.404 1.00 30.17 C ATOM 575 O SER A 72 7.224 13.288 36.175 1.00 29.68 O ATOM 576 CB SER A 72 8.817 14.005 33.463 1.00 30.95 C ATOM 577 OG SER A 72 9.225 14.976 32.522 1.00 48.38 O ATOM 578 N ARG A 73 6.301 14.928 34.927 1.00 28.57 N ATOM 579 CA ARG A 73 4.945 14.519 35.239 1.00 26.37 C ATOM 580 C ARG A 73 4.482 13.901 33.921 1.00 25.15 C ATOM 581 O ARG A 73 5.115 14.064 32.857 1.00 25.87 O ATOM 582 CB ARG A 73 4.110 15.689 35.739 1.00 25.25 C ATOM 583 CG ARG A 73 4.725 16.369 36.972 1.00 30.81 C ATOM 584 CD ARG A 73 4.540 15.496 38.207 1.00 34.64 C ATOM 585 NE ARG A 73 3.151 15.213 38.537 1.00 43.61 N ATOM 586 CZ ARG A 73 2.303 15.981 39.205 1.00 46.83 C ATOM 587 NH1 ARG A 73 2.710 17.159 39.668 1.00 49.43 N ATOM 588 NH2 ARG A 73 1.052 15.592 39.424 1.00 50.26 N ATOM 589 N ASN A 74 3.422 13.169 34.011 1.00 22.41 N ATOM 590 CA ASN A 74 2.720 12.476 32.956 1.00 18.90 C ATOM 591 C ASN A 74 1.230 12.741 33.232 1.00 18.51 C ATOM 592 O ASN A 74 0.479 11.826 33.539 1.00 17.69 O ATOM 593 CB ASN A 74 3.052 10.981 33.053 1.00 15.23 C ATOM 594 CG ASN A 74 2.375 10.143 32.010 1.00 17.61 C ATOM 595 OD1 ASN A 74 1.812 10.710 31.047 1.00 16.53 O ATOM 596 ND2 ASN A 74 2.432 8.834 32.130 1.00 17.98 N ATOM 597 N LEU A 75 0.849 14.011 33.069 1.00 17.76 N ATOM 598 CA LEU A 75 -0.548 14.370 33.362 1.00 18.17 C ATOM 599 C LEU A 75 -1.597 13.800 32.447 1.00 17.60 C ATOM 600 O LEU A 75 -2.780 13.699 32.869 1.00 17.69 O ATOM 601 CB LEU A 75 -0.666 15.895 33.476 1.00 20.05 C ATOM 602 CG LEU A 75 0.161 16.474 34.639 1.00 22.89 C ATOM 603 CD1 LEU A 75 0.201 17.987 34.532 1.00 24.89 C ATOM 604 CD2 LEU A 75 -0.371 16.009 35.979 1.00 26.46 C ATOM 605 N CYS A 76 -1.279 13.397 31.211 1.00 16.50 N ATOM 606 CA CYS A 76 -2.239 12.752 30.338 1.00 16.72 C ATOM 607 C CYS A 76 -2.246 11.248 30.516 1.00 16.24 C ATOM 608 O CYS A 76 -3.010 10.507 29.890 1.00 15.79 O ATOM 609 CB CYS A 76 -1.952 13.075 28.852 1.00 14.35 C ATOM 610 SG CYS A 76 -2.205 14.845 28.603 1.00 12.80 S ATOM 611 N ASN A 77 -1.351 10.778 31.398 1.00 17.38 N ATOM 612 CA ASN A 77 -1.222 9.373 31.716 1.00 17.28 C ATOM 613 C ASN A 77 -1.114 8.516 30.457 1.00 18.05 C ATOM 614 O ASN A 77 -1.879 7.602 30.190 1.00 19.07 O ATOM 615 CB AASN A 77 -2.330 8.861 32.635 0.50 19.29 C ATOM 616 CB BASN A 77 -2.458 8.917 32.513 0.50 17.92 C ATOM 617 CG AASN A 77 -1.953 7.532 33.270 0.50 22.46 C ATOM 618 CG BASN A 77 -2.707 9.722 33.771 0.50 23.19 C ATOM 619 OD1AASN A 77 -2.840 6.733 33.572 0.50 30.03 O ATOM 620 OD1BASN A 77 -1.899 9.711 34.694 0.50 22.14 O ATOM 621 ND2AASN A 77 -0.669 7.267 33.489 0.50 23.82 N ATOM 622 ND2BASN A 77 -3.834 10.425 33.785 0.50 22.71 N ATOM 623 N ILE A 78 -0.096 8.821 29.647 1.00 16.67 N ATOM 624 CA ILE A 78 0.089 8.093 28.375 1.00 16.83 C ATOM 625 C ILE A 78 1.571 8.062 28.071 1.00 16.79 C ATOM 626 O ILE A 78 2.288 9.015 28.373 1.00 15.80 O ATOM 627 CB ILE A 78 -0.703 8.911 27.332 1.00 20.32 C ATOM 628 CG1 ILE A 78 -0.977 8.184 26.020 1.00 22.75 C ATOM 629 CG2 ILE A 78 -0.075 10.268 27.049 1.00 18.60 C ATOM 630 CD1 ILE A 78 -2.121 7.193 26.190 1.00 27.62 C ATOM 631 N PRO A 79 2.060 6.975 27.491 1.00 15.84 N ATOM 632 CA PRO A 79 3.432 6.905 27.000 1.00 15.30 C ATOM 633 C PRO A 79 3.545 7.953 25.897 1.00 15.24 C ATOM 634 O PRO A 79 2.606 8.061 25.106 1.00 15.04 O ATOM 635 CB PRO A 79 3.527 5.505 26.379 1.00 15.36 C ATOM 636 CG PRO A 79 2.337 4.757 26.868 1.00 17.63 C ATOM 637 CD PRO A 79 1.269 5.775 27.121 1.00 15.90 C ATOM 638 N CYS A 80 4.657 8.684 25.769 1.00 13.72 N ATOM 639 CA CYS A 80 4.743 9.688 24.698 1.00 14.15 C ATOM 640 C CYS A 80 4.680 9.041 23.325 1.00 14.31 C ATOM 641 O CYS A 80 4.230 9.664 22.358 1.00 14.82 O ATOM 642 CB CYS A 80 6.043 10.492 24.833 1.00 12.75 C ATOM 643 SG CYS A 80 6.159 11.473 26.337 1.00 11.08 S ATOM 644 N SER A 81 5.097 7.787 23.190 1.00 14.14 N ATOM 645 CA SER A 81 5.035 7.100 21.892 1.00 15.73 C ATOM 646 C SER A 81 3.609 6.967 21.397 1.00 17.04 C ATOM 647 O SER A 81 3.336 6.912 20.179 1.00 18.31 O ATOM 648 CB SER A 81 5.724 5.730 22.096 1.00 20.50 C ATOM 649 OG SER A 81 4.882 4.936 22.919 1.00 21.04 O ATOM 650 N ALA A 82 2.613 6.925 22.284 1.00 17.55 N ATOM 651 CA ALA A 82 1.202 6.829 21.896 1.00 19.09 C ATOM 652 C ALA A 82 0.689 8.093 21.244 1.00 20.22 C ATOM 653 O ALA A 82 -0.351 8.134 20.564 1.00 20.99 O ATOM 654 CB ALA A 82 0.373 6.422 23.116 1.00 19.91 C ATOM 655 N LEU A 83 1.468 9.189 21.325 1.00 19.14 N ATOM 656 CA LEU A 83 1.114 10.441 20.672 1.00 18.22 C ATOM 657 C LEU A 83 1.631 10.500 19.241 1.00 17.88 C ATOM 658 O LEU A 83 1.591 11.556 18.590 1.00 18.02 O ATOM 659 CB LEU A 83 1.651 11.589 21.532 1.00 16.41 C ATOM 660 CG LEU A 83 1.159 11.566 22.988 1.00 17.08 C ATOM 661 CD1 LEU A 83 1.746 12.787 23.682 1.00 20.50 C ATOM 662 CD2 LEU A 83 -0.368 11.505 23.051 1.00 21.70 C ATOM 663 N LEU A 84 2.243 9.421 18.748 1.00 17.90 N ATOM 664 CA LEU A 84 2.812 9.395 17.404 1.00 17.56 C ATOM 665 C LEU A 84 2.023 8.554 16.425 1.00 17.56 C ATOM 666 O LEU A 84 2.314 8.439 15.224 1.00 18.07 O ATOM 667 CB LEU A 84 4.255 8.847 17.461 1.00 17.51 C ATOM 668 CG LEU A 84 5.188 9.587 18.402 1.00 19.56 C ATOM 669 CD1 LEU A 84 6.599 8.985 18.301 1.00 19.02 C ATOM 670 CD2 LEU A 84 5.288 11.076 18.134 1.00 15.18 C ATOM 671 N SER A 85 0.948 7.974 16.935 1.00 18.11 N ATOM 672 CA SER A 85 0.064 7.104 16.186 1.00 18.71 C ATOM 673 C SER A 85 -0.627 7.799 15.020 1.00 19.48 C ATOM 674 O SER A 85 -0.854 9.015 15.046 1.00 20.41 O ATOM 675 CB ASER A 85 -1.010 6.573 17.152 0.50 20.52 C ATOM 676 CB BSER A 85 -1.006 6.559 17.148 0.50 19.28 C ATOM 677 OG ASER A 85 -1.911 5.724 16.470 0.50 18.30 O ATOM 678 OG BSER A 85 -1.479 7.585 18.005 0.50 14.26 O ATOM 679 N SER A 86 -1.050 6.991 14.046 1.00 18.39 N ATOM 680 CA SER A 86 -1.833 7.518 12.925 1.00 18.98 C ATOM 681 C SER A 86 -3.233 7.946 13.363 1.00 19.55 C ATOM 682 O SER A 86 -3.877 8.800 12.755 1.00 21.91 O ATOM 683 CB SER A 86 -1.885 6.501 11.781 1.00 28.82 C ATOM 684 OG SER A 86 -2.374 5.244 12.224 1.00 32.53 O ATOM 685 N ASP A 87 -3.746 7.396 14.454 1.00 18.54 N ATOM 686 CA ASP A 87 -5.023 7.754 15.052 1.00 17.67 C ATOM 687 C ASP A 87 -4.738 8.880 16.050 1.00 16.94 C ATOM 688 O ASP A 87 -3.931 8.677 16.956 1.00 16.53 O ATOM 689 CB ASP A 87 -5.610 6.523 15.729 1.00 19.19 C ATOM 690 CG ASP A 87 -6.887 6.747 16.498 1.00 24.83 C ATOM 691 OD1 ASP A 87 -7.162 7.844 17.009 1.00 20.38 O ATOM 692 OD2 ASP A 87 -7.681 5.777 16.631 1.00 26.78 O ATOM 693 N ILE A 88 -5.331 10.059 15.885 1.00 15.81 N ATOM 694 CA ILE A 88 -5.012 11.198 16.734 1.00 14.90 C ATOM 695 C ILE A 88 -5.758 11.246 18.058 1.00 14.53 C ATOM 696 O ILE A 88 -5.580 12.228 18.803 1.00 14.34 O ATOM 697 CB ILE A 88 -5.215 12.540 15.976 1.00 13.07 C ATOM 698 CG1 ILE A 88 -6.689 12.822 15.713 1.00 16.88 C ATOM 699 CG2 ILE A 88 -4.378 12.529 14.692 1.00 15.25 C ATOM 700 CD1 ILE A 88 -6.921 14.242 15.210 1.00 17.37 C ATOM 701 N THR A 89 -6.524 10.212 18.404 1.00 13.93 N ATOM 702 CA THR A 89 -7.303 10.243 19.632 1.00 13.76 C ATOM 703 C THR A 89 -6.508 10.633 20.868 1.00 14.16 C ATOM 704 O THR A 89 -6.932 11.524 21.628 1.00 14.13 O ATOM 705 CB THR A 89 -8.021 8.888 19.844 1.00 17.13 C ATOM 706 OG1 THR A 89 -8.880 8.585 18.741 1.00 17.25 O ATOM 707 CG2 THR A 89 -8.833 8.891 21.128 1.00 17.08 C ATOM 708 N ALA A 90 -5.364 10.006 21.111 1.00 14.14 N ATOM 709 CA ALA A 90 -4.612 10.300 22.340 1.00 13.95 C ATOM 710 C ALA A 90 -4.102 11.727 22.387 1.00 13.93 C ATOM 711 O ALA A 90 -4.145 12.398 23.436 1.00 14.20 O ATOM 712 CB ALA A 90 -3.479 9.285 22.477 1.00 16.92 C ATOM 713 N SER A 91 -3.613 12.195 21.233 1.00 13.80 N ATOM 714 CA SER A 91 -3.117 13.570 21.141 1.00 12.79 C ATOM 715 C SER A 91 -4.258 14.556 21.417 1.00 12.65 C ATOM 716 O SER A 91 -4.021 15.544 22.088 1.00 13.98 O ATOM 717 CB SER A 91 -2.503 13.877 19.781 1.00 14.04 C ATOM 718 OG SER A 91 -1.194 13.311 19.667 1.00 12.81 O ATOM 719 N VAL A 92 -5.435 14.288 20.852 1.00 12.64 N ATOM 720 CA VAL A 92 -6.555 15.213 21.108 1.00 12.99 C ATOM 721 C VAL A 92 -6.975 15.179 22.564 1.00 14.03 C ATOM 722 O VAL A 92 -7.159 16.253 23.167 1.00 14.85 O ATOM 723 CB VAL A 92 -7.749 14.860 20.202 1.00 14.47 C ATOM 724 CG1 VAL A 92 -8.986 15.660 20.618 1.00 14.77 C ATOM 725 CG2 VAL A 92 -7.374 15.170 18.755 1.00 16.57 C ATOM 726 N ASN A 93 -7.101 13.990 23.173 1.00 14.37 N ATOM 727 CA ASN A 93 -7.508 13.930 24.580 1.00 14.89 C ATOM 728 C ASN A 93 -6.525 14.699 25.464 1.00 14.52 C ATOM 729 O ASN A 93 -6.903 15.377 26.433 1.00 15.52 O ATOM 730 CB ASN A 93 -7.544 12.465 25.066 1.00 16.92 C ATOM 731 CG ASN A 93 -8.702 11.650 24.545 1.00 26.22 C ATOM 732 OD1 ASN A 93 -8.668 10.408 24.525 1.00 29.79 O ATOM 733 ND2 ASN A 93 -9.762 12.320 24.141 1.00 25.39 N ATOM 734 N CYS A 94 -5.236 14.545 25.162 1.00 13.24 N ATOM 735 CA CYS A 94 -4.190 15.212 25.914 1.00 12.58 C ATOM 736 C CYS A 94 -4.207 16.707 25.624 1.00 13.66 C ATOM 737 O CYS A 94 -4.082 17.481 26.580 1.00 12.38 O ATOM 738 CB CYS A 94 -2.835 14.548 25.649 1.00 12.11 C ATOM 739 SG CYS A 94 -1.514 15.193 26.719 1.00 12.21 S ATOM 740 N ALA A 95 -4.383 17.116 24.375 1.00 14.17 N ATOM 741 CA ALA A 95 -4.474 18.539 24.058 1.00 13.88 C ATOM 742 C ALA A 95 -5.627 19.226 24.788 1.00 13.75 C ATOM 743 O ALA A 95 -5.504 20.373 25.219 1.00 13.71 O ATOM 744 CB ALA A 95 -4.636 18.738 22.553 1.00 12.66 C ATOM 745 N LYS A 96 -6.749 18.532 24.996 1.00 13.49 N ATOM 746 CA LYS A 96 -7.851 19.133 25.758 1.00 13.23 C ATOM 747 C LYS A 96 -7.407 19.461 27.180 1.00 13.65 C ATOM 748 O LYS A 96 -7.865 20.472 27.733 1.00 15.26 O ATOM 749 CB LYS A 96 -9.065 18.192 25.767 1.00 12.83 C ATOM 750 CG LYS A 96 -9.712 18.090 24.390 1.00 14.55 C ATOM 751 CD LYS A 96 -10.830 17.055 24.395 1.00 14.51 C ATOM 752 CE LYS A 96 -11.561 17.073 23.055 1.00 16.86 C ATOM 753 NZ LYS A 96 -12.571 15.944 23.061 1.00 20.49 N ATOM 754 N LYS A 97 -6.608 18.549 27.786 1.00 13.10 N ATOM 755 CA LYS A 97 -6.130 18.864 29.143 1.00 13.93 C ATOM 756 C LYS A 97 -5.180 20.051 29.129 1.00 14.45 C ATOM 757 O LYS A 97 -5.217 20.925 30.008 1.00 15.18 O ATOM 758 CB LYS A 97 -5.400 17.610 29.668 1.00 14.43 C ATOM 759 CG LYS A 97 -6.329 16.432 29.960 1.00 18.28 C ATOM 760 CD LYS A 97 -5.452 15.238 30.361 1.00 24.01 C ATOM 761 CE LYS A 97 -6.340 14.047 30.708 1.00 28.70 C ATOM 762 NZ LYS A 97 -7.055 14.346 31.984 1.00 38.09 N ATOM 763 N ILE A 98 -4.268 20.079 28.137 1.00 13.44 N ATOM 764 CA ILE A 98 -3.287 21.167 28.059 1.00 13.11 C ATOM 765 C ILE A 98 -3.979 22.518 27.938 1.00 13.98 C ATOM 766 O ILE A 98 -3.685 23.464 28.681 1.00 15.23 O ATOM 767 CB ILE A 98 -2.306 20.964 26.887 1.00 12.74 C ATOM 768 CG1 ILE A 98 -1.481 19.685 27.088 1.00 15.28 C ATOM 769 CG2 ILE A 98 -1.371 22.172 26.758 1.00 11.29 C ATOM 770 CD1 ILE A 98 -0.735 19.252 25.831 1.00 14.28 C ATOM 771 N VAL A 99 -4.919 22.634 27.008 1.00 14.00 N ATOM 772 CA VAL A 99 -5.610 23.904 26.746 1.00 14.71 C ATOM 773 C VAL A 99 -6.519 24.352 27.880 1.00 16.29 C ATOM 774 O VAL A 99 -6.919 25.513 27.943 1.00 16.68 O ATOM 775 CB VAL A 99 -6.376 23.821 25.414 1.00 12.95 C ATOM 776 CG1 VAL A 99 -7.622 22.972 25.507 1.00 15.00 C ATOM 777 CG2 VAL A 99 -6.709 25.216 24.891 1.00 14.40 C ATOM 778 N SER A 100 -6.822 23.431 28.794 1.00 16.10 N ATOM 779 CA SER A 100 -7.628 23.721 29.965 1.00 18.30 C ATOM 780 C SER A 100 -6.761 24.090 31.160 1.00 18.68 C ATOM 781 O SER A 100 -7.294 24.343 32.246 1.00 20.11 O ATOM 782 CB SER A 100 -8.440 22.470 30.296 1.00 16.65 C ATOM 783 OG SER A 100 -9.347 22.068 29.277 1.00 19.71 O ATOM 784 N ASP A 101 -5.443 24.100 31.044 1.00 18.52 N ATOM 785 CA ASP A 101 -4.551 24.318 32.188 1.00 19.48 C ATOM 786 C ASP A 101 -4.439 25.751 32.681 1.00 20.06 C ATOM 787 O ASP A 101 -3.870 25.967 33.767 1.00 21.45 O ATOM 788 CB ASP A 101 -3.182 23.737 31.833 1.00 22.09 C ATOM 789 CG ASP A 101 -2.307 23.447 33.042 1.00 28.56 C ATOM 790 OD1 ASP A 101 -2.751 22.672 33.906 1.00 30.69 O ATOM 791 OD2 ASP A 101 -1.194 24.006 33.100 1.00 29.39 O ATOM 792 N GLY A 102 -4.930 26.742 31.970 1.00 19.45 N ATOM 793 CA GLY A 102 -4.898 28.112 32.466 1.00 19.52 C ATOM 794 C GLY A 102 -4.594 29.167 31.427 1.00 18.90 C ATOM 795 O GLY A 102 -5.168 30.264 31.426 1.00 19.27 O ATOM 796 N ASN A 103 -3.671 28.834 30.526 1.00 17.54 N ATOM 797 CA ASN A 103 -3.234 29.800 29.526 1.00 16.22 C ATOM 798 C ASN A 103 -3.734 29.500 28.124 1.00 14.21 C ATOM 799 O ASN A 103 -3.217 30.124 27.192 1.00 15.07 O ATOM 800 CB ASN A 103 -1.700 29.866 29.488 1.00 18.05 C ATOM 801 CG ASN A 103 -1.175 30.310 30.848 1.00 23.49 C ATOM 802 OD1 ASN A 103 -1.730 31.232 31.431 1.00 25.06 O ATOM 803 ND2 ASN A 103 -0.135 29.619 31.312 1.00 27.78 N ATOM 804 N GLY A 104 -4.761 28.679 28.019 1.00 12.62 N ATOM 805 CA GLY A 104 -5.381 28.411 26.724 1.00 12.88 C ATOM 806 C GLY A 104 -4.356 27.869 25.736 1.00 12.56 C ATOM 807 O GLY A 104 -3.452 27.114 26.109 1.00 12.94 O ATOM 808 N MET A 105 -4.478 28.322 24.493 1.00 11.50 N ATOM 809 CA MET A 105 -3.564 27.832 23.461 1.00 11.04 C ATOM 810 C MET A 105 -2.196 28.486 23.535 1.00 11.84 C ATOM 811 O MET A 105 -1.329 28.071 22.757 1.00 11.99 O ATOM 812 CB MET A 105 -4.138 27.941 22.044 1.00 12.59 C ATOM 813 CG MET A 105 -5.218 26.866 21.851 1.00 10.74 C ATOM 814 SD MET A 105 -5.752 26.759 20.136 1.00 11.07 S ATOM 815 CE MET A 105 -4.396 25.782 19.457 1.00 15.91 C ATOM 816 N ASN A 106 -1.932 29.429 24.432 1.00 11.22 N ATOM 817 CA ASN A 106 -0.581 29.976 24.546 1.00 12.48 C ATOM 818 C ASN A 106 0.392 28.906 25.028 1.00 13.49 C ATOM 819 O ASN A 106 1.596 29.109 24.874 1.00 14.44 O ATOM 820 CB ASN A 106 -0.547 31.171 25.500 1.00 12.42 C ATOM 821 CG ASN A 106 -1.372 32.321 24.992 1.00 13.53 C ATOM 822 OD1 ASN A 106 -1.021 32.991 24.004 1.00 13.91 O ATOM 823 ND2 ASN A 106 -2.501 32.577 25.664 1.00 12.59 N ATOM 824 N ALA A 107 -0.105 27.768 25.505 1.00 12.94 N ATOM 825 CA ALA A 107 0.754 26.643 25.850 1.00 13.10 C ATOM 826 C ALA A 107 1.573 26.197 24.630 1.00 14.73 C ATOM 827 O ALA A 107 2.678 25.685 24.862 1.00 15.38 O ATOM 828 CB ALA A 107 -0.040 25.452 26.346 1.00 13.35 C ATOM 829 N TRP A 108 1.016 26.348 23.433 1.00 13.94 N ATOM 830 CA TRP A 108 1.792 26.032 22.228 1.00 14.95 C ATOM 831 C TRP A 108 2.468 27.324 21.766 1.00 15.45 C ATOM 832 O TRP A 108 1.820 28.222 21.212 1.00 15.12 O ATOM 833 CB TRP A 108 0.867 25.494 21.145 1.00 12.27 C ATOM 834 CG TRP A 108 0.340 24.125 21.451 1.00 12.97 C ATOM 835 CD1 TRP A 108 0.973 22.954 21.098 1.00 13.33 C ATOM 836 CD2 TRP A 108 -0.855 23.744 22.137 1.00 12.18 C ATOM 837 NE1 TRP A 108 0.242 21.878 21.541 1.00 11.97 N ATOM 838 CE2 TRP A 108 -0.892 22.345 22.187 1.00 12.29 C ATOM 839 CE3 TRP A 108 -1.895 24.475 22.738 1.00 14.22 C ATOM 840 CZ2 TRP A 108 -1.937 21.637 22.775 1.00 12.72 C ATOM 841 CZ3 TRP A 108 -2.963 23.788 23.327 1.00 14.91 C ATOM 842 CH2 TRP A 108 -2.957 22.362 23.357 1.00 12.30 C ATOM 843 N VAL A 109 3.785 27.422 21.963 1.00 16.15 N ATOM 844 CA VAL A 109 4.517 28.629 21.575 1.00 16.88 C ATOM 845 C VAL A 109 4.336 28.942 20.092 1.00 16.07 C ATOM 846 O VAL A 109 4.145 30.135 19.805 1.00 16.46 O ATOM 847 CB VAL A 109 6.003 28.493 21.958 1.00 25.60 C ATOM 848 CG1 VAL A 109 6.820 29.652 21.399 1.00 27.97 C ATOM 849 CG2 VAL A 109 6.171 28.432 23.477 1.00 29.37 C ATOM 850 N ALA A 110 4.314 27.956 19.210 1.00 16.70 N ATOM 851 CA ALA A 110 4.116 28.203 17.785 1.00 15.82 C ATOM 852 C ALA A 110 2.723 28.779 17.516 1.00 16.01 C ATOM 853 O ALA A 110 2.567 29.609 16.613 1.00 15.28 O ATOM 854 CB ALA A 110 4.344 26.954 16.948 1.00 18.42 C ATOM 855 N TRP A 111 1.730 28.332 18.291 1.00 13.89 N ATOM 856 CA TRP A 111 0.399 28.941 18.118 1.00 14.03 C ATOM 857 C TRP A 111 0.439 30.415 18.501 1.00 14.86 C ATOM 858 O TRP A 111 0.010 31.288 17.761 1.00 13.24 O ATOM 859 CB TRP A 111 -0.675 28.255 18.990 1.00 12.15 C ATOM 860 CG TRP A 111 -2.005 28.955 18.861 1.00 13.15 C ATOM 861 CD1 TRP A 111 -2.942 28.734 17.894 1.00 12.87 C ATOM 862 CD2 TRP A 111 -2.534 29.964 19.720 1.00 12.67 C ATOM 863 NE1 TRP A 111 -4.026 29.567 18.088 1.00 11.29 N ATOM 864 CE2 TRP A 111 -3.796 30.325 19.221 1.00 12.21 C ATOM 865 CE3 TRP A 111 -2.056 30.608 20.861 1.00 15.29 C ATOM 866 CZ2 TRP A 111 -4.579 31.296 19.834 1.00 14.29 C ATOM 867 CZ3 TRP A 111 -2.837 31.569 21.484 1.00 14.63 C ATOM 868 CH2 TRP A 111 -4.092 31.916 20.958 1.00 13.59 C ATOM 869 N ARG A 112 1.043 30.736 19.664 1.00 14.40 N ATOM 870 CA ARG A 112 1.124 32.137 20.051 1.00 15.21 C ATOM 871 C ARG A 112 1.908 32.967 19.033 1.00 14.80 C ATOM 872 O ARG A 112 1.460 34.071 18.682 1.00 14.84 O ATOM 873 CB ARG A 112 1.838 32.238 21.432 1.00 17.37 C ATOM 874 CG ARG A 112 1.874 33.702 21.871 1.00 18.15 C ATOM 875 CD ARG A 112 2.343 33.875 23.310 1.00 22.52 C ATOM 876 NE ARG A 112 3.668 33.306 23.498 1.00 28.83 N ATOM 877 CZ ARG A 112 4.829 33.868 23.156 1.00 29.79 C ATOM 878 NH1 ARG A 112 4.873 35.067 22.603 1.00 27.45 N ATOM 879 NH2 ARG A 112 5.956 33.219 23.383 1.00 27.38 N ATOM 880 N ASN A 113 3.030 32.412 18.553 1.00 14.69 N ATOM 881 CA ASN A 113 3.837 33.266 17.655 1.00 13.54 C ATOM 882 C ASN A 113 3.388 33.307 16.213 1.00 13.89 C ATOM 883 O ASN A 113 3.786 34.231 15.486 1.00 14.09 O ATOM 884 CB ASN A 113 5.298 32.763 17.745 1.00 13.29 C ATOM 885 CG ASN A 113 5.976 33.106 19.057 1.00 13.75 C ATOM 886 OD1 ASN A 113 5.639 34.127 19.672 1.00 17.11 O ATOM 887 ND2 ASN A 113 6.931 32.287 19.495 1.00 16.63 N ATOM 888 N ARG A 114 2.648 32.299 15.741 1.00 12.76 N ATOM 889 CA ARG A 114 2.319 32.226 14.308 1.00 12.70 C ATOM 890 C ARG A 114 0.847 32.134 13.982 1.00 12.74 C ATOM 891 O ARG A 114 0.500 32.353 12.810 1.00 13.48 O ATOM 892 CB ARG A 114 3.018 30.935 13.773 1.00 13.37 C ATOM 893 CG ARG A 114 4.518 31.009 14.035 1.00 12.57 C ATOM 894 CD ARG A 114 5.360 29.847 13.561 1.00 12.73 C ATOM 895 NE ARG A 114 5.499 29.930 12.087 1.00 13.18 N ATOM 896 CZ ARG A 114 6.225 29.022 11.429 1.00 13.66 C ATOM 897 NH1 ARG A 114 6.848 28.010 12.050 1.00 13.96 N ATOM 898 NH2 ARG A 114 6.298 29.123 10.104 1.00 15.20 N ATOM 899 N CYS A 115 0.005 31.827 14.983 1.00 13.02 N ATOM 900 CA CYS A 115 -1.405 31.650 14.691 1.00 11.82 C ATOM 901 C CYS A 115 -2.281 32.688 15.396 1.00 13.26 C ATOM 902 O CYS A 115 -3.245 33.175 14.808 1.00 14.49 O ATOM 903 CB CYS A 115 -1.930 30.266 15.111 1.00 10.17 C ATOM 904 SG CYS A 115 -1.065 28.894 14.310 1.00 10.81 S ATOM 905 N LYS A 116 -1.935 32.968 16.638 1.00 12.68 N ATOM 906 CA LYS A 116 -2.732 33.872 17.470 1.00 13.98 C ATOM 907 C LYS A 116 -2.981 35.200 16.773 1.00 14.67 C ATOM 908 O LYS A 116 -2.060 35.851 16.293 1.00 14.91 O ATOM 909 CB LYS A 116 -1.974 34.092 18.786 1.00 9.53 C ATOM 910 CG LYS A 116 -2.764 34.952 19.797 1.00 13.11 C ATOM 911 CD LYS A 116 -1.936 34.981 21.091 1.00 13.14 C ATOM 912 CE LYS A 116 -2.721 35.744 22.162 1.00 13.27 C ATOM 913 NZ LYS A 116 -1.883 35.787 23.422 1.00 14.45 N ATOM 914 N GLY A 117 -4.251 35.597 16.644 1.00 16.40 N ATOM 915 CA GLY A 117 -4.593 36.863 16.029 1.00 17.97 C ATOM 916 C GLY A 117 -4.611 36.902 14.513 1.00 18.71 C ATOM 917 O GLY A 117 -4.949 37.956 13.958 1.00 20.37 O ATOM 918 N THR A 118 -4.273 35.792 13.872 1.00 16.21 N ATOM 919 CA THR A 118 -4.239 35.755 12.415 1.00 16.01 C ATOM 920 C THR A 118 -5.554 35.225 11.883 1.00 15.77 C ATOM 921 O THR A 118 -6.407 34.825 12.673 1.00 14.91 O ATOM 922 CB THR A 118 -3.096 34.866 11.880 1.00 14.72 C ATOM 923 OG1 THR A 118 -3.430 33.495 12.120 1.00 13.23 O ATOM 924 CG2 THR A 118 -1.776 35.245 12.521 1.00 15.36 C ATOM 925 N ASP A 119 -5.718 35.241 10.547 1.00 14.92 N ATOM 926 CA ASP A 119 -6.949 34.738 9.956 1.00 15.76 C ATOM 927 C ASP A 119 -6.929 33.215 9.980 1.00 16.21 C ATOM 928 O ASP A 119 -6.689 32.571 8.976 1.00 16.06 O ATOM 929 CB ASP A 119 -7.014 35.278 8.510 1.00 16.37 C ATOM 930 CG ASP A 119 -8.220 34.797 7.739 1.00 24.33 C ATOM 931 OD1 ASP A 119 -9.221 34.412 8.388 1.00 25.30 O ATOM 932 OD2 ASP A 119 -8.107 34.823 6.490 1.00 26.75 O ATOM 933 N VAL A 120 -7.181 32.582 11.137 1.00 15.22 N ATOM 934 CA VAL A 120 -7.079 31.124 11.243 1.00 15.52 C ATOM 935 C VAL A 120 -8.153 30.362 10.497 1.00 15.62 C ATOM 936 O VAL A 120 -7.996 29.181 10.169 1.00 15.61 O ATOM 937 CB VAL A 120 -7.010 30.652 12.705 1.00 14.50 C ATOM 938 CG1 VAL A 120 -5.751 31.174 13.406 1.00 13.49 C ATOM 939 CG2 VAL A 120 -8.260 31.106 13.426 1.00 18.01 C ATOM 940 N GLN A 121 -9.273 30.994 10.132 1.00 16.86 N ATOM 941 CA GLN A 121 -10.273 30.336 9.315 1.00 15.91 C ATOM 942 C GLN A 121 -9.692 29.922 7.965 1.00 15.75 C ATOM 943 O GLN A 121 -10.167 28.980 7.335 1.00 16.32 O ATOM 944 CB GLN A 121 -11.495 31.223 9.070 1.00 22.07 C ATOM 945 CG GLN A 121 -12.657 30.497 8.406 1.00 27.94 C ATOM 946 CD GLN A 121 -12.614 30.561 6.892 1.00 32.52 C ATOM 947 OE1 GLN A 121 -12.924 29.580 6.207 1.00 35.65 O ATOM 948 NE2 GLN A 121 -12.206 31.703 6.348 1.00 37.52 N ATOM 949 N ALA A 122 -8.664 30.607 7.461 1.00 14.16 N ATOM 950 CA ALA A 122 -8.027 30.196 6.206 1.00 14.48 C ATOM 951 C ALA A 122 -7.622 28.740 6.245 1.00 15.17 C ATOM 952 O ALA A 122 -7.595 28.065 5.210 1.00 15.64 O ATOM 953 CB ALA A 122 -6.822 31.092 5.912 1.00 13.71 C ATOM 954 N TRP A 123 -7.234 28.188 7.408 1.00 14.85 N ATOM 955 CA TRP A 123 -6.812 26.817 7.556 1.00 15.09 C ATOM 956 C TRP A 123 -7.910 25.791 7.334 1.00 15.70 C ATOM 957 O TRP A 123 -7.574 24.626 7.131 1.00 15.01 O ATOM 958 CB TRP A 123 -6.153 26.558 8.944 1.00 13.01 C ATOM 959 CG TRP A 123 -4.873 27.352 9.027 1.00 13.65 C ATOM 960 CD1 TRP A 123 -4.669 28.519 9.711 1.00 15.17 C ATOM 961 CD2 TRP A 123 -3.632 27.050 8.386 1.00 14.16 C ATOM 962 NE1 TRP A 123 -3.381 28.967 9.494 1.00 14.36 N ATOM 963 CE2 TRP A 123 -2.731 28.074 8.704 1.00 16.38 C ATOM 964 CE3 TRP A 123 -3.214 25.995 7.561 1.00 15.02 C ATOM 965 CZ2 TRP A 123 -1.413 28.095 8.223 1.00 17.35 C ATOM 966 CZ3 TRP A 123 -1.915 26.031 7.067 1.00 20.19 C ATOM 967 CH2 TRP A 123 -1.026 27.061 7.411 1.00 18.61 C ATOM 968 N ILE A 124 -9.186 26.173 7.348 1.00 16.26 N ATOM 969 CA ILE A 124 -10.244 25.203 7.081 1.00 17.34 C ATOM 970 C ILE A 124 -10.958 25.562 5.783 1.00 18.63 C ATOM 971 O ILE A 124 -11.938 24.916 5.400 1.00 18.03 O ATOM 972 CB ILE A 124 -11.213 25.069 8.264 1.00 17.88 C ATOM 973 CG1 ILE A 124 -11.838 26.401 8.663 1.00 20.55 C ATOM 974 CG2 ILE A 124 -10.467 24.479 9.476 1.00 17.12 C ATOM 975 CD1 ILE A 124 -12.851 26.299 9.796 1.00 28.01 C ATOM 976 N ARG A 125 -10.388 26.510 5.037 1.00 18.58 N ATOM 977 CA ARG A 125 -10.994 26.967 3.786 1.00 19.52 C ATOM 978 C ARG A 125 -11.056 25.839 2.787 1.00 19.76 C ATOM 979 O ARG A 125 -10.131 25.059 2.624 1.00 20.17 O ATOM 980 CB ARG A 125 -10.203 28.151 3.221 1.00 27.40 C ATOM 981 CG ARG A 125 -10.855 28.871 2.055 1.00 39.28 C ATOM 982 CD ARG A 125 -11.064 30.346 2.332 1.00 48.18 C ATOM 983 NE ARG A 125 -9.952 31.034 2.972 1.00 53.75 N ATOM 984 CZ ARG A 125 -10.104 32.121 3.724 1.00 57.96 C ATOM 985 NH1 ARG A 125 -11.318 32.625 3.921 1.00 62.67 N ATOM 986 NH2 ARG A 125 -9.086 32.743 4.298 1.00 61.35 N ATOM 987 N GLY A 126 -12.222 25.666 2.127 1.00 19.98 N ATOM 988 CA GLY A 126 -12.389 24.617 1.148 1.00 20.71 C ATOM 989 C GLY A 126 -12.705 23.238 1.709 1.00 21.05 C ATOM 990 O GLY A 126 -13.006 22.326 0.933 1.00 22.69 O ATOM 991 N CYS A 127 -12.640 23.079 3.024 1.00 20.04 N ATOM 992 CA CYS A 127 -12.893 21.753 3.586 1.00 20.56 C ATOM 993 C CYS A 127 -14.398 21.535 3.715 1.00 21.00 C ATOM 994 O CYS A 127 -15.106 22.449 4.118 1.00 20.88 O ATOM 995 CB CYS A 127 -12.223 21.588 4.955 1.00 20.27 C ATOM 996 SG CYS A 127 -10.443 21.944 4.947 1.00 18.11 S ATOM 997 N ARG A 128 -14.786 20.327 3.360 1.00 22.07 N ATOM 998 CA ARG A 128 -16.165 19.845 3.461 1.00 24.51 C ATOM 999 C ARG A 128 -16.185 19.162 4.837 1.00 26.37 C ATOM 1000 O ARG A 128 -15.699 18.046 5.040 1.00 27.58 O ATOM 1001 CB ARG A 128 -16.407 18.893 2.307 1.00 23.69 C ATOM 1002 CG ARG A 128 -17.767 18.293 2.147 1.00 36.80 C ATOM 1003 CD ARG A 128 -18.606 18.878 1.000 1.00 47.34 C ATOM 1004 NE ARG A 128 -19.983 18.652 1.462 1.00 52.78 N ATOM 1005 CZ ARG A 128 -20.795 19.592 1.896 1.00 54.36 C ATOM 1006 NH1 ARG A 128 -20.372 20.856 1.947 1.00 59.14 N ATOM 1007 NH2 ARG A 128 -21.988 19.273 2.373 1.00 54.49 N ATOM 1008 N LEU A 129 -16.556 19.949 5.844 1.00 26.47 N ATOM 1009 CA LEU A 129 -16.464 19.472 7.216 1.00 27.32 C ATOM 1010 CB LEU A 129 -15.904 20.629 8.074 1.00 28.32 C ATOM 1011 CG LEU A 129 -14.376 20.692 7.953 1.00 31.26 C ATOM 1012 CD1 LEU A 129 -13.837 22.063 8.284 1.00 33.18 C ATOM 1013 CD2 LEU A 129 -13.758 19.629 8.845 1.00 31.52 C TER 1014 LEU A 129 HETATM 1015 CL CL A 200 -8.573 31.142 26.528 1.00 18.19 CL HETATM 1016 CL CL A 201 -11.694 28.713 12.250 1.00 21.82 CL HETATM 1017 O HOH A 202 10.879 10.613 32.587 1.00 10.97 O HETATM 1018 O HOH A 203 10.289 12.194 14.636 1.00 13.30 O HETATM 1019 O HOH A 204 0.644 15.551 19.649 1.00 13.78 O HETATM 1020 O HOH A 205 8.693 11.787 30.570 1.00 14.14 O HETATM 1021 O HOH A 206 12.958 13.612 26.453 1.00 14.15 O HETATM 1022 O HOH A 207 4.285 31.927 10.426 1.00 14.43 O HETATM 1023 O HOH A 208 -6.938 16.618 3.112 1.00 15.01 O HETATM 1024 O HOH A 209 7.639 26.744 8.912 1.00 15.19 O HETATM 1025 O HOH A 210 -2.286 10.409 19.133 1.00 15.48 O HETATM 1026 O HOH A 211 -1.195 23.202 4.340 1.00 15.81 O HETATM 1027 O HOH A 212 4.482 10.025 9.236 1.00 15.96 O HETATM 1028 O HOH A 213 -6.655 27.410 29.873 1.00 16.34 O HETATM 1029 O HOH A 214 7.137 13.059 31.577 1.00 16.43 O HETATM 1030 O HOH A 215 -2.967 26.170 28.758 1.00 16.45 O HETATM 1031 O HOH A 216 -2.846 31.753 10.013 1.00 16.64 O HETATM 1032 O HOH A 217 9.614 4.677 30.905 1.00 16.94 O HETATM 1033 O HOH A 218 8.449 21.343 18.679 1.00 17.27 O HETATM 1034 O HOH A 219 -0.414 31.336 8.901 1.00 17.68 O HETATM 1035 O HOH A 220 -4.069 11.072 25.838 1.00 17.78 O HETATM 1036 O HOH A 221 -3.386 37.687 25.212 1.00 18.02 O HETATM 1037 O HOH A 222 -1.662 11.131 16.406 1.00 18.03 O HETATM 1038 O HOH A 223 -3.281 31.115 5.697 1.00 18.28 O HETATM 1039 O HOH A 224 -10.939 30.911 24.243 1.00 18.32 O HETATM 1040 O HOH A 225 7.094 24.986 15.739 1.00 18.56 O HETATM 1041 O HOH A 226 -4.578 7.661 19.577 1.00 18.94 O HETATM 1042 O HOH A 227 -2.329 6.339 20.642 1.00 19.32 O HETATM 1043 O HOH A 228 -7.243 34.881 15.339 1.00 19.33 O HETATM 1044 O HOH A 229 2.942 20.443 25.408 1.00 19.37 O HETATM 1045 O HOH A 230 17.901 9.077 24.625 1.00 19.61 O HETATM 1046 O HOH A 231 -0.047 17.718 4.493 1.00 19.75 O HETATM 1047 O HOH A 232 13.490 13.490 18.605 0.50 19.79 O HETATM 1048 O HOH A 233 -6.318 33.019 17.248 1.00 19.85 O HETATM 1049 O HOH A 234 6.916 11.433 32.978 1.00 19.95 O HETATM 1050 O HOH A 235 -3.497 38.476 18.726 1.00 20.26 O HETATM 1051 O HOH A 236 -4.178 32.654 7.872 1.00 20.37 O HETATM 1052 O HOH A 237 -8.400 10.064 14.433 1.00 20.37 O HETATM 1053 O HOH A 238 -18.493 18.493 9.303 0.50 20.39 O HETATM 1054 O HOH A 239 1.385 32.790 10.400 1.00 20.43 O HETATM 1055 O HOH A 240 11.896 11.896 37.210 0.50 20.74 O HETATM 1056 O HOH A 241 -3.961 36.825 8.829 1.00 20.87 O HETATM 1057 O HOH A 242 -6.693 19.428 2.190 1.00 20.90 O HETATM 1058 O HOH A 243 -0.775 38.308 19.723 1.00 20.91 O HETATM 1059 O HOH A 244 -15.875 15.692 11.542 1.00 21.02 O HETATM 1060 O HOH A 245 7.123 16.827 10.556 1.00 21.29 O HETATM 1061 O HOH A 246 13.092 23.814 26.808 1.00 21.60 O HETATM 1062 O HOH A 247 5.471 25.661 20.192 1.00 21.65 O HETATM 1063 O HOH A 248 -4.933 12.015 28.281 1.00 21.85 O HETATM 1064 O HOH A 249 -9.500 32.799 16.423 1.00 22.13 O HETATM 1065 O HOH A 250 5.304 23.715 9.208 1.00 22.17 O HETATM 1066 O HOH A 251 4.829 22.431 21.586 1.00 22.23 O HETATM 1067 O HOH A 252 -15.332 19.844 17.081 1.00 22.29 O HETATM 1068 O HOH A 253 -11.938 12.129 10.600 1.00 22.30 O HETATM 1069 O HOH A 254 -0.010 37.715 22.558 1.00 22.39 O HETATM 1070 O HOH A 255 -7.202 10.129 13.243 1.00 22.76 O HETATM 1071 O HOH A 256 2.317 18.684 5.478 1.00 22.97 O HETATM 1072 O HOH A 257 -0.609 26.419 30.111 1.00 23.01 O HETATM 1073 O HOH A 258 -12.876 18.006 2.111 1.00 23.03 O HETATM 1074 O HOH A 259 6.914 23.143 20.073 1.00 23.39 O HETATM 1075 O HOH A 260 -3.577 28.426 5.186 1.00 23.46 O HETATM 1076 O HOH A 261 4.675 4.962 29.797 1.00 23.56 O HETATM 1077 O HOH A 262 -1.245 38.248 15.147 1.00 23.56 O HETATM 1078 O HOH A 263 -5.564 8.814 25.596 1.00 23.69 O HETATM 1079 O HOH A 264 7.009 23.807 11.646 1.00 23.70 O HETATM 1080 O HOH A 265 1.879 6.009 30.780 1.00 23.71 O HETATM 1081 O HOH A 266 5.216 25.173 22.892 1.00 23.72 O HETATM 1082 O HOH A 267 18.106 18.106 18.605 0.50 23.78 O HETATM 1083 O HOH A 268 -14.988 23.445 25.814 1.00 24.14 O HETATM 1084 O HOH A 269 -2.222 34.844 7.765 1.00 24.45 O HETATM 1085 O HOH A 270 0.390 12.722 16.652 1.00 24.62 O HETATM 1086 O HOH A 271 -14.530 24.676 5.590 1.00 24.72 O HETATM 1087 O HOH A 272 -3.166 5.199 23.295 1.00 24.86 O HETATM 1088 O HOH A 273 1.216 9.738 5.280 1.00 25.40 O HETATM 1089 O HOH A 274 -18.265 22.087 4.822 1.00 25.66 O HETATM 1090 O HOH A 275 -15.201 9.092 16.700 1.00 25.72 O HETATM 1091 O HOH A 276 4.600 36.742 19.557 1.00 25.73 O HETATM 1092 O HOH A 277 -5.992 9.643 32.090 1.00 25.80 O HETATM 1093 O HOH A 278 1.304 36.804 18.883 1.00 25.84 O HETATM 1094 O HOH A 279 -9.132 14.941 28.012 1.00 25.89 O HETATM 1095 O HOH A 280 4.528 7.893 34.391 1.00 26.12 O HETATM 1096 O HOH A 281 -2.547 30.453 34.163 1.00 26.23 O HETATM 1097 O HOH A 282 -0.620 35.566 26.185 1.00 26.24 O HETATM 1098 O HOH A 283 -7.927 11.450 28.856 1.00 26.35 O HETATM 1099 O HOH A 284 1.788 6.539 10.936 1.00 26.51 O HETATM 1100 O HOH A 285 13.159 10.398 34.593 1.00 26.79 O HETATM 1101 O HOH A 286 7.222 16.362 8.190 1.00 27.07 O HETATM 1102 O HOH A 287 -7.850 10.126 8.000 1.00 27.14 O HETATM 1103 O HOH A 288 -10.393 8.137 24.443 1.00 27.20 O HETATM 1104 O HOH A 289 -2.733 40.337 14.551 1.00 27.27 O HETATM 1105 O HOH A 290 -9.169 11.966 9.156 1.00 27.41 O HETATM 1106 O HOH A 291 -12.985 17.705 28.178 1.00 27.42 O HETATM 1107 O HOH A 292 10.547 21.468 20.359 1.00 27.50 O HETATM 1108 O HOH A 293 -10.183 33.721 11.223 1.00 27.72 O HETATM 1109 O HOH A 294 7.457 25.395 18.468 1.00 27.87 O HETATM 1110 O HOH A 295 -9.754 18.975 29.738 1.00 27.87 O HETATM 1111 O HOH A 296 17.581 14.317 21.216 1.00 28.20 O HETATM 1112 O HOH A 297 16.590 14.860 29.890 1.00 28.36 O HETATM 1113 O HOH A 298 14.635 15.792 18.064 1.00 28.37 O HETATM 1114 O HOH A 299 -7.275 39.098 15.412 1.00 28.45 O HETATM 1115 O HOH A 300 -2.211 38.505 10.070 1.00 28.51 O HETATM 1116 O HOH A 301 -8.571 37.178 15.480 1.00 28.80 O HETATM 1117 O HOH A 302 2.949 5.060 18.299 1.00 29.00 O HETATM 1118 O HOH A 303 -16.726 16.726 9.303 0.50 29.27 O HETATM 1119 O HOH A 304 13.606 24.178 23.338 1.00 29.57 O HETATM 1120 O HOH A 305 -4.676 26.495 38.634 1.00 29.58 O HETATM 1121 O HOH A 306 -10.948 29.213 27.557 1.00 29.60 O HETATM 1122 O HOH A 307 -2.697 11.318 4.964 1.00 29.70 O HETATM 1123 O HOH A 308 -1.522 3.392 27.650 1.00 29.71 O HETATM 1124 O HOH A 309 -4.251 17.996 33.619 1.00 29.91 O HETATM 1125 O HOH A 310 -5.110 6.413 24.610 1.00 29.96 O HETATM 1126 O HOH A 311 -19.900 22.788 12.165 1.00 30.10 O HETATM 1127 O HOH A 312 -6.817 8.451 27.930 1.00 30.10 O HETATM 1128 O HOH A 313 9.177 23.023 16.646 1.00 30.15 O HETATM 1129 O HOH A 314 19.217 16.433 25.700 1.00 30.15 O HETATM 1130 O HOH A 315 -16.634 14.607 19.679 1.00 30.53 O HETATM 1131 O HOH A 316 -1.426 28.041 3.446 1.00 30.85 O HETATM 1132 O HOH A 317 2.295 3.473 21.971 1.00 30.99 O HETATM 1133 O HOH A 318 2.568 37.386 21.852 1.00 31.04 O HETATM 1134 O HOH A 319 0.159 4.298 14.251 1.00 31.06 O HETATM 1135 O HOH A 320 0.692 3.808 19.651 1.00 31.15 O HETATM 1136 O HOH A 321 -5.834 20.579 32.561 1.00 31.19 O HETATM 1137 O HOH A 322 -4.693 5.674 29.352 1.00 31.20 O HETATM 1138 O HOH A 323 -15.358 14.932 22.145 1.00 31.42 O HETATM 1139 O HOH A 324 1.238 38.146 25.783 1.00 31.44 O HETATM 1140 O HOH A 325 20.230 21.631 26.500 1.00 31.80 O HETATM 1141 O HOH A 326 -16.031 19.645 22.360 1.00 31.83 O HETATM 1142 O HOH A 327 15.665 17.533 18.043 1.00 31.91 O HETATM 1143 O HOH A 328 5.286 16.954 41.850 1.00 31.92 O HETATM 1144 O HOH A 329 -8.597 28.043 31.993 1.00 32.24 O HETATM 1145 O HOH A 330 -3.444 19.251 2.289 1.00 32.41 O HETATM 1146 O HOH A 331 -10.753 12.992 8.668 1.00 32.58 O HETATM 1147 O HOH A 332 8.018 19.892 13.833 1.00 32.64 O HETATM 1148 O HOH A 333 -12.861 11.915 21.063 1.00 32.92 O HETATM 1149 O HOH A 334 -5.704 8.037 10.830 1.00 33.68 O HETATM 1150 O HOH A 335 -20.305 17.389 -0.507 1.00 33.75 O HETATM 1151 O HOH A 336 6.238 2.840 24.049 1.00 33.77 O HETATM 1152 O HOH A 337 -4.670 8.314 29.558 1.00 33.81 O HETATM 1153 O HOH A 338 2.193 22.444 25.296 1.00 33.85 O HETATM 1154 O HOH A 339 -16.498 21.632 23.971 1.00 34.03 O HETATM 1155 O HOH A 340 -9.160 5.260 18.834 1.00 34.18 O HETATM 1156 O HOH A 341 -8.559 19.349 0.671 1.00 34.21 O HETATM 1157 O HOH A 342 13.968 6.338 33.309 1.00 34.39 O HETATM 1158 O HOH A 343 10.492 18.283 39.312 1.00 34.80 O HETATM 1159 O HOH A 344 3.455 31.157 24.979 1.00 34.87 O HETATM 1160 O HOH A 345 -1.988 4.987 29.616 1.00 35.02 O HETATM 1161 O HOH A 346 -6.300 7.200 32.899 1.00 35.03 O HETATM 1162 O HOH A 347 -20.135 18.837 11.394 1.00 35.17 O HETATM 1163 O HOH A 348 -1.041 3.883 25.065 1.00 35.59 O HETATM 1164 O HOH A 349 -5.860 27.566 35.957 1.00 35.59 O HETATM 1165 O HOH A 350 7.817 17.167 38.889 1.00 35.72 O HETATM 1166 O HOH A 351 3.038 22.719 28.596 1.00 35.80 O HETATM 1167 O HOH A 352 7.383 23.092 5.206 1.00 36.12 O HETATM 1168 O HOH A 353 -1.512 28.095 34.397 1.00 36.18 O HETATM 1169 O HOH A 354 -3.790 22.766 1.644 1.00 36.56 O HETATM 1170 O HOH A 355 -1.740 19.862 3.414 1.00 37.02 O HETATM 1171 O HOH A 356 2.114 12.911 36.519 1.00 37.08 O HETATM 1172 O HOH A 357 3.724 24.464 3.530 1.00 37.13 O HETATM 1173 O HOH A 358 -18.346 29.918 18.378 1.00 37.27 O HETATM 1174 O HOH A 359 4.572 26.197 26.844 1.00 37.66 O HETATM 1175 O HOH A 360 20.535 20.940 24.404 1.00 37.82 O HETATM 1176 O HOH A 361 9.634 21.963 28.448 1.00 38.26 O HETATM 1177 O HOH A 362 -1.080 21.418 35.703 1.00 38.49 O HETATM 1178 O HOH A 363 1.677 28.597 28.743 1.00 38.60 O HETATM 1179 O HOH A 364 19.283 15.322 28.250 1.00 38.60 O HETATM 1180 O HOH A 365 7.015 18.690 35.740 1.00 38.72 O HETATM 1181 O HOH A 366 1.249 3.610 16.471 1.00 38.94 O HETATM 1182 O HOH A 367 1.582 19.199 38.346 1.00 39.28 O HETATM 1183 O HOH A 368 11.048 24.309 30.326 1.00 39.30 O HETATM 1184 O HOH A 369 -14.416 21.935 28.473 1.00 39.31 O HETATM 1185 O HOH A 370 18.376 19.088 24.588 1.00 39.45 O HETATM 1186 O HOH A 371 -10.984 36.550 14.845 1.00 39.83 O HETATM 1187 O HOH A 372 -2.577 29.992 1.311 1.00 40.38 O HETATM 1188 O HOH A 373 17.114 21.181 21.516 1.00 40.73 O HETATM 1189 O HOH A 374 5.265 21.346 3.207 1.00 41.14 O HETATM 1190 O HOH A 375 9.959 11.483 35.605 1.00 42.02 O HETATM 1191 O HOH A 376 -8.712 37.262 11.831 1.00 42.48 O HETATM 1192 O HOH A 377 9.550 17.444 6.125 1.00 44.76 O HETATM 1193 O HOH A 378 -19.917 19.989 14.602 1.00 49.67 O CONECT 50 996 CONECT 247 904 CONECT 522 643 CONECT 610 739 CONECT 643 522 CONECT 739 610 CONECT 904 247 CONECT 996 50 MASTER 292 0 2 7 3 0 2 6 1192 1 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REMARK 200 HIGHEST RESOLUTION SHELL, RANGE HIGH (A) : NULL REMARK 200 HIGHEST RESOLUTION SHELL, RANGE LOW (A) : NULL REMARK 200 COMPLETENESS FOR SHELL (%) : NULL REMARK 200 DATA REDUNDANCY IN SHELL : NULL REMARK 200 R MERGE FOR SHELL (I) : NULL REMARK 200 R SYM FOR SHELL (I) : NULL REMARK 200 FOR SHELL : NULL REMARK 200 REMARK 200 DIFFRACTION PROTOCOL: NULL REMARK 200 METHOD USED TO DETERMINE THE STRUCTURE: NULL REMARK 200 SOFTWARE USED: NULL REMARK 200 STARTING MODEL: NULL REMARK 200 REMARK 200 REMARK: NULL REMARK 280 REMARK 280 CRYSTAL REMARK 280 SOLVENT CONTENT, VS (%): 40.52 REMARK 280 MATTHEWS COEFFICIENT, VM (ANGSTROMS**3/DA): 2.07 REMARK 280 REMARK 280 CRYSTALLIZATION CONDITIONS: NULL REMARK 290 REMARK 290 CRYSTALLOGRAPHIC SYMMETRY REMARK 290 SYMMETRY OPERATORS FOR SPACE GROUP: P 43 21 2 REMARK 290 REMARK 290 SYMOP SYMMETRY REMARK 290 NNNMMM OPERATOR REMARK 290 1555 X,Y,Z REMARK 290 2555 -X,-Y,Z+1/2 REMARK 290 3555 -Y+1/2,X+1/2,Z+3/4 REMARK 290 4555 Y+1/2,-X+1/2,Z+1/4 REMARK 290 5555 -X+1/2,Y+1/2,-Z+3/4 REMARK 290 6555 X+1/2,-Y+1/2,-Z+1/4 REMARK 290 7555 Y,X,-Z REMARK 290 8555 -Y,-X,-Z+1/2 REMARK 290 REMARK 290 WHERE NNN -> OPERATOR NUMBER REMARK 290 MMM -> TRANSLATION VECTOR REMARK 290 REMARK 290 CRYSTALLOGRAPHIC SYMMETRY TRANSFORMATIONS REMARK 290 THE FOLLOWING TRANSFORMATIONS OPERATE ON THE ATOM/HETATM REMARK 290 RECORDS IN THIS ENTRY TO PRODUCE CRYSTALLOGRAPHICALLY REMARK 290 RELATED MOLECULES. REMARK 290 SMTRY1 1 1.000000 0.000000 0.000000 0.00000 REMARK 290 SMTRY2 1 0.000000 1.000000 0.000000 0.00000 REMARK 290 SMTRY3 1 0.000000 0.000000 1.000000 0.00000 REMARK 290 SMTRY1 2 -1.000000 0.000000 0.000000 0.00000 REMARK 290 SMTRY2 2 0.000000 -1.000000 0.000000 0.00000 REMARK 290 SMTRY3 2 0.000000 0.000000 1.000000 18.95000 REMARK 290 SMTRY1 3 0.000000 -1.000000 0.000000 39.55000 REMARK 290 SMTRY2 3 1.000000 0.000000 0.000000 39.55000 REMARK 290 SMTRY3 3 0.000000 0.000000 1.000000 28.42500 REMARK 290 SMTRY1 4 0.000000 1.000000 0.000000 39.55000 REMARK 290 SMTRY2 4 -1.000000 0.000000 0.000000 39.55000 REMARK 290 SMTRY3 4 0.000000 0.000000 1.000000 9.47500 REMARK 290 SMTRY1 5 -1.000000 0.000000 0.000000 39.55000 REMARK 290 SMTRY2 5 0.000000 1.000000 0.000000 39.55000 REMARK 290 SMTRY3 5 0.000000 0.000000 -1.000000 28.42500 REMARK 290 SMTRY1 6 1.000000 0.000000 0.000000 39.55000 REMARK 290 SMTRY2 6 0.000000 -1.000000 0.000000 39.55000 REMARK 290 SMTRY3 6 0.000000 0.000000 -1.000000 9.47500 REMARK 290 SMTRY1 7 0.000000 1.000000 0.000000 0.00000 REMARK 290 SMTRY2 7 1.000000 0.000000 0.000000 0.00000 REMARK 290 SMTRY3 7 0.000000 0.000000 -1.000000 0.00000 REMARK 290 SMTRY1 8 0.000000 -1.000000 0.000000 0.00000 REMARK 290 SMTRY2 8 -1.000000 0.000000 0.000000 0.00000 REMARK 290 SMTRY3 8 0.000000 0.000000 -1.000000 18.95000 REMARK 290 REMARK 290 REMARK: NULL REMARK 300 REMARK 300 BIOMOLECULE: 1 REMARK 300 SEE REMARK 350 FOR THE AUTHOR PROVIDED AND/OR PROGRAM REMARK 300 GENERATED ASSEMBLY INFORMATION FOR THE STRUCTURE IN REMARK 300 THIS ENTRY. THE REMARK MAY ALSO PROVIDE INFORMATION ON REMARK 300 BURIED SURFACE AREA. REMARK 350 REMARK 350 COORDINATES FOR A COMPLETE MULTIMER REPRESENTING THE KNOWN REMARK 350 BIOLOGICALLY SIGNIFICANT OLIGOMERIZATION STATE OF THE REMARK 350 MOLECULE CAN BE GENERATED BY APPLYING BIOMT TRANSFORMATIONS REMARK 350 GIVEN BELOW. BOTH NON-CRYSTALLOGRAPHIC AND REMARK 350 CRYSTALLOGRAPHIC OPERATIONS ARE GIVEN. REMARK 350 REMARK 350 BIOMOLECULE: 1 REMARK 350 AUTHOR DETERMINED BIOLOGICAL UNIT: MONOMERIC REMARK 350 APPLY THE FOLLOWING TO CHAINS: A REMARK 350 BIOMT1 1 1.000000 0.000000 0.000000 0.00000 REMARK 350 BIOMT2 1 0.000000 1.000000 0.000000 0.00000 REMARK 350 BIOMT3 1 0.000000 0.000000 1.000000 0.00000 REMARK 375 REMARK 375 SPECIAL POSITION REMARK 375 THE FOLLOWING ATOMS ARE FOUND TO BE WITHIN 0.15 ANGSTROMS REMARK 375 OF A SYMMETRY RELATED ATOM AND ARE ASSUMED TO BE ON SPECIAL REMARK 375 POSITIONS. REMARK 375 REMARK 375 ATOM RES CSSEQI REMARK 375 HOH A 318 LIES ON A SPECIAL POSITION. REMARK 500 REMARK 500 GEOMETRY AND STEREOCHEMISTRY REMARK 500 SUBTOPIC: CLOSE CONTACTS REMARK 500 REMARK 500 THE FOLLOWING ATOMS THAT ARE RELATED BY CRYSTALLOGRAPHIC REMARK 500 SYMMETRY ARE IN CLOSE CONTACT. AN ATOM LOCATED WITHIN 0.15 REMARK 500 ANGSTROMS OF A SYMMETRY RELATED ATOM IS ASSUMED TO BE ON A REMARK 500 SPECIAL POSITION AND IS, THEREFORE, LISTED IN REMARK 375 REMARK 500 INSTEAD OF REMARK 500. ATOMS WITH NON-BLANK ALTERNATE REMARK 500 LOCATION INDICATORS ARE NOT INCLUDED IN THE CALCULATIONS. REMARK 500 REMARK 500 DISTANCE CUTOFF: REMARK 500 2.2 ANGSTROMS FOR CONTACTS NOT INVOLVING HYDROGEN ATOMS REMARK 500 1.6 ANGSTROMS FOR CONTACTS INVOLVING HYDROGEN ATOMS REMARK 500 REMARK 500 ATM1 RES C SSEQI ATM2 RES C SSEQI SSYMOP DISTANCE REMARK 500 O HOH A 275 O HOH A 275 8555 0.35 REMARK 500 O HOH A 203 O HOH A 203 7556 1.38 REMARK 500 REMARK 500 REMARK: NULL REMARK 500 REMARK 500 GEOMETRY AND STEREOCHEMISTRY REMARK 500 SUBTOPIC: COVALENT BOND ANGLES REMARK 500 REMARK 500 THE STEREOCHEMICAL PARAMETERS OF THE FOLLOWING RESIDUES REMARK 500 HAVE VALUES WHICH DEVIATE FROM EXPECTED VALUES BY MORE REMARK 500 THAN 6*RMSD (M=MODEL NUMBER; RES=RESIDUE NAME; C=CHAIN REMARK 500 IDENTIFIER; SSEQ=SEQUENCE NUMBER; I=INSERTION CODE). REMARK 500 REMARK 500 STANDARD TABLE: REMARK 500 FORMAT: (10X,I3,1X,A3,1X,A1,I4,A1,3(1X,A4,2X),12X,F5.1) REMARK 500 REMARK 500 EXPECTED VALUES PROTEIN: ENGH AND HUBER, 1999 REMARK 500 EXPECTED VALUES NUCLEIC ACID: CLOWNEY ET AL 1996 REMARK 500 REMARK 500 M RES CSSEQI ATM1 ATM2 ATM3 REMARK 500 ARG A 5 NE - CZ - NH1 ANGL. DEV. = 4.8 DEGREES REMARK 500 ARG A 14 NE - CZ - NH1 ANGL. DEV. = 3.3 DEGREES REMARK 500 ASP A 18 CB - CG - OD1 ANGL. DEV. = 8.7 DEGREES REMARK 500 ASP A 18 CB - CG - OD2 ANGL. DEV. = -8.1 DEGREES REMARK 500 ARG A 45 NE - CZ - NH1 ANGL. DEV. = 3.3 DEGREES REMARK 500 ASP A 52 CB - CG - OD1 ANGL. DEV. = 5.5 DEGREES REMARK 500 ARG A 61 NE - CZ - NH1 ANGL. DEV. = 3.6 DEGREES REMARK 500 ARG A 73 NE - CZ - NH1 ANGL. DEV. = 3.3 DEGREES REMARK 500 ASP A 87 CB - CG - OD1 ANGL. DEV. = 5.6 DEGREES REMARK 500 ASP A 119 CB - CG - OD2 ANGL. DEV. = -5.6 DEGREES REMARK 500 ARG A 128 NE - CZ - NH1 ANGL. DEV. = 3.7 DEGREES REMARK 500 REMARK 500 REMARK: NULL REMARK 500 REMARK 500 GEOMETRY AND STEREOCHEMISTRY REMARK 500 SUBTOPIC: TORSION ANGLES REMARK 500 REMARK 500 TORSION ANGLES OUTSIDE THE EXPECTED RAMACHANDRAN REGIONS: REMARK 500 (M=MODEL NUMBER; RES=RESIDUE NAME; C=CHAIN IDENTIFIER; REMARK 500 SSEQ=SEQUENCE NUMBER; I=INSERTION CODE). REMARK 500 REMARK 500 STANDARD TABLE: REMARK 500 FORMAT:(10X,I3,1X,A3,1X,A1,I4,A1,4X,F7.2,3X,F7.2) REMARK 500 REMARK 500 EXPECTED VALUES: GJ KLEYWEGT AND TA JONES (1996). PHI/PSI- REMARK 500 CHOLOGY: RAMACHANDRAN REVISITED. STRUCTURE 4, 1395 - 1400 REMARK 500 REMARK 500 M RES CSSEQI PSI PHI REMARK 500 ARG A 68 19.50 -141.12 REMARK 500 REMARK 500 REMARK: NULL REMARK 525 REMARK 525 SOLVENT REMARK 525 REMARK 525 THE SOLVENT MOLECULES HAVE CHAIN IDENTIFIERS THAT REMARK 525 INDICATE THE POLYMER CHAIN WITH WHICH THEY ARE MOST REMARK 525 CLOSELY ASSOCIATED. THE REMARK LISTS ALL THE SOLVENT REMARK 525 MOLECULES WHICH ARE MORE THAN 5A AWAY FROM THE REMARK 525 NEAREST POLYMER CHAIN (M = MODEL NUMBER; REMARK 525 RES=RESIDUE NAME; C=CHAIN IDENTIFIER; SSEQ=SEQUENCE REMARK 525 NUMBER; I=INSERTION CODE): REMARK 525 REMARK 525 M RES CSSEQI REMARK 525 HOH A 188 DISTANCE = 6.89 ANGSTROMS REMARK 525 HOH A 189 DISTANCE = 6.32 ANGSTROMS REMARK 525 HOH A 190 DISTANCE = 5.68 ANGSTROMS REMARK 525 HOH A 228 DISTANCE = 6.51 ANGSTROMS REMARK 525 HOH A 230 DISTANCE = 5.30 ANGSTROMS REMARK 525 HOH A 243 DISTANCE = 5.19 ANGSTROMS REMARK 525 HOH A 249 DISTANCE = 6.99 ANGSTROMS REMARK 525 HOH A 258 DISTANCE = 6.92 ANGSTROMS REMARK 525 HOH A 268 DISTANCE = 8.36 ANGSTROMS REMARK 525 HOH A 278 DISTANCE = 6.81 ANGSTROMS REMARK 525 HOH A 304 DISTANCE = 7.48 ANGSTROMS REMARK 525 HOH A 305 DISTANCE = 6.21 ANGSTROMS REMARK 525 HOH A 308 DISTANCE = 19.71 ANGSTROMS DBREF 1HEL A 1 129 UNP P00698 LYSC_CHICK 19 147 SEQRES 1 A 129 LYS VAL PHE GLY ARG CYS GLU LEU ALA ALA ALA MET LYS SEQRES 2 A 129 ARG HIS GLY LEU ASP ASN TYR ARG GLY TYR SER LEU GLY SEQRES 3 A 129 ASN TRP VAL CYS ALA ALA LYS PHE GLU SER ASN PHE ASN SEQRES 4 A 129 THR GLN ALA THR ASN ARG ASN THR ASP GLY SER THR ASP SEQRES 5 A 129 TYR GLY ILE LEU GLN ILE ASN SER ARG TRP TRP CYS ASN SEQRES 6 A 129 ASP GLY ARG THR PRO GLY SER ARG ASN LEU CYS ASN ILE SEQRES 7 A 129 PRO CYS SER ALA LEU LEU SER SER ASP ILE THR ALA SER SEQRES 8 A 129 VAL ASN CYS ALA LYS LYS ILE VAL SER ASP GLY ASN GLY SEQRES 9 A 129 MET ASN ALA TRP VAL ALA TRP ARG ASN ARG CYS LYS GLY SEQRES 10 A 129 THR ASP VAL GLN ALA TRP ILE ARG GLY CYS ARG LEU FORMUL 2 HOH *185(H2 O) HELIX 1 H1 GLY A 4 GLY A 16 1 13 HELIX 2 H2 LEU A 25 PHE A 34 1 10 HELIX 3 H3 PRO A 79 LEU A 84 5 6 HELIX 4 H4 ILE A 88 SER A 100 1 13 HELIX 5 H5 GLY A 104 TRP A 108 5 5 HELIX 6 H6 VAL A 109 ARG A 114 1 6 HELIX 7 H7 VAL A 120 ILE A 124 5 5 SHEET 1 S1 3 ALA A 42 ASN A 46 0 SHEET 2 S1 3 GLY A 49 GLY A 54 -1 O SER A 50 N ASN A 46 SHEET 3 S1 3 LEU A 56 SER A 60 -1 O SER A 60 N THR A 51 SSBOND 1 CYS A 6 CYS A 127 1555 1555 1.99 SSBOND 2 CYS A 30 CYS A 115 1555 1555 2.11 SSBOND 3 CYS A 64 CYS A 80 1555 1555 2.00 SSBOND 4 CYS A 76 CYS A 94 1555 1555 2.10 CRYST1 79.100 79.100 37.900 90.00 90.00 90.00 P 43 21 2 8 ORIGX1 1.000000 0.000000 0.000000 0.00000 ORIGX2 0.000000 1.000000 0.000000 0.00000 ORIGX3 0.000000 0.000000 1.000000 0.00000 SCALE1 0.012642 0.000000 0.000000 0.00000 SCALE2 0.000000 0.012642 0.000000 0.00000 SCALE3 0.000000 0.000000 0.026385 0.00000 ATOM 1 N LYS A 1 3.294 10.164 10.266 1.00 11.18 N ATOM 2 CA LYS A 1 2.388 10.533 9.168 1.00 9.68 C ATOM 3 C LYS A 1 2.438 12.049 8.889 1.00 14.00 C ATOM 4 O LYS A 1 2.406 12.898 9.815 1.00 14.00 O ATOM 5 CB LYS A 1 0.949 10.101 9.559 1.00 13.29 C ATOM 6 CG LYS A 1 -0.050 10.621 8.573 1.00 13.52 C ATOM 7 CD LYS A 1 -1.425 10.081 8.720 1.00 22.15 C ATOM 8 CE LYS A 1 -2.370 10.773 7.722 1.00 20.23 C ATOM 9 NZ LYS A 1 -3.776 10.439 7.933 1.00 68.72 N ATOM 10 N VAL A 2 2.552 12.428 7.626 1.00 10.17 N ATOM 11 CA VAL A 2 2.524 13.840 7.282 1.00 10.02 C ATOM 12 C VAL A 2 1.120 14.180 6.770 1.00 27.84 C ATOM 13 O VAL A 2 0.737 13.798 5.675 1.00 22.87 O ATOM 14 CB VAL A 2 3.529 14.264 6.240 1.00 9.00 C ATOM 15 CG1 VAL A 2 3.313 15.765 5.983 1.00 11.37 C ATOM 16 CG2 VAL A 2 4.928 14.016 6.810 1.00 10.57 C ATOM 17 N PHE A 3 0.333 14.851 7.573 1.00 16.35 N ATOM 18 CA PHE A 3 -1.021 15.173 7.169 1.00 15.34 C ATOM 19 C PHE A 3 -1.097 16.285 6.126 1.00 14.79 C ATOM 20 O PHE A 3 -0.261 17.203 6.054 1.00 14.99 O ATOM 21 CB PHE A 3 -1.867 15.710 8.361 1.00 14.03 C ATOM 22 CG PHE A 3 -2.412 14.638 9.295 1.00 16.41 C ATOM 23 CD1 PHE A 3 -1.575 14.049 10.240 1.00 14.44 C ATOM 24 CD2 PHE A 3 -3.757 14.285 9.274 1.00 18.12 C ATOM 25 CE1 PHE A 3 -2.065 13.116 11.135 1.00 11.11 C ATOM 26 CE2 PHE A 3 -4.263 13.332 10.178 1.00 32.24 C ATOM 27 CZ PHE A 3 -3.413 12.758 11.132 1.00 14.31 C ATOM 28 N GLY A 4 -2.229 16.228 5.393 1.00 15.47 N ATOM 29 CA GLY A 4 -2.645 17.273 4.511 1.00 13.97 C ATOM 30 C GLY A 4 -3.456 18.261 5.350 1.00 10.95 C ATOM 31 O GLY A 4 -4.070 17.876 6.282 1.00 16.45 O ATOM 32 N ARG A 5 -3.414 19.518 5.009 1.00 14.28 N ATOM 33 CA ARG A 5 -4.106 20.560 5.674 1.00 11.63 C ATOM 34 C ARG A 5 -5.540 20.226 5.992 1.00 21.37 C ATOM 35 O ARG A 5 -5.963 20.258 7.138 1.00 9.74 O ATOM 36 CB ARG A 5 -3.952 21.857 4.900 1.00 13.31 C ATOM 37 CG ARG A 5 -4.508 23.053 5.610 1.00 13.02 C ATOM 38 CD ARG A 5 -4.414 24.335 4.775 1.00 19.72 C ATOM 39 NE ARG A 5 -5.013 24.223 3.447 1.00 23.52 N ATOM 40 CZ ARG A 5 -6.287 24.522 3.048 1.00 40.17 C ATOM 41 NH1 ARG A 5 -7.248 25.009 3.841 1.00 17.54 N ATOM 42 NH2 ARG A 5 -6.619 24.303 1.767 1.00 33.21 N ATOM 43 N CYS A 6 -6.327 19.866 4.967 1.00 15.04 N ATOM 44 CA CYS A 6 -7.767 19.572 5.189 1.00 12.93 C ATOM 45 C CYS A 6 -7.997 18.269 5.916 1.00 5.10 C ATOM 46 O CYS A 6 -8.992 18.125 6.630 1.00 13.60 O ATOM 47 CB CYS A 6 -8.607 19.637 3.859 1.00 16.72 C ATOM 48 SG CYS A 6 -8.669 21.273 3.104 1.00 16.68 S ATOM 49 N GLU A 7 -7.142 17.274 5.653 1.00 7.34 N ATOM 50 CA GLU A 7 -7.309 15.981 6.323 1.00 10.86 C ATOM 51 C GLU A 7 -7.129 16.181 7.848 1.00 17.71 C ATOM 52 O GLU A 7 -7.835 15.638 8.657 1.00 14.19 O ATOM 53 CB GLU A 7 -6.187 15.048 5.880 1.00 16.19 C ATOM 54 CG GLU A 7 -6.206 13.614 6.496 1.00 16.67 C ATOM 55 CD GLU A 7 -4.952 12.864 6.030 1.00 32.91 C ATOM 56 OE1 GLU A 7 -4.003 13.411 5.480 1.00 18.18 O ATOM 57 OE2 GLU A 7 -4.992 11.578 6.219 1.00 28.07 O ATOM 58 N LEU A 8 -6.148 16.987 8.221 1.00 14.04 N ATOM 59 CA LEU A 8 -5.919 17.285 9.637 1.00 8.65 C ATOM 60 C LEU A 8 -7.068 18.103 10.254 1.00 10.08 C ATOM 61 O LEU A 8 -7.500 17.827 11.353 1.00 15.66 O ATOM 62 CB LEU A 8 -4.607 18.084 9.809 1.00 14.88 C ATOM 63 CG LEU A 8 -4.384 18.432 11.299 1.00 12.61 C ATOM 64 CD1 LEU A 8 -4.110 17.104 12.053 1.00 12.51 C ATOM 65 CD2 LEU A 8 -3.147 19.299 11.372 1.00 13.98 C ATOM 66 N ALA A 9 -7.524 19.122 9.561 1.00 11.92 N ATOM 67 CA ALA A 9 -8.664 19.896 9.982 1.00 10.97 C ATOM 68 C ALA A 9 -9.841 18.971 10.304 1.00 15.73 C ATOM 69 O ALA A 9 -10.469 19.046 11.359 1.00 13.41 O ATOM 70 CB ALA A 9 -9.039 21.012 8.954 1.00 8.88 C ATOM 71 N ALA A 10 -10.124 18.049 9.425 1.00 12.11 N ATOM 72 CA ALA A 10 -11.262 17.129 9.595 1.00 12.19 C ATOM 73 C ALA A 10 -11.034 16.206 10.780 1.00 18.02 C ATOM 74 O ALA A 10 -11.932 15.902 11.522 1.00 17.70 O ATOM 75 CB ALA A 10 -11.457 16.297 8.313 1.00 14.75 C ATOM 76 N ALA A 11 -9.815 15.771 10.988 1.00 14.94 N ATOM 77 CA ALA A 11 -9.544 14.908 12.136 1.00 12.19 C ATOM 78 C ALA A 11 -9.651 15.641 13.494 1.00 7.51 C ATOM 79 O ALA A 11 -10.088 15.066 14.457 1.00 12.99 O ATOM 80 CB ALA A 11 -8.153 14.250 12.041 1.00 15.76 C ATOM 81 N MET A 12 -9.107 16.884 13.529 1.00 10.71 N ATOM 82 CA MET A 12 -9.160 17.683 14.710 1.00 12.27 C ATOM 83 C MET A 12 -10.599 17.988 15.028 1.00 16.76 C ATOM 84 O MET A 12 -10.964 17.966 16.195 1.00 17.43 O ATOM 85 CB MET A 12 -8.385 18.996 14.563 1.00 6.96 C ATOM 86 CG MET A 12 -6.872 18.717 14.593 1.00 7.53 C ATOM 87 SD MET A 12 -5.971 20.286 14.351 1.00 16.25 S ATOM 88 CE MET A 12 -4.392 19.972 15.137 1.00 11.48 C ATOM 89 N LYS A 13 -11.421 18.239 13.985 1.00 11.66 N ATOM 90 CA LYS A 13 -12.844 18.554 14.146 1.00 12.77 C ATOM 91 C LYS A 13 -13.552 17.402 14.762 1.00 17.21 C ATOM 92 O LYS A 13 -14.278 17.533 15.704 1.00 15.75 O ATOM 93 CB LYS A 13 -13.505 18.908 12.852 1.00 14.38 C ATOM 94 CG LYS A 13 -14.874 19.457 13.096 1.00 16.88 C ATOM 95 CD LYS A 13 -15.519 20.062 11.867 1.00 19.73 C ATOM 96 CE LYS A 13 -17.062 20.060 11.971 1.00 41.06 C ATOM 97 NZ LYS A 13 -17.725 20.836 10.899 1.00 61.80 N ATOM 98 N ARG A 14 -13.273 16.240 14.220 1.00 21.68 N ATOM 99 CA ARG A 14 -13.878 15.021 14.667 1.00 17.17 C ATOM 100 C ARG A 14 -13.480 14.746 16.099 1.00 27.88 C ATOM 101 O ARG A 14 -14.217 14.129 16.823 1.00 17.70 O ATOM 102 CB ARG A 14 -13.448 13.876 13.756 1.00 23.48 C ATOM 103 CG ARG A 14 -14.102 12.553 14.162 1.00 51.76 C ATOM 104 CD ARG A 14 -13.875 11.424 13.160 1.00 52.15 C ATOM 105 NE ARG A 14 -12.616 10.730 13.354 1.00 61.79 N ATOM 106 CZ ARG A 14 -12.406 9.681 14.156 1.00 47.00 C ATOM 107 NH1 ARG A 14 -13.357 9.121 14.898 1.00 35.04 N ATOM 108 NH2 ARG A 14 -11.177 9.169 14.196 1.00 55.70 N ATOM 109 N HIS A 15 -12.300 15.219 16.498 1.00 20.13 N ATOM 110 CA HIS A 15 -11.791 15.016 17.846 1.00 14.58 C ATOM 111 C HIS A 15 -12.221 16.074 18.888 1.00 18.83 C ATOM 112 O HIS A 15 -11.689 16.060 19.970 1.00 22.76 O ATOM 113 CB HIS A 15 -10.268 14.799 17.851 1.00 23.09 C ATOM 114 CG HIS A 15 -9.906 13.364 17.563 1.00 21.53 C ATOM 115 ND1 HIS A 15 -9.721 12.896 16.256 1.00 28.28 N ATOM 116 CD2 HIS A 15 -9.723 12.308 18.413 1.00 30.99 C ATOM 117 CE1 HIS A 15 -9.422 11.580 16.350 1.00 20.50 C ATOM 118 NE2 HIS A 15 -9.412 11.213 17.627 1.00 42.34 N ATOM 119 N GLY A 16 -13.146 16.952 18.551 1.00 14.90 N ATOM 120 CA GLY A 16 -13.687 17.956 19.401 1.00 16.84 C ATOM 121 C GLY A 16 -12.871 19.227 19.554 1.00 23.06 C ATOM 122 O GLY A 16 -13.121 20.016 20.460 1.00 19.24 O ATOM 123 N LEU A 17 -11.922 19.491 18.685 1.00 14.25 N ATOM 124 CA LEU A 17 -11.134 20.695 18.826 1.00 10.83 C ATOM 125 C LEU A 17 -11.728 21.961 18.295 1.00 16.61 C ATOM 126 O LEU A 17 -11.276 23.016 18.657 1.00 18.63 O ATOM 127 CB LEU A 17 -9.749 20.538 18.218 1.00 14.80 C ATOM 128 CG LEU A 17 -8.792 19.745 19.031 1.00 19.84 C ATOM 129 CD1 LEU A 17 -7.483 19.876 18.293 1.00 22.16 C ATOM 130 CD2 LEU A 17 -8.675 20.282 20.474 1.00 15.82 C ATOM 131 N ASP A 18 -12.704 21.930 17.405 1.00 18.32 N ATOM 132 CA ASP A 18 -13.261 23.178 16.884 1.00 18.68 C ATOM 133 C ASP A 18 -13.986 23.912 17.979 1.00 19.05 C ATOM 134 O ASP A 18 -14.952 23.375 18.512 1.00 21.74 O ATOM 135 CB ASP A 18 -14.275 23.002 15.717 1.00 25.90 C ATOM 136 CG ASP A 18 -14.712 24.288 15.010 1.00 37.66 C ATOM 137 OD1 ASP A 18 -14.134 25.393 15.038 1.00 26.98 O ATOM 138 OD2 ASP A 18 -15.751 24.055 14.248 1.00 63.41 O ATOM 139 N ASN A 19 -13.542 25.130 18.229 1.00 12.22 N ATOM 140 CA ASN A 19 -14.046 26.010 19.253 1.00 9.99 C ATOM 141 C ASN A 19 -13.851 25.507 20.671 1.00 15.97 C ATOM 142 O ASN A 19 -14.534 25.975 21.595 1.00 18.10 O ATOM 143 CB ASN A 19 -15.518 26.259 19.032 1.00 20.32 C ATOM 144 CG ASN A 19 -15.706 27.052 17.774 1.00 40.03 C ATOM 145 OD1 ASN A 19 -15.227 28.183 17.693 1.00 57.25 O ATOM 146 ND2 ASN A 19 -16.402 26.456 16.811 1.00 40.09 N ATOM 147 N TYR A 20 -12.956 24.552 20.827 1.00 12.49 N ATOM 148 CA TYR A 20 -12.652 24.027 22.106 1.00 8.91 C ATOM 149 C TYR A 20 -12.037 25.159 22.929 1.00 19.06 C ATOM 150 O TYR A 20 -10.978 25.687 22.602 1.00 16.99 O ATOM 151 CB TYR A 20 -11.717 22.810 22.005 1.00 17.23 C ATOM 152 CG TYR A 20 -11.532 22.151 23.355 1.00 13.76 C ATOM 153 CD1 TYR A 20 -12.444 21.206 23.832 1.00 17.40 C ATOM 154 CD2 TYR A 20 -10.475 22.556 24.184 1.00 24.53 C ATOM 155 CE1 TYR A 20 -12.311 20.657 25.111 1.00 20.84 C ATOM 156 CE2 TYR A 20 -10.331 22.023 25.461 1.00 16.26 C ATOM 157 CZ TYR A 20 -11.259 21.078 25.922 1.00 35.41 C ATOM 158 OH TYR A 20 -11.104 20.560 27.183 1.00 29.68 O ATOM 159 N ARG A 21 -12.721 25.593 23.977 1.00 19.00 N ATOM 160 CA ARG A 21 -12.250 26.715 24.791 1.00 15.05 C ATOM 161 C ARG A 21 -12.264 27.987 24.017 1.00 8.63 C ATOM 162 O ARG A 21 -11.450 28.877 24.295 1.00 13.69 O ATOM 163 CB ARG A 21 -10.847 26.601 25.387 1.00 18.33 C ATOM 164 CG ARG A 21 -10.694 25.514 26.442 1.00 27.37 C ATOM 165 CD ARG A 21 -11.577 25.864 27.598 1.00 40.81 C ATOM 166 NE ARG A 21 -11.597 24.902 28.676 1.00 57.85 N ATOM 167 CZ ARG A 21 -11.253 25.330 29.884 1.00 97.15 C ATOM 168 NH1 ARG A 21 -10.859 26.593 30.049 1.00 63.15 N ATOM 169 NH2 ARG A 21 -11.283 24.508 30.937 1.00 68.08 N ATOM 170 N GLY A 22 -13.173 28.076 23.045 1.00 11.97 N ATOM 171 CA GLY A 22 -13.290 29.312 22.253 1.00 13.56 C ATOM 172 C GLY A 22 -12.276 29.499 21.125 1.00 15.57 C ATOM 173 O GLY A 22 -12.274 30.537 20.508 1.00 15.13 O ATOM 174 N TYR A 23 -11.414 28.511 20.863 1.00 17.25 N ATOM 175 CA TYR A 23 -10.419 28.584 19.787 1.00 12.17 C ATOM 176 C TYR A 23 -10.964 27.832 18.564 1.00 7.69 C ATOM 177 O TYR A 23 -11.097 26.573 18.581 1.00 8.57 O ATOM 178 CB TYR A 23 -9.059 27.910 20.217 1.00 11.16 C ATOM 179 CG TYR A 23 -8.358 28.702 21.299 1.00 14.01 C ATOM 180 CD1 TYR A 23 -7.560 29.766 20.910 1.00 10.23 C ATOM 181 CD2 TYR A 23 -8.534 28.427 22.652 1.00 6.77 C ATOM 182 CE1 TYR A 23 -6.879 30.557 21.846 1.00 9.23 C ATOM 183 CE2 TYR A 23 -7.907 29.219 23.612 1.00 10.96 C ATOM 184 CZ TYR A 23 -7.061 30.276 23.207 1.00 12.99 C ATOM 185 OH TYR A 23 -6.411 31.069 24.111 1.00 13.78 O ATOM 186 N SER A 24 -11.219 28.590 17.517 1.00 12.88 N ATOM 187 CA SER A 24 -11.730 28.032 16.253 1.00 14.99 C ATOM 188 C SER A 24 -10.726 27.075 15.616 1.00 20.42 C ATOM 189 O SER A 24 -9.487 27.191 15.841 1.00 9.73 O ATOM 190 CB SER A 24 -12.060 29.179 15.305 1.00 9.90 C ATOM 191 OG SER A 24 -10.830 29.750 14.853 1.00 17.68 O ATOM 192 N LEU A 25 -11.267 26.110 14.822 1.00 16.10 N ATOM 193 CA LEU A 25 -10.460 25.111 14.092 1.00 11.40 C ATOM 194 C LEU A 25 -9.205 25.683 13.438 1.00 11.44 C ATOM 195 O LEU A 25 -8.145 25.073 13.536 1.00 10.56 O ATOM 196 CB LEU A 25 -11.293 24.412 12.993 1.00 13.62 C ATOM 197 CG LEU A 25 -10.826 23.089 12.491 1.00 16.00 C ATOM 198 CD1 LEU A 25 -10.359 22.212 13.644 1.00 15.17 C ATOM 199 CD2 LEU A 25 -12.018 22.437 11.805 1.00 15.75 C ATOM 200 N GLY A 26 -9.311 26.836 12.758 1.00 10.18 N ATOM 201 CA GLY A 26 -8.169 27.388 12.084 1.00 7.13 C ATOM 202 C GLY A 26 -6.984 27.643 12.997 1.00 9.12 C ATOM 203 O GLY A 26 -5.854 27.610 12.555 1.00 12.61 O ATOM 204 N ASN A 27 -7.232 27.928 14.280 1.00 10.01 N ATOM 205 CA ASN A 27 -6.132 28.159 15.255 1.00 10.13 C ATOM 206 C ASN A 27 -5.317 26.889 15.464 1.00 2.57 C ATOM 207 O ASN A 27 -4.057 26.899 15.477 1.00 7.08 O ATOM 208 CB ASN A 27 -6.688 28.636 16.631 1.00 9.13 C ATOM 209 CG ASN A 27 -7.131 30.092 16.624 1.00 4.84 C ATOM 210 OD1 ASN A 27 -6.292 30.979 16.582 1.00 9.37 O ATOM 211 ND2 ASN A 27 -8.466 30.324 16.587 1.00 8.00 N ATOM 212 N TRP A 28 -6.033 25.791 15.639 1.00 5.40 N ATOM 213 CA TRP A 28 -5.402 24.497 15.879 1.00 5.45 C ATOM 214 C TRP A 28 -4.584 24.047 14.657 1.00 6.38 C ATOM 215 O TRP A 28 -3.510 23.501 14.767 1.00 7.31 O ATOM 216 CB TRP A 28 -6.482 23.490 16.237 1.00 7.31 C ATOM 217 CG TRP A 28 -7.149 23.849 17.539 1.00 7.66 C ATOM 218 CD1 TRP A 28 -8.351 24.415 17.748 1.00 11.80 C ATOM 219 CD2 TRP A 28 -6.540 23.691 18.841 1.00 9.47 C ATOM 220 NE1 TRP A 28 -8.575 24.567 19.117 1.00 11.48 N ATOM 221 CE2 TRP A 28 -7.475 24.139 19.807 1.00 9.56 C ATOM 222 CE3 TRP A 28 -5.321 23.121 19.249 1.00 10.24 C ATOM 223 CZ2 TRP A 28 -7.187 24.066 21.210 1.00 11.03 C ATOM 224 CZ3 TRP A 28 -5.059 23.060 20.609 1.00 20.66 C ATOM 225 CH2 TRP A 28 -5.985 23.551 21.560 1.00 9.06 C ATOM 226 N VAL A 29 -5.166 24.262 13.469 1.00 3.89 N ATOM 227 CA VAL A 29 -4.458 23.870 12.217 1.00 5.65 C ATOM 228 C VAL A 29 -3.242 24.746 11.972 1.00 2.99 C ATOM 229 O VAL A 29 -2.170 24.273 11.571 1.00 7.90 O ATOM 230 CB VAL A 29 -5.456 23.881 11.020 1.00 7.66 C ATOM 231 CG1 VAL A 29 -4.630 23.646 9.743 1.00 13.23 C ATOM 232 CG2 VAL A 29 -6.516 22.751 11.149 1.00 6.73 C ATOM 233 N CYS A 30 -3.372 26.060 12.262 1.00 2.63 N ATOM 234 CA CYS A 30 -2.281 26.976 12.125 1.00 7.05 C ATOM 235 C CYS A 30 -1.151 26.582 13.072 1.00 10.47 C ATOM 236 O CYS A 30 0.054 26.552 12.766 1.00 4.93 O ATOM 237 CB CYS A 30 -2.756 28.428 12.303 1.00 2.61 C ATOM 238 SG CYS A 30 -1.467 29.667 12.134 1.00 10.20 S ATOM 239 N ALA A 31 -1.521 26.283 14.306 1.00 9.82 N ATOM 240 CA ALA A 31 -0.491 25.884 15.276 1.00 15.61 C ATOM 241 C ALA A 31 0.235 24.607 14.849 1.00 5.07 C ATOM 242 O ALA A 31 1.464 24.554 14.987 1.00 9.27 O ATOM 243 CB ALA A 31 -1.089 25.781 16.704 1.00 7.85 C ATOM 244 N ALA A 32 -0.483 23.609 14.315 1.00 7.79 N ATOM 245 CA ALA A 32 0.162 22.357 13.855 1.00 8.61 C ATOM 246 C ALA A 32 1.085 22.594 12.673 1.00 7.90 C ATOM 247 O ALA A 32 2.197 22.050 12.585 1.00 9.35 O ATOM 248 CB ALA A 32 -0.823 21.268 13.540 1.00 10.83 C ATOM 249 N LYS A 33 0.653 23.463 11.786 1.00 7.35 N ATOM 250 CA LYS A 33 1.542 23.795 10.635 1.00 6.50 C ATOM 251 C LYS A 33 2.867 24.333 11.097 1.00 6.89 C ATOM 252 O LYS A 33 3.936 23.889 10.727 1.00 10.45 O ATOM 253 CB LYS A 33 0.863 24.886 9.776 1.00 10.32 C ATOM 254 CG LYS A 33 1.793 25.437 8.676 1.00 13.52 C ATOM 255 CD LYS A 33 1.927 24.485 7.491 1.00 19.87 C ATOM 256 CE LYS A 33 3.138 24.764 6.621 1.00 27.04 C ATOM 257 NZ LYS A 33 3.217 23.793 5.511 1.00 45.44 N ATOM 258 N PHE A 34 2.807 25.345 11.961 1.00 9.24 N ATOM 259 CA PHE A 34 4.029 25.958 12.436 1.00 8.96 C ATOM 260 C PHE A 34 4.846 25.192 13.455 1.00 16.48 C ATOM 261 O PHE A 34 6.039 25.360 13.540 1.00 14.96 O ATOM 262 CB PHE A 34 3.856 27.469 12.721 1.00 9.21 C ATOM 263 CG PHE A 34 3.417 28.201 11.426 1.00 11.78 C ATOM 264 CD1 PHE A 34 4.231 28.189 10.282 1.00 11.49 C ATOM 265 CD2 PHE A 34 2.212 28.933 11.385 1.00 12.86 C ATOM 266 CE1 PHE A 34 3.830 28.854 9.136 1.00 12.74 C ATOM 267 CE2 PHE A 34 1.803 29.618 10.224 1.00 13.38 C ATOM 268 CZ PHE A 34 2.627 29.554 9.090 1.00 13.39 C ATOM 269 N GLU A 35 4.201 24.324 14.225 1.00 9.90 N ATOM 270 CA GLU A 35 4.889 23.543 15.263 1.00 11.92 C ATOM 271 C GLU A 35 5.641 22.352 14.706 1.00 13.79 C ATOM 272 O GLU A 35 6.781 22.129 15.054 1.00 8.23 O ATOM 273 CB GLU A 35 3.839 23.026 16.259 1.00 6.00 C ATOM 274 CG GLU A 35 3.409 24.107 17.322 1.00 11.89 C ATOM 275 CD GLU A 35 4.516 24.690 18.200 1.00 12.03 C ATOM 276 OE1 GLU A 35 5.640 24.296 18.226 1.00 12.97 O ATOM 277 OE2 GLU A 35 4.167 25.730 18.876 1.00 13.03 O ATOM 278 N SER A 36 4.983 21.591 13.819 1.00 8.49 N ATOM 279 CA SER A 36 5.541 20.369 13.283 1.00 8.82 C ATOM 280 C SER A 36 5.483 20.189 11.756 1.00 10.13 C ATOM 281 O SER A 36 5.800 19.070 11.251 1.00 14.88 O ATOM 282 CB SER A 36 4.684 19.256 13.831 1.00 7.77 C ATOM 283 OG SER A 36 3.330 19.336 13.297 1.00 8.30 O ATOM 284 N ASN A 37 4.975 21.223 11.050 1.00 11.55 N ATOM 285 CA ASN A 37 4.752 21.103 9.605 1.00 8.89 C ATOM 286 C ASN A 37 3.825 19.918 9.321 1.00 14.33 C ATOM 287 O ASN A 37 3.972 19.215 8.320 1.00 14.19 O ATOM 288 CB ASN A 37 6.061 21.002 8.788 1.00 20.93 C ATOM 289 CG ASN A 37 5.851 21.458 7.334 1.00 25.83 C ATOM 290 OD1 ASN A 37 5.061 22.365 7.057 1.00 26.84 O ATOM 291 ND2 ASN A 37 6.474 20.759 6.397 1.00 52.87 N ATOM 292 N PHE A 38 2.864 19.696 10.220 1.00 7.19 N ATOM 293 CA PHE A 38 1.862 18.625 10.075 1.00 11.76 C ATOM 294 C PHE A 38 2.411 17.214 10.168 1.00 10.63 C ATOM 295 O PHE A 38 1.747 16.276 9.742 1.00 9.49 O ATOM 296 CB PHE A 38 1.092 18.696 8.696 1.00 8.56 C ATOM 297 CG PHE A 38 0.280 19.956 8.505 1.00 13.59 C ATOM 298 CD1 PHE A 38 -0.367 20.558 9.597 1.00 8.45 C ATOM 299 CD2 PHE A 38 0.112 20.532 7.255 1.00 17.61 C ATOM 300 CE1 PHE A 38 -1.146 21.685 9.432 1.00 11.53 C ATOM 301 CE2 PHE A 38 -0.664 21.687 7.081 1.00 17.65 C ATOM 302 CZ PHE A 38 -1.316 22.268 8.162 1.00 13.17 C ATOM 303 N ASN A 39 3.667 17.073 10.600 1.00 8.40 N ATOM 304 CA ASN A 39 4.271 15.737 10.699 1.00 6.01 C ATOM 305 C ASN A 39 4.101 15.211 12.158 1.00 6.81 C ATOM 306 O ASN A 39 4.597 15.858 13.147 1.00 11.41 O ATOM 307 CB ASN A 39 5.776 15.925 10.373 1.00 6.39 C ATOM 308 CG ASN A 39 6.552 14.636 10.450 1.00 6.34 C ATOM 309 OD1 ASN A 39 5.992 13.541 10.684 1.00 10.75 O ATOM 310 ND2 ASN A 39 7.832 14.764 10.100 1.00 13.88 N ATOM 311 N THR A 40 3.430 14.054 12.314 1.00 8.05 N ATOM 312 CA THR A 40 3.222 13.509 13.676 1.00 10.13 C ATOM 313 C THR A 40 4.525 13.041 14.358 1.00 8.64 C ATOM 314 O THR A 40 4.546 12.831 15.542 1.00 12.11 O ATOM 315 CB THR A 40 2.279 12.302 13.663 1.00 12.49 C ATOM 316 OG1 THR A 40 2.862 11.250 12.880 1.00 10.89 O ATOM 317 CG2 THR A 40 0.843 12.666 13.219 1.00 9.90 C ATOM 318 N GLN A 41 5.594 12.819 13.559 1.00 6.61 N ATOM 319 CA GLN A 41 6.860 12.308 14.019 1.00 3.67 C ATOM 320 C GLN A 41 7.861 13.372 14.433 1.00 4.66 C ATOM 321 O GLN A 41 8.986 13.051 14.864 1.00 8.80 O ATOM 322 CB GLN A 41 7.463 11.344 12.979 1.00 9.30 C ATOM 323 CG GLN A 41 6.598 10.100 12.797 1.00 12.21 C ATOM 324 CD GLN A 41 7.402 8.999 12.104 1.00 22.47 C ATOM 325 OE1 GLN A 41 8.254 8.393 12.763 1.00 16.54 O ATOM 326 NE2 GLN A 41 7.257 8.847 10.744 1.00 13.11 N ATOM 327 N ALA A 42 7.460 14.657 14.305 1.00 6.51 N ATOM 328 CA ALA A 42 8.376 15.748 14.672 1.00 8.14 C ATOM 329 C ALA A 42 8.824 15.710 16.237 1.00 11.70 C ATOM 330 O ALA A 42 8.005 15.547 17.165 1.00 5.54 O ATOM 331 CB ALA A 42 7.744 17.108 14.349 1.00 8.97 C ATOM 332 N THR A 43 10.132 15.865 16.445 1.00 7.08 N ATOM 333 CA THR A 43 10.705 15.992 17.773 1.00 10.42 C ATOM 334 C THR A 43 11.694 17.112 17.692 1.00 11.09 C ATOM 335 O THR A 43 12.280 17.411 16.646 1.00 12.80 O ATOM 336 CB THR A 43 11.362 14.748 18.354 1.00 13.93 C ATOM 337 OG1 THR A 43 12.360 14.429 17.435 1.00 11.09 O ATOM 338 CG2 THR A 43 10.420 13.567 18.530 1.00 7.58 C ATOM 339 N ASN A 44 11.894 17.801 18.808 1.00 10.77 N ATOM 340 CA ASN A 44 12.828 18.909 18.863 1.00 6.02 C ATOM 341 C ASN A 44 13.258 19.036 20.281 1.00 16.51 C ATOM 342 O ASN A 44 12.426 19.127 21.185 1.00 11.61 O ATOM 343 CB ASN A 44 12.171 20.225 18.473 1.00 13.37 C ATOM 344 CG ASN A 44 11.932 20.272 16.966 1.00 55.95 C ATOM 345 OD1 ASN A 44 12.883 20.299 16.146 1.00 30.16 O ATOM 346 ND2 ASN A 44 10.659 20.233 16.594 1.00 20.67 N ATOM 347 N ARG A 45 14.545 19.035 20.479 1.00 13.41 N ATOM 348 CA ARG A 45 15.061 19.112 21.827 1.00 12.01 C ATOM 349 C ARG A 45 15.250 20.555 22.252 1.00 20.93 C ATOM 350 O ARG A 45 15.601 21.418 21.435 1.00 20.22 O ATOM 351 CB ARG A 45 16.408 18.438 21.953 1.00 19.70 C ATOM 352 CG ARG A 45 16.935 18.714 23.338 1.00 35.82 C ATOM 353 CD ARG A 45 16.730 17.468 24.141 1.00 27.63 C ATOM 354 NE ARG A 45 17.249 16.408 23.330 1.00 67.37 N ATOM 355 CZ ARG A 45 18.249 15.588 23.641 1.00 91.84 C ATOM 356 NH1 ARG A 45 18.868 15.589 24.830 1.00 36.08 N ATOM 357 NH2 ARG A 45 18.624 14.698 22.716 1.00 64.26 N ATOM 358 N ASN A 46 15.056 20.796 23.543 1.00 12.63 N ATOM 359 CA ASN A 46 15.247 22.135 24.062 1.00 12.92 C ATOM 360 C ASN A 46 16.508 22.245 24.900 1.00 8.11 C ATOM 361 O ASN A 46 17.149 21.253 25.274 1.00 15.77 O ATOM 362 CB ASN A 46 13.989 22.699 24.735 1.00 11.70 C ATOM 363 CG ASN A 46 12.659 22.418 24.007 1.00 21.14 C ATOM 364 OD1 ASN A 46 11.762 21.669 24.459 1.00 23.29 O ATOM 365 ND2 ASN A 46 12.508 23.062 22.886 1.00 24.99 N ATOM 366 N THR A 47 16.906 23.489 25.146 1.00 23.92 N ATOM 367 CA THR A 47 18.108 23.768 25.931 1.00 39.90 C ATOM 368 C THR A 47 17.996 23.269 27.358 1.00 25.44 C ATOM 369 O THR A 47 18.958 22.798 27.923 1.00 34.24 O ATOM 370 CB THR A 47 18.506 25.250 25.905 1.00 47.42 C ATOM 371 OG1 THR A 47 17.376 26.053 26.142 1.00 38.53 O ATOM 372 CG2 THR A 47 19.115 25.572 24.552 1.00 58.08 C ATOM 373 N ASP A 48 16.797 23.339 27.935 1.00 20.62 N ATOM 374 CA ASP A 48 16.626 22.832 29.261 1.00 9.90 C ATOM 375 C ASP A 48 16.700 21.306 29.306 1.00 19.23 C ATOM 376 O ASP A 48 16.586 20.723 30.361 1.00 22.36 O ATOM 377 CB ASP A 48 15.349 23.377 29.887 1.00 14.78 C ATOM 378 CG ASP A 48 14.119 22.821 29.267 1.00 19.04 C ATOM 379 OD1 ASP A 48 14.160 21.981 28.422 1.00 28.31 O ATOM 380 OD2 ASP A 48 13.002 23.315 29.717 1.00 28.61 O ATOM 381 N GLY A 49 16.883 20.637 28.166 1.00 17.28 N ATOM 382 CA GLY A 49 16.950 19.205 28.182 1.00 10.24 C ATOM 383 C GLY A 49 15.608 18.534 27.977 1.00 14.24 C ATOM 384 O GLY A 49 15.499 17.291 27.852 1.00 13.58 O ATOM 385 N SER A 50 14.564 19.331 27.973 1.00 9.07 N ATOM 386 CA SER A 50 13.311 18.716 27.712 1.00 7.32 C ATOM 387 C SER A 50 13.217 18.531 26.131 1.00 11.52 C ATOM 388 O SER A 50 14.085 19.016 25.374 1.00 13.96 O ATOM 389 CB SER A 50 12.113 19.490 28.182 1.00 4.67 C ATOM 390 OG SER A 50 12.074 20.716 27.461 1.00 9.76 O ATOM 391 N THR A 51 12.150 17.857 25.646 1.00 11.43 N ATOM 392 CA THR A 51 11.958 17.610 24.179 1.00 9.12 C ATOM 393 C THR A 51 10.485 17.806 23.917 1.00 16.87 C ATOM 394 O THR A 51 9.677 17.499 24.825 1.00 8.33 O ATOM 395 CB THR A 51 12.363 16.177 23.757 1.00 5.49 C ATOM 396 OG1 THR A 51 13.711 15.986 24.120 1.00 6.88 O ATOM 397 CG2 THR A 51 12.234 15.930 22.227 1.00 7.94 C ATOM 398 N ASP A 52 10.158 18.354 22.701 1.00 9.46 N ATOM 399 CA ASP A 52 8.767 18.608 22.181 1.00 5.88 C ATOM 400 C ASP A 52 8.451 17.463 21.198 1.00 5.87 C ATOM 401 O ASP A 52 9.311 17.033 20.476 1.00 5.53 O ATOM 402 CB ASP A 52 8.717 19.972 21.485 1.00 6.73 C ATOM 403 CG ASP A 52 9.014 21.046 22.449 1.00 17.46 C ATOM 404 OD1 ASP A 52 8.778 20.978 23.593 1.00 16.69 O ATOM 405 OD2 ASP A 52 9.531 22.065 21.923 1.00 28.92 O ATOM 406 N TYR A 53 7.279 16.908 21.280 1.00 7.33 N ATOM 407 CA TYR A 53 6.899 15.745 20.548 1.00 9.37 C ATOM 408 C TYR A 53 5.580 15.922 19.790 1.00 13.52 C ATOM 409 O TYR A 53 4.554 16.399 20.326 1.00 7.94 O ATOM 410 CB TYR A 53 6.630 14.562 21.517 1.00 7.91 C ATOM 411 CG TYR A 53 7.865 14.099 22.242 1.00 6.82 C ATOM 412 CD1 TYR A 53 8.335 14.742 23.399 1.00 8.88 C ATOM 413 CD2 TYR A 53 8.618 13.027 21.749 1.00 6.30 C ATOM 414 CE1 TYR A 53 9.548 14.382 24.006 1.00 1.83 C ATOM 415 CE2 TYR A 53 9.846 12.646 22.351 1.00 10.07 C ATOM 416 CZ TYR A 53 10.229 13.264 23.534 1.00 8.70 C ATOM 417 OH TYR A 53 11.374 12.889 24.151 1.00 12.40 O ATOM 418 N GLY A 54 5.598 15.446 18.516 1.00 11.04 N ATOM 419 CA GLY A 54 4.390 15.347 17.710 1.00 7.71 C ATOM 420 C GLY A 54 3.939 16.599 17.020 1.00 3.67 C ATOM 421 O GLY A 54 4.535 17.621 17.017 1.00 8.49 O ATOM 422 N ILE A 55 2.748 16.458 16.496 1.00 11.91 N ATOM 423 CA ILE A 55 2.096 17.435 15.686 1.00 7.88 C ATOM 424 C ILE A 55 1.893 18.749 16.386 1.00 10.21 C ATOM 425 O ILE A 55 1.904 19.805 15.761 1.00 9.17 O ATOM 426 CB ILE A 55 0.838 16.805 15.068 1.00 19.48 C ATOM 427 CG1 ILE A 55 0.390 17.438 13.734 1.00 15.27 C ATOM 428 CG2 ILE A 55 -0.262 16.528 16.106 1.00 16.63 C ATOM 429 CD1 ILE A 55 -0.353 16.483 12.846 1.00 21.60 C ATOM 430 N LEU A 56 1.765 18.677 17.706 1.00 9.90 N ATOM 431 CA LEU A 56 1.584 19.877 18.488 1.00 7.23 C ATOM 432 C LEU A 56 2.735 20.173 19.390 1.00 18.66 C ATOM 433 O LEU A 56 2.660 21.074 20.200 1.00 10.73 O ATOM 434 CB LEU A 56 0.216 19.957 19.205 1.00 11.28 C ATOM 435 CG LEU A 56 -0.990 20.157 18.283 1.00 12.31 C ATOM 436 CD1 LEU A 56 -2.255 19.795 19.036 1.00 11.09 C ATOM 437 CD2 LEU A 56 -1.074 21.607 17.850 1.00 11.43 C ATOM 438 N GLN A 57 3.804 19.441 19.202 1.00 7.01 N ATOM 439 CA GLN A 57 5.029 19.733 19.898 1.00 9.13 C ATOM 440 C GLN A 57 4.883 19.918 21.451 1.00 11.13 C ATOM 441 O GLN A 57 5.272 20.968 22.020 1.00 12.02 O ATOM 442 CB GLN A 57 5.767 20.937 19.263 1.00 10.29 C ATOM 443 CG GLN A 57 6.362 20.658 17.863 1.00 6.27 C ATOM 444 CD GLN A 57 7.544 19.747 17.936 1.00 2.25 C ATOM 445 OE1 GLN A 57 8.676 20.257 18.147 1.00 7.47 O ATOM 446 NE2 GLN A 57 7.279 18.413 17.746 1.00 7.69 N ATOM 447 N ILE A 58 4.303 18.898 22.061 1.00 9.58 N ATOM 448 CA ILE A 58 4.031 18.814 23.487 1.00 12.88 C ATOM 449 C ILE A 58 5.301 18.482 24.282 1.00 14.09 C ATOM 450 O ILE A 58 6.055 17.583 23.982 1.00 10.65 O ATOM 451 CB ILE A 58 2.839 17.923 23.711 1.00 12.15 C ATOM 452 CG1 ILE A 58 1.599 18.614 23.110 1.00 12.61 C ATOM 453 CG2 ILE A 58 2.704 17.544 25.215 1.00 12.37 C ATOM 454 CD1 ILE A 58 0.329 17.770 23.138 1.00 17.22 C ATOM 455 N ASN A 59 5.556 19.297 25.282 1.00 11.07 N ATOM 456 CA ASN A 59 6.797 19.305 26.034 1.00 6.68 C ATOM 457 C ASN A 59 6.893 18.239 27.099 1.00 8.46 C ATOM 458 O ASN A 59 5.904 18.002 27.761 1.00 12.15 O ATOM 459 CB ASN A 59 7.045 20.721 26.565 1.00 7.94 C ATOM 460 CG ASN A 59 8.434 20.839 27.178 1.00 12.92 C ATOM 461 OD1 ASN A 59 8.578 20.809 28.411 1.00 30.15 O ATOM 462 ND2 ASN A 59 9.469 20.939 26.342 1.00 15.71 N ATOM 463 N SER A 60 8.096 17.590 27.218 1.00 8.63 N ATOM 464 CA SER A 60 8.333 16.496 28.162 1.00 11.30 C ATOM 465 C SER A 60 8.586 17.015 29.647 1.00 7.42 C ATOM 466 O SER A 60 8.559 16.218 30.620 1.00 18.39 O ATOM 467 CB SER A 60 9.448 15.619 27.698 1.00 9.31 C ATOM 468 OG SER A 60 10.642 16.390 27.790 1.00 9.01 O ATOM 469 N ARG A 61 8.806 18.347 29.787 1.00 11.96 N ATOM 470 CA ARG A 61 8.981 18.933 31.125 1.00 18.58 C ATOM 471 C ARG A 61 7.701 18.806 31.935 1.00 21.66 C ATOM 472 O ARG A 61 7.730 18.363 33.063 1.00 24.43 O ATOM 473 CB ARG A 61 9.507 20.347 31.068 1.00 19.81 C ATOM 474 CG ARG A 61 9.259 21.125 32.338 1.00 40.52 C ATOM 475 CD ARG A 61 10.511 21.648 33.063 1.00 30.90 C ATOM 476 NE ARG A 61 11.777 21.523 32.353 1.00 58.97 N ATOM 477 CZ ARG A 61 12.722 20.587 32.539 1.00 70.61 C ATOM 478 NH1 ARG A 61 12.610 19.570 33.413 1.00 68.85 N ATOM 479 NH2 ARG A 61 13.829 20.673 31.795 1.00 56.33 N ATOM 480 N TRP A 62 6.542 19.071 31.329 1.00 12.69 N ATOM 481 CA TRP A 62 5.279 18.955 32.026 1.00 10.92 C ATOM 482 C TRP A 62 4.281 17.916 31.682 1.00 19.26 C ATOM 483 O TRP A 62 3.526 17.478 32.563 1.00 19.01 O ATOM 484 CB TRP A 62 4.455 20.234 31.875 1.00 14.48 C ATOM 485 CG TRP A 62 5.346 21.376 31.920 1.00 34.77 C ATOM 486 CD1 TRP A 62 5.937 21.965 30.857 1.00 48.56 C ATOM 487 CD2 TRP A 62 5.859 21.980 33.091 1.00 34.03 C ATOM 488 NE1 TRP A 62 6.753 22.970 31.303 1.00 60.61 N ATOM 489 CE2 TRP A 62 6.730 22.995 32.671 1.00 37.59 C ATOM 490 CE3 TRP A 62 5.619 21.790 34.443 1.00 44.85 C ATOM 491 CZ2 TRP A 62 7.373 23.823 33.582 1.00 74.91 C ATOM 492 CZ3 TRP A 62 6.254 22.606 35.347 1.00 49.52 C ATOM 493 CH2 TRP A 62 7.122 23.609 34.923 1.00 52.73 C ATOM 494 N TRP A 63 4.152 17.600 30.385 1.00 11.21 N ATOM 495 CA TRP A 63 3.036 16.858 29.848 1.00 9.63 C ATOM 496 C TRP A 63 3.155 15.396 29.592 1.00 4.89 C ATOM 497 O TRP A 63 2.183 14.725 29.581 1.00 11.10 O ATOM 498 CB TRP A 63 2.652 17.635 28.566 1.00 6.50 C ATOM 499 CG TRP A 63 2.429 19.101 28.874 1.00 5.59 C ATOM 500 CD1 TRP A 63 3.223 20.140 28.615 1.00 15.86 C ATOM 501 CD2 TRP A 63 1.364 19.632 29.695 1.00 11.68 C ATOM 502 NE1 TRP A 63 2.675 21.309 29.075 1.00 15.89 N ATOM 503 CE2 TRP A 63 1.567 21.028 29.780 1.00 12.27 C ATOM 504 CE3 TRP A 63 0.230 19.055 30.324 1.00 14.52 C ATOM 505 CZ2 TRP A 63 0.682 21.862 30.488 1.00 10.75 C ATOM 506 CZ3 TRP A 63 -0.678 19.891 30.985 1.00 10.21 C ATOM 507 CH2 TRP A 63 -0.421 21.271 31.057 1.00 13.33 C ATOM 508 N CYS A 64 4.324 14.905 29.353 1.00 8.53 N ATOM 509 CA CYS A 64 4.448 13.469 29.032 1.00 14.18 C ATOM 510 C CYS A 64 5.785 12.968 29.569 1.00 8.75 C ATOM 511 O CYS A 64 6.694 13.742 29.793 1.00 11.88 O ATOM 512 CB CYS A 64 4.366 13.241 27.432 1.00 12.87 C ATOM 513 SG CYS A 64 5.695 14.086 26.427 1.00 9.81 S ATOM 514 N ASN A 65 5.913 11.651 29.720 1.00 9.55 N ATOM 515 CA ASN A 65 7.127 11.114 30.200 1.00 16.84 C ATOM 516 C ASN A 65 7.999 10.547 29.073 1.00 4.97 C ATOM 517 O ASN A 65 7.529 9.623 28.435 1.00 10.83 O ATOM 518 CB ASN A 65 6.809 9.953 31.188 1.00 9.17 C ATOM 519 CG ASN A 65 8.120 9.322 31.715 1.00 22.59 C ATOM 520 OD1 ASN A 65 9.033 10.017 32.182 1.00 21.36 O ATOM 521 ND2 ASN A 65 8.276 8.015 31.524 1.00 36.98 N ATOM 522 N ASP A 66 9.254 10.993 28.982 1.00 7.66 N ATOM 523 CA ASP A 66 10.153 10.434 27.995 1.00 14.97 C ATOM 524 C ASP A 66 11.354 9.742 28.601 1.00 23.09 C ATOM 525 O ASP A 66 12.237 9.341 27.867 1.00 9.43 O ATOM 526 CB ASP A 66 10.641 11.448 26.948 1.00 13.58 C ATOM 527 CG ASP A 66 11.480 12.554 27.535 1.00 11.41 C ATOM 528 OD1 ASP A 66 11.787 12.613 28.717 1.00 18.43 O ATOM 529 OD2 ASP A 66 11.850 13.432 26.659 1.00 10.72 O ATOM 530 N GLY A 67 11.395 9.644 29.920 1.00 14.60 N ATOM 531 CA GLY A 67 12.449 8.941 30.665 1.00 9.04 C ATOM 532 C GLY A 67 13.738 9.677 30.697 1.00 13.04 C ATOM 533 O GLY A 67 14.726 9.165 31.164 1.00 23.22 O ATOM 534 N ARG A 68 13.787 10.891 30.194 1.00 8.50 N ATOM 535 CA ARG A 68 15.089 11.512 30.237 1.00 11.50 C ATOM 536 C ARG A 68 15.046 12.949 30.560 1.00 11.49 C ATOM 537 O ARG A 68 15.995 13.645 30.281 1.00 17.90 O ATOM 538 CB ARG A 68 15.872 11.277 28.959 1.00 18.67 C ATOM 539 CG ARG A 68 15.218 11.867 27.707 1.00 21.19 C ATOM 540 CD ARG A 68 16.251 12.103 26.592 1.00 19.51 C ATOM 541 NE ARG A 68 15.790 12.984 25.527 1.00 20.38 N ATOM 542 CZ ARG A 68 16.264 12.978 24.248 1.00 29.94 C ATOM 543 NH1 ARG A 68 17.253 12.102 23.926 1.00 13.00 N ATOM 544 NH2 ARG A 68 15.787 13.865 23.293 1.00 13.47 N ATOM 545 N THR A 69 13.937 13.376 31.145 1.00 12.12 N ATOM 546 CA THR A 69 13.674 14.782 31.586 1.00 17.22 C ATOM 547 C THR A 69 13.372 14.770 33.144 1.00 15.41 C ATOM 548 O THR A 69 12.260 14.526 33.618 1.00 19.26 O ATOM 549 CB THR A 69 12.464 15.410 30.798 1.00 12.81 C ATOM 550 OG1 THR A 69 12.589 15.107 29.412 1.00 17.25 O ATOM 551 CG2 THR A 69 12.392 16.932 30.990 1.00 8.98 C ATOM 552 N PRO A 70 14.431 14.960 33.874 1.00 30.00 N ATOM 553 CA PRO A 70 14.563 14.964 35.315 1.00 31.13 C ATOM 554 C PRO A 70 13.654 16.003 35.904 1.00 43.01 C ATOM 555 O PRO A 70 13.699 17.188 35.594 1.00 37.19 O ATOM 556 CB PRO A 70 16.056 15.221 35.541 1.00 43.11 C ATOM 557 CG PRO A 70 16.728 15.203 34.148 1.00 49.23 C ATOM 558 CD PRO A 70 15.635 15.319 33.119 1.00 44.60 C ATOM 559 N GLY A 71 12.698 15.573 36.672 1.00 29.79 N ATOM 560 CA GLY A 71 11.785 16.609 37.130 1.00 38.84 C ATOM 561 C GLY A 71 10.547 16.728 36.220 1.00 34.52 C ATOM 562 O GLY A 71 9.750 17.644 36.328 1.00 53.49 O ATOM 563 N SER A 72 10.339 15.797 35.324 1.00 30.26 N ATOM 564 CA SER A 72 9.157 15.860 34.502 1.00 32.28 C ATOM 565 C SER A 72 7.906 15.615 35.374 1.00 22.29 C ATOM 566 O SER A 72 7.914 14.715 36.197 1.00 26.48 O ATOM 567 CB SER A 72 9.249 14.700 33.473 1.00 31.83 C ATOM 568 OG SER A 72 8.038 14.552 32.612 1.00 33.11 O ATOM 569 N ARG A 73 6.801 16.311 35.113 1.00 20.31 N ATOM 570 CA ARG A 73 5.550 16.055 35.819 1.00 12.77 C ATOM 571 C ARG A 73 4.564 15.081 35.174 1.00 35.87 C ATOM 572 O ARG A 73 3.662 14.597 35.845 1.00 50.20 O ATOM 573 CB ARG A 73 4.830 17.322 36.128 1.00 19.66 C ATOM 574 CG ARG A 73 5.605 18.165 37.124 1.00 35.18 C ATOM 575 CD ARG A 73 4.864 19.471 37.396 1.00 86.10 C ATOM 576 NE ARG A 73 4.736 19.744 38.823 1.00 80.19 N ATOM 577 CZ ARG A 73 4.227 20.854 39.398 1.00 81.09 C ATOM 578 NH1 ARG A 73 3.742 21.891 38.705 1.00 81.15 N ATOM 579 NH2 ARG A 73 4.215 20.930 40.739 1.00 71.03 N ATOM 580 N ASN A 74 4.668 14.781 33.896 1.00 18.76 N ATOM 581 CA ASN A 74 3.715 13.833 33.313 1.00 10.40 C ATOM 582 C ASN A 74 2.194 14.147 33.501 1.00 8.97 C ATOM 583 O ASN A 74 1.355 13.278 33.697 1.00 15.29 O ATOM 584 CB ASN A 74 4.053 12.334 33.426 1.00 16.10 C ATOM 585 CG ASN A 74 3.479 11.413 32.309 1.00 15.75 C ATOM 586 OD1 ASN A 74 2.928 11.864 31.297 1.00 22.77 O ATOM 587 ND2 ASN A 74 3.593 10.101 32.490 1.00 17.62 N ATOM 588 N LEU A 75 1.851 15.405 33.334 1.00 13.92 N ATOM 589 CA LEU A 75 0.471 15.774 33.458 1.00 16.58 C ATOM 590 C LEU A 75 -0.505 15.089 32.565 1.00 21.84 C ATOM 591 O LEU A 75 -1.654 14.976 32.957 1.00 22.99 O ATOM 592 CB LEU A 75 0.245 17.277 33.466 1.00 17.10 C ATOM 593 CG LEU A 75 0.919 17.845 34.715 1.00 30.53 C ATOM 594 CD1 LEU A 75 0.889 19.358 34.725 1.00 35.25 C ATOM 595 CD2 LEU A 75 0.238 17.306 35.969 1.00 21.06 C ATOM 596 N CYS A 76 -0.146 14.663 31.359 1.00 18.42 N ATOM 597 CA CYS A 76 -1.153 13.970 30.513 1.00 10.67 C ATOM 598 C CYS A 76 -1.137 12.463 30.738 1.00 12.68 C ATOM 599 O CYS A 76 -1.935 11.725 30.131 1.00 17.21 O ATOM 600 CB CYS A 76 -1.094 14.295 28.984 1.00 9.97 C ATOM 601 SG CYS A 76 -1.329 16.050 28.713 1.00 13.70 S ATOM 602 N ASN A 77 -0.194 12.038 31.586 1.00 14.93 N ATOM 603 CA ASN A 77 -0.117 10.607 31.926 1.00 19.18 C ATOM 604 C ASN A 77 0.099 9.697 30.747 1.00 21.40 C ATOM 605 O ASN A 77 -0.626 8.715 30.538 1.00 17.53 O ATOM 606 CB ASN A 77 -1.421 10.174 32.620 1.00 36.06 C ATOM 607 CG ASN A 77 -1.361 8.783 33.215 1.00 80.95 C ATOM 608 OD1 ASN A 77 -2.358 8.042 33.188 1.00 78.33 O ATOM 609 ND2 ASN A 77 -0.186 8.412 33.715 1.00 38.97 N ATOM 610 N ILE A 78 1.114 10.006 29.979 1.00 14.33 N ATOM 611 CA ILE A 78 1.373 9.191 28.838 1.00 11.33 C ATOM 612 C ILE A 78 2.873 9.258 28.499 1.00 13.41 C ATOM 613 O ILE A 78 3.568 10.265 28.718 1.00 12.78 O ATOM 614 CB ILE A 78 0.764 9.855 27.598 1.00 15.98 C ATOM 615 CG1 ILE A 78 0.764 11.376 27.743 1.00 20.19 C ATOM 616 CG2 ILE A 78 -0.461 9.195 26.985 1.00 25.51 C ATOM 617 CD1 ILE A 78 0.735 12.094 26.406 1.00 31.88 C ATOM 618 N PRO A 79 3.343 8.210 27.843 1.00 14.97 N ATOM 619 CA PRO A 79 4.715 8.229 27.362 1.00 12.65 C ATOM 620 C PRO A 79 4.738 9.234 26.187 1.00 10.18 C ATOM 621 O PRO A 79 3.762 9.304 25.359 1.00 11.71 O ATOM 622 CB PRO A 79 4.962 6.830 26.843 1.00 11.25 C ATOM 623 CG PRO A 79 3.631 6.096 26.844 1.00 17.21 C ATOM 624 CD PRO A 79 2.621 6.951 27.581 1.00 9.85 C ATOM 625 N CYS A 80 5.798 10.020 26.078 1.00 11.09 N ATOM 626 CA CYS A 80 5.870 11.003 24.969 1.00 5.24 C ATOM 627 C CYS A 80 5.782 10.359 23.546 1.00 8.89 C ATOM 628 O CYS A 80 5.284 10.950 22.568 1.00 11.52 O ATOM 629 CB CYS A 80 7.126 11.894 25.061 1.00 7.40 C ATOM 630 SG CYS A 80 7.251 12.847 26.592 1.00 9.47 S ATOM 631 N SER A 81 6.259 9.115 23.442 1.00 9.85 N ATOM 632 CA SER A 81 6.242 8.432 22.154 1.00 7.67 C ATOM 633 C SER A 81 4.815 8.223 21.687 1.00 15.55 C ATOM 634 O SER A 81 4.554 8.156 20.510 1.00 15.82 O ATOM 635 CB SER A 81 6.995 7.111 22.234 1.00 15.31 C ATOM 636 OG SER A 81 6.295 6.245 23.119 1.00 17.97 O ATOM 637 N ALA A 82 3.857 8.169 22.598 1.00 11.39 N ATOM 638 CA ALA A 82 2.452 8.033 22.185 1.00 14.65 C ATOM 639 C ALA A 82 2.000 9.216 21.325 1.00 20.26 C ATOM 640 O ALA A 82 1.033 9.113 20.571 1.00 22.13 O ATOM 641 CB ALA A 82 1.481 8.009 23.384 1.00 17.51 C ATOM 642 N LEU A 83 2.659 10.349 21.528 1.00 9.56 N ATOM 643 CA LEU A 83 2.329 11.589 20.867 1.00 12.01 C ATOM 644 C LEU A 83 2.834 11.627 19.385 1.00 18.14 C ATOM 645 O LEU A 83 2.626 12.620 18.685 1.00 12.31 O ATOM 646 CB LEU A 83 2.986 12.761 21.651 1.00 15.90 C ATOM 647 CG LEU A 83 2.370 12.966 23.055 1.00 9.43 C ATOM 648 CD1 LEU A 83 3.076 14.069 23.849 1.00 12.61 C ATOM 649 CD2 LEU A 83 0.843 13.174 22.965 1.00 15.37 C ATOM 650 N LEU A 84 3.542 10.556 18.940 1.00 13.34 N ATOM 651 CA LEU A 84 4.131 10.512 17.618 1.00 11.55 C ATOM 652 C LEU A 84 3.361 9.657 16.630 1.00 16.60 C ATOM 653 O LEU A 84 3.704 9.570 15.475 1.00 22.63 O ATOM 654 CB LEU A 84 5.630 10.044 17.645 1.00 7.92 C ATOM 655 CG LEU A 84 6.546 10.859 18.552 1.00 18.00 C ATOM 656 CD1 LEU A 84 7.978 10.414 18.359 1.00 17.76 C ATOM 657 CD2 LEU A 84 6.513 12.306 18.116 1.00 8.41 C ATOM 658 N SER A 85 2.332 9.023 17.096 1.00 15.68 N ATOM 659 CA SER A 85 1.485 8.148 16.333 1.00 22.63 C ATOM 660 C SER A 85 0.792 8.827 15.194 1.00 14.76 C ATOM 661 O SER A 85 0.519 10.007 15.280 1.00 16.99 O ATOM 662 CB SER A 85 0.376 7.776 17.295 1.00 16.59 C ATOM 663 OG SER A 85 -0.373 6.761 16.741 1.00 23.89 O ATOM 664 N SER A 86 0.371 8.039 14.186 1.00 19.04 N ATOM 665 CA SER A 86 -0.430 8.505 13.025 1.00 17.09 C ATOM 666 C SER A 86 -1.827 8.884 13.487 1.00 21.77 C ATOM 667 O SER A 86 -2.481 9.696 12.857 1.00 24.42 O ATOM 668 CB SER A 86 -0.584 7.358 12.026 1.00 21.75 C ATOM 669 OG SER A 86 0.687 7.146 11.467 1.00 50.53 O ATOM 670 N ASP A 87 -2.288 8.227 14.575 1.00 13.55 N ATOM 671 CA ASP A 87 -3.611 8.483 15.195 1.00 14.83 C ATOM 672 C ASP A 87 -3.426 9.673 16.162 1.00 16.43 C ATOM 673 O ASP A 87 -2.640 9.585 17.147 1.00 17.32 O ATOM 674 CB ASP A 87 -4.025 7.244 15.987 1.00 17.38 C ATOM 675 CG ASP A 87 -5.365 7.435 16.676 1.00 36.42 C ATOM 676 OD1 ASP A 87 -5.875 8.512 16.868 1.00 21.05 O ATOM 677 OD2 ASP A 87 -5.952 6.315 17.005 1.00 56.25 O ATOM 678 N ILE A 88 -4.037 10.803 15.879 1.00 12.05 N ATOM 679 CA ILE A 88 -3.749 11.974 16.722 1.00 17.85 C ATOM 680 C ILE A 88 -4.490 12.067 18.055 1.00 12.40 C ATOM 681 O ILE A 88 -4.393 13.081 18.780 1.00 11.64 O ATOM 682 CB ILE A 88 -4.014 13.293 15.954 1.00 16.92 C ATOM 683 CG1 ILE A 88 -5.565 13.392 15.634 1.00 15.36 C ATOM 684 CG2 ILE A 88 -3.104 13.384 14.694 1.00 18.11 C ATOM 685 CD1 ILE A 88 -6.065 14.738 15.196 1.00 20.80 C ATOM 686 N THR A 89 -5.257 11.069 18.381 1.00 13.72 N ATOM 687 CA THR A 89 -6.058 11.103 19.584 1.00 12.45 C ATOM 688 C THR A 89 -5.326 11.520 20.907 1.00 8.02 C ATOM 689 O THR A 89 -5.777 12.403 21.614 1.00 14.43 O ATOM 690 CB THR A 89 -6.717 9.735 19.716 1.00 18.11 C ATOM 691 OG1 THR A 89 -7.492 9.539 18.564 1.00 18.36 O ATOM 692 CG2 THR A 89 -7.642 9.724 20.953 1.00 16.37 C ATOM 693 N ALA A 90 -4.186 10.900 21.216 1.00 10.40 N ATOM 694 CA ALA A 90 -3.483 11.250 22.444 1.00 14.23 C ATOM 695 C ALA A 90 -2.971 12.685 22.412 1.00 17.47 C ATOM 696 O ALA A 90 -2.981 13.344 23.413 1.00 10.92 O ATOM 697 CB ALA A 90 -2.331 10.290 22.751 1.00 15.55 C ATOM 698 N SER A 91 -2.504 13.185 21.257 1.00 8.67 N ATOM 699 CA SER A 91 -2.032 14.567 21.163 1.00 7.03 C ATOM 700 C SER A 91 -3.155 15.522 21.418 1.00 6.83 C ATOM 701 O SER A 91 -3.033 16.547 22.059 1.00 13.20 O ATOM 702 CB SER A 91 -1.445 14.870 19.783 1.00 7.71 C ATOM 703 OG SER A 91 -0.111 14.402 19.670 1.00 11.50 O ATOM 704 N VAL A 92 -4.289 15.234 20.839 1.00 9.57 N ATOM 705 CA VAL A 92 -5.449 16.101 21.004 1.00 7.79 C ATOM 706 C VAL A 92 -5.938 16.148 22.488 1.00 10.65 C ATOM 707 O VAL A 92 -6.254 17.195 23.018 1.00 12.26 O ATOM 708 CB VAL A 92 -6.523 15.597 19.994 1.00 23.58 C ATOM 709 CG1 VAL A 92 -7.936 16.117 20.303 1.00 19.93 C ATOM 710 CG2 VAL A 92 -6.110 15.987 18.555 1.00 17.17 C ATOM 711 N ASN A 93 -6.047 14.973 23.140 1.00 10.03 N ATOM 712 CA ASN A 93 -6.511 14.862 24.536 1.00 24.44 C ATOM 713 C ASN A 93 -5.602 15.647 25.472 1.00 10.79 C ATOM 714 O ASN A 93 -6.049 16.390 26.310 1.00 15.54 O ATOM 715 CB ASN A 93 -6.580 13.395 24.989 1.00 13.16 C ATOM 716 CG ASN A 93 -7.781 12.668 24.406 1.00 15.37 C ATOM 717 OD1 ASN A 93 -7.842 11.422 24.426 1.00 35.75 O ATOM 718 ND2 ASN A 93 -8.682 13.436 23.835 1.00 16.65 N ATOM 719 N CYS A 94 -4.284 15.477 25.249 1.00 10.49 N ATOM 720 CA CYS A 94 -3.267 16.178 25.984 1.00 7.62 C ATOM 721 C CYS A 94 -3.353 17.649 25.690 1.00 17.58 C ATOM 722 O CYS A 94 -3.298 18.462 26.598 1.00 9.76 O ATOM 723 CB CYS A 94 -1.875 15.620 25.709 1.00 5.33 C ATOM 724 SG CYS A 94 -0.613 16.312 26.762 1.00 13.87 S ATOM 725 N ALA A 95 -3.546 18.041 24.407 1.00 7.01 N ATOM 726 CA ALA A 95 -3.656 19.481 24.142 1.00 8.80 C ATOM 727 C ALA A 95 -4.864 20.156 24.849 1.00 8.68 C ATOM 728 O ALA A 95 -4.867 21.353 25.215 1.00 11.44 O ATOM 729 CB ALA A 95 -3.774 19.698 22.627 1.00 6.34 C ATOM 730 N LYS A 96 -5.932 19.405 24.966 1.00 9.62 N ATOM 731 CA LYS A 96 -7.108 19.927 25.596 1.00 9.41 C ATOM 732 C LYS A 96 -6.804 20.229 27.091 1.00 11.43 C ATOM 733 O LYS A 96 -7.271 21.199 27.627 1.00 15.34 O ATOM 734 CB LYS A 96 -8.195 18.868 25.472 1.00 12.74 C ATOM 735 CG LYS A 96 -8.927 18.820 24.137 1.00 9.62 C ATOM 736 CD LYS A 96 -9.976 17.699 24.147 1.00 14.08 C ATOM 737 CE LYS A 96 -10.973 17.784 22.960 1.00 16.34 C ATOM 738 NZ LYS A 96 -11.641 16.485 22.720 1.00 20.55 N ATOM 739 N LYS A 97 -5.944 19.447 27.750 1.00 13.54 N ATOM 740 CA LYS A 97 -5.538 19.706 29.158 1.00 14.41 C ATOM 741 C LYS A 97 -4.672 20.981 29.209 1.00 13.37 C ATOM 742 O LYS A 97 -4.809 21.878 30.014 1.00 13.38 O ATOM 743 CB LYS A 97 -4.710 18.544 29.689 1.00 10.77 C ATOM 744 CG LYS A 97 -5.493 17.342 30.140 1.00 32.04 C ATOM 745 CD LYS A 97 -6.434 17.637 31.297 1.00 45.76 C ATOM 746 CE LYS A 97 -7.073 16.369 31.886 1.00 70.47 C ATOM 747 NZ LYS A 97 -8.523 16.232 31.620 1.00 59.21 N ATOM 748 N ILE A 98 -3.760 21.072 28.264 1.00 12.65 N ATOM 749 CA ILE A 98 -2.856 22.204 28.161 1.00 10.78 C ATOM 750 C ILE A 98 -3.607 23.536 27.991 1.00 8.94 C ATOM 751 O ILE A 98 -3.322 24.532 28.701 1.00 12.98 O ATOM 752 CB ILE A 98 -1.778 22.026 27.022 1.00 17.91 C ATOM 753 CG1 ILE A 98 -0.899 20.798 27.234 1.00 15.21 C ATOM 754 CG2 ILE A 98 -0.932 23.292 26.811 1.00 10.73 C ATOM 755 CD1 ILE A 98 -0.035 20.440 26.059 1.00 5.59 C ATOM 756 N VAL A 99 -4.497 23.570 26.973 1.00 12.61 N ATOM 757 CA VAL A 99 -5.194 24.822 26.643 1.00 14.92 C ATOM 758 C VAL A 99 -6.158 25.244 27.757 1.00 17.60 C ATOM 759 O VAL A 99 -6.529 26.431 27.844 1.00 21.46 O ATOM 760 CB VAL A 99 -5.863 24.788 25.223 1.00 7.93 C ATOM 761 CG1 VAL A 99 -7.102 23.930 25.230 1.00 13.13 C ATOM 762 CG2 VAL A 99 -6.203 26.159 24.648 1.00 14.05 C ATOM 763 N SER A 100 -6.529 24.274 28.623 1.00 14.94 N ATOM 764 CA SER A 100 -7.469 24.559 29.728 1.00 23.99 C ATOM 765 C SER A 100 -6.810 25.233 30.952 1.00 23.57 C ATOM 766 O SER A 100 -7.460 25.872 31.759 1.00 30.51 O ATOM 767 CB SER A 100 -8.109 23.250 30.148 1.00 15.96 C ATOM 768 OG SER A 100 -9.019 22.837 29.120 1.00 33.46 O ATOM 769 N ASP A 101 -5.495 25.061 30.981 1.00 27.50 N ATOM 770 CA ASP A 101 -4.485 25.414 31.955 1.00 38.61 C ATOM 771 C ASP A 101 -4.239 26.879 32.265 1.00 31.46 C ATOM 772 O ASP A 101 -3.422 27.194 33.137 1.00 49.53 O ATOM 773 CB ASP A 101 -3.173 24.648 31.624 1.00 32.62 C ATOM 774 CG ASP A 101 -2.133 24.566 32.715 1.00 66.21 C ATOM 775 OD1 ASP A 101 -2.482 23.821 33.747 1.00 53.53 O ATOM 776 OD2 ASP A 101 -1.045 25.095 32.609 1.00 62.33 O ATOM 777 N GLY A 102 -4.876 27.820 31.617 1.00 31.57 N ATOM 778 CA GLY A 102 -4.525 29.170 32.093 1.00 42.83 C ATOM 779 C GLY A 102 -4.082 30.192 31.049 1.00 56.99 C ATOM 780 O GLY A 102 -4.713 31.264 30.990 1.00 31.68 O ATOM 781 N ASN A 103 -2.979 29.915 30.284 1.00 23.55 N ATOM 782 CA ASN A 103 -2.573 30.864 29.246 1.00 11.97 C ATOM 783 C ASN A 103 -3.176 30.497 27.876 1.00 9.86 C ATOM 784 O ASN A 103 -2.905 31.106 26.860 1.00 13.84 O ATOM 785 CB ASN A 103 -1.070 31.114 29.177 1.00 17.92 C ATOM 786 CG ASN A 103 -0.638 31.476 30.587 1.00 65.73 C ATOM 787 OD1 ASN A 103 0.384 30.993 31.105 1.00 74.71 O ATOM 788 ND2 ASN A 103 -1.509 32.224 31.271 1.00 54.30 N ATOM 789 N GLY A 104 -4.070 29.522 27.865 1.00 10.94 N ATOM 790 CA GLY A 104 -4.733 29.138 26.601 1.00 18.78 C ATOM 791 C GLY A 104 -3.725 28.668 25.570 1.00 8.28 C ATOM 792 O GLY A 104 -2.766 27.947 25.892 1.00 12.01 O ATOM 793 N MET A 105 -3.906 29.119 24.313 1.00 13.56 N ATOM 794 CA MET A 105 -3.014 28.684 23.198 1.00 9.18 C ATOM 795 C MET A 105 -1.637 29.372 23.232 1.00 8.69 C ATOM 796 O MET A 105 -0.727 29.013 22.506 1.00 9.67 O ATOM 797 CB MET A 105 -3.739 28.882 21.838 1.00 3.51 C ATOM 798 CG MET A 105 -4.790 27.788 21.646 1.00 9.82 C ATOM 799 SD MET A 105 -5.184 27.455 19.852 1.00 12.90 S ATOM 800 CE MET A 105 -3.617 26.757 19.326 1.00 6.80 C ATOM 801 N ASN A 106 -1.509 30.373 24.105 1.00 7.08 N ATOM 802 CA ASN A 106 -0.270 31.037 24.269 1.00 4.32 C ATOM 803 C ASN A 106 0.809 30.046 24.765 1.00 8.04 C ATOM 804 O ASN A 106 2.030 30.336 24.608 1.00 11.37 O ATOM 805 CB ASN A 106 -0.396 32.190 25.241 1.00 12.62 C ATOM 806 CG ASN A 106 -1.239 33.309 24.682 1.00 16.51 C ATOM 807 OD1 ASN A 106 -0.864 33.972 23.658 1.00 9.88 O ATOM 808 ND2 ASN A 106 -2.372 33.492 25.355 1.00 15.30 N ATOM 809 N ALA A 107 0.360 28.870 25.250 1.00 8.10 N ATOM 810 CA ALA A 107 1.308 27.840 25.625 1.00 10.48 C ATOM 811 C ALA A 107 2.113 27.450 24.395 1.00 16.77 C ATOM 812 O ALA A 107 3.191 26.948 24.511 1.00 16.10 O ATOM 813 CB ALA A 107 0.585 26.599 26.143 1.00 11.40 C ATOM 814 N TRP A 108 1.577 27.639 23.205 1.00 10.51 N ATOM 815 CA TRP A 108 2.303 27.285 21.966 1.00 9.27 C ATOM 816 C TRP A 108 2.970 28.504 21.404 1.00 9.11 C ATOM 817 O TRP A 108 2.312 29.428 20.865 1.00 9.30 O ATOM 818 CB TRP A 108 1.398 26.569 20.912 1.00 4.39 C ATOM 819 CG TRP A 108 1.005 25.176 21.256 1.00 2.06 C ATOM 820 CD1 TRP A 108 1.760 24.069 21.021 1.00 10.56 C ATOM 821 CD2 TRP A 108 -0.146 24.722 21.926 1.00 4.61 C ATOM 822 NE1 TRP A 108 1.131 22.972 21.471 1.00 11.23 N ATOM 823 CE2 TRP A 108 -0.048 23.321 22.045 1.00 11.25 C ATOM 824 CE3 TRP A 108 -1.256 25.348 22.436 1.00 8.16 C ATOM 825 CZ2 TRP A 108 -1.038 22.520 22.626 1.00 7.13 C ATOM 826 CZ3 TRP A 108 -2.220 24.549 23.074 1.00 13.29 C ATOM 827 CH2 TRP A 108 -2.156 23.123 23.101 1.00 8.74 C ATOM 828 N VAL A 109 4.320 28.523 21.508 1.00 10.59 N ATOM 829 CA VAL A 109 5.046 29.672 21.044 1.00 10.72 C ATOM 830 C VAL A 109 4.800 30.025 19.545 1.00 7.07 C ATOM 831 O VAL A 109 4.617 31.199 19.228 1.00 12.32 O ATOM 832 CB VAL A 109 6.549 29.491 21.342 1.00 15.75 C ATOM 833 CG1 VAL A 109 7.068 28.242 20.605 1.00 38.18 C ATOM 834 CG2 VAL A 109 7.327 30.751 20.898 1.00 17.01 C ATOM 835 N ALA A 110 4.761 28.998 18.662 1.00 7.28 N ATOM 836 CA ALA A 110 4.506 29.281 17.232 1.00 14.92 C ATOM 837 C ALA A 110 3.122 29.845 17.031 1.00 12.74 C ATOM 838 O ALA A 110 2.902 30.659 16.125 1.00 13.19 O ATOM 839 CB ALA A 110 4.783 28.117 16.262 1.00 12.16 C ATOM 840 N TRP A 111 2.190 29.398 17.892 1.00 7.58 N ATOM 841 CA TRP A 111 0.821 29.901 17.789 1.00 5.91 C ATOM 842 C TRP A 111 0.815 31.399 18.100 1.00 9.06 C ATOM 843 O TRP A 111 0.249 32.308 17.369 1.00 6.22 O ATOM 844 CB TRP A 111 -0.240 29.136 18.618 1.00 6.54 C ATOM 845 CG TRP A 111 -1.589 29.763 18.461 1.00 9.13 C ATOM 846 CD1 TRP A 111 -2.510 29.517 17.447 1.00 5.89 C ATOM 847 CD2 TRP A 111 -2.190 30.781 19.295 1.00 10.48 C ATOM 848 NE1 TRP A 111 -3.642 30.322 17.597 1.00 5.88 N ATOM 849 CE2 TRP A 111 -3.471 31.090 18.728 1.00 5.72 C ATOM 850 CE3 TRP A 111 -1.805 31.432 20.511 1.00 4.95 C ATOM 851 CZ2 TRP A 111 -4.306 32.057 19.314 1.00 13.37 C ATOM 852 CZ3 TRP A 111 -2.658 32.382 21.061 1.00 6.90 C ATOM 853 CH2 TRP A 111 -3.906 32.666 20.489 1.00 4.12 C ATOM 854 N ARG A 112 1.497 31.701 19.218 1.00 7.90 N ATOM 855 CA ARG A 112 1.527 33.107 19.659 1.00 11.81 C ATOM 856 C ARG A 112 2.221 34.013 18.630 1.00 9.34 C ATOM 857 O ARG A 112 1.746 35.118 18.330 1.00 9.72 O ATOM 858 CB ARG A 112 2.215 33.175 21.040 1.00 18.21 C ATOM 859 CG ARG A 112 2.053 34.513 21.722 1.00 52.15 C ATOM 860 CD ARG A 112 2.813 34.593 23.056 1.00 27.12 C ATOM 861 NE ARG A 112 3.479 33.351 23.413 1.00 52.40 N ATOM 862 CZ ARG A 112 4.785 33.247 23.639 1.00 49.41 C ATOM 863 NH1 ARG A 112 5.612 34.286 23.535 1.00 53.98 N ATOM 864 NH2 ARG A 112 5.274 32.058 23.981 1.00 51.24 N ATOM 865 N ASN A 113 3.331 33.501 18.078 1.00 8.96 N ATOM 866 CA ASN A 113 4.132 34.283 17.152 1.00 15.60 C ATOM 867 C ASN A 113 3.657 34.303 15.695 1.00 17.72 C ATOM 868 O ASN A 113 3.919 35.261 14.974 1.00 16.73 O ATOM 869 CB ASN A 113 5.657 33.938 17.244 1.00 8.06 C ATOM 870 CG ASN A 113 6.192 34.297 18.636 1.00 11.97 C ATOM 871 OD1 ASN A 113 5.714 35.228 19.278 1.00 19.44 O ATOM 872 ND2 ASN A 113 7.179 33.595 19.091 1.00 9.04 N ATOM 873 N ARG A 114 2.964 33.273 15.287 1.00 7.06 N ATOM 874 CA ARG A 114 2.604 33.129 13.873 1.00 11.57 C ATOM 875 C ARG A 114 1.171 33.002 13.552 1.00 19.78 C ATOM 876 O ARG A 114 0.827 33.118 12.375 1.00 14.27 O ATOM 877 CB ARG A 114 3.309 31.830 13.395 1.00 8.51 C ATOM 878 CG ARG A 114 4.766 31.877 13.898 1.00 21.43 C ATOM 879 CD ARG A 114 5.833 31.132 13.125 1.00 27.54 C ATOM 880 NE ARG A 114 5.898 31.278 11.660 1.00 16.59 N ATOM 881 CZ ARG A 114 6.631 30.413 10.970 1.00 12.23 C ATOM 882 NH1 ARG A 114 7.271 29.439 11.649 1.00 11.43 N ATOM 883 NH2 ARG A 114 6.744 30.477 9.659 1.00 12.83 N ATOM 884 N CYS A 115 0.351 32.723 14.572 1.00 5.86 N ATOM 885 CA CYS A 115 -1.055 32.487 14.333 1.00 8.05 C ATOM 886 C CYS A 115 -1.937 33.541 14.914 1.00 18.18 C ATOM 887 O CYS A 115 -2.914 34.024 14.264 1.00 11.36 O ATOM 888 CB CYS A 115 -1.488 31.114 14.872 1.00 7.31 C ATOM 889 SG CYS A 115 -0.553 29.849 14.022 1.00 10.81 S ATOM 890 N LYS A 116 -1.630 33.796 16.196 1.00 10.46 N ATOM 891 CA LYS A 116 -2.372 34.723 16.976 1.00 9.75 C ATOM 892 C LYS A 116 -2.562 36.032 16.228 1.00 9.63 C ATOM 893 O LYS A 116 -1.583 36.599 15.729 1.00 13.85 O ATOM 894 CB LYS A 116 -1.716 34.948 18.335 1.00 12.72 C ATOM 895 CG LYS A 116 -2.557 35.791 19.284 1.00 7.87 C ATOM 896 CD LYS A 116 -1.809 35.938 20.635 1.00 15.62 C ATOM 897 CE LYS A 116 -2.607 36.597 21.773 1.00 17.04 C ATOM 898 NZ LYS A 116 -1.889 36.524 23.073 1.00 11.32 N ATOM 899 N GLY A 117 -3.862 36.462 16.131 1.00 9.19 N ATOM 900 CA GLY A 117 -4.213 37.737 15.493 1.00 22.87 C ATOM 901 C GLY A 117 -4.091 37.759 13.972 1.00 33.97 C ATOM 902 O GLY A 117 -4.044 38.799 13.371 1.00 28.79 O ATOM 903 N THR A 118 -4.019 36.612 13.340 1.00 15.57 N ATOM 904 CA THR A 118 -3.940 36.537 11.885 1.00 20.24 C ATOM 905 C THR A 118 -5.285 35.977 11.407 1.00 18.25 C ATOM 906 O THR A 118 -6.080 35.563 12.249 1.00 18.09 O ATOM 907 CB THR A 118 -2.747 35.680 11.439 1.00 13.62 C ATOM 908 OG1 THR A 118 -3.060 34.321 11.639 1.00 12.88 O ATOM 909 CG2 THR A 118 -1.455 36.072 12.193 1.00 12.89 C ATOM 910 N ASP A 119 -5.573 35.973 10.102 1.00 20.97 N ATOM 911 CA ASP A 119 -6.848 35.408 9.620 1.00 17.43 C ATOM 912 C ASP A 119 -6.693 33.892 9.567 1.00 19.50 C ATOM 913 O ASP A 119 -6.430 33.280 8.509 1.00 24.87 O ATOM 914 CB ASP A 119 -7.228 35.933 8.234 1.00 27.62 C ATOM 915 CG ASP A 119 -8.359 35.154 7.625 1.00 57.62 C ATOM 916 OD1 ASP A 119 -9.168 34.529 8.288 1.00 36.72 O ATOM 917 OD2 ASP A 119 -8.349 35.190 6.315 1.00 41.26 O ATOM 918 N VAL A 120 -6.836 33.291 10.750 1.00 20.10 N ATOM 919 CA VAL A 120 -6.637 31.835 10.895 1.00 24.77 C ATOM 920 C VAL A 120 -7.664 31.011 10.149 1.00 16.15 C ATOM 921 O VAL A 120 -7.486 29.777 9.914 1.00 13.85 O ATOM 922 CB VAL A 120 -6.476 31.372 12.367 1.00 15.61 C ATOM 923 CG1 VAL A 120 -5.271 32.055 13.060 1.00 17.17 C ATOM 924 CG2 VAL A 120 -7.761 31.691 13.097 1.00 18.96 C ATOM 925 N GLN A 121 -8.761 31.679 9.776 1.00 18.05 N ATOM 926 CA GLN A 121 -9.808 30.981 9.039 1.00 20.34 C ATOM 927 C GLN A 121 -9.285 30.499 7.694 1.00 14.19 C ATOM 928 O GLN A 121 -9.831 29.566 7.093 1.00 15.61 O ATOM 929 CB GLN A 121 -10.896 31.993 8.746 1.00 39.58 C ATOM 930 CG GLN A 121 -12.076 31.743 9.628 1.00 39.30 C ATOM 931 CD GLN A 121 -13.286 31.887 8.785 1.00 58.93 C ATOM 932 OE1 GLN A 121 -13.734 30.908 8.174 1.00 64.91 O ATOM 933 NE2 GLN A 121 -13.757 33.131 8.683 1.00 55.29 N ATOM 934 N ALA A 122 -8.222 31.161 7.229 1.00 14.53 N ATOM 935 CA ALA A 122 -7.588 30.777 5.964 1.00 13.31 C ATOM 936 C ALA A 122 -7.152 29.312 5.955 1.00 18.32 C ATOM 937 O ALA A 122 -7.123 28.622 4.924 1.00 14.96 O ATOM 938 CB ALA A 122 -6.378 31.657 5.724 1.00 18.92 C ATOM 939 N TRP A 123 -6.792 28.829 7.138 1.00 14.16 N ATOM 940 CA TRP A 123 -6.304 27.460 7.305 1.00 21.27 C ATOM 941 C TRP A 123 -7.326 26.369 7.031 1.00 12.05 C ATOM 942 O TRP A 123 -6.976 25.209 6.736 1.00 15.94 O ATOM 943 CB TRP A 123 -5.545 27.274 8.649 1.00 14.43 C ATOM 944 CG TRP A 123 -4.302 28.098 8.663 1.00 16.32 C ATOM 945 CD1 TRP A 123 -4.115 29.310 9.238 1.00 16.23 C ATOM 946 CD2 TRP A 123 -3.066 27.733 8.045 1.00 6.26 C ATOM 947 NE1 TRP A 123 -2.826 29.737 8.996 1.00 13.70 N ATOM 948 CE2 TRP A 123 -2.184 28.799 8.248 1.00 10.20 C ATOM 949 CE3 TRP A 123 -2.680 26.618 7.323 1.00 14.76 C ATOM 950 CZ2 TRP A 123 -0.873 28.742 7.761 1.00 21.80 C ATOM 951 CZ3 TRP A 123 -1.413 26.574 6.804 1.00 21.47 C ATOM 952 CH2 TRP A 123 -0.520 27.623 7.023 1.00 22.40 C ATOM 953 N ILE A 124 -8.590 26.696 7.126 1.00 10.36 N ATOM 954 CA ILE A 124 -9.573 25.659 6.831 1.00 14.89 C ATOM 955 C ILE A 124 -10.353 25.927 5.530 1.00 16.52 C ATOM 956 O ILE A 124 -11.277 25.213 5.172 1.00 16.40 O ATOM 957 CB ILE A 124 -10.480 25.421 8.019 1.00 15.33 C ATOM 958 CG1 ILE A 124 -11.016 26.778 8.456 1.00 15.55 C ATOM 959 CG2 ILE A 124 -9.624 24.846 9.164 1.00 15.50 C ATOM 960 CD1 ILE A 124 -12.489 26.742 8.908 1.00 32.95 C ATOM 961 N ARG A 125 -9.977 27.003 4.848 1.00 17.59 N ATOM 962 CA ARG A 125 -10.598 27.366 3.586 1.00 26.87 C ATOM 963 C ARG A 125 -10.424 26.259 2.569 1.00 18.05 C ATOM 964 O ARG A 125 -9.339 25.658 2.433 1.00 24.03 O ATOM 965 CB ARG A 125 -10.123 28.708 3.068 1.00 29.23 C ATOM 966 CG ARG A 125 -10.586 29.089 1.669 1.00 46.98 C ATOM 967 CD ARG A 125 -10.321 30.571 1.370 1.00 51.79 C ATOM 968 NE ARG A 125 -8.921 30.857 1.669 1.00 80.40 N ATOM 969 CZ ARG A 125 -7.924 30.424 0.892 1.00 80.57 C ATOM 970 NH1 ARG A 125 -8.167 29.752 -0.234 1.00 50.97 N ATOM 971 NH2 ARG A 125 -6.657 30.677 1.239 1.00 73.99 N ATOM 972 N GLY A 126 -11.581 25.957 1.917 1.00 23.54 N ATOM 973 CA GLY A 126 -11.741 24.959 0.858 1.00 15.85 C ATOM 974 C GLY A 126 -11.903 23.570 1.356 1.00 22.07 C ATOM 975 O GLY A 126 -11.988 22.638 0.564 1.00 33.97 O ATOM 976 N CYS A 127 -11.912 23.409 2.685 1.00 12.41 N ATOM 977 CA CYS A 127 -12.009 22.059 3.164 1.00 10.52 C ATOM 978 C CYS A 127 -13.442 21.578 3.291 1.00 13.96 C ATOM 979 O CYS A 127 -14.383 22.316 3.676 1.00 19.22 O ATOM 980 CB CYS A 127 -11.259 21.795 4.516 1.00 16.08 C ATOM 981 SG CYS A 127 -9.562 22.365 4.503 1.00 17.92 S ATOM 982 N ARG A 128 -13.609 20.299 3.023 1.00 19.23 N ATOM 983 CA ARG A 128 -14.929 19.757 3.200 1.00 29.52 C ATOM 984 C ARG A 128 -15.116 19.387 4.645 1.00 19.23 C ATOM 985 O ARG A 128 -14.626 18.345 5.078 1.00 29.24 O ATOM 986 CB ARG A 128 -15.159 18.511 2.363 1.00 35.15 C ATOM 987 CG ARG A 128 -16.602 18.043 2.481 1.00 32.92 C ATOM 988 CD ARG A 128 -16.961 17.187 1.277 1.00 38.70 C ATOM 989 NE ARG A 128 -15.779 16.498 0.721 1.00 50.55 N ATOM 990 CZ ARG A 128 -15.503 16.208 -0.581 1.00 55.70 C ATOM 991 NH1 ARG A 128 -16.293 16.551 -1.610 1.00 46.94 N ATOM 992 NH2 ARG A 128 -14.377 15.541 -0.856 1.00 48.25 N ATOM 993 N LEU A 129 -15.775 20.226 5.404 1.00 22.75 N ATOM 994 CA LEU A 129 -15.976 19.869 6.811 1.00 33.38 C ATOM 995 C LEU A 129 -17.449 19.906 7.141 1.00 72.70 C ATOM 996 O LEU A 129 -18.191 20.465 6.277 1.00 49.87 O ATOM 997 CB LEU A 129 -15.235 20.742 7.845 1.00 23.67 C ATOM 998 CG LEU A 129 -13.711 20.917 7.641 1.00 28.34 C ATOM 999 CD1 LEU A 129 -13.308 22.315 8.150 1.00 36.38 C ATOM 1000 CD2 LEU A 129 -12.970 19.868 8.434 1.00 43.93 C ATOM 1001 OXT LEU A 129 -17.769 19.416 8.251 1.00 70.56 O TER 1002 LEU A 129 END bio3d/inst/examples/test.pdb0000644000176200001440000003474112544562303015530 0ustar liggesusersTITLE A CURATED PDB FILE FOR BIO3D FUNCTION TESTING COMPND 3 CHAIN: A, B; REMARK 470 MISSING ATOM REMARK 470 M RES CSSEQI ATOMS REMARK 470 ARG B 2 CA HET CA A 8 1 HET GDP A 7 40 HETNAM CA CALCIUM ION HETNAM GDP GUANOSINE-5'-DIPHOSPHATE FORMUL 2 CA CA 2+ FORMUL 3 GDP C10 H15 N5 O11 P2 FORMUL 4 HOH *5(H2 O) ATOM 1 N GLY A 1 16.622 88.040 40.142 1.00 0.00 N ATOM 2 H GLY A 1 16.225 88.048 41.071 1.00 0.00 H ATOM 3 CA GLY A 1 16.176 89.039 39.198 1.00 0.00 C ATOM 4 HA2 GLY A 1 16.896 89.857 39.171 1.00 0.00 H ATOM 5 HA3 GLY A 1 16.099 88.591 38.207 1.00 0.00 H ATOM 6 C GLY A 1 14.824 89.586 39.597 1.00 0.00 C ATOM 7 O GLY A 1 14.138 89.018 40.440 1.00 0.00 O ATOM 8 N TES A 2 14.454 90.706 38.990 1.00 0.00 N ATOM 9 H TES A 2 15.096 91.143 38.344 1.00 0.00 H ATOM 10 CA TES A 2 13.180 91.367 39.252 1.00 0.00 C ATOM 11 HA TES A 2 13.061 91.524 40.324 1.00 0.00 H ATOM 12 CB TES A 2 13.160 92.738 38.548 1.00 0.00 C ATOM 13 HB1 TES A 2 13.269 92.596 37.473 1.00 0.00 H ATOM 14 HB2 TES A 2 12.214 93.239 38.754 1.00 0.00 H ATOM 15 HB3 TES A 2 13.983 93.349 38.919 1.00 0.00 H ATOM 16 C TES A 2 12.002 90.500 38.785 1.00 0.00 C ATOM 17 O TES A 2 12.186 89.530 38.069 1.00 0.00 O ATOM 18 N GLY A 3 10.806 90.823 39.256 1.00 0.00 N ATOM 19 H GLY A 3 10.699 91.591 39.903 1.00 0.00 H ATOM 20 CA GLY A 3 9.618 90.086 38.859 1.00 0.00 C ATOM 21 HA2 GLY A 3 9.709 89.045 39.170 1.00 0.00 H ATOM 22 HA3 GLY A 3 8.737 90.528 39.325 1.00 0.00 H ATOM 23 C GLY A 3 9.458 90.138 37.351 1.00 0.00 C ATOM 24 O GLY A 3 9.767 91.152 36.714 1.00 0.00 O ATOM 25 N GLU A 4 9.011 89.022 36.777 1.00 0.00 N ATOM 26 H GLU A 4 8.933 88.195 37.351 1.00 0.00 H ATOM 27 CA GLU A 4 8.800 88.888 35.337 1.00 0.00 C ATOM 28 HA GLU A 4 8.427 87.889 35.110 1.00 0.00 H ATOM 29 CB GLU A 4 7.782 89.910 34.849 1.00 0.00 C ATOM 30 HB2 GLU A 4 8.175 90.910 35.034 1.00 0.00 H ATOM 31 HB3 GLU A 4 7.634 89.770 33.778 1.00 0.00 H ATOM 32 CG GLU A 4 6.447 89.747 35.573 1.00 0.00 C ATOM 33 HG2 GLU A 4 6.108 88.729 35.382 1.00 0.00 H ATOM 34 HG3 GLU A 4 6.622 89.877 36.641 1.00 0.00 H ATOM 35 CD GLU A 4 5.388 90.726 35.112 1.00 0.00 C ATOM 36 OE1 GLU A 4 5.496 91.926 35.460 1.00 0.00 O ATOM 37 OE2 GLU A 4 4.438 90.287 34.414 1.00 0.00 O ATOM 38 C GLU A 4 10.048 88.888 34.470 1.00 0.00 C ATOM 39 O GLU A 4 9.954 88.911 33.256 1.00 0.00 O ATOM 40 N SER A 5 11.217 88.779 35.087 1.00 0.00 N ATOM 41 H SER A 5 11.273 88.828 36.094 1.00 0.00 H ATOM 42 CA SER A 5 12.463 88.755 34.325 1.00 0.00 C ATOM 43 HA SER A 5 12.402 89.476 33.510 1.00 0.00 H ATOM 44 CB SER A 5 13.669 89.114 35.211 1.00 0.00 C ATOM 45 HB2 SER A 5 14.563 89.086 34.588 1.00 0.00 H ATOM 46 HB3 SER A 5 13.520 90.126 35.587 1.00 0.00 H ATOM 47 OG SER A 5 13.825 88.226 36.298 1.00 0.00 O ATOM 48 HG SER A 5 14.587 88.489 36.820 1.00 0.00 H ATOM 49 C SER A 5 12.700 87.449 33.545 1.00 0.00 C ATOM 50 O SER A 5 13.314 87.463 32.469 1.00 0.00 O ATOM 51 N GLY A 5I 12.210 86.338 34.087 1.00 0.00 N ATOM 52 H GLY A 5I 11.719 86.378 34.969 1.00 0.00 H ATOM 53 CA GLY A 5I 12.360 85.044 33.440 1.00 0.00 C ATOM 54 HA2 GLY A 5I 11.379 84.765 33.055 1.00 0.00 H ATOM 55 HA3 GLY A 5I 13.051 85.182 32.608 1.00 0.00 H ATOM 56 C GLY A 5I 12.879 83.922 34.335 1.00 0.00 C ATOM 57 O GLY A 5I 13.210 82.847 33.824 1.00 0.00 O TER ATOM 58 N SER B 1 3.347 80.074 30.363 1.00 0.00 N ATOM 59 H SER B 1 3.644 79.127 30.177 1.00 0.00 H ATOM 60 CA SER B 1 2.423 80.307 31.476 1.00 0.00 C ATOM 61 HA SER B 1 1.632 80.987 31.159 1.00 0.00 H ATOM 62 CB SER B 1 1.780 78.988 31.941 1.00 0.00 C ATOM 63 HB2 SER B 1 0.927 79.234 32.573 1.00 0.00 H ATOM 64 HB3 SER B 1 1.434 78.452 31.057 1.00 0.00 H ATOM 65 OG SER B 1 2.691 78.165 32.670 1.00 0.00 O ATOM 66 HG SER B 1 2.248 77.357 32.938 1.00 0.00 H ATOM 67 C SER B 1 3.060 81.006 32.684 1.00 0.00 C ATOM 68 O SER B 1 4.276 81.085 32.807 1.00 0.00 O ATOM 69 N ARG B 2 2.215 81.494 33.583 1.00 0.00 N ATOM 70 H ARG B 2 1.220 81.443 33.416 1.00 0.00 H ATOM 71 HA ARG B 2 3.667 81.786 35.055 1.00 0.00 H ATOM 72 CB ARG B 2 2.761 83.671 34.576 1.00 0.00 C ATOM 73 HB2 ARG B 2 3.353 83.835 33.676 1.00 0.00 H ATOM 74 HB3 ARG B 2 1.744 84.028 34.411 1.00 0.00 H ATOM 75 CG ARG B 2 3.391 84.442 35.753 1.00 0.00 C ATOM 76 HG2 ARG B 2 2.694 84.426 36.591 1.00 0.00 H ATOM 77 HG3 ARG B 2 4.316 83.942 36.040 1.00 0.00 H ATOM 78 CD ARG B 2 3.697 85.908 35.366 1.00 0.00 C ATOM 79 HD2 ARG B 2 4.359 85.878 34.500 1.00 0.00 H ATOM 80 HD3 ARG B 2 2.750 86.367 35.083 1.00 0.00 H ATOM 81 NE ARG B 2 4.329 86.735 36.417 1.00 0.00 N ATOM 82 HE ARG B 2 3.807 87.549 36.709 1.00 0.00 H ATOM 83 CZ ARG B 2 5.485 86.470 37.023 1.00 0.00 C ATOM 84 NH1 ARG B 2 6.159 85.389 36.705 1.00 0.00 N ATOM 85 HH11 ARG B 2 5.798 84.761 36.001 1.00 0.00 H ATOM 86 HH12 ARG B 2 7.035 85.190 37.166 1.00 0.00 H ATOM 87 NH2 ARG B 2 6.006 87.302 37.921 1.00 0.00 N ATOM 88 HH21 ARG B 2 5.521 88.156 38.155 1.00 0.00 H ATOM 89 HH22 ARG B 2 6.884 87.075 38.365 1.00 0.00 H ATOM 90 C ARG B 2 1.721 81.801 35.924 1.00 0.00 C ATOM 91 O ARG B 2 0.502 81.932 35.813 1.00 0.00 O ATOM 92 N TES B 3 2.277 81.237 36.980 1.00 0.00 N ATOM 93 H TES B 3 3.264 81.024 36.997 1.00 0.00 H ATOM 94 CA TES B 3 1.476 80.868 38.127 1.00 0.00 C ATOM 95 HA TES B 3 0.487 81.323 38.066 1.00 0.00 H ATOM 96 CB TES B 3 1.307 79.333 38.262 1.00 0.00 C ATOM 97 HB TES B 3 0.758 78.962 37.397 1.00 0.00 H ATOM 98 CG1 TES B 3 2.649 78.651 38.335 1.00 0.00 C ATOM 99 HG11 TES B 3 3.199 79.021 39.201 1.00 0.00 H ATOM 100 HG12 TES B 3 2.505 77.575 38.429 1.00 0.00 H ATOM 101 HG13 TES B 3 3.215 78.863 37.428 1.00 0.00 H ATOM 102 CG2 TES B 3 0.483 78.998 39.497 1.00 0.00 C ATOM 103 HG21 TES B 3 -0.502 79.458 39.413 1.00 0.00 H ATOM 104 HG22 TES B 3 0.373 77.917 39.579 1.00 0.00 H ATOM 105 HG23 TES B 3 0.987 79.379 40.385 1.00 0.00 H ATOM 106 C TES B 3 2.134 81.432 39.368 1.00 0.00 C ATOM 107 O TES B 3 3.351 81.598 39.421 1.00 0.00 O ATOM 108 N LYS B 4 1.308 81.763 40.345 1.00 0.00 N ATOM 109 H LYS B 4 0.311 81.683 40.205 1.00 0.00 H ATOM 110 CA LYS B 4 1.782 82.317 41.594 1.00 0.00 C ATOM 111 HA LYS B 4 2.696 82.888 41.429 1.00 0.00 H ATOM 112 CB LYS B 4 0.717 83.254 42.183 1.00 0.00 C ATOM 113 HB2 LYS B 4 -0.220 82.699 42.226 1.00 0.00 H ATOM 114 HB3 LYS B 4 1.032 83.513 43.194 1.00 0.00 H ATOM 115 CG LYS B 4 0.511 84.557 41.353 1.00 0.00 C ATOM 116 HG2 LYS B 4 -0.169 85.197 41.916 1.00 0.00 H ATOM 117 HG3 LYS B 4 1.482 85.045 41.268 1.00 0.00 H ATOM 118 CD LYS B 4 -0.068 84.308 39.935 1.00 0.00 C ATOM 119 HD2 LYS B 4 0.367 83.379 39.566 1.00 0.00 H ATOM 120 HD3 LYS B 4 -1.146 84.186 40.042 1.00 0.00 H ATOM 121 CE LYS B 4 0.225 85.450 38.931 1.00 0.00 C ATOM 122 HE2 LYS B 4 -0.218 86.364 39.327 1.00 0.00 H ATOM 123 HE3 LYS B 4 1.307 85.569 38.867 1.00 0.00 H ATOM 124 NZ LYS B 4 -0.332 85.177 37.557 1.00 0.00 N ATOM 125 HZ1 LYS B 4 -1.334 85.067 37.616 1.00 0.00 H ATOM 126 HZ2 LYS B 4 -0.113 85.950 36.945 1.00 0.00 H ATOM 127 HZ3 LYS B 4 0.078 84.330 37.190 1.00 0.00 H ATOM 128 C LYS B 4 2.147 81.178 42.544 1.00 0.00 C ATOM 129 O ALYS B 4 1.284 80.471 43.043 1.00 0.00 O ATOM 130 O BLYS B 4 1.284 80.471 43.043 1.00 0.00 O TER HETATM 131 O1B GDP A 7 8.875 86.447 37.778 1.00 0.00 O HETATM 132 PB GDP A 7 9.784 85.563 37.007 1.00 0.00 P HETATM 133 O2B GDP A 7 9.897 84.148 37.414 1.00 0.00 O HETATM 134 O3B GDP A 7 11.201 86.125 36.990 1.00 0.00 O HETATM 135 O3A GDP A 7 9.360 85.532 35.497 1.00 0.00 O HETATM 136 PA GDP A 7 8.710 84.388 34.580 1.00 0.00 P HETATM 137 O1A GDP A 7 7.423 83.996 35.135 1.00 0.00 O HETATM 138 O2A GDP A 7 9.740 83.380 34.358 1.00 0.00 O HETATM 139 O5* GDP A 7 8.528 85.213 33.238 1.00 0.00 O HETATM 140 C5* GDP A 7 7.532 86.212 33.034 1.00 0.00 C HETATM 141 H50 GDP A 7 7.861 87.119 33.469 1.00 0.00 H HETATM 142 H51 GDP A 7 6.616 85.919 33.477 1.00 0.00 H HETATM 143 C4* GDP A 7 6.730 85.907 31.739 1.00 0.00 C HETATM 144 H40 GDP A 7 5.939 86.652 31.657 1.00 0.00 H HETATM 145 O4* GDP A 7 7.571 86.158 30.624 1.00 0.00 O HETATM 146 C1* GDP A 7 7.617 85.023 29.734 1.00 0.00 C HETATM 147 H10 GDP A 7 7.037 85.139 28.819 1.00 0.00 H HETATM 148 N9 GDP A 7 9.008 84.960 29.259 1.00 0.00 N HETATM 149 C8 GDP A 7 10.142 84.689 29.984 1.00 0.00 C HETATM 150 H80 GDP A 7 10.044 84.440 31.030 1.00 0.00 H HETATM 151 N7 GDP A 7 11.252 84.763 29.319 1.00 0.00 N HETATM 152 C5 GDP A 7 10.826 85.121 28.035 1.00 0.00 C HETATM 153 C6 GDP A 7 11.584 85.355 26.856 1.00 0.00 C HETATM 154 O6 GDP A 7 12.802 85.280 26.720 1.00 0.00 O HETATM 155 N1 GDP A 7 10.785 85.705 25.782 1.00 0.00 N HETATM 156 H1N GDP A 7 11.237 85.903 24.912 1.00 0.00 H HETATM 157 C2 GDP A 7 9.418 85.808 25.821 1.00 0.00 C HETATM 158 N2 GDP A 7 8.800 86.145 24.706 1.00 0.00 N HETATM 159 H21 GDP A 7 9.226 86.351 23.813 1.00 0.00 H HETATM 160 H22 GDP A 7 7.794 86.225 24.754 1.00 0.00 H HETATM 161 N3 GDP A 7 8.689 85.595 26.916 1.00 0.00 N HETATM 162 C4 GDP A 7 9.460 85.249 27.988 1.00 0.00 C HETATM 163 C3* GDP A 7 6.222 84.464 31.525 1.00 0.00 C HETATM 164 H30 GDP A 7 5.697 83.680 32.071 1.00 0.00 H HETATM 165 C2* GDP A 7 7.196 83.836 30.552 1.00 0.00 C HETATM 166 H20 GDP A 7 8.029 83.460 31.146 1.00 0.00 H HETATM 167 O2* GDP A 7 6.667 82.782 29.758 1.00 0.00 O HETATM 168 H2* GDP A 7 7.378 82.474 29.191 1.00 0.00 H HETATM 169 O3* GDP A 7 4.933 84.502 30.955 1.00 0.00 O HETATM 170 H3* GDP A 7 4.539 83.643 30.787 1.00 0.00 H HETATM 171 CA CA A 8 8.662 83.157 38.911 1.00 0.00 CA HETATM 172 O HOH B 5 10.341 83.033 40.331 1.00 14.52 O HETATM 173 O HOH B 6 6.852 83.372 37.663 1.00 10.61 O HETATM 174 O HOH B 7 7.480 82.206 40.427 1.00 12.26 O HETATM 175 O HOH B 8 8.135 85.033 39.786 1.00 15.76 O HETATM 176 O HOH B 9 13.941 92.866 34.673 1.00 2.00 O END bio3d/inst/examples/transducin.fa0000644000176200001440000003100012322022452016513 0ustar liggesusers>http://www.rcsb.org/pdb/files/1TND.pdb -----------------------------ARTVKLLLLGAGESGKSTIVKQMKIIHQDGY SLEECLEFIAIIYGNTLQSILAIVRAMTTLNIQYGDSARQDDARKLMHMADTIEE-GTMP KEMSDIIQRLWKDSGIQACFDRASEYQLNDSAGYYLSDLERLVTPGYVPTEQDVLRSRVK TTGIIETQFSFKDLNFRMFDVGGQRSERKKWIHCFEGVTCIIFIAALSAYDMVLVEDDEV NRMHESLHLFNSICNHRYFATTSIVLFLNKKDVFSEKIKKAHLSICFPDYNGPNTYEDAG NYIKVQFLELNMRRDVKEIYSHMTCATDTQNVKFVFDAVTDIIIKENLKDCGL- >http://www.rcsb.org/pdb/files/1TAD.pdb -----------------------------ARTVKLLLLGAGESGKSTIVKQMKIIHQDGY SLEECLEFIAIIYGNTLQSILAIVRAMTTLNIQYGDSARQDDARKLMHMADTIEE-GTMP KEMSDIIQRLWKDSGIQACFDRASEYQLNDSAGYYLSDLERLVTPGYVPTEQDVLRSRVK TTGIIETQFSFKDLNFRMFDVGGQRSERKKWIHCFEGVTCIIFIAALSAYDMVLVEDDEV NRMHESLHLFNSICNHRYFATTSIVLFLNKKDVFSEKIKKAHLSICFPDYNGPNTYEDAG NYIKVQFLELNMRRDVKEIYSHMTCATDTQNVKFVFDAVTDIIIKE-------- >http://www.rcsb.org/pdb/files/1TAG.pdb -----------------------------ARTVKLLLLGAGESGKSTIVKQMKIIHQDGY SLEECLEFIAIIYGNTLQSILAIVRAMTTLNIQYGDSARQDDARKLMHMADTIEE-GTMP KEMSDIIQRLWKDSGIQACFDRASEYQLNDSAGYYLSDLERLVTPGYVPTEQDVLRSRVK TTGIIETQFSFKDLNFRMFDVGGQRSERKKWIHCFEGVTCIIFIAALSAYDMVLVEDDEV NRMHESLHLFNSICNHRYFATTSIVLFLNKKDVFSEKIKKAHLSICFPDYNGPNTYEDAG NYIKVQFLELNMRRDVKEIYSHMTCATDTQNVKFVFDAVTDIII---------- >http://www.rcsb.org/pdb/files/3V00.pdb --HMGAGASAEEKHSRELEKKLKEDAEKDARTVKLLLLGAGESGKSTIVKQMKIIHQDPY SLEECLEFIAIIYGNTLQSILAIVRAMTTLNIQYGDSARQDDARKLMHMADTIEE-GTMP KEMSDIIQRLWKDSGIQACFDRASEYQLNDSAGYYLSDLERLVTPGYVPTEQDVLRSRVK TTGIIETQFSFKDLNFRMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMHLFNSICNNKWFTDTSIILFLNKKDLFEEKIKKSPLTICYPEYAGSNTYEEAG NYIKVQFLELNMRRDVKEIYSHMTCATDTQNVKFVFDAVTDIIIKENLKDCGLF >http://www.rcsb.org/pdb/files/1FQJ.pdb ------------------------------RTVKLLLLGAGESGKSTIVKQMKIIHQDGY SLEECLEFIAIIYGNTLQSILAIVRAMTTLNIQYGDSARQDDARKLMHMADTIEE-GTMP KEMSDIIQRLWKDSGIQACFDRASEYQLNDSAGYYLSDLERLVTPGYVPTEQDVLRSRVK TTGIIETQFSFKDLNFRMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTDTSIILFLNKKDLFEEKIKKSPLTICYPEYAGSNTYEEAG NYIKVQFLELNMRRDVKEIYSHMTCATDTQNVKFVFDAVTDIIIKENL------ >http://www.rcsb.org/pdb/files/1FQK.pdb ------------------------------RTVKLLLLGAGESGKSTIVKQMKIIHQDGY SLEECLEFIAIIYGNTLQSILAIVRAMTTLNIQYGDSARQDDARKLMHMADTIEE-GTMP KEMSDIIQRLWKDSGIQACFDRASEYQLNDSAGYYLSDLERLVTPGYVPTEQDVLRSRVK TTGIIETQFSFKDLNFRMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTDTSIILFLNKKDLFEEKIKKSPLTICYPEYAGSNTYEEAG NYIKVQFLELNMRRDVKEIYSHMTCATDTQNVKFVFDAVTDIIIKENLK----- >http://www.rcsb.org/pdb/files/2XNS.pdb ------------------------------REVKLLLLGAGESGKSTIVKQMKIIHEAGY SEEECKQYKAVVYSNTIQSIIAIIRAMGRLKIDFGDSARADDARQLFVLAGAAEE-GFMT AELAGVIKRLWKDSGVQACFNRSREYQLNDSAAYYLNDLDRIAQPNYIPTQQDVLRTRVK TTGIVETHFTFKDLHFKMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTDTSIILFLNKKDLFEEKIKKSPLTICYPEYAGSNTYEEAA AYIQCQFEDLNKRKDTKEIYTHFTCATDTKNVQFVFDAVTDVIIKNN------- >http://www.rcsb.org/pdb/files/1KJY.pdb ----------------------------GAREVKLLLLGAGESGKSTIVKQMKIIHEAGY SEEECKQYKAVVYSNTIQSIIAIIRAMGRLKIDFGDSARADDARQLFVLAGAAEE-GFMT AELAGVIKRLWKDSGVQACFNRSREYQLNDSAAYYLNDLDRIAQPNYIPTQQDVLRTRVK TTGIVETHFTFKDLHFKMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTDTSIILFLNKKDLFEEKIKKSPLTICYPEYAGSNTYEEAA AYIQCQFEDLNKRKDTKEIYTHFTCATDTKNVQFVFDAVTDVIIKNNLK----- >http://www.rcsb.org/pdb/files/2OM2.pdb ----------------------------GAREVKLLLLGAGESGKSTIVKQMKIIHEAGY SEEECKQYKAVVYSNTIQSIIAIIRAMGRLKIDFGDSARADDARQLFVLAGAAEE-GFMT AELAGVIKRLWKDSGVQACFNRSREYQLNDSAAYYLNDLDRIAQPNYIPTQQDVLRTRVK TTGIVETHFTFKDLHFKMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTDTSIILFLNKKDLFEEKIKKSPLTICYPEYAGSNTYEEAA AYIQCQFEDLNKRKDTKEIYTHFTCATDTKNVQFVFDAVTDVIIKNNLK----- >http://www.rcsb.org/pdb/files/4G5Q.pdb -----------------------------AREVKLLLLGAGESGKSTIVKQMKIIHEAGY SEEECKQYKAVVYSNTIQSIIAIIRAMGRLKIDFGDSARADDARQLFVLAGAAEE-GFMT AELAGVIKRLWKDSGVQACFNRSREYQLNDSAAYYLNDLDRIAQPNYIPTQQDVLRTRVK TTGIVETHFTFKDLHFKMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTDTSIILFLNKKDLFEEKIKKSPLTICYPEYAGSNTYEEAA AYIQCQFEDLNKRKDTKEIYTHFTCATDTKNVQFVFDAVTDVIIKNNLKD---- >http://www.rcsb.org/pdb/files/1GP2.pdb ---LSAEDKAAVERSKMIDRNLREDGEKAAREVKLLLLGAGESGKSTIVKQMKIIHEAGY SEEECKQYKAVVYSNTIQSIIAIIRAMGRLKIDFGDAARADDARQLFVLAGAAEE-GFMT AELAGVIKRLWKDSGVQACFNRSREYQLNDSAAYYLNDLDRIAQPNYIPTQQDVLRTRVK TTGIVETHFTFKDLHFKMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTDTSIILFLNKKDLFEEKIKKSPLTICYPEYAGSNTYEEAA AYIQCQFEDLNKRKDTKEIYTHFTCATDTKNVQFVFDAVTDVIIKNNL------ >http://www.rcsb.org/pdb/files/1AGR.pdb ---LSAEDKAAVERSKMIDRNLREDGEKAAREVKLLLLGAGESGKSTIVKQMKIIHEAGY SEEECKQYKAVVYSNTIQSIIAIIRAMGRLKIDFGDAARADDARQLFVLAGAAEE-GFMT AELAGVIKRLWKDSGVQACFNRSREYQLNDSAAYYLNDLDRIAQPNYIPTQQDVLRTRVK TTGIVETHFTFKDLHFKMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTDTSIILFLNKKDLFEEKIKKSPLTICYPEYAGSNTYEEAA AYIQCQFEDLNKRKDTKEIYTHFTCATDTKNVQFVFDAVTDVIIKNNLKDCGLF >http://www.rcsb.org/pdb/files/1CIP.pdb ------------------------------REVKLLLLGAGESGKSTIVKQMKIIHEAGY SEEECKQYKAVVYSNTIQSIIAIIRAMGRLKIDFGDAARADDARQLFVLAGAAEE-GFMT AELAGVIKRLWKDSGVQACFNRSREYQLNDSAAYYLNDLDRIAQPNYIPTQQDVLRTRVK TTGIVETHFTFKDLHFKMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTDTSIILFLNKKDLFEEKIKKSPLTICYPEYAGSNTYEEAA AYIQCQFEDLNKRKDTKEIYTHFTCATDTKNVQFVFDAVTDVIIKNN------- >http://www.rcsb.org/pdb/files/1GFI.pdb -------------------------------EVKLLLLGAGESGKSTIVKQMKIIHEAGY SEEECKQYKAVVYSNTIQSIIAIIRAMGRLKIDFGDAARADDARQLFVLAGAAEE-GFMT AELAGVIKRLWKDSGVQACFNRSREYQLNDSAAYYLNDLDRIAQPNYIPTQQDVLRTRVK TTGIVETHFTFKDLHFKMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTDTSIILFLNKKDLFEEKIKKSPLTICYPEYAGSNTYEEAA AYIQCQFEDLNKRKDTKEIYTHFTCATDTKNVQFVFDAVTDVIIK--------- >http://www.rcsb.org/pdb/files/1GIA.pdb --------------------------------VKLLLLGAGESGKSTIVKQMKIIHEAGY SEEECKQYKAVVYSNTIQSIIAIIRAMGRLKIDFGDAARADDARQLFVLAGAAEE-GFMT AELAGVIKRLWKDSGVQACFNRSREYQLNDSAAYYLNDLDRIAQPNYIPTQQDVLRTRVK TTGIVETHFTFKDLHFKMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTDTSIILFLNKKDLFEEKIKKSPLTICYPEYAGSNTYEEAA AYIQCQFEDLNKRKDTKEIYTHFTCATDTKNVQFVFDAVTDVI----------- >http://www.rcsb.org/pdb/files/2ZJY.pdb ------------------------------REVKLLLLGAGESGKSTIVKQMKIIHEAGY SEEECKQYKAVVYSNTIQSIIAIIRAMGRLKIDFGDAARADDARQLFVLAGAAEE-GFMT AELAGVIKRLWKDSGVQACFNRSREYQLNDSAAYYLNDLDRIAQPNYIPTQQDVLRTRVK TTGIVETHFTFKDLHFKMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTDTSIILFLNKKDLFEEKIKKSPLTICYPEYAGSNTYEEAA AYIQCQFEDLNKRKDTKEIYTHFTCATDTKNVQFVFDAVTDVIIKNNL------ >http://www.rcsb.org/pdb/files/3ONW.pdb ------------------------------REVKLLLLGAGESGKSTIVKQMKIIHEAGY SEEECKQYKAVVYSNTIQSIIAIIRAMGRLKIDFGDSARADDARQLFVLAGAAEE-GFMT AELAGVIKRLWKDSGVQACFNRSREYLLNDSAAYYLNDLDRIAQPNYIPTQQDVLRTRVK TTGIVETHFTFKDLHFKMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTDTSIILFLNKKDLFEEKIKKSPLTICYPEYAGSNTYEEAA AYIQCQFEDLNKRKDTKEIYTHFTCATDTKNVQFVFDAVTDVIIKNN------- >http://www.rcsb.org/pdb/files/1BH2.pdb ------------------------------REVKLLLLGAGESGKSTIVKQMKIIHEAGY SEEECKQYKAVVYSNTIQSIIAIIRAMGRLKIDFGDAARADDARQLFVLAGAAEE-GFMT AELAGVIKRLWKDSGVQACFNRSREYQLNDSAAYYLNDLDRIAQPNYIPTQQDVLRTRVK TTGIVETHFTFKDLHFKMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTDTSIILFLNKKDLFEEKIKKSPLTICYPEYAGSNTYEEAA AYIQCQFEDLNKRKDTKEIYTHFTCSTDTKNVQFVFDAVTDVIIKN-------- >http://www.rcsb.org/pdb/files/1GG2.pdb ---LSAEDKAAVERSKMIDRNLREDGEKAAREVKLLLLGAGESGKSTIVKQMKIIHEAGY SEEECKQYKAVVYSNTIQSIIAIIRAMGRLKIDFGDAARADDARQLFVLAGAAEE-GFMT AELAGVIKRLWKDSGVQACFNRSREYQLNDSAAYYLNDLDRIAQPNYIPTQQDVLRTRVK TTGIVETHFTFKDLHFKMFDVGAQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTDTSIILFLNKKDLFEEKIKKSPLTICYPEYAGSNTYEEAA AYIQCQFEDLNKRKDTKEIYTHFTCATDTKNVQFVFDAVTDVIIKNNL------ >http://www.rcsb.org/pdb/files/1GIT.pdb ------------------------------REVKLLLLGAGESGKSTIVKQMKIIHEAGY SEEECKQYKAVVYSNTIQSIIAIIRAMGRLKIDFGDAARADDARQLFVLAGAAEE-GFMT AELAGVIKRLWKDSGVQACFNRSREYQLNDSAAYYLNDLDRIAQPNYIPTQQDVLRTRVK TTGIVETHFTFKDLHFKMFDVGAQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTDTSIILFLNKKDLFEEKIKKSPLTICYPEYAGSNTYEEAA AYIQCQFEDLNKRKDTKEIYTHFTCATDTKNVQFVFDAVTDVIIKNNL------ >http://www.rcsb.org/pdb/files/3QI2.pdb ----------------------------GAREVKLLLLGARESGKSTIVKQMKIIHEAGY SEEECKQYKAVVYSNTIQSIIAIIRAMGRLKIDFGDSARADDARQLFVLAGAAEE-GFMT AELAGVIKRLWKDSGVQACFNRSREYQLNDSAAYYLNDLDRIAQPNYIPTQQDVLRTRVK TTGIVETHFTFKDLHFKMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTDTSIILFLNKKDLFEEKIKKSPLTICYPEYAGSNTYEEAA AYIQCQFEDLNKRKDTKEIYTHFTCATDTKNVQFVFDAVTDVIIKNN------- >http://www.rcsb.org/pdb/files/1SVK.pdb -------------------------------EVKLLLLGAGESGKSTIVKQMKIIHEAGY SEEECKQYKAVVYSNTIQSIIAIIRAMGRLKIDFGDAARADDARQLFVLAGAAEE-GFMT AELAGVIKRLWKDSGVQACFNRSREYQLNDSAAYYLNDLDRIAQPNYIPTQQDVLRTRVP TTGIVETHFTFKDLHFKMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTDTSIILFLNKKDLFEEKIKKSPLTICYPEYAGSNTYEEAA AYIQCQFEDLNKRKDTKEIYTHFTCATDTKNVQFVFDAVTDVIIK--------- >http://www.rcsb.org/pdb/files/1SVS.pdb ------------------------------REVKLLLLGAGESGKSTIVKQMKIIHEAGY SEEECKQYKAVVYSNTIQSIIAIIRAMGRLKIDFGDAARADDARQLFVLAGAAEE-GFMT AELAGVIKRLWKDSGVQACFNRSREYQLNDSAAYYLNDLDRIAQPNYIPTQQDVLRTRVP TTGIVETHFTFKDLHFKMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTDTSIILFLNKKDLFEEKIKKSPLTICYPEYAGSNTYEEAA AYIQCQFEDLNKRKDTKEIYTHFTCATDTKNVQFVFDAVTDVIIKNN------- >http://www.rcsb.org/pdb/files/3FFA.pdb -------------------------------EVKLLLLGAGESGKSTIVKQMKIIHEAGY SEEECKQYKAVVYSNTIQSIIAIIRAMGRLKIDFGDAARADDARQLFVLAGAAEE-GFMT AELAGVIKRLWKDSGVQACFNRSREYQLNDSAAYYLNDLDRIAQPNYIPTQQDVLRTRVK TTGIVETHFTFKDLHFKMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTDTSIILFLNKKDLFEEKIKKSPLTICYPEYAGSNTYEEAA AYIQCQFEDLNKRKDTKEIYTHFTCATDAKNVQFVFDAVTDVIIKNNL------ >http://www.rcsb.org/pdb/files/1GIL.pdb --------------------------------VKLLLLGAGESGKSTIVKQMKIIHEAGY SEEECKQYKAVVYSNTIQSIIAIIRAMGRLKIDFGDAARADDARQLFVLAGAAEE-GFMT AELAGVIKRLWKDSGVQACFNRSREYQLNDSAAYYLNDLDRIAQPNYIPTQQDVLRTRVK TTGIVETHFTFKDLHFKMFDVGGLRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTDTSIILFLNKKDLFEEKIKKSPLTICYPEYAGSNTYEEAA AYIQCQFEDLNKRKDTKEIYTHFTCATDTKNVQFVFDAVTDVI----------- >http://www.rcsb.org/pdb/files/1AS0.pdb ------------------------------REVKLLLLGAVESGKSTIVKQMKIIHEAGY SEEECKQYKAVVYSNTIQSIIAIIRAMGRLKIDFGDAARADDARQLFVLAGAAEE-GFMT AELAGVIKRLWKDSGVQACFNRSREYQLNDSAAYYLNDLDRIAQPNYIPTQQDVLRTRVK TTGIVETHFTFKDLHFKMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTDTSIILFLNKKDLFEEKIKKSPLTICYPEYAGSNTYEEAA AYIQCQFEDLNKRKDTKEIYTHFTCATDTKNVQFVFDAVTDVII---------- >http://www.rcsb.org/pdb/files/1AS2.pdb ------------------------------REVKLLLLGAVESGKSTIVKQMKIIHEAGY SEEECKQYKAVVYSNTIQSIIAIIRAMGRLKIDFGDAARADDARQLFVLAGAAEE-GFMT AELAGVIKRLWKDSGVQACFNRSREYQLNDSAAYYLNDLDRIAQPNYIPTQQDVLRTRVK TTGIVETHFTFKDLHFKMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTDTSIILFLNKKDLFEEKIKKSPLTICYPEYAGSNTYEEAA AYIQCQFEDLNKRKDTKEIYTHFTCATDTKNVQFVFDAVTDVIIKN-------- >http://www.rcsb.org/pdb/files/2ODE.pdb -------------------------------EVKLLLLGAGESGKSTIVKQMKIIHEDGY SEDECKQYKVVVYSNTIQSIIAIIRAMGRLKIDFGEAARADDARQLFVLAGSAEE-GVMT PELAGVIKRLWRDGGVQACFSRSREYQLNDSASYYLNDLDRISQSNYIPTQQDVLRTRVK TTGIVETHFTFKDLYFKMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTETSIILFLNKKDLFEEKIKRSPLTICYPEYTGSNTYEEAA AYIQCQFEDLNRRKDTKEIYTHFTCATDTKNVQFVFDAVTDVIIKNNL------ >http://www.rcsb.org/pdb/files/2V4Z.pdb ------------------------------KEVKLLLLGAGESGKSTIVKQMKIIHEDGY SEDECKQYKVVVYSNTIQSIIAIIRAMGRLKIDFGEAARADDARQLFVLAGSAEE-GVMT PELAGVIKRLWRDGGVQACFSRSREYQLNDSASYYLNDLDRISQSNYIPTQQDVLRTRVK TTGIVETHFTFKDLYFKMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTETSIILFLNKKDLFEEKIKRSPLTICYPEYTGSNTYEEAA AYIQCQFEDLNRRKDTKEIYTHFTCATDTKNVQFVFDAVTDVIIKNN------- >http://www.rcsb.org/pdb/files/4G5R.pdb ------------------------------KEVKLLLLGAGESGKSTIVKQMKIIHEDGY SEDECKQYKVVVYSNTIQSIIAIIRAMGRLKIDFGEAARADDARQLFVLAGSAEE-GVMT PELAGVIKRLWRDGGVQACFSRSREYQLNDSASYYLNDLDRISQSNYIPTQQDVLRTRVK TTGIVETHFTFKDLYFKMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTETSIILFLNKKDLFEEKIKRSPLTICYPEYTGSNTYEEAA AYIQCQFEDLNRRKDTKEIYTHFTCATDTKNVQFVFDAVTDVIIKNNLKE---- >http://www.rcsb.org/pdb/files/2IHB.pdb -------------------------------EVKLLLLGAGESGKSTIVKQMKIIHEDGY SEDECKQYKVVVYSNTIQSIIAIIRAMGRLKIDFGEAARADDARQLFVLAGSAEE-GVMT PELAGVIKRLWRDGGVQACFSRSREYQLNDSASYYLNDLDRISQSNYIPTQQDVLRTRVK TTGIVETHFTFKDLYFKMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTETSIILFLNKKDLFEEKIKRSPLTICYPEYTGSNTYEEAA AYIQCQFEDLNRRKDTKEIYTHFTCATDTKNVQFVFDAVTDVIIKNNLK----- >http://www.rcsb.org/pdb/files/4G5O.pdb -----------------------------AKEVKLLLLGAGESGKSTIVKQMKIIHEDGY SEDECKQYKVVVYSNTIQSIIAIIRAMGRLKIDFGEAARADDARQLFVLAGSAEE-GVMT PELAGVIKRLWRDGGVQACFSRSREYLLNDSASYYLNDLDRISQSNYIPTQQDVLRTRVK TTGIVETHFTFKDLYFKMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEM NRMHESMKLFDSICNNKWFTETSIILFLNKKDLFEEKIKRSPLTICYPEYTGSNTYEEAA AYIQCQFEDLNRRKDTKEIYTHFTCATDTKNVQFVFDAVTDVIIKNNLKE---- bio3d/inst/examples/kif1a.fa0000644000176200001440000000303012322022452015336 0ustar liggesusers>http://www.rcsb.org/pdb/files/1bg2.pdb NIKVMCRFRPLNESEVNRGDKYIAKFQGEDTV----VIASK-------PYAFDRVFQSS- -------TSQEQVYNDCAKKIVKDVLEGYNGTIFAYGQTSSGKTHTMEGKLHDPEGMGII PRIVQDIFNYIYSMDENL-EFHIKVSYFEIYLDKIRDLL-DVSKT-NLSVHEDKNRVPYV KGCTERFVCSPDEVMDTIDEGKSNRHVAVTNMNEHSSRSHSIFLINVKQENTQT----EQ KLSGKLYLVDLAGSEKVSKTGAEGAVLDEAKNINKSLSALGNVISALAEGSTYVPYRDSK MTRILQDSLGGNCRTTIVICCSPSSYNESETKSTLLFGQRAKTI >http://www.rcsb.org/pdb/files/1i6i.pdb SVKVAVRVRPFNSREMSRDSKCIIQMSGSTTT----IVNPKQPKETPKSFSFDYSYWSHT SPEDINYASQKQVYRDIGEEMLQHAFEGYNVCIFAYGQTGAGKSYTMMGKQEK-DQQGII PQLCEDLFSRINDTTNDNMSYSVEVSYMEIYCERVRDLL-NPKNKGNLRVREHPLLGPYV EDLSKLAVTSYNDIQDLMDSGNKARTVAATNMNETSSRSHAVFNIIFTQKRHDAETNITT EKVSKISLVDLAGSE---------------ANINKSLTTLGKVISALAEMD-FIPYRDSV LTWLLRENLGGNSRTAMVAALSPADINYDETLSTLRYADRAKQI >http://www.rcsb.org/pdb/files/1i5s.pdb SVKVAVRVRPFNSREMSRDSKCIIQMSGSTTT----IVNPKQPKETPKSFSFDYSYWSHT SPEDINYASQKQVYRDIGEEMLQHAFEGYNVCIFAYGQTGAGKSYTMMGKQEK-DQQGII PQLCEDLFSRINDTTNDNMSYSVEVSYMEIYCERVRDLL-NPKNKGNLRVREHPLLGPYV EDLSKLAVTSYNDIQDLMDSGNKARTVAATNMNETSSRSHAVFNIIFTQKRHDAETNITT EKVSKISLVDLAGSER-----AKGTRLKEGANINKSLTTLGKVISALAEMD--IPYRDSV LTWLLRENLGGNSRTAMVAALSPADINYDETLSTLRYADRAK-- >http://www.rcsb.org/pdb/files/2ncd.pdb NIRVFCRIRPPLESEENRMC-CTWTYHDESTVELQSIDAQAKSKMGQQIFSFDQVFHPL- -------SSQSDIF-EMVSPLIQSALDGYNICIFAYGQTGSGKTYTMDGV---PESVGVI PRTVDLLFDSIRGYRNLGWEYEIKATFLEIYNEVLYDLLSNEQKDMEIRMAKNNKNDIYV SNITEETVLDPNHLRHLMHTAKMNRATASTAGNERSSRSHAVTKLELIGRHAEK----QE ISVGSINLVDLAGSES--------------PNINRSLSELTNVILALLQKQDHIPYRNSK LTHLLMPSLGGNSKTLMFINVSPFQDCFQESVKSLRFAASVNSC bio3d/inst/staticdocs/0000755000176200001440000000000012544562303014373 5ustar liggesusersbio3d/inst/staticdocs/index.r0000644000176200001440000001310112544562303015661 0ustar liggesusers##"io" sd_section("Input/Output:", "Read and Write Common Biomolecular Data Types", c( "read.pdb", "read.fasta", "read.fasta.pdb", "read.ncdf", "read.dcd", "read.crd", "read.pqr", "read.mol2", "read.all", "read.pdcBD", "aln2html", "get.pdb", "get.seq", "load.enmff", "write.pdb", "write.crd", "write.fasta", "write.ncdf", "write.pqr", "write.pir", "mktrj.nma", "mktrj.pca", "mktrj.enma", "view.dccm", "view.cna" ) ) ##"sequence" sd_section("Sequence Analysis:", "Do Interesting Things with Protein Sequence", c( "consensus", "conserv", "blast.pdb", "hmmer", "pfam", "uniprot", "entropy", "ide.filter", "seqidentity", "motif.find", "pdbaln", "seq2aln", "seqaln", "seqaln.pair", "seqbind" ) ) ##"structure" sd_section("Structure Analysis:", "Do Interesting Things with Protein Structure", c( "angle.xyz", "biounit", "blast.pdb", "get.blast", "atom.select", "combine.select", "cmap", "cmap.filter", "core.find", "com", "dccm", "dccm.filter", "dist.xyz", "dm", "dssp", "dssp.pdbs", "geostas", "mustang", "fit.xyz", "binding.site", "mktrj", "mktrj.pca", "overlap", "pca", "pca.xyz", "pca.pdbs", "pca.array", "pca.tor", "dccm.pca", "project.pca", "pdbaln", "pdb.annotate", "pdb2aln", "pdb2aln.ind", "pdbfit", "chain.pdb", "convert.pdb", "rgyr", "rmsd", "rmsd.filter", "rmsf", "rmsip", "struct.aln", "torsion.pdb", "torsion.xyz", "wrap.tor", "aa2mass", "aa.table", "atom.index", "atom2mass", "atom2ele", "cov.nma", "dccm.enma", "dccm.nma", "dccm.xyz", "deformation.nma", "fluct.nma", "inner.prod", "load.enmff", "mktrj.nma", "nma", "nma.pdb", "nma.pdbs", "normalize.vector", "pdbs2pdb", "pdbs.filter", "plot.enma", "plot.nma", "plot.rmsip", "sdENM", "sse.bridges", "view.dccm", "view.modes", "var.xyz", "inspect.connectivity" ) ) ##"trajectory" sd_section("Trajectory Analysis:", "Do Interesting Things with Simulation Data", c( "angle.xyz", "cmap", "cmap.filter", "core.find", "dccm", "dccm.pca", "dccm.filter", "lmi", "dist.xyz", "dm", "dssp.xyz", "geostas", "fit.xyz", "mktrj", "mktrj.pca", "overlap", "project.pca", "pca.tor", "pca.xyz", "pdbaln", "rgyr", "rmsd", "rmsd.filter", "rmsf", "rmsip", "torsion.pdb", "torsion.xyz", "wrap.tor" ) ) ##"nma" sd_section("Normal Mode Analysis:", "Probe Large-Scale Protein Motions", c( "aa2mass", "aa.table", "atom.index", "atom2mass", "atom2ele", "bhattacharyya", "cov.nma", "covsoverlap", "dccm.enma", "dccm.nma", "dccm.xyz", "deformation.nma", "geostas", "fluct.nma", "inner.prod", "load.enmff", "mktrj", "mktrj.nma", "mktrj.enma", "nma", "nma.pdb", "nma.pdbs", "normalize.vector", "pdbs2pdb", "plot.enma", "plot.nma", "plot.rmsip", "sdENM", "sse.bridges", "sip", "var.xyz", "var.pdbs", "view.dccm", "view.modes" ) ) ##"cna" sd_section("Correlation Network Analysis:", "Network analysis of dynamic coupling", c( "cna", "cnapath", "cov2dccm", "dccm", "lmi", "dccm.filter", "cmap", "community.tree", "network.amendment", "view.cna", "view.dccm", "view.cnapath", "plot.cna", "print.cna", "identify.cna", "layout.cna", "prune.cna" ) ) ##"graphics" sd_section("Graphics:", "Plotting and Graphic Display", c( "bwr.colors", "vmd.colors", "mono.colors", "plot.bio3d", "plot.blast", "plot.cmap", "plot.core", "plot.dccm", "plot.dmat", "plot.fluct", "plot.geostas", "plot.pca", "plot.pca.loadings", "hclustplot", "plot.cna", "plot.fasta", "plot.hmmer" ) ) ##"util" sd_section("Utilities:", "Convert and Manipulate Data", c( "aa.index", "aa123", "aa2index", "aln2html", "as.fasta", "as.pdb", "as.select", "as.xyz", "atom.select", "combine.select", "atom2xyz", "basename.pdb", "bio3d-package", "biounit", "bounds", "bounds.sse", "cat.pdb", "check.utility", "clean.pdb", "chain.pdb", "convert.pdb", "diag.ind", "difference.vector", "gap.inspect", "get.blast", "inspect.connectivity", "ide.filter", "is.gap", "is.pdb", "is.select", "is.xyz", "is.pdbs", "lbio3d", "orient.pdb", "pairwise", "plot.bio3d", "print.core", "print.cna", "print.fasta", "print.xyz", "print.cnapath", "print.enma", "print.geostas", "print.mol2", "print.nma", "print.pca", "print.pdb", "print.prmtop", "print.rle2", "print.select", "print.sse", "rle2", "rmsd.filter", "pdbseq", "seqbind", "pdbsplit", "store.atom", "trim.pdb", "unbound", "vec2resno", "setup.ncore", "elements", "formula2mass" ) ) ##"example" sd_section("Example Data:", "Bio3d Example Data", c("example.data") ) #sd_icon("Some title:", # "some sub-text", # c("pants") # ) bio3d/inst/CITATION0000644000176200001440000000122612526367343013400 0ustar liggesuserscitHeader("To cite bio3d in publications use:") citEntry(entry = "article", author = "Grant B.J., Rodrigues A.P.C., ElSawy K.M., McCammon J.A., Caves L.S.D.", title = "Bio3D: An R package for the comparative analysis of protein structures.", journal = "Bioinformatics", year = "2006", volume = "22", pages = "2695--2696", month = "Nov", organization = "University of California, San Diego, La Jolla, CA 92093, USA.", textVersion = "Grant, B.J. et al. (2006) Bioinformatics 22, 2695--2696.") citFooter("Original article and updates are available from http://thegrantlab.org/bio3d/") bio3d/inst/doc/0000755000176200001440000000000012632664353013006 5ustar liggesusersbio3d/inst/doc/bio3d_vignettes.html0000644000176200001440000002441112632664353016766 0ustar liggesusers bio3d Vignettes

We distribute a number of extended Bio3D vignettes that provide worked examples of using Bio3D to perform a particular type of structural bioinformatics analysis. An updated list of these can be found on-line.

At the time of writing these include:

  • Installing Bio3D ( PDF | HTML)
  • Getting started with Bio3D ( PDF | HTML )
  • PDB structure manipulation and analysis with Bio3D ( PDF | HTML)
  • Comparative sequence and structure analysis with Bio3D ( PDF | HTML)
  • Beginning trajectory analysis with Bio3D ( PDF | HTML)
  • Enhanced methods for Normal Mode Analysis with Bio3D ( PDF | HTML)
  • Ensemble NMA of E.coli DHFR structures ( PDF | HTML )
  • Ensemble NMA across multiple species of DHFR ( PDF | HTML )
  • Correlation network analysis with Bio3D ( PDF | HTML )
  • Protein structure network analysis with Bio3D ( PDF | HTML )

There is also extensive on-line documentation with worked examples (and their output) for all functions and a package manual (in PDF format) that is a concatenation of each functions documentation (without example output).

Note that for information on Bio3D development status or to report a bug, please refer to: https://bitbucket.org/Grantlab/bio3d

bio3d/inst/doc/bio3d_vignettes.Rmd0000644000176200001440000000604212632664353016544 0ustar liggesusers--- title: "bio3d Vignettes" date: "Feb 19 2015" output: rmarkdown::html_vignette vignette: > %\VignetteEngine{knitr::rmarkdown} %\VignetteIndexEntry{bio3d Vignettes} \usepackage[utf8]{inputenc} --- We distribute a number of extended **Bio3D vignettes** that provide worked examples of using Bio3D to perform a particular type of structural bioinformatics analysis. An updated list of these can be found on-line. At the time of writing these include: * Installing Bio3D ( PDF | HTML) * Getting started with Bio3D ( PDF | HTML ) * PDB structure manipulation and analysis with Bio3D ( PDF | HTML) * Comparative sequence and structure analysis with Bio3D ( PDF | HTML) * Beginning trajectory analysis with Bio3D ( PDF | HTML) * Enhanced methods for Normal Mode Analysis with Bio3D ( PDF | HTML) * Ensemble NMA of E.coli DHFR structures ( PDF | HTML ) * Ensemble NMA across multiple species of DHFR ( PDF | HTML ) * Correlation network analysis with Bio3D ( PDF | HTML ) * Protein structure network analysis with Bio3D ( PDF | HTML ) There is also extensive on-line documentation with worked examples (and their output) for all functions and a package manual (in PDF format) that is a concatenation of each functions documentation (without example output). Note that for information on Bio3D development status or to report a bug, please refer to: https://bitbucket.org/Grantlab/bio3d bio3d/inst/matrices/0000755000176200001440000000000012524171273014042 5ustar liggesusersbio3d/inst/matrices/bio3d.mat0000644000176200001440000000377612040627421015554 0ustar liggesusers# PET91 Matrix - Jones, Taylor and Thornton 1991 # This is an update of the MDM78 Dayhoff matrix normalised such that # all maxima are on the diagonal with a score of 10 A R N D C Q E G H I L K M F P S T W Y V B Z X - A 10 -1 0 -1 -1 -1 -1 1 -2 0 -1 -1 -1 -3 1 1 2 -4 -3 1 0 -1 0 0 R -1 10 0 -1 -1 2 0 0 2 -3 -3 4 -2 -4 -1 -1 -1 0 -2 -3 0 1 0 0 N 0 0 10 2 -1 0 1 0 1 -2 -3 1 -2 -3 -1 1 1 -4 -1 -2 2 0 0 0 D -1 -1 2 10 -3 0 4 1 0 -3 -4 0 -3 -5 -2 0 -1 -5 -2 -3 3 2 0 0 C -1 -1 -1 -3 10 -3 -4 -1 0 -2 -3 -3 -2 0 -2 1 -1 1 2 -2 -2 -3 0 0 Q -1 2 0 0 -3 10 2 -1 3 -3 -2 2 -2 -4 0 -1 -1 -3 -1 -3 0 3 0 0 E -1 0 1 4 -4 2 10 1 0 -3 -4 1 -3 -5 -2 -1 -1 -5 -4 -2 2 3 0 0 G 1 0 0 1 -1 -1 1 10 -2 -3 -4 -1 -3 -5 -1 1 0 -2 -4 -2 0 0 0 0 H -2 2 1 0 0 3 0 -2 10 -3 -2 1 -2 0 0 -1 -1 -3 4 -3 0 1 0 0 I 0 -3 -2 -3 -2 -3 -3 -3 -3 10 2 -3 3 0 -2 -1 1 -4 -2 4 -2 -3 0 0 L -1 -3 -3 -4 -3 -2 -4 -4 -2 2 10 -3 3 2 0 -2 -1 -2 -1 2 -3 -3 0 0 K -1 4 1 0 -3 2 1 -1 1 -3 -3 10 -2 -5 -2 -1 -1 -3 -3 -3 0 1 0 0 M -1 -2 -2 -3 -2 -2 -3 -3 -2 3 3 -2 10 0 -2 -1 0 -3 -3 2 -2 -2 0 0 F -3 -4 -3 -5 0 -4 -5 -5 0 0 2 -5 0 10 -2 -2 -2 -1 5 0 -4 -4 0 0 P 1 -1 -1 -2 -2 0 -2 -1 0 -2 0 -2 -2 -2 10 1 1 -5 -3 -1 -1 -1 0 0 S 1 -1 1 0 1 -1 -1 1 -1 -1 -2 -1 -1 -2 1 10 1 -3 -1 -1 0 -1 0 0 T 2 -1 1 -1 -1 -1 -1 0 -1 1 -1 -1 0 -2 1 1 10 -4 -3 0 0 -1 0 0 W -4 0 -4 -5 1 -3 -5 -2 -3 -4 -2 -3 -3 -1 -5 -3 -4 10 0 -4 -4 -4 0 0 Y -3 -2 -1 -2 2 -1 -4 -4 4 -2 -1 -3 -3 5 -3 -1 -3 0 10 -3 -1 -2 0 0 V 1 -3 -2 -3 -2 -3 -2 -2 -3 4 2 -3 2 0 -1 -1 0 -4 -3 10 -2 -2 0 0 B 0 0 2 3 -2 0 2 0 0 -2 -3 0 -2 -4 -1 0 0 -4 -1 -2 10 1 0 0 Z -1 1 0 2 -3 3 3 0 1 -3 -3 1 -2 -4 -1 -1 -1 -4 -2 -2 1 10 0 0 X 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 bio3d/inst/matrices/pam30.mat0000644000176200001440000000521412040627421015461 0ustar liggesusers# # This matrix was produced by "pam" Version 1.0.6 [28-Jul-93] # # PAM 30 substitution matrix, scale = ln(2)/2 = 0.346574 # # Expected score = -5.06, Entropy = 2.57 bits # # Lowest score = -17, Highest score = 13 # # Note. 'X' and '-' set to zero # A R N D C Q E G H I L K M F P S T W Y V B Z X - A 6 -7 -4 -3 -6 -4 -2 -2 -7 -5 -6 -7 -5 -8 -2 0 -1 -13 -8 -2 -3 -3 0 0 R -7 8 -6 -10 -8 -2 -9 -9 -2 -5 -8 0 -4 -9 -4 -3 -6 -2 -10 -8 -7 -4 0 0 N -4 -6 8 2 -11 -3 -2 -3 0 -5 -7 -1 -9 -9 -6 0 -2 -8 -4 -8 6 -3 0 0 D -3 -10 2 8 -14 -2 2 -3 -4 -7 -12 -4 -11 -15 -8 -4 -5 -15 -11 -8 6 1 0 0 C -6 -8 -11 -14 10 -14 -14 -9 -7 -6 -15 -14 -13 -13 -8 -3 -8 -15 -4 -6 -12 -14 0 0 Q -4 -2 -3 -2 -14 8 1 -7 1 -8 -5 -3 -4 -13 -3 -5 -5 -13 -12 -7 -3 6 0 0 E -2 -9 -2 2 -14 1 8 -4 -5 -5 -9 -4 -7 -14 -5 -4 -6 -17 -8 -6 1 6 0 0 G -2 -9 -3 -3 -9 -7 -4 6 -9 -11 -10 -7 -8 -9 -6 -2 -6 -15 -14 -5 -3 -5 0 0 H -7 -2 0 -4 -7 1 -5 -9 9 -9 -6 -6 -10 -6 -4 -6 -7 -7 -3 -6 -1 -1 0 0 I -5 -5 -5 -7 -6 -8 -5 -11 -9 8 -1 -6 -1 -2 -8 -7 -2 -14 -6 2 -6 -6 0 0 L -6 -8 -7 -12 -15 -5 -9 -10 -6 -1 7 -8 1 -3 -7 -8 -7 -6 -7 -2 -9 -7 0 0 K -7 0 -1 -4 -14 -3 -4 -7 -6 -6 -8 7 -2 -14 -6 -4 -3 -12 -9 -9 -2 -4 0 0 M -5 -4 -9 -11 -13 -4 -7 -8 -10 -1 1 -2 11 -4 -8 -5 -4 -13 -11 -1 -10 -5 0 0 F -8 -9 -9 -15 -13 -13 -14 -9 -6 -2 -3 -14 -4 9 -10 -6 -9 -4 2 -8 -10 -13 0 0 P -2 -4 -6 -8 -8 -3 -5 -6 -4 -8 -7 -6 -8 -10 8 -2 -4 -14 -13 -6 -7 -4 0 0 S 0 -3 0 -4 -3 -5 -4 -2 -6 -7 -8 -4 -5 -6 -2 6 0 -5 -7 -6 -1 -5 0 0 T -1 -6 -2 -5 -8 -5 -6 -6 -7 -2 -7 -3 -4 -9 -4 0 7 -13 -6 -3 -3 -6 0 0 W -13 -2 -8 -15 -15 -13 -17 -15 -7 -14 -6 -12 -13 -4 -14 -5 -13 13 -5 -15 -10 -14 0 0 Y -8 -10 -4 -11 -4 -12 -8 -14 -3 -6 -7 -9 -11 2 -13 -7 -6 -5 10 -7 -6 -9 0 0 V -2 -8 -8 -8 -6 -7 -6 -5 -6 2 -2 -9 -1 -8 -6 -6 -3 -15 -7 7 -8 -6 0 0 B -3 -7 6 6 -12 -3 1 -3 -1 -6 -9 -2 -10 -10 -7 -1 -3 -10 -6 -8 6 0 0 0 Z -3 -4 -3 1 -14 6 6 -5 -1 -6 -7 -4 -5 -13 -4 -5 -6 -14 -9 -6 0 6 0 0 X 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 * 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 bio3d/inst/matrices/blosum62.mat0000644000176200001440000000411312040627421016207 0ustar liggesusers# Matrix made by matblas from blosum62.iij # * column uses minimum score # BLOSUM Clustered Scoring Matrix in 1/2 Bit Units # Blocks Database = /data/blocks_5.0/blocks.dat # Cluster Percentage: >= 62 # Entropy = 0.6979, Expected = -0.5209 A R N D C Q E G H I L K M F P S T W Y V B Z X - A 4 -1 -2 -2 0 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -3 -2 0 -2 -1 0 0 R -1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3 -1 0 0 0 N -2 0 6 1 -3 0 0 0 1 -3 -3 0 -2 -3 -2 1 0 -4 -2 -3 3 0 0 0 D -2 -2 1 6 -3 0 2 -1 -1 -3 -4 -1 -3 -3 -1 0 -1 -4 -3 -3 4 1 0 0 C 0 -3 -3 -3 9 -3 -4 -3 -3 -1 -1 -3 -1 -2 -3 -1 -1 -2 -2 -1 -3 -3 0 0 Q -1 1 0 0 -3 5 2 -2 0 -3 -2 1 0 -3 -1 0 -1 -2 -1 -2 0 3 0 0 E -1 0 0 2 -4 2 5 -2 0 -3 -3 1 -2 -3 -1 0 -1 -3 -2 -2 1 4 0 0 G 0 -2 0 -1 -3 -2 -2 6 -2 -4 -4 -2 -3 -3 -2 0 -2 -2 -3 -3 -1 -2 0 0 H -2 0 1 -1 -3 0 0 -2 8 -3 -3 -1 -2 -1 -2 -1 -2 -2 2 -3 0 0 0 0 I -1 -3 -3 -3 -1 -3 -3 -4 -3 4 2 -3 1 0 -3 -2 -1 -3 -1 3 -3 -3 0 0 L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1 -4 -3 0 0 K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1 0 -1 -3 -2 -2 0 1 0 0 M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5 0 -2 -1 -1 -1 -1 1 -3 -1 0 0 F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6 -4 -2 -2 1 3 -1 -3 -3 0 0 P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7 -1 -1 -4 -3 -2 -2 -1 0 0 S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4 1 -3 -2 -2 0 0 0 0 T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -2 -2 0 -1 -1 0 0 W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11 2 -3 -4 -3 0 0 Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7 -1 -3 -2 0 0 V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4 -3 -2 0 0 B -2 -1 3 4 -3 0 1 -1 0 -3 -4 0 -3 -3 -2 0 -1 -4 -3 -3 4 1 0 0 Z -1 0 0 1 -3 3 4 -2 0 -3 -3 1 -1 -3 -1 0 -1 -3 -2 -2 1 4 0 0 X 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 bio3d/inst/matrices/custom.mat0000644000176200001440000000347212040627421016057 0ustar liggesusers A R N D C Q E G H I L K M F P S T W Y V B Z X * A 8 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 5 -4 R -1 8 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 5 -4 N -1 -1 8 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 5 -4 D -1 -1 -1 8 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 5 -4 C -1 -1 -1 -1 8 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 5 -4 Q -1 -1 -1 -1 -1 8 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 5 -4 E -1 -1 -1 -1 -1 -1 8 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 5 -4 G -1 -1 -1 -1 -1 -1 -1 8 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 5 -4 H -1 -1 -1 -1 -1 -1 -1 -1 8 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 5 -4 I -1 -1 -1 -1 -1 -1 -1 -1 -1 8 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 5 -4 L -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 8 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 5 -4 K -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 8 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 5 -4 M -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 8 -1 -1 -1 -1 -1 -1 -1 -1 -1 5 -4 F -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 8 -1 -1 -1 -1 -1 -1 -1 -1 5 -4 P -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 8 -1 -1 -1 -1 -1 -1 -1 5 -4 S -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 8 -1 -1 -1 -1 -1 -1 5 -4 T -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 8 -1 -1 -1 -1 -1 5 -4 W -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 8 -1 -1 -1 -1 5 -4 Y -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 8 -1 -1 -1 5 -4 V -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 8 -1 -1 5 -4 B -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 8 -1 5 -4 Z -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 8 5 -4 X 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 9 -3 * -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -3 1 bio3d/inst/matrices/similarity.mat0000644000176200001440000000256112040627421016731 0ustar liggesusers# Similarity Matrix derived from the PET91 matrix # of Jones, Taylor and Thornton 1991 with values # sacled to 0 or 1. Input with 'read.table' A R N D C Q E G H I L K M F P S T W Y V B Z X - A 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 0 0 1 0 0 0 0 R 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 N 0 0 1 1 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 D 0 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 C 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 Q 0 1 0 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 E 0 0 1 1 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 G 1 0 0 1 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 H 0 1 1 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 I 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 1 0 0 1 0 0 0 0 L 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 0 1 0 0 0 0 K 0 1 1 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 M 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 1 0 0 0 0 F 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 P 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 S 1 0 1 0 1 0 0 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 T 1 0 1 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 W 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 Y 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 V 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 1 0 0 0 0 B 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 Z 0 1 0 1 0 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 X 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 bio3d/inst/matrices/emboss_properties.mat0000644000176200001440000000112212040627421020277 0ustar liggesusers# EMBOSS DATA FILE # # This file contains the chemical classifications of the common amino # acids. # # # # # # aromatic polar positive # Tiny Small aliphatic non-polar charged negative A 1 1 0 0 1 0 0 0 0 C 1 1 0 0 1 0 0 0 0 D 0 1 0 0 0 1 1 0 1 E 0 0 0 0 0 1 1 0 1 F 0 0 0 1 1 0 0 0 0 G 1 1 0 0 1 0 0 0 0 H 0 0 0 1 0 1 1 1 0 I 0 0 1 0 1 0 0 0 0 K 0 0 0 0 0 1 1 1 0 L 0 0 1 0 1 0 0 0 0 M 0 0 0 0 1 0 0 0 0 N 0 1 0 0 0 1 0 0 0 P 0 1 0 0 1 0 0 0 0 Q 0 0 0 0 0 1 0 0 0 R 0 0 0 0 0 1 1 1 0 S 1 1 0 0 0 1 0 0 0 T 1 1 0 0 0 1 0 0 0 V 0 1 1 0 1 0 0 0 0 W 0 0 0 1 1 0 0 0 0 Y 0 0 0 1 1 0 0 0 0 // bio3d/inst/matrices/properties.mat0000644000176200001440000000336212040627421016737 0ustar liggesusers# Amino acid property index # The first 10 properties are derived from the venn diagram of # Taylor 1986 (J. Theor. Biol. 119, 205-218) and the work of # Zvelebil 1987 (J. Mol. Biol. 195, 957-961). # Properties 'burried', 'surface' and 'neutral' are based on # relative hydrophobicity and the extent to which residues are # distributed between the surface and interior of known structures, # see Chothia 1998 (J. Mol. Biol. 278, 457-479 ) and the work of # Miller 1986 (J. Mol. Biol. 196, 641-656) I L V C A G M F Y W H K R E Q D N S T P B Z X - hydrophobic 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 0 0 0 1 0 polar 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 small 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 1 0 proline 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 tiny 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 aliphatic 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 aromatic 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 positive 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 negative 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 charged 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 1 0 0 0 0 0 0 1 0 special 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 burried 1 1 1 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 surface 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 1 0 neutral 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 0 0 1 0 bio3d/tests/0000755000176200001440000000000012526367344012430 5ustar liggesusersbio3d/tests/testthat.R0000644000176200001440000000004612526367344014413 0ustar liggesuserslibrary(testthat) test_check("bio3d") bio3d/tests/testthat/0000755000176200001440000000000012632753312014260 5ustar liggesusersbio3d/tests/testthat/test-read.pdb.R0000644000176200001440000001060412632622153017036 0ustar liggesuserscontext("Testing basic PDB structure operation") test_that("read.pdb() reads a normal pdb file", { ## Simple test with PDB ID 1HEL file <- system.file("examples/1dpx.pdb",package="bio3d") invisible(capture.output(pdb <- read.pdb(file))) expect_is(pdb$atom, "data.frame") expect_true(inherits(pdb, "pdb")) expect_true(inherits(pdb$xyz, "xyz")) expect_equal(nrow(pdb$atom), 1177) expect_equal(sum(pdb$calpha), 129) expect_equal(sum(pdb$atom$resid=="HOH"), 177) expect_equal(sum(pdb$atom$resid=="CL"), 2) expect_that(sum(pdb$xyz), equals(44657.12, tolerance=1e-6)) expect_equal(sum(pdb$atom$type=="ATOM"), 998) expect_equal(sum(pdb$atom$type=="HETATM"), 179) expect_equal(pdb$remark$biomat$num, 1) expect_equal(pdb$remark$biomat$chain[[1]], "A") true_mat <- matrix(c(1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0), nrow=3, byrow=TRUE) expect_equivalent(pdb$remark$biomat$mat[[1]][[1]], true_mat) invisible(capture.output(spdb <- read.pdb(file, ATOM.only=TRUE))) expect_equal(spdb$atom, pdb$atom) expect_equal(spdb$xyz, pdb$xyz) expect_equal(spdb$calpha, pdb$calpha) expect_true(is.null(spdb$helix)) expect_true(is.null(spdb$sheet)) expect_true(is.null(spdb$seqres)) expect_true(is.null(spdb$remark)) }) test_that("read.pdb() reads and stores data properly", { skip_on_cran() datdir <- tempdir() invisible(capture.output(get.pdb(c("3DRC", "1P3Q", "1SVK", "1L2Y"), path=datdir, overwrite = FALSE, verbose = FALSE))) # "3DRC" example PDB has a CA calcium ion and a CA containing ligand. expect_error(read.pdb("nothing")) invisible(capture.output(pdb <- read.pdb(file.path(datdir, "3DRC.pdb")))) expect_equal(nrow(pdb$atom), 2954) expect_equal(sum(pdb$calpha), 318) # expect_equivalent(aa321(pdb$seqres), pdbseq(pdb)) expect_equal(pdb$xyz[1:6], c(24.317, 59.447, 4.079, 25.000, 58.475, 4.908), tolerance=1e-6) expect_equal(length(pdb$helix$start), 8) expect_equal(length(pdb$sheet$start), 16) # "1SVK" example PDB has alternate location indicator invisible(capture.output(pdb <- read.pdb(file.path(datdir, "1SVK.pdb")))) expect_equal(sum(pdb$calpha), 313) expect_equal(sum(pdb$atom$resno==47), 6) expect_equal(sum(pdb$atom$resid=="GDP"), 28) # multi-model structure invisible(capture.output(pdb <- read.pdb(file.path(datdir, "1L2Y.pdb"), multi=TRUE))) expect_equal(dim(pdb$xyz), c(38, 912)) expect_equal(pdb$xyz[20, 1:6], c(-8.559, 6.374, -1.226, -7.539, 6.170, -0.168), tolerance=1e-6) # one atom cat("ATOM 1 N SER Q 398 48.435 21.981 -6.393 1.00 56.10 N\n", file=file.path(datdir, "t1a.pdb")) pdb <- read.pdb(file.path(datdir, "t1a.pdb")) expect_is(pdb$atom, "data.frame") ### write.pdb() invisible(capture.output(pdb <- read.pdb(file.path(datdir, "3DRC.pdb")))) write.pdb(pdb, file=file.path(datdir, "t1.pdb")) invisible(capture.output(pdb1 <- read.pdb(file.path(datdir, "t1.pdb")))) expect_identical(pdb$atom, pdb1$atom) expect_identical(pdb$xyz, pdb1$xyz) expect_identical(pdb$calpha, pdb1$calpha) # multi-model structure invisible(capture.output(pdb <- read.pdb(file.path(datdir, "1L2Y.pdb"), multi=TRUE))) write.pdb(pdb, file=file.path(datdir, "t2.pdb")) invisible(capture.output(pdb2 <- read.pdb(file.path(datdir, "t2.pdb"), multi=TRUE))) # SSE and SEQRES missing in write.pdb() pdb[c("seqres", "helix", "sheet", "call")] <- NULL pdb2[c("seqres", "helix", "sheet", "call")] <- NULL expect_identical(pdb, pdb2) ### trim.pdb() invisible(capture.output(pdb <- read.pdb(file.path(datdir, "1P3Q.pdb")))) pdb1 <- trim.pdb(pdb, inds = atom.select(pdb, "calpha", verbose=FALSE)) expect_is(pdb1, "pdb") expect_equal(nrow(pdb1$atom), 228) expect_equal(sum(pdb1$calpha), 228) expect_equivalent(pdb1$helix$start, pdb$helix$start) expect_equivalent(sort(pdb1$sheet$end), sort(pdb$sheet$end)) pdb2 <- trim.pdb(pdb, inds = atom.select(pdb, "protein", chain="U", verbose=FALSE)) expect_equal(nrow(pdb2$atom), 593) expect_equal(sum(pdb2$calpha), 74) expect_equivalent(pdb2$helix, list(start=c(22, 37, 56), end=c(35, 39, 60), chain=rep("U",3), type=c("1", "5", "5"))) expect_equivalent(pdb2$sheet, list(start=c(12,2,66,41,48), end=c(16,7,71,45,49), chain=rep("U",5), sense=c("0","-1","1","-1","-1"))) }) bio3d/tests/testthat/test-clean.pdb.R0000644000176200001440000000463112632340443017207 0ustar liggesuserscontext("Testing the utility function clean.pdb()") test_that("clean.pdb() does nothing for 'clean' pdb by default", { file <- system.file("examples/1dpx.pdb", package="bio3d") invisible(capture.output(pdb <- read.pdb(file))) invisible(capture.output(npdb <- clean.pdb(pdb))) expect_true(is.null(npdb$log)) npdb$call <- NULL npdb$log <- NULL pdb$call <- NULL expect_equal(pdb, npdb) }) test_that("clean.pdb() does renumbering properly", { skip_on_cran() invisible(capture.output(pdb <- read.pdb("1tag"))) invisible(capture.output(npdb <- clean.pdb(pdb, force.renumber = TRUE))) resno <- npdb$atom[npdb$calpha, "resno"] expect_equal(resno[1:10], 1:10) # A PDB with 'insert' residues: Should do automatic renumbering invisible(capture.output(pdb <- read.pdb("1a7l"))) invisible(capture.output(npdb <- clean.pdb(pdb))) resno <- npdb$atom[npdb$calpha, "resno"] expect_equal(resno[1:10], 1:10) # Renumbering for each chain invisible(capture.output(npdb <- clean.pdb(pdb, consecutive = FALSE))) resno <- npdb$atom[npdb$calpha, "resno"] chain <- npdb$atom[npdb$calpha, "chain"] expect_equal(resno[chain=="B"][1:10], 1:10) # Is SSE update correct? invisible(capture.output(ss <- pdb2sse(pdb))) invisible(capture.output(nss <- pdb2sse(npdb))) expect_equal(as.character(ss), as.character(nss)) } ) test_that("clean.pdb() relabels chains properly (fix.chain = TRUE)", { file <- system.file("examples/1dpx.pdb", package="bio3d") invisible(capture.output(pdb <- read.pdb(file))) # remove chain ID pdb$atom[, "chain"] <- as.character(NA) pdb$helix$chain <- "" pdb$sheet$chain <- "" invisible(capture.output(npdb <- clean.pdb(pdb, fix.chain = TRUE))) expect_equal(npdb$atom[npdb$calpha, "chain"], rep("A", sum(npdb$calpha))) # A case with wrong chain labels but consecutive residue numbering file <- system.file("examples/hivp.pdb", package="bio3d") invisible(capture.output(pdb0 <- read.pdb(file))) # Manually renumbering all residues pdb <- pdb0 pdb$atom[, "resno"] <- vec2resno(1:sum(pdb$calpha), paste(pdb$atom[, "resno"], pdb$atom[, "chain"], sep = "_") ) # Label both chains as "A" pdb$atom[, "chain"] <- "A" invisible(capture.output(npdb <- clean.pdb(pdb, consecutive = FALSE, force.renumber = TRUE, fix.chain = TRUE))) pdb0$call <- NULL npdb$call <- NULL npdb$log <- NULL expect_equal(pdb0, npdb) } ) bio3d/tests/testthat/test-dssp.R0000644000176200001440000000471012602522006016322 0ustar liggesuserscontext("Testing dssp()") test_that("SSE assignment still works", { skip_on_cran() if(!check.utility('dssp')) { skip('Need DSSP installed to run this test') } ## Simple test with PDB ID 1HEL invisible(capture.output(pdb <- read.pdb("3ERJ"))) sse <- dssp(pdb) ## helices sse.stored <- c(17, 37, 58, 101, 19, 37, 58, 101) expect_that(as.numeric(sse$helix$start), equals(sse.stored)) sse.stored <- c(18, 9, 14, 8, 16, 10, 13, 7) expect_that(as.numeric(sse$helix$length), equals(sse.stored)) ## sheet sse.stored <- c(3, 50, 75, 93, 3, 50, 75, 93 ) expect_that(as.numeric(sse$sheet$start), equals(sse.stored)) sse.stored <- c(8, 6, 4, 8, 8, 6, 4, 8) expect_that(as.numeric(sse$sheet$length), equals(sse.stored)) ## With RESNO=FALSE sse <- dssp(pdb, resno=FALSE) ## helices sse.stored <- c(16, 36, 57, 100, 134, 152, 173, 216) expect_that(as.numeric(sse$helix$start), equals(sse.stored)) sse.stored <- c(rep("A", 4), rep("B", 4)) expect_that(as.character(sse$helix$chain), equals(sse.stored)) ## sheet sse.stored <- c(2, 49, 74, 92, 118, 165, 190, 208) expect_that(as.numeric(sse$sheet$start), equals(sse.stored)) sse.stored <- c(rep("A", 4), rep("B", 4)) expect_that(as.character(sse$sheet$chain), equals(sse.stored)) ## With FULL=TRUE sse <- dssp(pdb, full=TRUE) expect_that(sum(as.numeric(sse$hbonds[,"BP1"]), na.rm=T), equals(2127)) expect_that(sum(as.numeric(sse$hbonds[,"BP2"]), na.rm=T), equals(1355)) expect_that(sum(as.numeric(sse$hbonds[,"NH-O.1"]), na.rm=T), equals(12017)) expect_that(sum(as.numeric(sse$hbonds[,"E1"]), na.rm=T), equals(-315.8)) expect_that(sum(as.numeric(sse$hbonds[,"O-HN.1"]), na.rm=T), equals(13347)) expect_that(sum(as.numeric(sse$hbonds[,"E2"]), na.rm=T), equals(-313.4)) expect_that(sum(as.numeric(sse$hbonds[,"NH-O.2"]), na.rm=T), equals(11859)) expect_that(sum(as.numeric(sse$hbonds[,"E3"]), na.rm=T), equals(-51.2)) expect_that(sum(as.numeric(sse$hbonds[,"O-HN.2"]), na.rm=T), equals(13076)) expect_that(sum(as.numeric(sse$hbonds[,"E4"]), na.rm=T), equals(-53.6)) expect_that(length(which(sse$hbonds[,"Chain1"]=="A")), equals(112)) expect_that(length(which(sse$hbonds[,"Chain1"]=="B")), equals(105)) expect_that(length(which(sse$hbonds[,"Chain2"]=="A")), equals(115)) expect_that(length(which(sse$hbonds[,"Chain3"]=="B")), equals(108)) expect_that(length(which(sse$hbonds[,"Chain4"]=="A")), equals(116)) } ) bio3d/tests/testthat/test-core.find.R0000644000176200001440000000150212544562303017225 0ustar liggesuserscontext("Testing core.find function") test_that("core.find() works properly", { skip_on_cran() attach(transducin) inds <- unlist(lapply(c("1TND_A", "1TAG", "1AS0", "1AS2"), grep, pdbs$id)) pdbs <- trim.pdbs(pdbs, row.inds=inds) invisible(capture.output(core <- core.find(pdbs, ncore=1))) resnos.1 <- c(202, 206, 209, 205, 203, 201) resnos.2 <- c(332, 334, 335, 336, 337, 340) expect_equal(length(core$resno), 313) expect_equal(resnos.1, as.numeric(core$resno[1:6])) expect_equal(resnos.2, as.numeric(tail(core$resno))) xyz <- c(16, 17, 18, 19, 20, 21, 25, 26, 27, 34) expect_equal(xyz, core$xyz[1:10]) expect_equal(sum(core$xyz), 234006) ## Check multicore invisible(capture.output(core.mc <- core.find(pdbs, ncore=NULL))) expect_identical(core, core.mc) detach(transducin) }) bio3d/tests/testthat/test-aa2mass.R0000644000176200001440000000137312526367344016723 0ustar liggesuserscontext("Testing aa2mass()") test_that("Amino acid mass tests", { ## Simple test sequ <- c("ALA", "LYS", "TPO") masses <- c(71.078, 129.180, 181.084) expect_that(aa2mass(sequ, addter=FALSE, mmtk=FALSE, mass.custom=NULL), equals(masses, tolerance=1e-6)) ## With Terminal atoms added masses <- c(72.08594, 129.18000, 198.09134) expect_that(aa2mass(sequ, addter=TRUE, mmtk=FALSE, mass.custom=NULL), equals(masses, tolerance=1e-6)) ## With 'custom' residues sequ <- c("MLY", "HMM", "UNK") masses <- c(156.225, 10.000, 20.001) expect_that(aa2mass(sequ, addter=FALSE, mmtk=FALSE, mass.custom=list(HMM=10, UNK=20.001)), equals(masses, tolerance=1e-6)) } ) bio3d/tests/testthat/test-nma.pdbs.R0000644000176200001440000001300512544562303017061 0ustar liggesuserscontext("Testing nma.pdbs()") test_that("eNMA works", { skip_on_cran() "mysign" <- function(a,b) { if(all(sign(a)==sign(b))) return(1) else return(-1) } attach(transducin) inds <- unlist(lapply(c("1TND_A", "1TAG", "1AS0", "1AS2"), grep, pdbs$id)) pdbs <- trim.pdbs(pdbs, row.inds=inds) gaps <- gap.inspect(pdbs$xyz) ## Calc modes invisible(capture.output(modes <- nma.pdbs(pdbs, fit=TRUE, rm.gaps=TRUE, ncore=1))) ## check dimensions expect_that(dim(modes$U), equals(c(939, 933, 4))) expect_that(dim(modes$L), equals(c(4, 933))) expect_that(dim(modes$fluctuations), equals(c(4, 313))) ## structure 1- mode1: U1 <- c(-0.046380187, 0.007816393, 0.078933552, -0.041820111, 0.009819448, 0.047598954) nowU1 <- head(modes$U.subspace[,1,1], n=6) expect_that(nowU1 * mysign(U1, nowU1), equals(U1, tolerance=1e-6)) ## structure 1- mode2: U2 <- c(0.007819148, -0.005717370, -0.051933927, 0.002265788, -0.013386437, -0.039409712) nowU2 <- head(modes$U.subspace[,2,1], n=6) expect_that(nowU2 * mysign(U2, nowU2), equals(U2, tolerance=1e-6)) ## structure 4- mode3: U3 <- c(-0.13697744, -0.05401178, 0.09615700, -0.11026525, -0.01510157, 0.05796091) nowU3 <- head(modes$U.subspace[,3,4], n=6) expect_that(nowU3 * mysign(U3, nowU3), equals(U3, tolerance=1e-6)) ## structure 4-mode1 - tail: U1 <- c(0.009820174, 0.004566910, -0.055544781, 0.009938013, 0.006707436, -0.068154672) nowU1 <- tail(modes$U.subspace[,1,4], n=6) expect_that(nowU1 * mysign(U1, nowU1), equals(U1, tolerance=1e-6)) ## Fluctuations: f1 <- c(0.3657288, 0.2196504, 0.1449100, 0.1217517, 0.1130416, 0.1007862) f2 <- c(0.5295304, 0.3004094, 0.2011062, 0.1401276, 0.1205508, 0.1011349) f4 <- c(0.6488982, 0.3079024, 0.2166070, 0.1504672, 0.1282899, 0.1029634) expect_that(modes$fluctuations[1,1:6], equals(f1, tolerance=1e-6)) expect_that(modes$fluctuations[2,1:6], equals(f2, tolerance=1e-6)) expect_that(modes$fluctuations[4,1:6], equals(f4, tolerance=1e-6)) ## Orthognal expect_that(as.numeric(modes$U.subspace[,1,1] %*% modes$U.subspace[,1,1]), equals(1, tolerance=1e-6)) expect_that(as.numeric(modes$U.subspace[,1,1] %*% modes$U.subspace[,2,1]), equals(0, tolerance=1e-6)) ## RMSIP rmsips <- c(1.0000, 0.9174, 0.9441, 0.9251) expect_that(as.vector(modes$rmsip[1,]), equals(rmsips, tolerance=1e-6)) ## Multicore (same arguments as above!) invisible(capture.output(mmc <- nma.pdbs(pdbs, fit=TRUE, rm.gaps=TRUE, ncore=NULL))) expect_that(mmc$fluctuations, equals(modes$fluctuations, tolerance=1e-6)) expect_that(mmc$U.subspace, equals(modes$U.subspace, tolerance=1e-6)) ## Calc modes with rm.gaps=FALSE invisible(capture.output(modes <- nma.pdbs(pdbs, fit=TRUE, rm.gaps=FALSE, ncore=NULL))) ## structure 1-mode1 - tail: U1 <- c(0.04397369, -0.01400912, -0.02123377, 0.08124317, -0.02660929, -0.02619898) nowU1 <- tail(modes$U.subspace[,1,1], n=6) expect_that(nowU1 * mysign(U1, nowU1), equals(U1, tolerance=1e-6)) ## structure 1-mode1 - tail: U1 <- c(0.001300939, -0.053317847, 0.011292943, 0.003307034, -0.071684004, NA) nowU1 <- modes$U.subspace[938:943,1,2] U1[is.na(U1)] <- 0 nowU1[is.na(nowU1)] <- 0 expect_that(nowU1 * mysign(nowU1, U1), equals(U1, tolerance=1e-6)) ## fluctuations na.expected <- c(3, 4, 1258, 1259, 1262, 1263, 1266, 1267, 1268, 1270, 1271, 1272, 1274, 1275, 1276, 1278, 1279, 1280, 1282, 1283, 1284, 1286, 1287, 1288, 1290, 1291, 1292) expect_that(which(is.na(modes$fluctuations)), equals(na.expected)) f1 <- c(0.59967448, 0.34438649, 0.20382435, 0.13350449) f4 <- c(0.3335200, 0.4255609, 0.5941589, rep(NA, 7)) expect_that(modes$fluctuations[1,1:4], equals(f1, tolerance=1e-6)) expect_that(tail(modes$fluctuations[4,], n=10), equals(f4, tolerance=1e-6)) ## Calc modes with mass=FALSE and temp=NULL invisible(capture.output(modes <- nma.pdbs(pdbs, mass=FALSE, temp=NULL, ncore=NULL))) ## structure 1- mode1: U1 <- c(-0.04043330, 0.00730273, 0.07000757, -0.04520831, 0.01130271, 0.05337233) nowU1 <- head(modes$U.subspace[,1,1], n=6) expect_that(nowU1 * mysign(U1, nowU1), equals(U1, tolerance=1e-6)) ## structure 1- mode2: U2 <- c(-0.002813312, 0.005765808, 0.039807147, 0.001667637, 0.014587327, 0.038682763) nowU2 <- head(modes$U.subspace[,2,1], n=6) expect_that(nowU2 * mysign(U2, nowU2), equals(U2, tolerance=1e-6)) ## structure 5- mode3: U3 <- c(0.11324262, 0.04220159, -0.07597465, 0.10267147, 0.01483591, -0.05261675) nowU3 <- head(modes$U.subspace[,3,4], n=6) expect_that(nowU3 * mysign(U3, nowU3), equals(U3, tolerance=1e-6)) ## Calc modes with mass=FALSE and temp=NULL and ff="anm" invisible(capture.output(modes <- nma.pdbs(pdbs, mass=FALSE, temp=NULL, ff="anm", ncore=NULL))) ## structure 3- mode10: U1 <- c(0.03630660, 0.03078575, -0.02376714, 0.01906218, 0.01110582, -0.01361602) nowU1 <- head(modes$U.subspace[,10,3], n=6) expect_that(nowU1 * mysign(U1, nowU1), equals(U1, tolerance=1e-6)) ## structure 4- mode1: U1 <- c(-0.04113844, 0.01096919, 0.07368620, -0.04250786, 0.01320282, 0.05550216) nowU1 <- head(modes$U.subspace[,1,4], n=6) expect_that(nowU1 * mysign(U1, nowU1), equals(U1, tolerance=1e-6)) f1 <- c(0.3630744, 0.2768045, 0.1996179, 0.1766148) f2 <- c(0.5231570, 0.3519813, 0.2331503, 0.2003372) expect_that(modes$fluctuations[1,1:4], equals(f1, tolerance=1e-6)) expect_that(modes$fluctuations[2,1:4], equals(f2, tolerance=1e-6)) detach(transducin) }) bio3d/tests/testthat/test-pdbsplit.R0000644000176200001440000000512612526367344017215 0ustar liggesuserscontext("Testing pdbsplit()") test_that("pdbsplit works", { skip_on_cran() path <- tempdir() invisible(capture.output(rawfiles <- get.pdb("3R1C", path=path))) invisible(capture.output(files <- pdbsplit(rawfiles, ids=NULL, path=path))) expected <- c('3R1C_A.pdb', '3R1C_B.pdb', '3R1C_C.pdb', '3R1C_D.pdb', '3R1C_E.pdb', '3R1C_F.pdb', '3R1C_G.pdb', '3R1C_H.pdb', '3R1C_I.pdb', '3R1C_J.pdb', '3R1C_K.pdb', '3R1C_L.pdb', '3R1C_M.pdb', '3R1C_N.pdb', '3R1C_O.pdb', '3R1C_P.pdb', '3R1C_Q.pdb', '3R1C_R.pdb', '3R1C_S.pdb', '3R1C_Y.pdb', '3R1C_T.pdb', '3R1C_U.pdb', '3R1C_W.pdb', '3R1C_X.pdb', '3R1C_V.pdb', '3R1C_Z.pdb', '3R1C_a.pdb', '3R1C_b.pdb', '3R1C_c.pdb', '3R1C_d.pdb', '3R1C_e.pdb', '3R1C_f.pdb', '3R1C_g.pdb', '3R1C_h.pdb', '3R1C_i.pdb', '3R1C_j.pdb') expect_that(expected, equals(basename(files))) ids <- c('3R1C') invisible(capture.output(files <- pdbsplit(rawfiles, ids=ids , path=path))) expect_that(expected, equals(basename(files))) ids <- c('3R1C_e', '3R1C_E') invisible(capture.output(files <- pdbsplit(rawfiles, ids=ids , path=path))) expected <- c("3R1C_e.pdb", "3R1C_E.pdb") expect_that(expected, equals(basename(files))) ids <- c('3R1C_XX') invisible(capture.output(files <- pdbsplit(rawfiles, ids=ids , path=path))) expected <- NULL expect_that(expected, equals(files)) ## multi=TRUE invisible(capture.output(rawfiles <- get.pdb("1UD7", path=path))) invisible(capture.output(files <- pdbsplit(rawfiles, ids=NULL, path=path, multi=TRUE))) expected <- c('1UD7_A.01.pdb', '1UD7_A.02.pdb', '1UD7_A.03.pdb', '1UD7_A.04.pdb', '1UD7_A.05.pdb', '1UD7_A.06.pdb', '1UD7_A.07.pdb', '1UD7_A.08.pdb', '1UD7_A.09.pdb', '1UD7_A.10.pdb', '1UD7_A.11.pdb', '1UD7_A.12.pdb', '1UD7_A.13.pdb', '1UD7_A.14.pdb', '1UD7_A.15.pdb', '1UD7_A.16.pdb', '1UD7_A.17.pdb', '1UD7_A.18.pdb', '1UD7_A.19.pdb', '1UD7_A.20.pdb') expect_that(expected, equals(basename(files))) pdb <- read.pdb(files[1]) expect_that(nrow(pdb$atom), equals(1230)) ## non standard amino acids: invisible(capture.output(rawfiles <- get.pdb("1cdk", path=path))) invisible(capture.output(files <- pdbsplit(rawfiles, path=path))) invisible(capture.output(pdb <- read.pdb(files[1]))) invisible(capture.output(inds <- atom.select(pdb, resno=197)$atom)) inds.expected <- c(1568, 1569, 1570, 1571, 1572, 1573, 1574, 1575, 1576, 1577, 1578) expect_that(inds, equals(inds.expected)) } ) bio3d/tests/testthat/test-cmap.R0000644000176200001440000000167412526367344016320 0ustar liggesuserscontext("Testing cmap function") test_that("cmap() works properly", { ## Simple test with PDB ID 1HEL file <- system.file("examples/1hel.pdb",package="bio3d") invisible(capture.output(pdb <- read.pdb(file))) ## Calculate contact map on a small protein invisible(capture.output(inds <- atom.select(pdb, "protein"))) invisible(capture.output(cm <- cmap(pdb$xyz[, inds$xyz], grpby=pdb$atom[inds$atom, "resno"], ncore=1))) expect_equal(length(which(cm==1)), 285) expect_true(all(is.na(cm[1,1:3]))) expect_equal(cm[1,4], 0) expect_equal(cm[13,129], 1) ## Check multicore cmap skip_on_cran() trjfile <- system.file("examples/hivp.dcd", package="bio3d") invisible(capture.output(trj <- read.dcd(trjfile))) invisible(capture.output(cm <- cmap(trj, dcut=6, ncore=1))) invisible(capture.output(cm.mc <- cmap(trj, dcut=6, ncore=NULL))) expect_that(cm, equals(cm.mc, tolerance=1e-6)) }) bio3d/tests/testthat/test-nma.R0000644000176200001440000002307212632340443016134 0ustar liggesuserscontext("Testing nma()") test_that("NMA", { "mysign" <- function(a,b) { if(all(sign(a)==sign(b))) return(1) else return(-1) } ## Simple test with PDB ID 1HEL file <- system.file("examples/1hel.pdb",package="bio3d") invisible(capture.output(pdb <- read.pdb(file))) ## Calculate modes with default arguments invisible(capture.output(modes <- nma(pdb, ff='calpha', mass=TRUE, temp=300.0))) ## Check first eigenvector U7 <- c(-0.05471209, -0.054333625, 0.001052514, -0.041171891, -0.049232935, -0.001588035) nowU7 <- head(modes$U[,7]) expect_that(nowU7 * mysign(U7, nowU7), equals(U7, tolerance=1e-6)) ## Check second eigenvector U8 <- c(0.064185522, 0.027349834, -0.024359816, 0.011493963, 0.029426825, -0.014397686) nowU8 <- head(modes$U[,8]) expect_that(nowU8 * mysign(U8, nowU8), equals(U8, tolerance=1e-6)) ## Check Mode vector mode7 <- c(-0.092579348, -0.091938941, 0.001780978, -0.079838481, -0.095470057, -0.003079439) nowMode7 <- head(modes$modes[,7]) expect_that(nowMode7 * mysign(mode7, nowMode7), equals(mode7, tolerance=1e-6)) ## Check eigenvalues eival <- c(0.013383, 0.013933, 0.022355, 0.025518, 0.029944, 0.033954) nowEival <- modes$L[7:12] expect_that(nowEival, equals(eival, tolerance=1e-6)) ## Check frequencies freqs <- c(0.018411826, 0.018786352, 0.023796192, 0.025423975, 0.027540704, 0.029326863) nowFreqs <- modes$frequencies[7:12] expect_that(nowFreqs, equals(freqs, tolerance=1e-6)) ## Dimensions expect_that(dim(modes$U), equals(c(387, 387))) expect_that(dim(modes$modes), equals(c(387, 387))) expect_that(length(modes$L), equals(387)) expect_that(length(modes$frequencies), equals(387)) expect_that(length(modes$mass), equals(129)) expect_that(modes$natoms, equals(129)) expect_that(modes$temp, equals(300)) ## Orthognals expect_that(as.numeric(modes$U[,7] %*% modes$U[,7]), equals(1, tolerance=1e-6)) expect_that(as.numeric(modes$U[,7] %*% modes$U[,8]), equals(0, tolerance=1e-6)) expect_that(all((round(c(modes$U[,7] %*% modes$U),6)==0)[-7]), equals(TRUE)) expect_that(all(round(c(modes$L[1:6]), 6)==0), equals(TRUE)) ################################################################### # # Test with ouput from MMTK # ################################################################### "calpha.mmtk" <- function(r, ...) { ## MMTK Units: kJ / mol / nm^2 a <- 128; b <- 8.6 * 10^5; c <- 2.39 * 10^5; ifelse( r<4.0, b*(r/10) - c, a*(r/10)^(-6) ) } ## Vibrational Modes invisible(capture.output(modes <- nma(pdb, pfc.fun=calpha.mmtk, mmtk=TRUE, addter=FALSE))) ## Mode vector 7 (mmtk: modes[6]) mmtk7 <- c(0.009399498664059314, 0.009162216956173577, -0.00018940255982217028, 0.008013487647313355, 0.009521462401750403, 0.000300410055738782, 0.006725323170414416, 0.00613075811499374, 0.0007167801244317134, 0.003911230038334056, 0.0031036402193391484, 0.00011224732577516142, 5.015756380626851e-05, 0.00122307913030356, 0.0005064454471294721, 0.0014876013838084666, -0.003968053761191632, 0.00020389385408319644) nowMmtk7 <- head(modes$modes[,7], n=18) expect_that(nowMmtk7 * mysign(mmtk7, nowMmtk7), equals(mmtk7, tolerance=1e-6)) ## Raw mode vector 7 (mmtk: modes.rawMode(6)) mmtk7 <- c(0.05535176862194779, 0.053954464078107375, -0.0011153538121951093, 0.04133856415826275, 0.049117637874840574, 0.0015497023155839553, 0.04227251082807122, 0.03853532867244931, 0.00450537391343214) nowMmtk7 <- head(modes$U[,7], n=9) expect_that(nowMmtk7 * mysign(mmtk7, nowMmtk7), equals(mmtk7, tolerance=1e-6)) ## Frequencies mmtkFreqs <- c(0.18417800523842359, 0.18804324107310424, 0.23820080688206749, 0.25592672017449125, 0.2798133442063071, 0.29367413814307064) nowMmtkFreqs <- modes$frequencies[7:12] expect_that(nowMmtkFreqs, equals(mmtkFreqs, tolerance=1e-6)) ## Fluctuations mmtk.flucts <- c(0.00195060853392, 0.00113764918589, 0.00167187530508, 0.00175346604072, 0.00151209078542, 0.00130098648001, 0.00133495588156, 0.00107978100112, 0.000924829566202, 0.00109689698409) nowFlucts <- modes$fluctuations[1:10] expect_that(nowFlucts, equals(mmtk.flucts, tolerance=1e-6)) ## Energetic Modes (mass=FALSE) invisible(capture.output(modes <- nma(pdb, pfc.fun=calpha.mmtk, mass=FALSE, mmtk=TRUE, addter=FALSE))) mmtk7 <- c(0.010350805923938345, 0.009267077807430083, -3.701643999426641e-05, 0.008268033266170226, 0.009606710315232818, 0.0003705525203545053, 0.006767227535558591, 0.005694101052352917, 0.001077079483122824) nowMmtk7 <- head(modes$modes[,7], n=9) expect_that(nowMmtk7 * mysign(mmtk7, nowMmtk7), equals(mmtk7, tolerance=1e-6)) mmtk7 <- c(0.05478481030376396, 0.04904884735365174, -0.00019592084498809212, 0.04376109816000157, 0.05084645641420892, 0.001961262696318724, 0.03581762420652206, 0.030137773647408828, 0.005700773021792685) nowMmtk7 <- head(modes$U[,7], n=9) expect_that(nowMmtk7 * mysign(mmtk7, nowMmtk7), equals(mmtk7, tolerance=1e-6)) ## Fluctuations mmtk.flucts <- c(0.00195600136572, 0.00114595965451, 0.00168855332538, 0.00175330685712, 0.00152428233485, 0.00130978174806, 0.00134381308059, 0.00108408194319, 0.000924316154921, 0.0010985505357) nowFlucts <- modes$fluctuations[1:10] expect_that(nowFlucts, equals(mmtk.flucts, tolerance=1e-6)) ## Energetic Modes (mass=FALSE, temp=NULL) invisible(capture.output(modes <- nma(pdb, pfc.fun=calpha.mmtk, mass=FALSE, temp=NULL))) mmtk7 <- c(0.05478481030376396, 0.04904884735365174, -0.00019592084498809212, 0.04376109816000157, 0.05084645641420892, 0.001961262696318724, 0.03581762420652206, 0.030137773647408828, 0.005700773021792685) nowMmtk7 <- head(modes$modes[,7], n=9) expect_that(nowMmtk7 * mysign(mmtk7, nowMmtk7), equals(mmtk7, tolerance=1e-6)) mmtk7 <- c(0.05478481030376396, 0.04904884735365174, -0.00019592084498809212, 0.04376109816000157, 0.05084645641420892, 0.001961262696318724, 0.03581762420652206, 0.030137773647408828, 0.005700773021792685) nowMmtk7 <- head(modes$U[,7], n=9) expect_that(nowMmtk7 * mysign(mmtk7, nowMmtk7), equals(mmtk7, tolerance=1e-6)) ## Fluctuations mmtk.flucts <- c(0.000784175524735, 0.000459423765829, 0.000676953612192, 0.000702913785645, 0.000611096147848, 0.000525101264027, 0.00053874467886, 0.000434616530213, 0.00037056523503, 0.000440417096776) nowFlucts <- modes$fluctuations[1:10] expect_that(nowFlucts, equals(mmtk.flucts, tolerance=1e-6)) ## ANM eigenvectors invisible(capture.output(modes <- nma(pdb, ff='anm', mass=FALSE, temp=NULL, cutoff=15))) anm7 <- c(0.041345308400364066, 0.03345000499525146, 0.008604839963113613, 0.03755854024944313, 0.036973377719312125, 0.008638534251932818, 0.033187347539802, 0.022779436981185324, 0.004702511702428035) nowAnm7 <- head(modes$modes[,7], n=9) expect_that(nowAnm7 * mysign(anm7, nowAnm7), equals(anm7, tolerance=1e-6)) ## ANM eigenvalues check eivalsANM <- c(0.84962016107196869, 1.0327718030407862, 1.3724207202555807, 1.7545168246132175, 1.9606866740784614, 2.2429459260702607) nowEivalANM <- modes$L[7:12] expect_that(nowEivalANM, equals(eivalsANM, tolerance=1e-6)) ################################################################### # # Test mass custom stuff # ################################################################### mc <- list(ALA=500, SER=1000) invisible(capture.output(modes <- nma(pdb, mass.custom=mc))) mass.expected <- c(500.000, 500.000, 500.000, 131.196, 129.180, 157.194) expect_that(modes$mass[9:14], equals(mass.expected, tolerance=1e-6)) sum.expected <- 28564.36 expect_that(sum(modes$mass), equals(sum.expected, tolerance=1e-6)) modes.expected <- c(-0.128550854, -0.069409382, 0.011821391, -0.056729257, -0.076231424, 0.004736013) nowMode7 <- modes$modes[1:6, 7] expect_that(nowMode7 * mysign(nowMode7, modes.expected), equals(modes.expected, tolerance=1e-6)) L.expected <- c(0.007375, 0.009036, 0.013007, 0.015084, 0.020111, 0.022608) expect_that(modes$L[7:12], equals(L.expected, tolerance=1e-6)) ################################################################### # # Test build.hessian # ################################################################### sele <- atom.select(pdb, chain="A", elety="CA") xyz <- pdb$xyz[sele$xyz] i <- 2; j <- 5; hessian <- build.hessian(xyz, pfc.fun=calpha.mmtk, fc.weights=NULL) subhess <- matrix(c(-79.568546, 80.648662, -19.298073, 80.648662, -81.743440, 19.560037, -19.298073, 19.560037, -4.680438), ncol=3, byrow=TRUE) expect_that(hessian[atom2xyz(j), atom2xyz(i)], equals(subhess, tolerance=1e-6)) ## Force constant weighting weight <- 0.5; fc.mat <- matrix(1, nrow=length(xyz)/3, ncol=length(xyz)/3) fc.mat[i, j] <- weight; fc.mat[j, i] <- weight; hessian2 <- build.hessian(xyz, pfc.fun=calpha.mmtk, fc.weights=fc.mat) expect_that(hessian[atom2xyz(j), atom2xyz(i)] * weight, equals(hessian2[atom2xyz(j), atom2xyz(i)], tolerance=1e-6)) } ) bio3d/tests/testthat/test-atom2mass.R0000644000176200001440000000431612526367344017302 0ustar liggesuserscontext("Testing atom mass functions") test_that("atom to mass tests", { ## Simple test atom.names <- c("CA", "O", "N", "OXT") ##masses <- c(12.01, 16.00, 14.01, 16.00) masses <- c(12.0107, 15.9994, 14.0067, 15.9994) expect_that(atom2mass(atom.names), equals(masses, tolerance=1e-6)) ##masses <- c(42.02, 16.00) masses <- c(42.0168, 15.9994) expect_that(as.numeric(atom2mass(atom.names, grpby=c(1,1,1,2))), equals(masses, tolerance=1e-6)) ## Should end with error atom.names <- c("CA", "O", "N", "OXT", "CL2", "PT1") expect_that(atom2mass(atom.names, rescue=FALSE), throws_error()) expect_that(atom2mass(atom.names, rescue=TRUE), gives_warning()) ## Simple test with PDB ID 1HEL file <- system.file("examples/1hel.pdb",package="bio3d") invisible(capture.output(pdb <- read.pdb(file))) invisible(capture.output(prot.inds <- atom.select(pdb, "protein"))) invisible(capture.output(pdb.prot <- trim.pdb(pdb, prot.inds))) eletys <- pdb$atom$elety[ pdb$atom$type=="ATOM" ] expect_that(sum(atom2mass( eletys )), equals(13346.39, tolerance=1e-6)) expect_that(sum(atom2mass( eletys )), equals(sum(atom2mass(pdb.prot), tolerance=1e-6))) expect_that(sum(atom2mass( pdb.prot )), equals(13346.39, tolerance=1e-6)) ## Try center of mass at the same go coma <- c(-0.4991111, 20.5858389, 19.2604674) expect_that(c(com(pdb.prot)), equals(coma, tolerance=1e-6)) # coma <- c(-0.5829897, 20.5306061, 19.1081465) # expect_that(com(pdb), equals(coma, tolerance=1e-6)) ## Add custom masses atom.names <- c("CA", "O", "N", "OXT", "CL2", "PT1") masses <- c(12.0107, 15.9994, 14.0067, 15.9994, 35.4530, 195.0780) elety.cust <- data.frame(name = c("CL2","PT1"), symb = c("Cl","Pt")) ##mass.cust <- data.frame(symb = c("Cl","Pt"), mass = c(35.45, 195.08)) expect_that(atom2mass(atom.names, elety.custom=elety.cust), equals(masses, tolerance=1e-6)) ## mass from formula form <- "C5 H6 N O3" masses <- c(60.050, 6.048, 14.010, 48.000) masses <- c(60.05350, 6.04764, 14.00670, 47.99820) expect_that(formula2mass(form, sum.mass=FALSE), equals(masses, tolerance=1e-6)) form <- "C5H6" expect_that(formula2mass(form), throws_error()) } ) bio3d/tests/testthat/test-read.ncdf.R0000644000176200001440000000305312526367344017215 0ustar liggesuserscontext("Testing basic operation with NetCDF trajectory") test_that("read.ncdf() and write.ncdf() works properly", { skip_on_cran() ##- Prepare files trjfile <- tempfile() file <- system.file("examples/hivp.dcd", package="bio3d") invisible(capture.output(trj0 <- read.dcd(file))) time0 <- sort(round(runif(nrow(trj0), 0, 1000), digit=3)) cell0 <- matrix(rep(runif(6, 0, 100), nrow(trj0)), ncol=6, byrow=TRUE) rownames(trj0) <- time0 ##- Write out <- try(write.ncdf(trj0, trjfile, cell = cell0)) expect_false(inherits(out, "try-error")) ##- Read trj <- read.ncdf(trjfile, headonly = TRUE, verbose = FALSE) expect_output(str(trj), "frames: int 351" ) expect_output(str(trj), "atoms : int 198" ) trj <- read.ncdf(trjfile, cell = TRUE, verbose = FALSE) ##expect_equal(trj, as.data.frame(cell0, stringsAsFactors=FALSE), tolerance = 1e-6) expect_equal(trj, cell0, tolerance = 1e-6) trj <- read.ncdf(trjfile, verbose = FALSE, time = TRUE) expect_equal(as.numeric(rownames(trj)), time0, tolerance = 1e-6) expect_equivalent(trj, trj0) pdb <- read.pdb(system.file("examples/hivp.pdb", package="bio3d")) inds <- atom.select(pdb, chain="A", verbose=FALSE) trj <- read.ncdf(trjfile, verbose = FALSE, first=10, last=20, stride=2, at.sel = inds) expect_equivalent(trj, as.xyz(trj0[seq(10, 20, 2), inds$xyz])) # multiple files files <- rep(trjfile, 4) txt <- capture.output(trj <- read.ncdf(files, headonly = TRUE)) expect_output(txt, "Frames: 1404") expect_output(txt, "Atoms: 198") }) bio3d/tests/testthat/test-deformation.R0000644000176200001440000000374712526367344017712 0ustar liggesuserscontext("Testing deformation analysis") test_that("still works", { ## Simple test with PDB ID 1HEL file <- system.file("examples/1hel.pdb",package="bio3d") invisible(capture.output(pdb <- read.pdb(file))) invisible(capture.output(modes <- nma(pdb))) #sums0 <- c(59.89283, 141.39431, 109.09525, 122.52931, 172.63766, 317.01506) sums0 <- c(180.9078, 198.6242, 318.4639, 379.9139, 479.9795, 473.1810) defe <- deformation.nma(modes) expect_that(defe$sums[1:6], equals(sums0, tolerance=1e-6)) expect_that(defe$sums[1:6], equals(colSums(defe$ei[,1:6]), tolerance=1e-6)) }) test_that("fits with MMTK", { "calpha.mmtk" <- function(r, ...) { ## MMTK Units: kJ / mol / nm^2 a <- 128; b <- 8.6 * 10^5; c <- 2.39 * 10^5; ifelse( r<4.0, b*(r/10) - c, a*(r/10)^(-6) ) } ## Calc modes file <- system.file("examples/1hel.pdb",package="bio3d") invisible(capture.output(pdb <- read.pdb(file))) invisible(capture.output(modes <- nma(pdb, pfc.fun=calpha.mmtk, addter=FALSE, mmtk=TRUE))) ## deformation energies of mode 7 (using MMTK - with PDB id 1etl) #def.mmtk <- c(1306.17014108, 524.571239022, 66.6665951865, 820.62710645, # 154.703500149, 754.482784094, 382.993752804, 173.118373857, # 287.880418213, 205.968139938, 466.277540766, 814.845931887) def.mmtk <- c(38.416002, 9.468705, 36.652248, 23.372066, 28.379588, 22.746524, 35.267401, 58.006941, 48.556190, 46.155725, 92.189766, 75.059341) ## calc deformation energies defe <- deformation.nma(modes, mode.inds=seq(7,26), pfc.fun=calpha.mmtk) expect_that(defe$ei[1:12,1], equals(def.mmtk, tolerance=1e-6)) # mode 8 def.mmtk <- c(92.87263, 142.04833, 208.63627, 77.01778) expect_that(head(defe$ei[,2], n=4), equals(def.mmtk, tolerance=1e-6)) #mode 9 def.mmtk <- c(250.2483, 183.0401, 362.0342, 255.6288) expect_that(head(defe$ei[,3], n=4), equals(def.mmtk, tolerance=1e-6)) }) bio3d/tests/testthat/test-vector-funs.R0000644000176200001440000000133112430771420017625 0ustar liggesuserscontext("Testing vector functions") test_that("Vector functions", { ## vector normalization x <- 1:3 x.norm <- c(0.2672612, 0.5345225, 0.8017837) expect_that(normalize.vector(x), equals(x.norm, tolerance = 1e-6)) y <- matrix(1:9, ncol = 3, nrow = 3) y.norm <- matrix(c(x.norm, 0.4558423, 0.5698029, 0.6837635, 0.5025707, 0.5743665, 0.6461623), ncol=3, byrow=F) expect_that(normalize.vector(y), equals(y.norm, tolerance = 1e-6)) ## Inner product x <- 1:3 y <- diag(x) z <- matrix(1:9, ncol = 3, nrow = 3) xy <- c(1, 4, 9) yz <- c(1, 10, 27) expect_that(inner.prod(x,y), equals(xy)) expect_that(inner.prod(y,z), equals(yz)) } ) bio3d/tests/testthat/test-atom.select.R0000644000176200001440000001042012561207713017573 0ustar liggesuserscontext("Testing atom.select function") test_that("atom.select() gets correct selections with various options", { # Use the curated test-purpose pdb file file <- system.file("examples/test.pdb", package="bio3d") # invisible(capture.output(pdb <- read.pdb(file, rm.alt = FALSE))) invisible(capture.output(pdb <- read.pdb(file))) # Select all atoms: omit 1 ALT capture.output(all.inds <- atom.select(pdb, "all")) expect_equal(length(all.inds$atom), 175) # Select chain A: return everything in chain A capture.output(a.inds <- atom.select(pdb, chain = "A")) expect_equal(a.inds$xyz, atom2xyz(a.inds$atom)) # self-consistent expect_equal(length(a.inds$atom), 98) expect_equal(a.inds$atom[1], 1) expect_equal(a.inds$atom[98], 170) # Select protein: omit 1 ALT # omit 2 'unknown' AA capture.output(pro.inds <- atom.select(pdb, "protein")$atom) expect_equal(length(pro.inds), 103) # Select C-alpha: omit 2 'unknown' AA # omit 1 with missing 'CA' capture.output(ca.inds <- atom.select(pdb, "calpha")$atom) expect_equal(length(ca.inds), 7) expect_equal(ca.inds[c(1, 5, 7)], c(3, 53, 110)) capture.output(ca2.inds <- atom.select(pdb, elety = "CA")$atom) expect_equal(length(ca2.inds), 10) # include calcium # Select 'N': return the number of all amino acids capture.output(cb.inds <- atom.select(pdb, elety = "N")$atom) expect_equal(length(cb.inds), 10) # Select 'unknown' AA capture.output(unk.inds <- atom.select(pdb, resid = "TES")$atom) expect_equal(length(unk.inds), 26) expect_equal(unk.inds[c(3, 13)], c(10, 94)) # Select 'ATOM' record: omit 1 'ALT' capture.output(ATOM.inds <- atom.select(pdb, type = "ATOM")$atom) expect_equal(length(ATOM.inds), 129) # Select first 5 CA atoms by 'eleno' capture.output(ca5.inds <- atom.select(pdb, eleno = c(3, 10, 20, 27, 42))$atom) expect_equal(length(ca5.inds), 5) expect_true(all(pdb$atom[ca5.inds, "elety"] == "CA")) # Select hydrogen/ligand/water capture.output(h.inds <- atom.select(pdb, "h")$atom) expect_equal(length(h.inds), 77) capture.output(lig.inds <- atom.select(pdb, "ligand")$atom) expect_equal(length(lig.inds), 67) # include "TES" capture.output(wat.inds <- atom.select(pdb, "water")$atom) expect_equal(length(wat.inds), 5) capture.output(ion.inds <- atom.select(pdb, resid = "CA")$atom) expect_equal(ion.inds, 170) capture.output(gdp.inds <- atom.select(pdb, resid = "GDP")$atom) expect_equal(length(gdp.inds), 40) # More string test capture.output(bb.inds <- atom.select(pdb, "backbone")$atom) capture.output(bb2.inds <- atom.select(pdb, "back")$atom) expect_equal(bb.inds, bb2.inds) expect_equal(length(bb.inds), 31) capture.output(cb2.inds <- atom.select(pdb, "cbeta")$atom) expect_equal(length(cb2.inds), 36) capture.output(npro.inds <- atom.select(pdb, "notprotein")$atom) capture.output(npro2.inds <- atom.select(pdb, "protein", inverse = TRUE)$atom) expect_equal(npro.inds, npro2.inds) expect_equal(length(intersect(pro.inds, npro.inds)), 0) expect_equal(length(pro.inds) + length(npro.inds), nrow(pdb$atom)) # omit ALT capture.output(nwat.inds <- atom.select(pdb, "notwater")$atom) capture.output(nwat2.inds <- atom.select(pdb, "water", inverse = TRUE)$atom) expect_equal(nwat.inds, nwat2.inds) expect_equal(length(intersect(wat.inds, nwat.inds)), 0) expect_equal(length(wat.inds) + length(nwat.inds), nrow(pdb$atom)) # omit ALT capture.output(noh.inds <- atom.select(pdb, "noh")$atom) capture.output(noh2.inds <- atom.select(pdb, "h", inverse = TRUE)$atom) expect_equal(noh.inds, noh2.inds) expect_equal(length(intersect(h.inds, noh.inds)), 0) expect_equal(length(h.inds) + length(noh.inds), nrow(pdb$atom)) # omit ALT # Test on combination of select capture.output(comb1.inds <- atom.select(pdb, chain = "B", resno = c(1,4))) expect_equal(length(comb1.inds$atom), 33) # omit ALT capture.output(comb2.inds <- atom.select(pdb, resid = "GDP", elety = "PA") ) expect_equal(comb2.inds$atom, 135) capture.output(comb3.inds <- atom.select(pdb, "noh", resid = "TES", chain = "A") ) expect_equal(comb3.inds$atom, c(8, 10, 12, 16, 17)) capture.output(comb4.inds <- atom.select(pdb, chain = "B", resid = "GDP", operator = "OR") ) expect_equal(length(comb4.inds$atom), 117) }) bio3d/tests/testthat/test-pdb.annotate.R0000644000176200001440000000501512526367344017746 0ustar liggesuserscontext("Testing pdb.annotate()") test_that("PDB annotation works", { skip_on_cran() expected <- c('3R1C_X', '3R1C_B', '3R1C_C', '3R1C_D', '3R1C_E', '3R1C_F', '3R1C_G', '3R1C_H', '3R1C_I', '3R1C_J', '3R1C_K', '3R1C_L', '3R1C_M', '3R1C_N', '3R1C_O', '3R1C_P', '3R1C_Q', '3R1C_R', '3R1C_S', '3R1C_Y', '3R1C_T', '3R1C_U', '3R1C_W', '3R1C_A', '3R1C_V', '3R1C_Z', '3R1C_a', '3R1C_b', '3R1C_c', '3R1C_d', '3R1C_e', '3R1C_f', '3R1C_g', '3R1C_h', '3R1C_i', '3R1C_j') invisible(capture.output(anno <- pdb.annotate(expected))) expect_identical(rownames(anno), expected) expected <- c('3R1C_A', '3R1C_B', '3R1C_C', '3R1C_D', '3R1C_E', '3R1C_F', '3R1C_G', '3R1C_H', '3R1C_I', '3R1C_J', '3R1C_K', '3R1C_L', '3R1C_M', '3R1C_N', '3R1C_O', '3R1C_P', '3R1C_Q', '3R1C_R', '3R1C_S', '3R1C_T', '3R1C_U', '3R1C_V', '3R1C_W', '3R1C_X', '3R1C_Y', '3R1C_Z', '3R1C_a', '3R1C_b', '3R1C_c', '3R1C_d', '3R1C_e', '3R1C_f', '3R1C_g', '3R1C_h', '3R1C_i', '3R1C_j') invisible(capture.output(anno <- pdb.annotate('3R1C'))) expect_identical(rownames(anno), expected) expected <- c('3R1C_A', '3R1C_B', '3R1C_C', '3R1C_D', '3R1C_E', '3R1C_F', '3R1C_G', '3R1C_H', '3R1C_I', '3R1C_J', '3R1C_K', '3R1C_L', '3R1C_M', '3R1C_N', '3R1C_O', '3R1C_P', '3R1C_Q', '3R1C_R', '3R1C_S', '3R1C_T', '3R1C_U', '3R1C_V', '3R1C_W', '3R1C_X', '3R1C_Y', '3R1C_Z', '3R1C_a', '3R1C_b', '3R1C_c', '3R1C_d', '3R1C_e', '3R1C_f', '3R1C_g', '3R1C_h', '3R1C_i', '3R1C_j', '1CDK_A', '1CDK_B', '1CDK_I', '1CDK_J') invisible(capture.output(anno <- pdb.annotate(c('3R1C', '1CDK')))) expect_identical(rownames(anno), expected) invisible(capture.output(anno <- pdb.annotate(c('3R1C_A', '3r1c_a', '3r1c_q')))) expect_identical(rownames(anno), expected[c(1, 27)]) invisible(capture.output(anno <- pdb.annotate(c('3R1C_A', '3r1c_a', '3r1c_q'), unique=TRUE))) expect_identical(rownames(anno), "3R1C") expect_identical(anno$chainId, "A,B,C,D,E,F,G,H,I,J,K,L,M,N,O,P,Q,R,S,T,U,V,W,X,Y,Z,a,b,c,d,e,f,g,h,i,j") expected <- rep("ANP,MN,MYR,TPO", 2) invisible(capture.output(anno <- pdb.annotate(c('1cdk_A', '1cdk_B'), anno.terms="ligandId"))) expect_identical(anno$ligandId, expected) }) bio3d/tests/testthat/test-get.pdb.R0000644000176200001440000000233612526367344016717 0ustar liggesuserscontext("Testing get.pdb()") test_that("get.pdb() works properly", { skip_on_cran() ids <- c("1tag", "1tnd") # Gt tmp <- tempdir() files <- get.pdb(ids, tmp, verbose=FALSE) expect_identical(files, paste(tmp, "/", ids, ".pdb", sep="")) expect_warning(get.pdb("3c7kxxx", tmp, verbose=FALSE)) expect_warning(get.pdb("1tag", tmp, verbose=FALSE)) files <- get.pdb("1as0", tmp, verbose=FALSE, gzip=TRUE) expect_identical(files, paste(tmp, "/1as0.pdb", sep="")) # expect_error(get.pdb("aaaa", tmp, verbose=FALSE)) }) test_that("get.pdb() with ncore>1 works properly", { skip_on_cran() ids <- c("1tag", "1tnd", "3v00", "1got") tmp1 <- paste(tempdir(), "1", sep="") tmp2 <- paste(tempdir(), "2", sep="") time1 <- system.time(r1 <- get.pdb(ids, tmp1, ncore=1, verbose=FALSE))["elapsed"] time2 <- system.time(r2 <- get.pdb(ids, tmp2, ncore=NULL, verbose=FALSE))["elapsed"] expect_identical(r2, paste(tmp2, "/", ids, ".pdb", sep="")) expect_identical(list.files(tmp1), list.files(tmp2)) # cat("Speed up by ", round((time1-time2)/time2*100, 1), "%", sep="") # if(getOption("cores") > 1) # expect_true(time1 > time2) unlink(tmp1, recursive=TRUE) unlink(tmp2, recursive=TRUE) }) bio3d/tests/testthat/test-dccm.R0000644000176200001440000000451312526367344016301 0ustar liggesuserscontext("Testing dccm functions") test_that("Correlation matrix from NMA", { ## Calculate correl mat on a small protein file <- system.file("examples/1hel.pdb",package="bio3d") invisible(capture.output(pdb <- read.pdb(file))) invisible(capture.output(modes <- nma(pdb))) invisible(capture.output(cm <- dccm.nma(modes, ncore=1))) expect_that(cm[1,1], equals(1, tolerance=1e-6)) expect_that(cm[1,2], equals(0.4380029, tolerance=1e-6)) expect_that(cm[1,3], equals(0.1407395, tolerance=1e-6)) expect_that(cm[1,3], equals(cm[3,1])) expect_that(sum(cm), equals(57.71768, tolerance=1e-6)) ## Check multicore DCCM invisible(capture.output(cm.mc <- dccm.nma(modes, ncore=NULL))) expect_that(cm, equals(cm.mc, tolerance=1e-6)) } ) test_that("Correlation matrix from XYZ (dccm.xyz)", { skip_on_cran() ## Calculate correl mat on a short HIV protease simulation trjfile <- system.file("examples/hivp.dcd", package="bio3d") invisible(capture.output(trj <- read.dcd(trjfile))) invisible(capture.output(cm <- dccm(trj, ncore=1))) expect_that(cm[1,1], equals(1, tolerance=1e-6)) expect_that(cm[1,2], equals(0.9965964, tolerance=1e-6)) expect_that(cm[1,3], equals(0.992211, tolerance=1e-6)) expect_that(cm[1,3], equals(cm[3,1])) ## Check multicore DCCM invisible(capture.output(cm.mc <- dccm(trj, ncore=NULL))) expect_that(cm, equals(cm.mc, tolerance=1e-6)) }) test_that("Correlation matrix from PCA (dccm.pca)", { skip_on_cran() ## Calculate correl mat on a short HIV protease simulation trjfile <- system.file("examples/hivp.dcd", package="bio3d") invisible(capture.output(trj <- read.dcd(trjfile))) invisible(capture.output(xyz <- fit.xyz(trj[1, ], trj[1:20, ], 1:ncol(trj), 1:ncol(trj)) )) invisible(capture.output(pca <- pca.xyz(xyz))) invisible(capture.output(cm <- dccm(pca, ncore = 1))) pca$z <- NULL invisible(capture.output(cm2 <- dccm(pca, ncore = 1))) expect_that(cm[1,1], equals(1, tolerance=1e-6)) expect_that(cm[1,2], equals(0.7120510, tolerance=1e-6)) expect_that(cm[1,3], equals(0.5455956, tolerance=1e-6)) expect_that(cm[1,3], equals(cm[3,1])) expect_that(cm, equals(cm2, tolerance=1e-6)) ## Check multicore DCCM invisible(capture.output(cm.mc <- dccm(pca, ncore=NULL))) expect_that(cm, equals(cm.mc, tolerance=1e-6)) }) bio3d/tests/testthat/test-pca.R0000644000176200001440000000231012544562303016117 0ustar liggesuserscontext("Testing pca()") test_that("pca functions works", { "mysign" <- function(a,b) { if(all(sign(a)==sign(b))) return(1) else return(-1) } attach(transducin) inds <- unlist(lapply(c("1TND_A", "1TAG", "1AS0", "1AS2"), grep, pdbs$id)) pdbs <- trim.pdbs(pdbs, row.inds=inds) gaps <- gap.inspect(pdbs$xyz) ## Calc modes invisible(capture.output(pc <- pca(pdbs))) ## check dimensions expect_that(dim(pc$U), equals(c(939, 939))) expect_that(length(pc$L), equals(939)) ## check eigenvalues Lexpected <- c(1.964689e+02, 1.715903e+02, 7.091482e+01) expect_that(head(pc$L, n=3), equals(Lexpected, tolerance=1e-6)) ## check atom-wise loadings AUexpected <- c(0.013422274, 0.023879443, 0.022779307, 0.023490288, 0.006308588, 0.010057694) expect_that(head(pc$au[,1], n=6), equals(AUexpected, tolerance=1e-6)) Z1expected <- c(-3.555218, -12.081135, -4.602888, 20.239240) Z1now <- as.numeric(head(pc$z[,1], n=4)) expect_that(Z1now * mysign(Z1expected, Z1now), equals(Z1expected, tolerance=1e-6)) Mexpected <- c(30.12193, 67.76449, 43.36594, 27.01919, 69.66411, 44.47434) expect_that(head(pc$mean, n=6), equals(Mexpected, tolerance=1e-6)) detach(transducin) }) bio3d/tests/testthat/test-overlap.R0000644000176200001440000000330412544562303017030 0ustar liggesusers context("Testing overlap functions") test_that("Overlap functions", { ## Simple test with PDB ID 1HEL file <- system.file("examples/1hel.pdb",package="bio3d") invisible(capture.output(pdb.a <- read.pdb(file))) file <- system.file("examples/1dpx.pdb",package="bio3d") invisible(capture.output(pdb.b <- read.pdb(file))) ## Calculate modes with default arguments invisible(capture.output(modes <- nma(pdb.a, inds=NULL, ff='calpha', mass=FALSE, temp=300.0))) ca.inds.a <- atom.select(pdb.a, "calpha", verbose=FALSE) ca.inds.b <- atom.select(pdb.b, "calpha", verbose=FALSE) ## Set new coordinates of pdb.b xyz.b <- fit.xyz(pdb.a$xyz, pdb.b$xyz, fixed.inds=ca.inds.a$xyz, mobile.inds=ca.inds.b$xyz) pdb.b$xyz <- xyz.b ## difference vector dv <- difference.vector(rbind(pdb.a$xyz[ca.inds.a$xyz], pdb.b$xyz[ca.inds.b$xyz])) o1 <- overlap(modes, dv, nmodes=(modes$natoms*3)-6) expect_that(o1$overlap.cum[(modes$natoms*3)-6], equals(1, tolerance=1e-6)) expect_that(o1$overlap.cum[1], equals(0.2786508, tolerance=1e-6)) o2 <- overlap(modes$U[,7:26], dv) expect_that(all((round(o1$overlap[1:20] - o2$overlap, 10)==0)), equals(TRUE)) ## Calculate modes with default arguments invisible(capture.output(modes.b <- nma(pdb.b, inds=NULL, ff='calpha', mass=FALSE, temp=300.0))) r <- rmsip(modes, modes.b) expect_that(r$overlap[1,1], equals(0.704, tolerance=1e-6)) expect_that(r$overlap[1,2], equals(0.286, tolerance=1e-6)) expect_that(r$overlap[2,1], equals(0.289, tolerance=1e-6)) } ) bio3d/tests/testthat/test-rmsd.R0000644000176200001440000000333512526367344016341 0ustar liggesuserscontext("Testing RMSD function") test_that("rmsd() gets the same results as PyMOL", { file <- system.file(c("examples/1hel.pdb", "examples/1dpx.pdb"), package="bio3d") invisible(capture.output(pdb.a <- read.pdb(file[1]))) invisible(capture.output(pdb.b <- read.pdb(file[2]))) invisible(capture.output(inds.a <- atom.select(pdb.a, "calpha"))) invisible(capture.output(inds.b <- atom.select(pdb.b, "calpha"))) rd1 <- rmsd(a=pdb.a$xyz, b=pdb.b$xyz, a.inds=inds.a$xyz, b.inds=inds.b$xyz, fit=FALSE) rd2 <- rmsd(a=pdb.a$xyz, b=pdb.b$xyz, a.inds=inds.a$xyz, b.inds=inds.b$xyz, fit=TRUE) ## with pymol "pair_fit 1hel and name CA, 1dpx and name CA" = 0.293 rmsd1 <- 1.386 rmsd2 <- 0.293 expect_equal(round(rd1, 3), rmsd1) expect_equal(round(rd2, 3), rmsd2) }) test_that("rmsd() with ncore>1 works properly", { file <- system.file(c("examples/1hel.pdb", "examples/1dpx.pdb"), package="bio3d") invisible(capture.output(pdb.a <- read.pdb(file[1]))) invisible(capture.output(pdb.b <- read.pdb(file[2]))) invisible(capture.output(inds.a <- atom.select(pdb.a, "calpha"))) invisible(capture.output(inds.b <- atom.select(pdb.b, "calpha"))) ## check if ncore > 1 is really faster time1 <- system.time(rmsd1 <- rmsd(a=pdb.a$xyz, b=pdb.b$xyz, a.inds=inds.a$xyz, b.inds=inds.b$xyz, fit=TRUE, ncore=1)) time2 <- system.time(rmsd2 <- rmsd(a=pdb.a$xyz, b=pdb.b$xyz, a.inds=inds.a$xyz, b.inds=inds.b$xyz, fit=TRUE, ncore=NULL)) ##time1 <- time1["elapsed"] ##time2 <- time2["elapsed"] # expect_equivalent(rmsd1, rmsd2) expect_equal(rmsd1, rmsd2, tolerance=1e-6) # cat("Speed up by", round((time1-time2)/time2, 1)*100, "%", sep="") # if(getOption("cores") > 1) # expect_true(time1 > time2) }) bio3d/tests/testthat/test-fitting.R0000644000176200001440000001011012602522006017004 0ustar liggesuserscontext("Testing fitting functions") test_that("Fitting still works", { ## Test fit.xyz / gap.inspect attach(transducin) inds <- unlist(lapply(c("1TAG", "1AS0", "1AS2"), grep, pdbs$id)) pdbs <- trim.pdbs(pdbs, row.inds=inds) gaps <- gap.inspect(pdbs$xyz) rmsd.mat <- matrix(c(0.000, 1.792, 1.903, 1.792, 0.000, 1.881, 1.903, 1.881, 0.000), ncol=3, byrow=TRUE) ## Test rmsd() expect_that(rmsd( pdbs ), equals(rmsd.mat, tolerance = 1e-6)) ## Test fit.xyz() xyz <- fit.xyz( fixed = pdbs$xyz[1,], mobile = pdbs$xyz, fixed.inds = gaps$f.inds, mobile.inds = gaps$f.inds ) x1.expected <- c(29.72513, 64.03389, 42.98273, 30.15516, 67.73641, 43.70090) x2.expected <- c(NA, NA, NA, 29.28225, 67.23703, 43.05571) x3.expected <- c(NA, NA, NA, 30.17547, 67.15645, 43.46491) expect_that(head(xyz[1,]), equals(x1.expected, tolerance = 1e-6)) expect_that(head(xyz[2,]), equals(x2.expected, tolerance = 1e-6)) expect_that(head(xyz[3,]), equals(x3.expected, tolerance = 1e-6)) rmsd.mat <- matrix(c(0.000, 1.659, 1.814, 1.659, 0.000, 1.697, 1.814, 1.697, 0.000), ncol=3, byrow=TRUE) expect_that(rmsd( xyz[, gaps$f.inds] ), equals(rmsd.mat, tolerance = 1e-6)) expect_that(rmsd( pdbs, fit=TRUE ), equals(rmsd.mat, tolerance = 1e-6)) xyz2 <- fit.xyz( fixed = pdbs$xyz[1,], mobile = pdbs, fixed.inds = gaps$f.inds, mobile.inds = gaps$f.inds ) expect_that(xyz2, equals(xyz)) detach(transducin) } ) test_that("struct.aln still works", { skip_on_cran() if(!check.utility('muscle')) { skip('Need MUSCLE installed to run this test') } ## Simple test with PDB ID 1HEL file.a <- system.file("examples/1hel.pdb",package="bio3d") file.b <- system.file("examples/1dpx.pdb",package="bio3d") invisible(capture.output(pdb.a <- read.pdb(file.a))) invisible(capture.output(pdb.b <- read.pdb(file.b))) invisible(capture.output(aln <- struct.aln(pdb.a, pdb.b, write.pdbs=FALSE, cutoff=0.1, max.cycles=2, extra.args="-quiet"))) rmsda <- c(0.293, 0.229, 0.200) expect_that(aln$rmsd, equals(rmsda, tolerance = 1e-6)) expect_that(length(aln$a.inds$atom), equals(112)) expect_that(length(aln$b.inds$atom), equals(112)) expect_that(length(aln$b.inds$xyz), equals(112*3)) expect_that(length(aln$b.inds$xyz), equals(112*3)) } ) # A little bit more tests... test_that("fit.xyz() gets the same results as VMD", { skip_on_cran() if(!check.utility('muscle')) { skip('Need MUSCLE installed to run this test') } invisible(capture.output(pdbs <- pdbaln(c("1tag", "1as0")))) inds <- gap.inspect(pdbs$xyz)$f.inds expect_error(fit.xyz("string", pdbs$xyz, inds, inds)) expect_error(fit.xyz(pdbs$xyz, pdbs$xyz, inds, inds)) expect_error(fit.xyz(pdbs$xyz[1,], pdbs$xyz, inds, 1:4)) xyz <- fit.xyz(pdbs$xyz[1,], pdbs$xyz[2,], inds, inds) xyz <- xyz[!is.na(xyz)] xyz0 <- c(41.063, 15.667, 58.826, 43.041, 17.479, 56.113, 44.826, 15.317, 53.571) # VMD results expect_equal(round(xyz[1:9], 3), xyz0) }) test_that("fit.xyz() with ncore>1 works properly", { skip_on_cran() attach(transducin) inds <- unlist(lapply(c("1TAG", "1AS0", "1AS2"), grep, pdbs$id)) pdbs <- trim.pdbs(pdbs, row.inds=inds) #invisible(capture.output(pdbs <- pdbaln(c("1tag", "1as0", "1as2")))) #inds <- gap.inspect(pdbs$xyz)$f.inds # check if ncore > 1 is really faster time1 <- system.time(xyz1 <- fit.xyz(pdbs$xyz[1,], pdbs$xyz, inds, inds, ncore=1)) time2 <- system.time(xyz2 <- fit.xyz(pdbs$xyz[1,], pdbs$xyz, inds, inds, ncore=NULL)) time1 <- time1["elapsed"] time2 <- time2["elapsed"] expect_equivalent(xyz1, xyz2) # cat("Speed up by", round((time1-time2)/time2, 1)*100, "%", sep="") # if(getOption("cores") > 1) # expect_true(time1 > time2) detach(transducin) }) bio3d/NAMESPACE0000644000176200001440000000457012632622153012501 0ustar liggesusers#exportPattern("^[[:alpha:]]+") exportPattern("^[^\\.]") import(parallel, grid) import("graphics") import("grDevices") import("stats") import("utils") S3method(as.pdb, default) S3method(as.pdb, mol2) S3method(as.pdb, prmtop) S3method(atom2ele, default) S3method(atom2ele, pdb) S3method(atom2mass, default) S3method(atom2mass, pdb) S3method(atom.select, pdb) S3method(atom.select, prmtop) S3method(bhattacharyya, array) S3method(bhattacharyya, enma) S3method(bhattacharyya, matrix) S3method(bhattacharyya, nma) S3method(bhattacharyya, pca) S3method(cmap, default) S3method(cmap, pdb) S3method(cmap, xyz) S3method(cna, dccm) S3method(cna, ensmb) S3method(com, pdb) S3method(com, xyz) S3method(core.find, default) S3method(core.find, pdb) S3method(core.find, pdbs) S3method(covsoverlap, enma) S3method(covsoverlap, nma) S3method(dccm, enma) S3method(dccm, nma) S3method(dccm, pca) S3method(dccm, xyz) S3method(dm, pdb) S3method(dm, xyz) S3method(dssp, pdb) S3method(dssp, pdbs) S3method(dssp, xyz) S3method(geostas, default) S3method(geostas, enma) S3method(geostas, nma) S3method(geostas, pdb) S3method(geostas, pdbs) S3method(geostas, xyz) S3method(pdbfit, pdb) S3method(pdbfit, pdbs) S3method(identify, cna) S3method(mktrj, enma) S3method(mktrj, nma) S3method(mktrj, pca) S3method(nma, pdb) S3method(nma, pdbs) S3method(pca, array) S3method(pca, pdbs) S3method(pca, tor) S3method(pca, xyz) S3method(plot, bio3d) S3method(plot, blast) S3method(plot, cmap) S3method(plot, cna) S3method(plot, core) S3method(plot, dccm) S3method(plot, dmat) S3method(plot, enma) S3method(plot, fasta) S3method(plot, fluct) S3method(plot, geostas) S3method(plot, hmmer) S3method(plot, nma) S3method(plot, pca) S3method(plot, pca.loadings) S3method(plot, pca.score) S3method(plot, pca.scree) S3method(plot, rmsip) S3method(print, core) S3method(print, enma) S3method(print, fasta) S3method(print, nma) S3method(print, pca) S3method(print, pdb) S3method(print, rle2) S3method(print, select) S3method(print, sse) S3method(print, xyz) S3method(print, cna) S3method(print, cnapath) S3method(print, geostas) S3method(print, mol2) S3method(print, prmtop) S3method(read.crd, amber) S3method(read.crd, charmm) S3method(rmsip, default) S3method(rmsip, enma) S3method(sip, default) S3method(sip, enma) S3method(sip, nma) S3method(summary, pdb) S3method(summary, cna) S3method(summary, cnapath) S3method(trim, pdb) S3method(trim, pdbs) S3method(trim, xyz) bio3d/demo/0000755000176200001440000000000012526367343012211 5ustar liggesusersbio3d/demo/pca.R0000644000176200001440000000470312430771420013070 0ustar liggesusers### ### Example of PCA on a collection of PKA structures ### and a large collection of transducin structure ### ### Authors Xin-Qiu Yao ### Lars Skjaerven ### Barry J Grant ### require(bio3d); require(graphics); pause <- function() { cat("Press ENTER/RETURN/NEWLINE to continue.") readLines(n=1) invisible() } ################################################ ## # ## Basic PCA of related X-ray structures # ## (requires the 'muscle' program installed) # ## # ################################################ pause() ### Set temp dir to store PDB files tmp.dir <- tempdir() ## Specify PDB identifiers ids <- c("1cdk_A", "3agm_A", "1cmk_E", "3dnd_A", "1q8w_A") ## Download PDBs raw.files <- get.pdb(ids, path=tmp.dir) pause() ## Split PDBs by chain ID files <- pdbsplit(raw.files, ids, path=tmp.dir) pause() ## Sequence/structure alignment pdbs <- pdbaln(files) pause() ## Find invariant core core <- core.find(pdbs) pause() ## Fit structures to core region xyz <- pdbfit(pdbs, inds=core$c1A.xyz) ## outpath="core_fit/", full.pdbs=T, het2atom=T) pause() ## Locate gap containing positions gaps.pos <- gap.inspect(pdbs$xyz) ## Perform PCA on non-gap containing positions pc.xray <- pca.xyz(xyz[,gaps.pos$f.inds]) pause() ## Plot x-ray results plot(pc.xray) pause() ############################################# ## # ## Larger transducin example # ## # ############################################# data(transducin) attach(transducin, warn.conflicts=FALSE) ## data 'transducin' contains objects ## - pdbs: aligned C-alpha coordinates for 53 transducin ## structures from the PDB ## - annotation: annotation of the 53 PDBs ## Note that this data can be generated from scratch by following the ## Comparative Structure Analysis with Bio3D Vignette available both ## on-line and from within the Bio3D package. pdbs <- transducin$pdbs annotation <- transducin$annotation pause() ## Inspect gaps gaps.pos <- gap.inspect(pdbs$xyz) ## Previously fitted coordinates invariance core xyz <- pdbs$xyz ## Do PCA pc.xray <- pca.xyz(xyz[, gaps.pos$f.inds]) pause() ## Plot overview plot(pc.xray, col=annotation[, "color"]) ## Plot atom wise loadings plot.bio3d(pc.xray$au[,1], ylab="PC1 (A)") pause() unlink(tmp.dir) bio3d/demo/00Index0000644000176200001440000000026712526367343013350 0ustar liggesuserspdb PDB File Manipulation, Searching and Alignment pca Principal Component Analysis nma Normal Mode Analysis md Molecular Dynamics Trajectory Analysis bio3d/demo/md.R0000644000176200001440000000352012430771420012721 0ustar liggesusers### ### Example of basic molecular dynamics trajectory analysis ### ### Authors Xin-Qiu Yao ### Lars Skjaerven ### Barry J Grant ### require(bio3d); require(graphics); pause <- function() { cat("Press ENTER/RETURN/NEWLINE to continue.") readLines(n=1) invisible() } ############################################# ## # ## Basic analysis of HIVpr trajectory data # ## # ############################################# pause() # Read example trajectory file trtfile <- system.file("examples/hivp.dcd", package="bio3d") trj <- read.dcd(trtfile) # Read the starting PDB file to determine atom correspondence pdbfile <- system.file("examples/hivp.pdb", package="bio3d") pdb <- read.pdb(pdbfile) # Whats in the new pdb object print(pdb) pause() # How many rows (frames) and columns (coords) present in trj dim(trj) ncol(trj) == length(pdb$xyz) pause() # Trajectory Frame Superposition on Calpha atoms ca.inds <- atom.select(pdb, elety = "CA") xyz <- fit.xyz(fixed = pdb$xyz, mobile = trj, fixed.inds = ca.inds$xyz, mobile.inds = ca.inds$xyz) # Root Mean Square Deviation (RMSD) rd <- rmsd(xyz[1, ca.inds$xyz], xyz[, ca.inds$xyz]) plot(rd, typ = "l", ylab = "RMSD", xlab = "Frame No.") points(lowess(rd), typ = "l", col = "red", lty = 2, lwd = 2) summary(rd) pause() # Root Mean Squared Fluctuations (RMSF) rf <- rmsf(xyz[, ca.inds$xyz]) plot(rf, ylab = "RMSF", xlab = "Residue Position", typ="l") pause() # Principal Component Analysis pc <- pca.xyz(xyz[, ca.inds$xyz]) plot(pc, col = bwr.colors(nrow(xyz))) pause() # Cluster in PC space hc <- hclust(dist(pc$z[, 1:2])) grps <- cutree(hc, k = 2) plot(pc, col = grps) pause() # Cross-Correlation Analysis cij <- dccm(xyz[, ca.inds$xyz]) plot(cij) ## view.dccm(cij, pdb, launch = TRUE) bio3d/demo/pdb.R0000644000176200001440000000324412526367343013104 0ustar liggesusers### ### Example of PDB file manipulation, searching, alignment etc. ### ### Authors Xin-Qiu Yao ### Lars Skjaerven ### Barry J Grant ### require(bio3d); require(graphics); pause <- function() { cat("Press ENTER/RETURN/NEWLINE to continue.") readLines(n=1) invisible() } ############################################# ## # ## Basic PDB file reading and manipulation # ## # ############################################# pause() # Read an online RCSB Protein Data Bank structure pdb <- read.pdb("4q21") # Whats in the new pdb object print(pdb) pause() # Most bio3d functions, including read.pdb(), return list objects attributes(pdb) pdb$atom[1:3, c("resno", "resid", "elety", "x", "y", "z")] pause() # Selection of substructure regions with 'atom.select()'' function inds <- atom.select(pdb, elety = c("N","CA","C"), resno=4:6) pdb$atom[inds$atom,] pause() # Simple B-factor plot ca.inds <- atom.select(pdb, "calpha") plot.bio3d( pdb$atom[ca.inds$atom,"b"], sse=pdb, ylab="B-factor") ################################### ## # ## Search for similar structures # ## # ################################### # Use sequence aa <- pdbseq(pdb) aa pause() # Blast the RCSB PDB to find similar sequences blast <- blast.pdb(aa) head(blast$hit.tbl) pause() # Plot results top.hits <- plot(blast) head(top.hits$hits) pause() ## Download and and analyze further .... ## raw.files <- get.pdb(top.hits$pdb.id, path="raw_hits") ## files <- pdbsplit(raw.files, top.hits$hits, path="top_hits") ## pdbs <- pdbaln(files) ## ...... bio3d/demo/nma.R0000644000176200001440000000401312526367343013105 0ustar liggesusers### ### Examples from NMA Vignette ### ### Authors Lars Skjaerven ### Xin-Qiu Yao ### Barry J Grant ### require(bio3d); require(graphics); pause <- function() { cat("Press ENTER/RETURN/NEWLINE to continue.") readLines(n=1) invisible() } ############################################# ## # ## Basic usage # ## # ############################################# ### Read PDB and Calculate Normal Modes pdb <- read.pdb("1hel") modes <- nma(pdb) pause() ### Print a summary print(modes) pause() ### Plot the nma object for a quick overview plot(modes) pause() ### Calculate cross-correlations cm <- dccm(modes) pause() ### Plot correlation map plot(cm, sse=pdb) pause() ### Calculate modes with force field ANM modes.anm <- nma(pdb, ff="anm") pause() ### Investigate modes similarity with RMSIP r <- rmsip(modes, modes.anm) pause() ### Plot RMSIP results plot(r, xlab="ANM", ylab="C-alpha FF") pause() ################################################ ## # ## Ensemble NMA # ## (requires the 'muscle' program installed) # ## # ################################################ pause() ### Set temp dir to store PDB files tmp.dir <- tempdir() ### Download a set of DHFR structures ids <- c("1rx2_A", "1rx4_A", "1rg7_A", "3fyv_X", "3sgy_B") ### Download and split by chain ID raw.files <- get.pdb(ids, path=tmp.dir) pause() ### Split PDB files by chain ID files <- pdbsplit( raw.files, ids, path=tmp.dir) pause() ### Align structures pdbs <- pdbaln(files) pause() ### View sequence identity summary( c(seqidentity(pdbs)) ) pause() ### Calculate modes of aligned proteins modes <- nma(pdbs) pause() ## Print a summary print(modes) pause() ### Plot fluctuations plot(modes, pdbs) pause() ### Cluster Modes simiarlity heatmap(1-modes$rmsip, labCol=ids) unlink(tmp.dir) bio3d/NEWS0000644000176200001440000002047412544562302011763 0ustar liggesusersNEWS ==== v2.2 Version 2.2, released in Feb 2015, contains new facilities for sub-optimal path analysis of biomolecular correlation networks, constructing biological units, identification and tidying of malformed PDB files, and improved secondary structure annotation of 'pdbs' objects and various plots. We have also updated and enhanced atom selection functionality and developed a new vignette detailing PDB structure manipulation and analysis facilities. For a fine- grained list of changes, or to report a bug, please consult: * [The issues log](https://bitbucket.org/Grantlab/bio3d/issues) * [The commit log](https://bitbucket.org/Grantlab/bio3d/commits/all) Major new functions include: * cnapath: Suboptimal Path Analysis for Correlation Networks * biounit: Biological Unit Construction * clean.pdb: Inspect And Clean Up A PDB Object * cat.pdb: Concatenate Multiple PDB Objects * pdb2sse: Obtain An SSE Sequence Vector From A PDB Object * bounds.sse: Obtain A SSE Object From An SSE Sequence Vector * aa.table: Updated amino acid reference data that replaces older 'aa.mass' * as.fasta: Convert alignment/sequence in matrix/vector format to a FASTA object. * as.pdb: Convert coordinate data to PDB format * as.select: Convert atomic indices to a atom.select object * as.xyz: Convert vectors and matrices to 'xyz' class objects * atom.select.pdb: Atom selection from PDB objects has been extensively updated * basename.pdb: Utility for manipulation of PDB file names * check.utility: Check and Report on Missing Bio3D Utility Programs * cmapt: Update contact map methods for pdb and xyz objects * cna: Update correlation network analysis methods for dccm and ensmb objects" * cnapath: Suboptimal Path Analysis for Correlation Networks * com: Updated center of mass methods for pdb and xyz objects * combine.select: Combine atom.select objects, renamed from previous 'combine.sel' * cov.enma: New method to Calculate Covariance Matrix from Ensemble Normal Modes" * cov2dccm: Calculates the N-by-N cross-correlation matrix from a 3N-by-3N covariance matrix * covsoverlap: New methods for nma and enma objects * dm: Distance matrix gets new methods for pdb and xyz class objects * dssp: Secondary Structure Analysis with DSSP gets new methods for pdb, xyz and pdbs class objects * geostas: Geometrically stable domain finder gets new methods for nma, enma, pdb, pdbs and xyz objects. * is.pdbs: Is an Object of Class pdbs * mono.colors: New color palette * pdb2sse: Obtain An SSE Sequence Vector From A PDB Object * pdbfit: Coordinate superposition gets new methods for multi-model pdb objects and pdbs objects. * read.crd: Can Now Read Coordinate Data from Amber or CHARMM * read.prmtop: Read AMBER Parameter/Topology files * var.pdbs: Pairwise Distance Variance in Cartesian Coordinates * plot: New or updated plot methods for 'cmap', 'geostas', and 'pca' class objects as well as a new plot.fluct() function that expands on plot.bio3d() for plotting atomic fluctuations from MD and NMA results. * print: New print methods for cnapath, enma, geostas, mol2, nma, pca, pdb, prmtop, rle2, select and sse objects. v2.1 ---- Version 2.1, released in Sep 2014, contains new facilities for Correlation Network Analysis (cna) and Geometrically Stable Domain finding (geostas). We have also changed 'PDB object data' storage from a matrix to a data.frame format. Improved methods and functionality for ensemble NMA are now also included along with extensive improvements to package vignettes and function documentation. For a fine-grained list of changes, or to report a bug, please consult: * [The issues log](https://bitbucket.org/Grantlab/bio3d/issues) * [The commit log](https://bitbucket.org/Grantlab/bio3d/commits/all) Major new functions include: * cna: Protein Dynamic Correlation Network Construction and Community Analysis. * plot.cna: Protein Structure Network Plots in 2D and 3D. * print.cna: Summarize and Print Features of a cna Network Graph * identify.cna: Identify Points in a CNA Protein Structure Network Plot * layout.cna: Protein Structure Network Layout * view.cna: View CNA Protein Structure Network Community Output in VMD * prune.cna: Prune A cna Network Object * community.tree: Reconstruction of the Girvan-Newman Community Tree for a CNA Class Object. * network.amendment: Amendment of a CNA Network According To A Input Community Membership Vector. * lmi: Linear Mutual Information Matrix * dccm.pca: Dynamic Cross-Correlation from Principal Component Analysis * filter.dccm: Filter for Cross-correlation Matrices (Cij) * cmap.filter: Contact Map Consensus Filtering * geostas (amsm.xyz): GeoStaS Domain Finder * bhattacharyya Bhattacharyya Coefficient * covsoverlap: Covariance Overlap * sip: Square Inner Product * cov.nma: Calculate Covariance Matrix from Normal Modes * mktrj.enma: Ensemble NMA Atomic Displacement Trajectory * pca.array: Principal Component Analysis of an array of matrices * hmmer: HMMER Sequence Search * plot.hmmer: Plot a Summary of HMMER Hit Statistics. * uniprot: Fetch UniProt Entry Data. * pfam: Download Pfam FASTA Sequence Alignment * hclustplot: Dendrogram with Clustering Annotation * write.pir: Write PIR Formated Sequences * mustang: Structure-based Sequence Alignment with MUSTANG * pdbs.filter: Filter or Trim a pdbs PDBs Object * dssp.pdbs: Secondary Structure Analysis of Aligned PDB Structures with DSSP * plot.fasta: Plot a Multiple Sequence Alignment * print.fasta: Printing Sequence Alignments * inspect.connectivity: Check the Connectivity of Protein Structures * var.xyz: Pairwise Distance Variance in Cartesian Coordinates * is.xyz(as.xyz, print.xyz): Is an Object of Class * setup.ncore: Setup for Running Bio3D Functions using Multiple CPU Cores v2.0 ---- Version 2.0, released in Nov 2013, contains over 30 new functions including enhanced Normal Mode Analysis facilities as well extensive improvements to existing code and documentation. For a fine-grained list of changes or to report a bug, please consult: * [The issues log](https://bitbucket.org/Grantlab/bio3d/issues) * [The commit log](https://bitbucket.org/Grantlab/bio3d/commits/all) Major new functions include: * aa2mass: Amino Acid Residues to Mass Converter * atom.index: Index of Atomic Masses * atom2mass(atom2ele, formula2mass): Atom Names to Mass Converter * binding.site: Binding Site Residues * com(com.xyz): Center of Mass * combine.sel: Combine Atom Selections From PDB Structure * dccm.enma: Cross-Correlation for Ensemble NMA (eNMA) * dccm.mean: Filter DCCM matrices * dccm.nma: Dynamic Cross-Correlation from Normal Modes Analysis * dccm.xyz: DCCM: Dynamical Cross-Correlation Matrix * deformation.nma: Deformation Analysis * dssp.trj: Secondary Structure Analysis of Trajectories with DSSP * fluct.nma: NMA Fluctuations * inner.prod: Mass-weighted Inner Product * is.pdb: Is an Object of Class pdb * is.select: Is an Object of Class atom.select * load.enmff(ff.calpha, ff.calphax, ff.anm, ff.pfanm, ff.sdenm, ff.reach): ENM Force Field Loader * mktrj.nma: NMA Atomic Displacement Trajectory * nma(build.hessian, print.nma): Normal Mode Analysis * nma.pdbs(print.enma): Ensemble Normal Mode Analysis * normalize.vector: Mass-Weighted Normalized Vector * pdb.annotate: Get Customizable Annotations From PDB * pdb2aln: Align a PDB structure to an existing alignment * pdb2aln.ind: Mapping between PDB atomic indices and alignment positions * pdbfit: PDB File Coordinate Superposition * pdbs2pdb: PDBs to PDB Converter * plot.enma: Plot eNMA Results * plot.nma: Plot NMA Results * plot.rmsip: Plot RMSIP Results * read.mol2: Read MOL2 File * sdENM: Index for the sdENM ff * sse.bridges: SSE Backbone Hydrogen Bonding * struct.aln: Structure Alignment Of Two PDB Files * view.dccm: Visualization of Dynamic Cross-Correlation * view.modes: Vector Field Visualization of Modes * vmd.colors: Color as in VMD Molecular Viewer Versioning ---------- Releases will be numbered with the following semantic versioning format: .- E.g.: 2.0-1 And constructed with the following guidelines: * Breaking backward compatibility bumps the major (and resets the minor and patch) * New additions without breaking backward compatibility bumps the minor (and resets the patch) * Bug fixes and misc changes bumps the patch For more information on SemVer, please visit http://semver.org/. ----- For changes prior to v1.1-6 (Apr 2013) please see the bio3d wki: * [Whats new wki page](http://bio3d.pbworks.com/w/page/7824486/WhatsNew) bio3d/data/0000755000176200001440000000000012632622153012165 5ustar liggesusersbio3d/data/kinesin.RData0000644000176200001440000151024012632622153014545 0ustar liggesusers‹ì½wTË»6:äŒä†Œd$Ç~IÃ2¨Š¨ˆˆ˜%bB@@   3" È H”$Q2Üöܵ~g­Ïï[çî{~\îöìýÇ~ìêꪧ¦žyßžî*sç-ÌÎÌ †CCE¡¦AÿIKþ C‹aB‘áÐAßýÇú¢5¸ÑCô<Í/DOÑ°c¨Ç‚ç1¿j¥üƒ ÁiSÂ…ÃÒ î,ê¿ŠÝh>ÿSìlMK[‚CÀp™uï±ú¢æó?Á2R`/­|"ì¬Î‘~lÜ´Ñ|þ§ 8¹ªŒ¸ÛnÙÁV9…‡Íç ‚í‡Öš€ff0>ÎÂüPAm£ùüOA ÈÜK8!ûv¦—XH×Ðn4Ÿÿ)Ž'ñRú‘> Wåõn™‡u£ùüOA°þXshô 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媩–ÑѨhœù¤ÉïóÍnÝÐwa]íq»ÉZOnd7¦ó-硇ßfòˆ1;ZÖU͸‰"{ü6£3oòuU?¬ÖÑ“Q“‡æœ7ü°¡ÙRŸÇÖˆ—¤ªÞFi7ÖÕŦՄá°[ý°Z+ ãÛ ±\‡*áv|ƒUÅ‚³tFÖEÅÜ£9Kǵ_äk²Q…jhnUG{[ééá.G¸–wEÇÛPàÞìxÞæ1SÑ”¶îsØBT÷ t{”ohÝÐy_\a÷ÇuHë&¼,÷û´I¿ cÈ¿î/·um¹¥˜ýß“u}rP–#¢Ÿª¡–lšèlèF«£Éè‘ÅyÄÐ_ØIG¿ó1ÈÜù\ËÇ {Akš½v½Bd .[òΆáls˾þ_­U¥bio3d/R/0000755000176200001440000000000012632622171011455 5ustar liggesusersbio3d/R/pca.tor.R0000644000176200001440000000045412526367343013162 0ustar liggesusers"pca.tor" <- function(data, ... ) { data[data < 0] <- data[data < 0] + 360 data[data < -180] <- data[data < -180] + 360 data[data > 180] <- data[data > 180] - 360 ##cat("Rescaled (-180 to 180) and corrected for periodicity\n") data <- wrap.tor(data) return( pca.xyz(data, ...) ) } bio3d/R/is.select.R0000644000176200001440000000006012412621431013457 0ustar liggesusersis.select <- function(x) inherits(x, "select")bio3d/R/rmsd.R0000644000176200001440000001205412524171274012552 0ustar liggesusers"rmsd" <- function(a, b=NULL, a.inds=NULL, b.inds=NULL, fit=FALSE, ncore=1, nseg.scale=1) { # Calculate the RMSD between all rows of 'a' or between # the single structure 'a' and the one or more structures # contained in 'b' # Parallelized by parallel package -Wed Dec 12 11:15:20 EST 2012 # nseg.scale - to resolve the memory problem of using multicore ncore <- setup.ncore(ncore) if(ncore > 1) { # Issue of serialization problem # Maximal number of cells of a double-precision matrix # that each core can serialize: (2^31-1-61)/8 R_NCELL_LIMIT_CORE = 2.68435448e8 R_NCELL_LIMIT = ncore * R_NCELL_LIMIT_CORE if(nseg.scale < 1) { warning("nseg.scale should be 1 or a larger integer\n") nseg.scale=1 } } if(is.pdb(a) | is.pdbs(a)) a=a$xyz if(is.pdb(b) | is.pdbs(b)) b=b$xyz if( is.null(a.inds) && is.null(b.inds) ) { a.inds <- gap.inspect(a)$f.inds if(!is.null(b)) { a.inds <- intersect(a.inds, gap.inspect(b)$f.inds) } b.inds <- a.inds if(length(a.inds) != length(a)) { warning(paste("No indices provided, using the", length(a.inds)/3, "non NA positions\n")) } } if (is.null(a.inds)) a.inds <- gap.inspect(a)$f.inds if (is.null(b.inds) && !is.null(b)) b.inds <- gap.inspect(b)$f.inds if(is.vector(a) || nrow(a)==1) { if(is.null(b)) { stop("No comparison can be made, input was only a single vector 'a'") } } else { if(is.null(b)) { # Pair Wise Matrix 'a' if( any(is.na(a[,a.inds])) ) { stop("error: NA elements present in selected set") } nseq=nrow(a) inds=pairwise(nseq) ni <- nrow(inds) if(ncore > 1){ # Parallelized RLIMIT = R_NCELL_LIMIT nDataSeg = floor((ni-1)/RLIMIT)+1 nDataSeg = floor(nDataSeg * nseg.scale) lenSeg = floor(ni/nDataSeg) s = vector("list", nDataSeg) for(i in 1:nDataSeg) { istart = (i-1)*lenSeg + 1 iend = if(i 1)) { if (length(a.inds) != length(b.inds)) { stop("dimension mismatch: a[a.inds] and b[,b.inds] should be the same length") } if( any(is.na(a[a.inds])) || any(is.na(b[,b.inds])) ) { stop("error: NA elements present in selected set") } if(fit) { # Parallelized / single version b <- fit.xyz(fixed=a, mobile=b, fixed.inds=a.inds, mobile.inds=b.inds, ncore=ncore, nseg.scale=nseg.scale) } if(ncore > 1){ # Parallelized RLIMIT = R_NCELL_LIMIT nDataSeg = floor((nrow(b)-1)/RLIMIT)+1 nDataSeg = floor(nDataSeg * nseg.scale) lenSeg = floor(nrow(b)/nDataSeg) irmsd = vector("list", nDataSeg) for(i in 1:nDataSeg) { istart = (i-1)*lenSeg + 1 iend = if(i 20)) { id <- basename(id) if(any(nchar(id) > 17)) { id <- substr(id,1,10) } id <-paste0("[Truncated_Name:", 1:length(id),"]",id) } ##- Format sequence identifiers ids.nchar <- max(nchar(id))+3 ## with a gap of 3 spaces btwn id and sequence ids.format <- paste0("%-",ids.nchar,"s") ids <- sprintf(ids.format, id) ## Format for annotation printing (see below) pad.format <- paste0("%+",(ids.nchar+1),"s") ## Format for conservation annotation printing (see below) pad.format2 <- paste0("%+",(ids.nchar),"s") ##- Scale 'width' of output if not specified in input call tput.col <- 85 ## typical terminal width from system("tput cols") if(is.null(width)) { width <- tput.col - ids.nchar - 4 } ## Make sure we end on a 10 block width <- floor(width/10)*10 ##- Work out sequence block widths nseq <- length(ids) nres <- ncol(ali) block.start <- seq(1, nres, by=width) if(nres < width) { block.end <- nres } else { block.end <- unique(c( seq(width, nres, by=width), nres)) } nblocks <- length(block.start) block.annot <- rep(" ", width) block.annot[ c(1,seq(10, width, by=10)) ] = "." blocks <- matrix(NA, ncol=nblocks, nrow=nseq) for(i in 1:nblocks) { ##- Sequence block positions <- block.start[i]:block.end[i] blocks[,i] <- paste0(ids, apply(ali[, positions, drop=FALSE], 1, paste, collapse="")) ##-- Formated Printing of annotations (numbers & ticks) and sequence blocks if(numbers) { ##- Annotations for each sequence block annot = block.annot[1:length(positions)] annot[length(annot)] = block.end[i] annot[1] = sprintf(pad.format, block.start[i]) cat(paste(annot, collapse=""),"\n") } ##- Sequence block cat(blocks[,i], sep="\n") ##- Formated Printing of conservation (stars for conserved columns) if(conservation) { annot2 <- c("", cons[positions]) annot2[1] = sprintf(pad.format2, "") cat(paste(annot2, collapse=""),"\n") } ##- Ticks + numbers again if(numbers) { cat(paste(annot, collapse=""),"\n\n") } else{ cat("\n") } } ##invisible(blocks) ## Can be useful for plot.fasta() later!! } bio3d/R/dssp.pdb.R0000644000176200001440000002632712526367343013340 0ustar liggesusers## NOTE: ## We do not support old-version DSSP any longer ## Please update your DSSP program to the newest version "dssp.pdb" <- function (pdb, exefile = "dssp", resno=TRUE, full=FALSE, verbose=FALSE, ...) { ## Log the call cl <- match.call() ## Check if the program is executable os1 <- .Platform$OS.type status <- system(paste(exefile, "--version"), ignore.stderr = TRUE, ignore.stdout = TRUE) ### if(!(status %in% c(0,1))) ### stop(paste("Launching external program 'DSSP' failed\n", ### " make sure '", exefile, "' is in your search path", sep="")) ## check atom composition - need backbone atoms to continue SSE analysis checkatoms <- TRUE if(checkatoms) { inds <- atom.select(pdb, "backbone", verbose=verbose) tmp <- trim.pdb(pdb, inds) resid <- paste(tmp$atom$resno, tmp$atom$chain, sep="-") musthave <- c("C", "CA", "N", "O") incomplete <- sapply(unique(resid), function(x) { inds <- which(resid==x) elety <- sort(tmp$atom$elety[inds]) if(!all(musthave %in% elety)) return(TRUE) else return(FALSE) }) if(all(incomplete)) stop("No residues found with a complete set of backbone atoms") if(any(incomplete)) warning(paste("Residues with missing backbone atoms detected:", paste(unique(resid)[incomplete], collapse=", "), collapse=" ")) } infile <- tempfile() outfile <- tempfile() write.pdb(pdb, file = infile) cmd <- paste(exefile, infile, outfile) if(verbose) cat(paste("Running command:\n ", cmd , "\n")) if(os1 == "windows") success <- shell(cmd, ignore.stderr = !verbose, ignore.stdout = !verbose) else success <- system(cmd, ignore.stderr = !verbose, ignore.stdout = !verbose) if(success!=0) stop(paste("An error occurred while running command\n '", cmd, "'", sep="")) ## ## For Debug (Tue Aug 3 18:22:11 PDT 2010) ## -- Following multi chain error report from Heiko Strathmann ## outfile <- "2jk2.dssp" ## outfile <- "4q21.dssp" ## trim <- function(s) { s <- sub("^ +", "", s) s <- sub(" +$", "", s) s[(s == "")] <- NA s } split.line <- function(x, split=" ") { tmp <- unlist(strsplit(x, split=split)) inds <- which(tmp!="") return(trim(tmp[inds])) } raw.lines <- readLines(outfile) unlink(c(infile, outfile)) type <- substring(raw.lines, 1, 3) raw.lines <- raw.lines[-(1:which(type == " #"))] ## delete chain breaking lines aa <- substring(raw.lines, 14, 14) if(any(aa == "!")) raw.lines <- raw.lines[-which(aa == "!")] cha <- substring(raw.lines, 12, 12) sse <- substring(raw.lines, 17, 17) res.name <- substring(raw.lines, 14, 14) res.id <- as.numeric(substring(raw.lines, 1, 5)) ## dssp residue IDs res.num <- as.numeric(substring(raw.lines, 6, 10)) ## Residue numbers res.insert <- substring(raw.lines, 11, 11) ## Insertion codes res.ind <- 1:length(res.num) ## Internal indices if(any(res.insert!=" ")) { if(resno) { warning("Insertions are found in PDB: Residue numbers may be incorrect. Try again with resno=FALSE") } else { ii <- diff(res.num) ii[ii==0] <- 1 #Consecutive numbers at insertion residues ii[ii<0] <- 2 #Jumps at possible chain termination res.num <- res.num[1] + c(0, cumsum(ii)) } } if(full) { ## Difference between sse res id and internal res indices diff <- res.id - res.ind names(diff) <- res.id ## Beta bridge partner residue ids bp1 <- as.numeric(substring(raw.lines, 26, 29)) bp2 <- as.numeric(substring(raw.lines, 30, 33)) bp1[bp1==0] <- NA bp2[bp2==0] <- NA ## Convert from dssp SSE residue IDs to internal residue indices bp1[ !is.na(bp1) ] <- as.vector(bp1[ !is.na(bp1) ] - diff[as.character(bp1[!is.na(bp1)])]) bp2[ !is.na(bp2) ] <- as.vector(bp2[ !is.na(bp2) ] - diff[as.character(bp2[!is.na(bp2)])]) ## H-bond records hbonds <- split.line(split.line(substring(raw.lines, 40, 83), split=","), split=" ") hbonds <- matrix(as.numeric(hbonds), ncol=8, byrow=TRUE) hbonds <- as.data.frame(hbonds) for(i in seq(1,7,by=2)) { hbonds[[i]][ which(hbonds[[i]]==0) ] <- NA ## Convert from relative to absolute residue numbering hmm <- res.id + hbonds[[i]] ## Convert from dssp SSE residue IDs to internal residue indices hbonds[[i]][ !is.na(hmm) ] <- as.vector(hmm[ !is.na(hmm) ] - diff[as.character(hmm[!is.na(hmm)])]) } ## Bind bridge pair and H-bond records to one matrix hbonds <- cbind(bp1, bp2, hbonds) cnames <- c("BP1", "BP2", "NH-O.1", "E1", "O-HN.1", "E2", "NH-O.2", "E3", "O-HN.2", "E4") colnames(hbonds) <- cnames if(resno) { ## 2 col matrix mapping the res.ind's to res.num and chain id tmp.map <- cbind(res.num, cha) row.names(tmp.map) <- res.ind ## Add an additional matrix holding the Chain IDs hbonds <- cbind(hbonds, data.frame(matrix(NA, ncol=6, nrow=nrow(tmp.map)), stringsAsFactors=FALSE)) colnames(hbonds) <- c(cnames, "ChainBP1", "ChainBP2", "Chain1", "Chain2", "Chain3", "Chain4") ## Add chain IDs for each entry tmp.inds <- which(!is.na(hbonds[,"BP1"])) tmp.names <- as.character(hbonds[tmp.inds,"BP1"]) hbonds[tmp.inds,"BP1"] <- as.numeric(tmp.map[tmp.names, "res.num"]) hbonds[tmp.inds,"ChainBP1"] <- tmp.map[tmp.names, "cha"] tmp.inds <- which(!is.na(hbonds[,"BP2"])) tmp.names <- as.character(hbonds[tmp.inds,"BP2"]) hbonds[tmp.inds,"BP2"] <- as.numeric(tmp.map[tmp.names, "res.num"]) hbonds[tmp.inds,"ChainBP2"] <- tmp.map[tmp.names, "cha"] tmp.inds <- which(!is.na(hbonds[,"NH-O.1"])) tmp.names <- as.character(hbonds[tmp.inds,"NH-O.1"]) hbonds[tmp.inds,"NH-O.1"] <- as.numeric(tmp.map[tmp.names, "res.num"]) hbonds[tmp.inds,"Chain1"] <- tmp.map[tmp.names, "cha"] tmp.inds <- which(!is.na(hbonds[,"O-HN.1"])) tmp.names <- as.character(hbonds[tmp.inds,"O-HN.1"]) hbonds[tmp.inds,"O-HN.1"] <- as.numeric(tmp.map[tmp.names, "res.num"]) hbonds[tmp.inds,"Chain2"] <- tmp.map[tmp.names, "cha"] tmp.inds <- which(!is.na(hbonds[,"NH-O.2"])) tmp.names <- as.character(hbonds[tmp.inds,"NH-O.2"]) hbonds[tmp.inds,"NH-O.2"] <- as.numeric(tmp.map[tmp.names, "res.num"]) hbonds[tmp.inds,"Chain3"] <- tmp.map[tmp.names, "cha"] tmp.inds <- which(!is.na(hbonds[,"O-HN.2"])) tmp.names <- as.character(hbonds[tmp.inds,"O-HN.2"]) hbonds[tmp.inds,"O-HN.2"] <- as.numeric(tmp.map[tmp.names, "res.num"]) hbonds[tmp.inds,"Chain4"] <- tmp.map[tmp.names, "cha"] ## Set row names to "RESNUM-CHAINID" row.names(hbonds) <- apply(tmp.map, 1, paste, collapse="-") } } else { hbonds <- NULL } # column numbers of phi and psi are different between # the old and new versions of DSSP phi <- as.numeric(substring(raw.lines, 104, 109)) psi <- as.numeric(substring(raw.lines, 110, 115)) acc <- as.numeric(substring(raw.lines, 35, 38)) h.res <- bounds(res.num[which(sse == "H")], pre.sort=FALSE) g.res <- bounds(res.num[which(sse == "G")], pre.sort=FALSE) e.res <- bounds(res.num[which(sse == "E")], pre.sort=FALSE) t.res <- bounds(res.num[which(sse == "T")], pre.sort=FALSE) h.ind <- h.res; g.ind <- g.res e.ind <- e.res; t.ind <- t.res if(length(h.res) > 0) { res.ind <- which(sse == "H") h.ind[, "end"] <- res.ind[cumsum(h.res[, "length"])] h.ind[, "start"] <- h.ind[, "end"] - h.res[, "length"] + 1 } if(length(g.res) > 0) { res.ind <- which(sse == "G") g.ind[, "end"] <- res.ind[cumsum(g.res[, "length"])] g.ind[, "start"] <- g.ind[, "end"] - g.res[, "length"] + 1 } if(length(e.res) > 0) { res.ind <- which(sse == "E") e.ind[, "end"] <- res.ind[cumsum(e.res[, "length"])] e.ind[, "start"] <- e.ind[, "end"] - e.res[, "length"] + 1 } if(length(t.res) > 0) { res.ind <- which(sse == "T") t.ind[, "end"] <- res.ind[cumsum(t.res[, "length"])] t.ind[, "start"] <- t.ind[, "end"] - t.res[, "length"] + 1 } if(!resno) { h.res <- h.ind; g.res <- g.ind e.res <- e.ind; t.res <- t.ind } sheet = list(start=NULL, end=NULL, length=NULL, chain=NULL) helix = list(start=NULL, end=NULL, length=NULL, chain=NULL, type=NULL) turn = sheet ## ToDo: Add "type" for turns and strands too... if(length(h.res)>1) { # if(is.null(nrow(h.res))) # h.s <- as.matrix(t(h.res)) helix$start = c(helix$start,h.res[, "start"]) helix$end = c(helix$end, h.res[, "end"]) helix$length = c(helix$length, h.res[, "length"]) helix$chain = c(helix$chain, cha[h.ind[, "start"]]) helix$type = c(helix$type, sse[h.ind[, "start"]]) } if(length(g.res)>1) { # if(is.null(nrow(g.res))) # g.s <- as.matrix(t(g.res)) helix$start = c(helix$start,g.res[, "start"]) helix$end = c(helix$end, g.res[, "end"]) helix$length = c(helix$length, g.res[, "length"]) helix$chain = c(helix$chain, cha[g.ind[, "start"]]) helix$type = c(helix$type, sse[g.ind[, "start"]]) } if(length(helix$start) > 0) helix <- lapply(helix, function(x) {names(x) <- 1:length(helix$start); return(x)}) if(length(e.res)>1) { # if(is.null(nrow(e.res))) # e.s <- as.matrix(t(e.res)) sheet$start = c(sheet$start,e.res[, "start"]) sheet$end = c(sheet$end, e.res[, "end"]) sheet$length = c(sheet$length, e.res[, "length"]) sheet$chain = c(sheet$chain, cha[e.ind[, "start"]]) } if(length(sheet$start) > 0) sheet <- lapply(sheet, function(x) {names(x) <- 1:length(sheet$start); return(x)}) if(length(t.res)>1) { # if(is.null(nrow(t.res))) # t.s <- as.matrix(t(t.res)) turn$start = c(turn$start,t.res[, "start"]) turn$end = c(turn$end, t.res[, "end"]) turn$length = c(turn$length, t.res[, "length"]) turn$chain = c(turn$chain, cha[t.ind[, "start"]]) } if(length(turn$start) > 0) turn <- lapply(turn, function(x) {names(x) <- 1:length(turn$start); return(x)}) out <- list(helix = helix, sheet = sheet, hbonds = hbonds, turn = turn, phi = phi, psi = psi, acc = acc, sse = sse, call=cl) class(out) <- "sse" return(out) } bio3d/R/angle.xyz.R0000644000176200001440000000145712412621431013520 0ustar liggesusers"angle.xyz" <- function(xyz, atm.inc=3) { if(!is.vector(xyz) || !is.numeric(xyz)) stop("input 'xyz' should be a numeric vector") natm <- length(xyz)/3 if(natm < 3) stop("Need at least three atoms to define an angle") if(natm %% 1 != 0) stop("There should be three 'xyz' elements per atom") m.xyz <- matrix(xyz, nrow=3) atm.inds <- c(1:3); out <- NULL while(atm.inds[3] <= natm) { if( any(is.na( m.xyz[,atm.inds] )) ) { ang <- NA } else { d1 <- m.xyz[,atm.inds[1]] - m.xyz[,atm.inds[2]] d2 <- m.xyz[,atm.inds[3]] - m.xyz[,atm.inds[2]] ang <- sum(d1*d2) / (sqrt(sum(d1^2)) * sqrt(sum(d2^2)) ) ang[ang > 1] <- 1; ang[ang < -1] <- -1 ang <- acos(ang) * (180/pi) } out <- c(out, ang) atm.inds <- atm.inds + atm.inc } return(out) } bio3d/R/pdbaln.R0000644000176200001440000000601512632622153013042 0ustar liggesusers`pdbaln` <- function(files, fit=FALSE, pqr=FALSE, ncore=1, nseg.scale=1, ...) { ## Log the call cl <- match.call() ##- Quick and dirty alignment of pdb sequences ## pdbs <- pdbaln(files) ## ## ## 'files' should be a character vector of input PDB file names ## '...' extra arguments for seqaln ## ## Improvements to include 'atom.select' arguments (chain ## spliting etc), formalisation of 'pdb.list' into a specific ## bio3d object of multiple structures like 'pdbs'. ## ## pdb.list[[1]]$atom[1:3,] # Parallelized by parallel package (Fri Apr 26 19:24:18 EDT 2013) ncore <- setup.ncore(ncore) if(ncore > 1) { # Issue of serialization problem # Maximal number of cells of a double-precision matrix # that each core can serialize: (2^31-1-61)/8 R_NCELL_LIMIT_CORE = 2.68435448e8 R_NCELL_LIMIT = ncore * R_NCELL_LIMIT_CORE if(nseg.scale < 1) { warning("nseg.scale should be 1 or a larger integer\n") nseg.scale=1 } } ## Check if input PDB files exist localy or online toread.local <- file.exists(files) toread.online <- (substr(files,1,4)=="http") toread.id <- rep(FALSE, length(files)) toread <- as.logical(toread.local + toread.online) ## Check for 4 letter code and possible online file if(any(!toread)) { toread.id <- ((nchar(files)==4) + (!toread) == 2) files[toread.id] <- get.pdb(files[toread.id], URLonly=TRUE) cat(" Note: Accessing online PDB files using 4 letter PDBID\n") } ## Exit if we still have missing files missing <- !as.logical(toread + toread.id) if(any(missing)) { stop(paste(" ** Missing files: check filenames\n", paste( files[c(missing)], collapse="\n"),"\n",sep="") ) } # Avoid multi-thread downloading if(any(toread.online | toread.id)) { ncore = 1 options(cores = ncore) } cat("Reading PDB files:",files, sep="\n") mylapply <- lapply if(ncore > 1) mylapply <- mclapply pdb.list <- mylapply(1:length(files), function(i) { if(pqr) { pdb <- read.pqr(files[i]) } else { pdb <- read.pdb(files[i]) } cat(".") return( pdb ) } ) # pdb.list <- NULL # for(i in 1:length(files)) { # if(pqr) { # pdb.list[[ i ]] <- read.pqr(files[i]) # } else { # pdb.list[[ i ]] <- read.pdb(files[i]) # } # cat(".") # } cat("\n\nExtracting sequences\n") s <- mylapply(pdb.list, pdbseq) ## check for NULL in pdbseq output ## (this would indicate no amino acid sequence in PDB) tmpcheck <- unlist(lapply(s, is.null)) if(any(tmpcheck)) { err <- paste( "Could not align PDBs due to missing amino acid sequence in files:\n ", paste(files[tmpcheck], collapse=", ") ) stop(err) } s <- t(sapply(s, `[`, 1:max(sapply(s, length)))) s[is.na(s)] <- "-" ##s <- seqaln(s, id=files, extra.args="-quiet", ...) s <- seqaln(s, id=files, ...) cat("\n") s <- read.fasta.pdb(s, prefix = "", pdbext = "", ncore=ncore, nseg.scale=nseg.scale) s$call=cl if(fit) s$xyz <- pdbfit(s) return(s) } bio3d/R/entropy.R0000644000176200001440000000410712430771420013300 0ustar liggesusers"entropy" <- function(alignment) { # Calculate the Shannon entropy score for each position # in an alignment if(is.list(alignment)) alignment=alignment$ali aa <- c("V","I","L","M", "F","W","Y", "S","T", "N","Q", "H","K","R", "D","E", "A","G", "P", "C", "-","X") composition <- table(alignment) unk <- composition[!names( composition ) %in% aa] if(length(unk) > 0) { warning(paste("non standard residue code:",names(unk),"mapped to X\n ")) for(i in 1:length(unk)) alignment[alignment==names(unk[i])]="X" } len <- ncol(alignment) freq.22 <- matrix(0, nrow = 22, ncol = ncol(alignment), dimnames = list(aa,seq(1:len))) freq.10 <- matrix(0, nrow = 10, ncol = ncol(alignment), dimnames = list(c(1:10),c(1:len))) for (i in 1:len) { freq.22[names(summary((as.factor(toupper(alignment[,i]))))), i] <- (summary(as.factor(toupper(alignment[,i])))/length(alignment[,i])) freq.10[1,i] <- sum(freq.22[1:4,i]) # Hydrophobic, Aliphatic freq.10[2,i] <- sum(freq.22[5:7,i]) # Aromatic freq.10[3,i] <- sum(freq.22[8:9,i]) # Ser/Thr freq.10[4,i] <- sum(freq.22[10:11,i]) # Polar freq.10[5,i] <- sum(freq.22[12:14,i]) # Positive freq.10[6,i] <- sum(freq.22[15:16,i]) # Negative freq.10[7,i] <- sum(freq.22[17:18,i]) # Tiny freq.10[8,i] <- sum(freq.22[19,i]) # Proline freq.10[9,i] <- sum(freq.22[20,i]) # Cysteine freq.10[10,i] <- sum(freq.22[21:22,i]) # Gaps } entropy.22 <- vector(length = len) entropy.10 <- entropy.22 for (i in 1:len) { # entropy_i = sum[i] (P(X_i)log2(P(X_i))) entropy.22[i] <- -1*sum(freq.22[freq.22[, i] != 0, i] * log2(freq.22[freq.22[, i] != 0, i])) entropy.10[i] <- -1*sum(freq.10[freq.10[, i] != 0, i] * log2(freq.10[freq.10[, i] != 0, i])) } out <- list(H=entropy.22, H.10=entropy.10, H.norm=(1-(entropy.22/max(entropy.22))), H.10.norm=(1-(entropy.10/max(entropy.10))), freq=freq.22) } bio3d/R/normalize.vector.R0000644000176200001440000000062312412621431015074 0ustar liggesusers"normalize.vector" <- function(x, mass=NULL) { x <- as.matrix(x); dx <- dim(x); if(dx[2]==1) x <- as.numeric(x) if(!is.null(mass)) { if (dx[1] != (length(mass)*3)) stop("normalize.vector: incorrect length of mass") } if(is.matrix(x)) return(t( t(x) / sqrt(inner.prod(x,x,mass)))) else return(x / sqrt(inner.prod(x,x,mass))) } bio3d/R/hclustplot.R0000644000176200001440000001004212526367343014007 0ustar liggesusers"hclustplot" <- function(hc, k=NULL, h=NULL, colors=NULL, labels=NULL, fillbox=FALSE, heights = c(1, .3), mar = c(1, 1, 0, 1), ...) { if(!inherits(hc, "hclust")) stop("hc must be of type 'hclust'") if(is.null(k) & is.null(h) & is.null(colors)) stop("provide either k or h to function 'cutree', or colors for manual coloring") mtext.names <- names(formals( mtext )) plot.dendrogram <- get("plot.dendrogram", envir = getNamespace("stats")) plot.names <- c(names(formals( plot.dendrogram )), names(formals( plot.default ))) dots <- list(...) mtext.args <- dots[names(dots) %in% mtext.names] plot.args <- dots[names(dots) %in% plot.names] par.args <- dots[!(names(dots) %in% unique(c(names(mtext.args), names(plot.args))))] mtext.args <- c(mtext.args, par.args) plot.args <- c(plot.args, par.args) ## set default and allowed mtext arguments if(!any(names(mtext.args)=="line")) { if( fillbox ) mtext.args$line <- 0.5 else mtext.args$line <- -0.25 } if(!any(names(mtext.args)=="side")) mtext.args$side <- 1 if(!any(names(mtext.args)=="las")) mtext.args$las <- 2 if(any(names(mtext.args)=="col")) mtext.args$col <- NULL if(any(names(mtext.args)=="at")) mtext.args$at <- NULL ## set default and allowed plot.dendrogram arguments if(any(names(plot.args)=="axes")) { axes <- plot.args$axes plot.args$axes <- NULL } else { axes <- TRUE } if(any(names(plot.args)=="xaxs")) plot.args$xaxs <- NULL if(any(names(plot.args)=="leaflab")) plot.args$leaflab <- NULL if(any(names(plot.args)=="xlab")) plot.args$xlab <- NULL if(any(names(plot.args)=="horiz")) plot.args$horiz <- NULL ## print(mtext.args) ## print(par.args) ## print(plot.args) plot.labels <- TRUE if(is.logical(labels)) { if(labels) plot.labels <- TRUE else plot.labels <- FALSE labels <- NULL } if(is.null(labels)) { labels <- hc$labels if(is.null(labels)) labels <- seq(1, length(hc$order)) } else { if( length(hc$order) != length(labels) ) stop("labels must be of same length as hc") } if(!is.null(colors)) { unq.cols <- unique(colors) grps <- unlist(lapply(colors, function(x) which(x==unq.cols))) labelColors <- unq.cols } else { grps <- cutree(hc, k=k, h=h) labelColors <- seq(1, length(unique(grps))) } hcd <- as.dendrogram(hc) cols <- labelColors[grps][hc$order] ## set margins mar.default <- c(1, 1, 0, 1) if(all(mar==mar.default)) { mar <- mar.default mar[1] <- mar[1] + ifelse(plot.labels, 3, 0) mar[1] <- mar[1] + ifelse(fillbox, 2, 0) mar[2] <- mar[2] + ifelse(!is.null(plot.args$ylab), 2, 0) mar[2] <- mar[2] + ifelse(axes, 2, 0) mar[3] <- ifelse(!is.null(plot.args$ylab), 4, 2) } ## colored filled boxes below the dendrogram if( fillbox ) { ##| plot.labels ) { layout(as.matrix(c(2,1)), heights = heights) dev.hold() on.exit(dev.flush()) op <- par(no.readonly = TRUE) on.exit(par(op), add = TRUE) par(mar=c(mar[1], mar[2], 0, mar[4])) if( fillbox ) image(cbind(1:length(grps)), col = cols, axes = FALSE) else frame() if(plot.labels) { do.call('mtext', c(list(text=labels[ hc$order ], at=seq(0, 1, length.out=length(grps)), col=cols), mtext.args)) } } else { layout(1) } ## dendrogram par(mar = c(ifelse(fillbox, 0, mar[1]), mar[2], mar[3], mar[4])) do.call('plot', c(list(x=hcd, axes=FALSE, leaflab="none", xaxs="i"), plot.args)) if(axes) axis(2) ## labels when filled boxes are not drawn if(plot.labels & !fillbox ) { do.call('mtext', c(list(text=labels[ hc$order ], at=seq(1, length(grps)), col=cols), mtext.args)) } ##if(!is.null(sub)) { ## mtext(sub, side=3, line=-0.5) ##} } bio3d/R/dm.xyz.R0000644000176200001440000000621412632622153013034 0ustar liggesusersdm.xyz <- function(xyz, grpby=NULL, scut=NULL, mask.lower=TRUE, ncore=1, ...) { ## Parallelized by parallel package ncore <- setup.ncore(ncore, bigmem = FALSE) if(ncore > 1) { mcparallel <- get("mcparallel", envir = getNamespace("parallel")) mccollect <- get("mccollect", envir = getNamespace("parallel")) } ## function for multicore calculation of dmats calcdm <- function(r.inds, core.id, xyz) { j <- 1 out <- vector("list", length=length(r.inds)) for(i in r.inds) { dmi <- .dm.xyz1(xyz[i,], grpby=grpby, scut=scut, mask.lower=mask.lower) out[[j]] <- dmi j <- j+1 } return(out) } xyz <- as.xyz(xyz) if(nrow(xyz)>1) { ## dimensions of final array d3 <- nrow(xyz) if(!is.null(grpby)) d1 <- length(unique(grpby)) else d1 <- ncol(xyz)/3 dms <- array(data=0.00, dim=c(d1, d1, d3)) ## multicore setup if(ncore>1) { jobs <- list() } ## run calcdm() for each core core.ids <- sort(rep(1:ncore, length.out=d3)) for( i in 1:ncore ) { r.inds <- which(core.ids==i) if(ncore>1) { q <- mcparallel(calcdm(r.inds, i, xyz)) jobs[[i]] <- q } else { dm.list <- calcdm(r.inds, i, xyz) } } ## Collect all jobs if(ncore>1) res <- mccollect(jobs, wait=TRUE) else res <- list(dm.list) ## list to array i <- 1 for ( job in res ) { for(mat in job) { dms[,,i] <- mat i <- i+1 } } } else { dms <- .dm.xyz1(xyz, grpby=grpby, scut=scut, mask.lower=mask.lower) } return(dms) } .dm.xyz1 <- function(xyz, grpby=NULL, scut=NULL, mask.lower=TRUE) { ##-- New distance matrix function with 'grpby' option ## dm(pdb$xyz, grpby=pdb$atom[,"resno"], scut=3) xyz=as.xyz(xyz) if(dim(xyz)[1L]>1) { warning("multiple frames detected - using only the first row in xyz matrix") xyz = xyz[1,, drop=FALSE] } xyz=as.vector(xyz) ##- Full Distance matrix (could use 'dm' or 'dist.xyz') d <- as.matrix(dist(matrix(xyz, ncol = 3, byrow = TRUE))) ##- Mask lower.tri if( mask.lower ) d[lower.tri(d)] = NA ##- Mask concetive atoms if( is.null(grpby) ) { if (!is.null(scut)) { d[diag.ind(d, n = scut)] = NA if(!mask.lower) d[lower.tri(d)] = t(d)[lower.tri(d)] } return(d) } else { ##- Group by concetive numbers in 'grpby' if( length(xyz) != (length(grpby)*3) ) stop("dimension miss-match in 'xyz' and 'grpby', check lengths") ##- Bounds of 'grpby' numbers inds <- bounds(grpby, dup.inds=TRUE) nres <- nrow(inds) ##- Per-residue matrix m <- matrix(, ncol=nres, nrow=nres) ij <- pairwise(nres) ## Ignore concetive groups (residues) if (!is.null(scut)) ij <- ij[ij[,2]-ij[,1] > (scut-1),] ##- Min per residue for(k in 1 : nrow(ij) ) { m[ij[k,1],ij[k,2]] <- min( d[ (inds[ij[k,1],"start"]:inds[ij[k,1],"end"]), (inds[ij[k,2],"start"]:inds[ij[k,2],"end"])], na.rm=TRUE ) } if( !mask.lower ) m[lower.tri(m)] = t(m)[lower.tri(m)] return(m) } } bio3d/R/as.select.R0000644000176200001440000000071212544562302013462 0ustar liggesusers"as.select" <- function(x, ...) { cl <- match.call() if(is.select(x)) { return(x) } else { if(!is.vector(x)) stop("provide a numeric vector of atom indices") if(all(is.logical(x))) x <- which(x) if(!all(is.numeric(x))) stop("provide a numeric vector of atom indices") sele <- NULL sele$atom <- x sele$xyz <- atom2xyz(x) sele$call <- cl class(sele) <- "select" return(sele) } } bio3d/R/pca.array.R0000644000176200001440000000174612526367343013501 0ustar liggesusers"pca.array" <- function(x, use.svd=TRUE, ...) { if(!is.array(x)) stop("provide an array of matrices") ## Log the call cl <- match.call() ## Construct the input matrix for PCA x <- t(apply(x, 3, function(y) y[upper.tri(y)])) dx <- dim(x) n <- dx[1]; p <- dx[2] mean <- colMeans(x) if(!use.svd) { ## coverance matrix S <- var(x) ## eigenvectors ("U") & eigenvalues ("L"): [ U'SU=L ] prj <- eigen(S, symmetric = TRUE) L <- prj$values U <- prj$vectors } else { ## S = Q'Q, Q = UDV' Q <- t(t(x) - mean) / sqrt(n-1) prj <- svd(Q) L <- prj$d^2 U <- prj$v } ## fix negative eigenvalues ## (these are very small numbers and should be zero) L[L<0]<-0 sdev <- sqrt(L) ## scores of "xyz" on the pc's [ z=U'[x-x.mean] ] z <- sweep(x,2,mean) %*% (U) class(U)="pca.loadings" out <- list(L=L, U=U, z=z, sdev=sdev, mean=mean, call=cl) class(out)="pca" return(out) } bio3d/R/pdb2aln.R0000644000176200001440000000512112526367343013132 0ustar liggesusers"pdb2aln" <- function(aln, pdb, id="seq.pdb", aln.id=NULL, file="pdb2aln.fa", ...) { # check inputs if(!inherits(aln, "fasta") || !is.pdb(pdb)) stop("Incorrect type of input object: Should be 'pdb2aln(aln, pdb, ...)'") cl <- match.call() # Mask the gaps in the first sequence to get the # reference of original alignment positions aln$ali[1, is.gap(aln$ali[1,])] <- "X" aa1 <- pdbseq(pdb) if(!is.null(aln.id)) findid <- grep(aln.id, aln$id) if(is.null(aln.id) || length(findid)==0) { # do sequence-profile alignment naln <- seq2aln(seq2add=aa1, aln=aln, id=id, file=tempfile(), ...) # check if the old alignment doesn't change if(!identical(aln$ali, naln$ali[1:(nrow(naln$ali)-1), !is.gap(naln$ali[1,]), drop = FALSE])) warning("Alignment changed! Try aln.id with the closest sequence ID in the alignment") } else { # do pairwise sequence alignment if(length(findid) > 1) { warning(paste("Multiple entities found in alignment for id=", aln.id, ". Use the first one...", sep="")) } idhit <- findid[1] ##- Align seq to masked template from alignment tmp.msk <- aln$ali[idhit, ] tmp.msk[is.gap(tmp.msk)] <- "X" dots = list(...) dots$outfile = tempfile() args = c(list(aln = seqbind(tmp.msk, aa1)), dots) seq2tmp <- do.call(seqaln.pair, args) ##- check sequence identity ii <- seq2tmp$ali[1,]=='X' ide <- seqidentity(seq2tmp$ali[, !ii])[1,2] if(ide < 0.4) { warning(paste("Sequence identity is too low (<40%).", "You may want profile alignment (set aln.id=NULL)", sep=" ")) } ##- Insert gaps to adjust alignment ins <- is.gap( seq2tmp$ali[1,] ) ntmp <- matrix("-", nrow=nrow(aln$ali), ncol=(ncol(aln$ali)+sum(ins))) ntmp[,!ins] <- aln$ali ## Add seq to bottom of adjusted alignment naln <- seqbind(ntmp, seq2tmp$ali[2,]) rownames(naln$ali) <- c(rownames(aln$ali), id) naln$id <- c(aln$id, id) } # original alignment positions (include gaps) # and CA indices of PDB ref <- matrix(NA, nrow=2, ncol=ncol(naln$ali)) rownames(ref) <- c("ali.pos", "ca.inds") ref[1, !is.gap(naln$ali[1,])] <- 1:ncol(aln$ali) ref[2, !is.gap(naln$ali[id,])] <- atom.select(pdb, "calpha", verbose=FALSE)$atom # remove X naln$ali[1, naln$ali[1,]=="X"] <- "-" if(!is.null(file)) write.fasta(naln, file=file) out <- list(id=naln$id, ali=naln$ali, ref=ref, call=cl) class(out) <- "fasta" return (out) } bio3d/R/print.prmtop.R0000644000176200001440000000464712526367343014300 0ustar liggesusersprint.prmtop <- function(x, printseq=TRUE, ...) { if(!is.null(x$SOLVENT_POINTER)) sbox <- TRUE else sbox <- FALSE cn <- class(x) natom <- x$POINTERS[1] ##nca <- length(which(x$ATOM_NAME=="CA" & x$AMBER_ATOM_TYPE=="CX")) if(sbox) { nres.total <- x$POINTERS[12] nres.solute <- x$SOLVENT_POINTER[1] nmol.total <- x$SOLVENT_POINTER[2] nmol.solute <- x$SOLVENT_POINTER[3]-1 natom.per.mol <- x$ATOMS_PER_MOLECULE[1:nmol.solute] box.dim <- x$BOX_DIMENSIONS } else { nres.total <- x$POINTERS[12] nres.solute <- nres.total nmol.total <- length(which(x$ATOM_NAME=="OXT")) nmol.solute <- nmol.total } cat("\n Call:\n ", paste(deparse(x$call), sep = "\n", collapse = "\n"), "\n\n", sep = "") cat(" Class:\n ", paste(cn, collapse=", "), "\n\n", sep = "") cat(" System information:", "\n") cat(" Total number of atoms: ", natom, "\n", sep="") if(nres.solute!=nres.total) { cat(" Solute residues: ", nres.solute, " (of ", nres.total, ")\n", sep="") cat(" Solute molecules: ", nmol.solute, " (of ", nmol.total, ")\n", sep="") } else { cat(" Solute residues: ", nres.solute, "\n", sep="") cat(" Solute molecules: ", nmol.solute, "\n", sep="") } if(sbox) cat(" Box dimensions: ", paste(round(box.dim,2), collapse=" x "), "\n", sep="") if(printseq) { aa <- aa321(x$RESIDUE_LABEL[1:nres.solute]) if(nres.solute > 225) { ## Trim long sequences before output aa <- c(aa[1:225], "......", aa[(nres.solute-3):nres.solute]) } aa <- paste(" ", gsub(" ","", strwrap( paste(aa,collapse=" "), width=120, exdent=0) ), collapse="\n") cat("\n") cat(" Sequence:\n", aa, "\n", sep="") ## other residues if(nres.total>nres.solute) { unq.res <- unique(x$RESIDUE_LABEL[(nres.solute+1):length(x$RESIDUE_LABEL)]) unq.res <- paste(" ", gsub(" ","", strwrap( paste(unq.res,collapse=" "), width=120, exdent=0) ), collapse="\n") cat("\n") cat(" Residues in solvent:\n", unq.res, "\n", sep="") } } cat("\n") #i <- paste( attributes(x)$names, collapse=", ") #cat(strwrap(paste(" + attr:",i,"\n"),width=45, exdent=8), sep="\n") invisible( c(natom=natom, nres=nres.total, nres.solute=nres.solute, nmol=nmol.total, nmol.solute=nmol.solute) ) } bio3d/R/mktrj.nma.R0000644000176200001440000000222012632622171013475 0ustar liggesusers"mktrj.nma" <- function(nma=NULL, # nma data structure mode=7, # which mode to move along mag=10, # magnification factor step=1.25, # step size file=NULL, # output pdb file ... ) { # args for write.pdb ## make a trjactory of atomic displacments along a given mode if(!inherits(nma, "nma")) stop("mktrj.nma: must supply 'nma' object, i.e. from 'nma'") if(is.null(file)) file <- paste("mode_", mode, ".pdb", sep="") #if(nma$L[mode]<=0) # stop("Mode with eigenvalue <=0 detected. Check 'mode' index.") nma$xyz <- as.vector(nma$xyz) nstep <- c(seq(step, to=mag, by=step)) zcoor <- cbind(1) %*% nstep scor <- function(x,u,m) { return(x*u+m) } plus <- sapply(c(zcoor), scor, u=nma$modes[,mode], m=nma$xyz) minus <- sapply(c(-zcoor), scor, u=nma$modes[,mode], m=nma$xyz) coor <- t(cbind(nma$xyz, plus, plus[,rev(1:ncol(plus))], nma$xyz, minus, minus[,rev(1:ncol(minus))])) write.pdb(xyz=coor, file=file, ...) invisible(coor) } bio3d/R/write.ncdf.R0000644000176200001440000000730512632622153013650 0ustar liggesusers`write.ncdf` <- function(x, trjfile="R.ncdf", cell=NULL){ ##- Load ncdf4 package oops <- requireNamespace("ncdf4", quietly = TRUE) if(!oops) stop("Please install the ncdf4 package from CRAN") ## Error checking if(!is.matrix(x)) stop("input x should be a natom by nframe numeric matrix of coordinates") if(!is.null(cell)) { if(!is.matrix(cell)) stop("input cell should be a 6 by nframe numeric matrix") } nframe <- nrow(x) natom <- ncol(x)/3 ## Define dimensions frame <- ncdf4::ncdim_def(name="frame", units="", vals=c(1:nframe), unlim=TRUE, create_dimvar=FALSE) spatial <- ncdf4::ncdim_def(name="spatial", units="", vals=1:3, #c(1:3),#"xyz", unlim=FALSE, create_dimvar=TRUE) atom <- ncdf4::ncdim_def(name="atom", units="", vals=c(1:natom), unlim=FALSE, create_dimvar=FALSE) if(!is.null(cell)) { label <- ncdf4::ncdim_def(name="label", units="", vals=1:5, unlim=FALSE, create_dimvar=FALSE) cell_spatial <- ncdf4::ncdim_def(name="cell_spatial", units="", vals=1:3, unlim=FALSE, create_dimvar=TRUE) cell_angular <- ncdf4::ncdim_def(name="cell_angular", units="", vals=1:3, unlim=FALSE, create_dimvar=TRUE) } ## label <- dim.def.ncdf(name="label", units="", vals=1:5, ##??? ## unlim=FALSE, create_dimvar=FALSE) ## cells <- dim.def.ncdf(name="cell_spatial", units="", vals=1:3,# "abc", ## unlim=FALSE, create_dimvar=TRUE) ## cella <- dim.def.ncdf(name="cell_angular", units="", vals=1:3, ## #vals=c("alpha", "beta", "gamma"), ## unlim=FALSE, create_dimvar=TRUE) ## Define variables time <- ncdf4::ncvar_def(name="time", units="picosecond", dim=frame, missval=1e+30, prec="float") #"single" float coor <- ncdf4::ncvar_def(name="coordinates", units="angstrom", missval=1e+30, dim=list(spatial,atom,frame), prec="float")#"single" float if(!is.null(cell)) { cell_lengths <- ncdf4::ncvar_def(name="cell_lengths", units="angstrom", missval=1e+30, dim=list(cell_spatial, frame), prec="double") cell_angles <- ncdf4::ncvar_def(name="cell_angles", units="degree", missval=1e+30, dim=list(cell_angular, frame), prec="double") } ## cell.len <- var.def.ncdf(name="cell_lengths", units="angstrom", missval=1e+30, ## dim=list(cells,frame), prec="double") ## cell.ang <- var.def.ncdf(name="cell_angles", units="degrees", missval=1e+30, ## dim=list(cella,frame), prec="double") ## Create the file if(!is.null(cell)) { ncw <- ncdf4::nc_create( trjfile, list(time, coor, cell_lengths, cell_angles)) } else { ncw <- ncdf4::nc_create( trjfile, list(time, coor))#, cell.len, cell.ang) ) } ## Write data to file if(is.null(rownames(x))) ncdf4::ncvar_put(ncw, time, c(1:nframe), start=1, count=nframe) else ncdf4::ncvar_put(ncw, time, as.numeric(rownames(x)), start=1, count=nframe) ncdf4::ncvar_put( ncw, coor, t(x), start=c(1,1,1), count=c(3,natom,nframe)) if(!is.null(cell)) { ncdf4::ncvar_put( ncw, cell_lengths, t(cell[,1:3]), start=c(1,1), count=c(3,nframe)) ncdf4::ncvar_put( ncw, cell_angles, t(cell[,4:6]), start=c(1,1), count=c(3,nframe)) } ## Define Required Attributes ncdf4::ncatt_put(ncw, varid=0, attname="Conventions", attval="AMBER") ncdf4::ncatt_put(ncw, varid=0, attname="ConventionVersion", attval="1.0") ncdf4::ncatt_put(ncw, varid=0, attname="program",attval="bio3d") ncdf4::ncatt_put(ncw, varid=0, attname="programVersion", attval="1.2") null <- ncdf4::nc_close(ncw) } bio3d/R/is.gap.R0000644000176200001440000000040712526367343012773 0ustar liggesusers`is.gap` <- function(x, gap.char=c("-",".")) { if(is.pdbs(x) || class(x)=="fasta") { return( colSums( matrix( as.logical(is.na(x$ali)+(x$ali %in% gap.char)), ncol=ncol(x$ali)) ) > 0 ) } else { return( as.logical( is.na(x) + (x %in% gap.char) ) ) } } bio3d/R/cnapath.R0000644000176200001440000000765012632622153013226 0ustar liggesusers# Correlation network suboptimal path analysis # # Reference # Yen, J.Y. (1971) Finding the K Shortest Loopless Paths in a Network. # Management Science. 17(11):712-716. cnapath <- function(cna, from, to, k = 10, ncore = NULL, ...) { oops <- requireNamespace("igraph", quietly = TRUE) if (!oops) stop("igraph package missing: Please install, see: ?install.packages") if(!inherits(cna, "cna")) stop("Input cna is not a 'cna' object") ncore = setup.ncore(ncore) graph = cna$network # which path from the list is the shortest? select.shortest.path <- function(variants){ return( which.min( unlist( lapply( variants, function(x){x$dist} ) ) ) ) } # does a list contain this path? contains.path <- function(variants, variant){ return( any( unlist( lapply( variants, function(x){ isTRUE(all.equal(x$path, variant)) } ) ) ) ) } # first shortest path k0 <- igraph::get.shortest.paths(graph, from, to, output='both', ...) # if no shortest path found, network contains isolated parts. if(length(k0$vpath[[1]]) == 0) { cat(" No path found.\n", " Please check if the network contains isolated parts!\n\n", sep="") return(NULL) } # number of currently found shortest paths kk <- 1 # All shortest paths are stored in container A in order dist = sum(igraph::E(graph)$weight[k0$epath[[1]]]) A <- list(list(path=as.integer(k0$vpath[[1]]), epath=as.integer(k0$epath[[1]]), dist=dist)) # All candidates are stored in container B B <- list() # For progress bar pb <- txtProgressBar(min=0, max=k, style=3) # until k shortest paths are found while(kk < k){ # take last found shortest path last.path <- A[[length(A)]] tmpB <- mclapply(1:(length(last.path$path)-1), function(i) { spurNode <- last.path$path[i] rootPath <- last.path$path[1:i] if(i==1) rootePath = NULL else rootePath = last.path$epath[1:(i-1)] # Remove edges that coincide with the next step from the spur node on # those shortest paths stored in A that share the same root path here g <- graph for(j in 1:length(A)) { if(length(A[[j]]$path) > i && isTRUE(all.equal(rootPath, A[[j]]$path[1:i]))) { nn = A[[j]]$path[i+1] ee = igraph::E(g)[igraph::'%--%'(spurNode, nn)] if(length(ee)>0) g <- igraph::delete.edges(g, ee) } } # Remove all edges that link to nodes on the root path (excluding the spur node) if(i > 1) { for(j in rootPath[-(length(rootPath))]) { ee = igraph::E(g)[from(j)] if(length(ee)>0) g <- igraph::delete.edges(g, ee) } } # Suppress warnings because some nodes are intentionally isolated spurPath <- suppressWarnings(igraph::get.shortest.paths(g, spurNode, to, output='both'), ...) if(length(spurPath$vpath[[1]]) > 0 ) { vpath = c(rootPath, as.integer(spurPath$vpath[[1]][-1])) if(!contains.path(B, vpath)) { spurPath$epath <- as.integer(igraph::E(graph, path=as.integer(spurPath$vpath[[1]]))) epath = c(rootePath, spurPath$epath) return (list(path=vpath, epath = epath, dist = sum(igraph::E(graph)$weight[epath])) ) } } NULL } ) tmpB <- tmpB[ !sapply(tmpB, is.null) ] B <- c(B, tmpB) if(length(B) == 0) break # find shortest candidate sp <- select.shortest.path(B) # add to A, increase kk, remove shortest path from list of B A <- c(A, B[sp]) kk <- kk + 1 B <- B[-sp] setTxtProgressBar(pb, kk) } # stopped before reaching k paths if(kk < k) { setTxtProgressBar(pb, k) warning("Reaching maximal number of possible paths (", kk, ")") } close(pb) out <- list(path=lapply(A, "[[", "path"), epath = lapply(A, "[[", "epath"), dist = sapply(A, "[[", "dist")) class(out) <- c("cnapath", "list") return(out) } bio3d/R/plot.pca.score.R0000644000176200001440000000264112412621431014427 0ustar liggesusers"plot.pca.score" <- function(x, inds=NULL, col=rainbow(nrow(x)), lab="", ... ) { # Produces a z-score plot for PC1 vs PC2, # PC3 vs PC2 and PC1 vs PC3 if given a # matrix "z"that contains the column wise # scores obtained from PCA 'pca.xyz' if(is.list(x)) x=x$z # output from pca.xyz() if(is.null(inds)) inds <- 1:nrow(x) op <- par(no.readonly=TRUE) on.exit(par(op)) par(mfrow=c(2,2));par(pty="s") limits<-max(abs(c(range(x[,1]),range(x[,2]),range(x[,3])))) print(paste("axes limits: ",round(limits,2),sep="")) text.offset<-limits/19 plot(x[,1], x[,2], # pc1 vs pc2 xlim=c(-limits,limits), ylim=c(-limits,limits), xlab="PC1", ylab="PC2",col=col, ...) abline(h=0,col="gray",lty=2);abline(v=0,col="gray",lty=2) text(x[inds,1]+text.offset, x[inds,2]+text.offset, labels = lab[inds]) plot(x[,3], x[,2], # pc3 vs pc3 xlim=c(-limits,limits), ylim=c(-limits,limits), xlab="PC3", ylab="PC2", col=col, ...) abline(h=0,col="gray",lty=2);abline(v=0,col="gray",lty=2) text(x[inds,3]+text.offset, x[inds,2]+text.offset, labels = lab[inds]) plot(x[,1], x[,3], # pc1 vs pc3 xlim=c(-limits,limits), ylim=c(-limits,limits), xlab="PC1", ylab="PC3",col=col, ...) abline(h=0,col="gray",lty=2);abline(v=0,col="gray",lty=2) text(x[inds,1]+text.offset, x[inds,3]+text.offset, labels = lab[inds]) } bio3d/R/dssp.xyz.R0000644000176200001440000000076112544562302013407 0ustar liggesusersdssp.xyz <- function(xyz, pdb, ...) { if(!is.pdb(pdb)) stop("provide a pdb object as obtained from function 'read.pdb'") if(!is.xyz(xyz) && !is.matrix(xyz)) stop("provide an xyz object containing the trajectory coordinates") sse.mat <- NULL dims <- dim(xyz) for (i in 1:dims[1L]) { pdb.tmp <- pdb pdb.tmp$xyz <- as.xyz(xyz[i,]) sse <- dssp.pdb(pdb.tmp, ...)$sse sse.mat <- rbind(sse.mat, sse) } ##sse.mat[ sse.mat==" " ] <- "-" return(sse.mat) } bio3d/R/pca.R0000644000176200001440000000027012524171274012345 0ustar liggesusers"pca" <- function(...) { dots <- list(...) if(inherits(dots[[1]], "matrix")) { class(dots[[1]]) <- c("matrix", "xyz") UseMethod("pca", dots[[1]]) } UseMethod("pca") } bio3d/R/summary.cnapath.R0000644000176200001440000001307212632622153014715 0ustar liggesuserssummary.cnapath <- function(object, ..., pdb = NULL, label = NULL, col = NULL, plot = FALSE, concise = FALSE, cutoff = 0.1, normalize = TRUE) { pa <- list(object, ...) if(!all(sapply(pa, inherits, "cnapath"))) stop("Input pa is not a 'cnapath' object") if(is.null(label)) label = 1:length(pa) if(is.null(col)) col = 1:length(pa) out <- list() # read node numbers on paths y <- lapply(pa, function(x) unlist(x$path)) # store node degeneracy node.deg <- lapply(y, table) if(normalize) { node.deg <- lapply(node.deg, function(x) x/max(x)) } # find on-path node by the cutoff yy <- lapply(node.deg, function(x) x[x >= cutoff]) onpath.node <- unique(names(unlist(yy))) i <- as.numeric(onpath.node) onpath.node <- onpath.node[order(i)] # generate the node degeneracy table o <- lapply(node.deg, function(x) { x <- x[match(onpath.node, names(x))] x[is.na(x)] <- 0 names(x) <- onpath.node x } ) # replace node id with pdb resid and resno if(!is.null(pdb)) { ca.inds <- atom.select(pdb, elety="CA", verbose = FALSE) resno <- pdb$atom[ca.inds$atom, "resno"] resid <- pdb$atom[ca.inds$atom, "resid"] chain <- pdb$atom[ca.inds$atom, "chain"] lig.inds <- atom.select(pdb, "ligand", verbose = FALSE) islig <- paste(chain, resno, sep="_") %in% paste(pdb$atom[lig.inds$atom, "chain"], pdb$atom[lig.inds$atom, "resno"], sep="_") resid[!islig] <- aa321(resid[!islig]) o <- lapply(o, function(x) { node <- as.numeric(names(x)) if(length(unique(pdb$atom[, "chain"])) > 1) n <- paste(chain[node], paste(resid[node], resno[node], sep=""), sep="_") else n <- paste(resid[node], resno[node], sep="") names(x) <- n x } ) } names(o) <- label out$network <- label out$num.paths <- sapply(pa, function(x) length(x$path)) out$hist <- lapply(pa, function(x) table(cut(x$dist, breaks=5, include.lowest = TRUE))) if(length(out$hist)==1) out$hist = out$hist[[1]] out$degeneracy <- do.call(rbind, o) if(normalize) out$degeneracy <- round(out$degeneracy, digits=2) if(plot) { opar <- par(no.readonly = TRUE) on.exit(par(opar)) layout(matrix(1:2, nrow=1), respect = TRUE) rgbcolors <- sapply(col, col2rgb) / 255 rgbcolors <- rbind(rgbcolors, alpha = 0.6) ##- for path length distribution y1 <- lapply(pa, function(x) hist(x$dist, breaks = 20, plot = FALSE) ) par(mar=c(4, 4, 1, 1)) plot(y1[[1]], freq = FALSE, col = do.call(rgb, as.list(rgbcolors[,1])), border = col[1], main = "Path Length Distribution", xlim = range(unlist(lapply(y1, "[[", "breaks"))), ylim = c(0, max(unlist(lapply(y1, "[[", "density")))), xlab = "Path length", ylab = "Probability density") if(length(y1) > 1) for(i in 2:length(y1)) { plot(y1[[i]], freq = FALSE, col = do.call(rgb, as.list(rgbcolors[,i])), border = col[i], add = TRUE) } legend("topleft", legend = label, bty = "n", text.col = col) ##- for node degeneracy y2 <- lapply(pa, function(x) unlist(x$path)) if(!is.null(pdb)) y2 <- lapply(y2, function(x) resno[x]) if(concise) { # re-number node to get more concise plot ii <- sort(unique(unlist(y2))) y2 <- lapply(y2, match, ii) } y2 <- lapply(y2, function(x) hist(x, breaks = c(seq(min(x), max(x), 1) - 0.5, max(x) + 0.5), plot = FALSE) ) par(mar=c(4, 4, 1, 1)) plot(y2[[1]], freq = TRUE, col = do.call(rgb, as.list(rgbcolors[,1])), lty = 0, main = "Node Degeneracy", xlim = range(unlist(lapply(y2, "[[", "breaks"))), ylim = c(0, max(unlist(lapply(y2, "[[", "counts")))), xlab = "Node no", ylab = "Number of paths") if(length(y2) > 1) for(i in 2:length(y2)) plot(y2[[i]], freq = TRUE, col = do.call(rgb, as.list(rgbcolors[,i])), lty = 0, add = TRUE) } return(out) } print.cnapath <- function(x, ...) { dots = list(...) if(is.list(x) && all(sapply(x, inherits, "cnapath"))) { if(!"label" %in% names(dots) || is.null(dots$label)) dots$label = names(x) names(x) <- NULL args = c(x, dots) o <- do.call(summary, args) } else { o <- summary(x, ...) } if("plot" %in% names(dots)) plot = dots$plot else plot = FALSE if(!plot) { if("normalize" %in% names(dots)) normalize = dots$normalize else normalize = TRUE if(length(o$network) > 1) { cat("Number of networks: ", length(o$network), "(", paste(o$network, collapse=", "), ")\n") } cat("Number of paths in network(s):\n") if(length(o$network) > 1) { cat(paste(" ", o$network, ": ", o$num.paths, sep="", collapse="\n"), sep="\n") cat("\n") } else { cat(" ", o$num.paths, "\n\n") } cat("Path length distribution: \n") if(length(o$network) > 1) { for(i in 1:length(o$network)) { cat(" --- ", o$network[i], " ---") print(o$hist[[i]]) cat("\n") } } else { print(o$hist) cat("\n") } cat("Node degeneracy table: \n\n") if(length(o$network) == 1) rownames(o$degeneracy) = "" if(normalize) print(format(o$degeneracy, nsmall=2), quote=FALSE) else print(o$degeneracy) } } bio3d/R/sse.bridges.R0000644000176200001440000000406712412621431014011 0ustar liggesusers"sse.bridges" <- function(sse, type="helix", hbond=TRUE, energy.cut=-1.0 ) { if(missing(sse)) stop("sse missing") if(is.null(sse$hbonds)) stop("sse$hbonds does not exists. run dssp with 'resno=FALSE' and 'full=TRUE'") natoms <- nrow(sse$hbonds) if(type=="helix") { sse2 <- sse$helix stype <- "H" lim <- 4 } if(type=="sheet") { sse2 <- sse$sheet stype <- "S" lim <- 2 } if(length(sse2$start)==0) return(NULL) ## character array of SSE membership simple.sse <- sse$sse simple.sse[ !(sse$sse %in% c("H", "E", "G", "I")) ] <- "L" simple.sse[ sse$sse %in% c("E") ] <- "S" simple.sse[ sse$sse %in% c("H", "I", "G") ] <- "H" inds <- NULL for ( i in 1:(natoms-lim) ) { if(simple.sse[i]!=stype) next; paired <- NULL if(type=="helix") { paired <- i+4 if(simple.sse[paired]!=stype) next; } if (type=="sheet") { ## bridge pair info paired <- sse$hbonds[i,c("BP1", "BP2")] paired <- paired[ !is.na(paired) ] if(length(paired)==0) next; } if(hbond) { ## H-bond info resid <- sse$hbonds[i, c(3,5,7,9)] energ <- sse$hbonds[i, c(4,6,8,10)] energ <- energ[ !is.na(resid) ] resid <- resid[ !is.na(resid) ] inds.tmp <- which(resid %in% paired) resid <- resid[inds.tmp] energ <- energ[inds.tmp] dups <- duplicated(resid) resid <- resid[ !dups ] energ <- energ[ !dups ] if(length(resid)==0) next; for( j in 1:length(resid) ) { if(energ[j] < energy.cut) inds <- c(inds, i, resid[j]) } } else { for ( j in 1:length(paired) ) { inds <- c(inds, i, paired[j]) } } } if(length(inds)==0) return(NULL) mat <- matrix(inds, ncol=2, byrow=T) mat <- t(apply(mat, 1, sort)) pair.ids <- apply(mat, 1, function(x) paste(x, collapse="-")) mat <- matrix(mat[!duplicated(pair.ids), ], ncol=2) return(mat) } bio3d/R/print.core.R0000644000176200001440000000124312524171274013666 0ustar liggesusersprint.core <- function(x, vol=NULL, ...) { cv <- x$volume; cv[is.na(cv)]=min(cv,na.rm=TRUE) cl <- x$length; cl[is.na(cl)]=min(cl,na.rm=TRUE) ca <- NULL; cx <- NULL; cr <- NULL if(is.null(vol)) vol <- 1 ind <- (cv<=vol) ca <- sort(x$step.inds[ind]) cx <- atom2xyz(ca) cr <- sort(x$resno[ind]) nc <- length(ca) cat(paste("#",nc, "positions (cumulative volume <=", vol,"Angstrom^3)"),"\n") if(nc==0) { cat(paste("# Min volume is",round(min(cv),3)),"\n") } else { print(bounds(as.numeric(cr)), ...) } ##NextMethod("print", x, quote = FALSE, right = TRUE, ...) invisible(list(atom=ca, xyz=cx, resno=cr)) } bio3d/R/atom2xyz.R0000644000176200001440000000021512412621431013365 0ustar liggesusers"atom2xyz" <- function(num) { num3 <- num*3 c(t(matrix(c(((num3) - 2), ((num3) - 1), (num3)), ncol=3))) } bio3d/R/filter.identity.R0000644000176200001440000000222712544562302014721 0ustar liggesusersfilter.identity <- function(aln=NULL, ide=NULL, cutoff=0.6, verbose=TRUE, ...) { #k<-filter.identity(aln,cutoff=0.4) #aln$id[k$ind] #k<-filter.identity(ide=k$ide,cutoff=0.6) #plot(k$tree, axes = FALSE, ylab="%identity") #axis(2,labels =c(1,0.8,0.6,0.4)) #abline(h=0.6) if(is.null(ide)) { if(is.null(aln)) stop("Must provide either an alignment 'aln' or identity matrix 'ide'") ide <- seqidentity(aln, ...) } i.d <- as.dist(1-ide) tree <- hclust(i.d) h <- 1 - cutoff n <- nrow(tree$merge) + 1 k <- integer(length(h)) k <- n + 1 - apply(outer(c(tree$height, Inf), h, ">"),2, which.max) if(verbose) cat("filter.identity(): N clusters @ cutoff = ", k, "\n") #ans <- as.vector(.Call("R_cutree", tree$merge, k, PACKAGE = "stats")) ans <- as.vector(cutree(tree, k)) cluster.rep <- NULL for(i in 1:k) { ind <- which(ans==i) if (length(ind) == 1) { cluster.rep <- c(cluster.rep, ind) } else { cluster.rep <- c(cluster.rep, ind[ which.max( colSums(ide[ind,ind]) ) ]) # max similarity } } return(list(ind=cluster.rep, tree=tree, ide=ide)) } bio3d/R/mktrj.R0000644000176200001440000000005612526367343012741 0ustar liggesusers"mktrj" <- function(...) UseMethod("mktrj") bio3d/R/mono.colors.R0000644000176200001440000000024012412621431014036 0ustar liggesusers"mono.colors" <- function (n) { if(n<2) stop("need to ask for at least 2 colors") n=n-1 col <- rev(gray(0:(n) / (n))) col[1] = NA return(col) } bio3d/R/as.xyz.R0000644000176200001440000000035112544562302013034 0ustar liggesusers"as.xyz" <- function(x) { if(is.vector(x)) x = matrix(x, nrow=1) dims <- dim(x) if(!(dims[2L]%%3==0)) warning("number of cartesian coordinates not a multiple of 3") class(x) <- c("xyz", "matrix") return(x) } bio3d/R/plot.nma.R0000644000176200001440000000156312412621431013327 0ustar liggesusers"plot.nma" <- function(x, pch = 16, col = par("col"), cex = 0.8, mar = c(6, 4, 2, 2), ...) { opar <- par(no.readonly = TRUE) on.exit(par(opar)) par(cex = cex, mar = mar) layout(matrix(c(1,2,3,3), 2, 2, byrow = TRUE)) if(!is.null(x$frequencies)) { freqs <- x$frequencies main <- "Frequencies" } else { freqs <- x$force.constants main <- "Force constants" } if(length(freqs)>=100) n <- 100 else n <- length(freqs) plot(x$L[1:n], type = "h", pch = pch, xlab = "Mode index", ylab = "", main = "Eigenvalues", col = col) plot(freqs[1:n], type = "h", pch = pch, xlab = "Mode index", ylab = "", main = main, col = col) plot.bio3d(x$fluctuations, pch = pch, xlab = "Residue index", ylab = "", main = "Fluctuations", col = col, ...) } bio3d/R/as.pdb.R0000644000176200001440000001223712544562302012755 0ustar liggesusersas.pdb <- function(...) UseMethod("as.pdb") as.pdb.default <- function(pdb=NULL, xyz=NULL, type = NULL, resno = NULL, resid = NULL, eleno = NULL, elety = NULL, chain = NULL, insert= NULL, alt = NULL, o = NULL, b = NULL, segid = NULL, elesy = NULL, charge = NULL, verbose=TRUE, ...) { cl <- match.call() ## which input argument to determine number of atoms from input <- list(pdb=pdb, xyz=xyz, eleno=eleno, resno=resno, resid=resid) nulls <- unlist(lapply(input, is.null)) inds <- which(!nulls) if(length(inds)==0) stop("insufficient arguments. provide 'pdb', 'xyz', 'eleno', 'resno', and/or 'resid'") ## check content of pdb if(!is.null(pdb)) { if(!is.pdb(pdb)) stop("'pdb' must be of class 'pdb' as obtained from 'read.pdb'") } ## check content of xyz if(!is.null(xyz)) { if(!(is.numeric(xyz) & (is.matrix(xyz) | is.vector(xyz)))) stop("'xyz' must be a numeric vector/matrix") xyz <- as.xyz(xyz) } ## if pdb is provided use it to determine natoms if (inds[1]==1) { natoms <- nrow(pdb$atom) } ## if xyz is provided use it to determine natoms else if (inds[1]==2) { natoms <- ncol(xyz)/3 } ## else use eleno, resno, or resid else { natoms <- length(input[[inds[1]]]) } if(verbose) { cat("\n") cat(" Summary of PDB generation:\n") cat(paste(" .. number of atoms in PDB determined by '", names(input)[inds[1]], "'\n", sep="")) } ## set value of 'xyz' if(!is.null(xyz)) { if((ncol(xyz)/3)!=natoms) stop("ncol(xyz)/3 != length(resno)") } else { if(!is.null(pdb)) xyz <- as.xyz(pdb$xyz) else xyz <- as.xyz(rep(NA, natoms*3)) } ## generic function to set the values of remaining columns of PDB .setval <- function(values=NULL, typ=NULL, default=NULL, class="character", repfirst=FALSE) { if(!is.null(values)) { if(class=="character") fun=is.character if(class=="numeric") fun=is.numeric if(!fun(values)) stop(paste("'", typ, "' must be a ", class, " vector", sep="")) if(length(values)==1 & repfirst) values <- rep(values, natoms) if(length(values)!=natoms) stop(paste("length(", typ, ") != natoms", sep="")) } else { if(!is.null(pdb)) values <- pdb$atom[[typ]] else values <- default } return(values) } type <- .setval(type, typ="type", default=rep("ATOM", natoms), class="character", repfirst=TRUE) eleno <- .setval(eleno, typ="eleno", default=seq(1, natoms), class="numeric", repfirst=FALSE) elety <- .setval(elety, typ="elety", default=rep("CA", natoms), class="character", repfirst=TRUE) resno <- .setval(resno, typ="resno", default=seq(1, natoms), class="numeric", repfirst=FALSE) chain <- .setval(chain, typ="chain", default=rep("A", natoms), class="character", repfirst=TRUE) resid <- .setval(resid, typ="resid", default=rep("ALA", natoms), class="character", repfirst=TRUE) elesy <- .setval(elesy, typ="elesy", default=rep(NA, natoms), class="character", repfirst=TRUE) segid <- .setval(segid, typ="segid", default=rep(NA, natoms), class="character", repfirst=TRUE) o <- .setval(o, typ="o", default=rep(NA, natoms), class="numeric", repfirst=TRUE) b <- .setval(b, typ="b", default=rep(NA, natoms), class="numeric", repfirst=TRUE) alt <- .setval(alt, typ="alt", default=rep(NA, natoms), class="character", repfirst=FALSE) insert <- .setval(insert, typ="insert", default=rep(NA, natoms), class="character", repfirst=FALSE) charge <- .setval(charge, typ="charge", default=rep(NA, natoms), class="numeric", repfirst=TRUE) ## make the data frame for the final PDB object atom <- list() atom$type <- type atom$eleno <- eleno atom$elety <- elety atom$alt <- alt atom$resid <- resid atom$chain <- chain atom$resno <- resno atom$insert <- insert atom$x <- xyz[1, seq(1, natoms*3, by=3)] atom$y <- xyz[1, seq(2, natoms*3, by=3)] atom$z <- xyz[1, seq(3, natoms*3, by=3)] atom$o <- o atom$b <- b atom$segid <- segid atom$elesy <- elesy atom$charge <- charge atom <- data.frame(atom, stringsAsFactors=FALSE) out <- list() out$atom <- atom out$xyz <- xyz class(out) <- "pdb" ## should account for new resno and chain #if(!is.null(pdb)) { #out$helix <- pdb$helix #out$sheet <- pdb$sheet #out$seqres <- pdb$seqres #class(out) <- class(pdb) #} unwhich <- function(x, n) { out <- rep_len(FALSE, n) out[x] <- TRUE return(out) } ca.inds <- atom.select(out, "calpha", verbose=verbose) out$calpha <- unwhich(ca.inds$atom, natoms) out$call <- cl if(verbose) { resid <- unique(paste(atom$chain, atom$resno, sep="-")) cat(paste(" .. number of atoms in PDB: ", natoms, "\n")) cat(paste(" .. number of calphas in PDB:", sum(out$calpha), "\n")) cat(paste(" .. number of residues in PDB:", length(resid), "\n")) cat("\n") } return(out) } bio3d/R/pairwise.R0000644000176200001440000000203412412621431013414 0ustar liggesusers"pairwise" <- function(N) { # deternine the indices for pairwise # comparison of N things (where N = 'number.of.things') # Used in function 'identity' # # A <- read.fasta(system.file("examples/kinesin.fa", # package = "bio3d")) # # N<-nrow(A$ali) # # comparison indices # inds<-pairwise(N) # # store score in 's' # s<-rep(NA,nrow(inds)) # # go through all comparisons # for(i in 1:nrow(inds)) { # s[i]<-ide(A$ALI[ inds[i,1], ], A[ inds[i,2], ]) # } # # reformat 's' as a NxN matrix # mat<-matrix(NA,ncol=N,nrow=N) # mat[inds]<-s; mat[inds[,c(2,1)]]<-s if (!is.numeric(N) || N<0 || length(N)>1) { stop("pairwise: N must be positve numeric and of length 1") } pair <- matrix(NA, ncol=2, nrow= ( ((N*N)/2)-(N/2) )) start<-1 for(i in 1:(N-1)) { end = N-i inds = start:( (start-1)+end ) pair[inds,1] = rep(i, end) pair[inds,2] = (i+1):N start = start+end } return(pair) } bio3d/R/read.all.R0000644000176200001440000001116012526367343013272 0ustar liggesusers"read.all" <- function(aln, prefix ="", pdbext="", sel=NULL, ...) { ## Usage: ## sel <- c("N", "CA", "C", "O", "CB", "*G", "*D", "*E", "*Z") ## pdbs.all <- read.all(aln, sel=sel) files <- paste(prefix, aln$id, pdbext,sep="") ##cat(files,sep="\n") toread <- file.exists(files) ## check for online files toread[ substr(files,1,4)=="http" ] <- TRUE if(all(!toread)) stop("No corresponding PDB files found") coords <- NULL; res.nu <- NULL res.bf <- NULL; res.ch <- NULL res.id <- NULL blank <- rep(NA, ncol(aln$ali)) ## all atom data coords.all <- NULL elety.all <- NULL; resid.all <- NULL; resno.all <- NULL for (i in 1:length(aln$id)) { cat(paste("pdb/seq:",i," name:", aln$id[i]),"\n") if(!toread[i]) { warning(paste("No PDB file found for seq", aln$id[i], ": (with filename) ",files[i]), call.=FALSE) coords <- rbind(coords, rep(blank,3)) res.nu <- rbind(res.nu, blank) res.bf <- rbind(res.bf, blank) res.ch <- rbind(res.ch, blank) res.id <- rbind(res.id, blank) ## ##coords.all ## } else { pdb <- read.pdb( files[i], verbose=FALSE, ... ) pdbseq <- aa321(pdb$atom[pdb$calpha,"resid"]) aliseq <- toupper(aln$ali[i,]) tomatch <- gsub("X","[A-Z]",aliseq[aliseq!="-"]) ##-- Search for ali residues (1:15) in pdb start.num <- regexpr(pattern = paste(c(na.omit(tomatch[1:15])),collapse=""), text = paste(pdbseq,collapse=""))[1] if (start.num == -1) { stop("Starting residues of sequence not found in PDB") } ##-- Numeric vec, 'nseq', for mapping aln to pdb nseq <- rep(NA,length(aliseq)) ali.res.ind <- which(aliseq != "-") if( length(ali.res.ind) > length(pdbseq) ) { warning(paste(aln$id[i], ": sequence has more residues than PDB has Calpha's")) ali.res.ind <- ali.res.ind[1:length(pdbseq)] ## exclude extra tomatch <- tomatch[1:length(pdbseq)] ## terminal residues } nseq[ali.res.ind] = start.num:((start.num - 1) + length(tomatch)) ##-- Check for miss-matchs match <- aliseq != pdbseq[nseq] if ( sum(match, na.rm=TRUE) >= 1 ) { mismatch.ind <- which(match) mismatch <- cbind(aliseq, pdbseq[nseq])[mismatch.ind,] n.miss <- length(mismatch.ind) if(sum(mismatch=="X") != n.miss) { ## ignore masked X res details <- rbind(aliseq, !match, pdbseq[nseq], pdb$atom[pdb$calpha,"resno"][nseq] ) #### calpha[,"resno"][nseq] ) ###- typo?? rownames(details) = c("aliseq","match","pdbseq","pdbnum") msg <- paste("ERROR:", aln$id[i], "alignment and pdb sequences do not match") cat(msg,"\n"); print(details); cat(msg,"\n") print( cbind(details[,mismatch.ind]) ) stop(msg) } } ##-- Store nseq justified PDB data ca.ali <- pdb$atom[pdb$calpha,][nseq,] coords <- rbind(coords, as.numeric( t(ca.ali[,c("x","y","z")]) )) res.nu <- rbind(res.nu, ca.ali[, "resno"]) res.bf <- rbind(res.bf, as.numeric( ca.ali[,"b"] )) res.ch <- rbind(res.ch, ca.ali[, "chain"]) res.id <- rbind(res.id, ca.ali[, "resid"]) raw <- store.atom(pdb) if(is.null(sel)) { coords.all <- rbind(coords.all, as.numeric( raw[c("x","y","z"),,nseq] ) ) elety.all <- rbind(elety.all, c(raw[c("elety"),,nseq]) ) resid.all <- rbind(resid.all, c(raw[c("resid"),,nseq]) ) resno.all <- rbind(resno.all, c(raw[c("resno"),,nseq]) ) } else { coords.all <- rbind(coords.all, as.numeric( raw[c("x","y","z"), sel, nseq] ) ) elety.all <- rbind(elety.all, c(raw[c("elety"),sel,nseq]) ) resid.all <- rbind(resid.all, c(raw[c("resid"),sel,nseq]) ) resno.all <- rbind(resno.all, c(raw[c("resno"),sel,nseq]) ) } ## raw <- store.main(pdb) ## b <- cbind(b, raw[,,nseq]) } # end for } # end else rownames(aln$ali) <- aln$id ## out<-list(xyz=coords, resno=res.nu, b=res.bf, ## chain = res.ch, id=aln$id, ali=aln$ali) out<-list(xyz=coords, all=coords.all, resno=res.nu, b=res.bf, chain = res.ch, id=aln$id, ali=aln$ali, resid=res.id, all.elety=elety.all, all.resid=resid.all, all.resno=resno.all) atm <- rep( rep(sel,each=3), ncol(aln$ali)) colnames(out$all) = atm atm <- rep( sel, ncol(aln$ali)) colnames(out$all.elety) = atm colnames(out$all.resid) = atm colnames(out$all.resno) = atm class(out)=c("pdbs", "fasta") return(out) } bio3d/R/read.pqr.R0000644000176200001440000001760512524171274013330 0ustar liggesusers`read.pqr` <- function (file, maxlines=-1, multi=FALSE, rm.insert=FALSE, rm.alt=TRUE, verbose=TRUE) { if(missing(file)) { stop("read.pqr: please specify a PQR 'file' for reading") } if(!is.numeric(maxlines)) { stop("read.pqr: 'maxlines' must be numeric") } if(!is.logical(multi)) { stop("read.pqr: 'multi' must be logical TRUE/FALSE") } cl <- match.call() ## PDB FORMAT v3.3: colpos, datatype, name, description atom.format <- matrix(c(6, 'character', "type", # type(ATOM) 5, 'numeric', "eleno", # atom_no -1, NA, NA, # (blank) 4, 'character', "elety", # atom_ty 1, 'character', "alt", # alt_loc 4, 'character', "resid", # res_na 1, 'character', "chain", # chain_id 4, 'numeric', "resno", # res_no 1, 'character', "insert", # ins_code -3, NA, NA, # (blank) 8, 'numeric', "x", # x 8, 'numeric', "y", # y 8, 'numeric', "z", # z 8, 'numeric', "o", # o ### 6 for pdb 8, 'numeric', "b", # b ### 6 for pdb -6, NA, NA, # (blank) 4, 'character', "segid", # seg_id 2, 'character', "elesy", # element symbol 2, 'character', "charge" # atom_charge (should be 'numeric'] ), ncol=3, byrow=TRUE, dimnames = list(c(1:19), c("widths","what","name")) ) trim <- function(s) { ##- Remove leading and trailing spaces from character strings s <- sub("^ +", "", s) s <- sub(" +$", "", s) s[(s=="")]<-NA s } split.fields <- function(x) { ##- Split a character string for data.frame fwf reading ## First splits a string 'x' according to 'first' and 'last' ## then re-combines to new string with "," as separator x <- trim( substring(x, first, last) ) paste(x,collapse=",") } is.character0 <- function(x){length(x)==0 & is.character(x)} ##- Find first and last (substr) positions for each field widths <- as.numeric(atom.format[,"widths"]) # fixed-width spec drop.ind <- (widths < 0) # cols to ignore (i.e. -ve) widths <- abs(widths) # absolute vales for later st <- c(1, 1 + cumsum( widths )) first <- st[-length(st)][!drop.ind] # substr start last <- cumsum( widths )[!drop.ind] # substr end names(first) = na.omit(atom.format[,"name"]) names(last) = names(first) ##- Read 'n' lines of PDB file raw.lines <- readLines(file, n = maxlines) type <- substring(raw.lines, first["type"], last["type"]) ##- Check number of END/ENDMDL records raw.end <- sort(c(which(type == "END"), which(type == "ENDMDL"))) ## Check if we want to store multiple model data if (length(raw.end) > 1) { print("PDB has multiple END/ENDMDL records") if (!multi) { print("multi=FALSE: taking first record only") } else { print("multi=TRUE: 'read.dcd/read.ncdf' will be quicker!") raw.lines.multi <- raw.lines type.multi <- type } raw.lines <- raw.lines[ (1:raw.end[1]) ] type <- type[ (1:raw.end[1]) ] } ##- Check for 'n' smaller than total lines in PDB file if ( length(raw.end) !=1 ) { if (length(raw.lines) == maxlines) { print("You may need to increase 'maxlines'") print("check you have all data in $atom") } } ##- Split input lines by record type raw.header <- raw.lines[type == "HEADER"] raw.seqres <- raw.lines[type == "SEQRES"] raw.helix <- raw.lines[type == "HELIX "] raw.sheet <- raw.lines[type == "SHEET "] raw.atom <- raw.lines[type %in% c("ATOM ","HETATM")] if (verbose) { if (!is.character0(raw.header)) { cat(" ", raw.header, "\n") } } ## Edit from Baoqiang Cao Nov 29, 2009 ## Old version: ## seqres <- unlist(strsplit( trim(substring(raw.seqres,19,80))," +")) ## New version seqres <- unlist(strsplit( trim(substring(raw.seqres,19,80))," +")) if(!is.null(seqres)) { seqres.ch <- substring(raw.seqres, 12, 12) seqres.ln <- substring(raw.seqres, 13, 17) seqres.in <- ( !duplicated(seqres.ch) ) names(seqres) <- rep(seqres.ch[seqres.in], times=seqres.ln[seqres.in]) } ## End Edit from Baoqiang: ##- Secondary structure helix <- list(start = as.numeric(substring(raw.helix,22,25)), end = as.numeric(substring(raw.helix,34,37)), chain = trim(substring(raw.helix,20,20)), type = trim(substring(raw.helix,39,40))) sheet <- list(start = as.numeric(substring(raw.sheet,23,26)), end = as.numeric(substring(raw.sheet,34,37)), chain = trim(substring(raw.sheet,22,22)), sense = trim(substring(raw.sheet,39,40))) ## 2014-04-23 ## Update to use single data.frame for ATOM and HETATM records ## file="2RGF"; multi=TRUE; ## file="./4q21.pdb"; maxlines=-1; multi=FALSE; ## rm.insert=FALSE; rm.alt=TRUE; het2atom=FALSE; verbose=TRUE atom <- read.table(text=sapply(raw.atom, split.fields), stringsAsFactors=FALSE, sep=",", quote='', colClasses=atom.format[!drop.ind,"what"], col.names=atom.format[!drop.ind,"name"], comment.char="") ##-- End data.frame update ##- Coordinates only object ###xyz.models <- c(t(atom[,c("x","y","z")])) xyz.models <- matrix(as.numeric(c(t(atom[,c("x","y","z")]))), nrow=1) ##- Multi-model coordinate extraction if (length(raw.end) > 1 && multi) { raw.atom <- raw.lines.multi[ type.multi %in% c("ATOM ","HETATM") ] if( (length(raw.atom)/length(raw.end)) ==nrow(atom) ){ ## Only work with models with the same number of atoms) tmp.xyz=( rbind( substr(raw.atom, first["x"],last["x"]), substr(raw.atom, first["y"],last["y"]), substr(raw.atom, first["z"],last["z"]) ) ) ## Extract coords to nrow/frame * ncol/xyz matrix xyz.models <- matrix( as.numeric(tmp.xyz), ncol=nrow(atom)*3, nrow=length(raw.end), byrow=TRUE) } else { warning(paste("Unequal number of atoms in multi-model records:", file)) } rm(raw.lines.multi) } rm(raw.lines, raw.atom) ##- Possibly remove 'Alt records' (m[,"alt"] != NA) if (rm.alt) { if ( sum( !is.na(atom[,"alt"]) ) > 0 ) { first.alt <- sort( unique(na.omit(atom[,"alt"])) )[1] cat(paste(" PDB has ALT records, taking",first.alt,"only, rm.alt=TRUE\n")) alt.inds <- which( (atom[,"alt"] != first.alt) ) # take first alt only if(length(alt.inds)>0) { atom <- atom[-alt.inds,] xyz.models <- xyz.models[ ,-atom2xyz(alt.inds), drop=FALSE ] } } } ##- Possibly remove 'Insert records' if (rm.insert) { if ( sum( !is.na(atom[,"insert"]) ) > 0 ) { cat(" PDB has INSERT records, removing, rm.insert=TRUE\n") insert.inds <- which(!is.na(atom[,"insert"])) # rm insert positions atom <- atom[-insert.inds,] xyz.models <- xyz.models[ ,-atom2xyz(insert.inds), drop=FALSE ] } } ##- Vector of Calpha positions ## check for calcium resid and restrict to ATOM records only calpha = (atom[,"elety"]=="CA") & (atom[,"resid"]!="CA") & (atom[,"type"]=="ATOM") output<-list(atom=atom, #het=atom[atom$type=="HETATM",], helix=helix, sheet=sheet, seqres=seqres, xyz=xyz.models, calpha = calpha, call=cl) class(output) <- c("pdb", "sse") class(output$xyz) <- c("numeric","xyz") return(output) } bio3d/R/pdbs2pdb.R0000644000176200001440000000211712524171274013304 0ustar liggesusers"pdbs2pdb" <- function(pdbs, inds=NULL, rm.gaps=FALSE) { if(!inherits(pdbs, "pdbs")) { stop("Input 'pdbs' should be of class 'pdbs', e.g. from pdbaln() or read.fasta.pdb()") } if(is.null(inds)) inds <- seq(1, length(pdbs$id)) ## Temporaray file fname <- tempfile(fileext = "pdb") ## Set indicies gaps.res <- gap.inspect(pdbs$ali) gaps.pos <- gap.inspect(pdbs$xyz) all.pdbs <- list() for ( i in 1:length(inds) ) { j <- inds[i] ## Set indices for this structure only f.inds <- NULL if(rm.gaps) { f.inds$res <- gaps.res$f.inds f.inds$pos <- gaps.pos$f.inds } else { f.inds$res <- which(gaps.res$bin[j,]==0) f.inds$pos <- atom2xyz(f.inds$res) } ## Make a temporary PDB object write.pdb(pdb=NULL, xyz =pdbs$xyz[j,f.inds$pos], resno=pdbs$resno[j,f.inds$res], resid=pdbs$resid[j,f.inds$res], chain=pdbs$chain[j,f.inds$res], file=fname) all.pdbs[[i]] <- read.pdb(fname) } names(all.pdbs) <- sub(".pdb$", "", basename(pdbs$id[inds])) return(all.pdbs) } bio3d/R/mktrj.pca.R0000644000176200001440000000225212632622153013472 0ustar liggesusers"mktrj.pca" <- function(pca=NULL, # pca data structure pc=1, # which pc to move along mag=1, # magnification factor step=0.125, # step size file=NULL, # output pdb file ... ) { # args for write.pdb ## make a trjactory of atomic displacments along a given pc if(class(pca)!="pca") { stop("input should be a list object of class 'pca' (from 'pca.xyz')") } if(is.null(file)) file <- paste("pc_", pc, ".pdb", sep="") nstep <- c(seq(step, to=mag, by=step)) zcoor <- cbind(sqrt(pca$L[pc])) %*% nstep ##- Bug fix: Fri Jun 15 14:49:24 EDT 2012 ## plus <- apply(zcoor, 2, pca.z2xyz, pca) ## minus <- apply( (-(zcoor)), 2, pca.z2xyz, pca) scor <- function(x,u,m) { return(x*u+m) } plus <- sapply(c(zcoor), scor, u=pca$U[,pc], m=pca$mean) minus <- sapply(c(-zcoor), scor, u=pca$U[,pc], m=pca$mean) coor <- t(cbind(pca$mean, plus, plus[,rev(1:ncol(plus))], pca$mean, minus, minus[,rev(1:ncol(minus))])) write.pdb(xyz=coor, file=file, ...) invisible(coor) } bio3d/R/nma.pdb.R0000644000176200001440000002414512632622153013125 0ustar liggesusers"nma.pdb" <- function(pdb, inds=NULL, ff='calpha', pfc.fun=NULL, mass=TRUE, temp=300.0, keep=NULL, hessian=NULL, outmodes=NULL, ... ) { ## Log the call cl <- match.call() if(!is.pdb(pdb)) stop("please provide a 'pdb' object as obtained from 'read.pdb()'") if(!is.null(outmodes) & !is.select(outmodes)) stop("provide 'outmodes' as obtained from function atom.select()") ## Prepare PDB ## Take only first frame of multi model PDB files if(nrow(pdb$xyz)>1) { warning("multimodel PDB file detected - using only first frame") pdb$xyz=pdb$xyz[1,, drop=FALSE] } ## Trim to only CA atoms if(is.null(inds)) { ca.inds <- atom.select(pdb, "calpha", verbose=FALSE) pdb.in <- trim.pdb(pdb, ca.inds) } ## or to user selection else { pdb.in <- trim.pdb(pdb, inds) if(!all(pdb.in$atom$elety=="CA")) stop("non-CA atoms detected") } ## Indices for effective hessian if(is.select(outmodes)) { ## re-select since outmodes indices are based on input PDB inc.inds <- .match.sel(pdb, pdb.in, outmodes) pdb.out <- trim.pdb(pdb.in, inc.inds) } else { pdb.out <- pdb.in inc.inds <- atom.select(pdb.in, "all", verbose=FALSE) } ## fetch number of atoms and sequence natoms.in <- ncol(pdb.in$xyz)/3 natoms.out <- ncol(pdb.out$xyz)/3 sequ <- pdb.in$atom$resid if (natoms.in<3) stop("nma: insufficient number of atoms") ## check structure connectivity conn <- inspect.connectivity(pdb.in$xyz) if(!conn) { warning("Possible multi-chain structure or missing in-structure residue(s) present\n", " Fluctuations at neighboring positions may be affected.") } ## Process input arguments init <- .nma.init(ff=ff, pfc.fun=pfc.fun, sequ=sequ, ...) ## Use aa2mass to fetch residue mass if (mass) { masses.in <- do.call('aa2mass', c(list(pdb=sequ, inds=NULL), init$am.args)) masses.out <- masses.in[ inc.inds$atom ] } ## No mass-weighting else { masses.out <- NULL; } ## NMA hessian hessian <- .nma.hess(pdb.in$xyz, init=init, hessian=hessian, inc.inds=inc.inds) ## mass weight hessian if(!is.null(masses.out)) hessian <- .nma.mwhessian(hessian, masses=masses.out) ## diagaonalize - get eigenvectors ei <- .nma.diag(hessian) ## make a NMA object m <- .nma.finalize(ei, xyz=pdb.out$xyz, temp=temp, masses=masses.out, natoms=natoms.out, keep=keep, call=cl) return(m) } ".nma.init" <- function(ff=NULL, pfc.fun=NULL, ...) { ## Arguments to functions build.hessian and aa2mass bh.names <- names(formals( build.hessian )) am.names <- names(formals( aa2mass )) dots <- list(...) bh.args <- dots[names(dots) %in% bh.names] am.args <- dots[names(dots) %in% am.names] ## Define force field if (is.null(pfc.fun)) { ff <- load.enmff(ff) } else { ## Use customized force field if(!is.function(pfc.fun)) stop("'pfc.fun' must be a function") bh.args <- bh.args[ !('pfc.fun' %in% names(bh.args)) ] ff <- pfc.fun } ## Check for optional arguments to pfc.fun ff.names <- names(formals( ff )) ff.args <- dots[names(dots) %in% ff.names] ## Redirect them to build.hessian bh.args <- c(bh.args, ff.args) ## Arguments without destination all.names <- unique(c(bh.names, am.names, ff.names)) if(!all(names(dots) %in% all.names)) { oops <- names(dots)[!(names(dots) %in% all.names)] stop(paste("argument mismatch:", oops)) } if(length(bh.args)==0) bh.args=NULL if(length(am.args)==0) am.args=NULL #if(length(ff.args)==0) # ff.args=NULL out <- list(pfcfun=ff, bh.args=bh.args, am.args=am.args) return(out) } ## extract effective hessian ".nma.trim.hessian" <- function(hessian, inc.inds=NULL) { if(!is.matrix(hessian)) stop("hessian must be a matrix") if(is.null(inc.inds)) stop("indices must be provided") kaa <- hessian[inc.inds, inc.inds] kqq.inv <- solve(hessian[-inc.inds, -inc.inds]) kaq <- hessian[inc.inds, -inc.inds] kqa <- t(kaq) k <- kaa - ((kaq %*% kqq.inv) %*% kqa) return(k) } ## mass-weight hessian ".nma.mwhessian" <- function(hessian, masses=NULL) { if(!is.matrix(hessian)) stop("hessian must be a matrix") if(is.null(masses)) stop("masses must be provided") #cat(" Mass weighting Hessian...") #ptm <- proc.time() dims <- dim(hessian) natoms <- dims[1] / 3 if(length(masses)!=natoms) stop("dimension mismatch") masses <- sqrt(masses) inds <- rep(1:natoms, each=3) col.inds <- seq(1, ncol(hessian), by=3) for ( i in 1:natoms ) { m <- col.inds[i] hessian[,m:(m+2)] <- hessian[,m:(m+2)] * (1/masses[i]) hessian[,m:(m+2)] <- hessian[,m:(m+2)] * (1/masses[inds]) } #t <- proc.time() - ptm #cat("\tDone in", t[[3]], "seconds.\n") return(hessian) } ## wrapper for generating the hessian matrix ".nma.hess" <- function(xyz, init=NULL, hessian=NULL, inc.inds=NULL) { natoms <- ncol(as.xyz(xyz))/3 if(nrow(xyz)>1) xyz=xyz[1,,drop=FALSE] ## Build the Hessian Matrix if(is.null(hessian)) { cat(" Building Hessian...") ptm <- proc.time() H <- do.call('build.hessian', c(list(xyz=xyz, pfc.fun=init$pfcfun), init$bh.args)) t <- proc.time() - ptm cat("\t\tDone in", t[[3]], "seconds.\n") } else { H <- hessian } ## Effective Hessian if(!is.null(inc.inds)) { if(ncol(xyz)>length(inc.inds$xyz)) { cat(" Extracting effective Hessian..") ptm <- proc.time() H <- .nma.trim.hessian(H, inc.inds=inc.inds$xyz) t <- proc.time() - ptm cat("\tDone in", t[[3]], "seconds.\n") } } return(H) } ## diagonalize hessian ".nma.diag" <- function(H) { ## Diagonalize matrix cat(" Diagonalizing Hessian...") ptm <- proc.time() ei <- eigen(H, symmetric=TRUE) t <- proc.time() - ptm cat("\tDone in", t[[3]], "seconds.\n") return(ei) } ## build a NMA object ".nma.finalize" <- function(ei, xyz, temp, masses, natoms, keep, call) { if(length(masses)>0) mass <- TRUE else mass <- FALSE xyz=as.xyz(xyz) dims <- dim(ei$vectors) dimchecks <- c(ncol(xyz)/3==natoms, ifelse(mass, length(masses)==natoms, TRUE), dims[1]/3==natoms, dims[2]/3==natoms) if(!all(dimchecks)) stop(paste("dimension mismatch when generating nma object\n", paste(dimchecks, collapse=", "))) ## Raw eigenvalues ei$values <- round(ei$values, 6) ## Trivial modes first - sort on abs(ei$values) sort.inds <- order(abs(ei$values)) ei$values <- ei$values[sort.inds] ei$vectors <- ei$vectors[, sort.inds] ## hard code 6 trivial modes triv.modes <- seq(1, 6) ## keep only a subset of modes - including trivial modes if(!is.null(keep)) { if(keep>ncol(ei$vectors)) keep <- ncol(ei$vectors) keep.inds <- seq(1, keep) ei$vectors <- ei$vectors[,keep.inds] ei$values <- ei$values[keep.inds] } ## Frequencies are given by if (mass) { pi <- 3.14159265359 freq <- sqrt(abs(ei$values)) / (2 * pi) force.constants <- NULL } else { freq <- NULL force.constants <- ei$values } ## Raw unmodified eigenvectors: ## ei$vectors ## V holds the eigenvectors converted to unweighted Cartesian coords: V <- ei$vectors ## Change to non-mass-weighted eigenvectors if(mass) { wts.sqrt <- sqrt(masses) tri.inds <- rep(1:natoms, each=3) V <- apply(V, 2, '*', 1 / wts.sqrt[tri.inds]) } ## Temperature scaling kb <- 0.00831447086363271 if ( !is.null(temp) ) { if (!is.null(freq)) { amplitudes <- sqrt(2* temp * kb) / (2* pi * freq[ -triv.modes ]) amplitudes <- c(rep(1,length(triv.modes)), amplitudes) } else if(!is.null(force.constants)) { amplitudes <- sqrt((2* temp * kb) / force.constants[ -triv.modes ]) amplitudes <- c(rep(1,length(triv.modes)), amplitudes) } } else { amplitudes <- rep(1, times=3*natoms) } ## Temperature scaling of eigenvectors for ( i in (length(triv.modes)+1):ncol(V) ) { V[,i] <- (V[,i] * amplitudes[i]) } ## Check if first modes are zero-modes if(any(ei$values<0)) { warning("Negative eigenvalue(s) detected! \ This can be an indication of an unphysical input structure.") } ## Output to class "nma" nma <- list(modes=V, frequencies=NULL, force.constants=NULL, fluctuations=NULL, U=ei$vectors, L=ei$values, xyz=xyz, mass=masses, temp=temp, triv.modes=length(triv.modes), natoms=natoms, call=call) if(mass) { class(nma) <- c("VibrationalModes", "nma") nma$frequencies <- freq } else { class(nma) <- c("EnergeticModes", "nma") nma$force.constants <- force.constants } ## Calculate mode fluctuations nma$fluctuations <- fluct.nma(nma, mode.inds=NULL) ## Notes: ## U are the raw unmodified eigenvectors ## These mode vectors are in mass-weighted coordinates and not ## scaled by the thermal amplitudes, so they are orthonormal. ## V holds the eigenvectors converted to unweighted Cartesian ## coordinates. Unless you set temp=NULL, the modes are ## also scaled by the thermal fluctuation amplitudes. return(nma) } ".match.sel" <- function(a, b, inds) { ## a= original pdb ## b= trimmed pdb ## inds= indices of pdb 'a' to keep ## find corresponding atoms in b names.a <- paste(a$atom[inds$atom, "chain"], a$atom[inds$atom, "resno"], a$atom[inds$atom, "elety"], a$atom[inds$atom, "eleno"], sep="-") names.b <- paste(b$atom[, "chain"], b$atom[, "resno"], b$atom[, "elety"], b$atom[, "eleno"], sep="-") inds <- which(names.b %in% names.a) out <- list(atom=inds, xyz=atom2xyz(inds)) class(out) <- "select" return(out) } bio3d/R/print.geostas.R0000644000176200001440000000123512544562302014402 0ustar liggesusers"print.geostas" <- function(x, ...) { cn <- class(x) ndoms <- length(unique(x$grps)) dims <- dim(x$amsm) cat("\nCall:\n ", paste(deparse(x$call), sep = "\n", collapse = "\n"), "\n\n", sep = "") cat("Class:\n ", cn, "\n\n", sep = "") cat("Dimensions of AMSM:\n ", dims[1], "x", dims[2], "\n\n", sep="") cat("Number of domains:\n ", ndoms, "\n\n", sep="") cat("Domain size:\n") for(i in 1:ndoms) { cat(" #", i, ": ", length(x$inds[[i]]$atom), " atoms \n", sep="") } cat("\n") i <- paste( attributes(x)$names, collapse=", ") cat(strwrap(paste(" + attr:",i,"\n"),width=60, exdent=8), sep="\n") invisible(x) } bio3d/R/prune.cna.R0000644000176200001440000000405612526367343013507 0ustar liggesusersprune.cna <- function(x, edges.min=1, size.min=1) { ##-- Prune nodes based on number of edges and number of members ## prune.cna(net) ## ## Check for presence of igraph package oops <- requireNamespace("igraph", quietly = TRUE) if (!oops) { stop("igraph package missing: Please install, see: ?install.packages") } if(class(x)=="cna") { y <- summary.cna(x) network=x$community.network } else { warning("Input should be a 'cna' class object as obtained from cna()") network=x y <- NULL } if((edges.min==0) & (size.min==0)){ stop("Must specify a number greater than 0 for edges.min and/or size.min") } ## Identify nodes with less than 'edges.min' to other nodes. nodes.inds <- which(igraph::degree(network) < edges.min) ## Identify nodes with size less than 'size.min' ## cant use V(net$network)$size as these can be scaled ## so we will use the summary information in 'y' nodes.inds <- c(nodes.inds, which(y$size < size.min)) nodes.inds <- unique(nodes.inds) if( length(nodes.inds) == 0 ) { cat( "No Nodes Will Removed based on edges.min and size.min values" ) output = x } else { rm.vs <- igraph::V(network)[nodes.inds] cat( paste("Removing Nodes:", paste(rm.vs, collapse=", ")),"\n") ## Print details of removed with edges if(!is.null(y)) { w <- cbind(y$tbl[rm.vs,c("id","size")], "edges"=igraph::degree(network)[rm.vs], "members"=y$tbl[rm.vs,c("members")]) w <- w[order(w$id),] write.table(w, row.names=FALSE, col.names=TRUE, quote=FALSE,sep="\t") ## Residue raw network res2rm <- as.numeric(unlist(y$members[rm.vs])) x$communities$membership[res2rm] = NA } d <- igraph::delete.vertices(network, rm.vs) ## Will probably want to keep an edited old community object !!! output <- list("community.network"=d, "network"= x$network, ## UNCHANGED!!! "communities"=x$communities) } class(output) = class(x) return(output) } bio3d/R/write.pir.R0000644000176200001440000000471212524171274013532 0ustar liggesusers"write.pir" <- function(alignment = NULL, ids = NULL, seqs = alignment$ali, pdb.file = NULL, chain.first = NULL, resno.first = NULL, chain.last = NULL, resno.last = NULL, file, append = FALSE) { if (is.null(seqs)) stop("write.pir: please provide a 'seqs' or 'alignment' input object") if (!is.null(alignment)) { if (is.null(alignment$id) | is.null(alignment$ali)) { stop("write.pir: 'alignment' should be a list with '$id' and '$ali'components") } if (is.null(ids)) { ids=alignment$id } } else { if (is.null(ids)) { n.ids <- nrow(seqs) if(is.null(n.ids)) { n.ids=1 } ids=seq( 1, length=n.ids ) } } if(is.null(pdb.file)) pdb.file = rep("", nrow(seqs)) if(is.null(chain.first)) chain.first = rep("", nrow(seqs)) if(is.null(resno.first)) resno.first = rep("", nrow(seqs)) if(is.null(chain.last)) chain.last = rep("", nrow(seqs)) if(is.null(resno.last)) resno.last = rep("", nrow(seqs)) if (!append) { ##file.remove(file, showWarnings = FALSE) suppressWarnings( file.remove(file) ) } nseqs <- length(ids) if (nseqs == 1) { head = "sequence" if(pdb.file!="") { head = "structureX" if(chain.first == "") chain.first = "@" if(resno.first == "") resno.first = "FIRST" if(resno.last == "") resno.last = paste("+", sum(!is.gap(seqs)), sep="") } head = paste(head, pdb.file, resno.first, chain.first, resno.last, chain.last, "", "", "", "", sep=":") # change for shortening lines (<=60) - Xinqiu cat(">P1;", ids, "\n", file = file, append = TRUE, sep = "") cat(head, "\n", file = file, append = TRUE) cat(seqs, "*", file = file, append = TRUE, sep = "", fill = 60) } else { for (i in 1:nseqs) { head = "sequence" if(pdb.file[i]!="") { head = "structureX" if(chain.first[i] == "") chain.first[i] = "@" if(resno.first[i] == "") resno.first[i] = "FIRST" if(resno.last[i] == "") resno.last[i] = paste("+", sum(!is.gap(seqs[i,])), sep="") } head = paste(head, pdb.file[i], resno.first[i], chain.first[i], resno.last[i], chain.last[i], "", "", "", "", sep=":") cat(">P1;", ids[i], "\n", file = file, append = TRUE, sep = "") cat(head, "\n", file = file, append = TRUE) cat(seqs[i,], "*", file = file, append = TRUE, sep = "", fill = 60) } } } bio3d/R/binding.site.R0000644000176200001440000001030512561207744014162 0ustar liggesusers"binding.site" <- function(a, b = NULL, a.inds = NULL, b.inds = NULL, cutoff=5, hydrogens=TRUE, byres=TRUE, verbose=FALSE) { cl <- match.call() sep <- "_" trim <- function(s, leading=TRUE, trailing=TRUE) { if(leading) s <- sub("^ +", "", s) if(trailing) s <- sub(" +$", "", s) s[(s=="")]<-"" s } if (!is.pdb(a)) stop("must supply an input 'pdb' object 'a', i.e. from 'read.pdb'") ## workaround for NA chains if(any(is.na(a$atom$chain))) a$atom$chain[is.na(a$atom$chain)] <- " " ## backup of the original pdb provided a.orig <- a ## two PDBs provided if(!is.null(b)) { if(!is.pdb(b)) stop("'b' should be a 'pdb' object as obtained from 'read.pdb'") if ( hydrogens ) { if(is.null(a.inds)) a.inds <- atom.select(a, "all", verbose=verbose) if(is.null(b.inds)) b.inds <- atom.select(b, "all", verbose=verbose) } else { if(is.null(a.inds)) a.inds <- atom.select(a, string='noh', verbose=verbose) if(is.null(b.inds)) b.inds <- atom.select(b, string='noh', verbose=verbose) } } ## one PDB object is provided else { if(is.null(a.inds) & is.null(b.inds)) { a.inds <- atom.select(a, "protein", verbose=verbose) b.inds <- atom.select(a, "ligand", verbose=verbose) if(!length(a.inds$atom)>0) stop("insufficent 'protein' atoms in structure") if(!length(b.inds$atom)>0) stop("insufficent 'ligand' atoms in structure") } b <- trim.pdb(a, b.inds) a <- trim.pdb(a, a.inds) if ( hydrogens ) { a.inds <- atom.select(a, "all", verbose=verbose) b.inds <- atom.select(b, "all", verbose=verbose) } else { a.inds <- atom.select(a, string='noh', verbose=verbose) b.inds <- atom.select(b, string='noh', verbose=verbose) } } if(!(length(a.inds$atom)>0 | length(b.inds$atom)>0)) stop("insufficent atoms in selection(s)") ## omit hydrogens if any a <- trim.pdb(a, a.inds) b <- trim.pdb(b, b.inds) ## Calcuate pair-wise distances dmat <- dist.xyz(matrix(a$xyz, ncol=3, byrow=TRUE), matrix(b$xyz, ncol=3, byrow=TRUE)) ## atoms of a in contact with b cmap <- apply(dmat, 1, function(x) any(x <= cutoff)) atom.inds <- which(cmap) ## return NULL if no atoms are closer than cutoff if(length(atom.inds)<1) { cat(" no atoms found within", cutoff, "A\n") return(NULL) } ## get rid of any trailing and leading spaces a$atom$resid <- trim(a$atom$resid) a$atom$resno <- trim(a$atom$resno) a$atom$elety <- trim(a$atom$elety) a.orig$atom$resno <- trim(a.orig$atom$resno) a.orig$atom$elety <- trim(a.orig$atom$elety) ## return all atoms in a contacting residue, otherwise, just the atoms if(byres) { resno.map <- apply(a$atom[atom.inds, c("resno", "chain")], 1, paste, collapse=sep) all.resno <- apply(a.orig$atom[, c("resno", "chain")], 1, paste, collapse=sep) atom.inds2 <- which(all.resno %in% resno.map) } else { resno.map <- apply(a$atom[atom.inds, c("elety", "resno", "chain")], 1, paste, collapse=sep) all.resno <- apply(a.orig$atom[, c("elety", "resno", "chain")], 1, paste, collapse=sep) atom.inds2 <- which(all.resno %in% resno.map) } xyz.inds <- atom2xyz(atom.inds2) ## check for chain IDs tmp <- unique(paste(a$atom[atom.inds, "resid"], a$atom[atom.inds, "resno"], a$atom[atom.inds, "chain"], sep=sep)) resno <- as.numeric(unlist(lapply(strsplit(tmp, sep), function(x) x[2]))) chain <- unlist(lapply(strsplit(tmp, sep), function(x) x[3])) chain[chain==" "] <- NA if(all(is.na(chain))) { resnames <- unique(paste(a$atom[atom.inds, "resid"], " ", a$atom[atom.inds, "resno"], sep="")) } else { resnames <- unique(paste(a$atom[atom.inds, "resid"], " ", a$atom[atom.inds, "resno"], " (", a$atom[atom.inds, "chain"], ")", sep="")) } sele <- list(atom=atom.inds2, xyz=xyz.inds) class(sele) <- "select" out <- list(inds=sele, resnames=resnames, resno=resno, chain=chain, call=cl) return(out) } bio3d/R/read.crd.amber.R0000644000176200001440000000351212632622153014350 0ustar liggesusers"read.crd.amber" <- function(file, ...) { if (missing(file)) { stop("read.prmtop: please specify a crd 'file' for reading") } toread <- file.exists(file) if (!toread) { stop("No input crd file found: check filename") } trim <- function (s) { ## Remove leading and traling ## spaces from character strings s <- sub("^ +", "", s) s <- sub(" +$", "", s) s[(s=="")]<-NA s } parse.line <- function(line, fmt) { tmp <- seq(1, as.numeric(fmt[1])*as.numeric(fmt[2]), by=as.numeric(fmt[2])) substring(line, tmp, c(tmp[2:length(tmp)]-1, nchar(line))) } ## Read and parse file raw.lines <- readLines(file) name <- trim(raw.lines[1]) ##num.atoms <- as.numeric(trim(raw.lines[2])) info <- unlist(strsplit(trim(raw.lines[2]), " ")) num.atoms <- as.numeric(info[1]) simtime <- NULL if(length(info)>1) simtime=as.numeric(info[2]) num.crdlines <- ceiling(num.atoms*3 / 6) if(length(raw.lines) > num.crdlines*2) { vel=TRUE boxline.ind <- (num.crdlines*2)+3 } else { vel <- FALSE boxline.ind <- num.crdlines+3 } ## parse coordinates fmt <- c(6, 12, 0, "a") tmplines <- raw.lines[3:(num.crdlines+2)] crds <- trim(unlist(lapply(tmplines, parse.line, fmt))) crds=as.numeric(crds) crds=crds[!is.na(crds)] ## parse velocities if(vel) { fmt <- c(6, 12, 0, "a") tmplines <- raw.lines[(num.crdlines+3):((num.crdlines*2)+2)] vels <- trim(unlist(lapply(tmplines, parse.line, fmt))) vels=as.numeric(vels) vels=vels[!is.na(vels)] } else { vels <- NULL } boxline <- raw.lines[boxline.ind] if(!is.na(boxline)) box <- as.numeric(trim(parse.line(boxline, fmt))) else box <- NULL out <- list(xyz=as.xyz(crds), velocities=vels, time=simtime, natoms=num.atoms, box=box) class(out) <- c("amber", "crd") return(out) } bio3d/R/get.blast.R0000644000176200001440000000542312430771420013465 0ustar liggesusersget.blast <- function(urlget, time.out = NULL, chain.single=TRUE) { if(substr(urlget, 1, 4) == "http" && grep("Blast.cgi", urlget) && grep("RID[[:space:]]*=", urlget)) { rid <- sub("^.*RID[[:space:]]*=[[:space:]]*", "", urlget) names(urlget)=rid } else { stop("Illegal link for retrieving BLAST results") } cat(paste(" Searching ... please wait (updates every 5 seconds) RID =",rid,"\n ")) ##- Retrieve results via RID code and check for job completion ## (completion is based on retrieving HTML or CSV output) html <- 1 t.count <- 0 repeat { raw <- try(read.csv(urlget, header = FALSE, sep = ",", quote="\"", dec=".", fill = TRUE, comment.char=""), silent=TRUE) if(class(raw)=="try-error") { stop("No hits found: thus no output generated") } html <- grep("DOCTYPE", raw[1,]) if(!is.null(time.out) && (t.count > time.out) || (length(html) != 1)) break; cat("."); Sys.sleep(5) t.count <- t.count + 5 } if(length(html) == 1) { warning("\nTime out (", time.out, "s): Retrieve results with returned link\n", urlget, "\n", sep="") return(urlget) } colnames(raw) <- c("queryid", "subjectids", "identity", "positives", "alignmentlength", "mismatches", "gapopens", "q.start", "q.end", "s.start", "s.end", "evalue", "bitscore") ##- Expand 'raw' for each hit in 'subjectids' (i.e. split on ";") rawm <- as.matrix(raw) eachsubject <- strsplit(rawm[,"subjectids"],";") subjectids <- unlist(eachsubject) n.subjects <- sapply(eachsubject, length) rawm <- apply(rawm, 2, rep, times=n.subjects) rawm[,"subjectids"] <- subjectids ##- Parse ids all.ids <- strsplit(subjectids, "\\|") gi.id <- sapply(all.ids, '[', 2) pdb.4char <- sapply(all.ids, '[', 4) pdb.chain <- sapply(all.ids, '[', 5) ## Catch long chain IDs as in hits from "P12612" (e.g "1WF4_GG" => "1WF4_g") if(chain.single) { chain.ind <- nchar(pdb.chain) > 1 if(any(chain.ind)) { pdb.chain[ chain.ind ] <- tolower( substr(pdb.chain[ chain.ind ],1,1 ) ) } } pdb.id <- paste(pdb.4char,"_",pdb.chain,sep="") ##- Map zero evalues to arbitrarily high value for -log(evalue) mlog.evalue <- -log(as.numeric(rawm[,"evalue"])) mlog.evalue[is.infinite(mlog.evalue)] <- -log(1e-308) cat(paste("\n Reporting",length(pdb.id),"hits\n")) output <- list(bitscore= as.numeric(rawm[,"bitscore"]), evalue = as.numeric(rawm[,"evalue"]), mlog.evalue = mlog.evalue, gi.id = gi.id, pdb.id = pdb.id, hit.tbl = rawm, raw = raw, url = urlget) class(output) <- "blast" return(output) } bio3d/R/read.fasta.pdb.R0000644000176200001440000001464112632622153014362 0ustar liggesusers"read.fasta.pdb" <- function(aln, prefix="", pdbext="", fix.ali = FALSE, ncore=1, nseg.scale=1, ...) { ## Log the call cl <- match.call() # Parallelized by parallel package (Fri Apr 26 17:58:26 EDT 2013) ncore <- setup.ncore(ncore) if(ncore > 1) { # Issue of serialization problem # Maximal number of cells of a double-precision matrix # that each core can serialize: (2^31-1-61)/8 R_NCELL_LIMIT_CORE = 2.68435448e8 R_NCELL_LIMIT = ncore * R_NCELL_LIMIT_CORE if(nseg.scale < 1) { warning("nseg.scale should be 1 or a larger integer\n") nseg.scale=1 } } files <- paste(prefix, aln$id, pdbext,sep="") ##cat(files,sep="\n") toread <- file.exists(files) ## check for online files toread[ substr(files,1,4)=="http" ] <- TRUE if(all(!toread)) stop("No corresponding PDB files found") # Avoid multi-thread downloading if(any(substr(files,1,4) == "http")) { ncore = 1 options(cores = ncore) } blank <- rep(NA, ncol(aln$ali)) mylapply <- lapply if(ncore > 1) mylapply <- mclapply # for (i in 1:length(aln$id)) { retval <- mylapply(1:length(aln$id), function(i) { coords <- NULL; res.nu <- NULL res.bf <- NULL; res.ch <- NULL res.id <- NULL; res.ss <- NULL cat(paste("pdb/seq:",i," name:", aln$id[i]),"\n") if(!toread[i]) { warning(paste("No PDB file found for seq", aln$id[i], ": (with filename) ",files[i]), call.=FALSE) coords <- rbind(coords, rep(blank,3)) res.nu <- rbind(res.nu, blank) res.bf <- rbind(res.bf, blank) res.ch <- rbind(res.ch, blank) res.id <- rbind(res.id, blank) res.ss <- rbind(res.ss, blank) } else { pdb <- read.pdb( files[i], verbose=FALSE, ... ) ca.inds <- atom.select(pdb, "calpha", verbose=FALSE) pdbseq <- aa321(pdb$atom$resid[ca.inds$atom]) aliseq <- toupper(aln$ali[i,]) tomatch <- gsub("X","[A-Z]",aliseq[!is.gap(aliseq)]) if(length(pdbseq)<1) stop(paste(basename(aln$id[i]), ": insufficent Calpha's in PDB"), call.=FALSE) ##-- Search for ali residues (1:15) in pdb start.num <- regexpr(pattern = paste(c(na.omit(tomatch[1:15])),collapse=""), text = paste(pdbseq,collapse=""))[1] if (start.num == -1) { start.num <- 1 ##stop(paste(basename(aln$id[i]), ": starting residues of sequence does not match starting residues in PDB"), call.=FALSE) } ##-- Numeric vec, 'nseq', for mapping aln to pdb nseq <- rep(NA,length(aliseq)) ali.res.ind <- which(!is.gap(aliseq)) if( length(ali.res.ind) > (length(pdbseq) - start.num + 1) ) { warning(paste(aln$id[i], ": sequence has more residues than PDB has Calpha's"), call.=FALSE) ali.res.ind <- ali.res.ind[1:(length(pdbseq)-start.num+1)] ## exclude extra tomatch <- tomatch[1:(length(pdbseq)-start.num+1)] ## terminal residues } nseq[ali.res.ind] = start.num:((start.num - 1) + length(tomatch)) ##-- Check for miss-matches match <- aliseq != pdbseq[nseq] if ( sum(match, na.rm=TRUE) >= 1 ) { mismatch.ind <- which(match) mismatch <- cbind(aliseq, pdbseq[nseq])[mismatch.ind,] n.miss <- length(mismatch.ind) if(sum(mismatch=="X") != n.miss) { ## ignore masked X res details <- seqbind(aliseq, pdbseq[nseq]) details$ali[is.na(details$ali)] <- "-" rownames(details$ali) <- c("aliseq","pdbseq") details$id <- c("aliseq","pdbseq") resmatch <- which(!apply(details$ali, 2, function(x) x[1]==x[2])) resid <- paste(pdb$atom$resid[ca.inds$atom][nseq][resmatch][1], "-", pdb$atom$resno[ca.inds$atom][nseq][resmatch][1], " (", pdb$atom$chain[ca.inds$atom][nseq][resmatch][1], ")", sep="") cat("\n ERROR Alignment mismatch. See alignment below for further details\n") cat(" (row ", i, " of aln and sequence of '", aln$id[i], "').\n", sep="") cat(" First mismatch residue in PDB is:", resid, "\n") cat(" occurring at alignment position:", which(match)[1], "\n\n") .print.fasta.ali(details) msg <- paste(basename.pdb(aln$id[i]), " alignment and PDB sequence miss-match\n", " beginning at position ", which(match)[1], " (PDB RESNO ", resid, ")", sep="") stop(msg, call.=FALSE) } } ##-- Store nseq justified/aligned PDB data ca.ali <- pdb$atom[ca.inds$atom,][nseq,] coords <- rbind(coords, as.numeric( t(ca.ali[,c("x","y","z")]) )) res.nu <- rbind(res.nu, ca.ali[, "resno"]) res.bf <- rbind(res.bf, as.numeric( ca.ali[,"b"] )) res.ch <- rbind(res.ch, ca.ali[, "chain"]) res.id <- rbind(res.id, ca.ali[, "resid"]) sse <- pdb2sse(pdb, verbose = FALSE) res.ss <- rbind(res.ss, sse[nseq]) } ## end else for (non)missing PDB file return (list(coords=coords, res.nu=res.nu, res.bf=res.bf, res.ch=res.ch, res.id=res.id, res.ss=res.ss)) } ) ## end mylapply retval <- do.call(rbind, retval) coords <- matrix(unlist(retval[, "coords"]), nrow=length(aln$id), byrow=TRUE) res.nu <- matrix(unlist(retval[, "res.nu"]), nrow=length(aln$id), byrow=TRUE) res.bf <- matrix(unlist(retval[, "res.bf"]), nrow=length(aln$id), byrow=TRUE) res.ch <- matrix(unlist(retval[, "res.ch"]), nrow=length(aln$id), byrow=TRUE) res.id <- matrix(unlist(retval[, "res.id"]), nrow=length(aln$id), byrow=TRUE) if( any(sapply(retval[, "res.ss"], is.null)) ) { res.ss <- NULL } else { res.ss <- matrix(unlist(retval[, "res.ss"]), nrow=length(aln$id), byrow=TRUE) rownames(res.ss) <- aln$id } rownames(aln$ali) <- aln$id rownames(coords) <- aln$id rownames(res.nu) <- aln$id rownames(res.bf) <- aln$id rownames(res.ch) <- aln$id rownames(res.id) <- aln$id if(fix.ali) { i1 <- which(is.na(res.nu)) i2 <- which(is.gap(aln$ali)) if(!identical(i1, i2)) { aln$ali[i1] <- aln$ali[i2[1]] warning("$ali component is modified to match $resno") } } out<-list(xyz=coords, resno=res.nu, b=res.bf, chain = res.ch, id=aln$id, ali=aln$ali, resid=res.id, sse=res.ss, call = cl) class(out)=c("pdbs","fasta") class(out$xyz) = "xyz" return(out) } bio3d/R/write.fasta.R0000644000176200001440000000243412412621431014024 0ustar liggesusers"write.fasta" <- function(alignment=NULL, ids=NULL, seqs=alignment$ali, file, append = FALSE) { if (is.null(seqs)) stop("write.fasta: please provide a 'seqs' or 'alignment' input object") if (!is.null(alignment)) { if (is.null(alignment$id) | is.null(alignment$ali)) { stop("write.fasta: 'alignment' should be a list with '$id' and '$ali'components") } if (is.null(ids)) { ids=alignment$id } } else { if (is.null(ids)) { n.ids <- nrow(seqs) if(is.null(n.ids)) { n.ids=1 } ids=seq( 1, length=n.ids ) } } if (!append) { ##file.remove(file, showWarnings = FALSE) suppressWarnings( file.remove(file) ) } nseqs <- length(ids) if (nseqs == 1) { # change for shortening lines (<=60) - Xinqiu cat(">", ids, "\n", file = file, append = TRUE, sep = "") cat(seqs, file = file, append = TRUE, sep = "", fill = 60) # cat(">", ids, "\n", seqs, "\n", file = file, # append = TRUE, sep = "") } else { for (i in 1:nseqs) { cat(">", ids[i], "\n", file = file, append = TRUE, sep = "") cat(seqs[i,], file = file, append = TRUE, sep = "", fill = 60) # cat(">", ids[i], "\n", seqs[i,], # "\n", file = file, append = TRUE, sep = "") } } } bio3d/R/plot.geostas.R0000644000176200001440000000051612544562302014225 0ustar liggesusersplot.geostas <- function(x, at=seq(0, 1, 0.1), main="AMSM with Domain Assignment", col.regions=rev(heat.colors(200)), margin.segments=x$grps, ...) { plot.dccm(x$amsm, at=at, main=main, col.regions=col.regions, margin.segments=margin.segments, ...) } bio3d/R/print.cna.R0000644000176200001440000000147412526367343013513 0ustar liggesusersprint.cna <- function(x, ...) { ## Check for presence of igraph package oops <- requireNamespace("igraph", quietly = TRUE) if (!oops) { stop("igraph package missing: Please install, see: ?install.packages") } ## y <- summary.cna(x, ...) l1 <- paste( "\n - NETWORK NODES#: ", x$communities$vcount, "\tEDGES#:", igraph::ecount(x$network)) l2 <- paste( "\n - COMMUNITY NODES#:", max(x$communities$membership), "\tEDGES#:", igraph::ecount(x$community.network)) cat("\nCall:\n ", paste(deparse(x$call), sep = "\n", collapse = "\n"), "\n", sep = "") cat("\nStructure:",l1,l2,"\n\n ") i <- paste( attributes(x)$names, collapse=", ") cat(strwrap(paste(" + attr:",i,"\n"),width=60, exdent=8), sep="\n") #print.igraph(x$network) #print.igraph(x$community.network) } bio3d/R/plotb3.R0000644000176200001440000000771712561207744013025 0ustar liggesusersplotb3 <- function(x, resno=NULL, rm.gaps = FALSE, type="h", main="", sub="", xlim=NULL, ylim=NULL, ylim2zero=TRUE, xlab = "Residue", ylab = NULL, axes=TRUE, ann=par("ann"), col=par("col"), sse=NULL, sse.type="classic", sse.min.length=5, top=TRUE, bot=TRUE, helix.col="gray20", sheet.col="gray80", sse.border=FALSE, ...) { ## Check for gap positions gaps.pos = gap.inspect(x) if(is.matrix(x)) x = x[1, ] ## should support matrix in future if(!is.null(resno)) { if(is.pdb(resno)) { ## Take Calpha residue numbers from PDB input ca.inds <- atom.select(resno, "calpha", verbose = FALSE) resno <- resno$atom$resno[ca.inds$atom] } if(any(is.na(x))) { tmp.resno <- rep(NA, length(x)) tmp.resno[!is.na(x)] = resno resno = tmp.resno } if(length(resno) != length(x)) { warning("Length of input 'resno' does not equal the length of input 'x'; Ignoring 'resno'") resno=NULL } } if(rm.gaps) { xy <- xy.coords(x[gaps.pos$f.inds]) if(!is.null(resno)) resno <- resno[gaps.pos$f.inds] } else xy <- xy.coords(x) if (is.null(xlim)) xlim <- range(xy$x[is.finite(xy$x)]) if (is.null(ylim)) ylim <- range(xy$y[is.finite(xy$y)]) if(ylim2zero) ylim[1]=0 plot.new() plot.window(xlim, ylim, ...) points(xy$x, xy$y, col=col, type=type, ...) if(!is.null(sse)) { ## Obtain SSE vector from PDB input 'sse' if(is.pdb(sse)) sse$sse <- pdb2sse(sse) h <- bounds( which(sse$sse == "H") ) e <- bounds( which(sse$sse == "E") ) ## Remove short h and e elements that can crowd plots if(length(h) > 0) { h <- h[h[,"length"] >= sse.min.length,,drop=FALSE] } else { h <- NULL } if(length(e) > 0) { e <- e[e[,"length"] >= sse.min.length,,drop=FALSE] } else { e <- NULL } ## For gaps if(length(gaps.pos$t.inds) > 0) { # unwrap SSE after length filtering tmp.sse = rep(" ", length(x)) tmp.inds = which(!is.na(x)) if(length(h) > 0) tmp.sse[tmp.inds[unbound(h)]] = "H" if(length(e) > 0) tmp.sse[tmp.inds[unbound(e)]] = "E" # remove gaps if required if(rm.gaps) tmp.sse = tmp.sse[gaps.pos$f.inds] # new SSE segments h <- bounds( which(tmp.sse == "H") ) e <- bounds( which(tmp.sse == "E") ) } if(sse.type != "classic") warning("Only sse.type='classic' is currently available, 'fancy' coming soon") if(top) { ## Determine bottom and top of margin region bo <- max(ylim) + (diff(ylim)*0.001) # 0.1% to <- max(ylim) + (diff(ylim)*0.04) # 4% if(length(h) > 0) rect(xleft=h[,"start"], xright=h[,"end"], ybottom=bo, ytop=to, col=helix.col, border=sse.border) if(length(e) > 0) rect(xleft=e[,"start"], xright=e[,"end"], ybottom=bo, ytop=to, col=sheet.col, border=sse.border) } if(bot){ to <- min(ylim) - (diff(ylim)*0.001) bo <- min(ylim) - (diff(ylim)*0.04) if(length(h) > 0) rect(xleft=h[,"start"], xright=h[,"end"], ybottom=bo, ytop=to, col=helix.col, border=sse.border) if(length(e) > 0) rect(xleft=e[,"start"], xright=e[,"end"], ybottom=bo, ytop=to, col=sheet.col, border=sse.border) } } if(axes) { axis(2) box() at <- axTicks(1); at[1] = 1 if(is.null(resno)) { axis(1, at) } else { labels <- resno[at] labels[is.na(labels)] <- "" # for gaps, no label axis(1, at=at, labels=labels) } } if(ann) { if(is.null(xlab)) xlab=xy$xlab if(is.null(ylab)) ylab=xy$ylab title(main=main, sub=sub, xlab=xlab, ylab=ylab, ...) } } plot.bio3d <- function(...) { plotb3(...) } bio3d/R/mktrj.enma.R0000644000176200001440000000646512632622153013661 0ustar liggesusers"mktrj.enma" <- function(enma=NULL, # enma data structure pdbs=NULL, # pdbs object s.inds=NULL, # structure ids m.inds=NULL, # modes ids mag=10, # magnification factor step=1.25, # step size file=NULL, # output pdb file rock=TRUE, ncore=NULL, ... ) { # args for write.pdb ## make a trjactory of atomic displacments along a given mode if(!inherits(enma, "enma")) stop("mktrj.enma: must supply 'enma' object, i.e. from 'nma.pdbs'") ## Parallelized by parallel package ncore <- setup.ncore(ncore, bigmem = FALSE) if(ncore>1) mylapply <- mclapply else mylapply <- lapply if(is.null(s.inds)) s.inds <- 1:nrow(enma$fluctuations) if(is.null(m.inds)) m.inds <- 1:5 if(is.null(file) & length(s.inds)==1 & length(m.inds)==1) file <- paste("mode_", m.inds+6, "-s", s.inds, ".pdb", sep="") if(is.null(enma$call$rm.gaps)) rm.gaps <- TRUE else if(enma$call$rm.gaps=="T" || enma$call$rm.gaps=="TRUE") rm.gaps <- TRUE else rm.gaps <- FALSE if(!rm.gaps & length(s.inds)>1 & length(m.inds)>1) stop(paste("enma object must be calculated with argument rm.gaps=TRUE", "\n", "for trajectory generation of multiple structures and modes")) if(any(enma$L[s.inds, m.inds]<=0)) warning("Mode with eigenvalue <=0 detected. Check 'mode' index.") nstep <- c(seq(step, to=mag, by=step)) zcoor <- cbind(1) %*% nstep scor <- function(x,u,m) { return(x*u+m) } myMktrj <- function(i) { coor <- NULL ind <- s.inds[i] for(j in 1:length(m.inds)) { mode <- m.inds[j] u.inds <- which(!is.na(enma$U.subspace[,mode,ind])) if(rm.gaps) xyz.inds <- gap.inspect(enma$xyz)$f.inds else xyz.inds <- u.inds plus <- sapply(c(zcoor), scor, u=enma$U.subspace[u.inds,mode,ind], m=enma$xyz[ind,xyz.inds]) minus <- sapply(c(-zcoor), scor, u=enma$U.subspace[u.inds,mode,ind], m=enma$xyz[ind,xyz.inds]) if(rock) { tmp <- cbind(pdbs$xyz[ind,xyz.inds], plus, plus[,rev(1:ncol(plus))], enma$xyz[ind,xyz.inds], minus, minus[,rev(1:ncol(minus))]) } else { tmp <- cbind(plus[,rev(1:ncol(plus))], enma$xyz[ind,xyz.inds], minus) } coor <- rbind(coor, t(tmp)) } return(coor) } ## do the calc coor <- mylapply(1:length(s.inds), myMktrj) coor <- do.call(rbind, coor) class(coor) <- "xyz" if(!is.null(file)) { if(rm.gaps) xyz.inds <- gap.inspect(enma$xyz)$f.inds else xyz.inds <- which(!is.na(enma$U.subspace[,m.inds[1],s.inds[1]])) if(is.null(pdbs)) write.pdb(xyz=coor, file=file, ...) else { write.pdb(xyz=coor, file=file, chain=pdbs$chain[s.inds[1], xyz2atom(xyz.inds)], resno=pdbs$resno[s.inds[1], xyz2atom(xyz.inds)], resid=pdbs$resid[s.inds[1], xyz2atom(xyz.inds)], b=enma$fluctuations[s.inds[1], !is.gap(enma$fluctuations[s.inds[1],])], ...) } invisible(coor) } else { return(coor) } } bio3d/R/dccm.xyz.R0000644000176200001440000000277412524171274013354 0ustar liggesusers`dccm.xyz` <- function(x, reference=NULL, grpby=NULL, ncore=1, nseg.scale=1, ... ) { xyz <- x # Parallelized by parallel package (Wed Dec 12 18:36:39 EST 2012) ncore <- setup.ncore(ncore) if(is.null(reference)) ref = colMeans(xyz) else ref = reference dxyz <- sweep(xyz, 2, ref) covmat <- cov(dxyz) if(!is.null(reference)) { # moment instead of covariance mxyz <- colMeans(dxyz) covmat <- covmat + outer(mxyz, mxyz) } ccmat <- cov2dccm(covmat, ncore = ncore) if(is.null(grpby)) { return(ccmat) } else { ##- Group by concetive numbers in 'grpby' if( ncol(xyz) != (length(grpby)*3) ) stop("dimension miss-match in 'xyz' and 'grpby', check lengths") ##- Bounds of 'grpby' numbers inds <- bounds(grpby, dup.inds=TRUE) nres <- nrow(inds) ##- Per-residue matrix m <- matrix(, ncol=nres, nrow=nres) ij <- pairwise(nres) ##- Max (absolute value) per residue for(k in 1 : nrow(ij) ) { m[ij[k,1],ij[k,2]] <- min( ccmat[ (inds[ij[k,1],"start"]:inds[ij[k,1],"end"]), (inds[ij[k,2],"start"]:inds[ij[k,2],"end"])], na.rm=TRUE ) tmax <- max( ccmat[ (inds[ij[k,1],"start"]:inds[ij[k,1],"end"]), (inds[ij[k,2],"start"]:inds[ij[k,2],"end"])], na.rm=TRUE ) if(tmax > abs(m[ij[k,1],ij[k,2]])) m[ij[k,1],ij[k,2]] = tmax } # if( !mask.lower ) m[lower.tri(m)] = t(m)[lower.tri(m)] diag(m) <- 1 class(m)=c("dccm","matrix") return(m) } } bio3d/R/stride.R0000644000176200001440000001312012602330231013055 0ustar liggesusers"stride" <- function(pdb, exefile = "stride", resno=TRUE) { ## Log the call cl <- match.call() infile <- tempfile() outfile <- tempfile() write.pdb(pdb, file=infile) os1 <- .Platform$OS.type if(os1 == "windows") { shell( paste(exefile," -f",outfile," ",infile,sep="") ) } else { system( paste(exefile," -f",outfile," ",infile,sep="") ) } raw.lines <- readLines(outfile) type <- substring(raw.lines, 1, 3) unlink(c(infile, outfile)) raw.loc <- raw.lines[type == "LOC"] raw.tor <- raw.lines[type == "ASG"] phi <- as.numeric(substring(raw.tor, 43,49)) psi <- as.numeric(substring(raw.tor, 53,59)) # DEBUG: SSE length is inconsistent with the sequence length; # Read ASG instead of LOC lines sse <- substring(raw.tor, 25,25) cha <- substring(raw.tor, 10,10) acc <- as.numeric(substring(raw.tor, 65, 69)) res.num <- suppressWarnings(as.numeric(substring(raw.tor, 12, 15))) if(any(is.na(res.num))) { ins <- which(is.na(res.num)) res.num[ins] <- as.numeric(substring(raw.tor, 11, 14))[ins] if(resno) { warning("Insertions are found in PDB: Residue numbers may be incorrect. Try again with resno=FALSE") } else { ii <- diff(res.num) ii[ii==0] <- 1 #Consecutive numbers at insertion residues ii[ii<0] <- 2 #Jumps at possible chain termination res.num <- res.num[1] + c(0, cumsum(ii)) } } # res.ind <- 1:length(res.num) # res.name <- substring(raw.tor, 6, 8) h.res <- bounds(res.num[which(sse == "H")], pre.sort=FALSE) g.res <- bounds(res.num[which(sse == "G")], pre.sort=FALSE) e.res <- bounds(res.num[which(sse == "E")], pre.sort=FALSE) t.res <- bounds(res.num[which(sse == "T")], pre.sort=FALSE) ## sseInfo <- cbind(resIndex=res.ind, resNumber=res.num, ## resName=res.name, sse=sse) # start <- as.numeric(substring(raw.loc, 23,27)) # end <- as.numeric(substring(raw.loc, 42,45)) # chain <- substring(raw.loc, 29,29) # # sse <- substring(raw.loc, 6,9) # # h.ind <- sse == "Alph" # g.ind <- sse == "310H" # e.ind <- sse == "Stra" # t.ind <- sse == "Turn" # # sse.type <- sse # sse.type[h.ind] <- "H" # sse.type[g.ind] <- "G" # sse.type[e.ind] <- "E" # sse.type[t.ind] <- "T" h.ind <- h.res; g.ind <- g.res e.ind <- e.res; t.ind <- t.res if(length(h.res) > 0) { res.ind <- which(sse == "H") h.ind[, "end"] <- res.ind[cumsum(h.res[, "length"])] h.ind[, "start"] <- h.ind[, "end"] - h.res[, "length"] + 1 } if(length(g.res) > 0) { res.ind <- which(sse == "G") g.ind[, "end"] <- res.ind[cumsum(g.res[, "length"])] g.ind[, "start"] <- g.ind[, "end"] - g.res[, "length"] + 1 } if(length(e.res) > 0) { res.ind <- which(sse == "E") e.ind[, "end"] <- res.ind[cumsum(e.res[, "length"])] e.ind[, "start"] <- e.ind[, "end"] - e.res[, "length"] + 1 } if(length(t.res) > 0) { res.ind <- which(sse == "T") t.ind[, "end"] <- res.ind[cumsum(t.res[, "length"])] t.ind[, "start"] <- t.ind[, "end"] - t.res[, "length"] + 1 } if(!resno) { h.res <- h.ind; g.res <- g.ind e.res <- e.ind; t.res <- t.ind } sheet = list(start=NULL, end=NULL, length=NULL, chain=NULL) helix = list(start=NULL, end=NULL, length=NULL, chain=NULL, type=NULL) turn = sheet if(length(h.res)>1) { helix$start = c(helix$start,h.res[, "start"]) helix$end = c(helix$end, h.res[, "end"]) helix$length = c(helix$length, h.res[, "length"]) helix$chain = c(helix$chain, cha[h.ind[, "start"]]) helix$type = c(helix$type, sse[h.ind[, "start"]]) } if(length(g.res)>1) { helix$start = c(helix$start,g.res[, "start"]) helix$end = c(helix$end, g.res[, "end"]) helix$length = c(helix$length, g.res[, "length"]) helix$chain = c(helix$chain, cha[g.ind[, "start"]]) helix$type = c(helix$type, sse[g.ind[, "start"]]) } if(length(helix$start) > 0) helix <- lapply(helix, function(x) {names(x) <- 1:length(helix$start); return(x)}) if(length(e.res)>1) { sheet$start = c(sheet$start,e.res[, "start"]) sheet$end = c(sheet$end, e.res[, "end"]) sheet$length = c(sheet$length, e.res[, "length"]) sheet$chain = c(sheet$chain, cha[e.ind[, "start"]]) } if(length(sheet$start) > 0) sheet <- lapply(sheet, function(x) {names(x) <- 1:length(sheet$start); return(x)}) if(length(t.res)>1) { turn$start = c(turn$start,t.res[, "start"]) turn$end = c(turn$end, t.res[, "end"]) turn$length = c(turn$length, t.res[, "length"]) turn$chain = c(turn$chain, cha[t.ind[, "start"]]) } if(length(turn$start) > 0) turn <- lapply(turn, function(x) {names(x) <- 1:length(turn$start); return(x)}) # if(any(h.ind | g.ind)) { # helix=list(start = c(start[h.ind], start[g.ind]), # end = c(end[h.ind], end[g.ind]), # length = ( c(end[h.ind], end[g.ind]) - # c(start[h.ind], start[g.ind]) + 1), # chain = c(chain[h.ind], chain[g.ind]), # type = c(sse.type[h.ind], sse.type[g.ind])) # } # if(any(e.ind)) { # sheet = list(start = start[e.ind], # end = end[e.ind], # length = (end[e.ind] - start[e.ind] + 1), # chain = chain[e.ind]) # } # if(any(t.ind)) { # turn = list(start = start[t.ind], # end = end[t.ind], # length =(end[t.ind] - start[t.ind] + 1), # chain = chain[t.ind]) # } out <- list(helix = helix, sheet=sheet, hbonds=NULL, turn=turn, phi=phi, psi=psi, acc=acc, sse=sse, call=cl) class(out) <- "sse" return(out) } bio3d/R/dccm.enma.R0000644000176200001440000000442312632622153013430 0ustar liggesusers"dccm.enma" <- function(x, ncore=NULL, na.rm=FALSE, ...) { enma <- x if(!inherits(enma, "enma")) stop("input should be an 'enma' object as obtained from 'nma.pdbs'") ## Parallelized by parallel package ncore <- setup.ncore(ncore, bigmem = FALSE) if(ncore>1) mylapply <- mclapply else mylapply <- lapply mass <- TRUE if(!is.null(enma$call$mass)) mass <- enma$call$mass pi <- 3.14159265359 dims <- dim(enma$U.subspace) if(is.null(enma$full.nma)) { if((dims[1]-6)>dims[2]) warning(paste(dims[2], "modes used in the calculation of the DCCMs")) } myCalcDCCM <- function(i, enma, na.rm=FALSE) { if(is.null(enma$full.nma)) { if(mass) { freqs <- sqrt(abs(enma$L[i,])) / (2 * pi) fcs <- NULL } else { freqs <- NULL fcs <- enma$L[i,] } if(na.rm) { inds <- which( !is.na(enma$U.subspace[,1,i]) ) U <- enma$U.subspace[inds,,i] } else U <- enma$U.subspace[,,i] dummy.nma <- list(U=U, L=enma$L[i,], modes=NULL, frequencies=freqs, force.constants=fcs, triv.modes=0, natoms=nrow(U)/3) ##natoms=nrow(enma$U.subspace[,,i])/3) class(dummy.nma) <- "nma" invisible(capture.output( cm.tmp <- dccm.nma(dummy.nma, ncore=1) )) } else { invisible(capture.output( cm.tmp <- dccm.nma(enma$full.nma[[i]], ncore=1) )) } setTxtProgressBar(pb, i) return(cm.tmp) } ## do the calc pb <- txtProgressBar(min=1, max=dims[3L], style=3) all.dccm <- mylapply(1:dims[3L], myCalcDCCM, enma, na.rm=na.rm) close(pb) if(any(is.na(enma$U.subspace))) arr <- FALSE else arr <- TRUE if(arr) { ## convert to a 3d-array dccm.arr <- array(0, dim=c(dims[1L]/3, dims[1L]/3, dims[3L])) ## collect data for(i in 1:length(all.dccm)) { tmp.cm <- all.dccm[[i]] dccm.arr[,,i] <- tmp.cm } } if(arr) { avg <- apply(dccm.arr, 1:2, mean) class(avg) <- c("matrix", "dccm") out <- list(all.dccm=dccm.arr, avg.dccm=avg) } else { out <- list(all.dccm=all.dccm, avg.dccm=NULL) } return(out) } bio3d/R/trim.pdbs.R0000644000176200001440000000226012602522006013473 0ustar liggesusers## Use for trimming a pdbs object, either by removing structures, ## or by removing columns trim.pdbs <- function(pdbs, row.inds=NULL, col.inds=NULL, ...) { if(!inherits(pdbs, "pdbs")) stop("input 'pdbs' should be a list object as obtained from 'read.fasta.pdb'") ## Log the call cl <- match.call() if(is.null(row.inds)) row.inds <- seq(1, nrow(pdbs$resno), by=1) if(is.null(col.inds)) { gaps <- gap.inspect(pdbs$resno[row.inds,,drop=FALSE]) col.inds <- which(gaps$col < dim(pdbs$resno[row.inds,,drop=FALSE])[1L]) } if(any(col.inds<0)) col.inds.xyz <- atom2xyz(abs(col.inds)) * sign(rep(col.inds, each=3)) else col.inds.xyz <- atom2xyz(col.inds) new <- NULL new$id =pdbs$id[row.inds] new$xyz =pdbs$xyz[row.inds, col.inds.xyz, drop=FALSE] new$resno =pdbs$resno[row.inds, col.inds, drop=FALSE] new$b =pdbs$b[row.inds, col.inds, drop=FALSE] new$chain =pdbs$chain[row.inds, col.inds, drop=FALSE] new$ali =pdbs$ali[row.inds, col.inds, drop=FALSE] new$resid =pdbs$resid[row.inds, col.inds, drop=FALSE] new$sse =pdbs$sse[row.inds, col.inds, drop=FALSE] new$call =cl class(new) <- c("pdbs", "fasta") return(new) } bio3d/R/read.pdcBD.R0000644000176200001440000001272512412621431013467 0ustar liggesusers`read.pdcBD` <- function (file, maxlines=50000, multi=FALSE, rm.insert=FALSE, rm.alt=TRUE, verbose=TRUE) { if(missing(file)) { stop("read.pqr: please specify a PQR 'file' for reading") } if(!is.numeric(maxlines)) { stop("read.pqr: 'maxlines' must be numeric") } if(!is.logical(multi)) { stop("read.pqr: 'multi' must be logical TRUE/FALSE") } # PDB FORMAT v2.0: colpos, datatype, name, description atom.format <- matrix(c(-4, NA, NA, # (ATOM) 7, 'numeric', "eleno", # atom_no -1, NA, NA, # (blank) 3, 'character', "elety", # atom_ty 1, 'character', "alt", # alt_loc 4, 'character', "resid", # res_na 1, 'character', "chain", # chain_id 5, 'numeric', "resno", # res_no 1, 'character', "insert", # ins_code -3, NA, NA, # (blank) 10, 'numeric', "x", # x 10, 'numeric', "y", # y 10, 'numeric', "z", # z 8, 'numeric', "o", # o ### 6 for pdb 8, 'numeric', "b", # b ### 6 for pdb -6, NA, NA, # (blank) 4, 'character', "segid" # seg_id ), ncol=3, byrow=TRUE, dimnames = list(c(1:17), c("widths","what","name")) ) split.string <- function(x) { # split a string 'x' x <- substring(x, first, last) x[nchar(x) == 0] <- as.character(NA) x } is.character0 <- function(x){length(x)==0 & is.character(x)} trim <- function (s) { # Remove leading and traling # spaces from character strings s <- sub("^ +", "", s) s <- sub(" +$", "", s) s[(s=="")]<-NA s } # finds first and last (substr positions) widths <- as.numeric(atom.format[,"widths"]) # fixed-width spec drop.ind <- (widths < 0) # cols to ignore (i.e. -ve) widths <- abs(widths) # absolute vales for later st <- c(1, 1 + cumsum( widths )) first <- st[-length(st)][!drop.ind] # substr start last <- cumsum( widths )[!drop.ind] # substr end # read n lines of PDB file raw.lines <- readLines(file, n = maxlines) type <- substring(raw.lines,1,6) # check number of END/ENDMDL records raw.end <- sort(c(which(type == "END"), which(type == "ENDMDL"))) if (length(raw.end) > 1) { print("PDB has multiple END/ENDMDL records") if (!multi) { print("multi=FALSE: taking first record only") raw.lines <- raw.lines[ (1:raw.end[1]) ] type <- type[ (1:raw.end[1]) ] } else { print("multi=TRUE: 'read.dcd' will be quicker!") } } if ( length(raw.end) !=1 ) { if (length(raw.lines) == maxlines) { # have not yet read all the file print("You may need to increase 'maxlines'") print("check you have all data in $atom") } } # split by record type raw.header <- raw.lines[type == "HEADER"] raw.seqres <- raw.lines[type == "SEQRES"] raw.helix <- raw.lines[type == "HELIX "] raw.sheet <- raw.lines[type == "SHEET "] raw.atom <- raw.lines[type == "ATOM "] het.atom <- raw.lines[type == "HETATM"] # also look for "TER" records rm(raw.lines) if (verbose) { if (!is.character0(raw.header)) { cat(" ", raw.header, "\n") } } seqres <- unlist(strsplit( trim(substring(raw.seqres,19,80))," ")) helix <- list(start = as.numeric(substring(raw.helix,22,25)), end = as.numeric(substring(raw.helix,34,37)), chain = trim(substring(raw.helix,20,20)), type = trim(substring(raw.helix,39,40))) sheet <- list(start = as.numeric(substring(raw.sheet,23,26)), end = as.numeric(substring(raw.sheet,34,37)), chain = trim(substring(raw.sheet,22,22)), sense = trim(substring(raw.sheet,39,40))) # format ATOM records as a character matrix atom <- matrix(trim(sapply(raw.atom, split.string)), byrow=TRUE, ncol=nrow(atom.format[ !drop.ind,]), dimnames = list(NULL, atom.format[ !drop.ind,"name"]) ) # Alt records with m[,"alt"] != NA if (rm.alt) { if ( sum( !is.na(atom[,"alt"]) ) > 0 ) { cat(" PDB has ALT records, taking A only, rm.alt=TRUE\n") alt.inds <- which( (atom[,"alt"] != "A") ) # take first alt only if(length(alt.inds)>0) atom <- atom[-alt.inds,] } } # Insert records with m[,"insert"] != NA if (rm.insert) { if ( sum( !is.na(atom[,"insert"]) ) > 0 ) { cat(" PDB has INSERT records, removing, rm.insert=TRUE\n") insert.inds <- which(!is.na(atom[,"insert"])) # rm insert positions atom <- atom[-insert.inds,] } } het <- matrix(trim(sapply(het.atom, split.string)), byrow=TRUE, ncol=nrow(atom.format[ !drop.ind,]), dimnames = list(NULL, atom.format[ !drop.ind,"name"]) ) output<-list(atom=atom, het=het, helix=helix, sheet=sheet, seqres=seqres, xyz=as.numeric(t(atom[,c("x","y","z")])), calpha = as.logical(atom[,"elety"]=="CA")) class(output) <- "pdb" return(output) } bio3d/R/rgyr.R0000644000176200001440000000377312524171274012600 0ustar liggesusers# radius of gyration # xyz: length 3N, 1D array of atomic coordinates, # M*3N matrix, or a list object containing xyz # mass: length N 1D array of atomic masses [amu], # or a PDB object having masses stored in the # "B-factor" column rgyr <- function(xyz, mass=NULL, ncore=1, nseg.scale=1) { # Parallelized by parallel package ncore <- setup.ncore(ncore) if(ncore > 1) { # Issue of serialization problem # Maximal number of cells of a double-precision matrix # that each core can serialize: (2^31-1-61)/8 R_NCELL_LIMIT_CORE = 2.68435448e8 R_NCELL_LIMIT = ncore * R_NCELL_LIMIT_CORE if(nseg.scale < 1) { warning("nseg.scale should be 1 or a larger integer\n") nseg.scale=1 } } # check xyz, vector, matrix, or list if(is.list(xyz)) xyz <- xyz$xyz if(is.vector(xyz)) xyz <- matrix(xyz, nrow=1) #check mass array and load masses if(is.list(mass)) mass <- mass$atom[,"b"] if(is.null(mass)) # assume carbon mass <- rep(12.01, ncol(xyz)/3) if(ncol(xyz)/3 != length(mass)) stop("The length of masses doesn't match the number of atoms") rg <- function (xyz, mass) { nAtom <- length(mass) mc <- matrix(xyz, 3, nAtom) v <- replicate(3, mass) * t(mc) com <- colSums(v)/sum(mass) recenteredpos <- mc - replicate(nAtom, com) rog_sq <- sum(colSums(recenteredpos**2) * mass) rog_sq <- rog_sq / sum(mass) return( sqrt(rog_sq) ) } if(ncore > 1) { RLIMIT = R_NCELL_LIMIT nDataSeg = floor((nrow(xyz)-1)/RLIMIT)+1 nDataSeg = floor(nDataSeg * nseg.scale) lenSeg = floor(nrow(xyz)/nDataSeg) rog <- NULL for(i in 1:nDataSeg) { istart = (i-1)*lenSeg + 1 iend = if(i 3) { # if (nchar(elety) >= 3) { # if ((substr(elety, 2, 2) == "H") | (substr(elety, 1, 1) == "H")) { format <- "%-6s%5s %-4s%1s%-4s%1s%4s%1s%3s%8.3f%8.3f%8.3f%6.2f%6.2f%6s%4s%2s%2s" # } } sprintf(format, card, eleno, elety, alt, resid, chain, resno, insert, "", x, y, z, o, b, "", segid, elesy, charge) } if(nfile==1) { coords <- matrix(round(as.numeric(xyz), 3), ncol = 3, byrow = TRUE) if (verbose) { cat(paste("Writing 1 frame with",natom,"atoms ")) } lines <- NULL ii = 0 teleno <- as.numeric(eleno) for (i in 1:natom) { lines <- rbind(lines, atom.print( card = card[i], eleno = as.character(teleno[i] + ii), elety = elety[i], alt = alt[i], resid = resid[i], chain = chain[i], resno = resno[i], insert = insert[i], x = coords[i, 1], y = coords[i, 2], z = coords[i, 3], o = o[i], b = b[i], segid = segid[i], elesy = elesy[i], charge = charge[i])) ## Inserted Jul 8th 2008 for adding TER between chains ## Modified to be consistent to PDB format v3.3 if(chainter) { if(i %in% ter.lines) { # lines <- rbind(lines, "TER ") ii = ii + 1 lines <- rbind(lines, sprintf("%-6s%5s%6s%3s%1s%1s%4s%1s", "TER", as.character(teleno[i] + ii), "", resid[i], "", chain[i], resno[i], insert[i])) } } } ## Changed cat() for write.table() as sugested by Joao Martins ##cat(lines, file = file, sep = "\n", append = append) write.table(lines, file = file, quote = FALSE, row.names = FALSE, col.names = FALSE, append = append) if(chainter) { ii = ii + 1 cat(sprintf("%-6s%5s%6s%3s%1s%1s%4s%1s\n", "TER", as.character(teleno[i] + ii), "", resid[i], "", chain[i], resno[i], insert[i]), file = file, append = TRUE) } if(end) { cat("END \n", file = file, append = TRUE) } } else { if (verbose) { cat(paste("Writing",nfile,"frames with",natom,"atoms"),"\n") cat("Frame Progress (x50) ") } if(!append) unlink(file) for (j in 1:nfile) { coords <- matrix(round(as.numeric(xyz[j,]), 3), ncol = 3, byrow = TRUE) lines <- NULL ii = 0 teleno <- as.numeric(eleno) for (i in 1:natom) { lines <- rbind(lines, atom.print( eleno = as.character(teleno[i] + ii), elety = elety[i], alt = alt[i], resid = resid[i], chain = chain[i], resno = resno[i], insert = insert[i], x = coords[i, 1], y = coords[i, 2], z = coords[i, 3], o = o[i], b = b[i], segid = segid[i], elesy = elesy[i], charge = charge[i])) ## Inserted Jul 8th 2008 for adding TER between chains (untested) ## Modified to be consistent to PDB format v3.3 if(chainter) { if(i %in% ter.lines) { # lines <- rbind(lines, "TER ") ii = ii + 1 lines <- rbind(lines, sprintf("%-6s%5s%6s%3s%1s%1s%4s%1s", "TER", as.character(teleno[i] + ii), "", resid[i], "", chain[i], resno[i], insert[i])) } } } if (verbose) { if (j%%50 == 0) cat(".") } ##cat(lines, file = file, sep = "\n", append = TRUE) cat(sprintf("%-6s%4s%4d\n", "MODEL", " ", j), file = file, append = TRUE) write.table(lines, file = file, quote = FALSE, row.names = FALSE, col.names = FALSE, append = TRUE) if(chainter) { ii = ii + 1 cat(sprintf("%-6s%5s%6s%3s%1s%1s%4s%1s\n", "TER", as.character(teleno[i] + ii), "", resid[i], "", chain[i], resno[i], insert[i]), file=file, append=TRUE) } cat(sprintf("%-6s\n", "ENDMDL"), file = file, append = TRUE) } if(end) { cat("END \n", file = file, append = TRUE) } } if (verbose) cat(" DONE","\n") } bio3d/R/dccm.R0000644000176200001440000000037412412621431012504 0ustar liggesusers`dccm` <- function(x, ...) { if(inherits(x, "matrix")) { class(x) <- c("matrix", "xyz") UseMethod("dccm", x) } else if(inherits(x, "array")) { class(x) <- c("matrix", "mean") UseMethod("dccm", x) } else UseMethod("dccm") } bio3d/R/atom.select.R0000644000176200001440000000007212526367343014026 0ustar liggesusers"atom.select" <- function(...) UseMethod("atom.select") bio3d/R/cna.ensmb.R0000644000176200001440000000074212526367343013460 0ustar liggesusers# ensemble CNA calculation optimized with multicore cna.ensmb <- function(cij, ..., ncore = NULL) { ncore <- setup.ncore(ncore) cijs <- cij if("all.dccm" %in% names(cijs)) cijs <- cijs$all.dccm if(is.array(cijs) && length(dim(cijs))==3) cijs <- do.call("c", apply(cijs, 3, list)) if(is.list(cijs)) { net <- mclapply(cijs, cna.dccm, ...) } else { warning("cijs should be matrix, array(dim=3), or list") net <- NULL } return(net) } bio3d/R/mustang.R0000644000176200001440000000446312526367343013276 0ustar liggesusers"mustang" <- function(files, exefile="mustang", outfile="aln.mustang.fa", cleanpdb=FALSE, cleandir="mustangpdbs", verbose=TRUE) { ## Check if the program is executable os1 <- .Platform$OS.type status <- system(paste(exefile, "--version"), ignore.stderr = TRUE, ignore.stdout = TRUE) if(!(status %in% c(0,1))) stop(paste("Launching external program failed\n", " make sure '", exefile, "' is in your search path", sep="")) if(!all(file.exists(files))) stop(paste("Missing files:", paste(files[ !file.exists(files) ], collapse=", "))) ## produce cleaned CA pdb files for mustang if(cleanpdb) { if(!file.exists(cleandir)) dir.create(cleandir) newfiles <- c() for(i in 1:length(files)) { tmpout <- paste(cleandir, basename(files[i]), sep="/") pdb <- read.pdb(files[i]) sele <- atom.select(pdb, "calpha", verbose=verbose) new <- trim.pdb(pdb, sele) seq1 <- aa321(new$atom$resid) seq3 <- aa123(seq1) new$atom$type <- "ATOM" new$atom$resid <- seq3 write.pdb(new, file=tmpout) newfiles <- c(newfiles, tmpout) } files <- newfiles } infile <- tempfile() tmpout <- tempfile() dirn <- unique(dirname(files)) if(length(dirn)>1) stop("All files must be in one directory") files <- basename(files) rawlines <- NULL rawlines <- c(rawlines, paste(">", dirn)) for ( i in 1:length(files) ) rawlines <- c(rawlines, paste("+", files[i], sep="")) write.table(rawlines, file=infile, quote=FALSE, row.names=FALSE, col.names=FALSE) cmd <- paste(exefile, "-f", infile, "-o", tmpout, "-F fasta") if(verbose) cat("Running command\n", cmd, "\n") if (os1 == "windows") success <- shell(shQuote(cmd), ignore.stderr = !verbose, ignore.stdout = !verbose) else success <- system(cmd, ignore.stderr = !verbose, ignore.stdout = !verbose) if(success!=0) stop(paste("An error occurred while running command\n '", exefile, "'", sep="")) aln <- read.fasta(paste(tmpout, ".afasta", sep="")) rownames(aln$ali) <- paste(dirn, rownames(aln$ali), sep="/") aln$id <- rownames(aln$ali) unlink(infile); unlink(tmpout); if(!is.null(outfile)) write.fasta(aln, file=outfile) return(aln) } bio3d/R/get.pdb.R0000644000176200001440000000607712632622153013135 0ustar liggesusers"get.pdb" <- function (ids, path = ".", URLonly = FALSE, overwrite = FALSE, gzip = FALSE, split = FALSE, verbose = TRUE, ncore = 1, ... ) { if(.Platform$OS.type=="windows") gzip <- FALSE # Parallelized by parallel package (Tue Oct 15 15:23:36 EDT 2013) ncore <- setup.ncore(ncore) if(ncore > 4) { # To avoid too frequent access to PDB server if(!split) { warning("Exceed maximum ncore (=4) to access PDB server. Use ncore=4") ncore <- setup.ncore(ncore = 4) } else { setup.ncore(ncore = 4) } } if(inherits(ids, "blast")) ids = ids$pdb.id if (any(nchar(ids) < 4)) stop("ids should be standard 4 character PDB-IDs or 6 character PDB-ID_Chain-IDs") ids4 = ids if (any(nchar(ids) > 4)) { ids4 <- unlist(lapply(strsplit(ids, "_"), function(x) x[1])) if (any(nchar(ids4) > 4)) warning("ids should be standard 4 character PDB-IDs: trying first 4 characters...") ids4 <- substr(basename(ids), 1, 4) } ids4 <- unique(ids4) pdb.files <- paste(ids4, ".pdb", ifelse(gzip, ".gz", ""), sep = "") get.files <- file.path("http://www.rcsb.org/pdb/files", pdb.files) if (URLonly) return(get.files) put.files <- file.path(path, pdb.files) if(!file.exists(path)) dir.create(path) rtn <- rep(NA, length(pdb.files)) if(ncore > 1) { rtn <- unlist(mclapply(1:length(pdb.files), function(k) { if (!file.exists(sub(".gz$", "", put.files[k])) | overwrite ) { rtn <- try(download.file(get.files[k], put.files[k], quiet = !verbose), silent = TRUE) if(inherits(rtn, "try-error")) { rtn <- 1 file.remove(put.files[k]) } else if(gzip) { cmd <- paste("gunzip -f", put.files[k]) system(cmd) } } else { rtn <- put.files[k] warning(paste(put.files[k], " exists. Skipping download")) } return(rtn) })) } else { for (k in 1:length(pdb.files)) { if (!file.exists(sub(".gz$", "", put.files[k])) | overwrite ) { rt <- try(download.file(get.files[k], put.files[k], quiet = !verbose), silent=TRUE) rtn[k] <- rt if(inherits(rt, "try-error")) { rtn[k] <- 1 file.remove(put.files[k]) } else if(gzip) { cmd <- paste("gunzip -f", put.files[k]) system(cmd) } } else { rtn[k] <- put.files[k] warning(paste(put.files[k], " exists. Skipping download")) } } } names(rtn) <- file.path(path, paste(ids4, ".pdb", sep = "")) if (any(rtn == 1)) { warning("Some files could not be downloaded, check returned value") return(rtn) } else { if(split) { rtn = pdbsplit(pdb.files = names(rtn), ids = ids, path = file.path(path, "split_chain"), ncore = ncore, ...) return(rtn) } else { return(names(rtn)) } } } bio3d/R/deformation.nma.R0000644000176200001440000000420212475414747014674 0ustar liggesusers"deformation.nma" <- function(nma, mode.inds=NULL, pfc.fun=NULL, ncore=NULL) { if(!inherits(nma, "nma")) stop("provide input of class 'nma' as obtained from function 'nma'") if(is.null(mode.inds)) { nmodes <- 20 if(length(nma$L) < (nmodes+nma$triv.modes)) nmodes <- length(nma$L) - nma$triv.modes mode.inds <- seq(nma$triv.modes+1, nma$triv.modes+nmodes) } else { if(length(nma$L) < (length(mode.inds)+nma$triv.modes)) { nmodes <- length(nma$L) - nma$triv.modes mode.inds <- seq(nma$triv.modes+1, nma$triv.modes+nmodes) warning("'mode.inds' was modified to include all modes") } } ## Check for multiple cores ncore <- setup.ncore(ncore) ## calcualte deformation energies for a specific mode def.mode <- function(mode.id, nma, xyz, ff, natoms) { mode.vec <- nma$modes[,mode.id] ## for normalization norm <- sqrt(sum(mode.vec**2) / natoms) ## better work with a matrix mode.vec <- matrix(mode.vec, ncol=3, byrow=T) def.e <- rep(0, length=natoms) for ( i in 1:(natoms)) { ## distance vectors between a and b rs <- t(apply(xyz, 1, "-", xyz[i,])) ## differences mode vectors vs <- t(apply(mode.vec, 1, "-", mode.vec[i,])) ##rl <- apply(rs, 1, function(x) sqrt(sum(x^2))) rsq <- rowSums(rs^2) rl <- sqrt(rsq) ##l <- inner.prod(t(rs), t(vs)) / norm l <- colSums(t(rs)*t(vs)) / norm k <- ff(rl) l2 <- k*l*l / rsq l2[i] <- 0 def.e <- def.e + (0.5*l2) } return(def.e) } if(is.null(pfc.fun)) ff <- load.enmff("calpha") else { if (!is.function(pfc.fun)) stop("'pfc.fun' must be a function") ff <- pfc.fun } natoms <- length(nma$xyz)/3 xyz <- matrix(nma$xyz, ncol=3, byrow=T) if(ncore>1) hm <- mclapply(mode.inds, def.mode, nma, xyz, ff, natoms, mc.cores=ncore) else hm <- lapply(mode.inds, def.mode, nma, xyz, ff, natoms) ## Collect results and make a matrix to store results hm <- t(do.call(rbind, hm)) sums <- colSums(hm) out <- list(ei=hm, sums=sums) return(out) } bio3d/R/read.crd.R0000644000176200001440000000053712526367343013300 0ustar liggesusers"read.crd" <- function(file, ...) { ## from tools package: pos <- regexpr("\\.([[:alnum:]]+)$", file) ext <- ifelse(pos > -1L, substring(file, pos + 1L), "") if(ext %in% c("crd")) { class(file)=c("character", "charmm") } if(ext %in% c("inpcrd", "rst")) { class(file)=c("character", "amber") } UseMethod("read.crd", file) } bio3d/R/pfam.R0000644000176200001440000000217712526367343012543 0ustar liggesusers"pfam" <- function(id, alignment='seed', verbose=FALSE) { ##alignment <- 'full' ## seed, ncbi, full, metagenomics oops <- requireNamespace("RCurl", quietly = TRUE) if(!oops) stop("Please install the RCurl package from CRAN") cl <- match.call() format <- "fasta" url = paste('http://pfam.sanger.ac.uk/family/', id, '/acc', sep='') if(verbose) cat("Fetching accession from", url, "\n") if(!RCurl::url.exists(url)) { cat(url, "\n") stop("Url does not exist") } accid <- readLines(url, warn=FALSE)[1] ## download alignment url <- paste('http://pfam.sanger.ac.uk/family/', accid, '/alignment/', alignment, '/format?format=', format, sep='') if(verbose) cat("Fetching alignment from", url, "\n") if(!RCurl::url.exists(url)) { cat(url, "\n") stop("Url does not exist") } tmpfile <- tempfile() success <- download.file(url, tmpfile, quiet=!verbose) if(success==1) stop("Download failed") if(verbose) cat("Alignment successfully downloaded (", tmpfile, ")\n") fasta <- read.fasta(tmpfile) unlink(tmpfile) fasta$call=cl return(fasta) } bio3d/R/motif.find.R0000644000176200001440000000037512412621431013634 0ustar liggesusers`motif.find` <- function(motif, sequence) { ## return indices of motif within sequence position <- regexpr( paste(motif, collapse=""), paste(sequence,collapse="")) inds <- c(position):c(position+attr(position, "match.length")-1) return(inds) } bio3d/R/read.prmtop.R0000644000176200001440000000362112632622153014035 0ustar liggesusers"read.prmtop" <- function(file) { cl <- match.call() if (missing(file)) { stop("read.prmtop: please specify a prmtop 'file' for reading") } toread <- file.exists(file) if (!toread) { stop("No input prmtop file found: check filename") } readformat <- function(s) { s=trim(s) s=substring(s, 9, nchar(s)-1) tmp <- unlist(strsplit(s, "")) hmm <- c("a", "E", "I") type <- hmm[which(hmm %in% tmp)] dims <- unlist(strsplit(s, type)) if(type=="E") dims=c(dims[1], unlist(strsplit(dims[2], "\\."))) else dims=c(dims, NA) ## records per line ## record length ## type return(c(dims, type)) } trim <- function(s) { s <- sub("^ +", "", s) s <- sub(" +$", "", s) s[(s == "")] <- NA s } split.line <- function(x) { tmp <- unlist(strsplit(x, split=" ")) inds <- which(tmp!="") return(tmp[inds]) } ## Read and parse file raw.lines <- readLines(file) flags.ind <- grep("%FLAG", raw.lines) parse.line <- function(line, fmt) { tmp <- seq(1, as.numeric(fmt[1])*as.numeric(fmt[2]), by=as.numeric(fmt[2])) substring(line, tmp, c(tmp[2:length(tmp)]-1, nchar(line))) } all.data <- list() for(i in 1:length(flags.ind)) { ind.start <- flags.ind[i] if(i==length(flags.ind)) ind.end <- length(raw.lines) else ind.end <- flags.ind[i+1] - 1 flagname <- split.line(trim( raw.lines[ind.start] ))[2] tmp.lines <- raw.lines[ind.start:ind.end][-c(1,2)] if(flagname=="TITLE") fmt <- c(1, 20, NA, 'a') else fmt <- readformat(raw.lines[ind.start+1]) tmp.lines <- trim(unlist(lapply(tmp.lines, parse.line, fmt))) if(fmt[4]=="I" || fmt[4]=="E") tmp.lines=as.numeric(tmp.lines) tmp.lines=tmp.lines[!is.na(tmp.lines)] all.data[[flagname]]=tmp.lines } all.data$call=cl class(all.data)=c("amber", "prmtop") return(all.data) } bio3d/R/project.pca.R0000644000176200001440000000231412526367343014021 0ustar liggesusers"project.pca" <- function(data, pca, angular=FALSE, fit=FALSE, ...) { if(angular) data <- wrap.tor(data) if(is.null(dim(data))) { if(ncol(pca$U) != length(data)) stop("Dimensionality mismatch: length(data)!=ncol(pca$U)") if(fit) data <- fit.xyz(pca$mean, data, ...) z <- (data - pca$mean) %*% pca$U } else { if(ncol(pca$U) != ncol(data)) stop("Dimensionality mismatch: ncol(data)!=ncol(pca$U)") if(fit) data <- fit.xyz(pca$mean, data, ...) z <- sweep(data, 2, pca$mean) %*% pca$U } return(z) } z2xyz.pca <- function(z.coord, pca) { if(is.null(nrow(z.coord))) { if( length(z.coord) > ncol(pca$U) ) stop("Dimension miss-match: length(z.coord) > ncol(pca$U)") xyz.coord <- (z.coord %*% t(pca$U[, c(1:length(z.coord)) ]) ) + pca$mean } else { if( ncol(z.coord) > ncol(pca$U) ) stop("Dimension miss-match: ncol(z.coord) > ncol(pca$U)") xyz.coord <- NULL for(i in 1:nrow(z.coord)) { xyz.coord <- rbind(xyz.coord, (z.coord[i,] %*% t(pca$U[, c(1:length(z.coord[i,])) ]) ) + pca$mean) } } return(xyz.coord) } xyz2z.pca <- function(xyz.coord, pca) { return(project.pca(xyz.coord, pca)) } bio3d/R/gap.inspect.R0000644000176200001440000000200012524171274014006 0ustar liggesusers"gap.inspect" <- function(x) { # Report the number of gaps, ("-",".",NA), per # row (i.e. seq) and col (i.e. position) in a # given "alignment" 'x' if(is.vector(x)) { gaps <- is.gap(x) inds <- which(gaps) f.inds <- which(!gaps) gap.pos <- as.numeric(gaps) gap.col <- gap.pos gap.row <- sum(gaps) } else { if(is.list(x)) { if(inherits(x, "pdbs")) { x <- x$xyz; warning("Taking $xyz component (NOT $ali for which you should use 'gap.inspect(x$ali)')") } else { x <- x$ali } } gap.pos1 <-( as.numeric(x=="-") + as.numeric(x==".") ) gap.pos2 <- as.numeric(is.na(gap.pos1)) gap.pos<-matrix( colSums(rbind(gap.pos1,gap.pos2), na.rm=TRUE), ncol=ncol(x)) gap.col <- colSums(gap.pos) gap.row <- rowSums(gap.pos) inds <- which(gap.col!=0) ##f.inds=(1:ncol(x))[-inds] f.inds <- which(gap.col == 0) } output=list(t.inds=inds, f.inds=f.inds, row=gap.row, col=gap.col, bin=gap.pos) } bio3d/R/load.enmff.R0000644000176200001440000001637212631363217013624 0ustar liggesusers"ff.anm" <- function(r, cutoff=15, gamma=1, ...) { ifelse( r>cutoff, 0, gamma ) } "ff.pfanm" <- function(r, cutoff=NULL, ...) { if(is.null(cutoff)) return(r^(-2)) else ifelse( r>cutoff, 0, r^(-2)) } "ff.calpha" <- function(r, rmin=2.9, ...) { ## MMTK Units: kJ / mol / nm^2 ##a <- 128; b <- 8.6 * 10^5; c <- 2.39 * 10^5; ## Bio3D Units: kJ / mol / A^2 ## In case of unreasonable CA-CA distance if(!is.null(rmin)) r[(r 0 ) { dist.inds <- intersect(which(r<6.5), which(r>5.5)) inds.a <- intersect(dist.inds, inds.a) if ( length(inds.a) > 0 ) { if(verbose) { cat("\n\nHelix 1-4 interactions: ") cat(paste(atom.id, inds.a, sep=":")) cat("\n Default values: \n ") cat(paste(ks[inds.a], sep=" ")) } ## Apply new force constants ks.tmp <- ah.func(r[inds.a]) ## Avoid too large values ks.tmp[ which(ks.tmp>ah14.maxk) ] <- ah14.maxk ## Avoid values less than defaults old.vals <- ks[inds.a] reset.inds <- (ks.tmp < old.vals) ks.tmp[ reset.inds ] <- old.vals[ reset.inds ] ks[inds.a] <- ks.tmp if(verbose) { cat("\n New interactions: \n ") cat(ks[inds.a]) cat("\n Distances are: \n ") cat(r[inds.a]) cat("\n") } } } if ( length(inds.b) > 0 ) { dist.inds <- which(r<7) inds.b <- intersect(dist.inds, inds.b) if ( length(inds.b) > 0 ) { if(verbose) { cat("\n\nBeta bridge interactions: ") cat(paste(atom.id, inds.b, sep=":")) cat("\n Default values: \n ") cat(ks[inds.b]) } ## Apply new force constants ks.tmp <- bb.func(r[inds.b]) ## Avoid too large values ks.tmp[ which(ks.tmp>bb.maxk) ] <- bb.maxk ## Avoid values less than defaults old.vals <- ks[inds.b] reset.inds <- (ks.tmp < old.vals) ks.tmp[ reset.inds ] <- old.vals[ reset.inds ] ks[inds.b] <- ks.tmp if(verbose) { cat("\n New interactions: \n ") cat(ks[inds.b]) cat("\n Distances are: \n ") cat(r[inds.b]) cat("\n") } } } if ( length(inds.c) > 0 ) { if(verbose) { cat("\n\nSS-bond: ") cat(paste(atom.id, inds.c, sep=":")) cat("\n Default values: \n ") cat(ks[inds.c]) } ks[inds.c] <- ss.k if(verbose) { cat("\n New interactions: \n ") cat(ks[inds.c]) cat("\n Distances are: \n ") cat(r[inds.c]) cat("\n") } } return(ks) } "ff.sdenm" <- function(r, atom.id, ssdat=NULL, ...) { ## sdENM by lazyload. contains an array with dimensions ## 20 x 20 x 27 ## aa1 x aa2 x distance.category sdENM = bio3d::sdENM ## set sequence data to 1-letter aa code if(any(nchar(ssdat$seq)==3)) sequ <- suppressWarnings( aa321(ssdat$seq) ) else sequ <- ssdat$seq ## Check for non-standard amino acids if(any(sequ=="X")) { cat("\n") unk <- paste(unique(ssdat$seq[which(sequ=="X")]), collapse=", ") stop(paste("Unknown aminoacid identifier for:", unk)) } ## Initialize natoms <- length(r) aa.now <- sequ[atom.id] ## vector for spring constants ks <- rep(NA, natoms) ## Make distance categories map.dist <- c(0, seq(4, 16.5, by=0.5)) dist.cat <- cut(r, breaks=map.dist, labels=FALSE) dist.cat[is.na(dist.cat)] <- 27 dist.cat[atom.id] <- NA ## Unique distance categories unq.cat <- unique(dist.cat) for ( i in 1:length(unq.cat) ) { tmp.cat <- unq.cat[i] if(!is.na(tmp.cat)) { tmp.inds <- which(dist.cat==tmp.cat) ## Since the lower.tri is NA we look up twice :P a <- sdENM[ aa.now, sequ, tmp.cat ][ tmp.inds ] b <- sdENM[ sequ, aa.now, tmp.cat ][ tmp.inds ] ks[ tmp.inds ][!is.na(a)] <- a[!is.na(a)] ks[ tmp.inds ][!is.na(b)] <- b[!is.na(b)] } } ## Set special restraints for covalent pairs inds.k12 <- c(atom.id -1, atom.id+1) inds.k12 <- inds.k12[ intersect(which(inds.k12 > 0), which(inds.k12 <= natoms)) ] ks[inds.k12] <- 43.52 ks[atom.id]=0 ## should in principle not get this far ... if(any(is.na(ks))) { stop(paste("Incompatible protein sequence:\n", " Paramters only exists for standard amino acid residues")) } ## sdENM FF is in arbitrary units ## The values given were arbitrarily normalized, so that ## the average kappa (over all amino acid pairs) is equal to 1, at d = 6 Ang. ## scale to kJ / mol / A^2 range: ks <- ks * 0.0083144621 * 300 * 10 return(ks) } "ff.reach" <- function(r, atom.id, ssdat=NULL, ...) { natoms <- length(r) ## units in kJ/mol/A^2 ## Table 1 - line DHFR #af <- 6770; as <- 2.08; #bf <- 0.951; bs <- 0.0589; #k12 <- 860; k13 <- 26.7; k14 <- 17; ## by correspondance with Kei (29 aug'13) ## line 38, page 1644, 2008 Biophysical J af <- 4810; as <- 1.7; bf <- 0.872; bs <- 0.068; ## avgering over table 1 k12 <- 866; k13 <- 28.7; k14 <- 24.16667; ## Calculate default interactions ks <- (af * exp(-bf*r)) + (as * exp(-bs*r)) ## Differentiate between k12, k13, k14 inds.k12 <- c(atom.id -1, atom.id+1) inds.k13 <- c(atom.id -2, atom.id+2) inds.k14 <- c(atom.id -3, atom.id+3) inds.k12 <- inds.k12[ intersect(which(inds.k12 > 0), which(inds.k12 <= natoms)) ] inds.k13 <- inds.k13[ intersect(which(inds.k13 > 0), which(inds.k13 <= natoms)) ] inds.k14 <- inds.k14[ intersect(which(inds.k14 > 0), which(inds.k14 <= natoms)) ] ks[inds.k12] <- k12; ks[inds.k13] <- k13; ks[inds.k14] <- k14; return(ks) } "load.enmff" <- function(ff='calpha') { ## Bahar "ANM"-ff if (ff=="anm") { ff <- ff.anm } ## Yang Song and Jernigan (PNAS 2009) else if (ff=="pfanm") { ff <- ff.pfanm } ## Hinsen "C-alpha"-ff else if (ff=="calpha") { ff <- ff.calpha } ## Extended calpha-FF else if(ff=="calphax") { ff <- ff.calphax } ## sdENM else if(ff=="sdenm") { ff <- ff.sdenm } ## REACH else if(ff=="reach") { ff <- ff.reach } else { stop("force field not defined") } return(ff) } bio3d/R/print.sse.R0000644000176200001440000000340012412621431013514 0ustar liggesusers"print.sse" <- function(x, ...) { cn <- class(x) cat("\n") cat("Call:\n ", paste(deparse(x$call), sep = "\n", collapse = "\n"), "\n\n", sep = "") cat("Class:\n ", cn, "\n\n", sep = "") cat("Helices: ", length(x$helix$start), "") if(length(x$helix$start)>0) { j <- 0 for(i in 1:length(x$helix$start)) { if(j%%5==0) cat("\n ") tmpout <- paste(x$helix$start[i], "-", x$helix$end[i], " (", x$helix$chain[i], ")", sep="") cat(format(tmpout, justify="right", width=15)) j <- j+1 } } cat("\n\n") cat("Sheets: ", length(x$sheet$start), "") if(length(x$sheet$start)>0) { j <- 0 for(i in 1:length(x$sheet$start)) { if(j%%5==0) cat("\n ") tmpout <- paste(x$sheet$start[i], "-", x$sheet$end[i], " (", x$sheet$chain[i], ")", sep="") cat(format(tmpout, justify="right", width=15)) j <- j+1 } } cat("\n\n") cat("Turns: ", length(x$turn$start), "") if(length(x$turn$start)>0) { j <- 0 for(i in 1:length(x$turn$start)) { if(j%%5==0) cat("\n ") tmpout <- paste(x$turn$start[i], "-", x$turn$end[i], " (", x$turn$chain[i], ")", sep="") cat(format(tmpout, justify="right", width=15)) j <- j+1 } } cat("\n\n") if(is.null(x$call$resno)) resno <- TRUE else if(x$call$resno=="T" || x$call$resno=="TRUE") resno <- TRUE else resno <- FALSE if(resno) cat("Output is provided in residue numbers") else cat("Output is provided in residue (sequential) identifiers") cat("\n\n") invisible(x) } bio3d/R/conserv.R0000644000176200001440000000656712430771420013273 0ustar liggesusers`conserv` <- function(x, method=c("similarity","identity","entropy22","entropy10"), sub.matrix=c("bio3d","blosum62","pam30","other"), matrix.file=NULL, normalize.matrix = TRUE) { method <- match.arg(method) sub.matrix <- match.arg(sub.matrix) ## cat(paste("Options are, method=",method,", matrix=",sub.matrix, ## ", norm=",normalize.matrix),"\n" ) if(is.list(x)) x=x$ali aa <- c("V","I","L","M", "F","W","Y", "S","T", "N","Q", "H","K","R", "D","E", "A","G", "P", "C", "-","X") composition <- table(x) unk <- composition[!names( composition ) %in% aa] if(length(unk) > 0) { warning(paste("non standard residue code:",names(unk),"mapped to X\n ")) for(i in 1:length(unk)) x[x==names(unk[i])]="X" } if(method == "entropy10") { return(entropy(x)$H.10.norm) } if(method == "entropy22") { return(entropy(x)$H.norm) } if(method == "identity") { ## Identity (exact matchs score 1) freq.aa <- apply(x,2, function(i){ i.freq <- table(i[i!="-"]) if(length(i.freq)==0) { return(0) } else { return( max(table(i[i!="-"])) ) } } ) return( freq.aa/nrow(x) ) } if(method == "similarity") { #####cat(sub.matrix) ## Pairwise matches are assigned score from a 'similarity matrix' if(sub.matrix=="other") { if(is.null(matrix.file)) stop("Missing argument: similarity requires a 'matrix.file'") mat.file <- matrix.file } else { ##mat.file <- system.file("matrices/similarity.mat", package="bio3d") mat.file <- system.file(paste("matrices/",sub.matrix,".mat",sep=""), package="bio3d") ##mat.file <- paste("matrices/",sub.matrix,".mat",sep="") } mat <- read.table(mat.file) colnames(mat)[24]="-" if(normalize.matrix) { ## Karlin Normalize o.mat <- mat n <- nrow(o.mat) for(a.ind in 1:n) { for(b.ind in 1:n) { ab <- o.mat[a.ind,b.ind] aa <- o.mat[a.ind,a.ind] bb <- o.mat[b.ind,b.ind] aabb <- aa*bb if(ab==0 && aabb==0) { mat[a.ind,b.ind] <- 0 } else { if(aabb<0) { mat[a.ind,b.ind] <- ab / -sqrt(abs(aabb)) } else { mat[a.ind,b.ind] <- ab / sqrt(aabb) } } } } } scorecol <- function(col, mat) { freq.aa <- table(col) unique.aa <- names(freq.aa) missing.aa <- unique.aa[!unique.aa %in% colnames(mat)] count <- 0; score <- 0 for(i in 1:length(unique.aa)) { aa.i <- unique.aa[i]; freq.i <- freq.aa[i] for(j in i:length(unique.aa)) { aa.j <- unique.aa[j]; freq.j <- freq.aa[j] ##sim <- mat[aa.i,aa.j] if(length(missing.aa)>0) { if(i==missing.aa || j==missing.aa) { sim <- 0 } else { sim <- mat[aa.i,aa.j] } } else { sim <- mat[aa.i,aa.j] } ## number of comparisons if(aa.i == aa.j) { ncmp <- freq.i * (freq.i - 1)/2 } else { ncmp <- freq.i * freq.j } count <- count + ncmp score <- score + (ncmp * sim) } } return(score/count) } return( apply(x, 2, scorecol, mat=mat) ) } } bio3d/R/xyz2atom.R0000644000176200001440000000011312412621431013362 0ustar liggesusersxyz2atom <- function(xyz.ind) { return( unique( ceiling(xyz.ind/3) ) ) } bio3d/R/pdbfit.R0000644000176200001440000000224212544562302013051 0ustar liggesuserspdbfit <- function(...) UseMethod("pdbfit") pdbfit.pdb <- function(pdb, inds=NULL, ...) { if(!is.pdb(pdb)) stop("Input 'pdb' should be of class 'pdb', e.g. from read.pdb()") if(nrow(pdb$xyz)<2) stop("nothing to fit. < 2 frames in pdb object") if(is.null(inds)) { inds <- atom.select(pdb, "calpha") cat(" no indices provided. using all", length(inds$atom), "calpha atoms\n") } if(length(inds$xyz)<3) stop("insufficent atoms to superimpose") return(fit.xyz( fixed=pdb$xyz[1,], mobile=pdb$xyz, fixed.inds=inds$xyz, mobile.inds=inds$xyz)) } pdbfit.pdbs <- function(pdbs, inds=NULL, outpath=NULL, ...) { ## ## Quick Fit Fitter for PDBs ## was called 'fit.pdbs()' in model.R ## if(!inherits(pdbs, "pdbs")) { stop("Input 'pdbs' should be of class 'pdbs', e.g. from pdbaln() or read.fasta.pdb()") } full <- ifelse(is.null(outpath), FALSE, TRUE) if(is.null(inds)) { inds <-gap.inspect(pdbs$xyz)$f.inds } if(is.list(inds)){ inds=inds$xyz } invisible( fit.xyz( fixed=pdbs$xyz[1,], mobile=pdbs, fixed.inds=inds, mobile.inds=inds, outpath=outpath, full.pdbs=full, ... )) } bio3d/R/torsion.xyz.R0000644000176200001440000000231112412621431014115 0ustar liggesusers"torsion.xyz" <- function(xyz, atm.inc=4) { if(!is.vector(xyz) || !is.numeric(xyz)) stop("input 'xyz' should be a numeric vector") natm <- length(xyz)/3 if(natm < 4) stop("Need at least four atoms to define a dihedral") if(natm %% 1 != 0) stop("There should be three 'xyz' elements per atom") m.xyz <- matrix(xyz, nrow=3) atm.inds <- c(1:4); out<-NULL while(atm.inds[4] <= natm) { if( any(is.na( m.xyz[,atm.inds] )) ) { torp <- NA } else { d1 <- m.xyz[,atm.inds[2]] - m.xyz[,atm.inds[1]] d2 <- m.xyz[,atm.inds[3]] - m.xyz[,atm.inds[2]] d3 <- m.xyz[,atm.inds[4]] - m.xyz[,atm.inds[3]] u1 <- (d1[c(2,3,1)] * d2[c(3,1,2)]) - (d2[c(2,3,1)] * d1[c(3,1,2)]) u2 <- (d2[c(2,3,1)] * d3[c(3,1,2)]) - (d3[c(2,3,1)] * d2[c(3,1,2)]) ctor <- sum(u1*u2)/sqrt( sum(u1*u1) * sum(u2*u2) ) ctor[ctor > 1] <- 1; ctor[ctor < -1] <- -1 torp <- matrix(acos(ctor)*(180/pi),ncol=1) if( sum(u1 * ((u2[c(2,3,1)] * d2[c(3,1,2)]) - (u2[c(3,1,2)] * d2[c(2,3,1)]))) < 0) torp <- -torp } out <- c(out, torp) atm.inds <- atm.inds + atm.inc } if(atm.inc == 1 & natm > 4) out <- c(NA, out, NA, NA) return(out) } bio3d/R/plot.fasta.R0000644000176200001440000001256512524171274013667 0ustar liggesusers"plot.fasta" <- function(x, plot.labels=TRUE, plot.bars=TRUE, plot.lines=FALSE, plot.axis=TRUE, seq.index=NULL, color.conserved=FALSE, cutoff=0.5, col=NULL, bars.scale=2, row.spacing=0.5, aln.col="grey50", cex.text=1, add=FALSE, ... ) { if(!(inherits(x, "fasta") | inherits(x, "pdbs"))) stop("input 'x' should be a list object as obtained from 'read.fasta'") row.height <- 1 ## calculate sequence entropy ##ent <- entropy(x) ##hn <- ent$H.norm hn <- conserv(x$ali, method="entropy10") ## check for gaps gaps <- gap.inspect(x$ali) dims <- dim(x$ali) ## set x- and y-lim if(plot.labels) xlim <- c(1, dims[2]) else xlim <- c(1, dims[2]) if(plot.bars) { conserv.height <- row.height * bars.scale ylim <- c(0, (dims[1]*row.height) + conserv.height) } else { conserv.height <- NULL ylim <- c(0, dims[1]*row.height) } if(is.null(col)) { num.cols <- length(seq(cutoff, 1, by=0.1))-1 col <- heat.colors(num.cols) } else num.cols <- length(col) ## start the plot if(!add) { plot.new() plot.window(xlim, ylim) } ##a: space between row bars, b: height of grey row bars b <- 1 / (row.height + row.spacing) a <- b * row.spacing ## plot the sequence 'boxes' so that residue i is plotted at ## in coordinates [i,i+1) all.start <- NULL; all.end <- NULL; for ( i in 1:nrow(x$ali) ) { nongap.inds <- which(gaps$bin[i,]==0) bs <- bounds(nongap.inds) for ( j in 1:nrow(bs) ) { xstart <- bs[j,"start"] xend <- bs[j,"end"]+1 ystart <- ((i-1) * row.height) + 0.5 yend <- ystart + (row.height-a) rect(xstart, ystart, xend, yend, col=aln.col, border=NA) } all.start <- c(all.start, ystart) all.end <- c(all.end, yend) ## label on the side of the sequence if(plot.labels) mtext(x$id[i], side=4, line=-2, at=ystart+(b*.5), las=1, cex=cex.text) } aa2col <- function(aa, default="grey50") { if(aa %in% c("A", "V", "L", "I", "M", "F", "W")) return("dodgerblue3") if(aa %in% c("S", "T", "N", "Q")) return("forestgreen") if(aa %in% c("D", "E")) return("purple") if(aa %in% c("R", "K")) return("red") if(aa %in% c("H", "Y")) return("cyan") if(aa %in% c("G")) return("orange") if(aa %in% c("P")) return("yellow") if(aa %in% c("C")) return("darksalmon") else { warning(paste("color for amino acid", aa, "not found")) return(default) } } if(color.conserved) { ## which columns to color inds <- intersect(which(hn>=cutoff), which(gaps$col<(nrow(gaps$bin)/2))) for ( j in 1:length(inds) ) { col.ind <- inds[j] ## only non-gap positions nongap.inds <- which( gaps$bin[, col.ind]==0 ) if(length(nongap.inds)>0) { for (k in 1:length(nongap.inds) ) { row.ind <- nongap.inds[k] aa <- x$ali[row.ind, col.ind] #if(aa=="-") # print(aa) aacol <- aa2col(aa, default=aln.col) xstart <- inds[j] xend <- xstart+1 ystart <- ((row.ind-1) * row.height) + 0.5 yend <- ystart + (row.height-a) rect(xstart, ystart, xend, yend, col=aacol, border=NA) } } } } if(plot.bars || plot.lines) i=i+1 ## plot conservation bar if(plot.bars) { ints <- rev(seq(0, 1, by=0.1)) ints = ints[ints >= cutoff] for ( j in 1:(length(ints)-1) ) { cons.inds <- intersect( intersect(which(hn <= ints[j]), which(hn > ints[j+1])), gaps$f.inds) if(length(cons.inds)>0) { for (k in 1:length(cons.inds) ) { xstart <- cons.inds[k] xend <- xstart+1 ystart <- ((i-1) * row.height) + 0.5 yend <- ystart + (conserv.height*hn[cons.inds[k]]) ##print( paste(ystart, yend, hn[cons.inds[k]] )) rect(xstart, ystart, xend, yend, col=col[j], border=NA) } } } } if(plot.lines) { ystart <- ((i-1) * row.height) + 0.5 lines(1:dims[2]+0.5, ystart+(conserv.height*hn), type="o", cex=0.4) } if(plot.labels & (plot.lines | plot.bars)) { mtext("Conservation", side=4, line=-2., at=ystart+(b*.5), las=1, cex=cex.text) } ticks <- bounds(gaps$t.inds) starts <- c(1, ticks[,"start"]) ends <- c(ticks[,"end"], dims[2]) at <- c(starts, ends+1) if(plot.axis) { if(!is.null(seq.index)) { ig <- is.gap(x$ali[seq.index,]) tick.labs <- rep(NA, length=dims[2]) tick.labs[which(!ig)] = seq(1, length(which(!ig))) labs <- c(tick.labs[ starts ], tick.labs[ ends ]+1) labs[ 1 ] = 1 } else { tick.labs <- seq(1, dims[2]) labs <- c(tick.labs[ starts ], tick.labs[ ends ]+1) } if(!is.null(seq.index)) axis(1, at=at, labels=labs, tick=FALSE, line=-2.) axis(1, line=0.5, at=at) mtext("Alignment index", 1, line=2, cex=1.0) } out <- list(at=sort(unique(at)), start=all.start, end=all.end) invisible(out) } bio3d/R/basename.pdb.R0000644000176200001440000000066212526367343014134 0ustar liggesusersbasename.pdb <- function(x, mk4=FALSE) { ## ##- Extract PDB basename/identifier from filenames ## like "basename()" for PDB files ## E.g.: ## basename.pdb("/somedir/somewhere/1bg2_myfile.pdb") ## Will give: 1bg2_myfile ## basename.pdb("/somedir/somewhere/1bg2_myfile.pdb", TRUE) ## Will give: 1bg2 y <- sub("\\.pdb$","", basename(x)) if(mk4) { y <- substr(y,1,4) } names(y) <- x return(y) } bio3d/R/read.dcd.R0000644000176200001440000002430312526367343013257 0ustar liggesusers"read.dcd" <- function(trjfile, big=FALSE, verbose=TRUE, cell = FALSE){ # Version 0.2 ... Tue Jan 18 14:20:12 PST 2011 # Version 0.1 ... Thu Mar 9 21:18:54 PST 2005 # # Description: # Reads a CHARMM or X-PLOR/NAMD binary # trajectory file with either big- or # little-endian storage formats # # Details: # Reading is accomplished with two different # functions. # 1. 'dcd.header' which reads headder info # 2. 'dcd.frame' takes the header info and # reads frame by frame producing a # nframes/natom*3 matrix of cartisean # coordinates #===DCD=FORMAT============================================== #HDR NSET ISTRT NSAVC 5-ZEROS NATOM-NFREAT DELTA 9-ZEROS #CORD files step1 step zeroes (zero) timestep zeroes #C*4 INT INT INT 5INT INT DOUBLE 9INT # [CHARACTER*20] #=========================================================== #NTITLE TITLE #INT C*MAXTITL #C*2 C*80 #=========================================================== #NATOM #INT #=========================================================== #CELL(I), I=1,6 (DOUBLE) #=========================================================== #X(I), I=1,NATOM (SINGLE) #Y(I), I=1,NATOM #Z(I), I=1,NATOM #=========================================================== dcd.header <- function(trj,...) { # Read DCD Header section end = .Platform$endian # Check endianism check <- readBin(trj,"integer",1,endian=end) # first thing in file should be an '84' header if (check != 84) { # if not we have the wrong endianism if (end == "little") { end="big" } else { end="little" } check <- readBin(writeBin(check, raw()), "integer", 1, endian = end) if (check != 84) { close(trj) stop("PROBLEM with endian detection") } } hdr <- readChar(trj, nchars=4) # data => CORD or VELD # how big is the file 'end.pos' cur.pos <- seek(trj, where=1, origin = "end") # pos ? end.pos <- seek(trj, where=cur.pos, origin= "start") icntrl <- readBin(trj,"integer", 20, endian=end) # data => header info # header information: nframe = icntrl[1] # number of frames first = icntrl[2] # number of previous steps step = icntrl[3] # frequency of saving nstep = icntrl[4] # total number of steps nfile <- nstep/step # number of files last <- first + (step * nframe) # last step # 5 zeros ndegf = icntrl[8] # number of degrees of freedom nfixed = icntrl[9] # number of fixed atoms delta = icntrl[10] # coded time step cryst = icntrl[11] # crystallographic group block = icntrl[12] # extra block? # 9 zeros vers = icntrl[20] # flush to end of line a<-readBin(trj,"integer",1, endian=end) # should be '84' line tail ## cur.pos<-seek(trj, where=92, origin= "start") # position 92 rm(icntrl) # tidy up # Are we CHARMM or X-PLOR format charmm=FALSE; extrablock=FALSE; four.dims=FALSE if (vers != 0) { charmm=TRUE # charmm version number if (cryst == 1) { # check for extrablock = TRUE # extra free } # atom block & if (block == 1) { # extra four four.dims=TRUE # dimensions } } else { # re-read X-PLOR delta as a double cur.pos <- seek(trj, where=44, origin= "start") delta = readBin(trj,"double", 1, endian=end) seek(trj, where=cur.pos, origin= "start") } #=======# # Title # a<-readBin(trj,"integer",1, endian=end) # flush FORTRAN header ntitle <- readBin(trj,"integer",1, endian=end) # data => Num title lines title<-NULL # store title & date cur.pos <- seek(trj, where=NA) ## 100 for (i in 1:ntitle) { ### ==> !!!nasty hack due to invalid UTF-8 input (Jun 5th 07) !!! <=== ### ll<-try(title<-c( title, readChar(trj,80) ),silent=TRUE) } # OR: title<- readChar(trj, (ntitle*80)) if(class(ll)=="try-error") { warning("Check DCD header data is correct, particulary natom") ##cur.pos <- seek(trj, where=260, origin= "start") # pos 260 cur.pos <- seek(trj, where=(80*ntitle+cur.pos), origin= "start") } ### == end hack a<-readBin(trj,"integer",1, endian=end) # flush FORTRAN tail #=======# # Natom # a<-readBin(trj,"integer",1, endian=end) # flush FORTRAN header natom <- readBin(trj,"integer",1, endian=end) # number of atoms a<-readBin(trj,"integer",1, endian=end) # flush FORTRAN tail ##cur.pos <- seek(trj, where=276, origin= "start") # pos 276 #=============# # Freeindexes # if (nfixed != 0) { # Free (movable) atom indexes if nfixed > 0 a <- readBin(trj,"integer",1, endian=end) # flush FORTRAN header free.ind <- readBin(trj,"integer", (natom-nfixed), endian=end ) a <- readBin(trj,"integer",1, endian=end) # flush FORTRAN tail print("FIXED ATOMS IN SIMULATION => CAN'T READ YET") } if (verbose) { ## EDIT ## R version 2.11.0 does not like "\0", just remove for now - Apr 12 2010 ## cat( sub(" +$","",gsub(pattern="\0", replacement="", x=title)),sep="\n" ) cat(" NATOM =",natom,"\n") cat(" NFRAME=",nframe,"\n") cat(" ISTART=",first,"\n") cat(" last =",last,"\n") cat(" nstep =",nstep,"\n") cat(" nfile =",nfile,"\n") cat(" NSAVE =",step,"\n") cat(" NDEGF =",ndegf,"\n") cat(" version",vers,"\n") } # Done with Header :-) header <- list(natom=natom, nframe=nframe, first=first, last=last, nstep=nstep, nfile=nfile, step=step, ndegf=ndegf, nfixed=nfixed, charmm=charmm, extrablock=extrablock, four.dims=four.dims, end.pos=end.pos, end=end) } dcd.frame <- function(trj, head, cell) { # DCD step/frame data # read one frame from the current conection 'trj' # which should have been already through # 'dcd.header' so the "where" position is at # the start of the cooedinate section #============# # Free atoms # # Uncomment the next two lines if reading cell # parameters only works with CHARMM DCD files # if(!head$charmm && cell) # stop("Cell parameters can only be read from CHARMM dcd files.") if ( head$charmm && head$extrablock) { # CHARMM files may contain lattice parameters a <- readBin(trj,"integer",1, endian=head$end) # flush FORTRAN header u <- readBin(trj, "numeric", size = 8, n = (a/8),endian = head$end) a <- readBin(trj,"integer",1, endian=head$end) # flush FORTRAN tail } ##cur.pos <- seek(trj, where=332, origin= "start") # pos 332 #========# # Coords # if (head$nfixed == 0) { a <- readBin(trj,"integer",1, endian=head$end) # flush FORTRAN header x <- readBin(trj,"numeric", # read x coords size=4, n=(a/4), endian=head$end) a <- readBin(trj,"integer",1, endian=head$end) # flush FORTRAN tail a <- readBin(trj,"integer",1, endian=head$end) # flush FORTRAN header y <- readBin(trj,"numeric", # read y coords size=4, n=(a/4), endian=head$end) a <- readBin(trj,"integer",1, endian=head$end) # flush FORTRAN tail a <- readBin(trj,"integer",1, endian=head$end) # flush FORTRAN header z <- readBin(trj,"numeric", # read z coords size=4, n=(a/4), endian=head$end) a <- readBin(trj,"integer",1, endian=head$end) # flush FORTRAN tail } else { # not implemented yet! => cant cope with fixed atoms } #===============# # 4th dimension # if (head$charmm && head$four.dims) { # CHARMM files may contain an extra block? a <- readBin(trj,"integer",1, endian=head$end) # flush FORTRAN header seek(trj, where=a, origin= "current") # skip this block a <- readBin(trj,"integer",1, endian=head$end) # flush FORTRAN tail } # Done with coord frame :-) #coords <- list(x=x, # y=y, # z=z) if(cell) to.return <- c( u[c(1,3,6)], (180/pi)*acos(u[c(5,4,2)])) else to.return <- as.vector(rbind(x,y,z)) class(to.return) = "xyz" return(to.return) } # Check if file exists if( !file.exists(trjfile) ) { stop(paste("No input DCD file found with name:", trjfile)) } # Open file conection trj <- file(trjfile, "rb") #verbose=T head<-dcd.header(trj,verbose) nframes = head$nframe natoms = head$natom # blank xyz data structures # format: rows => nframes, cols => natoms ### ==> !!! Insert to read big dcd files (Sep 29th 08) !!! <=== ### ###xyz <- matrix(NA, nrow=nframes,ncol=natoms*3) if(!big) { if(cell) to.return <- matrix(NA, nrow=nframes,ncol=6) else to.return <- matrix(NA, nrow=nframes,ncol=natoms*3) } else { ##-! Insert to read big dcd files (Sep 29th 08) oops <- requireNamespace("bigmemory", quietly = TRUE) if(!oops) stop("Please install the bigmemory package from CRAN") if(cell) to.return <- bigmemory::big.matrix(nrow=nframes,ncol=6, init = NA, type = "double") else to.return <- bigmemory::big.matrix(nrow=nframes,ncol=natoms*3, init = NA, type = "double") } ### ==> !!! end big.matrix insert if(verbose){ cat("Reading (x100)") } store <- NULL # fill xyz with frame coords if(verbose) pb <- txtProgressBar(1, nframes, style=3) for(i in 1:nframes) { curr.pos <- seek(trj, where=0, origin= "current") if (curr.pos <= head$end.pos) { to.return[i,]<-dcd.frame(trj,head,cell) if (verbose) { setTxtProgressBar(pb, i) # if(i %% 100==0) { cat(".") } } # print(paste("frame:",i,"pos:",curr.pos)) store<-cbind(store,curr.pos) } else { print("Premature end of file") print(paste(" last frame:",i, "nframe:",head$nframe )) break } } # if(verbose) { cat("done",sep="\n") } if(verbose) cat("\n") close(trj) ##class(to.return) = "xyz" return( as.xyz(to.return) ) } bio3d/R/read.fasta.R0000644000176200001440000000330012430771420013602 0ustar liggesusers"read.fasta" <- function(file, rm.dup=TRUE, to.upper=FALSE, to.dash=TRUE) { ## Log the call cl <- match.call() ## Version 0.3 ... Thu Apr 26 19:17:09 PDT 2007 ## uses scan instead of read.table raw.fa <- scan(file, what=character(0), sep="\n", quiet = TRUE) ind <- grep(">", raw.fa) ## seq id lines if(length(ind) == 0) { stop("read.fasta: no '>' id lines found, check file format") } if (to.dash) { raw.fa[-ind] <- gsub("[/.]","-", raw.fa[-ind]) } if (to.upper) { raw.fa[-ind] <- toupper(raw.fa[-ind]) } ind.s <- ind+1 ## seq start and end lines ind.e <- c((ind-1)[-1], length(raw.fa)) seq.dim <- apply(cbind(ind.s, ind.e), 1, function(x) sum( nchar(raw.fa[ (x[1]:x[2])]) )) seq.format <- function(x, max.seq=max(seq.dim)) { fa <- rep("-",max.seq) fa[ c(1:x[3]) ] <- unlist(strsplit( raw.fa[ (x[1]:x[2]) ], split="")); return(fa) } ##seq.format( cbind(ind.s[1], ind.e[1], seq.dim[1]) ) store.fa <- t(matrix(apply(cbind(ind.s, ind.e, seq.dim), 1, seq.format), ncol=length(ind))) rownames(store.fa) <- gsub("^>| .*", "",raw.fa[ind], perl=TRUE) ## if (to.dash) { store.fa <- gsub("[/.]","-", store.fa ) } ## if (to.upper) { store.fa <- toupper(store.fa) } if (rm.dup) { ## remove duplicated seq id's ## dups <- as.numeric(duplicated(row.names(store.fa))) dups <- duplicated(row.names(store.fa)) if (any(dups)) { print(paste(" ** Duplicated sequence id's: ", row.names(store.fa)[dups]," **",sep="")) store.fa <- store.fa[!dups,] } } output <- list(id=rownames(store.fa), ali=store.fa, call=cl) class(output) <- "fasta" return(output) } bio3d/R/aln2html.R0000644000176200001440000001104312412621431013312 0ustar liggesusers`aln2html` <- function(aln, file = "alignment.html", Entropy = 0.5, append = TRUE, caption.css = "color: gray; font-size: 9pt", caption = "Produced by Bio3D", fontsize = "11pt", bgcolor = TRUE, colorscheme="clustal") { if(is.list(aln)) { x=aln$ali id=aln$id } else { x=aln id=dimnames(x)[[1]] } if(is.null(id)) stop("No $id list component or rownames for the alignment object") back <- ""; bold <- "" #bold <- "font-weight: bold" if (bgcolor) { back <- "background-"; bold <- "" } head <- paste("\n\n \n \n \n", collapse="",sep="" ) body <- paste("
\n") ## id <- paste("\n
",dimnames(x)[[1]],"    ",sep="") #- id justification # id <- dimnames(x)[[1]] len <- nchar(id, type="chars") pad <- NULL; if(!all(max(len)==len)) { for(i in 1:length(id)) { pad <- c(pad, paste( rep(" ", max(len)-len[i]),collapse="" )) } } id <- paste("\n
",id,pad,"   ",sep="") if(colorscheme=="clustal") { # Clustal coloring al <- matrix( paste("",x,"",sep=""), nrow=nrow(x)) } else { #- Color by entropy score he <- entropy(x) score <- he$H.10.norm; score[ which(he$freq[c("-"),]>0.6) ] = 0 rn <- cbind( (score > 0.4), (score > 0.575), (score > 0.75), (score > 0.9) ) rn[ rn[,2], 1] = FALSE; rn[ rn[,3], 2] = FALSE; rn[ rn[,4], 3] = FALSE al=x b <- matrix( paste("",x,"",sep=""), nrow=nrow(x)) l <- matrix( paste("",x,"",sep=""), nrow=nrow(x)) m <- matrix( paste("",x,"",sep=""), nrow=nrow(x)) h <- matrix( paste("",x,"",sep=""), nrow=nrow(x)) al[ , which(rn[,1])] = b[ , which(rn[,1])] al[ , which(rn[,2])] = l[ , which(rn[,2])] al[ , which(rn[,3])] = m[ , which(rn[,3])] al[ , which(rn[,4])] = h[ , which(rn[,4])] } #- Dont color unconserved positions if(Entropy > 0) { if(colorscheme=="clustal") he <- entropy(x) execlude <- unique( c(which(he$H.10.norm < Entropy), which(he$freq[c("-"),]>0.6)) ) al[,execlude] = x[,execlude] } #- Dont color gaps ind<-which(x=="-",arr.ind=TRUE); al[ind]=x[ind] cat(head, body, file=file, append=append) for(i in 1:length(id)) cat( id[i], al[i,], sep="", file=file, append=TRUE) cat(paste("\n
\n", "
", caption ,"
\n", " \n\n"), file=file, append=TRUE) } bio3d/R/lbio3d.R0000644000176200001440000000006112412621431012743 0ustar liggesusers"lbio3d" <- function () { ls("package:bio3d") } bio3d/R/filter.rmsd.R0000644000176200001440000000233512632622153014034 0ustar liggesusersfilter.rmsd <- function(xyz=NULL, rmsd.mat=NULL, cutoff=0.5, fit=TRUE, verbose=TRUE, inds=NULL, ...) { # k<-filter.rmsd(xyz=pdbs$xyz, cutoff=0.5) # k<-filter.rmsd(rmsd.mat=k$rmsd.mat, cutoff=2.0) if(is.null(rmsd.mat)) { if(is.null(xyz)) stop("Must provide either a 'xyz' matrix or RMSD matrix 'rmsd.mat'") if(is.list(xyz)) xyz=xyz$xyz if(is.null(inds)) { gaps <- gap.inspect(xyz) inds <- gaps$f.inds } rmsd.mat <- rmsd( xyz, a.inds=inds, fit=fit, ... ) } r.d <- as.dist(rmsd.mat) tree <- hclust(r.d) h <- cutoff n <- nrow(tree$merge) + 1 k <- integer(length(h)) k <- n + 1 - apply(outer(c(tree$height, Inf), h, ">"),2, which.max) if(verbose) cat("filter.rmsd(): N clusters @ cutoff = ", k, "\n") #ans <- as.vector(.Call("R_cutree", tree$merge, k, PACKAGE = "stats")) ans <- as.vector(cutree(tree, k)) cluster.rep <- NULL for(i in 1:k) { ind <- which(ans==i) if (length(ind) == 1) { cluster.rep <- c(cluster.rep, ind) } else { cluster.rep <- c(cluster.rep, ind[ which.min( colSums(rmsd.mat[ind,ind]) ) ]) } } return(list(ind=cluster.rep, tree=tree, rmsd.mat=rmsd.mat)) } bio3d/R/plot.cna.R0000644000176200001440000001045012526367343013327 0ustar liggesusersplot.cna <- function(x, pdb=NULL, weights=NULL, vertex.size=NULL, layout=NULL, col=NULL, full=FALSE, scale = TRUE, color.edge = FALSE, ...) { ##- Function for plotting cna networks the way we like them. ## Returns the plot layout coordinates silently. These can ## be passed to identify.cna() ## ##- Examples: ## plot.cna(net) ## plot.cna(net, pdb) ## plot.cna(net, layout=layout.cna(net,pdb)) ## plot.cna(net, layout=layout.cna(net,pdb, full=TRUE), full=TRUE) ## plot.cna(net, full=T, layout=layout.cna(net,pdb, full=T), vertex.size=3, weights=1, vertex.label=NA) ## ##- Other options: ## \dots can contain all ?igraph.plotting options, including: ## col=vmd.colors(), ## mark.groups=list() - A list of vertex id vectors ## mark.col=vmd.colors(alpha=0.3), ## mark.border=vmd.colors() ## etc... see ?plot.igraph ## AND ## vertex.size: Node sizes: V(x$network)$size ## vertex.color: Node colors: V(x$network)$color ## vertex.label: Node labels: V(x$network)$name - use NA to omit ## edge.width: Edge weights: E(x$network)$weight ## edge.color: Edge colors: E(x$network)$color ## (also vertex.label.color, vertex.label.cex etc. ## see ?igraph.plotting) ## ## Check for presence of igraph package oops <- requireNamespace("igraph", quietly = TRUE) if (!oops) { stop("igraph package missing: Please install, see: ?install.packages") } # if(color.edge) { # oops <- require(classInt) # if (!oops) { # warning("package classInt missing: color.edge is set to FALSE. # To make color.edge work, please install the missing package. See: ?install.packages") # color.edge = FALSE # } # } ##- Determine which network to plot along with node size if(full) { ## Plot the 'full' all-atom network y <- x$network if(is.null(vertex.size)) { ## Scale up the node size to something visible if(max(igraph::V(y)$size) < 10) { igraph::V(y)$size = igraph::V(y)$size + 13 } } } else { ## Plot the 'coarse-grained' community network y <- x$community.network ## here we will leave the node size as is } ##- Determine edge weights and scale values for plotting if(is.null(weights)){ ## Use weights defined in network weights <- igraph::E(y)$weight if(is.null(x$call$minus.log)){ ## If '$call$mins.log' is NULL => -log option was used in cna() ## so we we will revert back with exponential here weights <- exp(-weights) } else{ if(x$call$minus.log){ ## Again here we have 'minus.log=TRUE' weights <- exp(-weights) } } ## Lets scale the weights to lie between 1 and 5 # weights <- (weights - min(weights)) / max(weights - min(weights)) * (1 - 5) + 5 if(scale && (length(weights)>1)) weights <- (weights - min(weights)) / max(weights - min(weights)) * 4 + 1 else weights <- 10 * weights } ##- Obtain the plot layout coords if(!is.null(pdb) && is.null(layout)) { cat("Obtaining layout from PDB structure\n") layout = layout.cna(x, pdb, full=full) } if(is.null(pdb) && is.null(layout)) { cat("Obtaining estimated layout with fruchterman.reingold\n") layout <- igraph::layout.fruchterman.reingold(y, weights=weights) } if(dim(layout)[2] != 2){ stop("Input 'layout' must be an Nx2 matrix, where N is the number of communities") } if(color.edge) { # vec2color <- function(vec, pal=c("blue", "green", "red"), n=10) { # ##-- Define a color scale from a numeric vector # return( findColours(classIntervals(vec, n=n, style="equal"), pal) ) # } vec2color <- function(vec, pal=c("blue", "green", "red"), n=30) { col <- colorRampPalette(pal)(n) vec.cut <- cut(vec, seq(min(vec), max(vec), length.out=n), include.lowest = TRUE) levels(vec.cut) <- 1:length(col) return( col[vec.cut] ) } colors <- vec2color(weights) igraph::plot.igraph(y, edge.width=weights, edge.color = colors, layout=layout, vertex.color=col, vertex.size=vertex.size, ...) } else { igraph::plot.igraph(y, edge.width=weights, layout=layout, vertex.color=col, vertex.size=vertex.size, ...) } ## Silently return plot coordinates #class(layout) = "cna" layout <- layout } bio3d/R/pca.pdbs.R0000644000176200001440000000072512524171274013301 0ustar liggesusers"pca.pdbs" <- function(pdbs, core.find=FALSE, fit=FALSE, ...) { ## Log the call cl <- match.call() if(core.find & fit) { warning("incompatible arguments- neglecting 'fit=TRUE'") fit=FALSE } if(core.find) { core <- core.find(pdbs) pdbs$xyz = pdbfit(pdbs, core$c0.5A.xyz) } else if(fit) { pdbs$xyz = pdbfit(pdbs) } gaps.pos <- gap.inspect(pdbs$xyz) pc <- pca.xyz(pdbs$xyz[,gaps.pos$f.inds], ...) pc$call=cl return(pc) } bio3d/R/dist.xyz.R0000644000176200001440000000436412524171274013406 0ustar liggesusers`dist.xyz` <- function(a, b=NULL, all.pairs=TRUE, ncore=1, nseg.scale=1){ ## if 'a' is a vector (or matrix) and ## 'b' is a matrix ## compare (each row of) 'a' to all rows in 'b' ## if 'a' is a matrix and 'b' is NULL ## call 'dist' on 'a' ## if 'a' is a vector and 'b' is NULL ## make 'a' a 3 col matrix and call 'dist' # Parallelized by parallel package (Fri Jul 5 19:58:32 EDT 2013) ncore <- setup.ncore(ncore) if(ncore > 1) { # Issue of serialization problem # Maximal number of cells of a double-precision matrix # that each core can serialize: (2^31-1-61)/8 R_NCELL_LIMIT_CORE = 2.68435448e8 R_NCELL_LIMIT = ncore * R_NCELL_LIMIT_CORE if(nseg.scale < 1) { warning("nseg.scale should be 1 or a larger integer\n") nseg.scale=1 } } if(is.vector(a)) { a <- matrix(a, ncol=3, byrow=TRUE) } else { a <- as.matrix(a) } if(is.null(b)) { return(as.matrix(dist(a))) } else { if(is.vector(b)) { b <- matrix(b, ncol=3, byrow=TRUE) } else { b <- as.matrix(b) } } dima <- ncol(a) dimb <- ncol(b) if(dima != dimb) stop("Dimension miss-match of input 'a' and 'b'") if(dima != 3) { warning(paste("input does not have three columns: assuming you want", dima, "dimensional distances")) } if(!all.pairs) { ## distance between coresponding rows d <- rep( NA, max(nrow(a), nrow(b)) ) ind <- 1:min(nrow(a), nrow(b)) d[ind] <- sqrt( rowSums((a[ind,] - b[ind,])^2) ) ## return( sqrt( rowSums((a - b)^2) ) ) return(d) } else { if(ncore > 1) { RLIMIT = floor(R_NCELL_LIMIT / nrow(b)) nDataSeg = floor((nrow(a)-1)/RLIMIT) + 1 nDataSeg = floor(nDataSeg * nseg.scale) lenSeg = floor(nrow(a)/nDataSeg) d.l <- NULL for(i in 1:nDataSeg) { istart = (i-1)*lenSeg + 1 iend = if(i1){ members[i] <- paste0("c(", paste0(single.member, collapse=", "), ")") } else{ members[i] <- single.member } } } else{ ##- non numeric vectors can not be condensed members <- unlist(lapply(memb, paste, collapse=", ")) } ## Output silently as a list tbl <- data.frame( id=as.numeric(id), size=as.numeric(size), members=members, stringsAsFactors=FALSE ) y <- list("id"=id, "size"=size, "members"=memb, "tbl"=tbl) if(verbose) { print.data.frame(tbl, row.names=FALSE) } return(y) } bio3d/R/read.pdb.R0000644000176200001440000003052512632622153013264 0ustar liggesusers"read.pdb" <- function (file, maxlines=-1, multi=FALSE, rm.insert=FALSE, rm.alt=TRUE, ATOM.only = FALSE, verbose=TRUE) { if(missing(file)) { stop("read.pdb: please specify a PDB 'file' for reading") } if(!is.numeric(maxlines)) { stop("read.pdb: 'maxlines' must be numeric") } if(!is.logical(multi)) { stop("read.pdb: 'multi' must be logical TRUE/FALSE") } ##- Check if file exists locally or on-line toread <- file.exists(file) if(substr(file,1,4)=="http") { toread <- TRUE } ## Check for 4 letter code and possible on-line file if(!toread) { if(nchar(file)==4) { file <- get.pdb(file, URLonly=TRUE) cat(" Note: Accessing on-line PDB file\n") } else { stop("No input PDB file found: check filename") } } cl <- match.call() ## PDB FORMAT v3.3: colpos, datatype, name, description atom.format <- matrix(c(6, 'character', "type", # type(ATOM) 5, 'numeric', "eleno", # atom_no -1, NA, NA, # (blank) 4, 'character', "elety", # atom_ty 1, 'character', "alt", # alt_loc 4, 'character', "resid", # res_na 1, 'character', "chain", # chain_id 4, 'numeric', "resno", # res_no 1, 'character', "insert", # ins_code -3, NA, NA, # (blank) 8, 'numeric', "x", # x 8, 'numeric', "y", # y 8, 'numeric', "z", # z 6, 'numeric', "o", # o 6, 'numeric', "b", # b -6, NA, NA, # (blank) 4, 'character', "segid", # seg_id 2, 'character', "elesy", # element_symbol 2, 'character', "charge" # atom_charge (should be 'numeric'] ), ncol=3, byrow=TRUE, dimnames = list(c(1:19), c("widths","what","name")) ) trim <- function(s) { ##- Remove leading and trailing spaces from character strings s <- sub("^ +", "", s) s <- sub(" +$", "", s) s[(s=="")]<-"" s } split.fields <- function(x) { ##- Split a character string for data.frame fwf reading ## First splits a string 'x' according to 'first' and 'last' ## then re-combines to new string with ";" as separator x <- trim( substring(x, first, last) ) paste(x,collapse=";") } is.character0 <- function(x){length(x)==0 & is.character(x)} ##- Find first and last (substr) positions for each field widths <- as.numeric(atom.format[,"widths"]) # fixed-width spec drop.ind <- (widths < 0) # cols to ignore (i.e. -ve) widths <- abs(widths) # absolute vales for later st <- c(1, 1 + cumsum( widths )) first <- st[-length(st)][!drop.ind] # substr start last <- cumsum( widths )[!drop.ind] # substr end names(first) = na.omit(atom.format[,"name"]) names(last) = names(first) ##- Read 'n' lines of PDB file raw.lines <- readLines(file, n = maxlines) type <- substring(raw.lines, first["type"], last["type"]) ##- Check number of END/ENDMDL records raw.end <- sort(c(which(type == "END"), which(type == "ENDMDL"))) ## Check if we want to store multiple model data if (length(raw.end) > 1) { cat(" PDB has multiple END/ENDMDL records \n") if (!multi) { cat(" multi=FALSE: taking first record only \n") } else { cat(" multi=TRUE: 'read.dcd/read.ncdf' will be quicker! \n") raw.lines.multi <- raw.lines type.multi <- type } raw.lines <- raw.lines[ (1:raw.end[1]) ] type <- type[ (1:raw.end[1]) ] } ##- Check for 'n' smaller than total lines in PDB file if ( length(raw.end) !=1 ) { if (length(raw.lines) == maxlines) { cat(" You may need to increase 'maxlines' \n") cat(" check you have all data in $atom \n") } } ##- Shortened records if ATOM.only = TRUE if(ATOM.only) { raw.lines <- raw.lines[type %in% c("HEADER", "ATOM ", "HETATM")] type <- substring(raw.lines, first["type"], last["type"]) } ##- Parse REMARK records for storing symmetry matrices to ## build biological unit by calling 'biounit()' remark <- .parse.pdb.remark350(raw.lines) ##- Split input lines by record type raw.header <- raw.lines[type == "HEADER"] raw.seqres <- raw.lines[type == "SEQRES"] raw.helix <- raw.lines[type == "HELIX "] raw.sheet <- raw.lines[type == "SHEET "] raw.atom <- raw.lines[type %in% c("ATOM ","HETATM")] if (verbose) { if (!is.character0(raw.header)) { cat(" ", raw.header, "\n") } } ## Edit from Baoqiang Cao Nov 29, 2009 ## Old version: ## seqres <- unlist(strsplit( trim(substring(raw.seqres,19,80))," +")) ## New version seqres <- unlist(strsplit( trim(substring(raw.seqres,19,80))," +")) if(!is.null(seqres)) { seqres.ch <- substring(raw.seqres, 12, 12) seqres.ln <- substring(raw.seqres, 13, 17) seqres.in <- ( !duplicated(seqres.ch) ) names(seqres) <- rep(seqres.ch[seqres.in], times=seqres.ln[seqres.in]) } ## End Edit from Baoqiang: ##- Secondary structure if(length(raw.helix) > 0) { helix <- list(start = as.numeric(substring(raw.helix,22,25)), end = as.numeric(substring(raw.helix,34,37)), chain = trim(substring(raw.helix,20,20)), type = trim(substring(raw.helix,39,40))) ##- insert code for initial and end residues of helices insert.i <- trim(substring(raw.helix,26,26)) insert.e <- trim(substring(raw.helix,38,38)) names(helix$start) <- insert.i names(helix$end) <- insert.e } else { helix <- NULL } if(length(raw.sheet) > 0) { sheet <- list(start = as.numeric(substring(raw.sheet,23,26)), end = as.numeric(substring(raw.sheet,34,37)), chain = trim(substring(raw.sheet,22,22)), sense = trim(substring(raw.sheet,39,40))) ##- insert code for initial and end residues of sheets insert.i <- trim(substring(raw.sheet,27,27)) insert.e <- trim(substring(raw.sheet,38,38)) names(sheet$start) <- insert.i names(sheet$end) <- insert.e ##- remove repeated records for the same strand (e.g. in 1NH0) pa <- paste(sheet$start, insert.i, sheet$chain, sep='_') keep.inds <- which(!duplicated(pa)) sheet <- lapply(sheet, '[', keep.inds) } else { sheet <- NULL } ## 2014-04-23 ## Update to use single data.frame for ATOM and HETATM records ## file="2RGF"; multi=TRUE; ## file="./4q21.pdb"; maxlines=-1; multi=FALSE; ## rm.insert=FALSE; rm.alt=TRUE; het2atom=FALSE; verbose=TRUE atom <- read.table(text=sapply(raw.atom, split.fields), stringsAsFactors=FALSE, sep=";", quote='', colClasses=atom.format[!drop.ind,"what"], col.names=atom.format[!drop.ind,"name"], comment.char="", na.strings="") ##-- End data.frame update ##- Coordinates only object ###xyz.models <- c(t(atom[,c("x","y","z")])) xyz.models <- matrix(as.numeric(c(t(atom[,c("x","y","z")]))), nrow=1) ##- Multi-model coordinate extraction if (length(raw.end) > 1 && multi) { raw.atom <- raw.lines.multi[ type.multi %in% c("ATOM ","HETATM") ] if( (length(raw.atom)/length(raw.end)) ==nrow(atom) ){ ## Only work with models with the same number of atoms) tmp.xyz=( rbind( substr(raw.atom, first["x"],last["x"]), substr(raw.atom, first["y"],last["y"]), substr(raw.atom, first["z"],last["z"]) ) ) ## Extract coords to nrow/frame * ncol/xyz matrix xyz.models <- matrix( as.numeric(tmp.xyz), ncol=nrow(atom)*3, nrow=length(raw.end), byrow=TRUE) rownames(xyz.models) = NULL } else { warning(paste("Unequal number of atoms in multi-model records:", file)) } rm(raw.lines.multi) } rm(raw.lines, raw.atom) ##- Possibly remove 'Alt records' (m[,"alt"] != NA) if (rm.alt) { if ( sum( !is.na(atom[,"alt"]) ) > 0 ) { first.alt <- sort( unique(na.omit(atom[,"alt"])) )[1] cat(paste(" PDB has ALT records, taking",first.alt,"only, rm.alt=TRUE\n")) alt.inds <- which( (atom[,"alt"] != first.alt) ) # take first alt only if(length(alt.inds)>0) { atom <- atom[-alt.inds,] xyz.models <- xyz.models[ ,-atom2xyz(alt.inds), drop=FALSE ] } } } ##- Possibly remove 'Insert records' if (rm.insert) { if ( sum( !is.na(atom[,"insert"]) ) > 0 ) { cat(" PDB has INSERT records, removing, rm.insert=TRUE\n") insert.inds <- which(!is.na(atom[,"insert"])) # rm insert positions atom <- atom[-insert.inds,] xyz.models <- xyz.models[ ,-atom2xyz(insert.inds), drop=FALSE ] } } output<-list(atom=atom, #het=atom[atom$type=="HETATM",], helix=helix, sheet=sheet, seqres=seqres, xyz=as.xyz(xyz.models), calpha = NULL, remark = remark, call=cl) class(output) <- c("pdb", "sse") ca.inds <- atom.select.pdb(output, string="calpha", verbose=FALSE) output$calpha <- seq(1, nrow(atom)) %in% ca.inds$atom return(output) } ##- parse REMARK records for building biological unit ('biounit()') .parse.pdb.remark350 <- function(x) { raw.lines <- x # How many lines of REMARK 350? remark350 <- grep("^REMARK\\s+350", raw.lines) nlines <- length(remark350) # How many distinct biological unit? biolines <- grep("^REMARK\\s+350\\s+BIOMOLECULE", raw.lines) nbios <- length(biolines) if(nbios == 0) { # warning("REMARK 350 is incomplete.") return(NULL) } # End line number of each biological unit biolines2 <- c(biolines[-1], remark350[nlines]) # How the biological unit was determined? method <- sapply(1:nbios, function(i) { author <- intersect(grep("^REMARK\\s+350\\s+AUTHOR DETERMINED BIOLOGICAL UNIT", raw.lines), biolines[i]:biolines2[i]) if(length(author) >= 1) return("AUTHOR") else return("SOFTWARE") } ) # Get chain IDs to apply the transformation chain <- lapply(1:nbios, function(i) { chlines <- intersect(grep("^REMARK\\s+350\\s+APPLY THE FOLLOWING TO CHAINS", raw.lines), biolines[i]:biolines2[i]) if(length(chlines) >= 1) { chs <- gsub("\\s*", "", sub("^.*:", "", raw.lines[chlines])) chs <- unlist(strsplit(chs, split=",")) } else { # warning(paste("Can't determine chain IDs from REMARK 350 for biological unit", # i, sep="")) chs = NA } return(chs) } ) if(any(is.na(chain))) return(NULL) mat <- lapply(1:nbios, function(i) { # Get transformation matrices mtlines <- intersect(grep("^REMARK\\s+350\\s+BIOMT", raw.lines), biolines[i]:biolines2[i]) # Get chain ID again: different trans matrices may be applied to different chains chlines <- intersect(grep("^REMARK\\s+350\\s+APPLY THE FOLLOWING TO CHAINS", raw.lines), biolines[i]:biolines2[i]) chs <- gsub("\\s*", "", sub("^.*:", "", raw.lines[chlines])) chs <- strsplit(chs, split=",") if(length(mtlines) == 0 || length(mtlines) %% 3 != 0) { # warning("Incomplete transformation matrix") mat <- NA } else { mat <- lapply(seq(1, length(mtlines), 3), function(j) { mt <- matrix(NA, 3, 4) for(k in 1:3) { vals <- sub("^REMARK\\s+350\\s+BIOMT[123]\\s*", "", raw.lines[mtlines[j+k-1]]) vals <- strsplit(vals, split="\\s+")[[1]] mt[k, ] <- as.numeric(vals[-1]) } mt } ) chs.pos <- findInterval(mtlines[seq(1, length(mtlines), 3)], chlines) names(mat) <- sapply(chs[chs.pos], paste, collapse=" ") ## apply each mat to specific chains } return(mat) } ) if(any(is.na(mat))) return(NULL) out <- list(biomat = list(num=nbios, chain=chain, mat=mat, method=method)) return(out) } bio3d/R/pdbsplit.R0000644000176200001440000001243312632622153013424 0ustar liggesusers`pdbsplit` <- function(pdb.files, ids=NULL, path="split_chain", overwrite=TRUE, verbose=FALSE, mk4=FALSE, ncore=1, ...) { toread <- file.exists(pdb.files) toread[substr(pdb.files, 1, 4) == "http"] <- TRUE if (all(!toread)) stop("No corresponding PDB files found") if (any(!toread)) { warning(paste("Missing files:\n\t", paste(pdb.files[!toread], collapse = "\n\t"), sep = "")) pdb.files <- pdb.files[toread] } ## Parallelized by parallel package ncore <- setup.ncore(ncore, bigmem = FALSE) if(ncore>1) { mylapply <- mclapply prev.warn <- getOption("warn") options(warn=1) } else mylapply <- lapply ## Faster method to fetch chain IDs in a PDB file "quickscan" <- function(pdbfile) { fi <- readLines(pdbfile) fi = fi[ grep("^ATOM", fi) ] chains <- unique(substr(fi, 22,22)) chains[chains == " "] <- NA return(chains) } if(!verbose) pb <- txtProgressBar(min=0, max=length(pdb.files), style=3) if(!file.exists(path)) dir.create(path) "splitOnePdb" <- function(i, pdb.files, ids, path, overwrite, verbose, ...) { out <- c(); skipped <- c(); unused <- NULL; if(!overwrite && !verbose) { chains <- quickscan(pdb.files[i]) } else if(overwrite && !verbose) { invisible(capture.output( pdb <- read.pdb(pdb.files[i], verbose=verbose, ...) )) chains <- unique(pdb$atom[, "chain"]) } else { pdb <- read.pdb(pdb.files[i], verbose=verbose, ...) chains <- unique(pdb$atom[, "chain"]) } if(!is.null(ids)) { ids <- unique(ids) ## Match 'ids' with 'pdbId_chainId' combinations tmp.names <- paste0(basename.pdb(pdb.files[i], mk4=mk4), "_", chains) tmp.inds <- unique(unlist(lapply(ids, grep, tmp.names))) if(length(tmp.inds)==0) { ## Skip pdb file if no match were found unused <- basename.pdb(pdb.files[i], mk4=mk4) chains <- c() } else { chains <- chains[tmp.inds] } } if(!overwrite && !verbose) { tmp.names <- paste0(basename.pdb(pdb.files[i], mk4=mk4),"_", chains, ".pdb") new.name <- file.path(path, tmp.names) if(all(file.exists(new.name))) { out <- c(out, new.name) skipped <- paste(basename(pdb.files[i]), " (", paste(chains, collapse=","), ")", sep="") return( list(out=out, unused=unused, skipped=skipped) ) } else { if(!verbose) invisible(capture.output( pdb <- read.pdb(pdb.files[i], verbose=verbose, ...) )) else pdb <- read.pdb(pdb.files[i], verbose=verbose, ...) } } if (length(chains) > 0) { for (j in 1:length(chains)) { if(!verbose) setTxtProgressBar(pb, (i-1)+(j/length(chains))) ##if (!is.na(chains[j])) { new.pdb <- NULL sel <- atom.select(pdb, chain=chains[j], verbose=verbose) #==== new.pdb <- trim.pdb(pdb, sel, sse=FALSE) ## Multi-model records if (nrow(pdb$xyz)>1) { for ( k in 1:nrow(pdb$xyz) ) { str.len <- nchar(nrow(pdb$xyz)) new.name <- paste(basename.pdb(pdb.files[i], mk4=mk4), "_", chains[j], ".", formatC(k, width=str.len, format="d", flag="0"), ".pdb", sep = "") new.name <- file.path(path, new.name) xyz <- pdb$xyz[k, sel$xyz] write.pdb(new.pdb, file = new.name, xyz=xyz) out <- c(out, new.name) } } else { new.name <- paste0(basename.pdb(pdb.files[i], mk4=mk4), "_", chains[j], ".pdb") new.name <- file.path(path, new.name) if(!file.exists(new.name) || overwrite) write.pdb(new.pdb, file = new.name) out <- c(out, new.name) } ##} } } if(!verbose) setTxtProgressBar(pb, i) return( list(out=out, unused=unused, skipped=skipped) ) } outdata <- mylapply(1:length(pdb.files), splitOnePdb, pdb.files, ids, path, overwrite, verbose, ...) if(ncore>1) options(warn=prev.warn) ##### Collect data ##### outfiles <- c() unused <- c(); skipped <- c(); for(i in 1:length(outdata)) { tmp.out <- outdata[[i]] outfiles <- c(outfiles, tmp.out$out) unused <- c(unused, tmp.out$unused) skipped <- c(skipped, tmp.out$skipped) } if(!verbose) close(pb) if(!is.null(ids)) { ids.used <- NULL; nonmatch <- NULL if(length(outfiles)>0) { ids.used <- sub(".pdb$", "", basename(outfiles)) tmp.fun <- function(x, y) { ifelse(length(grep(x,y))>0, TRUE, FALSE) } tmp.inds <- unlist(lapply(ids, tmp.fun, ids.used)) nonmatch <- ids[!tmp.inds] } ## Elements of 'pdb.files' not in use if(length(unused)>0) { unused <- paste(unused, collapse=", ") warning(paste("unmatched pdb files:", unused)) } ## Elements of 'ids' not in use if(length(nonmatch)>0) { nonmatch <- paste(nonmatch, collapse=", ") warning(paste("unmatched ids:", nonmatch)) } } if(length(skipped)>0) { warning(paste(skipped, collapse=", ")) } return(outfiles) } bio3d/R/com.pdb.R0000644000176200001440000000133612526367343013136 0ustar liggesusers"com.pdb" <- function(pdb, inds=NULL, use.mass=TRUE, ... ) { if (missing(pdb)) stop("Please supply an input 'pdb' object, i.e. from 'read.pdb()'") if(!is.pdb(pdb)) stop("Input 'pdb' must be of type 'pdb'") if(is.null(inds)) { xyz <- pdb$xyz at <- pdb$atom[, "elety"] } else { if(!is.select(inds)) stop("provide a select object as obtained from 'atom.select'") if(length(inds$xyz)<3) stop("insufficient atoms in selection") xyz <- pdb$xyz[,inds$xyz] at <- pdb$atom[inds$atom, "elety"] } if(use.mass) { m <- atom2mass(at, ...) } else { m <- NULL } com <- com.xyz(xyz, m) return(com) } bio3d/R/pdb2aln.ind.R0000644000176200001440000000125012526367343013702 0ustar liggesusers"pdb2aln.ind" <- function(aln, pdb, inds = NULL, ...) { # get the new alignment; also check arguments internally naln <- pdb2aln(aln=aln, pdb=pdb, ...) if(is.null(inds)) inds <- gap.inspect(aln$ali)$f.inds ninds <- which(naln$ref["ali.pos",] %in% inds) ca.inds <- naln$ref["ca.inds", ninds] if(any(is.na(ca.inds))) { warning("Gaps are found in equivalent positions in PDB") } inds.a = inds[!is.na(ca.inds)] inds.b = ca.inds[!is.na(ca.inds)] a = list(atom=inds.a, xyz=atom2xyz(inds.a)) class(a) = "select" b = list(atom=inds.b, xyz=atom2xyz(inds.b)) class(b) = "select" out = list(a = a, b = b) return(out) } bio3d/R/formula2mass.R0000644000176200001440000000165512412621431014214 0ustar liggesusers"formula2mass" <- function(form, sum.mass=TRUE) { errmsg <- paste("error while parsing formula. \n", " provide input on the form: 'C3 H5 N O1'") if(class(form)!="character" || missing(form)) stop(errmsg) eles <- unlist(strsplit(form, " ")) mass <- c() for ( i in 1:length(eles) ) { ele <- unlist(strsplit(eles[i], "")) inds.a <- grep("[0-9]", ele) inds.b <- grep("[A-z]", ele) num <- paste(ele[inds.a], collapse="") char <- paste(ele[inds.b], collapse="") if(nchar(num)==0) num <- 1 if(length(inds.a)==0) inds.a <- 0 if(length(inds.b)==0) stop(errmsg) if(nrow(bounds(inds.a))>1 || nrow(bounds(inds.b))>1 ) stop(errmsg) mass <- c(mass, atom2mass(char) * as.numeric(num)) } if(sum.mass) mass <- sum(mass) return(mass) } bio3d/R/seqbind.R0000644000176200001440000000173212526367343013241 0ustar liggesusersseqbind <- function(..., blank = "-") { cl <- match.call() objs <- list(...) are.null <- unlist(lapply(objs, is.null)) objs <- objs[!are.null] if(length(objs)==0) stop("Incompatible input") if(length(objs)==1) return(unlist(objs)) is.fasta <- function(x) return(inherits(x, "fasta")) are.fas <- unlist(lapply(objs, is.fasta)) are.vec <- unlist(lapply(objs, is.vector)) are.mat <- unlist(lapply(objs, is.matrix)) if(!all(are.vec | are.mat | are.fas)) stop("'Can combine only vectors and/or matrices'") objs[are.fas] <- lapply(objs[are.fas], function(x) x$ali) objs[are.vec] <- lapply(objs[are.vec], matrix, nrow = 1) max.col <- max(unlist(lapply(objs, ncol))) extend <- function(x, n, add) cbind(x, matrix(add, nrow=nrow(x), ncol=n-ncol(x))) objs <- lapply(objs, extend, n = max.col, add = blank) objs <- do.call(rbind, objs) out <- as.fasta(objs, id=rownames(objs)) out$call <- cl return(out) } bio3d/R/var.xyz.R0000644000176200001440000000111612524171274013223 0ustar liggesusers"var.xyz" <- function(xyz, weights=TRUE) { ## Calculate pairwise distances natoms <- ncol(xyz) / 3 all <- array(0, dim=c(natoms,natoms,nrow(xyz))) for( i in 1:nrow(xyz) ) { dists <- dist.xyz(xyz[i,]) all[,,i] <- dists } ## Calculate variance of pairwise distances all.vars <- apply(all, 1:2, var) if(weights) { ## Make the final weights wts <- 1 - (all.vars / max(all.vars, na.rm=TRUE)) wts[is.na(wts)] <- 1 return(wts) } else { return(all.vars) } } "var.pdbs" <- function(pdbs, ...) { xyz <- pdbs$xyz return(var.xyz(xyz, ...)) } bio3d/R/cov.nma.R0000644000176200001440000000261512526367343013156 0ustar liggesuserscov.nma <- function(nma) { if(!inherits(nma, "nma")) stop("provide a 'nma' object as obtain from function 'nma.pdb()'") dims <- dim(nma$U) cov <- matrix(0, ncol=dims[1], nrow=dims[1]) tmpU <- nma$U[, (nma$triv.modes+1):ncol(nma$U)] tmpL <- nma$L[(nma$triv.modes+1):ncol(nma$U)] for(j in 1:ncol(tmpU) ) { cov <- cov + ( (tmpU[,j] %*% t(tmpU[,j])) / tmpL[j]) } return(cov) } cov.enma <- function(enma, ncore=NULL) { if(!inherits(enma, "enma")) stop("provide a 'enma' object as obtain from function 'nma.pdbs()'") if(any(is.na(enma$fluctuations))) stop("provide 'enma' object calculated with argument 'rm.gaps=TRUE'") ncore <- setup.ncore(ncore, bigmem = FALSE) if(ncore>1) mylapply <- mclapply else mylapply <- lapply if(!inherits(enma, "enma")) stop("provide 'enma' object as obtained from nma.pdbs") dims <- dim(enma$U.subspace) mycalc <- function(i, enma) { cov <- matrix(0, ncol=dims[1], nrow=dims[1]) tmpU <- enma$U.subspace[,,i] tmpL <- enma$L[i,] for(j in 1:ncol(tmpU) ) { cov = cov + ( (tmpU[,j] %*% t(tmpU[,j])) / tmpL[j]) } cat(".") return(cov) } covs.list <- mylapply(1:dims[3L], mycalc, enma) cat("\n") covs <- array(0, dim=c(dims[1], dims[1], dims[3])) for ( i in 1:dims[3L] ) covs[,,i]=covs.list[[i]] return(covs) } .tr <- function(mat) { return(sum(diag(mat))) } bio3d/R/unbound.R0000644000176200001440000000056712526367343013273 0ustar liggesusers"unbound" <- function(start, end=NULL) { if(is.matrix(start) && all(c("start", "end") %in% colnames(start))) { if(is.null(end)) end = start[, "end"] start = start[, "start"] } if(length(start)!=length(end)) stop("start and end must are not the same length") ex <- NULL for(i in 1:length(start)) { ex <- c(ex, start[i]:end[i]) } return(ex) } bio3d/R/covsoverlap.R0000644000176200001440000000363212526367343014160 0ustar liggesuserscovsoverlap <- function(...) UseMethod("covsoverlap") covsoverlap.enma <- function(enma, ncore=NULL, ...) { if(!inherits(enma, "enma")) stop("provide a 'enma' object as obtain from function 'nma.pdbs()'") if(any(is.na(enma$fluctuations))) stop("provide 'enma' object calculated with argument 'rm.gaps=TRUE'") ncore <- setup.ncore(ncore, bigmem = FALSE) if(ncore>1) mylapply <- mclapply else mylapply <- lapply cat("Calculating pairwise covariance overlap coefs") m <- dim(enma$U.subspace)[3] mat <- matrix(NA, m, m) ##inds <- pairwise(m) inds <- rbind(pairwise(m), matrix(rep(1:m,each=2), ncol=2, byrow=T)) mylist <- mylapply(1:nrow(inds), function(row) { i <- inds[row,1]; j <- inds[row,2]; a <- list(U=enma$U.subspace[,,i], L=enma$L[i, ]) b <- list(U=enma$U.subspace[,,j], L=enma$L[j, ]) val <- covsoverlap.nma(a, b, ...) out <- list(val=val, i=i, j=j) cat(".") return(out) }) for ( i in 1:length(mylist)) { tmp <- mylist[[i]] mat[tmp$i, tmp$j] <- tmp$val } mat[ inds[,c(2,1)] ] = mat[ inds ] ##diag(mat) <- rep(1, n) rownames(mat) <- basename(rownames(enma$fluctuations)) colnames(mat) <- basename(rownames(enma$fluctuations)) cat("\n") return(round(mat, 6)) } covsoverlap.nma <- function(a, b, subset=NULL, ...) { if(any(missing(a), missing(b))) stop("provide eigenvectors and eigenvalues") dims.a <- dim(a$U) dims.b <- dim(b$U) if(dims.a[1]!=dims.b[1]) stop("dimension mismatch") if(!is.null(subset)) { if(subset>ncol(a$U)) subset <- ncol(a$U) a$U <- a$U[,1:subset] b$U <- b$U[,1:subset] a$L <- a$L[1:subset] b$L <- b$L[1:subset] } sumb <- 0 for( k in 1:ncol(a$U) ) { tmp <- sqrt(a$L[k] * b$L) overlap <- c((t(a$U[,k]) %*% b$U)**2) sumb <- sumb + sum( tmp * overlap ) } return(1 - ( sum(a$L + b$L) - 2 *sumb ) / sum(a$L + b$L)) } bio3d/R/atom2mass.R0000644000176200001440000000235312524171273013513 0ustar liggesusersatom2mass <- function(...) UseMethod("atom2mass") atom2mass.default <- function(x, mass.custom=NULL, elety.custom=NULL, grpby=NULL, rescue=TRUE, ...){ if(!is.null(mass.custom)) { if(!all(c("symb","mass") %in% names(mass.custom))) stop("'mass.custom' must contains 'symb' and 'mass' components") inds <- unlist(lapply(mass.custom, is.factor)) mass.custom[inds] <- lapply(mass.custom[inds], as.character) } elements <- rbind(mass.custom[,c("symb","mass")], elements[,c("symb","mass")]) symb <- atom2ele.default(x, elety.custom, rescue, ...) M <- elements[match(symb, elements[,"symb"]), "mass"] if(any(is.na(M))) stop(paste("\n\tatom2mass: mass of element '", symb[is.na(M)], "' unknown", sep="")) if(!is.null(grpby)) { if(length(grpby) != length(M)) warning("'grpby' as been recycled") M <- unlist(lapply(split(M, grpby), sum)) } return(M) } atom2mass.pdb <- function(pdb, inds=NULL, mass.custom=NULL, elety.custom=NULL, grpby=NULL, rescue=TRUE, ...){ if(!is.null(inds)) pdb <- trim.pdb(pdb, inds) atom.names <- pdb$atom[,"elety"] M <- atom2mass.default(atom.names, mass.custom, elety.custom, grpby, rescue, ...) return(M) } bio3d/R/plot.pca.loadings.R0000644000176200001440000000154312430771420015120 0ustar liggesusers"plot.pca.loadings" <- function(x, resnums= seq(1,(length(x[,1])/3), 25), ... ) { # Plot residue loadings along PC1 to PC3 if given an xyz # C-alpha matrix of "loadings" (e.g. as returned from # 'pca.xyz' such a 'pca.trj$loadings') # For more info see 'pca.res.loadings' # # To Do: add gap.cols options if(is.list(x)) x=x$U pos <- resnums*3 op <- par(no.readonly=TRUE) on.exit(par(op)) par(mfrow=c(3,1), mar=c(4,4,2,2)) plot(abs(x[,1]),main="",type="h", axes=FALSE, xlab="Index Number", ylab="PC1") axis(1, at=pos, labels=resnums) axis(2) box() plot(abs(x[,2]),main="",type="h", axes=FALSE,xlab="Index Number",ylab="PC2") axis(1, at=pos,labels=(pos)/3) axis(2) box() plot(abs(x[,3]),main="",type="h", axes=FALSE,xlab="Index Number",ylab="PC3") axis(1, at=pos,labels=(pos)/3) axis(2) box() } bio3d/R/dccm.nma.R0000644000176200001440000000561612632622153013270 0ustar liggesusers"dccm.nma" <- function(x, nmodes=NULL, ncore=NULL, ...) { nma <- x if (missing(nma)) stop("dccm.nma: must supply a 'nma' object, i.e. from 'nma'") if(!"nma" %in% class(nma)) stop("dccm.nma: must supply 'nma' object, i.e. from 'nma'") ## Check for multiple cores ncore <- setup.ncore(ncore, bigmem = FALSE) if(ncore > 1) { mcparallel <- get("mcparallel", envir = getNamespace("parallel")) mccollect <- get("mccollect", envir = getNamespace("parallel")) } ## Inner product between all pairs of residues cross.inner.prod <- function(a, b) { mat <- apply(a, 1, "%*%", t(b)) return(mat) } ## Calc initial correlations for a subset of modes corrmats <- function(r.inds, core.id, nma, corr.mat, freqs) { for ( i in r.inds ) { mode <- matrix(nma$U[,i], ncol=3, byrow=TRUE) corr.mat <- corr.mat + (cross.inner.prod(mode, mode) / (freqs[i]**2)) if(core.id==1) setTxtProgressBar(pb, i) } return(corr.mat) } if(!is.null(nma$frequencies)) { freqs <- nma$frequencies } else { freqs <- nma$force.constants } if(is.null(nmodes)) nmodes <- length(nma$L) else { nmodes <- nmodes + nma$triv.modes if(nmodes>length(nma$L)) { warning("'nmodes' larger than the number of modes") nmodes <- length(nma$L) } } ## Initialize progress bar ##ptm <- proc.time() pb <- txtProgressBar(min=(nma$triv.modes+1), max=nmodes + nma$natoms, style=3) ## Allocate the correl matrix corr.mat <- matrix(0, nma$natoms, nma$natoms) ## Which modes to use for calculation mode.inds <- (nma$triv.modes+1):nmodes core.ids <- rep(1:ncore, length.out=length( mode.inds )) if(ncore>1) jobs <- list() for ( i in 1:ncore ) { rinds <- mode.inds[ which(core.ids==i) ] if(ncore>1) { q <- mcparallel(corrmats(rinds, i, nma, corr.mat, freqs)) jobs[[i]] <- q } else corr.mat <- corrmats(rinds, i, nma, corr.mat, freqs) } ## Collect all jobs, and sum matrices if(ncore>1) { res <- mccollect(jobs, wait=TRUE) for ( job in res ) { corr.mat <- corr.mat + job } } ## Basis for normalization a <- vector('numeric', length=nrow(corr.mat)) k <- length(mode.inds) ## for ProgressBar ! inds <- rep(1:nrow(corr.mat), each=3) for ( j in (nma$triv.modes+1):nmodes ) { v <- nma$U[, j] * nma$U[, j] a <- a + ( tapply( v, inds, sum) / (freqs[j]**2)) k <- k+1 setTxtProgressBar(pb, k) } close(pb) a <- sqrt(a) bn <- a%o%a ## Normalized correlation matrix corr.mat <- corr.mat / bn class(corr.mat) <- c("dccm", "matrix") ##t <- proc.time() - ptm ##cat(" Done in", t[[3]], "seconds.\n") return(corr.mat) } bio3d/R/uniprot.R0000644000176200001440000000320712526367343013313 0ustar liggesusersuniprot <- function(accid) { oops <- requireNamespace("XML", quietly = TRUE) if(!oops) stop("Please install the XML package from CRAN") url <- paste('http://www.uniprot.org/uniprot/', accid, '.xml', sep="") tmpfile <- tempfile() download.file(url, tmpfile) ##, method="wget") xml <- XML::xmlRoot(XML::xmlParse(tmpfile)) node.names <- XML::xmlSApply(xml[[1]], XML::xmlName) ## acession inds <- which(node.names=="accession") accession <- NULL for(i in 1:length(inds)) accession <- c(accession, XML::xmlValue(xml[[1]][[inds[i]]])) ## and name inds <- which(node.names=="name") name <- NULL for(i in 1:length(inds)) name <- c(name, XML::xmlValue(xml[[1]][[inds[i]]])) ## sequence inds <- which(node.names=="sequence") sequence <- gsub("\n", "", XML::xmlValue(xml[[1]][[inds]])) ## organism inds <- which(node.names=="organism") node <- xml[[1]][[inds]] organism <- XML::xmlValue(node[[1]]) ## taxon inds <- which(node.names=="organism") node <- xml[[1]][[inds]] taxon <- NULL for ( i in 1:XML::xmlSize(node[['lineage']]) ) { taxon <- c(taxon, XML::xmlValue(node[['lineage']][[i]])) } ## protein node <- xml[[1]][['protein']] fullName <- XML::xmlValue(node[['recommendedName']][['fullName']]) shortName <- XML::xmlValue(node[['recommendedName']][['shortName']]) ## gene node <- xml[[1]][['gene']] gene <- XML::xmlValue(node[[1]]) out <- list(accession = accession, name = name, fullName = fullName, shortName = shortName, sequence = sequence, gene = gene, organism = organism, taxon = taxon) return(out) } bio3d/R/com.R0000644000176200001440000000005212526367343012364 0ustar liggesusers"com" <- function(...) UseMethod("com") bio3d/R/summary.pdb.R0000644000176200001440000000770312544562303014052 0ustar liggesuserssummary.pdb <- function(object, printseq=FALSE, ...) { ## Print a summary of basic PDB object features if( !is.pdb(object) ) { stop("Input should be a pdb object, as obtained from 'read.pdb()'") } ## Multi-model check and total atom count nmodel <- nrow(object$xyz) if( is.null(nmodel) ) { ntotal <- length(object$xyz)/3 nmodel = 1 } else { ntotal <- length(object$xyz[1,])/3 } nxyz <- length(object$xyz) nres <- sum(object$calpha) chains <- unique(object$atom[,"chain"]) all.inds <- atom.select(object, "all", verbose=FALSE)$atom prot.inds <- atom.select(object, "protein", verbose=FALSE)$atom nuc.inds <- atom.select(object, "nucleic", verbose=FALSE)$atom other.inds <- all.inds[! (all.inds %in% c(prot.inds, nuc.inds)) ] nprot <-length(prot.inds) nnuc <-length(nuc.inds) nresnuc <- length(unique( paste(object$atom$chain[nuc.inds], object$atom$insert[nuc.inds], object$atom$resno[nuc.inds], sep="-"))) het <- object$atom[other.inds,] nhet.atom <- nrow(het) if(is.null(nhet.atom) | nhet.atom==0) { nhet.atom <- 0 nhet.res <- 0 hetres <- "none" } else { hetres.resno <- apply(het[,c("chain","resno","resid")], 1, paste, collapse=".") nhet.res <- length(unique(hetres.resno)) hetres.nres <- table(het[,c("resid")][!duplicated(hetres.resno)]) hetres <- paste( paste0( names(hetres.nres), " (",hetres.nres, ")"), collapse=", ") } if((nprot+nnuc+nhet.atom) != ntotal) warning("nPROTEIN + nNUCLEIC + nNON-PROTEIN + nNON-NUCLEIC != nTotal") cat("\n Call: ", paste(deparse(object$call), sep = "\n", collapse = "\n"), "\n", sep = "") s <- paste0("\n Total Models#: ", nmodel, "\n Total Atoms#: ", ntotal, ", XYZs#: ", nxyz, " Chains#: ", length(chains), " (values: ", paste(chains, collapse=" "),")", "\n\n Protein Atoms#: ", nprot, " (residues/Calpha atoms#: ", nres,")", "\n Nucleic acid Atoms#: ", nnuc, " (residues/phosphate atoms#: ", nresnuc,")", "\n\n Non-protein/nucleic Atoms#: ", nhet.atom, " (residues: ", nhet.res, ")", "\n Non-protein/nucleic resid values: [ ", hetres," ]", "\n\n") cat(s) if(printseq) { ##protein if(nres>0) { prot.pdb <- trim.pdb(object, as.select(prot.inds)) aa <- pdbseq(prot.pdb) if(!is.null(aa)) { if(nres > 225) { ## Trim long sequences before output aa <- c(aa[1:225], "......", aa[(nres-3):nres]) } aa <- paste(" ", gsub(" ","", strwrap( paste(aa,collapse=" "), width=120, exdent=0) ), collapse="\n") cat(" Protein sequence:\n", aa, "\n\n", sep="") } } ## nucleic if(nresnuc>0) { na.pdb <- trim.pdb(object, as.select(nuc.inds)) aa <- paste(object$atom$chain[nuc.inds], object$atom$insert[nuc.inds], object$atom$resno[nuc.inds], object$atom$resid[nuc.inds], sep="-") aa <- aa[!duplicated(aa)] aa <- unlist(lapply(strsplit(aa, "-"), function(x) x[4])) aa <- .aa321.na(aa) if(nresnuc > 225) { ## Trim long sequences before output aa <- c(aa[1:225], "......", aa[(nresnuc-3):nresnuc]) } aa <- paste(" ", gsub(" ","", strwrap( paste(aa,collapse=" "), width=120, exdent=0) ), collapse="\n") cat(" Nucleic acid sequence:\n", aa, "\n\n", sep="") } } i <- paste( attributes(object)$names, collapse=", ") cat(strwrap(paste(" + attr:",i,"\n"),width=45, exdent=8), sep="\n") invisible( c(nmodel=nmodel, natom=ntotal, nxyz=nxyz, nchains=length(chains), nprot=nprot, nprot.res=nres, nother=nhet.atom, nother.res=nhet.res) ) } bio3d/R/vmd.colors.R0000644000176200001440000000230412412621431013657 0ustar liggesusersvmd.colors <- function(n=33, picker=FALSE, ...){ ## RGB numbers red <- c(0, 1, 0.35, 1, 1, 0.5, 0.6, 0, 1, 1, 0.25, 0.65, 0.5, 0.9, 0.5, 0.5, 0, 0.88, 0.55, 0, 0, 0, 0, 0.02, 0.01, 0.27, 0.45, 0.9, 1, 0.98, 0.81, 0.89, 0.96) green <- c(0, 0, 0.35, 0.50, 1, 0.5, 0.6, 1, 1, 0.6, 0.75, 0, 0.9, 0.4, 0.3, 0.5, 0, 0.97, 0.9, 0.9, 0.9, 0.88, 0.76, 0.38, 0.04, 0, 0, 0, 0, 0, 0, 0.35, 0.72) blue <- c(1, 0, 0.35, 0, 0, 0.2, 0.6, 0, 1, 0.6, 0.75, 0.65, 0.4, 0.7, 0, 0.75, 0, 0.02, 0.02, 0.04, 0.5, 1, 1, 0.67, 0.93, 0.98, 0.9, 0.9, 0.66, 0.23, 0, 0, 0) ## Setup color indices max.col <- length(red) inds <- (1:n) if( n > max.col ) { inds <- inds %% max.col inds[inds==0] <- max.col warning( paste("Colors will be recycled: input 'n' >", max.col) ) } cols <- rgb(red[inds], green[inds], blue[inds], ...) names(cols) <- c(1:n) if(picker) { ## Draw a pie chart to help with color choice if(n > 50) { warning("Chart will likely be crowded, set n=33 to see all colors") } pie(rep(1, length(cols)), labels=paste(inds, cols), col=cols, cex=0.75) } return(cols) } bio3d/R/filter.dccm.R0000644000176200001440000000720612632622153013777 0ustar liggesusersfilter.dccm <- function(x, cutoff.cij = 0.4, cmap = NULL, xyz = NULL, fac = NULL, cutoff.sims = NULL, collapse = TRUE, extra.filter = NULL, ...) { # check cij format cij <- x if("all.dccm" %in% names(cij)) { cij <- cij$all.dccm } else if(is.list(cij)) { cij <- array(unlist(cij), dim = c(dim(cij[[1]]), length(cij))) } else if(is.matrix(cij)) { cij <- array(cij, dim = c(dim(cij), 1)) } else if(!is.array(cij)) { stop("Input x should be an array/list containing correlation matrices") } ## Check input is built of simmetric matrices if (dim(cij)[1] != dim(cij)[2]) { stop("Input 'x' should contain symmetric matrices.") } ## Check xyz and set cmap if(is.null(cmap)) { if(is.null(xyz)) cmap = FALSE else cmap = TRUE } if(cmap) { # Inspect cij values with respect to cutoff.cij and contact map if(is.null(xyz)) stop("xyz coordinates or a 'pdbs' object must be provided") # check factor vector for multiple networks construction if(!is.null(fac)) { if(!is.factor(fac)) fac = as.factor(fac) } else { fac = factor(rep("a", dim(cij)[3L])) } # check xyz for contact map calculation if(inherits(xyz, "pdbs")) { gaps.pos <- gap.inspect(xyz$xyz) xyz <- xyz$xyz[, gaps.pos$f.inds] } if(nrow(xyz) != dim(cij)[3L] && nlevels(fac) > 1) stop("xyz matrix doesn't match x. Set fac=NULL for single network construction") # convert cij to upper.tri matrix for internal use pcij <- apply(cij, 3, function(x) x[upper.tri(x)]) ncij <- tapply(1:dim(cij)[3L], fac, function(i) { # contact map if(nlevels(fac) > 1) cm <- cmap(xyz[i, ], ...) else cm <- cmap(xyz, ...) cij.min = apply(abs(pcij[, i]), 1, min) cij.max = apply(abs(pcij[, i]), 1, max) filter <- (cij.min >= cutoff.cij) | (cij.max >= cutoff.cij & cm[upper.tri(cm)]==1) if(!is.null(extra.filter)) filter <- filter * extra.filter[upper.tri(extra.filter)] ncij <- array(NA, dim=c(dim(cij[,,1]), length(i))) for(j in 1:dim(ncij)[3L]) { tcij <- cij[,,i[j]] tcij[upper.tri(tcij)] <- pcij[, i[j]] * filter tcij[lower.tri(tcij)] <- t(tcij)[lower.tri(tcij)] ncij[,,j] <- tcij } # if(length(i) == 1) ncij <- ncij[,,1] return(ncij) } ) if(collapse) ncij <- lapply(ncij, rowMeans, dims = 2) if(nlevels(fac)==1) ncij <- ncij[[1]] if(is.matrix(ncij)) class(ncij) = c("dccm", "matrix") return(ncij) } else { # Filter cijs with cutoff.sims and return mean dccm (dccm.mean()) if(is.null(cutoff.sims)) cutoff.sims = dim(cij)[3L] if (cutoff.sims > dim(cij)[3L] || cutoff.sims < 0) { stop("The cutoff.sims should be a number between 0 and N, where N is the the number of simulations in the input matrix") } ## Filter by cutoff.cij and sum across simulations cut.cij.inds <- (abs(cij) < cutoff.cij) count <- array(NA, dim = dim(cij)) count[!cut.cij.inds] = 1 cij.sum <- apply(count, c(1:2), sum, na.rm = TRUE) ## Mask cij values below cutoff and average across simulations cij[cut.cij.inds] = NA cij.ave <- apply(cij, c(1:2), mean, na.rm = TRUE) ## Mask average values if below cutoff.sims cut.sims.inds <- (cij.sum < cutoff.sims) cij.ave[cut.sims.inds] = 0 ## Could use NA here class(cij.ave) = c("dccm", "matrix") return(cij.ave) } } bio3d/R/atom2ele.R0000644000176200001440000000240012632622153013304 0ustar liggesusersatom2ele <- function(...) UseMethod("atom2ele") atom2ele.default <- function(x, elety.custom=NULL, rescue=TRUE, ...){ if(!is.null(elety.custom)) { if(!all(c("name","symb") %in% names(elety.custom))) stop("'elety.custom' must contains 'name' and 'symb' components") inds <- unlist(lapply(elety.custom, is.factor)) elety.custom[inds] <- lapply(elety.custom[inds], as.character) } atom.index <- rbind(elety.custom[,c("name","symb")], atom.index[,c("name","symb")]) # Why atom names starting by "H" are directly converted to "H" as follow? # x[substr(x,1,1) == "H"] <- "H" symb <- atom.index[match(x, atom.index[,"name"]), "symb"] is.unknown <- is.na(symb) if(any(is.unknown)) { if(rescue) { symb[is.unknown] <- substr(x[is.unknown],1,1) warning(paste("\n\tunknown element: mapped ", x[is.unknown], " to ", symb[is.unknown], sep="")) } else { stop(paste("\n\tatom2symb: element of '", x[is.unknown], "' unknown", sep="")) } } symb <- unlist(symb) return(symb) } atom2ele.pdb <- function(pdb, inds, elety.custom=NULL, rescue=TRUE, ...){ if(!is.null(inds)) pdb <- trim.pdb(pdb, inds) atom.names <- pdb$atom[,"elety"] symb <- atom2ele.default(atom.names, elety.custom, rescue, ...) return(symb) } bio3d/R/plot.hmmer.R0000644000176200001440000001265412524171274013700 0ustar liggesusers`plot.hmmer` <- function(x, cutoff=NULL, cut.seed=NULL, cluster=TRUE, mar=c(2, 5, 1, 1), cex=1.1, ...) { allowed <- c("phmmer", "hmmsearch", "jackhmmer") if(!any(inherits(x, allowed))) stop(paste("please provide the results of a hmmer search of type:", paste(allowed, collapse=", "))) if(is.null(x$evalue)) stop("missing evalues") ##x$mlog.evalue=-log(x$evalue) x$mlog.evalue=x$score panelplot <- function(z=x$mlog.evalue, ylab="-log(Evalue)", gp=gp, ...) { z=as.numeric(z) plot(z, xlab="", ylab=ylab, col=gps, ...) abline(v=gp, col="gray70", lty=3) pos=c(rep(3, length(gp))[-length(gp)],2) text( gp, z[gp], labels=paste0("Nhit=",gp ,", x=", round(z[gp])), col="black", pos=pos, cex=cex, ...) ##"gray50" } nrow <- 1 if(!is.null(x$kg) & !is.null(x$species)) nrow=nrow+2 ##- Setup plot arangment opar <- par(no.readonly = TRUE) on.exit(par(opar)) par(mfcol=c(nrow,1), mar=mar, cex.lab=cex) ##- Find the point pair with largest diff evalue dx <- abs(diff(x$mlog.evalue)) dx.cut = which.max(dx) if(!is.null(cutoff)) { ##- Use suplied cutoff gps = rep(2, length(x$mlog.evalue)) gps[ (x$mlog.evalue >= cutoff) ] = 1 } else { if(cluster) { ## Ask USER whether to continue with clustering with many hits nhit <- length(x$mlog.evalue) if(nhit > 1500) { cluster <- readline( paste0(" Note: ", nhit, " hits, continue with TIME-CONSUMING clustering [y/n/q](n): ") ) cluster <- switch(cluster, y=TRUE, yes=TRUE, q="QUIT", FALSE) if(cluster=="QUIT") { stop("user stop") } } } if(is.null(cut.seed)) { ## Use mid-point of largest diff pair as seed for ## cluster grps (typical PDB values are ~110) cut.seed = mean( x$mlog.evalue[dx.cut:(dx.cut+1)] ) } if(cluster){ ##- Partition into groups via clustering ## In future could use changepoint::cpt.var hc <- hclust( dist(x$mlog.evalue) ) if(!is.null(cutoff)) { cut.seed=cutoff } gps <- cutree(hc, h=cut.seed) } if(!cluster || (length(unique(gps))==1)) { ##- Either we don't want to run hclust or hclust/cutree ## has returned only one grp so here we will divide ## into two grps at point of largest diff gps = rep(2, length(x$mlog.evalue)) gps[1:dx.cut]=1 } } gp.inds <- na.omit(rle2(gps)$inds) gp.nums <- x$mlog.evalue[gp.inds] cat(" * Possible cutoff values: ", floor(gp.nums), "\n", " Yielding Nhits: ", gp.inds, "\n\n") if( is.null(cutoff) ) { ## Pick a cutoff close to cut.seed i <- which.min(abs(gp.nums - cut.seed)) cutoff <- floor( gp.nums[ i ] ) } inds <- x$mlog.evalue >= cutoff cat(" * Chosen cutoff value of: ", cutoff, "\n", " Yielding Nhits: ", sum(inds), "\n") ##- Plot each alignment statistic with annotated grps ##panelplot(gp=gp.inds) panelplot(x$score, ylab="Bitscore", gp=gp.inds) ## plot kigdom / species if(!is.null(x$kg) & !is.null(x$species)) { tmpfun <- function(s) { s=s[1:min(2, length(s))] if(length(s)>1) paste(substr(s[1], 1,1), substr(s[2], 1,6), collapse=".") else substr(s, 1,8) } ## make grps for x$species species <- unlist(lapply(strsplit(x$species, " "), tmpfun)) grps.sp <- rep(NA, length(species)) unq.sp <- unique(species) for(i in 1:length(unq.sp)) grps.sp[ which(species %in% unq.sp[i]) ] = i ## make grps for x$kg grps.kg <- rep(NA, length(x$kg)) unq.kg <- unique(x$kg) for(i in 1:length(unq.kg)) grps.kg[ which(x$kg %in% unq.kg[i]) ] = i ylim <- c(0, max(x$score)*1.2) xlim <- c(0, length(x$score)) plot.new() plot.window(xlim=xlim, ylim=ylim, ...) cols <- c("lightblue", "lightgreen", "lightpink", "lightsalmon", "lightyellow3", "lightcoral", "lightsteelblue2", "lightgoldenrod1") for(i in 1:length(unq.kg)) { bs <- bounds(which(grps.kg==i)) rect(bs[,"start"]-1, 1, bs[,"end"], max(x$score)*1.05, col=cols[i], border=NA) } mp <- barplot(x$score, col=grps.sp, width=1.0, space=0, border=par("fg"), ylab="Annotation", add=TRUE, ...) abline(v=gp.inds, col="gray70", lty=3) ncol <- 2 legend("topright", unq.kg, col=cols[1:i], pch=16, ncol=ncol, cex=cex*0.8, box.lwd = 0, box.col = "white",bg = "white") box(); axis(1); ## summary of chosen stuff unq.kg=unique(x$kg[which(inds)]) unq.sp=unique(x$species[which(inds)]) txt1 <- paste("N kingdoms: ", length(unq.kg), "/", length(unique(x$kg)), sep="") txt2 <- paste("N species: ", length(unq.sp), "/", length(unique(x$species)), sep="") txt <- paste(txt1, txt2, sep=", ") mtext(txt, side=3, line=-1.25, at=0, adj=0, cex=cex*0.8) } ## plot kigdom / species if(!is.null(x$kg) & !is.null(x$species)) { unq.kg <- unique(x$kg) tbl <- table(x$kg, cut(x$score, 30)) tbl=tbl[, seq(ncol(tbl), 1), drop=FALSE] cols <- seq(1,nrow(tbl)) barplot(tbl, col=cols, ylab="Frequency") legend("topright", rownames(tbl), col=cols, pch=16, cex=cex*0.8, box.lwd = 0, box.col = "white",bg = "white") box() } ##- Return details of hits above cutoff out <- cbind("acc"=x$acc[inds], "group"=gps[inds]) rownames(out) <- which(inds) invisible(list(hits=out, acc=x$acc[inds], "inds"=which(inds))) } bio3d/R/view.cna.R0000644000176200001440000001075112632622153013316 0ustar liggesusersview.cna <- function(x, pdb, layout=layout.cna(x, pdb, k=3), col.sphere=NULL, col.lines="silver", weights=NULL, radius=table(x$communities$membership)/5, alpha=1, vmdfile="network.vmd", pdbfile="network.pdb", launch=FALSE) { ## Draw a cna network in VMD ## Check for presence of igraph package oops <- requireNamespace("igraph", quietly = TRUE) if (!oops) { stop("igraph package missing: Please install, see: ?install.packages") } if(is.null(weights)){ weights <- igraph::E(x$community.network)$weight if(is.null(x$call$minus.log)){ weights <- exp(-weights) } else{ if(x$call$minus.log){ weights <- exp(-weights) } } } if(is.null(col.sphere)) { ## Get colors from network and convert to 0:17 VMD color index col.sphere <- match(igraph::V(x$community.network)$color, vmd.colors())-1 } else { ## Check supplied color(s) will work in VMD if(!all(col.sphere %in% c(0:17))) { warning("Input 'col.sphere' may not work properly in VMD - should be 0:17 color index value") } } ##-- VMD draw functions for sphere, lines and cone .vmd.sphere <- function(cent, radius=5, col="red", resolution=25) { ## .vmd.sphere( matrix(c(0,0,0, 1,1,1), ncol=3,byrow=T) ) if(ncol(cent) != 3) stop("Input 'cent' should be a 3 col xyz matrix") n <- nrow(cent); scr <- rep(NA, n) if(length(col) != n) col <- rep(col, length=n) if(length(radius) != n) radius <- rep(radius, length=n) for(i in 1:n) { scr[i] <- paste0("draw color ", col[i], "\ndraw sphere {", paste(cent[i,], collapse = " "), "} radius ", radius[i], " resolution ",resolution, "\n") } return(scr) } .vmd.lines <- function(start, end, radius=0.2, col="silver", resolution=25) { ## .vmd.lines( start=matrix(c(0,0,0), ncol=3,byrow=T), ## end=matrix(c(1,1,1), ncol=3,byrow=T) ) if(ncol(start) != 3) stop("Input 'start' and 'end' should be 3 col xyz matrices") n <- nrow(start); scr <- rep(NA, n) if(length(col) != n) col <- rep(col, length=n) if(length(radius) != n) radius <- rep(radius, length=n) for(i in 1:n) { scr[i] <- paste0("draw color ", col[i], "\ndraw cylinder {", paste(start[i,], collapse = " "), "} {", paste(end[i,], collapse = " "), "} radius ", radius[i], " resolution ",resolution, "\n") } return(scr) } .vmd.cone <- function(start, end, radius=5, col="silver", resolution=25) { warning("not here yet") } ##- Set alpha if needed scr <- NULL if(alpha != 1) scr <- paste("material change opacity Transparent", alpha,"\ndraw material Transparent\n") ##- Lets get drawing ##radius = V(x$community.network)$size ###radius = table(x$raw.communities$membership)/5 scr <- c(scr, .vmd.sphere( layout, radius=radius, col=col.sphere)) ## Edges ###edge.list <- unlist2(get.adjlist(x$community.network)) ###start.no <- as.numeric(names(edge.list)) ###end.no <- as.numeric((edge.list)) ###inds <- which(end.no > start.no) ###start <- layout[start.no[inds],] ###end <- layout[end.no[inds],] edge.list <- igraph::get.edges(x$community.network, 1:length(igraph::E(x$community.network))) start <- layout[edge.list[,1],] end <- layout[edge.list[,2],] ###weights=E(x$community.network)$weight ##/0.2 scr <- c(scr, .vmd.lines( start=start, end=end, radius=weights, col=col.lines)) cat(scr, file=vmdfile, sep="") ## Output a PDB file with chain color # Use the chain field to store cluster membership data for color in VMD ch <- vec2resno(vec=x$communities$membership, resno=pdb$atom[,"resno"]) write.pdb(pdb, chain=LETTERS[ch], file=pdbfile) ## Launch option ... ## vmd -pdb network.pdb -e network.vmd if(launch) { cmd <- paste("vmd", pdbfile, "-e", vmdfile) os1 <- .Platform$OS.type if (os1 == "windows") { shell(shQuote(cmd)) } else{ if(Sys.info()["sysname"]=="Darwin") { system(paste("/Applications/VMD\\ 1.9.*app/Contents/MacOS/startup.command",pdbfile, "-e", vmdfile)) } else{ system(cmd) } } } } bio3d/R/inner.prod.R0000644000176200001440000000132712412621431013653 0ustar liggesusers"inner.prod" <- function(x,y,mass=NULL) { x <- as.matrix(x); y <- as.matrix(y); dx <- dim(x); dy <- dim(y); if(dx[1]!=dy[1]) stop("inner.prod: unequal vector lengths") if(dx[2]>1 && dy[2]>1) { if(dx[2]!=dy[2]) stop("inner.prod: unequal vector lengths") } if(dx[2]==1) x <- as.numeric(x) if(dy[2]==1) y <- as.numeric(y) if(!is.null(mass)) { if (dx[1] != (length(mass)*3)) stop("inner.prod: incorrect length of mass") } if(is.null(mass)) mass <- 1 else mass <- rep(mass,each=3) if(is.matrix(x) || is.matrix(y)) return(colSums((x*y)*mass^2)) else return(sum(x*y*mass^2)) } bio3d/R/biounit.R0000644000176200001440000001337212600073757013264 0ustar liggesusers#' Biological Units Construction #' #' Construct biological assemblies/units based on a 'pdb' object. #' #' @details #' A valid structural/simulation study should be performed on the biological #' unit of a protein system. For example, the alpha2-beta2 tetramer form of #' hemoglobin. However, canonical PDB files usually contain the asymmetric unit of #' the crystal cell, which can be: #' \enumerate{ #' \item One biological unit #' \item A portion of a biological unit #' \item Multiple biological units #' } #' The function performs symmetry operations to the coordinates based on the #' transformation matrices stored in a 'pdb' object returned by #' \code{\link{read.pdb}}, and returns biological units stored as a list of #' \code{pdb} objects. #' #' @param pdb an object of class \code{pdb} as obtained from #' function \code{\link{read.pdb}}. #' @param biomat a list object as returned by \code{read.pdb} #' (pdb$remark$biomat), containing matrices for #' symmetry operation on individual chains to build biological units. #' It will override the matrices stored in \code{pdb}. #' @param multi logical, if TRUE the biological unit is returned as a #' 'multi-model' \code{pdb} object with each symmetric copy a distinct #' structural 'MODEL'. Otherwise, all copies are represented #' as separated chains. #' @param ncore number of CPU cores used to do the calculation. By default #' (\code{ncore=NULL}), use all available CPU cores. #' #' @return #' a list of \code{pdb} objects with each representing an individual #' biological unit. #' #' @seealso \code{\link{read.pdb}} #' #' @author Xin-Qiu Yao #' #' @examples #' \donttest{ #' pdb <- read.pdb("2dn1") #' biounit <- biounit(pdb) #' pdb #' biounit #' } #' \dontrun{ #' biounit <- biounit(read.pdb("2bfu"), multi=TRUE) #' write.pdb(biounit[[1]], file="biounit.pdb") #' # open the pdb file in VMD to have a look on the biological unit #' } biounit <- function(pdb, biomat = NULL, multi = FALSE, ncore = NULL) { if(!is.pdb(pdb)) stop("Please provide a 'pdb' object as obtained from 'read.pdb()'") if(!is.null(biomat)) remarks <- biomat else remarks <- pdb$remark$biomat if(is.null(remarks)) stop("Can't find 'remark' records for building biological units") ncore = setup.ncore(ncore) cl <- match.call() if(!is.null(remarks)) { # check max number of copies ncopy.max <- max(sapply(remarks$mat, length)) if(!multi && ncopy.max > 10) cat("It is slow to represent many symmetric copies as separated chains\n Try multi = TRUE\n") # Are chains treated differently? nn <- sapply(remarks$mat, function(x) length(unique(names(x)))) if(any(nn > 1) && multi) stop("Can't store as multiple models as separated symmetry operations are performed on distinct chains within one biological unit") biounits <- lapply(1:remarks$num, function(i) { # the transformation matrices mats <- remarks$mat[[i]] # applied to the chains chain <- remarks$chain[[i]] # number of copies ncopy <- length(mats) if(!multi) { ## save copies as individual chains # The original copy stored as spearated chains biounit0 <- lapply(chain, function(x) trim.pdb(pdb, chain=x, verbose=FALSE) ) # available chain ID repository chains0 <- setdiff(c(LETTERS, letters, 0:9), chain) jch <- 1 used.chain <- NULL biounit <- NULL for(j in 1:ncopy) { mt <- mats[[j]] chs <- strsplit(names(mats)[j], split=" ")[[1]] for(k in chs) { bio <- biounit0[[match(k, chain)]] xyz <- rbind(matrix(bio$xyz, nrow=3), 1) xyz <- matrix(mt %*% xyz, nrow = 1) if(! k %in% used.chain) { ch <- k used.chain <- c(used.chain, k) } else { ch <- chains0[jch] jch = jch + 1 } bio$xyz <- xyz bio$atom[, "chain"] <- ch bio$atom[, c("x", "y", "z")] <- round(matrix(xyz, ncol=3, byrow=TRUE), digits=3) biounit <- c(biounit, list(bio)) } } biounit <- do.call(cat.pdb, c(biounit, list(rechain = FALSE))) # # temporarily write the pdb of biounit and re-read it # tmpf <- tempfile() # write.pdb(biounit, file=tmpf) # biounit = read.pdb(tmpf, verbose=FALSE) } else { ## save copies as multi-models # The original copy biounit <- trim.pdb(pdb, chain=chain, verbose=FALSE) xyz = rbind(matrix(biounit$xyz, nrow=3), 1) ll <- mclapply(2:ncopy, function(j) { mt <- mats[[j]] xyz = matrix(mt %*% xyz, nrow=1) xyz }, mc.cores = ncore ) biounit$xyz <- rbind(biounit$xyz, do.call(rbind, ll)) class(biounit$xyz) <- "xyz" } biounit$call <- cl return(biounit) } ) # end of lapply(1:remarks$num) ## multimeric state nchs <- sapply(biounits, function(x) length(unique(x$atom[, "chain"])) * nrow(x$xyz)) mer <- c("monomer", "dimer", "trimer", "tetramer", "multimer") names(biounits) <- paste(remarks$method, ".determined.", mer[ifelse(nchs>5, 5, nchs)], " (", nchs, " chains)", sep="") # if(length(biounits) == 1) biounits = biounits[[1]] } return(biounits) } bio3d/R/rle2.R0000644000176200001440000000146412412621431012443 0ustar liggesusersrle2 <- function (x) { ## This is a modified version of base function "rle()" if (!is.vector(x) && !is.list(x)) stop("'x' must be an atomic vector") n <- length(x) if (n == 0L) return(structure(list(lengths = integer(), values = x, inds=integer), class = "rle2")) y <- x[-1L] != x[-n] i <- c(which(y | is.na(y)), n) structure(list(lengths = diff(c(0L, i)), values = x[i], inds=i), class = "rle2") } print.rle2 <- function(x, digits = getOption("digits"), prefix = "", ...) { if (is.null(digits)) digits <- getOption("digits") cat("", "Run Length Encoding\n", " lengths:", sep = prefix) utils::str(x$lengths) cat("", " values :", sep = prefix) utils::str(x$values, digits.d = digits) cat("", " indices:", sep = prefix) utils::str(x$inds, digits.d = digits) invisible(x) } bio3d/R/trim.xyz.R0000644000176200001440000000054312561207744013414 0ustar liggesusers"trim.xyz" <- function(xyz, row.inds=NULL, col.inds=NULL, ...) { xyz <- as.xyz(xyz) if(is.select(row.inds)) row.inds <- row.inds$xyz if(is.select(col.inds)) col.inds <- col.inds$xyz if(!is.null(row.inds)) xyz <- xyz[row.inds, , drop=FALSE] if(!is.null(col.inds)) xyz <- xyz[, col.inds, drop=FALSE] return(as.xyz(xyz)) } bio3d/R/bwr.colors.R0000644000176200001440000000156412412621431013672 0ustar liggesusers"bwr.colors" <- function (n) { # Derived from the function colorpanel # by Gregory R. Warnes if(n<3) warning("not sensible to ask for less than 3 colors") odd = FALSE if (n != as.integer(n/2) *2) { n <- n + 1 odd = TRUE } low <- col2rgb("blue") mid <- col2rgb("white") high <- col2rgb("red") lower <- floor(n/2) upper <- n - lower red <- c(seq(low[1, 1], mid[1, 1], length = lower), seq(mid[1,1], high[1, 1], length = upper))/255 green <- c(seq(low[3, 1], mid[3, 1], length = lower), seq(mid[3, 1], high[3, 1], length = upper))/255 blue <- c(seq(low[2, 1], mid[2, 1], length = lower), seq(mid[2, 1], high[2, 1], length = upper))/255 if (odd) { red <- red[-(lower + 1)] green <- green[-(lower + 1)] blue <- blue[-(lower + 1)] } rgb(red, blue, green) } bio3d/R/read.crd.charmm.R0000644000176200001440000000306412526367343014544 0ustar liggesusers"read.crd.charmm" <- function(file, ext=TRUE, verbose = TRUE, ...) { split.string <- function(x) { x <- substring(x, first, last) x[nchar(x) == 0] <- as.character(NA) x } trim <- function(s) { s <- sub("^ +", "", s) s <- sub(" +$", "", s) s[(s == "")] <- NA s } if(ext) atom.format <- c(10, 10, 8, 8, -4, 20,20,20, -1, 8, -1, 8, 20) else atom.format <- c(5, 5, 4, 5, -1, 10,10,10, -1, 4, -1, 4, 10) atom.names <- c("eleno", "resno", "resid", "elety", "blank", "x", "y", "z", "blank", "segid", "blank", "resno2", "b") widths <- abs(atom.format) drop.ind <- (atom.format < 0) st <- c(1, 1 + cumsum(widths)) first <- st[-length(st)][!drop.ind] last <- cumsum(widths)[!drop.ind] raw.lines <- readLines(file) head.ind <- which(substr(raw.lines,1,1)=="*") head.ind <- c(head.ind, (head.ind[length(head.ind)]+1) ) if(length(head.ind)>0) { raw.lines <- raw.lines[-head.ind] if(verbose) cat(raw.lines[head.ind],sep="\n") } atom <- matrix(trim(sapply(raw.lines, split.string)), byrow = TRUE, ncol = length(atom.format[!drop.ind]), dimnames = list(NULL, atom.names[!drop.ind]) ) output <- list(atom = atom, xyz = as.numeric(t(atom[, c("x", "y", "z")])), calpha = as.logical(atom[, "elety"] == "CA")) class(output) <- c("charmm", "crd") return(output) } bio3d/R/get.seq.R0000644000176200001440000000300012632622153013137 0ustar liggesusers`get.seq` <- function(ids, outfile="seqs.fasta", db="nr") { ## Download FASTA format sequences from the NR or ## SWISSPROT/UNIPROT databases via their gi or ## SWISSPROT identifer number if( !(db %in% c("nr", "swissprot", "uniprot")) ) stop("Option database should be one of nr, swissprot or uniprot") ids <- unique(ids) if(db=="nr") { get.files <- paste("http://www.ncbi.nlm.nih.gov/", "sviewer/viewer.fcgi?db=protein&val=", ids,"&report=fasta&retmode=text", sep="") ## ## Old pre Oct-18th-2010 format URL ## get.files <- paste("http://www.ncbi.nlm.nih.gov/entrez/", ## "viewer.fcgi?db=protein&val=", ## ids, "&dopt=fasta&sendto=t", sep="") } else { if(any(nchar(ids) != 6)) { warning("ids should be standard 6 character SWISSPROT/UNIPROT formart: trying first 6 char...") ids <- substr(basename(ids),1,6) } ids <- unique(ids) get.files <- file.path("http://www.uniprot.org/uniprot", paste(ids, ".fasta", sep="") ) } ## Remove existing file if(file.exists(outfile)) { warning(paste("Removing existing file:",outfile)) unlink(outfile) } rtn <- rep(NA, length(ids)) for(k in 1:length(ids)) { rtn[k] <- download.file( get.files[k], outfile, mode="a" ) } names(rtn) <- ids if(all(!rtn)) { return(read.fasta(outfile)) } else { warning("Not all downloads were sucesfull, see returned values") return(rtn) } } bio3d/R/plot.pca.scree.R0000644000176200001440000000235312412621431014415 0ustar liggesusers`plot.pca.scree` <- function(x, y=NULL, type="o", pch=18, main="", sub="", xlim=c(0,20), ylim=NULL, ylab="Proporton of Variance (%)", xlab="Eigenvalue Rank", axes=TRUE, ann=par("ann"), col=par("col"), lab=TRUE, ...) { if(is.list(x)) x=x$L # output from pca.xyz() PC <- c(1:length(x)) percent <- (x/sum(x))*100 cumv<-cumsum(percent) #xy <- xy.coords(x, y) xy <- xy.coords(percent, y=NULL) if (is.null(xlim)) xlim <- range(xy$x[is.finite(xy$x)]) if (is.null(ylim)) ylim <- range(xy$y[is.finite(xy$y)]) opar <- par(no.readonly=TRUE) on.exit(par(opar)) plot.new() plot.window(xlim, ylim)#, ...) points(xy$x, xy$y, col=col, type=type, pch=pch)#, ...) if (axes) { axis(1, at=c(1:8,max(xlim))) axis(2, at=c(round(percent[c(1:5)],1),0)) ##axis(1) ##axis(2) box() } if(lab) { text(c(1:4), percent[c(1:4)], labels=round(cumv[c(1:4)],1), pos=4) text(c(8,20), percent[c(8,20)], labels=round(cumv[c(8,20)],1), pos=3) } if (ann) { if(is.null(xlab)) xlab=xy$xlab if(is.null(ylab)) ylab=xy$ylab title(main=main, sub=sub, xlab=xlab, ylab=ylab, ...) } out<-list(pc=PC,percent=percent,cumv=cumv) } bio3d/R/diag.ind.R0000644000176200001440000000027212412621431013250 0ustar liggesusers`diag.ind` <- function (x, n=1, diag = TRUE) { x <- as.matrix(x) if (diag) { !(row(x) > col(x)) + (row(x) <= col(x)-n) } else { !(row(x) >= col(x)) + (row(x) <= col(x)-n) } } bio3d/R/is.xyz.R0000644000176200001440000000005312524171274013045 0ustar liggesusersis.xyz <- function(x) inherits(x, "xyz") bio3d/R/pdb2sse.R0000644000176200001440000000507012526367343013155 0ustar liggesusers#' Obtain An SSE Sequence Vector From A PDB Object #' #' Results are similar to that returned by stride(pdb)$sse and dssp(pdb)$sse. #' #' @details call for its effects. #' #' @param pdb an object of class \code{pdb} as obtained from #' function \code{\link{read.pdb}}. #' @param verbose logical, if TRUE warnings and other messages will be printed. #' #' @return a character vector indicating SSE elements for each amino acide residue. #' The 'names' attribute of the vector contains 'resno', 'chain', 'insert', and #' 'SSE segment number', seperated by the character '_'. #' #' @seealso \code{\link{dssp}}, \code{\link{stride}}, \code{\link{bounds.sse}} #' #' @author Barry Grant & Xin-Qiu Yao #' #' @examples #' \donttest{ #' pdb <- read.pdb("1a7l") #' sse <- pdb2sse(pdb) #' sse #' } pdb2sse <- function(pdb, verbose = TRUE) { ##- Function to obtain an SSE sequence vector from a PDB object ## Result similar to that returned by stride(pdb)$sse and dssp(pdb)$sse ## This could be incorporated into read.pdb() if found to be more generally useful ## if(is.null(pdb$helix) & is.null(pdb$sheet)) { if(verbose) warning("No helix and sheet defined in input 'sse' PDB object: try using dssp()") ##ss <- try(dssp(pdb)$sse) ## Probably best to get user to do this separately due to possible 'exefile' problems etc.. return(NULL) } ## An empty full length SSE vector ref <- pdb$atom[pdb$calpha, c("resno", "chain", "insert")] ref <- paste(ref$resno, ref$chain, ref$insert, sep="_") ss <- rep(" ", length(ref)) names(ss) <- ref ## loop over 'Helix' and 'Sheet' symbol <- c(helix="H", sheet="E") for(i in names(symbol)) { sse <- pdb[[i]] if(length(sse$start) > 0) { for(j in 1:length(sse$start)) { chain <- ifelse(sse$chain[j]=="", NA, sse$chain[j]) insert0 <- ifelse(names(sse$start[j])=="", NA, names(sse$start[j])) sse.ref0 <- paste(sse$start[j], chain, insert0, sep = "_") insert1 <- ifelse(names(sse$end[j])=="", NA, names(sse$end[j])) sse.ref1 <- paste(sse$end[j], chain, insert1, sep = "_") ii <- match(sse.ref0, ref); jj <- match(sse.ref1, ref) if(any(is.na(c(ii, jj)))) { if(verbose) warning(paste("The", i, "No.", j, "start/end with non-protein residue.")) } else { inds <- seq(ii, jj) ss[inds] <- symbol[i] names(ss)[inds] <- paste(names(ss)[inds], "_", j, sep="") } } } } return(ss) } bio3d/R/network.amendment.R0000644000176200001440000000713112526367343015253 0ustar liggesusersnetwork.amendment <- function(x, membership, minus.log=TRUE){ ## Check for presence of igraph package oops <- requireNamespace("igraph", quietly = TRUE) if (!oops) { stop("igraph package missing: Please install, see: ?install.packages") } if(class(x) != "cna"){ stop("Input x must be a 'cna' class object as obtained from cna()") } if(!is.numeric(membership)){ stop("Input membership must be a numeric vector") } if(length(membership) != length(x$communities$membership)){ stop("Input membership and x$community$membership must be of the same length") } contract.matrix <- function(cij.network, membership,## membership=comms$membership, collapse.method="max", minus.log=TRUE){ ## Function to collapse a NxN matrix to an mxm matrix ## where m is the communities of N. The collapse method ## can be one of the 'collapse.options' below ## convert to the original cij values if "-log" was used if(minus.log){ cij.network[cij.network>0] <- exp(-cij.network[cij.network>0]) } collapse.options=c("max", "median", "mean", "trimmed") collapse.method <- match.arg(tolower(collapse.method), collapse.options) ## Fill a 'collapse.cij' nxn community by community matrix node.num <- max(x$communities$membership) if(node.num > 1){ collapse.cij <- matrix(0, nrow=node.num, ncol=node.num) inds <- pairwise(node.num) for(i in 1:nrow(inds)) { comms.1.inds <- which(membership==inds[i,1]) comms.2.inds <- which(membership==inds[i,2]) submatrix <- cij.network[comms.1.inds, comms.2.inds] ## Use specified "collapse.method" to define community couplings collapse.cij[ inds[i,1], inds[i,2] ] = switch(collapse.method, max = max(submatrix), median = median(submatrix), mean = mean(submatrix), trimmed = mean(submatrix, trim = 0.1)) } if(minus.log){ collapse.cij[collapse.cij>0] <- -log(collapse.cij[collapse.cij>0]) } ## Copy values to lower triangle of matrix and set colnames collapse.cij[ inds[,c(2,1)] ] = collapse.cij[ inds ] colnames(collapse.cij) <- 1:ncol(collapse.cij) } else{ warning("There is only one community in the $communities object. $community.cij object will be set to 0 in the contract.matrix() function.") collapse.cij <- 0 } class(collapse.cij) <- c("dccm", "matrix") return(collapse.cij) } x$communities$membership <- membership x$community.cij <- contract.matrix(x$cij, membership, minus.log=minus.log) cols=vmd.colors() if(sum(x$community.cij)>0){ x$community.network <- igraph::graph.adjacency(x$community.cij, mode="undirected", weighted=TRUE, diag=FALSE) ##-- Annotate the two networks with community information ## Check for duplicated colors if(max(x$communities$membership) > length(unique(cols)) ) { warning("The number of communities is larger than the number of unique 'colors' provided as input. Colors will be recycled") } ## Set node colors igraph::V(x$network)$color <- cols[x$communities$membership] igraph::V(x$community.network)$color <- cols[ 1:max(x$communities$membership)] ## Set node sizes igraph::V(x$network)$size <- 1 igraph::V(x$community.network)$size <- table(x$communities$membership) } return(x) } bio3d/R/plot.dmat.R0000644000176200001440000000375512412621431013506 0ustar liggesusers"plot.dmat" <- function(x, key = TRUE, resnum.1 = c(1:ncol(x)), resnum.2 = resnum.1, axis.tick.space = 20, zlim = range(x, finite = TRUE), nlevels = 20, levels = pretty(zlim, nlevels), color.palette = bwr.colors, col = color.palette(length(levels) -1), axes = TRUE, key.axes, xaxs = "i", yaxs = "i", las = 1, grid = TRUE, grid.col = "yellow", grid.nx = floor(ncol(x)/30), grid.ny = grid.nx, center.zero = TRUE, flip=TRUE, ...) { if (missing(x)) { stop("no 'x' distance matrix specified") } if(center.zero) { if(zlim[1]<0) { ## make levels equidistant around 0 levels = pretty(c(-max(abs(zlim)),max(abs(zlim))), nlevels) } } mar.orig <- (par.orig <- par(c("mar", "las", "mfrow")))$mar on.exit(par(par.orig)) # Color key if(key) { w <- (3 + mar.orig[2]) * par("csi") * 2.54 layout(matrix(c(2, 1), ncol = 2), widths = c(1, lcm(w))) par(las = las) mar <- mar.orig mar[4] <- mar[2] mar[2] <- 1 par(mar = mar) plot.new() plot.window(xlim = c(0, 1), ylim = range(levels), xaxs = "i", yaxs = "i") rect(0, levels[-length(levels)], 1, levels[-1], col = col) if (missing(key.axes)) { if (axes) axis(4) } else key.axes box() } # Matrix plot mar <- mar.orig mar[4] <- 1 par(mar = mar) class(x)=NULL z <- as.matrix(as.data.frame(t(x))) nums <- seq(1,ncol(x),by=axis.tick.space) a2 <- resnum.2[nums] if(flip) { z=as.matrix(rev(as.data.frame(t(x)))); a2 <- rev(resnum.2[nums]) } image(x=1:ncol(x), y=1:nrow(x), z=z, col=col, yaxt="n", xaxt="n", ...) #xlab="Residue Number", ylab="Residue Number") axis(side=1, at=nums, labels=resnum.1[nums]) axis(side=2, at=nums, labels=a2) if(grid) grid(grid.nx ,grid.ny, col=grid.col) box() } bio3d/R/seqaln.pair.R0000644000176200001440000000075312526367343014033 0ustar liggesusers`seqaln.pair` <- function(aln, ...) { cl <- match.call() dots <- list(...) dots$extra.args = paste("-matrix", system.file("matrices/custom.mat", package="bio3d"), "-gapopen -3.0 ", "-gapextend -0.5", "-center 0.0", dots$extra.args) args <- c(list(aln=aln), dots) l <- do.call(seqaln, args) if(!all((seqidentity(l))==1)) { warning("Sequences are not identical, use seqaln()") } l$call <- cl return(l) } bio3d/R/clean.pdb.R0000644000176200001440000003312312632622153013430 0ustar liggesusers#' Inspect And Clean Up A PDB Object #' #' Inspect alternative coordinates, chain breaks, bad residue #' numbering, non-standard/unknow amino acids, etc. Return #' a 'clean' pdb object with fixed residue numbering and optionally #' relabeled chain IDs, corrected amino acid names, removed water, #' ligand, or hydrogen atoms. All changes are recorded in a log in the #' returned object. #' #' @details call for its effects. #' #' @param pdb an object of class \code{pdb} as obtained from #' function \code{\link{read.pdb}}. #' @param consecutive logical, if TRUE renumbering will result in #' consecutive residue numbers spanning all chains. Otherwise new residue #' numbers will begin at 1 for each chain. #' @param force.renumber logical, if TRUE atom and residue records are renumbered #' even if no 'insert' code is found in the \code{pdb} object. #' @param fix.chain logical, if TRUE chains are relabeled based on chain breaks detected. #' @param fix.aa logical, if TRUE non-standard amino acid names are converted into #' equivalent standard names. #' @param rm.wat logical, if TRUE water atoms are removed. #' @param rm.lig logical, if TRUE ligand atoms are removed. #' @param rm.h logical, if TRUE hydrogen atoms are removed. #' @param verbose logical, if TRUE details of the conversion process are printed. #' #' @return a 'pdb' object with an additional \code{$log} component storing #' all the processing messages. #' #' @seealso \code{\link{read.pdb}} #' #' @author Xin-Qiu Yao & Barry Grant #' #' @examples #' \donttest{ #' pdb <- read.pdb("1a7l") #' clean.pdb(pdb) #' } "clean.pdb" <- function(pdb, consecutive=TRUE, force.renumber = FALSE, fix.chain = FALSE, fix.aa = FALSE, rm.wat = FALSE, rm.lig = FALSE, rm.h = FALSE, verbose=FALSE) { if(!is.pdb(pdb)) stop("Input should be a 'pdb' object") cl <- match.call() ## processing message ## stored as an N-by-3 matrix with columns: ## FACT, OPERATION, IMPORTANT NOTE log <- NULL ## a flag to indicate if the pdb is clean clean <- TRUE ## Recognized amino acid names prot.aa <- bio3d::aa.table$aa3 ## for residues and atoms renumbering first.eleno = 1 first.resno = 1 ## remove water if(rm.wat) { wat <- atom.select(pdb, "water", verbose = FALSE) if(length(wat$atom) > 0) { pdb$atom <- pdb$atom[-wat$atom, ,drop=FALSE] pdb$xyz <- pdb$xyz[, -wat$xyz, drop=FALSE] log <- .update.log(log, paste("Found", length(wat$atom), "water atoms"), "REMOVED") } } ## remove ligands if(rm.lig) { lig <- atom.select(pdb, "ligand", verbose = FALSE) if(length(lig$atom) > 0) { pdb$atom <- pdb$atom[-lig$atom, ,drop=FALSE] pdb$xyz <- pdb$xyz[, -lig$xyz, drop=FALSE] log <- .update.log(log, paste("Found", length(lig$atom), "ligand atoms"), "REMOVED") } } ## remove hydrogens if(rm.h) { h.inds <- atom.select(pdb, "h", verbose = FALSE) if(length(h.inds$atom) > 0) { pdb$atom <- pdb$atom[-h.inds$atom, ,drop=FALSE] pdb$xyz <- pdb$xyz[, -h.inds$xyz, drop=FALSE] log <- .update.log(log, paste("Found", length(h.inds$atom), "hydrogens"), "REMOVED") } } ## check if 'alt' coords exist if(any(rm.p <- !is.na(pdb$atom$alt) & pdb$atom$alt != "A")) { pdb$atom <- pdb$atom[!rm.p, , drop=FALSE] pdb$xyz <- pdb$xyz[, -atom2xyz(which(rm.p)), drop=FALSE] log <- .update.log(log, paste("Found", sum(rm.p), "ALT records"), "REMOVED") } ## Some initial check: ## 1. Are all amino acid and/or nucleic acid residues ## distinguished by the combination chainID_resno_insert? .check.residue.ambiguity(pdb) ## 2. Check and clean up SSE annotation pdb <- .check.sse(pdb) log <- .update.log(log, pdb$log) ## 3. Fix pdb$calpha if it is mismatch pdb$atom ca.inds <- atom.select(pdb, "calpha", verbose = FALSE) calpha <- seq(1, nrow(pdb$atom)) %in% ca.inds$atom if(!identical(pdb$calpha, calpha)) { pdb$calpha <- calpha log <- .update.log(log, "pdb$calpha", "UPDATED") } ## 4. Fix object class if it is incorrect if(!inherits(pdb, "pdb") || !inherits(pdb, "sse") || !inherits(pdb$xyz, "xyz")) { class(pdb) <- c("pdb", "sse") class(pdb$xyz) <- "xyz" log <- .update.log(log, "Object class", "UPDATED") } ########### ## following operations are on an independent object npdb <- pdb ## check chain breaks and missing chain ids has.fixed.chain <- FALSE capture.output( new.chain <- chain.pdb(npdb) ) chain <- npdb$atom[, "chain"] if(any(is.na(npdb$atom[, "chain"]))) { log <- .update.log(log, "Found empty chain IDs", ifelse(fix.chain, "FIXED", "NO CHANGE"), ifelse(fix.chain, "ALL CHAINS ARE RELABELED", "")) if(fix.chain) { npdb$atom[, "chain"] <- new.chain has.fixed.chain <- TRUE } else if(clean) clean <- FALSE } else { ## check if new chain id assignment is consistent to original one chn.brk <- bounds(chain[ca.inds$atom], dup.inds=TRUE, pre.sort=FALSE) new.chn.brk <- bounds(new.chain[ca.inds$atom], dup.inds=TRUE, pre.sort=FALSE) if(!isTRUE(all.equal(chn.brk, new.chn.brk))) { log <- .update.log(log, "Found inconsistent chain breaks", ifelse(fix.chain, "FIXED", "NO CHANGE"), ifelse(fix.chain, "ALL CHAINS ARE RELABELED", "")) log <- .update.log(log, "Original chain breaks:") if(nrow(chn.brk) == 1) { log <- .update.log(log, " No chain break") } else { pre.ca <- ca.inds$atom[chn.brk[-nrow(chn.brk), "end"]] pre.log <- capture.output( print(npdb$atom[pre.ca, c("resid", "resno", "chain")], row.names = FALSE) ) log <- .update.log(log, pre.log) } log <- .update.log(log) log <- .update.log(log, "New chain breaks:") if(nrow(new.chn.brk) == 1) { log <- .update.log(log, " No chain breaks") } else { new.ca <- ca.inds$atom[new.chn.brk[-nrow(new.chn.brk), "end"]] new.log <- capture.output( print(npdb$atom[new.ca, c("resid", "resno", "chain")], row.names = FALSE) ) log <- .update.log(log, new.log) } if(fix.chain) { npdb$atom[, "chain"] <- new.chain has.fixed.chain <- TRUE } else if(clean) clean <- FALSE } } ## Renumber residues and atoms renumber <- FALSE if( any(!is.na(npdb$atom[, "insert"])) ) { renumber <- TRUE log <- .update.log(log, "Found INSERT records", "RENUMBERED") npdb$atom[, "insert"] <- as.character(NA) } else if(force.renumber) { renumber <- TRUE log <- .update.log(log, "force.renumber = TRUE", "RENUMBERED") } else if(has.fixed.chain) { ## check again the ambiguity of residue labeling chk <- try(.check.residue.ambiguity(npdb)) if(inherits(chk, "try-error")) { renumber <- TRUE log <- .update.log(log, "Found ambiguious residues after chain relabeling", "RENUMBERED") } } if(renumber) { ## Assign consecutive atom numbers npdb$atom[,"eleno"] <- seq(first.eleno, length=nrow(npdb$atom)) ## Determine what chain ID we have chain <- unique(npdb$atom[, "chain"]) ##- Assign new (consecutive) residue numbers for each chain prev.chain.res = 0 ## Number of residues in previous chain for(i in 1:length(chain)) { inds <- which(npdb$atom[, "chain"] == chain[i]) ## Combination of chain id, resno and insert code uniquely defines a residue (wwpdb.org) ## Here we use original pdb because we assume it should at least ## distinguish different residues by above combination. ## We don't use the modified pdb (npdb) because all non-protein residues ## are assigned a chain ID as "X" after calling chain.pdb(); ## These residues could have the same resno (which are still in original ## form) as they may be assigned different chain IDs in the original pdb. res <- paste(pdb$atom[inds, "chain"], pdb$atom[inds, "resno"], pdb$atom[inds, "insert"], sep="_") n.chain.res <- length(unique(res)) new.nums <- (first.resno+prev.chain.res):(first.resno+n.chain.res-1+prev.chain.res) npdb$atom[inds, "resno"] <- vec2resno(new.nums, res) if(consecutive) { ## Update prev.chain.res for next iteration prev.chain.res = prev.chain.res + n.chain.res } } } ## update SSE if(has.fixed.chain || renumber) { ## Must use original pdb to unfold SSE sse <- pdb2sse(pdb, verbose = FALSE) if(!is.null(sse)) { id <- sub(".*_.*_.*_([^_]*)$", "\\1", names(sse)) names(sse) <- paste(npdb$atom[ca.inds$atom, "resno"], npdb$atom[ca.inds$atom, "chain"], npdb$atom[ca.inds$atom, "insert"], id, sep = "_") new.sse <- bounds.sse(sse) if(length(new.sse$helix$start) > 0) { npdb$helix$start <- new.sse$helix$start npdb$helix$end <- new.sse$helix$end npdb$helix$chain <- new.sse$helix$chain } if(length(new.sse$sheet$start) > 0) { npdb$sheet$start <- new.sse$sheet$start npdb$sheet$end <- new.sse$sheet$end npdb$sheet$chain <- new.sse$sheet$chain } if(!isTRUE(all.equal(npdb$helix, pdb$helix)) || !isTRUE(all.equal(npdb$sheet, pdb$sheet)) ) log <- .update.log(log, "SSE annotation", "UPDATED") } } ## update seqres if(has.fixed.chain && !is.null(npdb$seqres)) { chs <- unique(npdb$atom[ca.inds$atom, "chain"]) names(npdb$seqres) <- vec2resno(chs, names(npdb$seqres)) if(!identical(npdb$seqres, pdb$seqres)) log <- .update.log(log, "SEQRES", "UPDATED") } ## update amino acid name naa.atom <- which(npdb$atom[, "resid"] %in% prot.aa[-c(1:20)]) naa.res <- intersect(ca.inds$atom, naa.atom) unk.atom <- which(!npdb$atom[, "resid"] %in% prot.aa) unk.res <- intersect(ca.inds$atom, unk.atom) if(length(naa.res) > 0) { log <- .update.log(log, paste("Found", length(naa.res), "non-standard amino acids"), ifelse(fix.aa, "FIXED", "NO CHANGE"), ifelse(fix.aa, "AMINO ACID NAMES ARE CHANGED", "")) tbl <- table(npdb$atom[naa.res, "resid"]) tbl <- paste(" ", names(tbl), "(", tbl, ")", collapse = ",") log <- .update.log(log, tbl) if(fix.aa) { npdb$atom[naa.atom, "resid"] <- aa123(aa321(npdb$atom[naa.atom, "resid"])) log <- .update.log(log, " Converted to") tbl <- table(npdb$atom[naa.res, "resid"]) tbl <- paste(" ", aa123(aa321(names(tbl))), "(", tbl, ")", collapse = ",") log <- .update.log(log, tbl) } else if(clean) clean <- FALSE } if(length(unk.res) > 0) { log <- .update.log(log, paste("Found", length(unk.res), "unknow amino acids"), "NO CHANGE") tbl <- table(npdb$atom[unk.res, "resid"]) tbl <- paste(" ", names(tbl), "(", tbl, ")", collapse = ",") log <- .update.log(log, tbl) # if(clean) clean <- FALSE } ## update pdb$call npdb$call <- cl ## is the pdb clean? # if(clean) # log <- .update.log(log, "PDB is clean!") if(!clean) { msg <- "PDB is still not clean. Try fix.chain=TRUE and/or fix.aa=TRUE" # log <- .update.log(log, msg) warning(msg) } ## format log # log <- .format.log(log) if(verbose) print(log) npdb$log <- log return(npdb) } .update.log <- function(log, fact="", op="", note="") { if(is.null(fact)) log else if(is.data.frame(fact)) .update.log(log, fact[,1], fact[,2], fact[,3]) else rbind(log, data.frame(Data = fact, Action = op, Note = note)) } .format.log <- function(log, format = c("print", "cat"), op.sign = "->", note.sign = "!!") { format <- match.arg(format) if(!is.null(log)) { log <- apply(log, 1, function(x) { if(nchar(x[2]) == 0) sprintf("%-40s", x[1]) else if(nchar(x[3]) == 0) paste(sprintf("%-40s", x[1]), op.sign, sprintf("%-12s", x[2])) else paste(paste(sprintf("%-40s", x[1]), op.sign, sprintf("%-12s", x[2]), note.sign, x[3], note.sign) ) } ) } else { log <- "No problem found" } log <- switch(format, print = log, cat = paste(paste(log, collapse="\n"), "\n", sep="") ) return(log) } .check.residue.ambiguity <- function(pdb) { ca.inds <- atom.select(pdb, "calpha", verbose = FALSE) c1p.inds <- atom.select(pdb, "nucleic", elety = "C1'", verbose = FALSE) inds <- combine.select(ca.inds, c1p.inds, operator = "+", verbose = FALSE) if(length(inds$atom) > 0) { strings <- paste(pdb$atom[inds$atom, "resno"], pdb$atom[inds$atom, "chain"], pdb$atom[inds$atom, "insert"], sep = "_") if(any(duplicated(strings))) stop(".check.residue.ambiguity(): Found ambiguous residue labeling") } invisible(NULL) } .check.sse <- function(pdb) { log <- NULL if(!is.null(pdb$helix) | !is.null(pdb$sheet)) { if(is.null(pdb$helix)) { pdb$helix <- list(start=NULL, end=NULL, chain=NULL, type=NULL) log <- .update.log(log, "Helix is null but sheet is not", "UPDATED") } if(is.null(pdb$sheet)) { pdb$sheet <- list(start=NULL, end=NULL, chain=NULL, sense=NULL) log <- .update.log(log, "Sheet is null but helix is not", "UPDATED") } } # if there is problem to generate sse vector ss <- try(pdb2sse(pdb, verbose = FALSE)) if(inherits(ss, "try-error")) stop(".check.sse(): Unable to generate SSE sequence") pdb$log <- log return(pdb) } bio3d/R/print.select.R0000644000176200001440000000122112524171274014211 0ustar liggesusersprint.select <- function(x, ...){ ## Print a summary of atom selection object features if(!inherits(x, "select")) { stop("Input should be a 'select' object, as obtained from 'atom.select()'") } cat("\n Call: ", paste(deparse(x$call), sep = "\n", collapse = "\n"), "\n", sep = "") cat( paste0("\n Atom Indices#: ", length(x$atom), " ($atom)", "\n XYZ Indices#: ",length(x$xyz), " ($xyz)\n\n" ) ) ## Add call if( length(x$atom) != (length(x$xyz)/3) ) warning("Atom and XYZ Indices Miss-Match") i <- paste(attributes(x)$names, collapse = ", ") cat(strwrap(paste(" + attr:", i), width = 45, exdent = 8), sep = "\n") } bio3d/R/plot.enma.R0000644000176200001440000002662312544562302013507 0ustar liggesusers"plot.enma" <- function(x, pdbs=NULL, conservation=NULL, variance=FALSE, spread = FALSE, offset = 1, col=NULL, signif=FALSE, pcut=0.005, qcut=0.04, xlab="Alignment Position", ylab=c("Fluctuations", "Fluct.variance", "Seq.conservation"), xlim=NULL, ylim=NULL, mar = c(4, 5, 2, 2), ...) { if(!(inherits(x, "enma") | inherits(x, "matrix"))) stop("provide a enma object as obtained from 'nma.pdbs'") if(spread & (variance | !is.null(conservation))) { warning(paste("incompatible arguments:", "\n", " when 'spread=TRUE' conservation and variance will not be plotted")) variance <- FALSE conservation <- FALSE } ## configure what to plot if(inherits(x, "enma")) yval <- x$fluctuations else yval <- x ## indices to plot if(!is.null(col) && any(is.na(col))) row.inds <- which(!is.na(col)) else row.inds <- 1:nrow(yval) ## indices for none-"all NA" columns gaps.tmp <- gap.inspect(yval[row.inds,,drop=FALSE]) col.inds <- which(gaps.tmp$col < length(row.inds)) ## colors if(is.null(col)) col <- seq(1, nrow(yval)) ## full dimensions of yval dims.full <- dim(yval) ## check for gaps gaps <- gap.inspect(yval) if(any(gaps$col>0)) rm.gaps <- FALSE else rm.gaps <- TRUE ## check if pdbs match enma object gaps.pdbs <- NULL if(!is.null(pdbs)) { if(!inherits(pdbs, "pdbs")) { warning("argument 'pdbs' is not a 'pdbs' object (as obtained from pdbaln())") pdbs <- NULL } else { gaps.pdbs <- gap.inspect(pdbs$ali) if(rm.gaps) dims.pdbs <- dim(pdbs$ali[, gaps.pdbs$f.inds, drop=FALSE]) else dims.pdbs <- dim(pdbs$ali) if(!identical(dims.full, dims.pdbs)) { warning("dimenension mismatch between modes and pdbs object") pdbs <- NULL } } } ## reduce all objects to match what we plot yval <- yval[row.inds, col.inds, drop=FALSE] if(!is.null(pdbs)) { if(rm.gaps) pdbs <- trim.pdbs(pdbs, row.inds=row.inds, col.inds=gaps.pdbs$f.inds) else pdbs <- trim.pdbs(pdbs, row.inds=row.inds, col.inds=col.inds) } col <- col[!is.na(col)] ## Sequence conservation h <- NULL cons.options <- c("similarity", "identity", "entropy22", "entropy10") if(is.null(conservation)) conservation=FALSE if(!is.logical(conservation)) { if(length(conservation)>1) { h <- conservation conservation=TRUE if(length(h)!=dims.full[2L]) { warning("dimension mismatch of supplied 'conservation' vector") h <- NULL conservation=FALSE } } if(!is.logical(conservation) && length(conservation)==1) { if(all(conservation %in% cons.options)) { conserv.method <- conservation } else { warning("unknown option for 'conservation'") conserv.method <- "similarity" } conservation=TRUE } } else { conserv.method <- cons.options[1] } if(is.null(pdbs) & conservation) { conservation=FALSE warning("forcing 'conservation=FALSE': sequence conservation plot requires the corresponding 'pdbs' object") } if(conservation && is.null(h)) { h <- conserv(pdbs, method=conserv.method) } ## x- and ylim if(is.null(ylim)) { if(spread) ylim=c(0, length(unique(col))*offset) else ylim <- c(0,max(yval, na.rm=TRUE)) } if(is.null(xlim)) { xlim <- c(0, ncol(yval)) } ## SSE information dots <- list(...) sse.aln <- NULL if(!is.null(pdbs) & !spread) { if( "sse" %in% names(dots) ) warning("SSE information from 'pdbs' will not be generated when 'sse' is provided") else sse.aln <- .pdbs2sse(pdbs, ind=1, rm.gaps=rm.gaps) } if( "sse" %in% names(dots) ) { sse.aln <- dots$sse dots$sse <- NULL } ## Perform test of significance ns <- levels(as.factor(col)) if((length(ns) !=2) & signif) { warning("Number of states is not equal to 2. Ignoring significance test") signif <- FALSE } sig <- NULL if(signif) { inds1 <- which(col==ns[1]) inds2 <- which(col==ns[2]) if(length(inds1)>1 & length(inds2)>1) { p <- NULL; q <- NULL for(i in 1:ncol(yval)) { p <- c(p, t.test(yval[inds1,i], yval[inds2,i], alternative="two.sided")$p.value) m <- mean(yval[inds1,i]) n <- mean(yval[inds2,i]) q <- c(q, abs(m-n)) } sig <- which(pqcut) } ## Plot significance as shaded blocks if(is.null(sig)) warning("Too few data points. Ignoring significance test") if(length(sig)==0) { ##warning("No significant differences found") sig <- NULL } } ## Configure plot nrows <- 1 if(conservation) nrows=nrows+1 if(variance) nrows=nrows+1 if(nrows>length(ylab) & !is.null(ylab)) warning("insufficient y labels") op <- par(no.readonly=TRUE) on.exit(par(op)) if(nrows>1) par(mfrow=c(nrows,1), mar=mar) else par(mar=mar) plot.new() plot.window(xlim=xlim, ylim=ylim, ...) ## If significance test was performed successfully if(!is.null(sig)) { ##maxy <- max(yval, na.rm=TRUE) bds <- bounds(sig) ii <- 1:nrow(bds) rect(bds[ii,1], rep(0, length(ii)), bds[ii,2], rep(ylim[2], length(ii)), col=rep("lightblue", length(ii)), border=NA) } ## Plot fluctuations / deformations par(new=TRUE) if(!spread) { do.call('plotb3', c(list(x=yval[1,], xlab=xlab, ylab=ylab[1], ylim=ylim, xlim=xlim, type='h', col=1, sse=sse.aln), dots)) ## Plot all lines (col==NA will not be plotted) for(i in 1:nrow(yval)) { lines( yval[i,], col=col[i], lwd=2, ... ) } } else { do.call('.plot.enma.spread', c(list(x=yval, pdbs=pdbs, col=col, offset=offset, xlab=xlab, ylab=ylab[1], ylim=ylim, xlim=xlim), dots)) } ## Fluctuation / deformations variance if (variance) { fluct.sd <- apply(yval, 2, var, na.rm=T) do.call('plotb3', c(list(x=fluct.sd, xlab=xlab, ylab=ylab[2], ##ylim=ylim, xlim=xlim, col=1), dots)) } ## Plot sequence conservation / entropy if (conservation) { do.call('plotb3', c(list(x=h, ylab=ylab[3], xlab=xlab, col=1), dots)) } out <- list(signif=sig, sse=sse.aln) invisible(out) } ".plot.enma.spread" <- function(x, pdbs=NULL, col=NULL, xlab="Alignment Position", ylab="Fluctuations", xlim=NULL, ylim=NULL, offset=1, ...) { if(!inherits(x, "enma") & !inherits(x, "matrix")) stop("provide a enma object as obtained from 'nma.pdbs'") if(inherits(x, "enma")) fluct <- x$fluctuations else fluct <- x if(is.null(col)) stop("group argument missing") if(length(col) != nrow(fluct)) stop("dimension mismatch: col argument should be of same length as x") if(length(unique(col)) < 2) stop("provide > 2 unique groups") row.inds <- which(!is.na(col)) newfluct <- fluct[row.inds,, drop=FALSE ] gaps <- gap.inspect(newfluct) col.inds <- which(gaps$col < length(row.inds)) newfluct <- newfluct[, col.inds, drop=FALSE] ## check for gaps gaps <- gap.inspect(newfluct) if(any(gaps$col>0)) rm.gaps <- FALSE else rm.gaps <- TRUE sse <- NULL dots <- list(...) if( "sse" %in% names(dots) ) { sse <- dots$sse dots$sse <- NULL } else { if(!is.null(pdbs)) { if(rm.gaps) { gs <- gap.inspect(pdbs$ali) pdbs <- trim.pdbs(pdbs, col.inds=gs$f.inds) } pdbs <- trim.pdbs(pdbs, row.inds=row.inds, col.inds=col.inds) sse <- .pdbs2sse(pdbs, ind=1, rm.gaps=rm.gaps) } } dims <- dim(newfluct) if(is.null(xlim)) xlim <- c(0, dims[2]) if(is.null(ylim)) ylim <- c(0, (length(unique(col))*offset)-offset) plotb3(newfluct[1, ], col=1, type='l', ylab=ylab, xlab=xlab, ylim=ylim, xlim=xlim, sse=sse, ...) col <- col[!is.na(col)] for(i in 1:length(unique(col))) { tmpinds <- which(col==i) off <- ((i-1)* offset ) for(j in 1:length(tmpinds)) lines(newfluct[tmpinds[j], ] + off, col=i) } } ".pdbs2sse" <- function(pdbs, ind=1, rm.gaps=FALSE) { ind <- ind[1] if(file.exists(pdbs$id[ind])) id <- pdbs$id[ind] else if(file.exists(rownames(pdbs$ali)[ind])) id <- rownames(pdbs$ali)[ind] sse.aln <- NULL pdb.ref <- try(read.pdb(id), silent=TRUE) if(inherits(pdb.ref, "try-error")) pdb.ref <- try(read.pdb(substr(basename(id), 1, 4)), silent=TRUE) gaps.res <- gap.inspect(pdbs$ali) sse.ref <- NULL if(!inherits(pdb.ref, "try-error")) sse.ref <- try(dssp(pdb.ref), silent=TRUE) if(!inherits(sse.ref, "try-error") & !inherits(pdb.ref, "try-error")) { if(rm.gaps) { resid <- paste0(pdbs$resno[ind, gaps.res$f.inds], pdbs$chain[ind, gaps.res$f.inds]) } else { resid <- paste0(pdbs$resno[ind, ], pdbs$chain[ind, ]) } ## Helices resid.helix <- unbound(sse.ref$helix$start, sse.ref$helix$end) resid.helix <- paste0(resid.helix, rep(sse.ref$helix$chain, sse.ref$helix$length)) inds <- which(resid %in% resid.helix) ## inds points now to the position in the alignment where the helices are new.sse <- bounds( seq(1, length(resid))[inds] ) if(length(new.sse) > 0) { sse.aln$helix$start <- new.sse[,"start"] sse.aln$helix$end <- new.sse[,"end"] sse.aln$helix$length <- new.sse[,"length"] } ## Sheets resid.sheet <- unbound(sse.ref$sheet$start, sse.ref$sheet$end) resid.sheet <- paste0(resid.sheet, rep(sse.ref$sheet$chain, sse.ref$sheet$length)) inds <- which(resid %in% resid.sheet) new.sse <- bounds( seq(1, length(resid))[inds] ) if(length(new.sse) > 0) { sse.aln$sheet$start <- new.sse[,"start"] sse.aln$sheet$end <- new.sse[,"end"] sse.aln$sheet$length <- new.sse[,"length"] } ## SSE vector sse <- rep(" ", length(resid)) for(i in 1:length(sse.aln$helix$start)) sse[sse.aln$helix$start[i]:sse.aln$helix$end[i]] <- "H" for(i in 1:length(sse.aln$sheet$start)) sse[sse.aln$sheet$start[i]:sse.aln$sheet$end[i]] <- "E" sse.aln$sse <- sse } else { msg <- NULL if(inherits(pdb.ref, "try-error")) msg = c(msg, paste("File not found:", pdbs$id[1])) if(inherits(sse.ref, "try-error")) msg = c(msg, "Launching external program 'DSSP' failed") warning(paste("SSE cannot be drawn", msg, sep="\n ")) } return(sse.aln) } bio3d/R/orient.pdb.R0000644000176200001440000000335212561207744013655 0ustar liggesusers"orient.pdb" <- function (pdb, atom.subset = NULL, verbose = TRUE ) { ## x <- c(rep(10,3), rep(0,3), rep(-10,3)) ## write.pdb(xyz=x, file="t1.pdb") ## write.pdb(xyz=orient.pdb(x), file="t2.pdb") if (missing(pdb)) { stop("pdb.orient: must supply 'pdb' object, e.g. from 'read.pdb'") } if(is.list(pdb)) { xyz <- pdb$xyz } else { if (!is.vector(pdb)) { stop("pdb.orient: input 'pdb' should NOT be a matrix") } xyz <- pdb } xyz <- matrix( xyz, ncol=3, byrow=TRUE ) if (is.null(atom.subset)) atom.subset <- c(1:nrow(xyz)) if (length(atom.subset) > nrow(xyz)) { stop("pdb.orient: there are more 'atom.subset' inds than there atoms") } ## Center on mean xyz positions xyz.bar <- apply(xyz[atom.subset, ], 2, mean) xyz <- sweep(xyz, 2, xyz.bar) ## Determine principal axis S <- var(xyz[atom.subset, ]) prj <- eigen(S, symmetric = TRUE) ## Mke rotation explicitly rh system ## z <- xyz %*% (prj$vectors) A <- prj$vectors b <- A[,1]; c <- A[,2] A[1,3] <- (b[2] * c[3]) - (b[3] * c[2]) A[2,3] <- (b[3] * c[1]) - (b[1] * c[3]) A[3,3] <- (b[1] * c[2]) - (b[2] * c[1]) ## Rotate z <- xyz %*% (A) if (verbose) { cat("Dimensions:", "\n") cat(" x min=", round(min(z[, 1]), 3), " max=", round(max(z[, 1]), 3), " range=", round(max(z[, 1]) - min(z[, 1]), 3), "\n") cat(" y min=", round(min(z[, 2]), 3), " max=", round(max(z[, 2]), 3), " range=", round(max(z[, 2]) - min(z[, 2]), 3), "\n") cat(" z min=", round(min(z[, 3]), 3), " max=", round(max(z[, 3]), 3), " range=", round(max(z[, 3]) - min(z[, 3]), 3), "\n") } z <- round(as.vector(t(z)),3) z <- as.xyz(z) invisible(z) } bio3d/R/bounds.R0000644000176200001440000000264412412621431013072 0ustar liggesusers"bounds" <- function (nums, dup.inds=FALSE, pre.sort=TRUE) { if(dup.inds) { ## bounds of concetive duplicated numbers s.ind <- which(!duplicated(nums)) e.ind <- c(s.ind[-1]-1, length(nums)) return(cbind(1:length(s.ind),"start"=s.ind,"end"=e.ind, "length"=(e.ind - s.ind + 1))) } else { if (!is.numeric(nums)) stop("must supply a numeric vector") if (length(nums)==0) return(nums) if(pre.sort) { ## should we pre-sort... nums <- sort(unique(nums)) } if (length(nums) == 1) { bounds <- c(nums, nums, 1) names(bounds) <- c("start", "end", "length") ## Edit here to return matrix (not vector) ## following dssp bug report from Yun Liu ## Fri, Apr 29, 2011 return( t(as.matrix(bounds)) ) } bounds <- nums[1] nums.start <- nums[1] diff.i <- 1 for (i in 2:length(nums)) { if ((nums[i] - diff.i) != nums.start) { bounds <- c(bounds, nums[i - 1], nums[i]) nums.start <- nums[i] diff.i <- 1 } else { diff.i <- diff.i + 1 } } bounds <- c(bounds, nums[length(nums)]) bounds <- matrix(bounds, ncol = 2, byrow = TRUE, dimnames = list(c(1:(length(bounds)/2)), c("start", "end"))) bounds <- cbind(bounds, length = (bounds[, 2] - bounds[,1]) + 1) return(bounds) } } bio3d/R/plot.dccm.R0000644000176200001440000001253212526367343013477 0ustar liggesusersplot.dccm <-function(x, sse=NULL, colorkey=TRUE, at=c(-1, -0.75, -0.5, -0.25, 0.25, 0.5, 0.75, 1), main="Residue Cross Correlation", # pad=0.022 helix.col = "gray20", sheet.col = "gray80", inner.box=TRUE, outer.box=FALSE, xlab="Residue No.", ylab="Residue No.", margin.segments=NULL, segment.col=vmd.colors(), segment.min=1, ...) { requireNamespace("lattice", quietly = TRUE) colnames(x) = NULL; rownames(x)=NULL draw.segment <- function(start, length, xymin, xymax, fill.col="gray", side=1) { ##-- Draw Annotation On Plot Margins, used for SSE and CLUSTER members ## draw.segment(store.grps[,"start"], store.grps[,"length"], ## xymin=xymin, xymax=xymax, side=1, fill.col="red") if(side==1) { ## Bottom Margin grid.rect(x=unit(start-0.5, "native"), y=0, gp = gpar(fill=fill.col, col=NA), just=c("left","bottom"), width=unit(length-0.5, "native"), height=xymin, vp=vpPath("plot_01.toplevel.vp","plot_01.panel.1.1.vp")) } if(side==2) { ## Left Margin grid.rect(x=0, y=unit(start-0.5, "native"), gp = gpar(fill=fill.col, col=NA), just=c("left","bottom"), width=xymin, height=unit(length-0.5, "native"), vp=vpPath("plot_01.toplevel.vp","plot_01.panel.1.1.vp")) } if(side==3) { ## Top Margin grid.rect(x=unit(start-0.5, "native"), y=xymax, gp = gpar(fill=fill.col,col=NA), just=c("left","bottom"), width=unit(length-0.5, "native"), height=unit(1, "npc"), vp=vpPath("plot_01.toplevel.vp","plot_01.panel.1.1.vp")) } if(side==4) { ## Right Margin grid.rect(x=xymax, y=unit(start-0.5, "native"), gp = gpar(fill=fill.col,col=NA), just=c("left","bottom"), width=unit(1, "npc"), height=unit(length-0.5, "native"), vp=vpPath("plot_01.toplevel.vp","plot_01.panel.1.1.vp")) } } ##-- Main Plot p1 <- lattice::contourplot(x, region = TRUE, labels=F, col="gray40", at=at, xlab=xlab, ylab=ylab, colorkey=colorkey, main=main, ...) xymin=0; xymax=1 if (is.null(sse) && is.null(margin.segments)) { print(p1) } else { xlim <- p1$x.limits ylim <- p1$y.limits uni <- 1/(max(xlim)-min(xlim)) pad=0.02 ## This should be setable! padref <- pad/uni if(!is.null(sse)) { ##-- Adjust Top and Right margins for 'sse' xymax <- 1-(pad) p1$x.limits[2]=xlim[2]+padref p1$y.limits[2]=ylim[2]+padref } if(!is.null(margin.segments)) { ##-- Adjust Bottom and Left margins for 'segments' xymin = pad p1$x.limits[1]=xlim[1]-padref p1$y.limits[1]=ylim[1]-padref ##- Format margin annotation object grps <- table(margin.segments) ## Exclude small grps less than 'segment.min' grps = names( grps[grps > segment.min] ) store.grps <- NULL; for(i in 1:length(grps)) { store.grps <- rbind(store.grps, cbind( bounds(which(margin.segments == grps[i])), "grp"=as.numeric(grps[i])) ) } ## Margin segment colors if(is.null(segment.col)) { segment.col <- (store.grps[,"grp"]) } else { segment.col <- segment.col[(store.grps[,"grp"])] } } print(p1) if(!is.null(sse)) { ##-- SSE annotation ## TOP ## dont have a pdb$helix$length if( is.null(sse$helix$length) ) { sse$helix$length <- (sse$helix$end+1)-sse$helix$start sse$sheet$length <- (sse$sheet$end+1)-sse$sheet$start } draw.segment(sse$helix$start, sse$helix$length, xymin=xymin, xymax=xymax, fill.col=helix.col, side=3) draw.segment(sse$sheet$start, sse$sheet$length, xymin=xymin, xymax=xymax, fill.col=sheet.col, side=3) ## RIGHT draw.segment(sse$helix$start, sse$helix$length, xymin=xymin, xymax=xymax, fill.col=helix.col, side=4) draw.segment(sse$sheet$start, sse$sheet$length, xymin=xymin, xymax=xymax, fill.col=sheet.col, side=4) } if(!is.null(margin.segments)) { ##-- Cluster annotation ## BOTTOM draw.segment(store.grps[,"start"], store.grps[,"length"], xymin=xymin, xymax=xymax, fill.col=segment.col, side=1) ## LEFT draw.segment(store.grps[,"start"], store.grps[,"length"], xymin=xymin, xymax=xymax, fill.col=segment.col, side=2) } if(!outer.box) { grid.rect(x=0, y=0, gp = gpar(fill=NA,col="white"), just=c("left","bottom"), width=1,height=1, vp=vpPath("plot_01.toplevel.vp","plot_01.panel.1.1.vp")) } if(inner.box) { grid.rect(x=xymin, y=xymin, gp = gpar(fill=NA,col="black"), just=c("left","bottom"), width=xymax, height=xymax, vp=vpPath("plot_01.toplevel.vp","plot_01.panel.1.1.vp")) } } } bio3d/R/store.atom.R0000644000176200001440000000354712526367343013715 0ustar liggesusers"store.atom" <- function(pdb) { colpaste <- function(x, col.names = colnames(x)) { apply(x, 1, function(row) paste(row[col.names], collapse = ".")) } getinds <- function(atoms, ref = atom.names) { sort(atom2xyz(charmatch(atoms, ref))) } repadd <- function(num, nrep = nres, toadd = nxyz) { c(num, rep(num, (nrep - 1)) + rep(cumsum(rep(toadd, (nrep - 1))), each = length(num))) } atom.data <- colpaste(pdb$atom, c("elety", "resno", "chain")) atom.list <- matrix(unlist(strsplit(atom.data, "\\.")), ncol = 3, byrow = TRUE) res.data <- colpaste(pdb$atom, c("resno", "chain")) res.list <- unique(res.data) atom.names <- c("N", "CA", "C", "O", "CB", "*G", "*G1", "*G2", "*D", "*D1", "*D2", "*E", "*E1", "*E2", "*Z", "NH1", "NH2", "OH") ## atom.greek <- c("N", "CA", "C", "O", "CB", "G", "G1", "G2", "D", "D1", "D2", "E", "E1", "E2", "Z", "*", "*", "*") coords <- NULL # Changed for PDB format v3.3 # blank <- matrix(NA, nrow = 13, ncol = length(atom.names)) blank <- matrix(NA, nrow = ncol(pdb$atom), ncol = length(atom.names)) for (i in 1:length(res.list)) { res.blank <- blank res.ind <- which(res.list[i] == res.data) blank.ind <- charmatch(atom.list[res.ind, 1], atom.names, nomatch = 0) + charmatch(substr(atom.list[res.ind,1], 2, 4), atom.greek, nomatch = 0) res.blank[, blank.ind[blank.ind != 0]] <- t( pdb$atom[(res.ind[blank.ind != 0]),] ) coords <- cbind(coords, res.blank) } natm <- length(atom.names) # PDB format v3.3 nxyz <- ncol(pdb$atom) * natm # nxyz <- 13 * natm nres <- length(coords)/(nxyz) dim(coords) <- c(ncol(pdb$atom), natm, nres) # dim(coords) <- c(13, natm, nres) dimnames(coords) = list(atom = colnames(pdb$atom), type = atom.names, res = res.list) return(coords) } bio3d/R/blast.pdb.R0000644000176200001440000000326112632622153013453 0ustar liggesusers`blast.pdb` <- function(seq, database="pdb", time.out=NULL, chain.single=TRUE) { if(inherits(seq, "fasta")) { if(is.matrix(seq$ali)) { if(nrow(seq$ali)>1) warning("Multiple sequences detected - using only the first sequence in object") seq <- as.vector(seq$ali[1,]) } else { seq <- as.vector(seq$ali) } } ## Run NCBI blastp on a given 'seq' sequence against a given 'database' if(!is.vector(seq)) { stop("Input 'seq' should be a single sequence as a single or multi element character vector") } seq <- paste(seq, collapse="") if( !(database %in% c("pdb", "nr", "swissprot")) ) stop("Option database should be one of pdb, nr or swissprot") ##- Submit # urlput <- paste("http://www.ncbi.nlm.nih.gov/BLAST/Blast.cgi?CMD=Put&DATABASE=", # database,"&HITLIST_SIZE=20000&PROGRAM=blastp&CLIENT=web&QUERY=", # paste(seq,collapse=""), # sep="") urlput <- paste("http://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Put&DATABASE=", database,"&HITLIST_SIZE=20000&PROGRAM=blastp&CLIENT=web&QUERY=", paste(seq,collapse=""), sep="") txt <- scan(urlput, what="raw", sep="\n", quiet=TRUE) rid <- sub("^.*RID = " ,"",txt[ grep("RID =",txt) ]) urlget <- paste("http://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Get", "&FORMAT_OBJECT=Alignment", "&ALIGNMENT_VIEW=Tabular", "&RESULTS_FILE=on", "&FORMAT_TYPE=CSV", "&ALIGNMENTS=20000", "&RID=",rid, sep="") blast <- get.blast(urlget, time.out = time.out, chain.single=chain.single) return(blast) } bio3d/R/geostas.R0000644000176200001440000001210712632622153013246 0ustar liggesusers#.getverbose <- function(...) { # dots <- list(...) # if(length(dots[names(dots) %in% "verbose"])==0) # verbose <- FALSE # else # verbose <- (dots[names(dots) %in% "verbose"])$verbose # return(verbose) #} geostas <- function(...) UseMethod("geostas") geostas.nma <- function(nma, m.inds=7:11, verbose=TRUE, ...) { if(verbose) cat(" .. generating trajectory from", length(m.inds), "modes\n") trj <- NULL for(i in m.inds) { trj <- rbind(trj, mktrj.nma(nma, mode=i)) } gs <- geostas.xyz(trj, verbose=verbose, ...) return(gs) } geostas.enma <- function(enma, pdbs=NULL, m.inds=1:5, verbose=TRUE, ...) { if(!inherits(enma, "enma")) stop("provide an 'enma' object as obtained by function 'nma.pdbs()'") if(!inherits(pdbs, "pdbs")) stop("provide an 'pdbs' object as obtained by function 'pdbaln' or 'read.fasta.pdb'") if(verbose) cat(" .. generating trajectory from", length(m.inds), "modes\n") trj <- mktrj.enma(enma, pdbs, m.inds=m.inds, rock=FALSE, ...) gs <- geostas.xyz(trj, verbose=verbose, ...) return(gs) } geostas.pdb <- function(pdb, inds=NULL, verbose=TRUE, ...) { if(!is.pdb(pdb)) stop("provide a 'pdb' object as obtained from function 'read.pdb'") if(!nrow(pdb$xyz)>2) stop("provide a multi-model (>2) 'pdb' with more than ") if(is.null(inds)) { inds <- atom.select(pdb, "calpha") if(verbose) cat(" ..", length(inds$atom), "'calpha' atoms selected\n") } xyz <- pdb$xyz[,inds$xyz] gs <- geostas.xyz(xyz, verbose=verbose, ...) ## map back so that indices matches input 'pdb' if(verbose) { cat(" .. converting indices to match input 'pdb' object \n") cat(" (additional attribute 'atomgrps' generated) \n") } gs$fit.inds <- inds$xyz[gs$fit.inds] resid <- paste(pdb$atom$resid, pdb$atom$resno, pdb$atom$chain, sep="-") grps <- rep(NA, nrow(pdb$atom)) for(i in 1:length(gs$inds)) { gs$inds[[i]] <- as.select(inds$atom[gs$inds[[i]]$atom]) gs$inds[[i]]$call <- NA tmp.inds <- which(resid %in% resid[ gs$inds[[i]]$atom ]) grps[ tmp.inds ] <- i } gs$atomgrps <- grps return(gs) } geostas.pdbs <- function(pdbs, verbose=TRUE, ...) { if(!inherits(pdbs, "pdbs")) stop("provide an 'pdbs' object as obtained by function 'pdbaln' or 'read.fasta.pdb'") ## identify non-gap regions gaps.res <- gap.inspect(pdbs$ali) gaps.pos <- gap.inspect(pdbs$xyz) if(verbose) cat(" ..", length(gaps.pos$f.inds), "non-gap positions selected\n") xyz <- pdbs$xyz[, gaps.pos$f.inds] gs <- geostas.xyz(xyz, verbose=verbose, ...) ## map back so that indices matches input 'pdbs' gs$fit.inds <- gaps.pos$f.inds[gs$fit.inds] grps <- rep(NA, ncol(pdbs$ali)) for(i in 1:length(gs$inds)) { gs$inds[[i]] <- as.select(gaps.res$f.inds[ gs$inds[[i]]$atom ]) gs$inds[[i]]$call <- NA grps[ gs$inds[[i]]$atom ] <- i } return(gs) } geostas.default <- function(...) geostas.xyz(...) geostas.xyz <- function(xyz, amsm=NULL, k=3, pairwise=TRUE, clustalg="kmeans", fit=TRUE, ncore=NULL, verbose=TRUE, ...) { cl <- match.call() xyz <- as.xyz(xyz) if(!nrow(xyz)>2) stop("provide a trajectory (e.g xyz object) with multiple (>2) frames") if(verbose) cat(" .. 'xyz' coordinate data with", nrow(xyz), "frames \n") if(!clustalg %in% c("hclust", "kmeans")) stop("'clustalg' should be 'kmeans' or 'hclust'") if(k<2) stop("provide 'k>1'") if(fit & is.null(amsm)) { if(verbose) cat(" .. 'fit=TRUE': running function 'core.find'\n") invisible(capture.output( core <- core.find(xyz) )) fit.inds <- core$xyz xyz <- fit.xyz(xyz[1,], xyz, fixed.inds=fit.inds, mobile.inds=fit.inds, ncore=ncore) if(is.null(fit.inds)) warning("core indices not found. fitting to all atoms") if(verbose) cat(" .. coordinates are superimposed to core region\n") } else { if(verbose) cat(" .. coordinates are not superimposed prior to geostas calculation\n") fit.inds <- NULL } if(is.null(amsm)) { if(verbose) cat(" .. calculating atomic movement similarity matrix ('amsm.xyz()') \n") amsm <- amsm.xyz(xyz, ncore=ncore) dims <- dim(amsm) if(verbose) cat(" .. dimensions of AMSM are ", dims[1], "x", dims[2], "\n", sep="") } else { if(!all(dim(amsm)==ncol(xyz)/3)) stop("dimension mismatch ('xyz' and 'amsm')") } if(pairwise) { cm <- 1-amsm } else { cm.tmp <- normalize.vector(amsm) cm <- 1 - apply(cm.tmp, 2, function(x,y) x %*% y, cm.tmp) } ## hierarchical clustering if(clustalg=="hclust") { if(verbose) cat(" .. clustering AMSM using 'hclust' \n") dis <- as.dist(cm) hc <- hclust(dis, ...) grps <- cutree(hc, k=k) } ## k-means clustering if(clustalg=="kmeans") { if(verbose) cat(" .. clustering AMSM using 'kmeans' \n") grps <- kmeans(cm, centers=k, ...)$cluster } ## return indices for the identified domains inds <- list() for(i in 1:length(unique(grps))) { inds[[i]] <- as.select(grps==i) } out <- list(call=cl, amsm=amsm, fit.inds=fit.inds, grps=grps, inds=inds) class(out) <- "geostas" return(out) } bio3d/R/seqidentity.R0000644000176200001440000000555612524171274014160 0ustar liggesusers"seqidentity" <- function( alignment , normalize=TRUE, similarity=FALSE, ncore=1, nseg.scale=1) { # Parallelized by parallel package (Sun Jul 7 17:35:38 EDT 2013) ncore <- setup.ncore(ncore) if(ncore > 1) { # Issue of serialization problem # Maximal number of cells of a double-precision matrix # that each core can serialize: (2^31-1-61)/8 R_NCELL_LIMIT_CORE = 2.68435448e8 R_NCELL_LIMIT = ncore * R_NCELL_LIMIT_CORE if(nseg.scale < 1) { warning("nseg.scale should be 1 or a larger integer\n") nseg.scale=1 } } ids <- NULL if(is.list(alignment)) { if(inherits(alignment, c("fasta", "pdbs"))) ids <- alignment$id alignment <- alignment$ali } else { ids <- rownames(alignment) } ## calculate similarity instead of identity? if(similarity) { alnTo10 <- function(x) { new <- rep(NA, length(x)) aa <- c("V","I","L","M", "F","W","Y", "S","T", "N","Q", "H","K","R", "D","E", "A","G", "P", "C", "-","X") new[ x %in% aa[1:4] ] = "V" # Hydrophobic, Aliphatic new[ x %in% aa[5:7] ] = "F" # Aromatic new[ x %in% aa[8:9] ] = "S" # Ser/Thr new[ x %in% aa[10:11] ] = "N" # Polar new[ x %in% aa[12:14] ] = "R" # Positive new[ x %in% aa[15:16] ] = "D" # Negative new[ x %in% aa[17:18] ] = "A" # Tiny new[ x %in% aa[19] ] = "P" # Proline new[ x %in% aa[20] ] = "C" # Cysteine new[ x %in% aa[21:22] ] = "-" # Gaps return(matrix(new, nrow=1)) } alignment <- t(apply(alignment, 1, alnTo10) ) } alignment[is.gap(alignment)] = NA ide <- function(x, y) { #### Edit by Heiko Strathmann #### Wed Aug 4 10:48:16 PDT 2010 #### Fix for bug with all gap sequences r <- sum(x==y, na.rm=TRUE) t <- sum(complete.cases(cbind(x,y))) if (normalize && t != 0) { r <- r/t } ################################## return( round(r, 3) ) } nseq <- nrow(alignment) inds <- pairwise( nseq ) ni <- nrow(inds) if(ncore > 1) { RLIMIT = R_NCELL_LIMIT nDataSeg = floor((ni-1)/RLIMIT) + 1 nDataSeg = floor(nDataSeg * nseg.scale) lenSeg = floor(ni/nDataSeg) s = NULL for(i in 1:nDataSeg) { istart = (i-1)*lenSeg + 1 iend = if(i1) mylapply <- mclapply else mylapply <- lapply gaps <- gap.inspect(enma$fluctuations) dims <- dim(enma$fluctuations) m <- dims[1] mat <- matrix(NA, m, m) ##inds <- pairwise(m) inds <- rbind(pairwise(m), matrix(rep(1:m,each=2), ncol=2, byrow=T)) mylist <- mylapply(1:nrow(inds), function(row) { i <- inds[row,1]; j <- inds[row,2]; out <- list(val=sip.default( enma$fluctuations[i,gaps$f.inds], enma$fluctuations[j,gaps$f.inds]), i=i, j=j) return(out) }) for ( i in 1:length(mylist)) { tmp <- mylist[[i]] mat[tmp$i, tmp$j] <- tmp$val } mat[ inds[,c(2,1)] ] = mat[ inds ] ##diag(mat) <- rep(1, n) colnames(mat) <- basename(rownames(enma$fluctuations)) rownames(mat) <- basename(rownames(enma$fluctuations)) return(round(mat, 6)) } sip.default <- function(v, w, ...) { if(length(v)!=length(w)) stop("dimension mismatch") return(as.numeric(((t(v) %*% w)**2) / ((t(v) %*% v)*(t(w) %*% w)))) } bio3d/R/print.pca.R0000644000176200001440000000156712524171274013512 0ustar liggesusers"print.pca" <- function(x, nmodes=6, ...) { cn <- class(x) cat("\nCall:\n ", paste(deparse(x$call), sep = "\n", collapse = "\n"), "\n\n", sep = "") cat("Class:\n ", cn, "\n\n", sep = "") cat("Number of eigenvalues:\n ", length(x$L), "\n\n", sep="") inds <- 1:nmodes e <- round(x$L[inds], 3) p <- (x$L[inds]/sum(x$L)) * 100 d <- data.frame( "Eigenvalue"=e, "Variance"=round(p,3), "Cumulative"=round(cumsum(p),3), row.names = paste(" PC",inds)) #cat("Eigenvalues:\n", sep="") print(d) cat("\n",paste(" (Obtained from", nrow(x$z), "conformers with", ncol(x$z), "xyz input values).")) i <- paste( attributes(x)$names, collapse=", ") cat("\n",strwrap(paste(" + attr:",i,"\n"),width=60, exdent=8), sep="\n") invisible(d) } bio3d/R/dssp.pdbs.R0000644000176200001440000000274612526367343013522 0ustar liggesusers"dssp.pdbs" <- function(pdbs, ...) { if(!is.pdbs(pdbs)) stop("provide a pdbs object as obtained from pdbaln()") dots <- list(...) if(any(c("resno", "full") %in% names(dots))) stop("arguments resno and full not allowed in dssp.pdbs()") gaps.res <- gap.inspect(pdbs$ali) sse <- matrix(NA, ncol=ncol(pdbs$resno), nrow=nrow(pdbs$resno)) for ( i in 1:length(pdbs$id) ) { ##- Check for local and/or online PDB file to run dssp on file <- pdbs$id[i] toread <- file.exists(file) if ((substr(file, 1, 4) == "http") | (nchar(file) == 4)) { toread <- TRUE } if (!toread) { stop(paste("Corresponding PDB file could not be found for entry:\n\t-", pdbs$id[i])) } tmp.pdb = read.pdb(pdbs$id[i]) tmp.sse = dssp.pdb(tmp.pdb, resno=FALSE, full=FALSE, ...) ##sse[i, which(gaps.res$bin[i,]==0)] = tmp.sse$sse ##- this old way (line 30) will have problems if alignment contains ## only a portion of the full PDB structure read on line 27 above. ## Thus the edit below uses residue number and chain id from the ## alignment (ali.names) as a reference to populate the sse matrix ## from the PDB dssp result (pdb.names) ali.names <- paste(pdbs$resno[i,], pdbs$chain[i,], sep="_") pdb.names <- paste(tmp.pdb$atom$resno[tmp.pdb$calpha], tmp.pdb$atom$chain[tmp.pdb$calpha], sep="_") names(tmp.sse$sse) <- pdb.names sse[i,] <- tmp.sse$sse[ ali.names ] } return(sse) } bio3d/R/rmsf.R0000644000176200001440000000145112632622153012550 0ustar liggesusers"rmsf" <- function(xyz, average = FALSE) { if(is.null(dim(xyz))) stop("input 'xyz' has NULL dimension") ## Cov function changed ~ R.2.7 my.sd <- function (x, na.rm = FALSE) { if (is.matrix(x)) apply(x, 2, my.sd, na.rm = na.rm) else if (is.vector(x)) { if(na.rm) x <- x[!is.na(x)] if(length(x) == 0) return(NA) sqrt(var(x, na.rm = na.rm)) } else if (is.data.frame(x)) sapply(x, my.sd, na.rm = na.rm) else { x <- as.vector(x) my.sd(x,na.rm=na.rm) } } fluct = rowSums( matrix( my.sd(xyz, na.rm = TRUE), ncol=3, byrow=TRUE )^2, na.rm=TRUE ) if(average) { if(ncol(xyz) %% 3 == 0) d = ncol(xyz) / 3 else d = ncol(xyz) return( sqrt( sum(fluct)/d ) ) } else { return( sqrt( fluct ) ) } } bio3d/R/plot.core.R0000644000176200001440000000173512412621431013505 0ustar liggesusers`plot.core` <- function(x, y=NULL, type="h", main="", sub="", xlim=NULL, ylim=NULL, xlab = "Core Size (Number of Residues)", ylab = "Total Ellipsoid Volume (Angstrom^3)", axes=TRUE, ann=par("ann"), col=par("col"), ...) { if(is.list(x)) { len <- x$length ## hack!! fix later x <- x$volume } else{ len <- rev(1:length(x)) } xy <- xy.coords(x, y) if (is.null(xlim)) xlim <- range(xy$x[is.finite(xy$x)]) if (is.null(ylim)) ylim <- range(xy$y[is.finite(xy$y)]) ## opar <- par(no.readonly=TRUE) ## on.exit(par(opar)) plot.new() plot.window(xlim, ylim, ...) points(xy$x, xy$y, col=col, type=type, ...) if (axes) { ax.ind <- c(1,seq(10,length(x),by=10)) axis(1, at=ax.ind, labels = len[ax.ind]) axis(2) box() } if (ann) { if(is.null(xlab)) xlab=xy$xlab if(is.null(ylab)) ylab=xy$ylab title(main=main, sub=sub, xlab=xlab, ylab=ylab, ...) } } bio3d/R/aa123.R0000644000176200001440000000151512412621431012403 0ustar liggesusers"aa123" <- function (aa) { if(any(nchar(aa)!=1)) stop("Provide a character vector of individual 1-letter aminoacid codes") # convert one-letter IUPAC amino-acid code into # three-letter PDB style, for instance "A" into "ALA". aa1 <- c("-","X", "A","C","D","E","F","G", "H","I","K","L","M","N","P","Q", "R","S","T","V","W","Y") aa3 <- c("---","UNK", "ALA", "CYS", "ASP", "GLU", "PHE", "GLY", "HIS", "ILE", "LYS", "LEU", "MET", "ASN", "PRO", "GLN", "ARG", "SER", "THR", "VAL", "TRP", "TYR") convert <- function(x) { if(is.na(x)) return(NA) if (all(x != aa1)) { warning("Unknown one letter code for aminoacid") return("UNK") } else { return(aa3[which(x == aa1)]) } } return(as.vector(unlist(sapply(aa, convert)))) } bio3d/R/plot.pca.R0000644000176200001440000000304612526367343013334 0ustar liggesusers`plot.pca` <- function(x, pc.axes=NULL, pch=16, col=par("col"), cex=0.8, mar=c(4, 4, 1, 1), ...) { opar <- par(no.readonly=TRUE) par(pty="s", cex=cex, mar=mar) if(is.null(pc.axes)) { ##-- Overview plot has been requested par(mfrow=c(2, 2)) pc.axes <- 1:3 ## Variance per PC for axis label annotation p <- paste0("PC",1:3," (", round((x$L[1:3]/sum(x$L)) * 100, 2),"%)") plot(x$z[,1],x$z[,2], type="p", pch=pch, xlab=p[1], ylab=p[2], col=col, ...) abline(h=0,col="gray",lty=2); abline(v=0,col="gray",lty=2) plot(x$z[,3],x$z[,2], type="p", pch=pch, xlab=p[3], ylab=p[2], col=col,...) abline(h=0,col="gray",lty=2); abline(v=0,col="gray",lty=2) plot(x$z[,1],x$z[,3], type="p", pch=pch, xlab=p[1], ylab=p[3], col=col,... ) abline(h=0,col="gray",lty=2); abline(v=0,col="gray",lty=2) plot.pca.scree(x$L, ...) par(opar) ##- Reset par for subsequent plot to this device } else { ##-- Score plot of an individual PC pair (e.g. 1 vs 2) has been requested if(length(pc.axes) != 2) { stop("Input 'pc.axes' should be NULL (for overview plots) or numeric and of length two (for individual score plot)") } ## Variance for axis labels p <- paste0("PC",pc.axes," (", round((x$L[pc.axes]/sum(x$L)) * 100, 2),"%)") par(cex=cex+0.2) ## Make axis legend text a little larger plot(x$z[,pc.axes[1]],x$z[,pc.axes[2]], type="p", pch=pch, xlab=p[1], ylab=p[2], col=col, ...) abline(h=0,col="gray",lty=2);abline(v=0,col="gray",lty=2) } invisible(x$z[,pc.axes, drop=FALSE]) } bio3d/R/aa2mass.R0000644000176200001440000000576612544562302013146 0ustar liggesusers"aa2mass" <- function(pdb, inds=NULL, mass.custom=NULL, addter=TRUE, mmtk=FALSE) { if (missing(pdb)) stop("must supply 'pdb' object or vector of amino acid residue names") if(is.pdb(pdb)) { if(!is.null(inds)) { pdb <- trim.pdb(pdb, inds) } sequ <- pdb$atom[pdb$calpha,"resid"] } else { if(!is.null(inds)) warning("'inds' has no effect when 'pdb' is vector") sequ <- pdb if(any(nchar(sequ)==1)) sequ <- aa123(sequ) if(any(nchar(sequ)!=3)) stop("must supply 'pdb' object or vector of amino acid residue names") } ## Define residues masses if(mmtk) { ## MMTK (for reproduction purposes!) w <- c( 71.079018, 157.196106, 114.104059, 114.080689, 103.143407, 128.131048, 128.107678, 57.05203, 137.141527, 113.159985, 113.159985, 129.18266, 131.197384, 147.177144, 97.117044, 87.078323, 101.105312, 186.213917, 163.176449, 99.132996) aa <- c("ALA", "ARG", "ASN", "ASP", "CYS", "GLN", "GLU", "GLY", "HIS", "ILE", "LEU", "LYS", "MET", "PHE", "PRO", "SER", "THR", "TRP", "TYR", "VAL") mat <- data.frame(aa3=aa, aa1=aa321(aa), mass=w, formula=NA, name=NA) rownames(mat) <- aa } else { ## Read data matrix mat <- bio3d::aa.table } ## Data frame with column names: aa3, aa1, mass, formula, name if (!is.null(mass.custom)) { if(class(mass.custom) != "list") stop("'mass.custom' must be of class 'list'") new.aas <- names(mass.custom) if(any(duplicated(new.aas))) { mass.custom[duplicated(new.aas)] <- NULL warning("duplicate residue name(s) in 'mass.custom'. using first occurrence(s) only.") } new.aas <- names(mass.custom) if(any(new.aas %in% mat$aa3)) { dups <- paste(unique(new.aas[new.aas %in% mat$aa3]), collapse=", ") warning(paste("residue name(s)", dups, "exists in 'aa.table'. overwriting with provided value(s).")) } for(new.aa in new.aas) { if( new.aa %in% rownames(mat) ) { ## Replace residue mass mat[new.aa, "mass"] = mass.custom[[ new.aa ]] } else { ## Add new residue to data frame (aa.table) nr <- data.frame(list(aa3=new.aa, aa1="X", mass=mass.custom[[ new.aa ]], formula=NA, name=NA)) rownames(nr) <- new.aa mat <- rbind(mat, nr) } } } ## Fetch mass from data frame wts <- mat[sequ, "mass"] ## Check for missing masses if(NA %in% wts) { inds <- which(wts %in% NA) unknown <- paste(unique(sequ[inds]), collapse=" ") stop(paste("Unknown amino acid identifier: ", unknown, sep="")) } if(addter) { wts[1] <- wts[1] + atom2mass("H") wts[length(wts)] <- wts[length(wts)] + atom2mass("O") + atom2mass("H") } return(wts) } bio3d/R/aa321.R0000644000176200001440000000242412544562302012412 0ustar liggesusers"aa321" <- function (aa) { # convert three-letters amino-acid code into # one-letter IUPAC code, for instance "ALA" into "A". # new residues should be added to through the util/make_aatable.R script aa1 <- c("-", ".", "X", bio3d::aa.table$aa1) aa3 <- c("---", "---","UNK", bio3d::aa.table$aa3) convert <- function(x) { if(is.na(x)) return(NA) if (all(x != aa3)) { warning(paste("Unknown 3-letters code for aminoacid:",x)) return("X") # mask unk } else { return(aa1[which(x == aa3)]) } } return(as.vector(unlist(sapply(aa, convert)))) } ".aa321.na" <- function (aa) { # convert three-letters amino-acid code into # one-letter IUPAC code, for instance "ALA" into "A". aa1 <- c("-",".","X", "C", "G", "T", "A", "U", "I", "C", "G", "T"," A", "U", "I") aa3 <- c("---", "---","UNK", "DC", "DG", "DT", "DA", "DU", "DI", "C", "G", "T", "A", "U", "I") convert <- function(x) { if(is.na(x)) return(NA) if (all(x != aa3)) { warning(paste("Unknown 3-letters code for residue:",x)) return("X") # mask unk } else { return(aa1[which(x == aa3)]) } } return(as.vector(unlist(sapply(aa, convert)))) } bio3d/R/read.mol2.R0000644000176200001440000001263612526367343013404 0ustar liggesusersprint.mol2 <- function(x, ...) { cat(paste("... Name:", x$name, "\n")) cat(paste("...", nrow(x$atom), "atoms in molecule", "\n")) cat(paste("...", nrow(x$bond), "bonds in molecule", "\n")) cat(paste("...", nrow(x$xyz), "frame(s) stored", "\n")) cat("\n") i <- paste( attributes(x)$names, collapse=", ") cat(strwrap(paste(" + attr:",i,"\n"),width=60, exdent=8), sep="\n") cat("\n") } "read.mol2" <- function (file, maxlines = -1L) { if (missing(file)) { stop("read.mol2: please specify a MOL2 'file' for reading") } if (!is.numeric(maxlines)) { stop("read.mol2: 'maxlines' must be numeric") } toread <- file.exists(file) if (!toread) { stop("No input MOL2 file found: check filename") } atom.format <- matrix(c("eleno", 'numeric', "elena", 'character', "x", 'numeric', "y", 'numeric', "z", 'numeric', "elety", 'character', "resno", 'numeric', "resid", 'character', "charge", 'numeric', "statbit", 'character'), ncol=2, byrow=TRUE, dimnames = list(c(1:10), c("name","what")) ) bond.format <-matrix( c("id", 'numeric', "origin", 'numeric', "target", 'numeric', "type", 'character', "statbit", 'character'), ncol=2, byrow=TRUE, dimnames = list(c(1:5), c("name","what")) ) trim <- function(s) { s <- sub("^ +", "", s) s <- sub(" +$", "", s) s[(s == "")] <- NA s } split.line <- function(x, collapse=TRUE, ncol=NULL) { tmp <- unlist(strsplit(x, split=" ")) inds <- which(tmp!="") if(!collapse) return(tmp[inds]) else { tmp <- tmp[inds] if(length(tmp)MOLECULE", raw.lines) atom.start <- grep("@ATOM", raw.lines) bond.start <- grep("@BOND", raw.lines) num.mol <- length(mol.start) if (!num.mol>0) { stop("read.mol2: mol2 file contains no molecules") } ## Fetch molecule names and info mol.names <- raw.lines[mol.start+1] mol.info <- trim( raw.lines[mol.start+2] ) mol.info <- as.numeric(unlist(lapply(mol.info, split.line, collapse=FALSE))) ## mol.info should contain num_atoms, num_bonds, num_subs, num_feat, num_sets mol.info <- matrix(mol.info, nrow=num.mol, byrow=T) num.atoms <- as.numeric(mol.info[,1]) num.bonds <- as.numeric(mol.info[,2]) atom.end <- atom.start + num.atoms bond.end <- bond.start + num.bonds ## Build a list containing ATOM record indices se <- matrix(c(atom.start, atom.end), nrow=length(atom.start)) atom.indices <- lapply(1:num.mol, function(d) seq(se[d,1]+1, se[d,2])) se <- matrix(c(bond.start, bond.end), nrow=length(bond.start)) bond.indices <- lapply(1:num.mol, function(d) seq(se[d,1]+1, se[d,2])) ## Check if file consist of identical molecules same.mol <- TRUE mol.first <- NULL mols <- list() for ( i in 1:num.mol ) { raw.atom <- raw.lines[ atom.indices[[i]] ] raw.bond <- raw.lines[ bond.indices[[i]] ] ## Split by space txt <- unlist(lapply(raw.atom, split.line, ncol=10, collapse=TRUE)) ncol <- length(unlist(strsplit(txt[1], ";"))) atom <- read.table(text=txt, stringsAsFactors=FALSE, sep=";", quote='', colClasses=atom.format[1:ncol,"what"], col.names=atom.format[1:ncol,"name"], comment.char="", na.strings=" ") ## Split by space txt <- unlist(lapply(raw.bond, split.line, ncol=5, collapse=TRUE)) ncol <- length(unlist(strsplit(txt[1], ";"))) bond <- read.table(text=txt, stringsAsFactors=FALSE, sep=";", quote='', colClasses=bond.format[1:ncol,"what"], col.names=bond.format[1:ncol,"name"], comment.char="", na.strings=" ") ## Same molecules as the previous ones? mol.str <- paste(atom$elena, collapse="") if ( i==1 ) { mol.first <- mol.str } else if (mol.str != mol.first) { same.mol <- FALSE } ## Store data xyz <- as.xyz(as.numeric(t(atom[, c("x", "y", "z")]))) out <- list("atom" = atom, "bond" = bond, "xyz" = xyz, "info" = mol.info[i,], "name" = mol.names[i]) class(out) <- "mol2" mols[[i]] <- out } ## If identical molecules if ( length(unique(num.atoms)) == 1 && same.mol == TRUE ) { atom <- mols[[1]]$atom bond <- mols[[1]]$bond xyz <- t(sapply(lapply(mols, function(x) x$xyz), rbind)) xyz <- as.xyz(xyz) out <- list("atom" = atom, "bond" = bond, "xyz" = xyz, "info" = mol.info[1,], "name" = mol.names[1]) class(out) <- "mol2" } else { out <- mols ##class(out) <- "mol2s" } return(out) } bio3d/R/view.modes.R0000644000176200001440000001005112632622153013655 0ustar liggesusers"view.modes" <- function(modes, mode=NULL, outprefix="mode_vecs", scale=5, dual=FALSE, launch=FALSE, exefile = "pymol") { if(! (inherits(modes, "nma") || inherits(modes,"pca")) ) stop("must supply a 'nma' or 'pca' object, i.e. from 'nma()' or 'pca.xyz()'") ## Check if the program is executable if(launch) { ver <- "-cq" os1 <- .Platform$OS.type status <- system(paste(exefile, ver), ignore.stderr = TRUE, ignore.stdout = TRUE) if(!(status %in% c(0,1))) stop(paste("Launching external program failed\n", " make sure '", exefile, "' is in your search path", sep="")) } if(inherits(modes, "nma")) { if(is.null(mode)) mode <- 7 xyz <- modes$xyz mode.vecs <- matrix(modes$modes[,mode], ncol=3, byrow=T) } else if (inherits(modes,"pca")) { if(is.null(mode)) mode <- 1 xyz <- modes$mean mode.vecs <- matrix(modes$U[,mode], ncol=3, byrow=T) } ## calc all vec lengths (for coloring later) all.lens <- apply(mode.vecs, 1, function(x) sqrt(sum(x**2))) ## make temp-files if(is.null(outprefix)) { pdbfile <- tempfile(fileext = ".inpcrd.pdb") outfile <- tempfile(fileext = ".py") } else { pdbfile <- paste(outprefix, ".inpcrd.pdb", sep="") outfile <- paste(outprefix, ".py", sep="") } ## start building pymol script scr <- c("from pymol import cmd") scr <- c(scr, "from pymol.cgo import *") scr <- c(scr, paste("cmd.load('", pdbfile, "', 'prot')", sep="")) scr <- c(scr, "cmd.show('cartoon')") scr <- c(scr, "cmd.set('cartoon_trace_atoms', 1)") ## define color range blues <- colorRamp(c("blue", "white", "red")) ## Arrow widths w.body <- 0.15; w.head <- 0.2 scr <- c(scr, "obj=[]") for ( i in 1:nrow(mode.vecs)) { inds <- atom2xyz(i) coords <- xyz[inds] ## For coloring (longest vec has length=1) tmp.len <- sqrt(sum((mode.vecs[i,]/max(all.lens))**2)) if(tmp.len>1) tmp.len <- 1 col <- blues(tmp.len) col <- round(col/256,4) col <- paste(col, collapse=", ") ## Main vector tmp.vec <- mode.vecs[i,] * scale ## For arrow head if(sqrt(sum(tmp.vec**2))<1) norm.vec <- tmp.vec else norm.vec <- normalize.vector(mode.vecs[i,]) ## Set vectors arrow.vec.a <- (coords + tmp.vec) head.vec.a <- (arrow.vec.a + (norm.vec)) arrow.vec.b <- (coords - tmp.vec) head.vec.b <- (arrow.vec.b - (norm.vec)) a <- paste(coords, collapse=",") b1 <- paste(arrow.vec.a, collapse=",") c1 <- paste(head.vec.a, collapse=",") b2 <- paste(arrow.vec.b, collapse=",") c2 <- paste(head.vec.b, collapse=",") ## Arrow body scr <- c(scr, paste("obj.extend([CYLINDER", a, b1, w.body, col, col, "])", sep=", ")) if(dual) scr <- c(scr, paste("obj.extend([CYLINDER", a, b2, w.body, col, col, "])", sep=", ")) ## Arrow heads scr <- c(scr, paste("obj.extend([CONE", b1, c1, w.head, 0.0, col, col, 1.0, 1.0,"])", sep=", ")) if(dual) scr <- c(scr, paste("obj.extend([CONE", b2, c2, w.head, 0.0, col, col, 1.0, 1.0,"])", sep=", ")) } name <- "vecs" scr <- c(scr, paste("cmd.load_cgo(obj, '", name, "')", sep="")) ## Write PDB structure file write.pdb(xyz=xyz, file=pdbfile) ## Write python script or PDB with conect records write(scr, file=outfile, sep="\n") if(launch) { ## Open pymol cmd <- paste(exefile, outfile) os1 <- .Platform$OS.type if (os1 == "windows") { success <- shell(shQuote(cmd)) } else { if(Sys.info()["sysname"]=="Darwin") { success <- system(paste("open -a MacPyMOL", outfile)) } else { success <- system(cmd) } } if(success!=0) stop(paste("An error occurred while running command\n '", exefile, "'", sep="")) } } bio3d/R/dssp.R0000644000176200001440000000005412526367343012561 0ustar liggesusers"dssp" <- function(...) UseMethod("dssp") bio3d/R/layout.cna.R0000644000176200001440000000476512526367343013702 0ustar liggesuserslayout.cna <- function(x, pdb, renumber=TRUE, k=2, full=FALSE){ ## Return the coordinate centers of network communities ## as defined in "x$communities$membership" 'membership vector' ## using Calpha's in 'pdb'. ## ## If k=3 the xyz geometric centers are returned. if k<3 then ## multidimensional scaling is used for k space ordination. ## ## co2 <- layout.cna(net, pdb) ## co3 <- layout.cna(net, pdb, k=3) ## all2 <- layout.cna(net, pdb, k=2, full=TRUE) ## plot.cna(net, layout=co2) if( !inherits(pdb, "pdb") ) { stop("Input 'pdb' is not of class 'pdb' as obtained from 'read.pdb()'") } if(!k %in% c(1,2,3)) { stop("Input 'k' should have a value of 3, 2 or 1") } if(inherits(x, "cna") || is.list(x)) { if(!full) { ## We want community coords membership <- x$communities$membership } else { ## We want full all-atom/Calpha coords (check network for number) membership <- c(1:length(x$communities$membership)) } } else { if(full) { ## Assuming we want Calpha coords - this is a BIG assumption! membership <- c(1:sum(pdb$calpha)) } stop("Input object 'x' should be of class cna") } ## Renumber 'pdb' to match membership resno indices if(renumber) { pdb <- convert.pdb(pdb, renumber=TRUE, rm.h=FALSE, verbose=FALSE) } ##-- Check if the number of residues in 'pdb' equals ## the length of 'membership' vector notprotein.inds <- atom.select(pdb, "notprotein", verbose=FALSE) if(length(notprotein.inds$atom)>0){ num.res <- length(pdb$atom[pdb$calpha,"resno"]) + length(unique(pdb$atom[notprotein.inds$atom,7])) } if(length(notprotein.inds$atom)==0){ num.res <- length(pdb$atom[pdb$calpha,"resno"]) } ##-- Calculate the geometric center of each community n <- unique(membership[!is.na(membership)]) cent <- matrix(NA, nrow=length(n), ncol=3) a <- 1 for(i in n){ inds <- atom.select(pdb, resno=which(membership==i), elety="CA", verbose=FALSE) cent[a,] <- apply( matrix(pdb$xyz[inds$xyz], nrow=3), 1, mean) a <- a + 1 } if(k != 3) { ##-- Multidimensional scaling for 2D or 1D projection ## note. dist(centers) and dist.xyz(centers) give same answer if(nrow(cent) - 1 < k) { # e.g. only two communities cent <- cmdscale(dist(cent), k = nrow(cent) - 1) cent <- cbind(cent, matrix(0, nrow=nrow(cent), ncol=k-nrow(cent)+1)) } else { cent <- cmdscale(dist(cent),k=k) } } return(cent) } bio3d/R/inspect.connectivity.R0000644000176200001440000000215312526367343015774 0ustar liggesusers## Useful for checking the connectivity in a pdb(s) object "inspect.connectivity" <- function(pdbs, cut=4.) { xyz <- NULL; ids <- NULL; if(inherits(pdbs, "pdbs")) { xyz <- pdbs$xyz n <- length(pdbs$id) ids <- pdbs$id } else if(is.pdb(pdbs)) { ca.inds <- atom.select(pdbs, 'calpha', verbose=FALSE) xyz <- as.xyz(pdbs$xyz)[1, ca.inds$xyz, drop=FALSE] n <- 1 } else if(inherits(pdbs, "xyz")) { xyz <- pdbs n <- nrow(xyz) } else { stop("Please provide coordinates as a \n 'pdbs', 'pdb', or xyz matrix format") } if(length(xyz)<6) { warning("Insufficient C-alpha atoms in structure to determine connectivity") return(FALSE) } is.connected <- function(xyz) { xyz <- matrix(xyz[!is.na(xyz)], ncol=3, byrow=T) for(i in 1:(nrow(xyz)-1)) { d <- sqrt((xyz[i,1]-xyz[i+1,1])**2 + (xyz[i,2]-xyz[i+1,2])**2 + (xyz[i,3]-xyz[i+1,3])**2 ) if(d>cut) return(FALSE) } return(TRUE) } cons <- rep(NA, length=n) for(i in 1:n) { cons[i] <- is.connected(xyz[i,]) } names(cons) <- ids return(cons) } bio3d/R/overlap.R0000644000176200001440000000236212412621431013245 0ustar liggesusers"overlap" <- function(modes, dv, nmodes=20) { if(missing(modes)) stop("overlap: 'modes' must be prodivded") if(missing(dv)) stop("overlap: 'dv' must be prodivded") if ("pca" %in% class(modes)) { ev <- modes$U mass <- NULL first.mode <- 1 } else if("nma" %in% class(modes)) { ev <- modes$modes mass <- modes$mass first.mode <- modes$triv.modes+1 nmodes <- modes$triv.modes + nmodes } else { if(class(modes)!="matrix" && class(modes)!="pca.loadings") stop("overlap: 'modes' must be an object of type 'pca', 'nma', or 'matrix'") ev <- modes mass <- NULL first.mode <- 1 } if (nrow(ev)!=length(dv)) stop("overlap: unequal vector lengths") if ( ncol(ev) < nmodes ) { nmodes <- dim(ev)[2L] warning("nmodes larger than dimensions of 'modes'") } inds <- seq(first.mode, nmodes) ev <- ev[,inds] ## Normalize vectors - mass-weighted if normal modes are ev <- normalize.vector(ev, mass) dvn <- normalize.vector(dv, mass) overlap.values <- inner.prod(ev, dvn, mass)**2 cum <- cumsum(overlap.values) out <- list(overlap=overlap.values, overlap.cum=cum) return(out) } bio3d/R/rot.lsq.R0000644000176200001440000000425312412621431013200 0ustar liggesusers"rot.lsq" <-function(xx, yy, xfit=rep(TRUE,length(xx)), yfit=xfit, verbose=FALSE) { # Coordinate superposition with the Kabsch algorithm # from Acta Cryst (1978) A34 pp827-828 (to which equation no. refer) # yy is the target (i.e. fixed) xx <- matrix(xx,nrow=3, ) x <- matrix(xx[xfit],nrow=3, ) y <- matrix(yy[yfit],nrow=3, ) if(length(x) != length(y)) stop("dimension mismatch in x and y") # mean positions xbar <- apply(x,1,mean) ; ybar <- apply(y,1,mean) # center both sets xx <- sweep(xx,1,xbar) # NB xx centred on xbar x <- sweep(x,1,xbar) ; y <- sweep(y,1,ybar) #irmsd <- sqrt(sum((x-y)^2)/dim(y)[2]) #cat("#irmsd= ",round(irmsd,6),"\n") # generate the 3x3 moment matrix: R (Equation 3) R <- y %*% t(x) # form R'R RR <- t(R) %*% R # diagonalize R'R prj <- eigen(RR) prj$values[prj$values < 0 & prj$values >= -1.0E-12]<-1.0E-12 # form A A <- prj$vectors # make explicitly rh system # A[,3] <- v3cross(A[,1],A[,2]) # inline the cross-product function call. b<-A[,1]; c <- A[,2] A[1,3] <- (b[2] * c[3]) - (b[3] * c[2]) A[2,3] <- (b[3] * c[1]) - (b[1] * c[3]) A[3,3] <- (b[1] * c[2]) - (b[2] * c[1]) # form B (==RA) (Equation 8) B <- R %*% A # normalize B # B <- sweep(B,2,sqrt(apply(B^2,2,sum)),"/") B <- sweep(B,2,sqrt(prj$values),"/") # make explicitly rh system # B[,3] <- v3cross(B[,1],B[,2]) # inline the cross-product function call. b<-B[,1]; c <- B[,2] B[1,3] <- (b[2] * c[3]) - (b[3] * c[2]) B[2,3] <- (b[3] * c[1]) - (b[1] * c[3]) B[3,3] <- (b[1] * c[2]) - (b[2] * c[1]) # form U (==Ba) (Equation 7) # U is the rotation matrix U <- B %*% t(A) # here we apply transformation matrix to *all* elements of xx # rotate xx (Uxx) xx <- U %*% xx if(verbose) { ## also apply it to the subset, in order to compute residual x <- U %*% x ## estimate of residuals frmsd <- sqrt(sum((x-y)^2)/dim(y)[2]) cat("#rmsd= ",round(frmsd,6),"\n") } # fest <- iest - sum(sqrt(prj$values)) # print(sqrt((2*fest)/dim(y)[2])) # return xx centred on y xx <- sweep(xx,1,ybar,"+") as.vector(xx) } bio3d/R/amsm.xyz.R0000644000176200001440000000611212544562302013367 0ustar liggesusers## class DistanceCalculator --> calculate() "amsm.xyz" <- function(xyz, ncore=NULL) { if(!is.matrix(xyz)) stop("'xyz' must be a trajectory matrix") natoms <- ncol(xyz) / 3 atom.pairs <- natoms * (natoms -1) / 2 ## Distance Calculator ## returns a list of 4x4 matrices M <- .amsm.distCalc(xyz, ncore=ncore) ## we use 'list' in this version of the code if(class(M)=="list") Mlist <- TRUE else Mlist <- FALSE ## assign the atom mov. sim. matrix matrixCorr <- matrix(0, nrow=natoms, ncol=natoms) maxEigenVal <- 0 ## solve eigenvalue problems ij <- combn(natoms,2) for ( i in 1:atom.pairs ) { atom.inds <- ij[,i] if(Mlist) { ev <- eigen(M[[i]]) maxDist <- M[[i]][1,1] } else { ev <- eigen(M[,,i]) maxDist <- M[1,1,i] } maxEigenVal <- max(ev$values) hei <- 1 - (sqrt(maxDist / maxEigenVal)) matrixCorr[atom.inds[1], atom.inds[2]] <- hei matrixCorr[atom.inds[2], atom.inds[1]] <- hei ##matrixCorr[atom.inds[1], atom.inds[2]] <- maxDist ##matrixCorr[atom.inds[2], atom.inds[1]] <- maxDist ##if(max(ev$values)>maxEigenVal) ## maxEigenVal <- max(ev$values) } ##matrixCorr <- 1 - sqrt(matrixCorr / maxEigenVal) diag(matrixCorr) <- 1 return(matrixCorr) } ## class DistanceCalculator --> DistanceCalculator() ".amsm.distCalc" <- function(xyz, ncore=NULL) { if(!is.matrix(xyz)) stop("'xyz' must be a trajectory matrix") ## Parallelized by package 'parallel' ncore <- setup.ncore(ncore, bigmem = FALSE) ## used for vectProdSum vectPS <- function(xyz.ab) { a <- xyz.ab[1:3]; b <- xyz.ab[4:6]; m <- (a[2] * b[3]) - (a[3] * b[2]) n <- (a[3] * b[1]) - (a[1] * b[3]) o <- (a[1] * b[2]) - (a[2] * b[1]) return(c(m,n,o)) } ## used for matrixPSum matrPS <- function(xyz.ab) { a <- xyz.ab[1:3]; b <- xyz.ab[4:6]; m <- a[1] * b + b[1] * a n <- a[2] * b + b[2] * a o <- a[3] * b + b[3] * a return(c(m,n,o)) } for.atompair2 <- function(i, xyz, ij, M) { ij <- ij[,i] inds <- rep(ij*3,each=3) - c(2,1,0) xyz.ab <- xyz[, inds] vectProdSum <- rowSums(apply(xyz.ab, 1, vectPS)) matrixPSum <- rowSums(apply(xyz.ab, 1, matrPS)) suma <- sum((xyz.ab[,1:3]-xyz.ab[,4:6])^2) ##M <- matrix(0, ncol=4, nrow=4) M[1,1] = suma M[1,2] = vectProdSum[1]; M[1,3] = vectProdSum[2]; M[1,4] = vectProdSum[3]; M[2,2] = matrixPSum[1]; M[2,3] = matrixPSum[2]; M[2,4] = matrixPSum[3]; M[3,3] = matrixPSum[5]; M[3,4] = matrixPSum[6]; M[4,4] = matrixPSum[9]; ## symmerty M[2,1] = M[1,2] M[3,1] = M[1,3] M[4,1] = M[1,4] M[3,2] = M[2,3] M[4,2] = M[2,4] M[4,3] = M[3,4] return(M) } ##atom.pairs <- natoms * (natoms -1) / 2 natoms <- ncol(xyz) / 3 M <- matrix(0, ncol=4, nrow=4) ij <- combn(natoms,2) if(ncore==1) { all.Ms <- lapply(1:ncol(ij), for.atompair2, xyz, ij, M) } else { all.Ms <- mclapply(1:ncol(ij), for.atompair2, xyz, ij, M, mc.cores=ncore) } return(all.Ms) } bio3d/R/lmi.R0000644000176200001440000000202512524171274012363 0ustar liggesuserslmi <- function (trj, grpby = NULL, ncore=1) { ncore <- setup.ncore(ncore) # rm:r-value matrix cm <- var(trj) # mclapply or lapply if (ncore > 1) { lmiapply = mclapply } else { lmiapply = lapply } rm <- cov2dccm(cm, method = "lmi", ncore = ncore) # group by or not if (!is.null(grpby)) { if (ncol(trj) != (length(grpby) * 3)) stop("dimension miss-match in 'trj' and 'grpby', check lengths") inds <- bounds(grpby, dup.inds = TRUE) l <- dim(inds)[1] m <- matrix(, ncol = l, nrow = l) ij <- pairwise(l) # list3: lmi list3 <- lmiapply(1:nrow(ij), function(k) max(rm[(inds[ij[k, 1], "start"]:inds[ij[k, 1], "end"]), (inds[ij[k, 2], "start"]:inds[ij[k, 2], "end"])], na.rm = TRUE)) list3 <- unlist(list3) for (k in 1:nrow(ij)) { m[ij[k, 1], ij[k, 2]] <- list3[k] } m[lower.tri(m)] = t(m)[lower.tri(m)] diag(m) <- 1; rm=m } class(rm) = c("dccm", "matrix") return(rm) } bio3d/R/vec2resno.R0000644000176200001440000000063312524171274013513 0ustar liggesusers`vec2resno` <- function(vec, resno) { ## replicate vec based on concetive ## similar resno entries if(is.pdb(resno)) resno <- resno$atom[,"resno"] res.len <- rle(resno)$lengths if(length(vec) != length(res.len)) stop("Length miss-match of 'vec' and concetive 'resno'") if( sum(res.len) != length(resno) ) stop("Replicated length Miss-match") return( rep(vec, times=res.len)) } bio3d/R/identify.cna.R0000644000176200001440000000250312526367343014164 0ustar liggesusersidentify.cna <- function(x, labels=NULL, cna=NULL, ...){ ## Be carefull with input argument order ## - 'labels' can take any input and screw up priniting ## e.g. if you pass cna as the second argument! ## Should this perhaps be able to take just a cna object as input ## - Possible if cna object has layout defined ## - Could take extra layout option for custom graphs ## x <- plot(net) ## ## d <- identify.cna(x, cna=net) ## d <- identify.cna(x, labels=summary(net)$members) oops <- requireNamespace("igraph", quietly = TRUE) if (!oops) { stop("igraph package missing: Please install, see: ?install.packages") } if(dim(x)[2] != 2){ stop("'x' object must be a Nx2 numeric matrix") } x.norm <- igraph::layout.norm(x, -1, 1, -1, 1) if( !is.null(labels) ) { ## Use input labels inds <- identify(x.norm[,1], x.norm[,2], labels, ...) return( labels[inds] ) } else { if(is.null(cna)) { ## Use standard labels inds <- identify(x.norm[,1], x.norm[,2], ...) return(inds) } else { ## Take labels from cna object!! labels.all <- summary.cna(cna) labels.short <- labels.all$tbl$members labels.full <- labels.all$members inds <- identify(x.norm[,1], x.norm[,2], labels.short, ...) return( labels.full[inds] ) } } } bio3d/R/bounds.sse.R0000644000176200001440000000627712632622153013677 0ustar liggesusers#' Obtain A SSE Object From An SSE Sequence Vector #' #' Inverse process of the funciton \code{\link{pdb2sse}}. #' #' @details call for its effects. #' #' @param x a character vector indicating SSE for each amino acid residue. #' @param pdb an object of class \code{pdb} as obtained from #' function \code{\link{read.pdb}}. Can be ignored if \code{x} has 'names' #' attribute for residue labels. #' #' @return a 'sse' object. #' #' @note In both \code{$helix} and \code{$sheet}, an additional #' \code{$id} component is added to indicate the original numbering of the sse. #' This is particularly useful in e.g. \code{trim.pdb()} function. #' #' @seealso \code{\link{pdb2sse}} #' #' @author Xin-Qiu Yao & Barry Grant #' #' @examples #' \donttest{ #' pdb <- read.pdb("1a7l") #' sse <- pdb2sse(pdb) #' sse.ind <- bounds.sse(sse) #' sse.ind #' } bounds.sse <- function(x, pdb=NULL) { if(length(x) == 0) return (NULL) strings <- names(x) if(is.null(strings)) { if(!is.null(pdb)) { strings <- paste(pdb$atom[pdb$calpha, "resno"], pdb$atom[pdb$calpha, "chain"], pdb$atom[pdb$calpha, "insert"], sep = "_") if(length(strings) != length(x)) stop("pdb doesn't match x") } else { strings <- paste(seq_along(x), NA, NA, sep="_") } } else { if(!is.null(pdb)) { warning("The x has 'names' attributes. The pdb is ignored") } } lstrings <- strsplit(strings, split="_") resno <- as.numeric(sapply(lstrings, "[", 1)) chain <- sapply(lstrings, "[", 2) chain[chain == "NA"] <- "" insert <- sapply(lstrings, "[", 3) insert[insert == "NA"] <- "" id <- as.numeric(sapply(lstrings, "[", 4)) sse.string <- paste(x, chain, id, sep="_") # bounds doesn't work, use rle2 instead rl <- rle2(sse.string) inds <- cbind(seq_along(rl$inds), start = c(1, rl$inds[-length(rl$inds)]+1), end = rl$inds, length = rl$lengths ) # inds <- bounds(sse.string, dup.inds=TRUE, pre.sort=FALSE) # sort segments based on sse id (i.e. keep the original order of sse) ind.order <- order(id[inds[, "start"]]) inds <- inds[ind.order, , drop = FALSE] # helix h.inds <- which(x[inds[, "start"]] == "H") if(length(h.inds) > 0) { h.id <- id[inds[h.inds, "start"]] if(any(is.na(h.id))) h.id <- seq_along(h.id) h <- list(start = resno[inds[h.inds, "start"]], end = resno[inds[h.inds, "end"]], chain = chain[inds[h.inds, "start"]], id = h.id) names(h$start) <- insert[inds[h.inds, "start"]] names(h$end) <- insert[inds[h.inds, "end"]] } else { h <- list(start=NULL, end=NULL, chain=NULL, id=NULL) } # sheet e.inds <- which(x[inds[, "start"]] == "E") if(length(e.inds) > 0) { e.id <- id[inds[e.inds, "start"]] if(any(is.na(e.id))) e.id <- seq_along(e.id) e <- list(start = resno[inds[e.inds, "start"]], end = resno[inds[e.inds, "end"]], chain = chain[inds[e.inds, "start"]], id = e.id) names(e$start) <- insert[inds[e.inds, "start"]] names(e$end) <- insert[inds[e.inds, "end"]] } else { e <- list(start=NULL, end=NULL, chain=NULL, id=NULL) } sse <- list(helix = h, sheet = e) class(sse) <- 'sse' return( sse ) } bio3d/R/atom.select.pdb.R0000644000176200001440000001441412632622153014566 0ustar liggesusers".is.protein" <- function(pdb, byres=TRUE) { if(byres) { return(.is.protein1(pdb)) } else { ## possible option to issue a warning when the two methods diverge sel1 <- .is.protein1(pdb) sel2 <- .is.protein2(pdb) if(!(identical(sel1, sel2))) { sel <- cbind(sel1, sel2) sums <- apply(sel, 1, sum) inds <- which(sums==1) unq <- paste(unique(pdb$atom$resid[inds]), collapse=",") warning(paste("possible protein residue(s) with non-standard residue name(s) \n (", unq, ")")) } return(sel1) } } ".is.protein1" <- function(pdb) { aa <- bio3d::aa.table$aa3 return(pdb$atom$resid %in% aa) } ".is.protein2" <- function(pdb) { resid <- paste(pdb$atom$chain, pdb$atom$insert, pdb$atom$resno, sep="-") at.ca <- resid[ pdb$atom$elety == "CA"] at.o <- resid[ pdb$atom$elety == "O" ] at.c <- resid[ pdb$atom$elety == "C" ] at.n <- resid[ pdb$atom$elety == "N" ] common <- intersect(intersect(intersect(at.ca, at.o), at.n), at.c) return(resid %in% common) } ".is.nucleic" <- function(pdb) { nuc.aa <- c("A", "U", "G", "C", "T", "I", "DA", "DU", "DG", "DC", "DT", "DI") return(pdb$atom$resid %in% nuc.aa) } ".is.water" <- function(pdb) { hoh <- c("H2O", "OH2", "HOH", "HHO", "OHH", "SOL", "WAT", "TIP", "TIP2", "TIP3", "TIP4") return(pdb$atom$resid %in% hoh) } ".is.hydrogen" <- function(pdb) { return(substr( gsub("^[123]", "",pdb$atom$elety) , 1, 1) %in% "H") } .match.type <- function(pdb, t) { if(!is.character(t)) stop("'type' must be a character vector") pdb$atom$type %in% t } .match.eleno <- function(pdb, eleno) { if(!is.numeric(eleno)) stop("'eleno' must be a numeric vector") pdb$atom$eleno %in% eleno } .match.elety <- function(pdb, elety) { if(!is.character(elety)) stop("'elety' must be a character vector") pdb$atom$elety %in% elety } .match.resid <- function(pdb, resid) { if(!is.character(resid)) stop("'resid' must be a character vector") pdb$atom$resid %in% resid } .match.chain <- function(pdb, chain) { if(!is.character(chain)) stop("'chain' must be a character vector") pdb$atom$chain %in% chain } .match.resno <- function(pdb, resno) { if(!is.numeric(resno)) stop("'resno' must be a numeric vector") pdb$atom$resno %in% resno } .match.segid <- function(pdb, segid) { if(!is.character(segid)) stop("'segid' must be a character vector") pdb$atom$segid %in% segid } atom.select.pdb <- function(pdb, string = NULL, type = NULL, eleno = NULL, elety = NULL, resid = NULL, chain = NULL, resno = NULL, segid = NULL, operator = "AND", inverse = FALSE, value = FALSE, verbose=FALSE, ...) { if(!is.pdb(pdb)) stop("'pdb' must be an object of class 'pdb'") ## check input operator op.tbl <- c(rep("AND",3), rep("OR",4)) operator <- op.tbl[match(operator, c("AND","and","&","OR","or","|","+"))] if(!operator %in% c("AND", "OR")) stop("Allowed values for 'operator' are 'AND' or 'OR'") ## check input string if(!is.null(string)) { str.allowed <- c("all", "protein", "notprotein", "nucleic", "notnucleic", "water", "notwater", "calpha", "cbeta", "backbone", "back", "ligand", "h", "noh") if(!(string %in% str.allowed)) stop("Unknown 'string' keyword. See documentation for allowed values") } ## verbose message output if(verbose) cat("\n") .verboseout <- function(M, type) { cat(" .. ", sprintf("%08s", length(which(M))), " atom(s) from '", type, "' selection \n", sep="") } ## combine logical vectors .combinelv <- function(L, M, operator) { if(operator=="AND") M <- L & M if(operator=="OR") M <- L | M return(M) } cl <- match.call() if(operator=="AND") M <- rep(TRUE, nrow(pdb$atom)) if(operator=="OR") M <- rep(FALSE, nrow(pdb$atom)) if(!is.null(string)) { M <- switch(string, all = M <- rep(TRUE, nrow(pdb$atom)), protein = .is.protein(pdb), notprotein = !.is.protein(pdb), nucleic = .is.nucleic(pdb), notnucleic = !.is.nucleic(pdb), water = .is.water(pdb), notwater = !.is.water(pdb), calpha = .is.protein(pdb) & .match.elety(pdb, "CA"), cbeta = .is.protein(pdb) & .match.elety(pdb, c("CA", "N", "C", "O", "CB")), backbone = .is.protein(pdb) & .match.elety(pdb, c("CA", "N", "C", "O")), back = .is.protein(pdb) & .match.elety(pdb, c("CA", "N", "C", "O")), ligand = !.is.protein(pdb) & !.is.nucleic(pdb) & !.is.water(pdb), h = .is.hydrogen(pdb), noh = !.is.hydrogen(pdb), NA ) if(verbose) { .verboseout(M, 'string') } } if(!is.null(type)) { L <- .match.type(pdb, type) if(verbose) .verboseout(L, 'type') M <- .combinelv(L, M, operator) } if(!is.null(eleno)) { L <- .match.eleno(pdb, eleno) if(verbose) .verboseout(L, 'eleno') M <- .combinelv(L, M, operator) } if(!is.null(elety)) { L <- .match.elety(pdb, elety) if(verbose) .verboseout(L, 'elety') M <- .combinelv(L, M, operator) } if(!is.null(resid)) { L <- .match.resid(pdb, resid) if(verbose) .verboseout(L, 'resid') M <- .combinelv(L, M, operator) } if(!is.null(chain)) { L <- .match.chain(pdb, chain) if(verbose) .verboseout(L, 'chain') M <- .combinelv(L, M, operator) } if(!is.null(resno)) { L <- .match.resno(pdb, resno) if(verbose) .verboseout(L, 'resno') M <- .combinelv(L, M, operator) } if(!is.null(segid)) { L <- .match.segid(pdb, segid) if(verbose) .verboseout(L, 'segid') M <- .combinelv(L, M, operator) } if(verbose) cat(" ..", sprintf("%08s", length(which(M))), "atom(s) in final combined selection \n") if(inverse) { if(verbose) { cat(" ..", sprintf("%08s", length(which(!M))), "atom(s) in inversed selection \n") } sele <- as.select(which(!M)) } else sele <- as.select(which(M)) sele$call <- cl if(verbose) cat("\n") if(value) return(trim.pdb(pdb, sele)) else return(sele) } bio3d/R/setup.ncore.R0000644000176200001440000000202112526367343014051 0ustar liggesuserssetup.ncore <- function(ncore, bigmem = FALSE) { if(is.null(ncore) || ncore > 1) { os1 <- .Platform$OS.type if(os1 == "windows") { if(is.null(ncore)) ncore = 1 else stop("Multicore is NOT supported in Windows (Set ncore = 1 or NULL)") } else { if(bigmem) { oops <- requireNamespace("bigmemory", quietly = TRUE) if(!oops) { if(is.null(ncore)) ncore <- 1 else stop("Please install the bigmemory package from CRAN for running with multicore") } } if(is.null(ncore)) ncore = parallel::detectCores() # Following lines check R internal varible for potential limit on multicore usage # Normally it does nothing, but will be helpful in running `R CMD check --as-cran` if(ncore > 1) { chk <- tolower(Sys.getenv("_R_CHECK_LIMIT_CORES_", "")) if (nzchar(chk) && (chk != "false")) ncore = 1 } } } options(mc.cores = ncore) return(ncore) } bio3d/R/fit.xyz.R0000644000176200001440000001722712544562302013225 0ustar liggesusers"fit.xyz" <- function(fixed, mobile, fixed.inds = NULL, mobile.inds = NULL, verbose = FALSE, prefix = "", pdbext = "", outpath = "fitlsq", full.pdbs=FALSE, ncore=1, nseg.scale=1, # to resolve the memory problem in using multicore ...) { # Parallelized by parallel package (Tue Dec 11 17:41:08 EST 2012) ncore <- setup.ncore(ncore) if(ncore > 1) { # Issue of serialization problem # Maximal number of cells of a double-precision matrix # that each core can serialize: (2^31-1-61)/8 R_NCELL_LIMIT_CORE = 2.68435448e8 R_NCELL_LIMIT = ncore * R_NCELL_LIMIT_CORE if(nseg.scale < 1) { warning("nseg.scale should be 1 or a larger integer\n") nseg.scale=1 } } ### Addation (Mon Jul 23 17:26:16 PDT 2007) if( is.null(fixed.inds) && is.null(mobile.inds) ) { if(is.list(mobile)) { fixed.inds <- intersect(which(!is.gap(fixed)), gap.inspect(mobile$xyz)$f.inds ) } else { fixed.inds <- intersect(which(!is.gap(fixed)), gap.inspect(mobile)$f.inds ) } mobile.inds <- fixed.inds warning(paste("No fitting indices provided, using the", length(fixed.inds)/3, "non NA positions\n")) } if (is.null(fixed.inds)) fixed.inds=which(!is.gap(fixed)) if (is.null(mobile.inds)) mobile.inds=gap.inspect(mobile)$f.inds if (length(fixed.inds) != length(mobile.inds)) stop("length of 'fixed.inds' != length of 'mobile.inds'") # if(!is.xyz(fixed) || !is.numeric(fixed)) if(!is.numeric(fixed)) stop("input 'fixed' should be a numeric 'xyz' vector or matrix") if(is.vector(mobile)) { # INPUT is a single vector if(!is.numeric(mobile)) stop("input 'mobile' should be numeric") if( any(is.na(fixed[fixed.inds])) || any(is.na(mobile[mobile.inds])) ) { stop(" NA elements selected for fitting (check indices)") } fit <- rot.lsq(xx=mobile, yy=fixed, xfit=mobile.inds, yfit=fixed.inds, verbose=verbose) return(as.xyz(fit)) } else { if(is.list(mobile)) { # INPUT is a list object if(!is.numeric(mobile$xyz)) stop("non numeric input 'mobile$xyz'") if( any(is.na(fixed[fixed.inds])) || any(is.na(mobile$xyz[,mobile.inds])) ) { stop(" NA elements selected for fitting (check indices)") } if(ncore>1 && is.matrix(mobile$xyz) ) { # Parallelized RLIMIT = floor(R_NCELL_LIMIT/ncol(mobile$xyz)) nDataSeg = floor((nrow(mobile$xyz)-1)/RLIMIT)+1 nDataSeg = floor(nDataSeg * nseg.scale) lenSeg = floor(nrow(mobile$xyz)/nDataSeg) fit = vector("list", nDataSeg) for(i in 1:nDataSeg) { istart = (i-1)*lenSeg + 1 iend = if(i1) mylapply <- mclapply # for(i in 1:length(mobile$id)) { mylapply(1:length(mobile$id), function(i) { ### pdb <- read.pdb( paste(pdb.path,"/",mobile$id[i],pdbext,sep=""), ... ) pdb <- read.pdb( full.files[i], ... ) res.resno <- mobile$resno[i,core.inds.atom] res.chains <- mobile$chain[i,core.inds.atom] chains <- unique(res.chains[!is.na(res.chains)]) if(length(chains)==0) { ##string <- paste("///", ## paste(mobile$resno[i,core.inds.atom],collapse = ","), ## "///CA/", sep="") inds <- atom.select(pdb, resno=res.resno, elety="CA", verbose=verbose)$xyz } else { if(length(chains)==1) { #string <- paste("//",chains,"/", # paste(res.resno, collapse = ","), # "///CA/", sep="") inds <- atom.select(pdb, resno=res.resno, chain=chains, elety="CA", verbose=verbose)$xyz } else { # indices for each chain inds <- NULL for(j in 1:length(chains)) { #string <- paste("//",chains[j],"/", # paste(res.resno[ res.chains==chains[j] ], # collapse = ","), # "///CA/", sep="") inds <- c(inds, atom.select(pdb, resno=res.resno[ res.chains==chains[j] ], chain=chains[j], elety="CA", verbose=verbose)$xyz) } } } pdb.xyz <- pdb$xyz #if (het) # pdb.xyz <- c(pdb.xyz, # as.numeric(t(pdb$het[,c("x","y","z")]))) if(length(inds) > length(fixed.inds)) { warning("Looks like we have a multi-chain pdb with no chain id: ignoring extra indices\n\t") inds <- inds[1:length(fixed.inds)] } xyz.fit <- rot.lsq(xx=pdb.xyz, yy=fixed, xfit=inds, # sort!! yfit=fixed.inds) write.pdb(xyz = xyz.fit, pdb = pdb, ##het = het, file = file.path(outpath, paste(basename(mobile$id[i]), "_flsq.pdb",sep = "")) ) return (NULL) } ) } return(as.xyz(fit)) } else { if(full.pdbs) warning("Need 'mobile' list object for 'full.pdbs=TRUE'") if(is.matrix(mobile)) { # INPUT is a matrix if(!is.numeric(mobile)) stop("input 'mobile' should be numeric") if( any(is.na(fixed[fixed.inds])) || any(is.na(mobile[,mobile.inds])) ) { stop("error: NA elements selected for fitting") } if(ncore > 1) { # Parallelized RLIMIT = floor(R_NCELL_LIMIT/ncol(mobile)) nDataSeg = floor((nrow(mobile)-1)/RLIMIT)+1 nDataSeg = floor(nDataSeg * nseg.scale) lenSeg = floor(nrow(mobile)/nDataSeg) fit = vector("list", nDataSeg) for(i in 1:nDataSeg) { istart = (i-1)*lenSeg + 1 iend = if(i1) xyz=xyz[1,,drop=FALSE] xyz=as.vector(xyz) xyz <- matrix(xyz, ncol=3, byrow=TRUE) natoms <- nrow(xyz) ## Check provided weight matrix if(!is.null(fc.weights)) { if(!is.matrix(fc.weights)) stop("'fc.weights' must be a numeric matrix") if((nrow(fc.weights) != natoms) || (ncol(fc.weights) != natoms) ) stop("'fc.weights' must be numeric matrix with dimensions NxN") } build.submatrix <- function(xyz, natoms, fc.weights=NULL, ssdat=NULL, ...) { ## Full Hessian Hsm <- matrix(0, ncol=3*natoms, nrow=3*natoms) ## Indices relating atoms and columns in the sub-hessian col.inds <- seq(1, ncol(Hsm), by=3) ## Weight indices inds <- rep(1:natoms, each=3) ## Convenient indices for accessing the hessian inds.x <- seq(1, natoms*3, by=3) inds.y <- inds.x+1 inds.z <- inds.x+2 for ( i in 1:natoms ) { ## Calculate difference vectors and force constants diff.vect <- t(t(xyz) - xyz[i,]) ##dists <- apply(diff.vect, 1, function(x) sqrt(sum(x**2))) dists <- sqrt(rowSums(diff.vect**2)) ## quicker ! ## pfc.fun takes a vector of distances if("ssdat" %in% names(formals( pfc.fun )) && "atom.id" %in% names(formals( pfc.fun )) ) force.constants <- pfc.fun(dists, atom.id=i, ssdat=ssdat, ...) else force.constants <- pfc.fun(dists, ...) ## Scale the force constants if(!is.null(fc.weights)) { force.constants <- force.constants * fc.weights[i,] } force.constants <- (-1) * force.constants / (dists**2) ## since we divide on zero, ensure no Inf values force.constants[i] <- 0 diff.vect[i,] <- 0 ## Hessian elements dxx <- diff.vect[,1] * diff.vect[,1] * force.constants dyy <- diff.vect[,2] * diff.vect[,2] * force.constants dzz <- diff.vect[,3] * diff.vect[,3] * force.constants dxy <- diff.vect[,1] * diff.vect[,2] * force.constants dxz <- diff.vect[,1] * diff.vect[,3] * force.constants dyz <- diff.vect[,2] * diff.vect[,3] * force.constants ## Place the elements m <- col.inds[i] ## Off-diagonals Hsm[inds.x, m ] <- dxx Hsm[inds.y, m+1 ] <- dyy Hsm[inds.z, m+2 ] <- dzz Hsm[inds.y, m ] <- dxy Hsm[inds.z, m ] <- dxz Hsm[inds.x, m+1 ] <- dxy Hsm[inds.z, m+1 ] <- dyz Hsm[inds.x, m+2 ] <- dxz Hsm[inds.y, m+2 ] <- dyz ## Diagonal super elements Hsm[inds.x[i], m] <- sum(Hsm[inds.x, m]) * (-1) Hsm[inds.y[i], m] <- sum(Hsm[inds.y, m]) * (-1) Hsm[inds.z[i], m] <- sum(Hsm[inds.z, m]) * (-1) Hsm[inds.x[i], m+1] <- sum(Hsm[inds.x, m+1]) * (-1) Hsm[inds.y[i], m+1] <- sum(Hsm[inds.y, m+1]) * (-1) Hsm[inds.z[i], m+1] <- sum(Hsm[inds.z, m+1]) * (-1) Hsm[inds.x[i], m+2] <- sum(Hsm[inds.x, m+2]) * (-1) Hsm[inds.y[i], m+2] <- sum(Hsm[inds.y, m+2]) * (-1) Hsm[inds.z[i], m+2] <- sum(Hsm[inds.z, m+2]) * (-1) } return(Hsm) } ## Sequence-structure properties in a list ssdat <- list() ## Sequence if(!is.null(sequ)) { ssdat$seq <- sequ } else { ssdat$seq <- NULL } ## SSE if(!is.null(sse)) { if(is.null(sse$call$resno) | is.null(sse$call$full)) { use.sse <- FALSE } else { if((sse$call$resno=="F" | sse$call$resno=="FALSE") & (sse$call$full=="T" | sse$call$full=="TRUE" )) use.sse <- TRUE else use.sse <- FALSE } ssdat$sse <- sse if(!use.sse) warning("sse argument ignored: use 'dssp' with 'resno=FALSE' and 'full=TRUE'") } else { use.sse <- FALSE ssdat$sse <- NULL } ## SS-bonds if(!is.null(ss.bonds)) { if(class(ss.bonds)!="matrix") stop("ss.bonds must be two a column matrix") if(ncol(ss.bonds)!=2) stop("ss.bonds must be two a column matrix") ssdat$ss.bonds <- rbind(ss.bonds, ss.bonds[,2:1]) } else { ssdat$ss.bonds <- NULL } ## Identify bridge pairs if(use.sse) { ## Helix 1-4 interactions ssdat$helix14 <- sse.bridges(sse, type="helix", hbond=TRUE, energy.cut=-1.0) ssdat$helix14 <- rbind(ssdat$helix14, ssdat$helix14[,2:1]) ## Beta bridges ssdat$beta.bridges <- sse.bridges(sse, type="sheet", hbond=TRUE, energy.cut=-1.0) ssdat$beta.bridges <- rbind(ssdat$beta.bridges, ssdat$beta.bridges[,2:1]) } else { ssdat$helix14 <- NULL ssdat$beta.bridges <- NULL } H <- build.submatrix(xyz=xyz, natoms=natoms, fc.weights=fc.weights, ssdat=ssdat, ... ) return(H) } bio3d/R/nma.pdbs.R0000644000176200001440000003100112632622153013275 0ustar liggesusers## Two options - both calculating modes on the FULL structure: ## 1 - use k <- kaa - ((kaq %*% kqq.inv) %*% kqa) to derive hessian for core atoms ## 2 - return the full objects "nma.pdbs" <- function(pdbs, fit=TRUE, full=FALSE, subspace=NULL, rm.gaps=TRUE, varweight=FALSE, outpath = NULL, ncore=1, ...) { if(!inherits(pdbs, "pdbs")) stop("input 'pdbs' should be a list object as obtained from 'read.fasta.pdb'") ## Log the call cl <- match.call() if(!is.null(outpath)) dir.create(outpath, FALSE) ## Parallelized by parallel package ncore <- setup.ncore(ncore, bigmem = TRUE) prev.warn <- getOption("warn") if(ncore>1) { mylapply <- mclapply options(warn=1) } else mylapply <- lapply ## Passing arguments to functions aa2mass and nma am.names <- names(formals( aa2mass )) nm.names <- names(formals( nma.pdb )) dots <- list(...) am.args <- dots[names(dots) %in% am.names] nm.args <- dots[names(dots) %in% nm.names] ## Limiting input if("mass" %in% names(nm.args)) mass <- nm.args$mass else mass <- TRUE if("ff" %in% names(nm.args)) ff <- nm.args$ff else ff <- 'calpha' if("temp" %in% names(nm.args)) temp <- nm.args$temp else temp <- 300 if("keep" %in% names(nm.args)) nm.keep <- nm.args$keep else nm.keep <- NULL if(!all((names(nm.args) %in% c("mass", "ff", "temp", "keep")))) { war <- paste(names(nm.args)[! names(nm.args) %in% c("mass", "ff", "temp", "keep") ], collapse=", ") warning(paste("ignoring arguments:", war)) } ## Force field pfc.fun <- load.enmff(ff) ## Check for optional arguments to pfc.fun ff.names <- names(formals( pfc.fun )) ff.args <- dots[names(dots) %in% ff.names] ## Set indicies gaps.res <- gap.inspect(pdbs$resno) gaps.pos <- gap.inspect(pdbs$xyz) ## check for missing masses before we start calculating if(any(pdbs$ali=="X") & mass==TRUE) { resnames <- c(bio3d::aa.table$aa3, names(am.args$mass.custom)) ops.inds <- which(pdbs$ali=="X", arr.ind=TRUE) unknowns <- c() for(i in 1:nrow(ops.inds)) { j <- ops.inds[i, ] resid <- pdbs$resid[j[1], j[2]] if(any(!(resid %in% resnames))) unknowns <- c(unknowns, resid[!resid%in%resnames]) } if(length(unknowns)>0) { options(warn=prev.warn) unknowns <- paste(unique(unknowns), collapse=", ") stop(paste0("Unknown mass for amino acid(s): ", unknowns, "\n Provide mass with argument 'mass.custom=list(", unknowns[1], "=100.00)',", "\n or ommit mass weighting with argument 'mass=FALSE'.")) } } ## Check connectivity con <- inspect.connectivity(pdbs, cut=4.05) if(!all(con)) { warning(paste(paste(basename(pdbs$id[which(!con)]), collapse=", "), "might have missing residue(s) in structure:\n", " Fluctuations at neighboring positions may be affected.")) } ## Use for later indexing pdbs$inds <- matrix(NA, ncol=ncol(pdbs$resno), nrow=nrow(pdbs$resno)) ## Number of modes to store in U.subspace if(is.null(subspace)) { keep <- length(gaps.pos$f.inds)-6 } else { keep <- subspace if (length(gaps.pos$f.inds) < (keep+6)) keep <- length(gaps.pos$f.inds)-6 } ## Coordiantes - fit or not if(fit) { xyz <- fit.xyz(fixed = pdbs$xyz[1, ], mobile = pdbs, fixed.inds = gaps.pos$f.inds, mobile.inds = gaps.pos$f.inds, ncore = ncore) } else xyz <- pdbs$xyz ## Fluctuations for each structure if(rm.gaps) flucts <- matrix(NA, nrow=nrow(gaps.res$bin), ncol=length(gaps.res$f.inds)) else flucts <- matrix(NA, nrow=nrow(gaps.res$bin), ncol=ncol(gaps.res$bin)) ## store residue numbers (same as pdbs$inds[,gaps$f.inds]) ##resnos <- flucts ## List object to store each modes object if(full) all.modes <- list() else all.modes <- NULL ## 3D array- containing the modes vectors for each structure if(rm.gaps) modes.array <- array(NA, dim=c(length(gaps.pos$f.inds), keep, nrow(gaps.res$bin))) else modes.array <- array(NA, dim=c(ncol(pdbs$xyz), keep, nrow(gaps.res$bin))) ## store eigenvalues of the first modes L.mat <- matrix(NA, ncol=keep, nrow=nrow(gaps.res$bin)) if(is.null(outpath)) fname <- tempfile(fileext = "pdb") ##### Start calculation of variance weighting ##### wts <- NULL if(is.logical(varweight)) { if(varweight) wts <- var.xyz(xyz, weights=TRUE) } else { dims.vw <- dim(varweight) if(all(dims==ncol(xyz)/3)) { wts <- varweight varweight <- TRUE } else stop("incompatible length of varweight vector") } ### Memory usage ### dims <- dim(modes.array) mem.usage <- sum(c(as.numeric(object.size(modes.array)), as.numeric(object.size(L.mat)), as.numeric(object.size(flucts)), as.numeric(object.size(matrix(NA, ncol=dims[3], nrow=dims[3]))) ))*2 if(full) { if(is.null(nm.keep)) tmpncol <- dims[2] else tmpncol <- nm.keep size.mat <- object.size(matrix(0.00000001, ncol=tmpncol, nrow=dims[1])) size.vec <- object.size(vector(length=dims[1], 'numeric')) tot.size <- ((size.mat * 2) + (size.vec * 4)) * length(pdbs$id) mem.usage <- mem.usage+tot.size } mem.usage=round(mem.usage/1048600,1) #### Print overview of scheduled calcualtion #### cat("\nDetails of Scheduled Calculation:\n") cat(paste(" ...", length(pdbs$id), "input structures", "\n")) if(keep>0) cat(paste(" ...", "storing", keep, "eigenvectors for each structure", "\n")) if(keep>0) cat(paste(" ...", "dimension of x$U.subspace: (", paste(dims[1], dims[2], dims[3], sep="x"), ")\n")) if(fit) cat(paste(" ...", "coordinate superposition prior to NM calculation", "\n")) if(varweight) cat(paste(" ...", "weighting force constants based on structural variance", "\n")) if(full) cat(paste(" ... individual complete 'nma' objects will be stored", "\n")) if(rm.gaps) cat(paste(" ... aligned eigenvectors (gap containing positions removed) ", "\n")) if(mem.usage>0) cat(paste(" ...", "estimated memory usage of final 'eNMA' object:", mem.usage, "Mb \n")) cat("\n") ##### Start modes calculation ##### pb <- txtProgressBar(min=0, max=length(pdbs$id), style=3) ## shared memory to follow progress bar if(ncore>1) iipb <- bigmemory::big.matrix(1, length(pdbs$id), init=NA) ## call .calcAlnModes for each structure in 'pdbs' alnModes <- mylapply(1:length(pdbs$id), .calcAlnModes, pdbs, xyz, gaps.res, mass, am.args, nm.keep, temp, keep, wts, rm.gaps, full, pfc.fun, ff, ff.args, outpath, pb, ncore, env=environment()) close(pb) ##### Collect data ##### for(i in 1:length(alnModes)) { tmp.modes <- alnModes[[i]] modes.array[,,i] = tmp.modes$U L.mat[i, ] = tmp.modes$L flucts[i, ] = tmp.modes$flucts if(full) all.modes[[i]] = tmp.modes$modes } remove(alnModes) invisible(gc()) ##### RMSIP ###### rmsip.map <- NULL if(rm.gaps) { rmsip.map <- .calcRMSIP(modes.array, ncore=ncore) rownames(rmsip.map) <- basename(rownames(pdbs$xyz)) colnames(rmsip.map) <- basename(rownames(pdbs$xyz)) if(!fit) warning("rmsip calculated on non-fitted structures: ensure that your input coordinates are pre-fitted.") } if(ncore>1) { # rm(iipb, pos = ".GlobalEnv") ## remove global iipb variable options(warn=prev.warn) ## restore warning option } rownames(flucts) <- basename(rownames(pdbs$xyz)) out <- list(fluctuations=flucts, rmsip=rmsip.map, U.subspace=modes.array, L=L.mat, full.nma=all.modes, xyz=xyz, call=cl) class(out) = "enma" return(out) } .calcRMSIP <- function(x, ncore=1) { if(ncore>1) mylapply <- mclapply else mylapply <- lapply n <- dim(x)[3] mat <- matrix(NA, n, n) inds <- rbind(pairwise(n), matrix(rep(1:n,each=2), ncol=2, byrow=T)) mylist <- mylapply(1:nrow(inds), function(row) { return(list( rmsip=rmsip(x[,,inds[row,1]], x[,,inds[row,2]])$rmsip, i=inds[row,1], j=inds[row,2]) ) }) for ( i in 1:length(mylist)) { tmp.rmsip <- mylist[[i]] mat[tmp.rmsip$i, tmp.rmsip$j] <- tmp.rmsip$rmsip } mat[ inds[,c(2,1)] ] = mat[ inds ] return(round(mat, 4)) } ## Calculate 'aligned' normal modes of structure i in pdbs .calcAlnModes <- function(i, pdbs, xyz, gaps.res, mass, am.args, nm.keep, temp, keep, wts, rm.gaps, full, pfc.fun, ff, ff.args, outpath, pb, ncore, env=NULL) { ## Set indices for this structure only f.inds <- NULL f.inds$res <- which(gaps.res$bin[i,]==0) f.inds$pos <- atom2xyz(f.inds$res) ## similar to $resno but sequential indices pdbs$inds[i, f.inds$res] <- seq(1, length(f.inds$res)) ## Indices to extract from Hessian inds.inc <- pdbs$inds[i, gaps.res$f.inds] inds.exc <- pdbs$inds[i, gaps.res$t.inds][ !is.na(pdbs$inds[i, gaps.res$t.inds]) ] inds.inc.xyz <- atom2xyz(inds.inc) inds.exc.xyz <- atom2xyz(inds.exc) ## Generate content of PDB object tmp.xyz <- xyz[i, f.inds$pos] resno <- pdbs$resno[i,f.inds$res] chain <- pdbs$chain[i,f.inds$res] ## Fix for missing chain IDs chain[is.na(chain)] <- "" ## 3-letter AA code is provided in the pdbs object ## avoid using aa123() here (translates TPO to THR) resid <- pdbs$resid[i,f.inds$res] sequ <- resid ## Build a dummy PDB to use with function nma.pdb() pdb.in <- as.pdb.default(xyz=tmp.xyz, elety="CA", resno=resno, chain=chain, resid=resid, verbose=FALSE) if(!is.null(outpath)) { fname <- file.path(outpath, basename(pdbs$id[i])) write.pdb(pdb.in, file=fname) } if(mass) { masses <- try( do.call('aa2mass', c(list(pdb=sequ, inds=NULL), am.args)), silent=TRUE ) if(inherits(masses, "try-error")) { hmm <- attr(masses,"condition") cat("\n\n") stop(paste(hmm$message, "in file", basename(pdbs$id[i]))) } masses.in <- masses masses.out <- masses } else { masses.in <- NULL masses.out <- NULL } natoms.in <- nrow(pdb.in$atom) natoms.out <- natoms.in if(rm.gaps) { ## Second PDB - containing only the aligned atoms sele <- list(atom=inds.inc, xyz=inds.inc.xyz) class(sele) <- "select" pdb.out <- trim.pdb(pdb.in, sele) natoms.out <- nrow(pdb.out$atom) if(mass) masses.out <- masses.in[ inds.inc ] inc.inds <- list(xyz=inds.inc.xyz) } else { pdb.out <- pdb.in inc.inds <- NULL } ## Build effective hessian bh.args <- c(list(sequ=sequ, fc.weights=wts[f.inds$res, f.inds$res]), ff.args) init <- list(pfcfun=pfc.fun, bh.args=bh.args) ##print(init$bh.args$fc.weights) invisible(capture.output( hessian <- .nma.hess(pdb.in$xyz, init=init, hessian=NULL, inc.inds=inc.inds) )) ## Mass-weight hessian if(!is.null(masses.out)) invisible(capture.output( hessian <- .nma.mwhessian(hessian, masses=masses.out))) ## Diagonalize invisible(capture.output( ei <- .nma.diag(hessian) )) ## Build an NMA object invisible(capture.output( modes <- .nma.finalize(ei, xyz=pdb.out$xyz, temp=temp, masses=masses.out, natoms=natoms.out, keep=nm.keep, call=NULL) )) if(rm.gaps) modes.mat <- matrix(NA, ncol=keep, nrow=nrow(modes$U)) else modes.mat <- matrix(NA, ncol=keep, nrow=ncol(pdbs$xyz)) j <- 1 for(k in 7:(keep+6)) { if(rm.gaps) modes.mat[, j] <- modes$U[,k] else modes.mat[f.inds$pos, j] <- modes$U[,k] j <- j+1 } if(rm.gaps) { flucts <- modes$fluctuations } else { flucts <- rep(NA, length=ncol(pdbs$resno)) flucts[f.inds$res] <- modes$fluctuations } ## Progress bar if(ncore>1) { iipb <- get("iipb", envir = env) iipb[1,i] <- 1 j <- length(which(!is.na(bigmemory::as.matrix(iipb)))) } else j <- i setTxtProgressBar(pb, j) L <- modes$L[seq(7, keep+6)] if(!full) { remove(modes) modes <- NULL } out <- list(modes=modes, U=modes.mat, L=L, flucts=flucts) ##f.inds=f.inds) invisible(gc()) return(out) } bio3d/R/check.utility.R0000644000176200001440000000131612526367343014371 0ustar liggesuserscheck.utility <- function(x = c("muscle", "dssp", "stride", "mustang", "makeup"), quiet = TRUE) { utilities <- match.arg(x, several.ok = TRUE) ##- Check on missing utility programs missing.util <- nchar(Sys.which(utilities)) == 0 if( any(missing.util) ) { if(!quiet) { warning(paste0(" Checking for external utility programs failed\n", " Please make sure '", paste(names(missing.util[missing.util]), collapse="', '"), "' is in your search path, see:\n", " http://thegrantlab.org/bio3d/tutorials/installing-bio3d#utilities")) } pass = FALSE } else { if(!quiet) cat("External utility programs found\n") pass = TRUE } invisible(pass) } bio3d/R/cov2dccm.R0000644000176200001440000000353312524171273013306 0ustar liggesuserscov2dccm <- function(vcov, method = c("pearson", "lmi"), ncore = NULL) { method = match.arg(method) ncore = setup.ncore(ncore) if(ncore == 1) mclapply = lapply x <- vcov ccmat = switch(method, pearson = { n <- nrow(x) np <- pairwise(n/3) d <- sqrt(colSums(matrix(diag(x), nrow=3))) ltv <- mclapply(1:nrow(np), function(i) { i1 <- (np[i, 2] - 1) * 3 + 1 i2 <- (np[i, 1] - 1) * 3 + 1 sum(diag(x[i1:(i1+2), i2:(i2+2)]))/ # sum of diagnol of submatrix (d[np[i, 2]] * d[np[i, 1]]) # divided by product of standard deviations } ) ccmat <- matrix(0, n/3, n/3) ccmat[lower.tri(ccmat)] <- unlist(ltv) # make full matrix ccmat <- ccmat + t(ccmat) diag(ccmat) <- 1 ccmat }, lmi = { # rm:r-value matrix cm <- x l <- dim(cm)[1]/3 rm <- matrix(nrow=l, ncol=l) d <- 3 ij <- pairwise(l) # list1: marginal-covariance list1 <- mclapply(1:l, function(i) det(cm[(3*i-2):(3*i), (3*i-2):(3*i)]) ) dm <- unlist(list1) # list2: pair-covariance list2 <- mclapply(1:nrow(ij), function(i) { x <- det(cm[c((3*ij[i,1]-2):(3*ij[i,1]),(3*ij[i,2]-2):(3*ij[i,2])), c((3*ij[i,1]-2):(3*ij[i,1]),(3*ij[i,2]-2):(3*ij[i,2]))]) y <- 1/2 * (log(dm[ij[i,1]]) + log(dm[ij[i,2]]) - log(x)) (1 - exp(-2 * y / d))^(1/2) } ) list2 <- unlist(list2) for (k in 1:nrow(ij)) { rm[ij[k, 1], ij[k, 2]] <- list2[k] } rm[lower.tri(rm)] = t(rm)[lower.tri(rm)] diag(rm) <- 1 rm } ) class(ccmat)=c("dccm","matrix") return(ccmat) } bio3d/R/read.ncdf.R0000644000176200001440000001227312632622153013431 0ustar liggesusers`read.ncdf` <- function(trjfile, headonly = FALSE, verbose = TRUE, time=FALSE, first = NULL, last= NULL, stride = 1, cell = FALSE, at.sel = NULL){ # Adding option 'at.sel' to select a subset of the structure using # an object of class 'select' # Currently file open in SHARE mode is not supported by NCDF package. # Multicore support is suppressed # ncore = 1 ##- Load ncdf4 package oops <- requireNamespace("ncdf4", quietly = TRUE) if(!oops) stop("Please install the ncdf4 package from CRAN") # open files nc <- lapply(trjfile, function (fnm) { nc <- ncdf4::nc_open(fnm, readunlim=FALSE) conv <- ncdf4::ncatt_get( nc, varid=0, "Conventions")$value if(conv!="AMBER") warning(paste("File conventions is not set to AMBER", fnm, sep=": ")) return(nc) }) # set first and last frame no for each file # used for time/frame range selection flen <- unlist(lapply(nc, function(n) return(n$dim$frame$len))) frange <- matrix(c(1, cumsum(flen[-length(nc)])+1, cumsum(flen)), nrow=length(nc)) if(verbose) { if(length(trjfile)>1) print(paste("Reading ", length(trjfile), "files")) else print(paste("Reading file", trjfile)) print(paste("Produced by program:", ncdf4::ncatt_get( nc[[1]], varid=0, "program")$value)) print(paste("File conventions", ncdf4::ncatt_get( nc[[1]], varid=0, "Conventions")$value, "version", ncdf4::ncatt_get( nc[[1]], varid=0, "ConventionVersion")$value)) print(paste("Frames:", sum(flen))) print(paste("Atoms:", nc[[1]]$dim$atom$len)) } # read heads, cell, or coordinates retval <- lapply(seq_along(nc), function (inc) { first.atom <- 1 count.atom <- -1 if(!is.null(at.sel)) { if(!is.select(at.sel)) stop("'at.sel' must be an object of class 'select'. See 'atom.select'.") atom.ind <- xyz2atom(at.sel$xyz) first.atom <- min(atom.ind) count.atom <- diff(range(atom.ind)) + 1 } nc <- nc[[inc]] frange <- frange[inc,] ss = 1 ee = nc$dim$frame$len if (!is.null(c(first, last))) { #check frame No or time if(time) { btime <- ncdf4::ncvar_get(nc, "time", 1, 1) etime <- ncdf4::ncvar_get(nc, "time", nc$dim$frame$len, 1) } else { btime = frange[1] etime = frange[2] } if((!is.null(first) && (etime < first)) || (!is.null(last) && last >=0 && btime > last) || (!is.null(first) && !is.null(last) && last >=0 && last < first) ) { # if(verbose) print(paste("Skip file", nc$filename)) ncdf4::nc_close(nc) return() } if(!headonly) { timeall <- btime:etime if(time) timeall <- ncdf4::ncvar_get(nc, "time") ss <- if(is.null(first)) 1 else which((timeall - first) >=0 )[1] if(is.null(last) || last < 0 || last > etime) { ee = nc$dim$frame$len } else { ee <- which((timeall - last) <= 0) ee <- ee[length(ee)] } } } tlen = ee - ss + 1 conv <- ncdf4::ncatt_get( nc, varid=0, "Conventions")$value if(headonly) { ## Report only header information return(list("file"=nc$filename, "conv"=conv, "frames"=nc$dim$frame$len, "atoms"=nc$dim$atom$len)) ##time <- ncdf4::ncvar_get(nc,"time") } if(cell) { celldata <- ncdf4::ncvar_get(nc, "cell_lengths", c(1, ss), c(-1, tlen)) celldata <- t( rbind(celldata, ncdf4::ncvar_get(nc, "cell_angles", c(1, ss), c(-1, tlen))) ) if(time) rownames(celldata) <- ncdf4::ncvar_get(nc, "time", ss, tlen) ncdf4::nc_close(nc) return( celldata ) } if(count.atom < 0) count.atom = nc$dim$atom$len # To solve 32-bit limitation problem with large trajectory file .check <- (3 * count.atom * tlen) / (2^31 - 1) if(.check > 1) { .nb <- floor(.check) + 1 .nn <- floor(tlen / .nb) .ss <- seq(ss, ss + tlen - 1, .nn) .tlen <- rep(.nn, length(.ss) - 1) .tlen <- c(.tlen, tlen - sum(.tlen)) coords <- sapply(1:length(.ss), function(i) ncdf4::ncvar_get(nc, "coordinates", c(1, first.atom, .ss[i]), c(-1, count.atom, .tlen[i])) ) } else { coords <- ncdf4::ncvar_get(nc, "coordinates", c(1, first.atom, ss), c(-1, count.atom, tlen)) } if(!is.null(at.sel)) coords <- coords[,atom.ind - first.atom + 1,] coords <- matrix( coords, ncol=(dim(coords)[2]*3), byrow=TRUE ) if(time) rownames(coords) <- ncdf4::ncvar_get(nc, "time", ss, tlen) ncdf4::nc_close(nc) return( coords ) } ) if(headonly) { retval <- do.call("c", retval) } else if(cell) { retval <- do.call(rbind, retval) ##retval <- as.data.frame(retval, stringsAsFactors=FALSE) } else { retval <- do.call(rbind, retval) ## take every "stride" frame retval <- as.xyz(subset(retval, (1:nrow(retval)) %in% seq(1, nrow(retval), stride))) } return( retval ) } bio3d/R/difference.vector.R0000644000176200001440000000134312412621431015166 0ustar liggesusers"difference.vector" <- function(xyz, xyz.inds=NULL, normalize=FALSE) { xyz <- as.matrix(xyz) if (dim(xyz)[1L] < 2) stop("xyz must be a matrix with two rows") if (dim(xyz)[2L] < 6) stop("xyz does not contain sufficient coordinates") if (dim(xyz)[1L] > 2) { xyz <- xyz[1:2,] warning("xyz has more than two rows - using only the two first") } if ( is.null(xyz.inds) ) xyz.inds <- seq(1, ncol(xyz)) if ( length(which(is.na(xyz[,xyz.inds]))) > 0 ) stop("xyz has NA values") a <- xyz[1, xyz.inds] b <- xyz[2, xyz.inds] if (length(a)!=length(b)) stop("unequal lengths of the two coordinate sets") diff <- b-a if(normalize) diff <- normalize.vector(diff) return( diff ) } bio3d/R/write.crd.R0000644000176200001440000000577612526367343013531 0ustar liggesusers"write.crd" <- function(pdb = NULL, xyz = pdb$xyz, resno = NULL, resid = NULL, eleno = NULL, elety = NULL, segid = NULL, resno2 = NULL, b = NULL, verbose = FALSE, file = "R.crd") { if (is.null(xyz) || !is.numeric(xyz)) stop("write.crd: please provide a 'pdb' object or numeric 'xyz' coordinates") if (any(is.na(xyz))) stop("write.crd: 'xyz' coordinates must have no NA's.") if (is.matrix(xyz) && nrow(xyz) == 1) xyz = as.vector(xyz) if (is.vector(xyz)) { natom <- length(xyz)/3 } else { stop("write.crd: 'xyz' or 'pdb$xyz' must contain only one structure") } if (!is.null(pdb)) { if (is.null(resno)) resno = pdb$atom[, "resno"] if (is.null(resid)) resid = pdb$atom[, "resid"] if (is.null(eleno)) eleno = pdb$atom[, "eleno"] if (is.null(elety)) elety = pdb$atom[, "elety"] if (is.null(segid)) segid = pdb$atom[, "chain"] if (is.null(resno2)) resno2 = pdb$atom[, "resno"] if (is.null(b)) b = pdb$atom[, "b"] } else { if (is.null(resno)) resno = c(1:natom) if (is.null(resno2)) resno2 = c(1:natom) if (is.null(resid)) resid = rep("ALA", natom) if (is.null(eleno)) eleno = c(1:natom) if (is.null(elety)) elety = rep("CA", natom) if (is.null(segid)) segid = rep("seg", natom) if (is.null(b)) b = rep("0.00", natom) } if (length(as.vector(xyz))%%3 != 0) { stop("write.crd: 'length(xyz)' must be divisable by 3.") } check.lengths <- sum(length(resno), length(resid), length(eleno), length(elety), length(resno2)) if (check.lengths%%natom != 0) { stop("write.crd: the lengths of all input vectors != 'length(xyz)/3'.") } b <- as.numeric(b) eleno <- as.character(eleno) resno <- as.character(resno) resno2 <- as.character(resno2) crd.print <- function(eleno, resno, resid, elety, x, y, z, segid="seg", resno2, b="0.00") { format <- "%5s%5s%4s %-4s%10.5f%10.5f%10.5f %-4s %-4s%10.5f" sprintf(format, eleno, resno, resid, elety, x, y, z, segid, resno2, b) } coords <- matrix(round(as.numeric(xyz), 3), ncol = 3, byrow = TRUE) if (verbose) cat(paste("Writing CRD file with", natom, "atoms ")) lines <- NULL for (i in 1:natom) { lines <- rbind(lines, crd.print( eleno = eleno[i], resno = resno[i], resid = resid[i], elety = elety[i], x = coords[i,1], y = coords[i, 2], z = coords[i, 3], segid = segid[i], resno2 = resno2[i], b = b[i])) } cat("* CRD from bio3d", file = file, "\n") cat("*", file = file, "\n", append = TRUE) cat(sprintf("%5g", natom), file = file, "\n", append = TRUE) cat(lines, file = file, sep = "\n", append = TRUE) if (verbose) cat(" DONE", "\n") } bio3d/R/community.tree.R0000644000176200001440000000374612526367343014605 0ustar liggesuserscommunity.tree <- function(x, rescale=FALSE){ ## Check for presence of igraph package oops <- requireNamespace("igraph", quietly = TRUE) if (!oops) { stop("igraph package missing: Please install, see: ?install.packages") } if(class(x) != "cna"){ stop("Input should be a 'cna' class object as obtained from cna()") } rescaling <- function(membership){ original.comms <- unique(membership) new.comms <- c(1:length(unique(membership))) a<-1 ## index to keep track of community number for(j in 1:length(membership)){ membership[membership == original.comms[a]] <- new.comms[a] a <- a +1 } return(membership) } num.of.nodes <- length(igraph::V(x$network)) membership <- c(1:num.of.nodes) merge.table <- x$communities$merges membership.table <- matrix(NA, nrow=dim(merge.table)[1]+1, ncol=num.of.nodes) membership.table[1,] <- c(1:num.of.nodes) num.of.comms <- c(num.of.nodes, rep(NA,dim(merge.table)[1])) for(i in 1:dim(merge.table)[1]){ comm.number <- num.of.nodes + i if(merge.table[i,1] < num.of.nodes){ membership[merge.table[i,1]] <- comm.number } else{ change.inds <- which(membership == merge.table[i,1]) membership[change.inds] <- comm.number } if(merge.table[i,2] < num.of.nodes){ membership[merge.table[i,2]] <- comm.number } else{ change.inds <- which(membership == merge.table[i,2]) membership[change.inds] <- comm.number } membership.table[i+1,] <- membership ## i+1 because the first line is where each node forms a separated community (it will match also the modularity values) num.of.comms[i+1] <- length(unique(membership)) } ## Rescale community number starting from 1 if(rescale){ membership.table <- t(apply(membership.table,1,rescaling)) } output <- list("tree" = membership.table, "modularity" = x$communities$modularity, "num.of.comms" = num.of.comms) return(output) } bio3d/R/dm.R0000644000176200001440000000101512526367343012206 0ustar liggesusers"dm" <- function(...) UseMethod("dm") "dm.pdb" <- function(pdb, inds=NULL, grp=TRUE, verbose=TRUE, ...) { if(!is.pdb(pdb)) { stop("input 'pdb' should be either: 1. an object returned from from 'read.pdb' or 2. a numeric 'xyz' vector of coordinates") } if(!is.null(inds)) { pdb <- trim.pdb(pdb, inds) } if(grp) grpby <- paste(pdb$atom$resno, pdb$atom$chain, sep="-") else grpby <- NULL d <- dm.xyz(pdb$xyz, grpby=grpby, ...) class(d) <- "dmat" return(d) } bio3d/R/torsion.pdb.R0000644000176200001440000001130212412621431014030 0ustar liggesusers"torsion.pdb" <- function(pdb) { colpaste <- function(x,col.names=colnames(x)) { apply(x, 1, function(row) paste(row[col.names], collapse=".")) } getinds <- function(atoms,ref=atom.names) { sort(atom2xyz(charmatch(atoms, ref))) } repadd <- function(num, nrep=nres, toadd=nxyz) { c(num, rep(num, (nrep-1)) + rep(cumsum(rep(toadd, (nrep-1))), each=length(num))) } ##-- List atoms form each residue of each chain atom.data <- colpaste(pdb$atom,c("elety","resno","chain")) atom.list <- matrix(unlist(strsplit(atom.data,"\\.")), ncol=3, byrow=TRUE) res.data <- colpaste(pdb$atom,c("resno","chain")) res.list <- unique(res.data) atom.names <- c("N","CA","C","O","CB", "*G","*G1","*G2","*D","*D1", "*D2","*E","*E1","*E2","*Z", "NH1","NH2") atom.greek <- c("N","CA","C","O","CB", "G","G1","G2","D","D1", "D2", "E","E1","E2", "Z", "*","*") coords <- NULL; blank <- matrix(NA, nrow=3, ncol=length(atom.names)) ##-- Store coords as a 3 x Natm x Nres = [xyz,atm,res] matrix for(i in 1:length(res.list)) { res.blank <- blank res.ind <- which(res.list[i]==res.data) ### --- Start Edit: Wed Dec 8 18:30:07 PST 2010 ### blank.ind <- charmatch(atom.list[res.ind,1], atom.names, nomatch=0) + ### charmatch(substr(atom.list[res.ind,1],2,4), atom.greek, nomatch=0) ### ### Bug Fix for NMR structures: make sure we have no Hydrogens atoms.noh <- atom.list[res.ind,1] atoms.noh[ grep("H",atoms.noh) ] = "H" blank.ind <- charmatch(atoms.noh, atom.names, nomatch=0) + charmatch(substr(atoms.noh,2,4), atom.greek, nomatch=0) ### --- End Edit res.blank[,blank.ind[blank.ind!=0]] <- matrix(pdb$xyz[atom2xyz(res.ind[blank.ind!=0])], nrow=3) coords <- cbind(coords,res.blank) } natm <- length(atom.names); nxyz <- 3*natm nres <- length(coords)/(nxyz) dim(coords) <- c(3, natm, nres) dimnames(coords)=list(xyz=c("x","y","z"), atm=atom.names, res=res.list) ##-- Torsions for selected atoms co <- c(coords) chi1 <- torsion.xyz( co[ repadd( getinds( c("N","CA","CB","*G") )) ] ) chi11 <- torsion.xyz( co[ repadd( getinds( c("N","CA","CB","*G1") )) ]) ###chi12 <- torsion.xyz( co[ repadd( getinds( c("N","CA","CB","*G2") )) ]) chi2 <- torsion.xyz( co[ repadd( getinds( c("CA","CB","*G","*D") )) ]) chi21 <- torsion.xyz( co[ repadd( getinds( c("CA","CB","*G","*D1") )) ]) ###chi22 <- torsion.xyz( co[ repadd( getinds( c("CA","CB","*G","*D2") )) ]) ## New catch for atom name CG1 of ILE residues chi2.ILE <- torsion.xyz( co[ repadd( getinds( c("CA","CB","*G1","*D1") )) ]) chi3 <- torsion.xyz( co[ repadd( getinds( c("CB","*G","*D","*E") )) ]) chi31 <- torsion.xyz( co[ repadd( getinds( c("CB","*G","*D","*E1") )) ]) ##chi32 <- torsion.xyz( co[ repadd( getinds( c("CB","*G","*D","*E2") )) ]) chi4 <- torsion.xyz( co[ repadd( getinds( c("*G","*D","*E","*Z") )) ]) chi51 <- torsion.xyz( co[ repadd( getinds( c("*D","*E","*Z", "NH1") )) ]) ###chi52 <- torsion.xyz( co[ repadd( getinds( c("*D","*E","*Z", "NH2") )) ]) omega <- torsion.xyz( co[ repadd( c(4:9, 52:57) ) ]) alpha <- c(NA, torsion.xyz( co[ repadd( c(4:6,55:57,106:108,157:159) ) ])) phi <- c(NA, torsion.xyz( co[ repadd( c(7:9,52:60) ) ])) psi <- torsion.xyz( co[ repadd( c(1:9,52:54) ) ]) ## alpha c("CA","CA","CA","CA") ## omega c("CA","C","N","CA") ## phi c("C","N","CA","C") ## psi c("N","CA","C","N") ##- Old Output with redundent angles (e.g. chi22 etc.). ### out <- list(psi=psi, phi=phi[-(nres+1)], omega=omega, ### chi1=chi1, chi11=chi11, chi12=chi12, ### chi2=chi2, chi21=chi21, chi22=chi22, ### chi3=chi3, chi31=chi31, chi32=chi32, ### chi4=chi4, ### chi51=chi51, chi52=chi52, ### alpha=alpha[-(nres+1)], coords=coords) ##- New reduced output with only one chi per sidechain position tor.collapse <- function(a1, a11) { a <- a1 got.a11 <- !(is.na(a11)) a[got.a11] <- a11[got.a11] return(a) } chi1.F <- tor.collapse(chi1, chi11) chi2.F <- tor.collapse(chi2, chi21) chi2.F <- tor.collapse(chi2.F, chi2.ILE) chi3.F <- tor.collapse(chi3, chi31) ## New table/matrix for output tbl=cbind(phi[-(nres+1)], psi, chi1.F, chi2.F, chi3.F, chi4, chi51) colnames(tbl) <- c("phi", "psi", "chi1", "chi2", "chi3", "chi4", "chi5") out <- list(psi=psi, phi=phi[-(nres+1)], omega=omega, chi1=chi1.F, ##chi11=chi11, chi12=chi12, chi2=chi2.F, ##chi21=chi21, chi22=chi22, chi3=chi3.F, ##chi31=chi31, chi32=chi32, chi4=chi4, chi5=chi51, ##chi51=chi51, chi52=chi52, alpha=alpha[-(nres+1)], coords=coords, tbl=tbl ) } bio3d/R/cmap.R0000644000176200001440000000453712632340443012530 0ustar liggesuserscmap <- function(...) UseMethod("cmap") cmap.default <- function(...) return(cmap.xyz(...)) cmap.xyz <- function(xyz, grpby=NULL, dcut=4, scut=3, pcut=1, mask.lower = TRUE, ncore=1, nseg.scale=1, ...) { # Parallelized by parallel package (Mon Apr 22 16:32:19 EDT 2013) ncore <- setup.ncore(ncore) if(ncore > 1) { # Issue of serialization problem # Maximal number of cells of a double-precision matrix # that each core can serialize: (2^31-1-61)/8 R_NCELL_LIMIT_CORE = 2.68435448e8 R_NCELL_LIMIT = ncore * R_NCELL_LIMIT_CORE if(nseg.scale < 1) { warning("nseg.scale should be 1 or a larger integer\n") nseg.scale=1 } } if (!(is.numeric(pcut) && pcut >= 0 && pcut <= 1)) { stop("Input 'pcut' should a number between 0 and 1") } xyz=as.xyz(xyz) if(nrow(xyz)>1) { if(is.null(grpby)) { nres <- ncol(xyz)/3 } else { inds <- bounds(grpby, dup.inds = TRUE) nres <- nrow(inds) } if(ncore > 1) { ni = nrow(xyz) RLIMIT = floor(R_NCELL_LIMIT/(0.5*nres*(nres+1))) nDataSeg = floor((ni-1)/RLIMIT)+1 nDataSeg = floor(nDataSeg * nseg.scale) lenSeg = floor(ni/nDataSeg) cmap.list <- NULL for(i in 1:nDataSeg) { istart = (i-1)*lenSeg + 1 iend = if(i= pcut ) cont.map <- matrix(NA, nrow=nres, ncol=nres) cont.map[!lower.tri(cont.map)] <- cmap.t if(!mask.lower) cont.map[lower.tri(cont.map)] <- t(cont.map)[lower.tri(cont.map)] } else { ## Distance matrix (all-atom) dmat <- dm.xyz( xyz, grpby, scut, mask.lower = mask.lower, ncore=ncore) ## Contact map return(matrix(as.numeric(dmat < dcut), ncol = ncol(dmat), nrow = nrow(dmat))) } return (cont.map) } bio3d/R/is.pdb.R0000644000176200001440000000013212524171274012756 0ustar liggesusersis.pdb <- function(x) inherits(x, "pdb") is.pdbs <- function(x) inherits(x, "pdbs") bio3d/R/dccm.pca.R0000644000176200001440000000560112526367343013263 0ustar liggesusers"dccm.pca" <- function(x, pc = NULL, ncore = NULL, ...) { if (missing(x) || !"pca" %in% class(x)) stop("dccm.pca: must supply a 'pca' object, i.e. from 'pca.xyz'") modes = pc ## Check for multiple cores ncore = setup.ncore(ncore) if(ncore > 1) { mcparallel <- get("mcparallel", envir = getNamespace("parallel")) mccollect <- get("mccollect", envir = getNamespace("parallel")) } ## Set modes to be included if(is.null(modes)) modes <- 1:length(x$L) ## If modes are negative, take modes complementary to them if( any(!is.numeric(modes)) || any(!(abs(modes) %in% c(1:length(x$L)))) || !(all(modes>0) || all(modes<0)) ) stop("Incorrect mode index") if(all(modes < 0)) { modes <- setdiff(c(1:length(x$L)), abs(modes)) if(length(modes) == 0) stop("No mode is selected") } modes <- unique(modes) nmodes <- length(modes) ## Calc variance-covariance matrix over a subset of modes vcovmat <- function(r.inds, pca, vcov.mat = 0) { for ( i in seq_along(r.inds) ) { vcov.mat <- vcov.mat + (pca$U[, r.inds[i]] %o% pca$U[, r.inds[i]]) * pca$L[r.inds[i]] if(ncore > 1) writeBin(1, fpb) else setTxtProgressBar(pb, i) } return(vcov.mat) } ## Calculate variance-covariance matrix first ## ## If contain $z, straightforward if(!is.null(x$z)) { q = x$z[, modes] %*% t(x$U[, modes]) vcov = cov(q) } else { ## Initialize progress bar pb <- txtProgressBar(min=1, max=nmodes, style=3) if(ncore > 1) { # Parallel # For progress bar fpb <- fifo(tempfile(), open = "w+b", blocking = T) # spawn a child process for message printing child <- mcparallel({ progress <- 0.0 while(progress < nmodes && !isIncomplete(fpb)) { msg <- readBin(fpb, "double") progress <- progress + as.numeric(msg) setTxtProgressBar(pb, progress) } } ) ################### jobid <- rep(1:ncore, ceiling(nmodes/ncore)) jobid <- jobid[1:nmodes] ltv <- mclapply(1:ncore, function(i) { j <- which(jobid %in% i) if(length(j) > 0) { m <- vcovmat(modes[j], x) m <- m[lower.tri(m, diag = TRUE)] } else { m = 0 } return(m) } ) ltv <- colSums(do.call(rbind, ltv)) vcov <- matrix(0, nrow(x$U), nrow(x$U)) vcov[lower.tri(vcov, diag = TRUE)] <- ltv vcov <- vcov + t(vcov) diag(vcov) <- diag(vcov) / 2 close(fpb) mccollect(child) # End the child for message printing } else { # Serial vcov <- vcovmat(modes, x) } close(pb) } corr.mat <- cov2dccm(vcov, ncore = ncore, ...) return(corr.mat) } bio3d/R/cat.pdb.R0000644000176200001440000000615412600075112013111 0ustar liggesuserscat.pdb <- function(..., renumber=FALSE, rechain=TRUE) { cl <- match.call() objs <- list(...) are.null <- unlist(lapply(objs, is.null)) objs <- objs[!are.null] if(length(objs)==1) if(is.null(cl$rechain)) rechain = FALSE else if(length(objs)<1) return(NULL) if(any(!unlist(lapply(objs, is.pdb)))) stop("provide PDB objects as obtained from read.pdb()") ## avoid NA as chain ID na.inds <- lapply(objs, function(x) is.na(x$atom$chain)) if(any(unlist(na.inds))) { na.inds <- which(unlist(lapply(na.inds, function(x) any(x)))) for(i in na.inds) { tmp <- objs[[i]] tmp$atom$chain[ is.na(tmp$atom$chain) ] <- " " objs[[i]] <- tmp } } ## save original chain IDs ori.chain <- unlist(lapply(objs, function(x) unique(x$atom$chain))) ## always assign new chain identifiers ## and bring back original chain ID later if rechain=FALSE k <- 1 chain.repo <- c(LETTERS, letters, 0:9) for(i in 1:length(objs)) { x <- objs[[i]] objs[[i]] <- .update.chain(x, chain.repo[k:length(chain.repo)]) k <- k + length(unique(x$atom$chain)) } ## concat objects new <- objs[[1]] if(length(objs) > 1) { for(i in 2:length(objs)) { new$atom <- rbind(new$atom, objs[[i]]$atom) new$xyz <- cbind(new$xyz, objs[[i]]$xyz) new$seqres <- c(new$seqres, objs[[i]]$seqres) new$helix <- c(new$helix, objs[[i]]$helix) new$sheet <- c(new$sheet, objs[[i]]$sheet) } } ## merge SSE info for(i in c("helix", "sheet")) { sse <- new[[i]] if(!is.null(sse)) { coms <- names(sse) names(sse) <- NULL # avoid nested naming in results inds <- which(!duplicated(coms)) for(j in inds) sse[[j]] <- do.call(c, sse[coms %in% coms[j]]) sse <- sse[inds] names(sse) <- coms[inds] new[[i]] <- sse } } ## renumber residues chk <- try(clean.pdb(new, consecutive = !rechain, force.renumber = renumber, verbose=FALSE), silent=TRUE) if(inherits(chk, "try-error")) { warning("cat.pdb(): Bad format pdb generated. Try rechain=TRUE and/or renumber=TRUE") new['helix'] <- list(NULL) new['sheet'] <- list(NULL) } else new <- chk if(!rechain) new <- .update.chain(new, ori.chain) ## build new PDB object new$call <- cl ## remap " " chain IDs to NA values new$atom$chain[ new$atom$chain==" " ] <- as.character(NA) ## check connectivity chains <- unique(new$atom$chain) for(i in 1:length(chains)) { sele <- atom.select(new, chain=chains[i], verbose=FALSE) tmp <- trim.pdb(new, sele) if(!inspect.connectivity(tmp)) warning(paste("possible chain break in molecule: chain", chains[i])) } return(new) } .update.chain <- function(x, chain.repo = LETTERS) { new <- x chains <- unique(x$atom$chain) for(j in 1:length(chains)) { inds <- which(x$atom$chain==chains[j]) new$atom$chain[inds] <- chain.repo[j] if(!is.null(x$helix)) new$helix$chain[x$helix$chain==chains[j]] <- chain.repo[j] if(!is.null(x$sheet)) new$sheet$chain[x$sheet$chain==chains[j]] <- chain.repo[j] } new } bio3d/R/as.pdb.mol2.R0000644000176200001440000000226412526367343013634 0ustar liggesusers as.pdb.mol2 <- function(mol2, ...) { natoms <- nrow(mol2$atom) xyz <- mol2$xyz tmp.pdb <- list() tmp.pdb$atom <- data.frame(cbind(rep("ATOM", natoms), seq(1, natoms), mol2$atom$elena, NA, mol2$atom$resid, rep(" ", natoms), rep(1, natoms), NA, mol2$atom$x, mol2$atom$y, mol2$atom$z, NA, NA, NA, unlist(lapply(strsplit(mol2$atom$elety, split="[.]"), function(x) x[1])), NA), stringsAsFactors=FALSE) colnames(tmp.pdb$atom) <- c("type", "eleno", "elety", "alt", "resid", "chain", "resno", "insert", "x", "y", "z", "o", "b", "segid", "elesy", "charge") tmp.pdb$xyz <- xyz class(tmp.pdb) <- "pdb" ca.inds <- rep(FALSE, natoms) return(tmp.pdb) } bio3d/R/fluct.nma.R0000644000176200001440000000207412430771420013470 0ustar liggesusers"fluct.nma" <- function(nma, mode.inds=NULL) { kb <- 0.00831447086363271 pi <- 3.14159265359 if(!"nma" %in% class(nma)) stop("fluct.nma: must supply 'nma' object, i.e. from 'nma'") if("VibrationalModes" %in% class(nma)) mass <- TRUE else mass <- FALSE if(is.null(mode.inds)) mode.inds <- seq(nma$triv.modes+1, length(nma$L)) if(min(mode.inds)<=nma$triv.modes) stop("'mode.inds' should not contain indices to trivial modes") f <- apply(nma$U, 2, function(x) { rowSums(matrix(x, ncol=3, byrow=TRUE)**2) }) if(mass) freq <- nma$frequencies**2 else freq <- nma$force.constants for ( i in mode.inds ) { f[,i] <- f[,i] / freq[i] } if(length(mode.inds)>1) f <- rowSums(f[,mode.inds]) else f <- f[,mode.inds] if(mass) { f <- f / nma$mass s <- 1/(2*pi)**2 if(!is.null(nma$temp)) s <- s*kb*nma$temp f <- f*s } else { if(!is.null(nma$temp)) f <- f*kb*nma$temp } return(f) } bio3d/R/cmap.pdb.R0000644000176200001440000000061012632622153013261 0ustar liggesuserscmap.pdb <- function(pdb, inds=NULL, verbose=FALSE, ...) { if(!is.pdb(pdb)) stop("provide a pdb object as obtained from function 'pdb'") if(is.null(inds)) { inds <- atom.select(pdb, "notwater", verbose=verbose) } pdb <- trim.pdb(pdb, inds) xyz <- pdb$xyz grpby <- paste(pdb$atom$chain, pdb$atom$insert, pdb$atom$resno, sep="-") return(cmap.xyz(xyz, grpby, ...)) } bio3d/R/plot.rmsip.R0000644000176200001440000000072412430771420013710 0ustar liggesusers"plot.rmsip" <- function(x, xlab=NULL, ylab=NULL, col=gray(50:0/50), zlim=c(0,1), ...) { ##opar <- par(no.readonly = TRUE) ##on.exit(par(opar)) if(is.null(xlab)) xlab <- "a" if(is.null(ylab)) ylab <- "b" image(1:ncol(x$overlap), 1:nrow(x$overlap), x$overlap, col=col, zlim=zlim, xlab=xlab, ylab=ylab, ...) mtext(paste("RMSIP:", round(x$rmsip, 2)), side=3, line=0.5, at=0.5, adj=0, ...) } bio3d/R/seq2aln.R0000644000176200001440000000113312526367343013154 0ustar liggesusersseq2aln <- function(seq2add, aln, id="seq", file = "aln.fa", ...) { ##- Add a sequence 'seq2add' to an existing alignment 'aln' ## Adds at the bottom of alignment cl <- match.call() if(!inherits(aln, "fasta")) stop("Input 'aln' should be a 'fasta' object") tmp.seq = as.fasta(seq2add) if(nrow(tmp.seq$ali) > 1) warning("Multiple sequences in 'seq2add' should be pre-aligned") dots = list(...) # fixed arguments dots$profile = aln dots$outfile = file args = c(list(aln=seq2add, id=id), dots) naln = do.call(seqaln, args) naln$call = cl return(naln) } bio3d/R/core.find.R0000644000176200001440000002277712632622153013466 0ustar liggesuserscore.find <- function(...) UseMethod("core.find") core.find.default <- function(xyz, ...) core.find.pdbs(xyz, ...) core.find.pdb <- function(pdb, verbose=TRUE, ...) { if(nrow(pdb$xyz)<4) stop("provide a multi model PDB file with 4 or more frames") inds1 <- atom.select(pdb, "calpha", verbose=verbose) inds2 <- atom.select(pdb, "nucleic", elety="P", verbose=verbose) inds <- combine.select(inds1, inds2, operator="OR", verbose=verbose) tmp <- trim.pdb(pdb, inds) core <- core.find.pdbs(tmp$xyz, verbose=verbose, ...) ## map to pdb inds full.ids <- paste(pdb$atom$elety, pdb$atom$resno, pdb$atom$chain, sep="-") tmp.ids <- paste(tmp$atom$elety, tmp$atom$resno, tmp$atom$chain, sep="-") core$step.inds <- which(full.ids %in% tmp.ids[core$step.inds])[core$step.inds] core$atom <- which(full.ids %in% tmp.ids[core$atom]) core$c1A.atom <- which(full.ids %in% tmp.ids[core$c1A.atom]) core$c0.5A.atom <- which(full.ids %in% tmp.ids[core$c0.5A.atom]) core$xyz <- atom2xyz(core$atom) core$c1A.xyz <- atom2xyz(core$c1A.atom) core$c0.5A.xyz <- atom2xyz(core$c0.5A.atom) ##core$resno <- which(full.ids %in% tmp.ids[core$resno])[core$resno] ##core$c1A.resno <- which(full.ids %in% tmp.ids[core$c1A.resno])[core$c1A.resno] ##core$c0.5A.resno <- which(full.ids %in% tmp.ids[core$c0.5A.resno])[core$c0.5A.resno] core$resno <- tmp$atom$resno[core$resno] core$c1A.resno <- tmp$atom$resno[core$c1A.resno] core$c0.5A.resno <- tmp$atom$resno[core$c0.5A.resno] return(core) } "core.find.pdbs" <- function(pdbs, shortcut = FALSE, rm.island = FALSE, verbose = TRUE, stop.at = 15, stop.vol = 0.5, write.pdbs = FALSE, outpath="core_pruned", ncore = 1, nseg.scale = 1, ...) { ## Itterative core deffination for lsq fit optimisation ## (core positions are those with low ellipsoid volume) # Parallelized by parallel package (Fri Apr 26 16:49:38 EDT 2013) ncore <- setup.ncore(ncore) if(ncore > 1) { # Issue of serialization problem # Maximal number of cells of a double-precision matrix # that each core can serialize: (2^31-1-61)/8 R_NCELL_LIMIT_CORE = 2.68435448e8 R_NCELL_LIMIT = ncore * R_NCELL_LIMIT_CORE if(nseg.scale < 1) { warning("nseg.scale should be 1 or a larger integer\n") nseg.scale=1 } } error.ellipsoid<-function(pos.xyz) { S<-var(pos.xyz) prj <- eigen(S, symmetric = TRUE) prj$values[prj$values < 0 & prj$values >= -1.0E-12]<-1.0E-12 vol<-4/3*pi*prod( sqrt( prj$values ) ) out<-list(vol=vol, U=prj$vectors, L=prj$values) } if(is.matrix(pdbs)) { xyz <- pdbs xyz.inds <- which(apply(is.na( xyz ), 2, sum)==0) res.inds<-xyz.inds[seq(3,length(xyz.inds),by=3)]/3 pdbseq = rep("ALA",length(xyz.inds)/3) pdbnum = c(1:(length(xyz.inds)/3)) } else { if( (is.list(pdbs)) && (class(pdbs)=="pdbs") ) { xyz=pdbs$xyz xyz.inds <- which(apply(is.na( xyz ), 2, sum)==0) res.inds <- which(apply(pdbs$ali=="-", 2, sum)==0) pdbseq = aa123(pdbs$ali[1,]); pdbnum = pdbs$resno[1,] } else { stop("input 'pdbs' should either be: a list object from 'read.fasta.pdb' or a numeric 'xyz' matrix of aligned coordinates") } } # First core = all non gap positions res.still.in <- res.inds # indices of core residues xyz.still.in <- xyz.inds # indices of core xyz's new.xyz.inds <- xyz.inds # indices of core xyz's xyz.moved <- xyz # core-fitted coords throwout.res <- NULL # non-core res inds throwout.xyz <- NULL # non-core xyz inds remain.vol <- NULL core.length <- NULL fit.to = rep(FALSE,ncol(xyz.moved)) # Preliminary fitting fit.to[ as.vector(xyz.still.in) ]<-TRUE # on first structure # xyz.tmp <- t(apply(xyz.moved, 1, # to find mean structure # rot.lsq, # for next fitting # yy=xyz.moved[1,], # xfit=fit.to)) xyz.tmp <- fit.xyz(xyz.moved[1,], xyz.moved, which(fit.to), which(fit.to), ncore=ncore, nseg.scale=nseg.scale) mean.xyz <- apply(xyz.tmp,2,mean) if(write.pdbs) { dir.create(outpath,FALSE) } while(length(res.still.in) > stop.at) { # Core fitting, (core => pdbnum[ res.still.in ]) fit.to = rep(FALSE,ncol(xyz.moved)) fit.to[ as.vector(xyz.still.in) ]<-TRUE # xyz.moved <- t(apply(xyz.moved, 1, # rot.lsq, # #yy=xyz.moved[1,], # yy=mean.xyz, # xfit=fit.to)) xyz.moved <- fit.xyz(mean.xyz, xyz.moved, which(fit.to), which(fit.to), ncore=ncore, nseg.scale=nseg.scale) mean.xyz <- apply(xyz.moved,2,mean) i<-1; j<-3 volume<-NULL # ellipsoid volume if(ncore > 1) { e <- mclapply(1:(length(new.xyz.inds)/3), function(j) { error.ellipsoid( xyz.moved[, new.xyz.inds[atom2xyz(j)]] )$vol }) volume <- unlist(e) } else { while(j<=length( new.xyz.inds )) { e<-error.ellipsoid(xyz.moved[,new.xyz.inds[i:j]]) volume<-c(volume,e$vol) i<-i+3;j<-j+3 } } record <- cbind(res.still.in , # store indices and volumes matrix(new.xyz.inds,ncol=3,byrow=3), volume) # Find highest volume (most variable position) if (shortcut) { if (length(res.still.in) >= 35) { # remove four at a time highest.vol.ind <- rev(order(volume))[1:4] } else { highest.vol.ind <- which.max(volume) } } else { # no shortcut rm one at a time highest.vol.ind <- which.max(volume) } if (rm.island) { # Exclude length 4 residue islands check <- bounds( res.still.in ) check.ind <- which(check[,"length"] < 4) if ( length(check.ind) > 0 ) { res.cut=NULL for (r in 1:length(check.ind)) { res.cut <- c(res.cut, check[check.ind[r],"start"]: check[check.ind[r],"end"]) } highest.vol.ind <- unique( c(highest.vol.ind, which( is.element(res.still.in, res.cut)) )) } } # rm position from "new.xyz.inds" xyz.exclude <- record[highest.vol.ind,c(2:4)] inds.torm <- which(is.element( new.xyz.inds, as.vector(xyz.exclude) )) new.xyz.inds <- new.xyz.inds[ -inds.torm ] # Store details of the residue we excluded tmp.vol <- sum(record[-highest.vol.ind,5]) throwout.res <- c( throwout.res, as.vector(record[highest.vol.ind,1])) throwout.xyz <- rbind( throwout.xyz, record[highest.vol.ind,2:4] ) remain.vol <- c(remain.vol, tmp.vol) res.still.in <- record[-highest.vol.ind,1] xyz.still.in <- record[-highest.vol.ind,2:4] core.length <- c(core.length,length(res.still.in)) if(verbose) { # Progress report cat( paste(" core size",length(res.still.in),"of", length(res.inds))," vol =", round(tmp.vol,3),"\n" ) if(write.pdbs) { # Write current core structure write.pdb(file = file.path(outpath, paste("core_", sprintf("%04.0f", length(res.still.in)),".pdb",sep="")), #xyz = xyz[1, new.xyz.inds ], xyz = mean.xyz[ new.xyz.inds ], resno = pdbnum[ res.still.in ], resid = pdbseq[ res.still.in ], b = round((volume[-highest.vol.ind] / max(volume[-highest.vol.ind]) * 1),2) ) } } if(tmp.vol < stop.vol) { cat(paste(" FINISHED: Min vol (",stop.vol,") reached\n")) break } } # ordered thro-out lists ordered.res<-as.vector(c(throwout.res, res.still.in)) ordered.xyz<-rbind(throwout.xyz, xyz.still.in) rownames(ordered.xyz)=NULL vol = c(remain.vol, rep(NA,stop.at)) len = c(core.length,rep(NA,stop.at)) blank<-rep(NA, len[1]); blank[na.omit(len)]=na.omit(vol) ordered.vol<-c(rev(blank),NA); blank[na.omit(len)]=na.omit(len) ordered.len<-c(rev(blank),NA) # sample cores (volume < 1 A^3, < 0.5 A^3, or the final core) if( min(ordered.vol,na.rm=TRUE) < 1) { a.atom <- sort(ordered.res[which(ordered.vol<1)[1]:length(ordered.vol)]) a.xyz <- sort(as.vector(ordered.xyz[which(ordered.vol<1)[1]: length(ordered.vol),])) a.resno <- as.numeric(pdbnum[a.atom]) } else { a.atom <- NULL a.xyz <- NULL a.resno <- NULL } if( min(ordered.vol,na.rm=TRUE) < 0.5) { b.atom <- sort(ordered.res[which(ordered.vol<0.5)[1]:length(ordered.vol)]) b.xyz <- sort(as.vector(ordered.xyz[which(ordered.vol<0.5)[1]: length(ordered.vol),])) b.resno <- as.numeric(pdbnum[b.atom]) } else { b.atom <- NULL b.xyz <- NULL b.resno <- NULL } tmp.inds <- which(!is.na(ordered.vol)) c.atom <- sort(ordered.res[tmp.inds[length(tmp.inds)]:length(ordered.vol)]) c.xyz <- atom2xyz(c.atom) c.resno <- as.numeric(pdbnum[c.atom]) output <- list(volume = ordered.vol, length = ordered.len, resno = pdbnum[ ordered.res ], step.inds = ordered.res, atom = c.atom, xyz = c.xyz, c1A.atom = a.atom, c1A.xyz = a.xyz, c1A.resno = a.resno, c0.5A.atom = b.atom, c0.5A.xyz = b.xyz, c0.5A.resno = b.resno ) class(output)="core"; return(output) } bio3d/R/com.xyz.R0000644000176200001440000000073712526367343013227 0ustar liggesusers"com.xyz" <- function(xyz, mass=NULL, ...) { xyz <- as.xyz(xyz) natoms <- ncol(xyz)/3 if(is.null(mass)) mass <- rep(1, times=natoms) if (natoms != length(mass)) stop("com.xyz: length of input vector 'mass' uequal to number of atoms (ncol(xyz)/3)") com1 <- function(x) { xyz <- matrix(x, ncol=3, byrow=T) com <- colSums(xyz * mass) / sum(mass) return(com) } com <- t(apply(xyz, 1, com1)) colnames(com) <- c("x", "y", "z") return(com) } bio3d/R/aa2index.R0000644000176200001440000000366112526367343013312 0ustar liggesusers"aa2index" <- function (aa, index = "KYTJ820101", window = 1) { if (!is.vector(aa)) stop("aa2index: non vector argument") if (!is.numeric(window) || window < 1) stop("aa2index: 'window' must be numeric and positive") if (window >= length(aa)) stop("aa2index: 'window' must be smaller than the sequence length") # Use LazyData to import data - changed Jul 23, 2013 # if (!exists("aa.index")) # data(aa.index) aa.index = bio3d::aa.index if (is.numeric(index)) { if (index > length(names(aa.index))) { stop("aa2index: 'index' number does not exist") } } else { if (!is.element(index, names(aa.index))) { stop("aa2index: 'index' name does not exist") } } x <- aa.index[[index]]$I[aa] if (window == 1) { y <- x } else { n <- length(x) y <- rep(NA, n) w <- ceiling(window/2) if ( (window %% 2) == 0 ) { from <- w to <- n - w y[from:to] <- sapply(from:to, function(i) mean(x[(i - (w-1)):(i+w)], na.rm=TRUE)) if (from-1 > 0) { y[1:(from-1)] <- sapply(1:(from-1), function(i) mean(x[1: (i + w)], na.rm=TRUE)) } y[(to+1):n] <- sapply((to+1):n, function(i) mean(x[(i- (w-1)): n], na.rm=TRUE)) } else { from <- w to <- n - (w-1) y[from:to] <- sapply(from:to, function(i) mean(x[(i - (w-1)):(i + (w-1))], na.rm=TRUE)) y[1:(from-1)] <- sapply(1:(from-1), function(i) mean(x[1: (i + (w-1))], na.rm=TRUE)) y[(to+1):n] <- sapply((to+1):n, function(i) mean(x[(i - (w-1)): n], na.rm=TRUE)) } y <- round(y,2) names(y) <- aa } return(y) } bio3d/R/plot.fluct.R0000644000176200001440000001226012632622153013673 0ustar liggesusers"plot.fluct" <- function(x, col = NULL, signif = FALSE, p.cutoff = 0.005, q.cutoff = 0.04, s.cutoff = 5, n.cutoff = 2, mean = FALSE, polygon = FALSE, ncore = NULL, ...) { ## check input data if(is.vector(x)) x = matrix(x, nrow=1) if(!is.matrix(x) || !is.numeric(x)) stop("provide a numeric matrix or vector") ## check colors, which also define groups of input data if signif=TRUE if(is.null(col)) col <- seq(1, nrow(x)) if(length(col) != nrow(x)) stop("length of col doesn't match dimension of x") if(any(is.na(col))) { x = x[!is.na(col), ] col = col[!is.na(col)] } ## check for significance calculation if(signif) { ns <- table(col) inds.signif <- which(ns >= s.cutoff) if(length(inds.signif) < 2) { warning("Insufficient samples to calculate significance") signif = FALSE } } ## extract some values from '...' since we still do some plots here ## These could be removed after merging this function with plot.bio3d() dots = list(...) if("rm.gaps" %in% names(dots)) rm.gaps = dots$rm.gaps else rm.gaps = formals(plotb3)$rm.gaps ## gaps positions gaps.pos <- gap.inspect(x) if(rm.gaps) yvals = x[, gaps.pos$f.inds, drop=FALSE] else yvals = x if("ylim2zero" %in% names(dots)) ylim2zero = dots$ylim2zero else ylim2zero = formals(plotb3)$ylim2zero if("xlim" %in% names(dots)) xlim = dots$xlim else xlim = c(1, ncol(yvals)) if("ylim" %in% names(dots)) ylim = dots$ylim else ylim = range(yvals, na.rm = TRUE) if(ylim2zero) ylim[1] = 0 if(! "ylab" %in% names(dots)) dots$ylab = "Fluctuation" dots$xlim = xlim dots$ylim = ylim ##################################################################### if(signif) { ncore = setup.ncore(ncore) # op = par(no.readonly = TRUE) op = par()$new on.exit(par(new=op)) pairs <- pairwise(length(inds.signif)) ## get p-value and q-value for each non-gap position p.all <- mclapply(gaps.pos$f.inds, function(i) { p.i <- apply(pairs, 1, function(j) { inds1 <- which(col == names(ns)[inds.signif[j[1]]]) inds2 <- which(col == names(ns)[inds.signif[j[2]]]) p = t.test(x[inds1, i], x[inds2,i], alternative="two.sided")$p.value q <- abs(mean(x[inds1, i]) - mean(x[inds2, i])) c(p, q) }) c(p=min(p.i[1, ]), q=p.i[2, which.min(p.i[1,])]) }) ## p-values with gaps inserted pvalue <- rep(NA, ncol(x)) pvalue[gaps.pos$f.inds] <- sapply(p.all, "[", "p") ## q-values, i.e. difference of mean values, with gaps inserted qvalue <- rep(NA, ncol(x)) qvalue[gaps.pos$f.inds] <- sapply(p.all, "[", "q") if(rm.gaps){ pvalue = pvalue[gaps.pos$f.inds] qvalue = qvalue[gaps.pos$f.inds] } sig <- which(pvalue<=p.cutoff & qvalue >= q.cutoff) ## - start plotting if(length(sig) > 0) { ## Plot significance as shaded blocks bds <- bounds(sig) ii <- which(bds[, "length"] >= n.cutoff) if(length(ii) > 0) { plot.new() plot.window(xlim=xlim, ylim=ylim) ## to show bricks for single site significance adjust = 0.1 rect(bds[ii,1]-adjust, rep(ylim[1], length(ii)), bds[ii,2]+adjust, rep(ylim[2], length(ii)), col=rep("lightblue", length(ii)), border=NA) ## add this for plot.bio3d on the same device par(new=TRUE) } } } if(mean) { # calculate mean values and replace yvals = apply(x, 2, tapply, col, mean, na.rm=TRUE) col = unique(col) if(!is.matrix(yvals)) yvals = matrix(yvals, nrow=1) else yvals = yvals[col, , drop=FALSE] # correct order change due to tapply # still keep the same gaps in first row # this will help plot SSE in plot.bio3d() yvals[1, is.na(x[1, ])] = NA x = yvals if(rm.gaps) yvals = x[, gaps.pos$f.inds, drop=FALSE] # trick to leave gap position unchanged. # Won't affect plot because plot.bio3d() only picks up the first row # All plots in this function should be done with yvals!! x = rbind(x, gap.mark=rep(0, ncol(x))) x["gap.mark", gaps.pos$t.inds] = NA } ## Plot fluctuations if(polygon) { dots$type = "n" do.call(plot.bio3d, c(list(x=x), dots)) xx = yvals[1, ] ylim2 = range(xx, na.rm = TRUE) if(ylim2zero) ylim2[1] = 0 n = bounds(which(is.na(xx))) if(length(n)>0) xx[n[, 1:2]] = ylim2[1] # color for polygon n.col = do.call(rgb, c(as.list(col2rgb(col[1])/255), list(alpha=0.4))) polygon(c(1, seq_along(xx), length(xx)), c(ylim2[1], xx, ylim2[1]), col = n.col, border=NA) } else { do.call(plot.bio3d, c(list(x=x), dots)) } ## Plot all lines for(i in 1:nrow(yvals)) lines(yvals[i, ], col=col[i], lwd=2) if(signif) out <- list(signif=sig) else out <- NULL invisible(out) } bio3d/R/chain.pdb.R0000644000176200001440000000335112526367343013441 0ustar liggesusers`chain.pdb` <- function(pdb, ca.dist=4, blank="X", rtn.vec=TRUE) { ##- Find possible chian breaks ## i.e. Concetive Caplpa's that are further than 'ca.dist' apart, ## print basic chain info and rtn a vector of chain ids ## consisting of the 26 upper-case letters of the Roman ## alphabet ## ## chn <- chain.pdb(pdb) ## pdb$atom[,"chain"] <- chain.pdb(pdb) ## ## Distance between concetive C-alphas ca <- atom.select(pdb, "calpha", verbose=FALSE) xyz <- matrix(pdb$xyz[ca$xyz], nrow=3) d <- sqrt( rowSums( apply(xyz , 1, diff)^2 ) ) ## Chain break distance check ind <- which(d > ca.dist) len <- diff( c(1,ind,length(d)) ) cat(paste("\t Found",length(ind), "possible chain breaks\n")) if(length(ind) > 0) { cat(paste("\t After resno(s):", paste( pdb$atom[ca$atom,"resno"][(ind)], collapse=", " ),"\n" )) cat(paste("\t Chain length(s):", paste(len+1, collapse=", " ),"\n" )) } ## Make a chain id vector if(rtn.vec) { resno.ind <- as.numeric(c(1, sort(as.numeric(c(ind,(ind+1)))), (length(d)+1) )) ## Renumber residues first, in case that original resnos are not ## consecutive crossing multiple chains res <- paste(pdb$atom[, "chain"], pdb$atom[, "resno"], pdb$atom[, "insert"], sep="_") pdb$atom[, "resno"] <- vec2resno(1:length(unique(res)), res) resno.val <- pdb$atom[ca$atom,"resno"][resno.ind] resno.val <- matrix(as.numeric(resno.val),nrow=2) vec <- rep(blank, nrow(pdb$atom)) for(i in 1:(length(resno.val)/2)) { sel.ind <- atom.select(pdb, resno=c(resno.val[1,i]:resno.val[2,i]), verbose=FALSE) vec[sel.ind$atom]=LETTERS[i] } return(vec) } } bio3d/R/view.dccm.R0000644000176200001440000001351112632622153013460 0ustar liggesusers"view.dccm" <- function(dccm, pdb, step=0.2, omit=0.2, radius = 0.15, type="pymol", outprefix="corr", launch=FALSE, exefile = "pymol") { ## Check if the program is executable if(launch) { ver <- "-cq" os1 <- .Platform$OS.type status <- system(paste(exefile, ver), ignore.stderr = TRUE, ignore.stdout = TRUE) if(!(status %in% c(0,1))) stop(paste("Launching external program failed\n", " make sure '", exefile, "' is in your search path", sep="")) } if(is.pdb(pdb)) { ca.inds <- atom.select(pdb, 'calpha', verbose=FALSE) bb.inds <- atom.select(pdb, 'backbone', verbose=FALSE) xyz <- pdb$xyz[ca.inds$xyz] ## If more than CA atoms are provided, assume its enough to draw cartoon in pymol if(length(pdb$xyz[bb.inds$xyz])==length(xyz)) ca.pdb <- TRUE else ca.pdb <- FALSE } else { xyz <- pdb } if(missing(dccm)) stop("correlation matrix must be provided") if(missing(xyz)) stop("cartesian coordinates missing") if(type!="pdb" && type!="pymol") stop("provide type 'pdb' or 'pymol'") dims <- dim(dccm) if((length(xyz)/3)!=dims[1L]) stop("unequal vector lengths") ## make temp-files if(is.null(outprefix)) { pdbfile <- tempfile(fileext = ".inpcrd.pdb") if(type=="pymol") outfile <- tempfile(fileext = ".py") else outfile <- tempfile(fileext = ".pdb") } else { pdbfile <- paste(outprefix, ".inpcrd.pdb", sep="") if(type=="pymol") outfile <- paste(outprefix, ".py", sep="") else outfile <- paste(outprefix, ".pdb", sep="") } ## Build the new PDB or pymol script in a vector scr <- c() if(type=="pymol") { ## start pymol script scr <- c("from pymol import cmd") scr <- c(scr, "from pymol.cgo import *") scr <- c(scr, paste("cmd.load('", pdbfile, "', 'prot')", sep="")) scr <- c(scr, "cmd.show('cartoon')") if(!is.pdb(pdb) || ca.pdb) scr <- c(scr, "cmd.set('cartoon_trace_atoms', 1)") ## define color range blues <- colorRamp(c("white", "blue")) reds <- colorRamp(c("white", "red")) w <- radius } else { m <- 0 } ## mask lower tri of correlation matrix dccm[lower.tri(dccm, diag=TRUE)] <- NA lims <- c(-1, 1) intervals <- seq(lims[1], lims[2], by=step) ## get rid of interval around 0 if(!is.null(omit)) { i <- which(intervals>(omit-0.001)) j <- which(intervals<(-omit+0.001)) inds <- sort(c(i,j)) intervals <- sort(intervals[inds]) } for ( i in 1:(length(intervals)-1) ) { lower <- intervals[i] upper <- intervals[i+1] if(lower<0 && upper>0) next sele <- intersect( which(dccm>lower), which(dccm<=upper) ) if(length(sele)==0) next f <- matrix(FALSE, ncol(dccm), nrow(dccm)) f[sele] <- TRUE inds <- which(f, arr.ind=TRUE) if(type=="pymol") { scr <- c(scr, "obj=[]") } else { m <- m+1 chain <- LETTERS[m] } for ( j in 1:nrow(inds) ) { x <- inds[j,1]; y <- inds[j,2]; if(x==y) next val <- dccm[x,y] ## corr coeff k <- atom2xyz(inds[j,1]) ## resi 1 l <- atom2xyz(inds[j,2]) ## resi 2 if(type=="pymol") { a <- paste(xyz[k], collapse=",") b <- paste(xyz[l], collapse=",") if(val<=0) col <- blues( abs(val) ) else col <- reds( abs(val) ) col <- round(col/256,4) col <- paste(col, collapse=", ") str <- paste("obj.extend([CYLINDER", a, b, w, col, col, "])", sep=", ") scr <- c(scr, str) } else { a <- paste(format(xyz[k], justify="right", width=8), collapse="") b <- paste(format(xyz[l], justify="right", width=8), collapse="") val <- format(round(val,2), justify="right", width=6) res.str <- format(x, justify="right", width=4) str <- paste("ATOM ", res.str, " CA ALA ", chain, res.str, " ", a, " 0.00", val, sep="") scr <- c(scr, str) res.str <- format(y, justify="right", width=4) str <- paste("ATOM ", res.str, " CA ALA ", chain, res.str, " ", b, " 0.00", val, sep="") scr <- c(scr, str) str <- paste("CONECT", format(x, justify="right", width=5), format(y, justify="right", width=5), sep="") scr <- c(scr, str) } } if(type=="pymol") { tmpa <- gsub("\\.", "", as.character(lower)) tmpb <- gsub("\\.", "", as.character(upper)) name <- paste("cor_", tmpa, "_", tmpb, sep="") str <- paste("cmd.load_cgo(obj, '", name, "')", sep="") scr <- c(scr, str) } else { str <- "TER" scr <- c(scr, str) } } ## Write PDB structure file if(is.pdb(pdb)) write.pdb(pdb, file=pdbfile) else write.pdb(xyz=xyz, file=pdbfile) ## Write python script or PDB with conect records write(scr, file=outfile, sep="\n") if(launch) { ## Open pymol cmd <- paste('pymol', outfile) os1 <- .Platform$OS.type if (os1 == "windows") { success <- shell(shQuote(cmd)) } else { if(Sys.info()["sysname"]=="Darwin") { success <- system(paste("open -a MacPyMOL", outfile)) } else { success <- system(cmd) } } if(success!=0) stop(paste("An error occurred while running command\n '", exefile, "'", sep="")) } } bio3d/R/bhattacharyya.R0000644000176200001440000000423612632622153014431 0ustar liggesusersbhattacharyya <- function(...) UseMethod("bhattacharyya") bhattacharyya.nma <- function(...) bhattacharyya.matrix(...) bhattacharyya.pca <- function(...) bhattacharyya.matrix(...) bhattacharyya.enma <- function(enma, covs=NULL, ncore=NULL, ...) { if(!inherits(enma, "enma")) stop("provide a 'enma' object as obtain from function 'nma.pdbs()'") if(any(is.na(enma$fluctuations))) stop("provide 'enma' object calculated with argument 'rm.gaps=TRUE'") if(is.null(covs)) { cat("Calculating covariance matrices") covs <- cov.enma(enma, ncore=ncore) } cat("Calculating pairwise bhattacharyya coefs") sim.mat <- bhattacharyya.array(covs, ncore=ncore) rownames(sim.mat) <- basename(rownames(enma$fluctuations)) colnames(sim.mat) <- basename(rownames(enma$fluctuations)) return(sim.mat) } bhattacharyya.matrix <- function(a, b, q=90, n=NULL, ...) { if(!is.matrix(a) & is.matrix(b)) stop("provide covariance matrices") a <- ((1 / .tr(a)) * a)*1000 b <- ((1 / .tr(b)) * b)*1000 ei <- eigen( (a + b)/2 ) if(is.null(n)) { percent <- (ei$values/sum(ei$values))*100 cumv <- cumsum(percent) n <- which(cumv>q)[1] } ca <- det((t(ei$vectors[,1:n]) %*% a) %*% ei$vectors[,1:n]) cb <- det((t(ei$vectors[,1:n]) %*% b) %*% ei$vectors[,1:n]) d <- prod(ei$values[1:n]) ndb <- (1/(2*n)) * log( d / sqrt(ca*cb) ) bc <- exp( -ndb ) return(bc) } bhattacharyya.array <- function(covs, ncore=NULL, ...) { ncore <- setup.ncore(ncore, bigmem = FALSE) if(ncore>1) mylapply <- mclapply else mylapply <- lapply dims <- dim(covs) m <- dims[3] mat <- matrix(NA, m, m) ##inds <- pairwise(m) inds <- rbind(pairwise(m), matrix(rep(1:m,each=2), ncol=2, byrow=T)) mylist <- mylapply(1:nrow(inds), function(row) { i <- inds[row,1]; j <- inds[row,2]; val <- bhattacharyya.matrix(covs[,,i], covs[,,j], ...) out <- list(val=val, i=i, j=j) cat(".") return(out) }) for ( i in 1:length(mylist)) { tmp <- mylist[[i]] mat[tmp$i, tmp$j] <- tmp$val } mat[ inds[,c(2,1)] ] = mat[ inds ] ##diag(mat) <- rep(1, n) cat("\n") return(round(mat, 6)) } bio3d/R/pdb.annotate.R0000644000176200001440000001451612632622153014164 0ustar liggesusers"pdb.annotate" <- function(ids, anno.terms=NULL, unique=FALSE, verbose=FALSE) { oopsa <- requireNamespace("XML", quietly = TRUE) oopsb <- requireNamespace("RCurl", quietly = TRUE) if(!all(c(oopsa, oopsb))) stop("Please install the XML and RCurl package from CRAN") if(!is.vector(ids)) { stop("Input argument 'ids' should be a vector of PDB identifiers/accession codes") } ## All available annotation terms (note 'citation' is a meta term) anno.allterms <- c("structureId", "experimentalTechnique", "resolution", "chainId", "ligandId", "ligandName", "source", "scopDomain", "classification", "compound", "title", "citation", "citationAuthor", "journalName", "publicationYear", "structureTitle","depositionDate","structureMolecularWeight","macromoleculeType", "chainId","entityId","sequence","chainLength","db_id","db_name") ##"molecularWeight","secondaryStructure","entityMacromoleculeType") if(is.null(anno.terms)) { anno.terms <- anno.allterms } else { ## Check and exclude invalid annotation terms term.found <- (anno.terms %in% anno.allterms) if( any(!term.found) ) { warning( paste("Requested annotation term not available:", paste(anno.terms[!term.found], collapse=", "), "\n Available terms are:\n\t ", paste(anno.allterms, collapse=", ")) ) } anno.terms <- match.arg(anno.terms, anno.allterms, several.ok=TRUE) } ## Check if we have any valid terms remaining if( length(anno.terms) == 0 ) { stop( paste("No valid anno.terms specified. Please select from:\n\t ", paste(anno.allterms, collapse=", ")) ) } ## force the structureId and chainId term to be present if(any(anno.terms=="citation")) req.terms <- c("structureId", "chainId", "ligandId", "citationAuthor", "journalName", "publicationYear") else req.terms <- c("structureId", "chainId", "ligandId") anno.terms.input <- anno.terms inds <- req.terms %in% anno.terms if(!all(inds)) anno.terms <- c(req.terms[!inds], anno.terms) if (missing(ids)) stop("please specify PDB ids for annotating") if (any(nchar(ids) != 4)) { warning("ids should be standard 4 character PDB-IDs: trying first 4 characters...") if(unique) ids <- unique(substr(basename(ids), 1, 4)) ## first 4 chars should be upper ## any chainId should remain untouched - see e.g. PDB ID 3R1C mysplit <- function(x) { str <- unlist(strsplit(x, "_")) if(length(str)>1) paste(toupper(str[1]), "_", str[2], sep="") else toupper(str[1]) } ids <- unlist(lapply(ids, mysplit)) } else { ids <- toupper(ids) } ids.short <- substr(basename(ids), 1, 4) ids.string <- paste(unique(ids.short), collapse=",") ## prepare query query1 = paste(anno.terms, collapse=",") url <- paste('http://www.rcsb.org/pdb/rest/customReport') curl.opts <- list(httpheader = "Expect:", httpheader = "Accept:text/xml", verbose = verbose, followlocation = TRUE ) curl <- RCurl::postForm(url, pdbids=ids.string, customReportColumns=query1, ssa='n', primaryOnly=1, style = "POST", .opts = curl.opts, .contentEncodeFun=RCurl::curlPercentEncode, .checkParams=TRUE ) ## parse XML xml <- XML::xmlParse(curl) data <- XML::xmlToDataFrame(XML::getNodeSet(xml, "/dataset/record"), stringsAsFactors=FALSE) if(nrow(data)==0) stop("Retrieving data from the PDB failed") ## change colnames (e.g. dimEntity.structureId -> structureId) for( i in 1:ncol(data)) { a <- unlist(strsplit(colnames(data)[i], "\\.")) colnames(data)[i] <- a[2] } ## merge data for unique structureId_chainId entries if(! (is.null(data$ligandId) & is.null(data$ligandName)) ) { if(unique) pdbc.ids <- data$structureId else pdbc.ids <- paste(data$structureId, data$chainId, sep="_") unq.ids <- unique(pdbc.ids) excl.inds <- NULL for( i in 1:length(unq.ids) ) { inds <- which(pdbc.ids==unq.ids[i]) if(!is.null(data$ligandId)) { tmp.ligId <- paste(unique(data$ligandId[inds]), collapse=",") data$ligandId[inds] <- tmp.ligId } if(!is.null(data$ligandName)) { tmp.ligName <- paste(unique(data$ligandName[inds]), collapse=",") data$ligandName[inds] <- tmp.ligName } if(!is.null(data$chainId)) { tmp.chainId <- paste(unique(data$chainId[inds]), collapse=",") data$chainId[inds] <- tmp.chainId } excl.inds <- c(excl.inds, inds[-1]) } } if(length(excl.inds)>0) data <- data[-excl.inds,] if(unique) rownames(data) <- data$structureId else rownames(data) <- paste(data$structureId, data$chainId, sep="_") ## include only requested "structureId_chainID" row.inds <- unique(unlist(lapply(ids, function(x) grep(x, rownames(data))))) data <- data[row.inds,, drop=FALSE] ## Format citation information if (any(anno.terms == "citation") ) { citation <- NULL lig.auth <- data[,"citationAuthor"] lig.year <- data[,"publicationYear"] lig.jnal <- data[,"journalName"] for(i in 1:length(lig.auth)) { citation <- c(citation, paste( unlist(strsplit(lig.auth[[i]], ","))[1], " et al. ", lig.jnal[i], " (", lig.year[i],")",sep="")) } data <- cbind(data, citation) } ## include only requested terms col.inds <- which(colnames(data) %in% anno.terms.input) data <- data[, col.inds, drop=FALSE] if(any(data=="null")) { inds <- which(data=="null", arr.ind=TRUE) for(i in 1:nrow(inds)) data[ inds[i,"row"], inds[i,"col"] ] = NA } ## check for missing entries mygrep <- function(x, y) { inds <- grep(x, y) if(length(inds)==0) return(NA) else return(inds) } collected.ids <- rownames(data) requested.ids <- ids missing <- is.na(unlist(lapply(requested.ids, mygrep, collected.ids))) if(any(missing)) { missing.str <- paste(requested.ids[which(missing)], collapse=", ") warning(paste("Annotation data could not be found for PDB ids:\n ", missing.str)) } return(data) } bio3d/R/hmmer.R0000644000176200001440000001302012632622153012704 0ustar liggesusers"hmmer" <- function(seq, type='phmmer', db=NULL, verbose=TRUE, timeout=90) { cl <- match.call() oopsa <- requireNamespace("XML", quietly = TRUE) oopsb <- requireNamespace("RCurl", quietly = TRUE) if(!all(c(oopsa, oopsb))) stop("Please install the XML and RCurl package from CRAN") seqToStr <- function(seq) { if(inherits(seq, "fasta")) seq <- seq$ali if(is.matrix(seq)) { if(nrow(seq)>1) warning(paste("Alignment with multiple sequences detected. Using only the first sequence")) seq <- as.vector(seq[1,!is.gap(seq[1,])]) } else seq <- as.vector(seq[!is.gap(seq)]) return(paste(seq, collapse="")) } alnToStr <- function(seq) { if(!inherits(seq, "fasta")) stop("seq must be of type 'fasta'") tmpfile <- tempfile() write.fasta(seq, file=tmpfile) rawlines <- paste(readLines(tmpfile), collapse="\n") unlink(tmpfile) return(rawlines) } types.allowed <- c("phmmer", "hmmscan", "hmmsearch", "jackhmmer") if(! type%in%types.allowed ) stop(paste("Input type should be either of:", paste(types.allowed, collapse=", "))) ## PHMMER (protein sequence vs protein sequence database) ## seq is a sequence if(type=="phmmer") { seq <- seqToStr(seq) if(is.null(db)) db="pdb" db.allowed <- c("env_nr", "nr", "refseq", "pdb", "rp15", "rp35", "rp55", "rp75", "swissprot", "unimes", "uniprotkb", "uniprotrefprot", "pfamseq") db <- tolower(db) if(!db%in%db.allowed) stop(paste("db must be either:", paste(db.allowed, collapse=", "))) seqdb <- db hmmdb <- NULL iter <- NULL rcurl <- TRUE } ## HMMSCAN (protein sequence vs profile-HMM database) ## seq is a sequence if(type=="hmmscan") { seq <- seqToStr(seq) if(is.null(db)) db="pfam" db.allowed <- tolower(c("pfam", "gene3d", "superfamily", "tigrfam")) db <- tolower(db) if(!db%in%db.allowed) stop(paste("db must be either:", paste(db.allowed, collapse=", "))) seqdb <- NULL hmmdb <- db iter <- NULL rcurl <- TRUE } ## HMMSEARCH (protein alignment/profile-HMM vs protein sequence database) ## seq is an alignment if(type=="hmmsearch") { if(!inherits(seq, "fasta")) stop("please provide 'seq' as a 'fasta' object") ##alnfile <- tempfile() ##seq <- write.fasta(seq, file=alnfile) seq <- alnToStr(seq) if(is.null(db)) db="pdb" db.allowed <- tolower(c("pdb", "swissprot")) db <- tolower(db) if(!db%in%db.allowed) stop(paste("db must be either:", paste(db.allowed, collapse=", "))) seqdb <- db hmmdb <- NULL iter <- NULL rcurl <- TRUE } ## JACKHMMER (iterative search vs protein sequence database) ## seq can be sequence, HMM, or multiple sequence alignment ## HMM not implemented here yet if(type=="jackhmmer") { if(!inherits(seq, "fasta")) stop("please provide 'seq' as a 'fasta' object") ##alnfile <- tempfile() ##seq <- write.fasta(seq, file=alnfile) seq <- alnToStr(seq) if(is.null(db)) db="pdb" db.allowed <- tolower(c("pdb", "swissprot")) db.allowed <- c("env_nr", "nr", "refseq", "pdb", "rp15", "rp35", "rp55", "rp75", "swissprot", "unimes", "uniprotkb", "uniprotrefprot", "pfamseq") db <- tolower(db) if(!db%in%db.allowed) stop(paste("db must be either:", paste(db.allowed, collapse=", "))) seqdb <- db hmmdb <- NULL iter <- NULL rcurl <- TRUE } ## Make the request to the HMMER website ##url <- paste('http://hmmer.janelia.org/search/', type, sep="") url <- paste("http://www.ebi.ac.uk/Tools/hmmer/search/", type, sep="") curl.opts <- list(httpheader = "Expect:", httpheader = "Accept:text/xml", verbose = verbose, followlocation = TRUE ) hmm <- RCurl::postForm(url, hmmdb=hmmdb, seqdb=seqdb, seq=seq, style = "POST", .opts = curl.opts, .contentEncodeFun=RCurl::curlPercentEncode, .checkParams=TRUE ) add.pdbs <- function(x, ...) { hit <- XML::xpathSApply(x, '@*') pdbs <- unique(XML::xpathSApply(x, 'pdbs', XML::xmlToList)) new <- as.matrix(hit, ncol=1) if(length(pdbs) > 1) { for(i in 1:length(pdbs)) { hit["acc"]=pdbs[i] new=cbind(new, hit) } colnames(new)=NULL } return(new) } ##fetch.pdbs <- function(x) { ## unique(XML::xpathSApply(x, 'pdbs', XML::xmlToList)) ##} xml <- XML::xmlParse(hmm) data <- XML::xpathSApply(xml, '///hits', XML::xpathSApply, '@*') pdb.ids <- NULL if(db=="pdb") { tmp <- XML::xpathSApply(xml, '///hits', add.pdbs) data <- as.data.frame(tmp, stringsAsFactors=FALSE) colnames(data) <- NULL } data <- as.data.frame(t(data), stringsAsFactors=FALSE) data <- data[!duplicated(data$acc), ] ##rownames(data) <- data[, "acc"] ## convert to numeric fieldsToNumeric <- c("evalue", "pvalue", "score", "archScore", "ndom", "nincluded", "niseqs", "nregions", "nreported", "bias", "dcl", "hindex") inds <- which(names(data) %in% fieldsToNumeric) for(i in 1:length(inds)) { tryCatch({ data[[inds[i]]] = as.numeric(data[[inds[i]]]) }, warning = function(w) { #print(w) return(data[[inds[i]]]) }, error = function(e) { #print(e) return(data[[inds[i]]]) } ) } class(data) <- c("hmmer", type, "data.frame") out <- data return(out) } bio3d/R/cna.dccm.R0000644000176200001440000001631212526367343013262 0ustar liggesuserscna.dccm <- function(cij, cutoff.cij=0.4, cm=NULL, vnames=colnames(cij), cluster.method="btwn", collapse.method="max", cols=vmd.colors(), minus.log=TRUE, ...){ ## Check for presence of igraph package oops <- requireNamespace("igraph", quietly = TRUE) if (!oops) { stop("igraph package missing: Please install, see: ?install.packages") } if (dim(cij)[1] != dim(cij)[2]) { stop("Input 'cij' should be a square matrix as obtained from the 'dccm()' function") } ## Check vnames/colnames if present. These are used to name nodes if( is.null( vnames ) ) { vnames <- 1:ncol(cij) } if( length(vnames) != ncol(cij) ) { stop("Length of input 'vnames' and number of cols in input 'cij' do not match") } colnames(cij) <- vnames ## Check 'cm' contact map if present. if(!is.null(cm)){ if (dim(cm)[1] != dim(cm)[2]) { stop("Input 'cm' should be a square contact matrix as obtained from the 'cmap()' function") } if (any(range(cm, na.rm=T) != c(0,1))) { stop("Input 'cm' should be a binary contact matrix as obtained from the 'cmap()' function") } if (dim(cm)[1] != dim(cij)[1]) { stop("Inputs 'cij' and 'cm' should have the same dimensions") } # convert NAs to 0 cm[is.na(cm)] = 0 } ##-- Functions for later cluster.network <- function(network, cluster.method="btwn"){ ## Function to define community clusters from network, ## cluster methods can be one of of 'cluster.options' cluster.options=c("btwn", "walk", "greed") cluster.method <- match.arg(tolower(cluster.method), cluster.options) comms <- switch( cluster.method, btwn = igraph::edge.betweenness.community(network, directed=FALSE), walk = igraph::walktrap.community(network), greed = igraph::fastgreedy.community(network) ) names(comms$membership) <- igraph::V(network)$name return(comms) } contract.matrix <- function(cij.network, membership,## membership=comms$membership, collapse.method="max", minus.log=minus.log){ ## Changed from minus.log=TRUE ## Function to collapse a NxN matrix to an mxm matrix ## where m is the communities of N. The collapse method ## can be one of the 'collapse.options' below ## convert to the original cij values if "-log" was used if(minus.log){ cij.network[cij.network>0] <- exp(-cij.network[cij.network>0]) } collapse.options=c("max", "median", "mean", "trimmed") collapse.method <- match.arg(tolower(collapse.method), collapse.options) ## Fill a 'collapse.cij' nxn community by community matrix node.num <- max(membership) if(node.num > 1){ collapse.cij <- matrix(0, nrow=node.num, ncol=node.num) inds <- pairwise(node.num) for(i in 1:nrow(inds)) { comms.1.inds <- which(membership==inds[i,1]) comms.2.inds <- which(membership==inds[i,2]) submatrix <- cij.network[comms.1.inds, comms.2.inds] ## Use specified "collapse.method" to define community couplings collapse.cij[ inds[i,1], inds[i,2] ] = switch(collapse.method, max = max(submatrix), median = median(submatrix), mean = mean(submatrix), trimmed = mean(submatrix, trim = 0.1)) } if(minus.log){ collapse.cij[collapse.cij>0] <- -log(collapse.cij[collapse.cij>0]) } ## Copy values to lower triangle of matrix and set colnames collapse.cij[ inds[,c(2,1)] ] = collapse.cij[ inds ] colnames(collapse.cij) <- 1:ncol(collapse.cij) } else{ warning("There is only one community in the $communities object. $community.cij object will be set to 0 in the contract.matrix() function.") collapse.cij <- 0 } class(collapse.cij) <- c("dccm", "matrix") return(collapse.cij) } ## Store the command used to submit the calculation cl <- match.call() ##- Take absolute value of 'cij' cij.abs <- abs(cij) ## Filter: set to 0 all values below the cutoff cij.abs[cij.abs < cutoff.cij] = 0 if(minus.log){ ##-- Calculate the -log of cij ## change cij >= 0.9999 to 0.9999 to avoid numerical problems ## (-log is too close to zero) cij.network <- cij.abs cij.network[cij.network >= 0.9999] = 0.9999 cij.network <- -log(cij.network) ## remove infinite values cij.network[is.infinite(cij.network)] = 0 } else{ cij.network <- cij.abs } if(!is.null(cm)){ ##-- Filter cij by contact map cij.network <- cij.network * cm } ## cij.network contains either the -log(abs.cij) or just abs.cij. ## (the default is minus.log=TRUE) ##-- Make an igraph network object network <- igraph::graph.adjacency(cij.network, mode="undirected", weighted=TRUE, diag=FALSE) ##-- Calculate the first set of communities communities <- cluster.network(network, cluster.method) ##-- Coarse grain the cij matrix to a new cluster/community matrix community.cij <- contract.matrix(cij.network, communities$membership, collapse.method, minus.log) ##-- Generate a coarse grained network --## if(sum(community.cij)>0){ community.network <- igraph::graph.adjacency(community.cij, mode="undirected", weighted=TRUE, diag=FALSE) ##-- Cluster the community network to obtain super-communities -- OLD VERSION ## clustered.communities <- cluster.network(community.network, cluster.method) ##-- Annotate the two networks with community information ## Check for duplicated colors if(max(communities$membership) > length(unique(cols)) ) { warning("The number of communities is larger than the number of unique 'colors' provided as input. Colors will be recycled") } ## Set node colors igraph::V(network)$color <- cols[communities$membership] igraph::V(community.network)$color <- cols[ 1:max(communities$membership)] ## Set node sizes igraph::V(network)$size <- 1 igraph::V(community.network)$size <- table(communities$membership) } else{ warning("The $communities structure does not allow a second clustering (i.e. the collapsed community.cij matrix contains only 0). 'community.network' object will be set to NA") community.network <- NA clustered.communities <- NA if(max(communities$membership) > length(unique(cols)) ) { warning("The number of communities is larger than the number of unique 'colors' provided as input. Colors will be recycled") } ## Set node colors igraph::V(network)$color <- cols[communities$membership] ## Set node sizes igraph::V(network)$size <- 1 } ## Output output <- list("network"=network, "communities"=communities, "community.network"=community.network, "community.cij"=community.cij, "cij"=cij.network, call = cl) class(output)="cna" return(output) } bio3d/R/as.pdb.prmtop.R0000644000176200001440000000412412561207744014276 0ustar liggesusersas.pdb.prmtop <- function(prmtop, crd=NULL, inds=NULL, inds.crd=inds, ncore=NULL, ...) { ncore <- setup.ncore(ncore, bigmem=FALSE) if(ncore>1) mylapply <- mclapply else mylapply <- lapply if(!inherits(prmtop, "prmtop")) stop("provide a prmtop object as obtained from function read.prmtop") natoms.prmtop <- prmtop$POINTERS[1] if(is.null(crd)) { warning("producing PDB object with no XYZ coordinates") crd <- as.numeric(rep(NA, natoms.prmtop*3)) } if(!inherits(crd, "amber")) { new <- list() new$xyz <- as.xyz(crd) new$natoms <- ncol(new$xyz)/3 crd <- new } natoms.crd <- crd$natoms if( any(c(!is.null(inds), !is.null(inds.crd))) ) { if(is.null(inds)) { inds$atom = seq(1, natoms.prmtop) inds$xyz = atom2xyz(inds$atom) class(inds) = "select" } if(is.null(inds.crd)) { inds.crd$atom = seq(1, natoms.crd) inds.crd$xyz = atom2xyz(inds.crd$atom) class(inds.crd)="select" } natoms.prmtop = length(inds$atom) natoms.crd = length(inds.crd$atom) } if(natoms.prmtop != natoms.crd) stop(paste("atom number mismatch:", natoms.prmtop, "(prmtop) vs", natoms.crd, "(crds)")) resmap <- function(i, type='resid') { if(i==length(prmtop$RESIDUE_POINTER)) j <- prmtop$POINTERS[1] - prmtop$RESIDUE_POINTER[i] + 1 else j <- prmtop$RESIDUE_POINTER[i+1] - prmtop$RESIDUE_POINTER[i] if(type=='resno') return(rep(i,j)) if(type=='resid') return(rep(prmtop$RESIDUE_LABEL[i], j)) } resno <- unlist(mylapply(1:length(prmtop$RESIDUE_POINTER), resmap, 'resno')) resid <- unlist(mylapply(1:length(prmtop$RESIDUE_POINTER), resmap, 'resid')) if(any(c(!is.null(inds), !is.null(inds.crd)))) { pdb <- as.pdb.default(xyz=crd$xyz[,inds.crd$xyz], elety=prmtop$ATOM_NAME[inds$atom], resno=resno[inds$atom], chain=as.character(NA), resid=resid[inds$atom]) } else { pdb <- as.pdb.default(xyz=crd$xyz, elety=prmtop$ATOM_NAME, resno=resno, chain=as.character(NA), resid=resid) } pdb$call = match.call() return(pdb) } bio3d/R/rmsip.R0000644000176200001440000000576112526367343012754 0ustar liggesusersrmsip <- function(...) UseMethod("rmsip") rmsip.enma <- function(enma, ncore=NULL, subset=10, ...) { if(!inherits(enma, "enma")) stop("provide a 'enma' object as obtain from function 'nma.pdbs()'") if(any(is.na(enma$fluctuations))) stop("provide 'enma' object calculated with argument 'rm.gaps=TRUE'") ncore <- setup.ncore(ncore, bigmem = FALSE) if(ncore>1) mylapply <- mclapply else mylapply <- lapply dims <- dim(enma$fluctuations) m <- dims[1] mat <- matrix(NA, m, m) ##inds <- pairwise(m) inds <- rbind(pairwise(m), matrix(rep(1:m,each=2), ncol=2, byrow=T)) mylist <- mylapply(1:nrow(inds), function(row) { i <- inds[row,1]; j <- inds[row,2]; r <- rmsip.default(enma$U.subspace[,,i], enma$U.subspace[,,j], subset=subset) out <- list(val=r$rmsip, i=i, j=j) cat(".") return(out) }) for ( i in 1:length(mylist)) { tmp <- mylist[[i]] mat[tmp$i, tmp$j] <- tmp$val } mat[ inds[,c(2,1)] ] = mat[ inds ] ##diag(mat) <- rep(1, n) colnames(mat) <- basename(rownames(enma$fluctuations)) rownames(mat) <- basename(rownames(enma$fluctuations)) cat("\n") return(round(mat, 6)) } rmsip.default <- function(modes.a, modes.b, subset = 10, row.name="a", col.name="b", ...) { if(missing(modes.a)) stop("rmsip: 'modes.a' must be prodivded") if(missing(modes.b)) stop("rmsip: 'modes.b' must be prodivded") m1 <- .fetchmodes(modes.a, subset=subset) m2 <- .fetchmodes(modes.b, subset=subset) dims.a <- dim(m1$U) dims.b <- dim(m2$U) subset <- dims.a[2] if( dims.a[1] != dims.b[1] ) stop("dimension mismatch") if( dims.a[2] != dims.b[2] ) stop("dimension mismatch") mass.a <- NULL; mass.b <- NULL; x <- normalize.vector(m1$U, mass.b) y <- normalize.vector(m2$U, mass.b) if(is.null(mass.a)) o <- ( t(x) %*% y ) **2 else o <- t(apply(x, 2, inner.prod, y, mass.a) **2) if (!is.null(row.name)) { rownames(o) <- paste(row.name, c(1:subset), sep="") } if (!is.null(col.name)) { colnames(o) <- paste(col.name, c(1:subset), sep="") } rmsip <- sqrt(sum(o)/subset) out <- list(overlap=round(o,3), rmsip=rmsip) class(out) <- "rmsip" return( out ) } .fetchmodes <- function(x, subset=NULL) { if (inherits(x, "pca")) { U <- x$U; L <- x$L; first.mode <- 1 } else if (inherits(x, "nma")) { U <- x$U; L <- x$L; mass <- x$mass first.mode <- x$triv.modes+1 } else { if( !(inherits(x, "matrix") | inherits(x, "pca.loadings")) ) stop("provide an object of type 'pca', 'nma', or 'matrix'") U <- x; L <- NULL; first.mode <- 1 } dims <- dim(U) if(is.null(subset)) { n <- dims[2] } else { n <- subset + first.mode - 1 if(n>dims[2]) n <- dims[2] } U <- U[, first.mode:n, drop=FALSE] L <- L[first.mode:n] out <- list(U=U, L=L, mass=NULL) return(out) } bio3d/R/combine.select.R0000644000176200001440000000245112544562302014475 0ustar liggesuserscombine.select <- function(sel1=NULL, sel2=NULL, ..., operator="AND", verbose=TRUE) { cl <- match.call() sels <- list(sel1, sel2, ...) if(any(sapply(sels, function(x) !is.null(x) && !inherits(x, "select")))) stop("Invalid atom select(s)") rm.inds = sapply(sels, is.null) sels = sels[!rm.inds] if(length(sels) == 0) return(NULL) else if(length(sels) == 1) return(sels[[1]]) op.tbl <- c(rep("AND",3), rep("OR",4), rep("NOT",4)) operator <- op.tbl[match(operator, c("AND","and","&","OR","or","|","+","NOT","not","!","-"))] # Print message if(verbose) { msg <- switch(operator, AND = " Intersect of selects", OR = " Union of selects", NOT = " Select 2 (, 3, ...) is subtracted from select 1", stop("Unknown operation") ) cat(msg, "\n", sep="") } sel <- sels[[1]]$atom for(i in 2:length(sels)) { sel <- switch(operator, "AND" = intersect(sel, sels[[i]]$atom), "OR" = sort(union(sel, sels[[i]]$atom)), "NOT" = setdiff(sel, sels[[i]]$atom), stop("Unknown operation") ) } sel <- list(atom = sel, xyz = atom2xyz(sel), call=cl) if(verbose) { cat(paste(" * Selected a total of:", length(sel$atom), "atoms *\n")) } class(sel) = "select" return(sel) } bio3d/R/wrap.tor.R0000644000176200001440000000251312526367343013366 0ustar liggesusers"wrap.tor" <- function(data,wrapav=TRUE,avestruc=NULL){ wrap180 <- function(x) { x[which(x > 180, arr.ind=TRUE)] <- x[which(x > 180, arr.ind=TRUE)] - 360 x[which(x < -180, arr.ind=TRUE)] <- x[which(x < -180, arr.ind=TRUE)] + 360 x } if(!wrapav && is.null(avestruc)) stop("Average structure is missing") if(is.vector(data)) { data <- matrix(data,ncol=1) return.vec = TRUE } else { return.vec = FALSE } avestruc.i<-avestruc datawrap <- NULL for(i in 1:ncol(data)) { struc <- data[,i] if(all(is.na(struc))) { struc <- rep(NA, length(struc)) } else { if(wrapav){ avestruc <- wrap180( mean(struc, na.rm=TRUE) ) } else { avestruc<-avestruc.i[i] } difvar <- avestruc - struc while (length(difvar[ as.vector(na.omit(abs(difvar)>180)) ]) > 0) { struc[which(difvar > 180, arr.ind=TRUE)] <- struc[which(difvar > 180, arr.ind=TRUE)] + 360 struc[which(difvar < -180, arr.ind=TRUE)] <- struc[which(difvar < -180, arr.ind=TRUE)] - 360 if(wrapav){avestruc <- wrap180( mean(struc, na.rm=TRUE) %% 360 )} difvar <- avestruc - struc } } datawrap <- cbind(datawrap,struc) } if(return.vec) datawrap = as.vector(datawrap) return(datawrap) } bio3d/R/trim.pdb.R0000644000176200001440000000476112544562303013331 0ustar liggesuserstrim <- function(...) UseMethod("trim") "trim.pdb" <- function(pdb, ..., inds=NULL, sse=TRUE) { if(!is.pdb(pdb)) stop("input 'pdb' must be a PDB list object as returned from 'read.pdb'") cl <- match.call() extra.args <- list(...) if(length(extra.args)>0) { if(!is.null(inds)) warning("Multiple atom selection terms provided. Using only argument 'inds'") else if(is.select(extra.args[[1]])) # to be back-compatible with the habit calling trim.pdb(pdb, inds) inds = extra.args[[1]] else inds = atom.select(pdb, ...) } if(is.null(inds)) stop("no selection indices provided") if(!is.list(inds)) stop("selection indices must be provided i.e. from 'atom.select'") if(is.null(inds$atom) || is.null(inds$xyz)) stop("selection indices must be provided i.e. from 'atom.select'") ## Trim atom components atom <- pdb$atom[inds$atom,] ## Add calpha indices if non-existing if(is.null(pdb$calpha)) { ca.inds <- atom.select(pdb, "calpha") pdb$calpha <- rep(FALSE, nrow(pdb$atom)) pdb$calpha[ca.inds$atom] <- TRUE } ## Trim calpha indices calpha <- pdb$calpha[inds$atom] ## Trim xyz components xyz <- trim.xyz(pdb$xyz, col.inds = inds$xyz) ## Trim SSE components helix <- NULL; sheet <- NULL; if(sse) { ss <- pdb2sse(pdb, verbose = FALSE) ##- Trim sse vector calpha2 <- which(pdb$calpha) %in% inds$atom ss <- ss[calpha2] ##- New sse new.sse <- bounds.sse(ss) helix <- new.sse$helix if(length(helix$start) > 0) { ##- add back other components add <- pdb$helix[!names(pdb$helix) %in% names(new.sse$helix)] ##- match sse number in case some sse are completely removed add <- lapply(add, function(x) x[new.sse$helix$id]) helix <- c(helix, add) } sheet <- new.sse$sheet if(length(sheet$start) > 0) { ##- add back other components add <- pdb$sheet[!names(pdb$sheet) %in% names(new.sse$sheet)] ##- match sse number in case some sse are completely removed add <- lapply(add, function(x) x[new.sse$sheet$id]) sheet <- c(sheet, add) } ##- remove 'id'; Maybe we don't need it? helix$id <- NULL sheet$id <- NULL } output <- list(atom = atom, helix = helix, sheet = sheet, seqres = pdb$seqres, ## return unmodified xyz = xyz, calpha = calpha, call = cl) class(output) <- class(pdb) return(output) } bio3d/R/write.pqr.R0000644000176200001440000002101612412621431013525 0ustar liggesusers`write.pqr` <- function (pdb = NULL, xyz = pdb$xyz, resno = NULL, resid = NULL, eleno = NULL, elety = NULL, chain = NULL, o = NULL, b = NULL, het = FALSE, append = FALSE, verbose =FALSE, chainter = FALSE, file = "R.pdb") { ## For Testing: ## resno = NULL; resid = NULL;eleno = NULL;elety = NULL;chain = NULL;o = NULL;b = NULL; het = FALSE;append = FALSE;verbose =FALSE;chainter = FALSE ## pdb=mt; eleno=eleno.new; resno=resno.new; file="t3.pqr" if(is.null(xyz) || !is.numeric(xyz)) stop("write.pqr: please provide a 'pdb' object or numeric 'xyz' vector") if(any(is.na(xyz))) stop("write.pqr: 'xyz' coordinates must have no NA's.") if(is.vector(xyz)) { natom <- length(xyz)/3 nfile <- 1 } else if (is.matrix(xyz)) { stop("write.pqr: no multimodel PQR support") ##natom <- ncol(xyz)/3 ##nfile <- nrow(xyz) ##if (verbose) { ## cat("Multiple 'xyz' rows will be interperted as multimodels/frames\n") ##} } else { stop("write.pdb: 'xyz' or 'pdb$xyz' must be either a vector or matrix") } card <- rep("ATOM", natom) if(!is.null(pdb)) { if(natom == 1) ## make sure we are a matrix pdb$atom <- t(as.matrix(pdb$atom)) if (het) card <- c( rep("ATOM", nrow(pdb$atom)), rep("HETATM", nrow(pdb$het)) ) if (is.null(resno)) { resno = pdb$atom[, "resno"] if (het) { resno = c(resno, pdb$het[, "resno"]) }} if (is.null(resid)) { resid = pdb$atom[, "resid"] if (het) { resid = c(resid, pdb$het[, "resid"]) }} if (is.null(eleno)) { eleno = pdb$atom[, "eleno"] if (het) { eleno = c(eleno, pdb$het[, "eleno"]) }} if (is.null(elety)) { elety = pdb$atom[, "elety"] if (het) { elety = c(elety, pdb$het[, "elety"]) }} if (is.null(chain)) { chain = pdb$atom[, "chain"] if (het) { chain = c(chain, pdb$het[, "chain"]) }} if (is.null(o)) { o = pdb$atom[, "o"] if (het) { o = c(o, pdb$het[, "o"]) }} if (is.null(b)) { b = pdb$atom[, "b"] if (het) { b = c(b, pdb$het[, "b"]) }} if (any(is.na(o))) { o = rep("1.00", natom) } if (any(is.na(b))) { b = rep("0.00", natom) } #if (any(is.na(chain))) { chain = rep(" ", natom) } chain[is.na(chain)]= " " } else { if (is.null(resno)) resno = c(1:natom) if (is.null(resid)) resid = rep("ALA", natom) if (is.null(eleno)) eleno = c(1:natom) if (is.null(elety)) elety = rep("CA", natom) if (is.null(chain)) chain = rep(" ", natom) if (is.null(o)) o = rep("1.00",natom) if (is.null(b)) b = rep("0.00", natom) } if (!is.logical(append)) stop("write.pqr: 'append' must be logical TRUE/FALSE") if (length(as.vector(xyz))%%3 != 0) { stop("write.pqr: 'length(xyz)' must be divisable by 3.") } check.lengths <- sum(length(resno), length(resid), length(eleno), length(elety), length(o), length(b)) if (check.lengths%%natom != 0) { stop("write.pqr: the lengths of all input vectors != 'length(xyz)/3'.") } o <- as.numeric(o) b <- as.numeric(b) eleno <- as.character(eleno) resno <- as.character(resno) ## Inserted Jul 8th 2008 for adding TER between chains ter.lines <- (which(!duplicated(chain))[-1] - 1) #### #### ## Edit: Sat Aug 1 14:48:48 PDT 2009 #### ## for speed imporvment and for #### ## implementing 6 character atom numbers #### if(nfile==1) { coords <- matrix(round(as.numeric(xyz), 3), ncol = 3, byrow = TRUE) if (verbose) { cat(paste("Writing 1 frame with",natom,"atoms ")) } coords <- matrix(round(as.numeric(xyz), 3), ncol = 3, byrow = TRUE) lines <- matrix(, ncol=1, nrow=natom) ## Four format otions: regular; elety > 3; eleno > 5; eleno > 5 & elety > 3 ## cases nchar(elety) > 3; nchar(eleno) > 5 cases <- matrix(1,ncol=2,nrow=natom) cases[(nchar(eleno) > 5) ,1] = 3 cases[(nchar(elety) < 4) ,2] = 0 cases <- rowSums(cases) ind.1 <- which(cases==1) ind.2 <- which(cases==2) ind.3 <- which(cases==3) ind.4 <- which(cases==4) atom.print.1 <- function(card = "ATOM", eleno, elety, alt = "", resid, chain = "", resno, insert = "", x, y, z, o = "1.00", b = "0.00", segid = "") { format <- "%-6s%5s %-3s%1s%-4s%1s%4s%1s%3s%8.3f%8.3f%8.3f%8.4f%7.4f%6s%4s" sprintf(format, card, eleno, elety, alt, resid, chain, resno, insert, "", x, y, z, o, b, "", segid) } atom.print.2 <- function(card = "ATOM", eleno, elety, alt = "", resid, chain = "", resno, insert = "", x, y, z, o = "1.00", b = "0.00", segid = "") { format <- "%-6s%5s %-4s%1s%-4s%1s%4s%1s%3s%8.3f%8.3f%8.3f%8.4f%7.4f%6s%4s" sprintf(format, card, eleno, elety, alt, resid, chain, resno, insert, "", x, y, z, o, b, "", segid) } atom.print.3 <- function(card = "ATOM", eleno, elety, alt = "", resid, chain = "", resno, insert = "", x, y, z, o = "1.00", b = "0.00", segid = "") { format <- "%-4s%7s %-3s%1s%-4s%1s%4s%1s%3s%8.3f%8.3f%8.3f%8.4f%7.4f%6s%4s" sprintf(format, card, eleno, elety, alt, resid, chain, resno, insert, "", x, y, z, o, b, "", segid) } atom.print.4 <- function(card = "ATOM", eleno, elety, alt = "", resid, chain = "", resno, insert = "", x, y, z, o = "1.00", b = "0.00", segid = "") { format <- "%-4s%7s %-4s%1s%-4s%1s%4s%1s%3s%8.3f%8.3f%8.3f%8.4f%7.4f%6s%4s" sprintf(format, card, eleno, elety, alt, resid, chain, resno, insert, "", x, y, z, o, b, "", segid) } if(length(ind.1)>0) { lines[ind.1,] <- atom.print.1( card = card[ind.1], eleno = eleno[ind.1], elety = elety[ind.1], resid = resid[ind.1], chain = chain[ind.1], resno = resno[ind.1], x = coords[ind.1, 1], y = coords[ind.1, 2], z = coords[ind.1, 3], o = o[ind.1], b = b[ind.1] ) } if(length(ind.2)>0) { lines[ind.2,] <- atom.print.2( card = card[ind.2], eleno = eleno[ind.2], elety = elety[ind.2], resid = resid[ind.2], chain = chain[ind.2], resno = resno[ind.2], x = coords[ind.2, 1], y = coords[ind.2, 2], z = coords[ind.2, 3], o = o[ind.2], b = b[ind.2] ) } if(length(ind.3)>0) { lines[ind.3,] <- atom.print.3( card = card[ind.3], eleno = eleno[ind.3], elety = elety[ind.3], resid = resid[ind.3], chain = chain[ind.3], resno = resno[ind.3], x = coords[ind.3, 1], y = coords[ind.3, 2], z = coords[ind.3, 3], o = o[ind.3], b = b[ind.3] ) } if(length(ind.4)>0) { lines[ind.4,] <- atom.print.4( card = card[ind.4], eleno = eleno[ind.4], elety = elety[ind.4], resid = resid[ind.4], chain = chain[ind.4], resno = resno[ind.4], x = coords[ind.4, 1], y = coords[ind.4, 2], z = coords[ind.4, 3], o = o[ind.4], b = b[ind.4] ) } write.table(lines, file=file, quote=FALSE, row.names=FALSE, col.names=FALSE, append=append) #### #### End of Edit: removed big chunks of old code #### } else { if (verbose) { cat(paste("Writing",nfile,"frames with",natom,"atoms"),"\n") cat("Frame Progress (x50) ") } stop("REMOVED code for multimodel PQR as these files dont have much support") } if (verbose) cat(" DONE","\n") } bio3d/R/seqaln.R0000644000176200001440000000713512561207744013077 0ustar liggesusers"seqaln" <- function(aln, id=NULL, profile=NULL, exefile = "muscle", outfile = "aln.fa", protein = TRUE, seqgroup = FALSE, refine = FALSE, extra.args = "", verbose = FALSE) { ## Log the call cl <- match.call() ## alignment to fasta object aln <- as.fasta(aln, id=id) ## nothing to align? if(!nrow(aln$ali) > 1 && is.null(profile)) { warning("nothing to align") aln$ali <- aln$ali[ , !is.gap(aln$ali), drop=FALSE] colnames(aln$ali) <- NULL return(aln) } if(!is.null(profile) & !inherits(profile, "fasta")) stop("profile must be of class 'fasta'") if(grepl("clustalo", tolower(exefile))) { prg <- "clustalo" ver <- "--version" if(!is.null(profile)) args <- c("", "--profile1", "--in", "--out") else args <- c("--in", "--out") extra.args <- paste(extra.args,"--force") if(seqgroup) extra.args <- paste(extra.args, "--output-order=tree-order") else extra.args <- paste(extra.args, "--output-order=input-order") if(verbose) extra.args <- paste(extra.args,"--verbose") if(!is.null(profile) && length(grep("dealign", extra.args))==0) warning("profile alignment with clustalo: consider using extra.args='--dealign'") #if(protein) # extra.args <- paste(extra.args,"--seqtype Protein") #else # extra.args <- paste(extra.args,"--seqtype DNA") } else { prg <- "muscle" ver <- "-version" if(!is.null(profile)) args <- c("-profile", "-in1", "-in2", "-out") else args <- c("-in", "-out") if(refine) extra.args <- paste(extra.args,"-refine") if(protein) extra.args <- paste(extra.args,"-seqtype protein") else extra.args <- paste(extra.args,"-seqtype dna") } ## Check if the program is executable os1 <- .Platform$OS.type status <- system(paste(exefile, ver), ignore.stderr = TRUE, ignore.stdout = TRUE) if(!(status %in% c(0,1))) stop(paste("Launching external program failed\n", " make sure '", exefile, "' is in your search path", sep="")) ## Generate temporary files toaln <- tempfile() write.fasta(aln, file=toaln) profilealn <- NULL if(!is.null(profile)) { profilealn <- tempfile() write.fasta(profile, file=profilealn) } if(is.null(outfile)) fa <- tempfile() else fa <- outfile ## Build command to external program if(is.null(profile)) { cmd <- paste(exefile, args[1], toaln, args[2], fa, extra.args, sep=" ") } else { cmd <- paste(exefile, args[1], args[2], profilealn, args[3], toaln, args[4], fa, extra.args, sep=" ") } if(verbose) cat(paste("Running command:\n ", cmd , "\n")) ## Run command if (os1 == "windows") success <- shell(shQuote(cmd), ignore.stderr = !verbose, ignore.stdout = !verbose) else success <- system(cmd, ignore.stderr = !verbose, ignore.stdout = !verbose) if(success!=0) stop(paste("An error occurred while running command\n '", exefile, "'", sep="")) ## Re-group sequences to initial alignment order ## (muscle groups similar sequences by default) naln <- read.fasta(fa, rm.dup=FALSE) if(!seqgroup) { if(is.null(profile)) { ord <- match(aln$id, naln$id) naln$id <- naln$id[ord] naln$ali <- naln$ali[ord,] } } ## Delete temporary files if(!is.null(profile)) unlink(profilealn) unlink(toaln) if(is.null(outfile)) unlink(fa) naln$call=cl return(naln) } bio3d/R/print.xyz.R0000644000176200001440000000162412524171274013573 0ustar liggesusersprint.xyz <- function(x, ...) { ## Print a summary of bio3d 'xyz' object features if(!inherits(x, "xyz")) { stop("Input should be a bio3d 'xyz' object") } if( is.null(nrow(x)) ) x <- t(as.matrix(x)) cat( paste0("\n Total Frames#: ", nrow(x), "\n Total XYZs#: ", ncol(x), ", (Atoms#: ", round(ncol(x)/3,3), ")\n\n") ) if(ncol(x) > 7) { s <- paste(" [1] ", paste(round(x[1,1:3],3),collapse=" "), " <...> ", paste( round(x[nrow(x),(ncol(x)-2):ncol(x)], 3), collapse=" "), " [",length(x),"]", sep="") } else { s <- paste(" [1] ", paste(round(x[1,],3),collapse=" "), " [",length(x),"]", sep="") } cat(s,"\n\n") i <- paste(attributes(x)$names, collapse = ", ") j <- paste("Matrix DIM =", nrow(x), "x", ncol(x)) cat(strwrap(paste(" + attr:", i, "\n",j), width = 45, exdent = 8), sep = "\n") } bio3d/R/struct.aln.R0000644000176200001440000001521412524171274013703 0ustar liggesusers"struct.aln" <- function(fixed, mobile, fixed.inds = NULL, mobile.inds = NULL, write.pdbs = TRUE, outpath = "fitlsq", prefix = c("fixed", "mobile"), max.cycles = 10, cutoff = 0.5, ... ) { if(missing(fixed)) stop("align: must supply 'pdb' object, i.e. from 'read.pdb'") if(missing(mobile)) stop("align: must supply 'pdb' object, i.e. from 'read.pdb'") if(!is.pdb(fixed)) stop("align: 'fixed' must be of type 'pdb'") if(!is.pdb(mobile)) stop("align: 'mobile' must be of type 'pdb'") ## if indices are provided, make new PDB entities if ( !is.null(fixed.inds) ) { if(length(fixed.inds$atom)<2) stop("align: insufficent atom indices for fitting") #a <- NULL #a$atom <- fixed$atom[fixed.inds$atom, ] #a$xyz <- fixed$xyz[fixed.inds$xyz] #a$calpha <- as.logical(a$atom[,"elety"] == "CA") a <- trim.pdb(fixed, fixed.inds) } else { a <- fixed fixed.inds <- atom.select(fixed, 'all', verbose=FALSE) } if ( !is.null(mobile.inds) ) { if(length(mobile.inds$atom)<2) stop("align: insufficent atom indices for fitting") #b <- NULL #b$atom <- mobile$atom[mobile.inds$atom, ] #b$xyz <- mobile$xyz[mobile.inds$xyz] #b$calpha <- as.logical(b$atom[,"elety"] == "CA") b <- trim.pdb(mobile, mobile.inds) } else { b <- mobile mobile.inds <- atom.select(mobile, 'all', verbose=FALSE) } "xyz.dist" <- function(v) { a <- v[1:3]; b <- v[4:6] sqrt(sum((a-b)**2)) } "resi.dev" <- function(xyz.a, xyz.b, cycle=1, cutoff = 0.5) { k <- matrix(xyz.a, ncol=3, byrow=T) l <- matrix(xyz.b, ncol=3, byrow=T) devs <- apply( cbind(k,l), 1, "xyz.dist") m <- median(devs) std <- sd(devs) cut <- m + (2*std) inds <- which( devs > cut ) if ( (std < cutoff) || (length(inds)==0) ) { return( NULL ) } else { cat( " Cycle ", i, ": ", length(inds), " atoms rejected", "\n", sep="") cat(" Mean: ", round(m,1), " Std: ", round(std,1), " Cut: ", round(cut,1), "\n", sep="" ) return(inds) } } "remap.inds" <- function(pdb.init, inds.init, inds.trunc.atom) { ## Map back to indices for the entire PDB given inds.full <- NULL inds.full$atom <- inds.init$atom[inds.trunc.atom] inds.full$xyz <- atom2xyz(inds.full$atom) inds.full$logical <- atom2xyz(seq(1, nrow(pdb.init$atom))) %in% inds.full$xyz return(inds.full) } "parse.pdb" <- function(pdb, gaps, s, i) { pdbseq <- aa321(pdb$atom[pdb$calpha, "resid"]) aliseq <- toupper(s$ali[i, ]) tomatch <- gsub("X", "[A-Z]", aliseq[!is.gap(aliseq)]) start.num <- regexpr(pattern = paste(c(na.omit(tomatch[1:15])), collapse = ""), text = paste(pdbseq, collapse = ""))[1] nseq <- rep(NA, length(aliseq)) ali.res.ind <- which(!is.gap(aliseq)) ali.res.ind <- ali.res.ind[1:length(pdbseq)] nseq[ali.res.ind] = start.num:((start.num - 1) + length(tomatch)) pdb$atom <- cbind(pdb$atom, index=seq(1, nrow(pdb$atom))) ca.ali <- pdb$atom[pdb$calpha, ][nseq, ] at.inds <- ca.ali[, "index"] return(at.inds) } ## PDB list for sequence alignment pdb.list <- NULL pdb.list[[1]] <- a pdb.list[[2]] <- b ## Sequence alignment s <- lapply(pdb.list, pdbseq) s <- t(sapply(s, `[`, 1:max(sapply(s, length)))) s[is.na(s)] <- "-" s <- seqaln(s, id = c("fixed", "mobile"), ...) gaps <- gap.inspect(s$ali) ## Parse truncated PDBs at.inds.a <- parse.pdb(a, gaps, s, 1) at.inds.b <- parse.pdb(b, gaps, s, 2) ## Fetch indices for fitting (truncated pdb) at.a <- as.numeric(at.inds.a[gaps$f.inds]) at.b <- as.numeric(at.inds.b[gaps$f.inds]) ## Indices for full pdb - done with the truncated ones a.inds.full <- remap.inds(fixed, fixed.inds, at.a) b.inds.full <- remap.inds(mobile, mobile.inds, at.b) ## Perform the initial fitting fit <- rot.lsq(mobile$xyz, fixed$xyz, xfit=b.inds.full$logical, yfit=a.inds.full$logical) rmsd.init <- rmsd(as.vector(fixed$xyz), fit, a.inds=a.inds.full$xyz, b.inds=b.inds.full$xyz) cat("\n") cat(" Initial RMSD (", length(gaps$f.inds), " atoms): ", rmsd.init, "\n", sep="") if ( write.pdbs ) { dir.create(outpath, FALSE) fname <- file.path(outpath, paste(prefix[2], "_", 0, ".pdb", sep="")) write.pdb(mobile, xyz=fit, file=fname) } ## Refinement process rmsd.all <- c(rmsd.init) for ( i in seq(1,max.cycles) ) { if(i>max.cycles) break ## Find residues with largest structural deviation exc <- resi.dev(fixed$xyz[a.inds.full$xyz], fit[b.inds.full$xyz], cycle = i, cutoff = cutoff) if ( is.null(exc) ) { break } else { ## Remove atoms for new round of fitting exc <- atom2xyz(exc) tmp <- seq(1,length( a.inds.full$logical )) exc.a <- tmp[which( a.inds.full$logical )][exc] a.inds.full$logical[exc.a] <- FALSE tmp <- seq(1,length( b.inds.full$logical )) exc.b <- tmp[which( b.inds.full$logical )][exc] b.inds.full$logical[exc.b] <- FALSE ## Build new xyz and atom indices a.inds.full$xyz <- which(a.inds.full$logical) b.inds.full$xyz <- which(b.inds.full$logical) a.inds.full$atom <- xyz2atom(a.inds.full$xyz) b.inds.full$atom <- xyz2atom(b.inds.full$xyz) ## Fit based on new indices fit <- rot.lsq(mobile$xyz, fixed$xyz, xfit=b.inds.full$logical, yfit=a.inds.full$logical) if ( write.pdbs ) { fname <- file.path(outpath, paste(prefix[2], "_", i, ".pdb", sep="")) write.pdb(mobile, xyz=fit, file=fname) } ## Calculate RMSD tmp.rmsd <- rmsd(as.vector(fixed$xyz), fit, a.inds=a.inds.full$xyz, b.inds.full$xyz) rmsd.all <- c(rmsd.all, tmp.rmsd) num.resi <- length(which(a.inds.full$logical))/3 cat(" RMSD (", num.resi, " of ", length(gaps$f.inds), " atoms): ", tmp.rmsd, "\n", sep="") } } if ( write.pdbs ) { fname <- file.path(outpath, paste(prefix[1], ".pdb", sep="")) write.pdb(fixed, file=fname) } a.inds.full$logical <- NULL b.inds.full$logical <- NULL out <- list("a.inds"=a.inds.full, "b.inds"=b.inds.full, xyz=as.xyz(fit), rmsd=rmsd.all) return(out) } bio3d/R/atom.select.prmtop.R0000644000176200001440000000051012561207744015340 0ustar liggesusers"atom.select.prmtop" <- function(prmtop, ...) { if(!inherits(prmtop, "prmtop")) stop("provide a PRMTOP object as obtained from read.prmtop()") cl <- match.call() tmp.pdb <- as.pdb.prmtop(prmtop, crd=as.numeric(rep(NA, prmtop$POINTERS[1]*3))) sele <- atom.select.pdb(tmp.pdb, ...) sele$call <- cl return(sele) } bio3d/R/plot.cmap.R0000644000176200001440000000523712544562302013505 0ustar liggesusersplot.cmap <- function(x, col=2, pch=16, main="Contact map", sub="", xlim=NULL, ylim=NULL, xlab = "Residue index", ylab = xlab, axes=TRUE, ann=par("ann"), sse=NULL, sse.type="classic", sse.min.length=5, bot=TRUE, left=TRUE, helix.col="gray20", sheet.col="gray80", sse.border=FALSE, add=FALSE, ...) { dims <- dim(x) if(is.null(xlim)) xlim <- c(1, dims[1]) if(is.null(ylim)) ylim <- c(1, dims[2]) if(!add) { plot.new() } else { axes <- FALSE xlab <- NA; ylab <- NA; main <- NA; sub <- NA; sse <- NULL; } plot.window(xlim=xlim, ylim=ylim, ...) inds <- which(x==1, arr.ind=TRUE) points(inds, pch=pch, col=col) if(!is.null(sse)) { ## Obtain SSE vector from PDB input 'sse' if(is.pdb(sse)) sse$sse <- pdb2sse(sse) h <- bounds( which(sse$sse == "H") ) e <- bounds( which(sse$sse == "E") ) ## Remove short h and e elements that can crowd plots if(length(h) > 0) { inds <- which(h[,"length"]>=sse.min.length) h <- h[inds,,drop=FALSE] } else { h <- NULL } if(length(e) > 0) { inds <- which(e[,"length"]>=sse.min.length) e <- e[inds,,drop=FALSE] } else { e <- NULL } if(sse.type != "classic") warning("Only sse.type='classic' is currently available, 'fancy' coming soon") off <- c(0.006, 0.039) if(left) { ## Determine bottom and top of margin region bo <- min(xlim) - (diff(xlim)*off[1]) to <- min(xlim) - (diff(xlim)*off[2]) if(length(h) > 0) rect(xleft=bo, xright=to, ybottom=h[,"start"], ytop=h[,"end"], col=helix.col, border=sse.border) if(length(e) > 0) rect(xleft=bo, xright=to, ybottom=e[,"start"], ytop=e[,"end"], col=sheet.col, border=sse.border) } if(bot){ to <- min(ylim) - (diff(ylim)*off[1]) bo <- min(ylim) - (diff(ylim)*off[2]) if(length(h) > 0) rect(xleft=h[,"start"], xright=h[,"end"], ybottom=bo, ytop=to, col=helix.col, border=sse.border) if(length(e) > 0) rect(xleft=e[,"start"], xright=e[,"end"], ybottom=bo, ytop=to, col=sheet.col, border=sse.border) } } if(axes) { box() at <- axTicks(1); at[1] = 1 axis(1, at) axis(2, at) } if(ann) { # if(is.null(xlab)) xlab=xy$xlab # if(is.null(ylab)) ylab=xy$ylab title(main=main, sub=sub, xlab=xlab, ylab=ylab, ...) } } bio3d/R/print.pdb.R0000644000176200001440000000022112526367343013504 0ustar liggesusersprint.pdb <- function(x, printseq=TRUE, ...) { ## Print a summary of basic PDB object features y <- summary.pdb(x, printseq=printseq, ...) } bio3d/R/pdbseq.R0000644000176200001440000000100512524171274013055 0ustar liggesusers`pdbseq` <- function(pdb, inds=NULL, aa1=TRUE) { ## b.inds <- atom.select(pdb, "//B////CA/") ## seq.pdb(pdb, b.inds) if(is.null(inds)) inds <- atom.select(pdb, "calpha", verbose=FALSE) # inds <- which(pdb$calpha) # inds <- atom.select(pdb, "//////CA/", verbose=FALSE)$atom if(is.list(inds)) inds <- inds$atom if(aa1) { aa <- aa321(pdb$atom[inds,"resid"]) } else { aa <- pdb$atom[inds,"resid"] } if(length(aa) > 0) { names(aa) <- pdb$atom[inds,"resno"] } return(aa) } bio3d/R/print.enma.R0000644000176200001440000000277512412621431013660 0ustar liggesusers"print.enma" <- function(x, ...) { cn <- class(x) nstruct <- nrow(x$fluctuations) dims <- dim(x$U.subspace) if(is.null(x$call$rm.gaps)) rm.gaps <- TRUE else if(x$call$rm.gaps=="T" || x$call$rm.gaps=="TRUE") rm.gaps <- TRUE else rm.gaps <- FALSE if(is.null(x$call$fit)) fit <- TRUE else if(x$call$fit=="T" || x$call$fit=="TRUE") fit <- TRUE else fit <- FALSE cat("\nCall:\n ", paste(deparse(x$call), sep = "\n", collapse = "\n"), "\n\n", sep = "") cat("Class:\n ", cn, "\n\n", sep = "") cat("Number of structures:\n ", nstruct, "\n\n", sep="") cat("Attributes stored:\n") if(!is.null(x$full.nma)) cat(" - Full 'nma' objects\n") if(!is.null(x$rmsip)) cat(" - Root mean square inner product (RMSIP)\n") if(!is.null(x$fluctuations)) cat(" - Aligned atomic fluctuations\n") if(rm.gaps) cat(" - Aligned eigenvectors (gaps removed)\n") else cat(" - Aligned eigenvectors (gaps not removed)\n") cat(" - Dimensions of x$U.subspace: ", dims[1L], "x", dims[2L], "x", dims[3L], sep="") cat("\n\n") if(fit) cat("Coordinates were aligned prior to NMA calculations") else cat("Coordinates were NOT aligned prior to NMA calculations") cat("\n\n") i <- paste( attributes(x)$names, collapse=", ") cat(strwrap(paste(" + attr:",i,"\n"),width=60, exdent=8), sep="\n") invisible(x) } bio3d/R/as.fasta.R0000644000176200001440000000164212526367343013314 0ustar liggesusers"as.fasta" <- function(x, id=NULL, ...) { cl <- match.call() if(is.list(x)) { if(is.null(id)) id <- x$id x <- x$ali } if(is.vector(x)) { if(any(nchar(x)>1)) stop("provide a matrix/vector of one letter amino acid codes") ##x <- seqbind(lapply(lapply(x, strsplit, ""), unlist)) x <- as.matrix(t(x)) } if(is.matrix(x)) { if(is.null(id)) id <- rownames(x) if(is.null(id)) id <- paste("seq",1:nrow(x), sep="") if(any(id=="") | any(is.na(id))) { id[id==""] <- NA inds <- which(is.na(id)) id[inds] <- paste("seq", inds, sep="") } if(nrow(x) != length(id)) stop("length of 'id' does not match number of rows in alignment") rownames(x) <- id } else { stop("provide a sequence character matrix/vector") } out <- list(id=id, ali=x, call=cl) class(out) <- "fasta" return(out) } bio3d/R/nma.R0000644000176200001440000000005712524171274012360 0ustar liggesusers"nma" <- function(...) { UseMethod("nma") } bio3d/R/filter.cmap.R0000644000176200001440000000146712544562302014015 0ustar liggesusersfilter.cmap <- function(cm, cutoff.sims = dim(cm)[3]) { ## Check input if (length(dim(cm)) != 3) { stop("Input 'cm' should be a NxNxS 3d array,\n where N is the number of atoms and S is the number of simulations") } if (!(is.numeric(cutoff.sims) && (cutoff.sims <= dim(cm)[3]))) { stop("Input 'cutoff.sims' should a number between 1 and dim(cm)[3],\n i.e. the number of simulations upon which filtering is based") } if ((is.numeric(cutoff.sims)) <= 0) { print("WARNING!! cutoff.sims' should a number between 1 and dim(cm)[3],\n i.e. the number of simulations upon which filtering is based") } ## Sum across simulations and filter by cutoff.sims de cm.sum <- apply(cm, c(1:2), sum, na.rm = TRUE) return(array(as.numeric(cm.sum >= cutoff.sims), dim = dim(cm.sum))) } bio3d/R/plot.blast.R0000644000176200001440000000607712524171274013677 0ustar liggesusers`plot.blast` <- function(x, cutoff=NULL, cut.seed=NULL, cluster=TRUE, mar=c(2, 5, 1, 1), cex=1.5, ...) { ## b <- blast.pdb( pdbseq( read.pdb("4q21") ) ) ## plot(b, 188) ## cut.seed=110 panelplot <- function(z=x$mlog.evalue, ylab="-log(Evalue)", gp=gp, ...) { z=as.numeric(z) plot(z, xlab="", ylab=ylab, col=gps, ...) abline(v=gp, col="gray70", lty=3) pos=c(rep(4, length(gp))[-length(gp)],2) text( gp, z[gp], labels=paste0("Nhit=",gp ,", x=", round(z[gp])), col="black", pos=pos, cex=cex, ...) ##"gray50" } ##- Setup plot arangment opar <- par(no.readonly = TRUE) on.exit(par(opar)) par(mfcol=c(4,1), mar=mar, cex.lab=cex) ##- Find the point pair with largest diff evalue dx <- abs(diff(x$mlog.evalue)) dx.cut = which.max(dx) if(!is.null(cutoff)) { ##- Use suplied cutoff gps = rep(2, length(x$mlog.evalue)) gps[ (x$mlog.evalue >= cutoff) ] = 1 } else { if(cluster) { ## Ask USER whether to continue with clustering with many hits nhit <- length(x$mlog.evalue) if(nhit > 1500) { cluster <- readline( paste0(" Note: ", nhit, " hits, continue with TIME-CONSUMING clustering [y/n/q](n): ") ) cluster <- switch(cluster, y=TRUE, yes=TRUE, q="QUIT", FALSE) if(cluster=="QUIT") { stop("user stop") } } } if(is.null(cut.seed)) { ## Use mid-point of largest diff pair as seed for ## cluster grps (typical PDB values are ~110) cut.seed = mean( x$mlog.evalue[dx.cut:(dx.cut+1)] ) } if(cluster){ ##- Partition into groups via clustering ## In future could use changepoint::cpt.var hc <- hclust( dist(x$mlog.evalue) ) if(!is.null(cutoff)) { cut.seed=cutoff } gps <- cutree(hc, h=cut.seed) } if(!cluster || (length(unique(gps))==1)) { ##- Either we don't want to run hclust or hclust/cutree ## has returned only one grp so here we will divide ## into two grps at point of largest diff gps = rep(2, length(x$mlog.evalue)) gps[1:dx.cut]=1 } } gp.inds <- na.omit(rle2(gps)$inds) gp.nums <- x$mlog.evalue[gp.inds] cat(" * Possible cutoff values: ", floor(gp.nums), "\n", " Yielding Nhits: ", gp.inds, "\n\n") if( is.null(cutoff) ) { ## Pick a cutoff close to cut.seed i <- which.min(abs(gp.nums - cut.seed)) cutoff <- floor( gp.nums[ i ] ) } inds <- x$mlog.evalue >= cutoff cat(" * Chosen cutoff value of: ", cutoff, "\n", " Yielding Nhits: ", sum(inds), "\n") ##- Plot each alignment statistic with annotated grps panelplot(gp=gp.inds) panelplot(x$bitscore, ylab="Bitscore", gp=gp.inds) panelplot(x$hit.tbl[,"identity"], ylab="Identity", gp=gp.inds) panelplot(x$hit.tbl[,"alignmentlength"], ylab="Length", gp=gp.inds) ##- Return details of hits above cutoff out <- cbind("pdb.id"=x$pdb.id[inds], "gi.id"=x$gi.id[inds], "group"=gps[inds]) rownames(out) <- which(inds) o <- list(hits=out, pdb.id=x$pdb.id[inds], gi.id=x$gi.id[inds]) class(o) <- "blast" return(o) } bio3d/R/convert.pdb.R0000644000176200001440000001276612544562302014041 0ustar liggesusers"convert.pdb" <- function(pdb, type = c("original", "pdb", "charmm", "amber", "gromacs"), renumber=FALSE, first.resno=1, first.eleno=1, consecutive=TRUE, rm.h=TRUE, rm.wat=FALSE, verbose=TRUE) { ##-- Check the requested output format is one of 'type.options' type <- match.arg(type) ##-- Water and hydrogen removal inds <- NULL if(rm.wat) { inds <- atom.select(pdb, "notwater", verbose=FALSE) if(verbose){ cat(paste("\t Retaining", length(inds$atom),"non-water atoms\n")) } } if(rm.h) { inds <- combine.select(inds, atom.select(pdb, "noh", verbose=FALSE), verbose=FALSE) if(verbose){ cat(paste("\t Retaining", length(inds$atom),"non-hydrogen atoms\n")) } } if(!is.null(inds)){ nrm <- nrow(pdb$atom) - length(inds$atom) if( nrm > 0) { if(verbose){ cat(paste("\t Removing a total of", nrm," atoms\n")) } pdb <- trim.pdb(pdb, inds) } } ##-- Renumbering of residues and atoms if(renumber) { if(verbose){ cat(paste("\t Renumbering residues ( from",first.resno,") and atoms ( from",first.eleno,")\n")) } ## Assign consecutive atom numbers pdb$atom[,"eleno"] <- seq(first.eleno, length=nrow(pdb$atom)) ## Determine chain start and end indices s.ind <- which(!duplicated(pdb$atom[,"chain"])) e.ind <- c(s.ind[-1]-1, nrow(pdb$atom)) ##- Assign new (consecutive) residue numbers for each chain prev.chain.res = 0 ## Number of residues in previous chain for (i in 1:length(s.ind)) { ## Combination of resno and insert code define a residue (wwpdb.org) insert = pdb$atom[s.ind[i]:e.ind[i], "insert"] insert[is.na(insert)] = "" resno0 <- paste0(pdb$atom[s.ind[i]:e.ind[i], "resno"], insert) ## Ordered table of residue occurrences tbl <- table(resno0)[unique(resno0)] n.chain.res <- length(tbl) new.nums <- (first.resno+prev.chain.res):(first.resno+n.chain.res-1+prev.chain.res) pdb$atom[s.ind[i]:e.ind[i],"resno"] <- rep(new.nums, tbl) ## SSE if(length(pdb$helix)>0) { chs = unique(pdb$helix$chain) t.inds = match(pdb$helix$start[pdb$helix$chain %in% chs[i]], unique(resno0)) pdb$helix$start[pdb$helix$chain %in% chs[i]] = new.nums[t.inds] t.inds = match(pdb$helix$end[pdb$helix$chain %in% chs[i]], unique(resno0)) pdb$helix$end[pdb$helix$chain %in% chs[i]] = new.nums[t.inds] } if(length(pdb$sheet)>0) { chs = unique(pdb$sheet$chain) t.inds = match(pdb$sheet$start[pdb$sheet$chain %in% chs[i]], unique(resno0)) pdb$sheet$start[pdb$sheet$chain %in% chs[i]] = new.nums[t.inds] t.inds = match(pdb$sheet$end[pdb$sheet$chain %in% chs[i]], unique(resno0)) pdb$sheet$end[pdb$sheet$chain %in% chs[i]] = new.nums[t.inds] } if(consecutive) { ## Update prev.chain.res for next iteration prev.chain.res = prev.chain.res + n.chain.res } } } ##-- Format conversion if(type != "original") { if(verbose){ cat(paste0("\t Converting to '", type, "' format\n")) } ## residue and atom types from PDB restype <- unique(pdb$atom[,"resid"]) #eletype <- unique(pdb$atom[,"elety"]) ## In future could determine 'input type' based on resid/elety ##- Check for non-standard residue names if(verbose){ not.prot.inds <- atom.select(pdb, "notprotein", verbose=FALSE)$atom if(length(not.prot.inds) > 0) { not.prot.res <- paste(unique(pdb$atom[not.prot.inds, "resid"]), collapse = " ") cat(paste("\t Non-standard residue names present (",not.prot.res,")\n") ) } } ##- Convert HIS resid his <- matrix( c("HIS", "HSD", "HID","HISA", "HIS", "HSE", "HIE","HISB", "HIS", "HSP", "HIP","HISH"), nrow=3, byrow=TRUE, dimnames = list(c("d","e","b"), c("pdb","charmm","amber","gromacs")) ) type.inds <- (colnames(his) %in% type) conv.inds <- !(colnames(his) %in% c(type,"pdb")) his.d.ind <- (pdb$atom[,"resid"] %in% his["d", !type.inds ]) his.e.ind <- (pdb$atom[,"resid"] %in% his["e", conv.inds ]) his.b.ind <- (pdb$atom[,"resid"] %in% his["b", conv.inds ]) pdb$atom[his.d.ind,"resid"] <- his["d", type.inds ] pdb$atom[his.e.ind,"resid"] <- his["e", type.inds ] pdb$atom[his.b.ind,"resid"] <- his["b", type.inds ] ##- Convert ILE CD1 to CD elety and remove chainID if (type=="charmm") { ile.ind <- atom.select(pdb, resid="ILE", elety="CD1", verbose=FALSE)$atom pdb$atom[ile.ind,"elety"] <- "CD" pdb$atom[,"chain"]=NA ## strip chain ID ## Could also add a SEGID via call to chain.pdb() function pdb$atom[,"segid"] <- chain.pdb(pdb) } else { ile.ind <- atom.select(pdb, resid="ILE", elety="CD", verbose=FALSE)$atom pdb$atom[ile.ind,"elety"] <- "CD1" } } ## END type != "original" (conversion) ##-- Convert hydrogen atom types (unfinished!) if(!rm.h) { if(type=="pdb") { pdb$atom[ pdb$atom[,"elety"]=="HN", "elety"] = "H" ###!!! ADD Many MORE ATOM TYPE CONVERSIONS HERE !!!### } if(verbose){ warning(paste("\t Additional hydrogen elety names may need converting.", "\t N.B. It is often best to remove hydrogen (rm.h=TRUE)", "\t before building systems for simulation",sep="\n")) } ## Add other atom name conversions here as the need arises... } return(pdb) } bio3d/R/consensus.R0000644000176200001440000000213212412621431013610 0ustar liggesusers"consensus" <- function(alignment, cutoff=0.6) { # Determine the consensus sequence for a given alignment if(is.list(alignment)) alignment=alignment$ali aa <- c("V","I","L","M", "F","W","Y", "S","T", "N","Q", "H","K","R", "D","E", "A","G", "P","C", "-","X") composition <- table(alignment) unk <- composition[!names( composition ) %in% aa] if(length(unk) > 0) { warning(paste("\nnon standard residue code:",names(unk),"maped to X")) for(i in 1:length(unk)) alignment[alignment==names(unk[i])]="X" } len <- ncol(alignment) freq <- matrix(0, nrow = 22, ncol = ncol(alignment), dimnames = list(aa,seq(1:len))) for (i in 1:len) { freq[names(summary((as.factor(toupper(alignment[,i]))))), i] <- (summary(as.factor(toupper(alignment[,i])))/length(alignment[,i])) } cons.freq <- apply(freq[1:20,], 2, max) cons.tmp <- aa[apply(freq[1:20,], 2, which.max)] cons.tmp[cons.freq <= cutoff] = "-" return(list(seq=cons.tmp, freq=freq, seq.freq=cons.freq, cutoff=cutoff)) } bio3d/R/print.nma.R0000644000176200001440000000163312412621431013503 0ustar liggesusers"print.nma" <- function(x, nmodes=6, ...) { cn <- class(x) cat("\nCall:\n ", paste(deparse(x$call), sep = "\n", collapse = "\n"), "\n\n", sep = "") cat("Class:\n ", cn[1], " (", cn[2], ")", "\n\n", sep = "") cat("Number of modes:\n ", length(x$L), " (", x$triv.modes, " trivial)", "\n\n", sep="") inds <- (x$triv.modes+1):(x$triv.modes+nmodes) if(!is.null(x$frequencies)) { freqs <- round(x$frequencies[inds], 3) cat("Frequencies:\n", sep="") } else { freqs <- round(x$force.constants[inds], 3) cat("Force constants:\n", sep="") } i <- x$triv.modes for ( f in freqs ) { i <- i+1 cat(" Mode ", i, ": \t", f, "\n", sep="") } cat("\n") i <- paste( attributes(x)$names, collapse=", ") cat(strwrap(paste(" + attr:",i,"\n"),width=60, exdent=8), sep="\n") invisible(x) } bio3d/R/view.cnapath.R0000644000176200001440000001416212632622153014173 0ustar liggesusersview.cnapath <- function(x, pdb, out.prefix = "view.cnapath", spline = FALSE, colors = c("blue", "red"), launch = FALSE, ...) { if(!inherits(x, "cnapath")) stop("Input x is not a 'cnapath' object") # Check colors if(is.character(colors)) { cols <- colorRamp(colors) } else { if(length(colors) == 1 && is.numeric(colors)) cols <- colorRamp(vmd.colors()[colors + 1]) else stop("colors should be a character vector or an integer indicating a VMD color ID") } file = paste(out.prefix, ".vmd", sep="") pdbfile = paste(out.prefix, ".pdb", sep="") res <- unique(unlist(x$path)) ind.source <- match(x$path[[1]][1], res) ind.sink <- match(x$path[[1]][length(x$path[[1]])], res) ca.inds <- atom.select(pdb, elety="CA", verbose = FALSE) res.pdb <- pdb$atom[ca.inds$atom[res], "resno"] chain.pdb <- pdb$atom[ca.inds$atom[res], "chain"] names(res.pdb) <- chain.pdb # make VMD atom selection string .vmd.atomselect <- function(res) { if(any(is.na(names(res)))) return(paste("resid", paste(res, collapse=" "))) else { res <- res[order(names(res))] inds <- bounds(names(res), dup.inds=TRUE) string <- NULL for(i in 1:nrow(inds)) { string <- c(string, paste("chain", names(res)[inds[i, "start"]], "and resid", paste(res[inds[i, "start"]:inds[i, "end"]], collapse=" "))) } return(paste(string, collapse=" or ")) } } # Draw molecular structures cat("mol new ", pdbfile, " type pdb first 0 last -1 step 1 filebonds 1 autobonds 1 waitfor all mol delrep 0 top mol representation NewCartoon 0.300000 10.000000 4.100000 0 mol color colorID 8 mol selection {all} mol material Opaque mol addrep top mol representation Licorice 0.300000 10.000000 10.000000 mol color name mol selection {(", .vmd.atomselect(res.pdb[c(ind.source, ind.sink)]), ")} mol material Opaque mol addrep top mol representation VDW 0.4 10 mol color colorID 2 mol selection {(", .vmd.atomselect(res.pdb), ") and name CA} mol material Opaque mol addrep top ", file=file) # Draw paths cat("\n# start drawing suboptimal paths\n", file=file, append=TRUE) rad <- function(r, rmin, rmax, radmin = 0.01, radmax = 0.5) { (rmax - r) / (rmax - rmin) * (radmax - radmin) + radmin } rmin <- min(x$dist) rmax <- max(x$dist) # turn off display update for speed up cat("display update off\n", file=file, append=TRUE) # find start color id for new colors cat("set color_start [colorinfo num]\n", file=file, append=TRUE) if(!spline) { col.mat <- array(list(), dim=c(length(res), length(res))) conn <- matrix(0, length(res), length(res)) rr <- conn for(j in 1:length(x$path)) { y = x$path[[j]] for(i in 1:(length(y)-1)) { i1 = match(y[i], res) i2 = match(y[i+1], res) if(conn[i1, i2] == 0) conn[i1, i2] = conn[i2, i1] = 1 r = rad(x$dist[j], rmin, rmax) ic = (rmax - x$dist[j]) / (rmax - rmin) col = list(cols(ic)[1:3]) if(r > rr[i1, i2]) { rr[i1, i2] = rr[i2, i1] = r col.mat[i1, i2] = col.mat[i2, i1] = col } } } rownames(conn) <- res colnames(conn) <- res rownames(rr) <- res colnames(rr) <- res k = 0 for(i in 1:(nrow(conn)-1)) { for(j in (i+1):ncol(conn)) { if(conn[i, j] == 1) { if(!is.numeric(colors)) { # col = as.numeric(col2rgb(col.mat[i, j]))/255 col = unlist(col.mat[i, j]) / 255 cat("color change rgb [expr ", k, " + $color_start] ", paste(col, collapse=" "), "\n", sep="", file=file, append=TRUE) cat("graphics top color [expr ", k, " + $color_start]\n", sep="", file=file, append=TRUE) } else { cat("graphics top color ", colors, "\n", sep="", file=file, append=TRUE) } cat("draw cylinder {", pdb$xyz[atom2xyz(ca.inds$atom[res[i]])], "} {", pdb$xyz[atom2xyz(ca.inds$atom[res[j]])], "} radius", rr[i, j], " resolution 6 filled 0\n", sep=" ", file=file, append=TRUE) k = k + 1 } } } } else { k = 0 for(j in 1:length(x$path)) { # get spline coordinates # interpolate at five points evenly distributed between two nodes xyz = matrix(pdb$xyz[atom2xyz(ca.inds$atom[x$path[[j]]])], nrow=3) spline.x = spline(xyz[1, ], n = ncol(xyz)+(ncol(xyz)-1)*5)$y spline.y = spline(xyz[2, ], n = ncol(xyz)+(ncol(xyz)-1)*5)$y spline.z = spline(xyz[3, ], n = ncol(xyz)+(ncol(xyz)-1)*5)$y # spline radius r = rad(x$dist[j], rmin, rmax, radmax=0.1) # spline color ic = (rmax - x$dist[j]) / (rmax - rmin) col = cols(ic)[1:3] / 255 if(!is.numeric(colors)) { cat("color change rgb [expr ", k, " + $color_start] ", paste(col, collapse=" "), "\n", sep="", file=file, append=TRUE) cat("graphics top color [expr ", k, " + $color_start]\n", sep="", file=file, append=TRUE) } else { cat("graphics top color ", colors, "\n", sep="", file=file, append=TRUE) } for(i in 1:(length(spline.x) - 1)) { cat("draw cylinder {", spline.x[i], spline.y[i], spline.z[i], "} {", spline.x[i+1], spline.y[i+1], spline.z[i+1], "} radius", r, " resolution 6 filled 0\n", sep=" ", file=file, append=TRUE) } k = k + 1 } } # turn on display update cat("display update on\n", file=file, append=TRUE) write.pdb(pdb, file=pdbfile) if(launch) { cmd <- paste("vmd -e", file) os1 <- .Platform$OS.type if (os1 == "windows") { shell(shQuote(cmd)) } else{ if(Sys.info()["sysname"]=="Darwin") { system(paste("/Applications/VMD\\ 1.9.*app/Contents/MacOS/startup.command -e", file)) } else { system(cmd) } } } } bio3d/R/pca.xyz.R0000644000176200001440000000720612526367343013212 0ustar liggesusers"pca.xyz" <- function(xyz, subset = rep(TRUE, nrow(as.matrix(xyz))), use.svd = FALSE, rm.gaps=FALSE, mass = NULL, ...) { ## Performs principal components analysis on the given "xyz" numeric data ## matrix and return the results as an object of class "pca.xyz" ## Log the call cl <- match.call() xyz <- as.xyz(xyz) if (any(!is.finite(xyz))) { ## Check for GAP positions in input if(rm.gaps) { gapC <- colSums(is.na(xyz)) == 0 if (sum(gapC) > 3) { xyz <- xyz[,gapC] cat(paste("NOTE: Removing", sum(!gapC)/3, "gap positions with missing coordinate data\n", " retaining", sum(gapC)/3, "non-gap positions for analysis.\n")) } else { stop("No non-gap containing positions (cols) available for analysis.") } } else { stop( paste(" Infinite or missing values in 'xyz' input.", "\t Likely solution is to remove gap positions (cols)", "\t or gap containing structures (rows) from input.", sep="\n") ) } } dx <- dim(xyz) n <- dx[1]; p <- dx[2] if (!n || !p) stop("0 extent dimensions") # for mass-weighted PCA if(!is.null(mass)) { if(is.pdb(mass)) mass = aa2mass(mass) if(length(mass) != ncol(xyz)/3) stop("Input mass vector does not match xyz") q = t( t(xyz) * rep(sqrt(mass), each=3) ) # mass weighted xyz # re-do fitting: iteratively fit to the mean mean <- colMeans(q[subset, ]) tolerance = 1.0 # convergence check maxiter = 10 # maximum number of iteration iter = 0 repeat { q <- fit.xyz(mean, q, 1:ncol(q), 1:ncol(q), ...) mean.now <- colMeans(q[subset, ]) mean.diff <- rmsd(mean, mean.now, 1:ncol(q), 1:ncol(q)) mean = mean.now iter = iter + 1 if(iter >= maxiter || mean.diff <= tolerance) break } if(mean.diff > tolerance) warning("Iteration stops before convergent") xyz <- q } # mean <- apply(xyz[subset,],2,mean) ## mean structure mean <- colMeans(xyz[subset,]) ## Faster n <- sum(subset) # Check number of columns if(p > 3000 && n <= 0.4*p && !use.svd) { cat("NOTE: In input xyz (MxN), N > 3000 and M < N\n", " Singular Value Decomposition (SVD) approach is faster\n", " and is recommended (set 'use.svd = TRUE')\n\n", sep=" ") flush(stdout()) } if(!use.svd) { S <- var(xyz[subset,]) ## coverance matrix ## eigenvectors ("U") & eigenvalues ("L"): [ U'SU=L ] prj <- eigen(S, symmetric = TRUE) L <- prj$values U <- prj$vectors } else { if(n < p) warning(paste("In input xyz (MxN), M < N:\n", " Only",n,"eigenvalues and eigenvectors are returned!\n\n")) ## S = Q'Q, Q = UDV' Q <- t(t(xyz[subset,]) - mean) / sqrt(n-1) prj <- svd(Q) L <- prj$d^2 U <- prj$v } ## fix negative eigenvalues ## (these are very small numbers and should be zero) L[L<0]<-0 sdev <- sqrt(L) ## scores of "xyz" on the pc's [ z=U'[x-x.mean] ] z <- sweep(xyz,2,mean) %*% (U) ## atom-wise loadings (norm of xyz eigenvectors) ## Skip the calculation if the input is not xyz coordinates, ## e.g. for PCA over correlaiton matrices (see pca.array()). if(ncol(U) %% 3 == 0) { au <- apply(U, 2, function(x) { sqrt(colSums(matrix(x^2, nrow=3))) }) } else { au <- NULL } class(U)="pca.loadings" if(!is.null(mass)) { mean = mean / sqrt(rep(mass, each=3)) out <- list(L=L, U=U, z=z, au=au, sdev=sdev, mean=mean, mass=mass, call=cl) } else out <- list(L=L, U=U, z=z, au=au, sdev=sdev, mean=mean, call=cl) class(out)="pca"; out } bio3d/vignettes/0000755000176200001440000000000012632664353013274 5ustar liggesusersbio3d/vignettes/bio3d_vignettes.Rmd0000644000176200001440000000604212632622153017022 0ustar liggesusers--- title: "bio3d Vignettes" date: "Feb 19 2015" output: rmarkdown::html_vignette vignette: > %\VignetteEngine{knitr::rmarkdown} %\VignetteIndexEntry{bio3d Vignettes} \usepackage[utf8]{inputenc} --- We distribute a number of extended **Bio3D vignettes** that provide worked examples of using Bio3D to perform a particular type of structural bioinformatics analysis. An updated list of these can be found on-line. At the time of writing these include: * Installing Bio3D ( PDF | HTML) * Getting started with Bio3D ( PDF | HTML ) * PDB structure manipulation and analysis with Bio3D ( PDF | HTML) * Comparative sequence and structure analysis with Bio3D ( PDF | HTML) * Beginning trajectory analysis with Bio3D ( PDF | HTML) * Enhanced methods for Normal Mode Analysis with Bio3D ( PDF | HTML) * Ensemble NMA of E.coli DHFR structures ( PDF | HTML ) * Ensemble NMA across multiple species of DHFR ( PDF | HTML ) * Correlation network analysis with Bio3D ( PDF | HTML ) * Protein structure network analysis with Bio3D ( PDF | HTML ) There is also extensive on-line documentation with worked examples (and their output) for all functions and a package manual (in PDF format) that is a concatenation of each functions documentation (without example output). Note that for information on Bio3D development status or to report a bug, please refer to: https://bitbucket.org/Grantlab/bio3d bio3d/README.md0000644000176200001440000000604712544562303012544 0ustar liggesusers# Documentation The Bio3D package for structural bioinformatics consists of sets of functions for: - input/output, - sequence analysis, - structure analysis, - simulation analysis, - normal mode analysis, - correlation network analysis, - format conversion and data manipulation, and - graphics and visualization.. Major functions are listed below with links to further documentation that includes example code and results. Note that you can also get help on any particular function by using the command `?function` or `help(function)` (e.g. `help(read.pdb)`) and directly execute the example code for a given function with the command `example(function)` from within R itself. We also distribute a number of extended **Bio3D vignettes** that provide worked examples of using Bio3D to perform a particular type of analysis. Currently available vignettes include: - Installing Bio3D ( PDF | HTML) - Getting started with Bio3D ( PDF | HTML ) - PDB structure manipulation and analysis with Bio3D ( PDF | HTML) - Beginning trajectory analysis with Bio3D ( PDF | HTML) - Enhanced methods for Normal Mode Analysis with Bio3D ( PDF | HTML) - Comparative sequence and structure analysis with Bio3D ( PDF | HTML) - Correlation network analysis with Bio3D ( PDF | HTML ) - Protein structure network analysis with Bio3D ( PDF | HTML ) There is also a package manual (in PDF format) that is a concatenation of each functions documentation. 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_¸bio3d/DESCRIPTION0000644000176200001440000000261612632753312012771 0ustar liggesusersPackage: bio3d Title: Biological Structure Analysis Version: 2.2-4 Author: Barry Grant, Xin-Qiu Yao, Lars Skjaerven, Julien Ide VignetteBuilder: knitr Imports: parallel, grid, graphics, grDevices, stats, utils Suggests: XML, RCurl, lattice, ncdf4, igraph, bigmemory, knitr, testthat (>= 0.9.1) Depends: R (>= 3.1.0) LazyData: yes Description: Utilities to process, organize and explore protein structure, sequence and dynamics data. Features include the ability to read and write structure, sequence and dynamic trajectory data, perform sequence and structure database searches, data summaries, atom selection, alignment, superposition, rigid core identification, clustering, torsion analysis, distance matrix analysis, structure and sequence conservation analysis, normal mode analysis, principal component analysis of heterogeneous structure data, and correlation network analysis from normal mode and molecular dynamics data. In addition, various utility functions are provided to enable the statistical and graphical power of the R environment to work with biological sequence and structural data. Please refer to the URLs below for more information. Maintainer: Barry Grant License: GPL (>= 2) URL: http://thegrantlab.org/bio3d/, http://bitbucket.org/Grantlab/bio3d NeedsCompilation: yes Packaged: 2015-12-12 00:23:07 UTC; xinqyao Repository: CRAN Date/Publication: 2015-12-12 09:11:54 bio3d/man/0000755000176200001440000000000012632664234012035 5ustar liggesusersbio3d/man/seqbind.Rd0000644000176200001440000000236312526367344013761 0ustar liggesusers\name{seqbind} \alias{seqbind} \title{ Combine Sequences by Rows Without Recycling } \description{ Take vectors and/or matrices arguments and combine them row-wise without recycling them (as is the case with \code{\link{rbind}}). } \usage{ seqbind(..., blank = "-") } \arguments{ \item{\dots}{ vectors, matrices, and/or alignment \sQuote{fasta} objects to combine. } \item{blank}{ a character to add to short arguments, to achieve the same length as the longer argument. } } \value{ Returns a list of class \code{"fasta"} with the following components: \item{ali}{ an alignment character matrix with a row per sequence and a column per equivalent aminoacid/nucleotide. } \item{id}{ sequence names as identifers.} \item{call}{ the matched call. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \seealso{ \code{\link{seqaln}}, \code{\link{read.fasta}}, \code{\link{read.pdb}}, \code{\link{write.fasta}}, \code{\link{rbind}} } \examples{ \dontrun{ ## Read two pdbs a.pdb <- read.pdb("1bg2") b.pdb <- read.pdb("1goj") seqs <- seqbind(aa321(a.pdb$atom[a.pdb$calpha,"resid"]), aa321(b.pdb$atom[b.pdb$calpha,"resid"])) # seqaln(seqs) } } \keyword{ utilities } bio3d/man/cov.nma.Rd0000644000176200001440000000220112524171274013655 0ustar liggesusers\name{cov.nma} \alias{cov.nma} \alias{cov.enma} \title{ Calculate Covariance Matrix from Normal Modes } \description{ Calculate the covariance matrix from a normal mode object. } \usage{ \method{cov}{nma}(nma) \method{cov}{enma}(enma, ncore=NULL) } \arguments{ \item{nma}{ an \code{nma} object as obtained from function \code{nma.pdb}. } \item{enma}{ an \code{enma} object as obtained from function \code{nma.pdbs}. } \item{ncore }{ number of CPU cores used to do the calculation. \code{ncore>1} requires package \sQuote{parallel} installed. } } \details{ This function calculates the covariance matrix from a \code{nma} object as obtained from function \code{nma.pdb} or covariance matrices from a \code{enma} object as obtain from function \code{nma.pdbs}. } \value{ Returns the calculated covariance matrix (function \code{cov.nma}), or covariance matrices (function \code{cov.enma}). } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. Fuglebakk, E. et al. (2013) \emph{JCTC} \bold{9}, 5618--5628. } \author{ Lars Skjaerven } \seealso{ \code{\link{nma}} } \keyword{ utilities } bio3d/man/aln2html.Rd0000644000176200001440000000371412544562303014046 0ustar liggesusers\name{aln2html} \alias{aln2html} \title{ Create a HTML Page For a Given Alignment } \description{ Renders a sequence alignment as coloured HTML suitable for viewing with a web browser. } \usage{ aln2html(aln, file="alignment.html", Entropy=0.5, append=TRUE, caption.css="color: gray; font-size: 9pt", caption="Produced by Bio3D", fontsize="11pt", bgcolor=TRUE, colorscheme="clustal") } \arguments{ \item{aln}{ an alignment list object with \code{id} and \code{ali} components, similar to that generated by \code{\link{read.fasta}}. } \item{file}{ name of output html file. } \item{Entropy}{ conservation \sQuote{cuttoff} value below which alignment columns are not coloured. } \item{append}{ logical, if TRUE output will be appended to \code{file}; otherwise, it will overwrite the contents of \code{file}. } \item{caption.css}{ a character string of css options for rendering \sQuote{caption} text. } \item{caption}{ a character string of text to act as a caption. } \item{fontsize}{ the font size for alignment characters. } \item{bgcolor}{ background colour. } \item{colorscheme}{ conservation colouring scheme, currently only \dQuote{clustal} is supported with alternative arguments resulting in an entropy shaded alignment.} } \value{ Called for its effect. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \note{ Your web browser should support style sheets. } \seealso{ \code{\link{read.fasta}}, \code{\link{write.fasta}}, \code{\link{seqaln}} } \examples{ \dontrun{ ## Read an example alignment aln <- read.fasta(system.file("examples/hivp_xray.fa",package="bio3d")) ## Produce a HTML file for this alignment aln2html(aln, append=FALSE, file=file.path("eg.html")) aln2html(aln, colorscheme="ent", file="eg.html") ## View/open the file in your web browser #browseURL("eg.html") } } \keyword{ IO } bio3d/man/biounit.Rd0000644000176200001440000000406612544562303013777 0ustar liggesusers% Generated by roxygen2 (4.1.0): do not edit by hand % Please edit documentation in R/biounit.R \name{biounit} \alias{biounit} \title{Biological Units Construction} \usage{ biounit(pdb, biomat = NULL, multi = FALSE, ncore = NULL) } \arguments{ \item{pdb}{an object of class \code{pdb} as obtained from function \code{\link{read.pdb}}.} \item{biomat}{a list object as returned by \code{read.pdb} (pdb$remark$biomat), containing matrices for symmetry operation on individual chains to build biological units. It will override the matrices stored in \code{pdb}.} \item{multi}{logical, if TRUE the biological unit is returned as a 'multi-model' \code{pdb} object with each symmetric copy a distinct structural 'MODEL'. Otherwise, all copies are represented as separated chains.} \item{ncore}{number of CPU cores used to do the calculation. By default (\code{ncore=NULL}), use all available CPU cores.} } \value{ a list of \code{pdb} objects with each representing an individual biological unit. } \description{ Construct biological assemblies/units based on a 'pdb' object. } \details{ A valid structural/simulation study should be performed on the biological unit of a protein system. For example, the alpha2-beta2 tetramer form of hemoglobin. However, canonical PDB files usually contain the asymmetric unit of the crystal cell, which can be: \enumerate{ \item One biological unit \item A portion of a biological unit \item Multiple biological units } The function performs symmetry operations to the coordinates based on the transformation matrices stored in a 'pdb' object returned by \code{\link{read.pdb}}, and returns biological units stored as a list of \code{pdb} objects. } \examples{ \donttest{ # PDB server connection required - testing excluded pdb <- read.pdb("2dn1") biounit <- biounit(pdb) pdb biounit } \dontrun{ biounit <- biounit(read.pdb("2bfu"), multi=TRUE) write.pdb(biounit[[1]], file="biounit.pdb") # open the pdb file in VMD to have a look on the biological unit } } \author{ Xin-Qiu Yao } \seealso{ \code{\link{read.pdb}} } bio3d/man/read.ncdf.Rd0000644000176200001440000000633412632622153014150 0ustar liggesusers\name{read.ncdf} \alias{read.ncdf} \title{ Read AMBER Binary netCDF files } \description{ Read coordinate data from a binary netCDF trajectory file. } \usage{ read.ncdf(trjfile, headonly = FALSE, verbose = TRUE, time = FALSE, first = NULL, last = NULL, stride = 1, cell = FALSE, at.sel = NULL) } \arguments{ \item{trjfile}{ name of trajectory file to read. A vector if treat a batch of files } \item{headonly}{ logical, if TRUE only trajectory header information is returned. If FALSE only trajectory coordinate data is returned. } \item{verbose}{ logical, if TRUE print details of the reading process. } \item{time}{ logical, if TRUE the \code{first} and \code{last} have the time unit ps; Otherwise the unit is the frame number. } \item{first}{ starting time or frame number to read; If NULL, start from the begining of the file(s). } \item{last}{ read data until \code{last} time or frame number; If NULL or equal to -1, read until the end of the file(s). } \item{stride}{ take at every \code{stride} frame(s) } \item{cell}{ logical, if TRUE and \code{headonly} is FALSE return cell information only. Otherwise, return header or coordinates.} \item{at.sel}{an object of class \sQuote{select} indicating a subset of atomic coordinates to be read.} } \details{ Reads a AMBER netCDF format trajectory file with the help of David W. Pierce's (UCSD) ncdf4 package available from CRAN. } \value{ A list of trajectory header data, a numeric matrix of xyz coordinates with a frame/structure per row and a Cartesian coordinate per column, or a numeric matrix of cell information with a frame/structure per row and lengths and angles per column. If time=TRUE, row names of returned coordinates or cell are set to be the physical time of corresponding frames. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. \url{http://www.unidata.ucar.edu/software/netcdf/} \url{http://cirrus.ucsd.edu/~pierce/ncdf/} \url{http://ambermd.org/formats.html#netcdf} } \author{ Barry Grant } \note{ See AMBER documentation for netCDF format description. NetCDF binary trajectory files are supported by the AMBER modules sander, pmemd and ptraj. Compared to formatted trajectory files, the binary trajectory files are smaller, higher precision and significantly faster to read and write. NetCDF provides for file portability across architectures, allows for backwards compatible extensibility of the format and enables the files to be self-describing. Support for this format is available in VMD. If you experience problems reading your trajectory file with read.ncdf() consider first reading your file into VMD and from there exporting a new DCD trajectory file with the 'save coordinates' option. This new file should be easily read with read.dcd(). } \seealso{ \code{\link{read.dcd}}, \code{\link{write.ncdf}}, \code{\link{read.pdb}}, \code{\link{write.pdb}}, \code{\link{atom.select}} } \examples{ \dontrun{ ##-- Read example trajectory file trtfile <- system.file("examples/hivp.dcd", package="bio3d") trj <- read.dcd(trtfile) ## Write to netCDF format write.ncdf(trj, "newtrj.nc") ## Read trj trj <- read.ncdf("newtrj.nc") } } \keyword{ IO } bio3d/man/orient.pdb.Rd0000644000176200001440000000265412544562303014373 0ustar liggesusers\name{orient.pdb} \alias{orient.pdb} \title{ Orient a PDB Structure } \description{ Center, to the coordinate origin, and orient, by principal axes, the coordinates of a given PDB structure or xyz vector. } \usage{ orient.pdb(pdb, atom.subset = NULL, verbose = TRUE) } \arguments{ \item{pdb}{ a pdb data structure obtained from \code{\link{read.pdb}} or a vector of \sQuote{xyz} coordinates. } \item{atom.subset}{ a subset of atom positions to base orientation on. } \item{verbose}{ print dimension details. } } \value{ Returns a numeric vector of re-oriented coordinates. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \note{ Centering and orientation can be restricted to a \code{atom.subset} of atoms. } \seealso{ \code{\link{read.pdb}}, \code{\link{write.pdb}}, \code{\link{fit.xyz}}, \code{\link{rot.lsq}} , \code{\link{atom.select}}} \examples{ \donttest{ # PDB server connection required - testing excluded pdb <- read.pdb( "1bg2" ) xyz <- orient.pdb(pdb) #write.pdb(pdb, xyz = xyz, file = "mov1.pdb") # Based on C-alphas inds <- atom.select(pdb, "calpha") xyz <- orient.pdb(pdb, atom.subset=inds$atom) #write.pdb(pdb, xyz = xyz, file = "mov2.pdb") # Based on a central Beta-strand inds <- atom.select(pdb, resno=c(224:232), elety='CA') xyz <- orient.pdb(pdb, atom.subset=inds$atom) #write.pdb(pdb, xyz = xyz, file = "mov3.pdb") } } \keyword{ manip } bio3d/man/pdbsplit.Rd0000644000176200001440000000563712632622153014152 0ustar liggesusers\name{pdbsplit} \alias{pdbsplit} \title{ Split a PDB File Into Separate Files, One For Each Chain. } \description{ Split a Protein Data Bank (PDB) coordinate file into new separate files with one file for each chain. } \usage{ pdbsplit(pdb.files, ids = NULL, path = "split_chain", overwrite=TRUE, verbose = FALSE, mk4=FALSE, ncore = 1, \dots) } \arguments{ \item{pdb.files}{ a character vector of PDB file names. } \item{ids}{ a character vector of PDB and chain identifiers (of the form: \sQuote{pdbId_chainId}, e.g. \sQuote{1bg2_A}). Used for filtering chain IDs for output (in the above example only chain A would be produced). } \item{path}{ output path for chain-split files. } \item{overwrite}{ logical, if FALSE the PDB structures will not be read and written if split files already exist. } \item{verbose}{ logical, if TRUE details of the PDB header and chain selections are printed. } \item{mk4}{ logical, if TRUE output filenames will use only the first four characters of the input filename (see \code{basename.pdb} for details). } \item{ncore}{ number of CPU cores used for the calculation. \code{ncore>1} requires package \sQuote{parallel} be installed. } \item{...}{ additional arguments to \code{read.pdb}. Useful e.g. for parsing multi model PDB files, including ALT records etc. in the output files. } } \details{ This function will produce single chain PDB files from multi-chain input files. By default all separate filenames are returned. To return only a subset of select chains the optional input \sQuote{ids} can be provided to filter the output (e.g. to fetch only chain C, of a PDB object with additional chains A+B ignored). See examples section for further details. Note that multi model atom records will only split into individual PDB files if \code{multi=TRUE}, else they are omitted. See examples. } \value{ Returns a character vector of chain-split file names. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. For a description of PDB format (version3.3) see:\cr \url{http://www.wwpdb.org/documentation/format33/v3.3.html}. } \author{ Barry Grant } \seealso{ \code{\link{read.pdb}}, \code{\link{atom.select}}, \code{\link{write.pdb}}, \code{\link{get.pdb}}. } \examples{ \dontrun{ ## Save separate PDB files for each chain of a local or on-line file pdbsplit( get.pdb("2KIN", URLonly=TRUE) ) ## Split several PDBs by chain ID and multi-model records raw.files <- get.pdb( c("1YX5", "3NOB") , URLonly=TRUE) chain.files <- pdbsplit(raw.files, path=tempdir(), multi=TRUE) basename(chain.files) ## Output only desired pdbID_chainID combinations ## for the last entry (1f9j), fetch all chains ids <- c("1YX5_A", "3NOB_B", "1F9J") raw.files <- get.pdb( ids , URLonly=TRUE) chain.files <- pdbsplit(raw.files, ids, path=tempdir()) basename(chain.files) } } \keyword{ utilities } bio3d/man/plot.dccm.Rd0000644000176200001440000000762512544562303014215 0ustar liggesusers\name{plot.dccm} \alias{plot.dccm} \title{ DCCM Plot } \description{ Plot a dynamical cross-correlation matrix. } \usage{ \method{plot}{dccm}(x, sse=NULL, colorkey=TRUE, at=c(-1, -0.75, -0.5, -0.25, 0.25, 0.5, 0.75, 1), main="Residue Cross Correlation", helix.col = "gray20", sheet.col = "gray80", inner.box=TRUE, outer.box=FALSE, xlab="Residue No.", ylab="Residue No.", margin.segments=NULL, segment.col=vmd.colors(), segment.min=1, ...) } \arguments{ \item{x}{ a numeric matrix of atom-wise cross-correlations as output by the \sQuote{dccm} function. } \item{sse}{ secondary structure object as returned from \code{\link{dssp}}, \code{\link{stride}} or \code{\link{read.pdb}}. } \item{colorkey}{ logical, if TRUE a key is plotted. } \item{at}{ numeric vector specifying the levels to be colored. } \item{main}{ a main title for the plot. } \item{helix.col}{ The colors for rectangles representing alpha helices. } \item{sheet.col}{ The colors for rectangles representing beta strands. } \item{inner.box}{ logical, if TRUE an outer box is drawn. } \item{outer.box}{ logical, if TRUE an outer box is drawn. } \item{xlab}{ a label for the x axis. } \item{ylab}{ a label for the y axis. } \item{margin.segments}{ a numeric vector of cluster membership as obtained from cutree() or other community detection method. This will be used for bottom and left margin annotation. } \item{segment.col}{ a vector of colors used for each cluster group in margin.segments. } \item{segment.min}{ a single element numeric vector that will cause margin.segments with a length below this value to be excluded from the plot. } \item{\dots}{ additional graphical parameters for contourplot. } } \details{ See the \sQuote{contourplot} function from the lattice package for plot customization options, and the functions \code{\link{dssp}} and \code{\link{stride}} for further details. } \value{ Called for its effect. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \note{ Be sure to check the correspondence of your \sQuote{sse} object with the \sQuote{cij} values being plotted as no internal checks are currently performed. } \seealso{ \code{\link{plot.bio3d}}, \code{\link{plot.dmat}}, \code{\link{filled.contour}}, \code{\link{contour}}, \code{\link{image}} \code{\link{plot.default}}, \code{\link{dssp}}, \code{\link{stride}} } \examples{ \dontrun{ ##-- Read example trajectory file trtfile <- system.file("examples/hivp.dcd", package="bio3d") trj <- read.dcd(trtfile) ## Read the starting PDB file to determine atom correspondence pdbfile <- system.file("examples/hivp.pdb", package="bio3d") pdb <- read.pdb(pdbfile) ## select residues 24 to 27 and 85 to 90 in both chains inds <- atom.select(pdb, resno=c(24:27,85:90), elety='CA') ## lsq fit of trj on pdb xyz <- fit.xyz(pdb$xyz, trj, fixed.inds=inds$xyz, mobile.inds=inds$xyz) ## Dynamic cross-correlations of atomic displacements cij <- dccm(xyz) ## Default plot plot.dccm(cij) ## Change the color scheme and the range of colored data levels plot.dccm(cij, contour=FALSE, col.regions=bwr.colors(200), at=seq(-1,1,by=0.01) ) ## Add secondary structure annotation to plot margins sse <- dssp(read.pdb("1W5Y"), resno=FALSE) plot.dccm(cij, sse=sse) ## Add additional margin annotation for chains.. ch <- ifelse(pdb$atom[pdb$calpha,"chain"]=="A", 1,2) plot.dccm(cij, sse=sse, margin.segments=ch) ## Plot with cluster annotation from dynamic network analysis #net <- cna(cij) #plot.dccm(cij, margin.segments=net$raw.communities$membership) ## Focus on major communities (i.e. exclude those below a certain total length) #plot.dccm(cij, margin.segments=net$raw.communities$membership, segment.min=25) } } \keyword{ hplot } bio3d/man/core.find.Rd0000644000176200001440000001567012632622153014176 0ustar liggesusers\name{core.find} \alias{core.find} \alias{core.find.default} \alias{core.find.pdbs} \alias{core.find.pdb} \title{ Identification of Invariant Core Positions } \description{ Perform iterated rounds of structural superposition to identify the most invariant region in an aligned set of protein structures. } \usage{ core.find(\dots) \method{core.find}{pdbs}(pdbs, shortcut = FALSE, rm.island = FALSE, verbose = TRUE, stop.at = 15, stop.vol = 0.5, write.pdbs = FALSE, outpath="core_pruned", ncore = 1, nseg.scale = 1, \dots) \method{core.find}{default}(xyz, \dots) \method{core.find}{pdb}(pdb, verbose=TRUE, \dots) } \arguments{ \item{pdbs}{ a numeric matrix of aligned C-alpha xyz Cartesian coordinates. For example an alignment data structure obtained with \code{\link{read.fasta.pdb}} or \code{\link{pdbaln}}. } \item{shortcut}{ if TRUE, remove more than one position at a time. } \item{rm.island}{ remove isolated fragments of less than three residues. } \item{verbose}{ logical, if TRUE a \dQuote{core\_pruned} directory containing \sQuote{core structures} for each iteraction is written to the current directory. } \item{stop.at}{ minimal core size at which iterations should be stopped. } \item{stop.vol}{ minimal core volume at which iterations should be stopped. } \item{write.pdbs}{ logical, if TRUE core coordinate files, containing only core positions for each iteration, are written to a location specified by \code{outpath}. } \item{outpath}{ character string specifying the output directory when \code{write.pdbs} is TRUE. } \item{ncore }{ number of CPU cores used to do the calculation. \code{ncore>1} requires package \sQuote{parallel} installed. } \item{nseg.scale }{ split input data into specified number of segments prior to running multiple core calculation. See \code{\link{fit.xyz}}.} \item{xyz}{ a numeric matrix of xyz Cartesian coordinates, e.g. obtained from \code{\link{read.dcd}} or \code{\link{read.ncdf}}. } \item{pdb}{ an object of type \code{pdb} as obtained from function \code{\link{read.pdb}} with multiple frames (>=4) stored in its \code{xyz} component. Note that the function will attempt to identify C-alpha and phosphate atoms (for protein and nucleic acids, respectively) in which the calculation should be based. } \item{\dots}{ arguments passed to and from functions. } } \details{ This function attempts to iteratively refine an initial structural superposition determined from a multiple alignment. This involves iterated rounds of superposition, where at each round the position(s) displaying the largest differences is(are) excluded from the dataset. The spatial variation at each aligned position is determined from the eigenvalues of their Cartesian coordinates (i.e. the variance of the distribution along its three principal directions). Inspired by the work of Gerstein \emph{et al.} (1991, 1995), an ellipsoid of variance is determined from the eigenvalues, and its volume is taken as a measure of structural variation at a given position. Optional \dQuote{core PDB files} containing core positions, upon which superposition is based, can be written to a location specified by \code{outpath} by setting \code{write.pdbs=TRUE}. These files are useful for examining the core filtering process by visualising them in a graphics program. } \value{ Returns a list of class \code{"core"} with the following components: \item{volume }{total core volume at each fitting iteration/round.} \item{length }{core length at each round.} \item{resno }{residue number of core residues at each round (taken from the first aligned structure) or, alternatively, the numeric index of core residues at each round.} \item{step.inds}{atom indices of core atoms at each round.} \item{atom }{atom indices of core positions in the last round.} \item{xyz }{xyz indices of core positions in the last round.} \item{c1A.atom }{atom indices of core positions with a total volume under 1 Angstrom\^3.} \item{c1A.xyz }{xyz indices of core positions with a total volume under 1 Angstrom\^3.} \item{c1A.resno }{residue numbers of core positions with a total volume under 1 Angstrom\^3.} \item{c0.5A.atom }{atom indices of core positions with a total volume under 0.5 Angstrom\^3.} \item{c0.5A.xyz }{xyz indices of core positions with a total volume under 0.5 Angstrom\^3.} \item{c0.5A.resno }{residue numbers of core positions with a total volume under 0.5 Angstrom\^3.} } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. Gerstein and Altman (1995) \emph{J. Mol. Biol.} \bold{251}, 161--175. Gerstein and Chothia (1991) \emph{J. Mol. Biol.} \bold{220}, 133--149. } \note{ The relevance of the \sQuote{core positions} identified by this procedure is dependent upon the number of input structures and their diversity. } \author{ Barry Grant } \seealso{ \code{\link{read.fasta.pdb}}, \code{\link{plot.core}}, \code{\link{fit.xyz}} } \examples{ \dontrun{ ##-- Generate a small kinesin alignment and read corresponding structures pdbfiles <- get.pdb(c("1bg2","2ncd","1i6i","1i5s"), URLonly=TRUE) pdbs <- pdbaln(pdbfiles) ##-- Find 'core' positions core <- core.find(pdbs) plot(core) ##-- Fit on these relatively invarient subset of positions #core.inds <- print(core, vol=1) core.inds <- print(core, vol=0.5) xyz <- pdbfit(pdbs, core.inds, outpath="corefit_structures") ##-- Compare to fitting on all equivalent positions xyz2 <- pdbfit(pdbs) ## Note that overall RMSD will be higher but RMSF will ## be lower in core regions, which may equate to a ## 'better fit' for certain applications gaps <- gap.inspect(pdbs$xyz) rmsd(xyz[,gaps$f.inds]) rmsd(xyz2[,gaps$f.inds]) plot(rmsf(xyz[,gaps$f.inds]), typ="l", col="blue", ylim=c(0,9)) points(rmsf(xyz2[,gaps$f.inds]), typ="l", col="red") } \dontrun{ ##-- Run core.find() on a multimodel PDB file pdb <- read.pdb('1d1d', multi=TRUE) core <- core.find(pdb) ##-- Run core.find() on a trajectory trtfile <- system.file("examples/hivp.dcd", package="bio3d") trj <- read.dcd(trtfile) ## Read the starting PDB file to determine atom correspondence pdbfile <- system.file("examples/hivp.pdb", package="bio3d") pdb <- read.pdb(pdbfile) ## select calpha coords from a manageable number of frames ca.ind <- atom.select(pdb, "calpha")$xyz frames <- seq(1, nrow(trj), by=10) core <- core.find( trj[frames, ca.ind], write.pdbs=TRUE ) ## have a look at the various cores "vmd -m core_pruned/*.pdb" ## Lets use a 6A^3 core cutoff inds <- print(core, vol=6) write.pdb(xyz=pdb$xyz[inds$xyz],resno=pdb$atom[inds$atom,"resno"], file="core.pdb") ##- Fit trj onto starting structure based on core indices xyz <- fit.xyz( fixed = pdb$xyz, mobile = trj, fixed.inds = inds$xyz, mobile.inds = inds$xyz) ##write.pdb(pdb=pdb, xyz=xyz, file="new_trj.pdb") ##write.ncdf(xyz, "new_trj.nc") } } \keyword{ utilities } bio3d/man/bhattacharyya.Rd0000644000176200001440000000346612526367344015165 0ustar liggesusers\name{bhattacharyya} \alias{bhattacharyya} \alias{bhattacharyya.nma} \alias{bhattacharyya.pca} \alias{bhattacharyya.enma} \alias{bhattacharyya.array} \alias{bhattacharyya.matrix} \title{ Bhattacharyya Coefficient } \description{ Calculate the Bhattacharyya Coefficient as a similarity between two modes objects. } \usage{ bhattacharyya(...) \method{bhattacharyya}{enma}(enma, covs=NULL, ncore=NULL, ...) \method{bhattacharyya}{array}(covs, ncore=NULL, ...) \method{bhattacharyya}{matrix}(a, b, q=90, n=NULL, ...) \method{bhattacharyya}{nma}(...) \method{bhattacharyya}{pca}(...) } \arguments{ \item{enma}{ an object of class \code{"enma"} obtained from function \code{nma.pdbs}. } \item{covs}{ an array of covariance matrices of equal dimensions. } \item{ncore }{ number of CPU cores used to do the calculation. \code{ncore>1} requires package \sQuote{parallel} installed. } \item{a}{ covariance matrix to be compared with \code{b}. } \item{b}{ covariance matrix to be compared with \code{a}. } \item{q}{ a numeric value (in percent) determining the number of modes to be compared. } \item{n}{ the number of modes to be compared. } \item{\dots}{ arguments passed to associated functions. } } \details{ Bhattacharyya coefficient provides a means to compare two covariance matrices derived from NMA or an ensemble of conformers (e.g. simulation or X-ray conformers). } \value{ Returns the similarity coefficient(s). } \references{ Skjaerven, L. et al. (2014) \emph{BMC Bioinformatics} \bold{15}, 399. Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. Fuglebakk, E. et al. (2013) \emph{JCTC} \bold{9}, 5618--5628. } \author{ Lars Skjaerven } \seealso{Other similarity measures: \code{\link{sip}}, \code{\link{covsoverlap}}, \code{\link{rmsip}}. } \keyword{ utilities } bio3d/man/atom2mass.Rd0000644000176200001440000000562212544562303014233 0ustar liggesusers\encoding{UTF-8} \name{atom2mass} \alias{atom2mass} \alias{atom2mass.default} \alias{atom2mass.pdb} \title{ Atom Names/Types to Mass Converter } \description{ Convert atom names/types into atomic masses. } \usage{ atom2mass(\dots) \method{atom2mass}{default}(x, mass.custom=NULL, elety.custom=NULL, grpby=NULL, rescue=TRUE, \dots) \method{atom2mass}{pdb}(pdb, inds=NULL, mass.custom=NULL, elety.custom=NULL, grpby=NULL, rescue=TRUE, \dots) } \arguments{ \item{x}{ a character vector containing atom names/types to be converted. } \item{mass.custom}{ a customized data.frame containing atomic symbols and corresponding masses. } \item{elety.custom}{ a customized data.frame containing atom names/types and corresponding atomic symbols.} \item{grpby}{a \sQuote{factor}, as returned by \code{as.factor}, used to group the atoms.} \item{rescue}{ logical, if TRUE the atomic symbols will be mapped to the first character of the atom names/types.} \item{pdb}{ an object of class \sQuote{pdb} for which \code{elety} will be converted.} \item{inds}{ an object of class \sQuote{select} indicating a subset of the \code{pdb} object to be used (see \code{\link{atom.select}} and \code{\link{trim.pdb}}).} \item{\dots}{.} } \details{ The default method first convert atom names/types into atomic symbols using the \code{\link{atom2ele}} function. Then, atomic symbols are searched in the \code{elements} data set and their corresponding masses are returned. If \code{mass.custom} is specified it is combined with \code{elements} (using \code{rbind}) before searching. Therefore, \code{mass.custom} must have columns named \code{symb} and \code{mass} (see examples). If \code{grpby} is specified masses are splitted (using \code{split}) to compute the mass of groups of atoms defined by \code{grpby}. The S3 method for object of class \sQuote{pdb}, pass \code{pdb$atom$elety} to the default method. } \value{Return a numeric vector of masses.} \author{Julien Ide, Lars Skjaerven} \seealso{ \code{\link{elements}}, \code{\link{atom.index}}, \code{\link{atom2ele}}, \code{\link{read.pdb}} } \examples{ atom.names <- c("CA", "O", "N", "OXT") atom2mass(atom.names) \donttest{ # PDB server connection required - testing excluded ## Get atomic symbols from a PDB object with a customized data set pdb <- read.pdb("3RE0", verbose=FALSE) inds <- atom.select(pdb, resno=201, verbose=FALSE) ## selected atoms print(pdb$atom$elety[inds$atom]) ## default will map CL2 to C atom2mass(pdb, inds) ## map element CL2 correctly to Cl myelety <- data.frame(name = c("CL2","PT1","N1","N2"), symb = c("Cl","Pt","N","N")) atom2mass(pdb, inds, elety.custom = myelety) ## custom masses mymasses <- data.frame(symb = c("Cl","Pt"), mass = c(35.45, 195.08)) atom2mass(pdb, inds, elety.custom = myelety, mass.custom = mymasses) } } \keyword{ utilities } bio3d/man/seqidentity.Rd0000644000176200001440000000375612544562303014675 0ustar liggesusers\name{seqidentity} \alias{seqidentity} \title{ Percent Identity} \description{ Determine the percent identity scores for aligned sequences. } \usage{ seqidentity(alignment, normalize=TRUE, similarity=FALSE, ncore=1, nseg.scale=1) } \arguments{ \item{alignment}{ sequence alignment obtained from \code{\link{read.fasta}} or an alignment character matrix. } \item{normalize}{ logical, if TRUE output is normalized to values between 0 and 1 otherwise percent identity is returned. } \item{similarity}{ logical, if TRUE sequence similarity is calculated instead of identity. } \item{ncore}{ number of CPU cores used to do the calculation. \code{ncore>1} requires package \sQuote{parallel} installed. } \item{nseg.scale}{ split input data into specified number of segments prior to running multiple core calculation. See \code{\link{fit.xyz}}. } } \details{ The percent identity value is a single numeric score determined for each pair of aligned sequences. It measures the number of identical residues (\dQuote{matches}) in relation to the length of the alignment. } \value{ Returns a numeric matrix with all pairwise identity values. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \seealso{ \code{\link{read.fasta}}, \code{\link{filter.identity}}, \code{\link{entropy}}, \code{\link{consensus}} } \examples{ \dontrun{ aln <- read.fasta( system.file("examples/kif1a.fa", package = "bio3d") ) } data(kinesin) attach(kinesin, warn.conflicts=FALSE) ide.mat <- seqidentity(pdbs) # Plot identity matrix plot.dmat(ide.mat, color.palette=mono.colors, main="Sequence Identity", xlab="Structure No.", ylab="Structure No.") # Histogram of pairwise identity values hist(ide.mat[upper.tri(ide.mat)], breaks=30,xlim=c(0,1), main="Sequence Identity", xlab="Identity") # Compare two sequences seqidentity( rbind(pdbs$ali[1,], pdbs$ali[20,]) ) detach(kinesin) } \keyword{ utilities } bio3d/man/plot.pca.loadings.Rd0000644000176200001440000000173012412623040015626 0ustar liggesusers\name{plot.pca.loadings} \alias{plot.pca.loadings} \title{ Plot Residue Loadings along PC1 to PC3 } \description{ Plot residue loadings along PC1 to PC3 from a given xyz C-alpha matrix of \code{loadings}. } \usage{ \method{plot}{pca.loadings}(x, resnums = seq(1, (length(x[, 1])/3), 25), ...) } \arguments{ \item{x}{ the results of principal component analysis obtained from \code{\link{pca.xyz}}, or just the loadings returned from \code{\link{pca.xyz}}. } \item{resnums}{ a numeric vector of residue numbers. } \item{\dots}{ extra plotting arguments. } } \value{ Called for its effect. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \seealso{ \code{\link{pca.xyz}}, \code{\link{plot.pca}} } \examples{ data(transducin) attach(transducin, warn.conflicts=FALSE) pc.xray <- pca.xyz(pdbs$xyz[, gap.inspect(pdbs$xyz)$f.inds]) plot.pca.loadings(pc.xray$U) detach(transducin) } \keyword{ hplot } bio3d/man/unbound.Rd0000644000176200001440000000230412526367344014001 0ustar liggesusers\name{unbound} \alias{unbound} \title{ Sequence Generation from a Bounds Vector } \description{ Generate a sequence of consecutive numbers from a \code{\link{bounds}} vector. } \usage{ unbound(start, end = NULL) } \arguments{ \item{start}{ vector of starting values, or a matrix containing starting and end values such as that obtained from \code{\link{bounds}}. } \item{end}{ vector of (maximal) end values, such as that obtained from \code{\link{bounds}}. } } \details{ This is a simple utility function that does the opposite of the \code{\link{bounds}} function. If \code{start} is a vector, \code{end} must be a vector having the same length as \code{start}. If \code{start} is a matrix with column names contain 'start' and 'end', such as that returned from \code{\link{bounds}}, \code{end} can be skipped and both starting and end values will be extracted from \code{start}. } \value{ Returns a numeric sequence vector. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \seealso{ \code{\link{bounds}} } \author{ Barry Grant } \examples{ test <- c(seq(1,5,1),8,seq(10,15,1)) b <- bounds(test) unbound(b) } \keyword{ utilities } bio3d/man/bounds.sse.Rd0000644000176200001440000000205612544562303014406 0ustar liggesusers% Generated by roxygen2 (4.1.0): do not edit by hand % Please edit documentation in R/bounds.sse.R \name{bounds.sse} \alias{bounds.sse} \title{Obtain A SSE Object From An SSE Sequence Vector} \usage{ bounds.sse(x, pdb = NULL) } \arguments{ \item{x}{a character vector indicating SSE for each amino acid residue.} \item{pdb}{an object of class \code{pdb} as obtained from function \code{\link{read.pdb}}. Can be ignored if \code{x} has 'names' attribute for residue labels.} } \value{ a 'sse' object. } \description{ Inverse process of the funciton \code{\link{pdb2sse}}. } \details{ call for its effects. } \note{ In both \code{$helix} and \code{$sheet}, an additional \code{$id} component is added to indicate the original numbering of the sse. This is particularly useful in e.g. \code{trim.pdb()} function. } \examples{ \donttest{ # PDB server connection required - testing excluded pdb <- read.pdb("1a7l") sse <- pdb2sse(pdb) sse.ind <- bounds.sse(sse) sse.ind } } \author{ Xin-Qiu Yao & Barry Grant } \seealso{ \code{\link{pdb2sse}} } bio3d/man/entropy.Rd0000644000176200001440000000715012412623040014011 0ustar liggesusers\name{entropy} \alias{entropy} \title{ Shannon Entropy Score } \description{ Calculate the sequence entropy score for every position in an alignment. } \usage{ entropy(alignment) } \arguments{ \item{alignment}{ sequence alignment returned from \code{\link{read.fasta}} or an alignment character matrix. } } \details{ Shannon's information theoretic entropy (Shannon, 1948) is an often-used measure of residue diversity and hence residue conservation. } \value{ Returns a list with five components: \item{H }{standard entropy score for a 22-letter alphabet.} \item{H.10 }{entropy score for a 10-letter alphabet (see below).} \item{H.norm }{ normalized entropy score (for 22-letter alphabet), so that conserved (low entropy) columns (or positions) score 1, and diverse (high entropy) columns score 0.} \item{H.10.norm }{ normalized entropy score (for 10-letter alphabet), so that conserved (low entropy) columns score 1 and diverse (high entropy) columns score 0.} \item{freq }{residue frequency matrix containing percent occurrence values for each residue type.} } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. Shannon (1948) \emph{The System Technical J.} \bold{27}, 379--422. Mirny and Shakhnovich (1999) \emph{J. Mol. Biol.} \bold{291}, 177--196. } \author{ Barry Grant } \note{ In addition to the standard entropy score (based on a 22-letter alphabet of the 20 standard amino-acids, plus a gap character \sQuote{-} and a mask character \sQuote{X}), an entropy score, \code{H.10}, based on a 10-letter alphabet is also returned. For \code{H.10}, residues from the 22-letter alphabet are classified into one of 10 types, loosely following the convention of Mirny and Shakhnovich (1999): Hydrophobic/Aliphatic [V,I,L,M], Aromatic [F,W,Y], Ser/Thr [S,T], Polar [N,Q], Positive [H,K,R], Negative [D,E], Tiny [A,G], Proline [P], Cysteine [C], and Gaps [-,X]. The residue code \sQuote{X} is useful for handling non-standard aminoacids. } \seealso{ \code{\link{consensus}}, \code{\link{read.fasta}} } \examples{ # Read HIV protease alignment aln <- read.fasta(system.file("examples/hivp_xray.fa",package="bio3d")) # Entropy and consensus h <- entropy(aln) con <- consensus(aln) names(h$H)=con$seq print(h$H) # Entropy for sub-alignment (positions 1 to 20) h.sub <- entropy(aln$ali[,1:20]) # Plot entropy and residue frequencies (excluding positions >=60 percent gaps) H <- h$H.norm H[ apply(h$freq[21:22,],2,sum)>=0.6 ] = 0 col <- mono.colors(32) aa <- rev(rownames(h$freq)) oldpar <- par(no.readonly=TRUE) layout(matrix(c(1,2),2,1,byrow = TRUE), widths = 7, heights = c(2, 8), respect = FALSE) # Plot 1: entropy par(mar = c(0, 4, 2, 2)) barplot(H, border="white", ylab = "Entropy", space=0, xlim=c(3.7, 97.3),yaxt="n" ) axis(side=2, at=c(0.2,0.4, 0.6, 0.8)) axis(side=3, at=(seq(0,length(con$seq),by=5)-0.5), labels=seq(0,length(con$seq),by=5)) box() # Plot2: residue frequencies par(mar = c(5, 4, 0, 2)) image(x=1:ncol(con$freq), y=1:nrow(con$freq), z=as.matrix(rev(as.data.frame(t(con$freq)))), col=col, yaxt="n", xaxt="n", xlab="Alignment Position", ylab="Residue Type") axis(side=1, at=seq(0,length(con$seq),by=5)) axis(side=2, at=c(1:22), labels=aa) axis(side=3, at=c(1:length(con$seq)), labels =con$seq) axis(side=4, at=c(1:22), labels=aa) grid(length(con$seq), length(aa)) box() for(i in 1:length(con$seq)) { text(i, which(aa==con$seq[i]),con$seq[i],col="white") } abline(h=c(3.5, 4.5, 5.5, 3.5, 7.5, 9.5, 12.5, 14.5, 16.5, 19.5), col="gray") par(oldpar) } \keyword{ utilities } bio3d/man/community.tree.Rd0000644000176200001440000000546512544562303015314 0ustar liggesusers\name{community.tree} \alias{community.tree} \title{ Reconstruction of the Girvan-Newman Community Tree for a CNA Class Object. } \description{ This function reconstructs the community tree of the community clustering analysis performed by the \sQuote{cna} function. It allows the user to explore different network community partitions. } \usage{ community.tree(x, rescale=FALSE) } \arguments{ \item{x}{ A protein network graph object as obtained from the \sQuote{cna} function. } \item{rescale}{ Logical, indicating whether to rescale the community names starting from 1. If FALSE, the community names will start from N+1, where N is the number of nodes. } } \value{ Returns a list object that includes the following components: \item{modularity}{ A numeric vector containing the modularity values. } \item{tree}{ A numeric matrix containing in each row the community residue memberships corresponding to a modularity value. The rows are ordered according to the \sQuote{modularity} object. } \item{num.of.comms}{ A numeric vector containing the number of communities per modularity value. The vector elements are ordered according to the \sQuote{modularity} object. } } \details{ The input of this function should be a \sQuote{cna} class object containing \sQuote{network} and \sQuote{communities} attributes. This function reconstructs the community residue memberships for each modularity value. The purpose is to facilitate inspection of alternate community partitioning points, which in practice often corresponds to a value close to the maximum of the modularity, but not the maximum value itself. } \author{ Guido Scarabelli } \seealso{ \code{\link{cna}}, \code{\link{network.amendment}}, \code{\link{summary.cna}} } \examples{ \donttest{ # PDB server connection required - testing excluded ###-- Build a CNA object pdb <- read.pdb("4Q21") modes <- nma(pdb) cij <- dccm(modes) net <- cna(cij, cutoff.cij=0.2) ##-- Reconstruct the community membership vector for each clustering step. tree <- community.tree(net, rescale=TRUE) ## Plot modularity vs number of communities plot( tree$num.of.comms, tree$modularity ) ## Inspect the maximum modularity value partitioning max.mod.ind <- which.max(tree$modularity) ## Number of communities (k) at max modularity tree$num.of.comms[ max.mod.ind ] ## Membership vector at this partition point tree$tree[max.mod.ind,] # Should be the same as that contained in the original CNA network object net$communities$membership == tree$tree[max.mod.ind,] # Inspect a new membership partitioning (at k=7) memb.k7 <- tree$tree[ tree$num.of.comms == 7, ] ## Produce a new k=7 community network net.7 <- network.amendment(net, memb.k7) plot(net.7, pdb) #view.cna(net.7, trim.pdb(pdb, atom.select(pdb,"calpha")), launch=TRUE ) } } \keyword{analysis} bio3d/man/view.dccm.Rd0000644000176200001440000000464312632622153014204 0ustar liggesusers\name{view.dccm} \alias{view.dccm} \title{ Visualization of Dynamic Cross-Correlation } \description{ Structural visualization of a cross-correlation matrix. } \usage{ view.dccm(dccm, pdb, step=0.2, omit=0.2, radius=0.15, type="pymol", outprefix="corr", launch=FALSE, exefile="pymol") } \arguments{ \item{dccm}{ an object of class \code{dccm} as obtained from function \code{dccm} or \code{dccm.nma}. } \item{pdb}{ an object of class \code{pdb} as obtained from function \code{read.pdb} or a numerical vector of Cartesian coordinates. } \item{step}{ binning interval of cross-correlation coefficents. } \item{omit}{ correlation coefficents with values (0-omit, 0+omit) will be omitted from visualization. } \item{radius}{ radius of visualized correlations. } \item{type}{ character string specifying the type of visualization: \sQuote{pymol} or \sQuote{pdb}. } \item{outprefix}{ character string specifying the file prefix. If \code{NULL} the temp directory will be used. } \item{launch}{ logical, if TRUE PyMol will be launched. } \item{exefile}{ file path to the \sQuote{PYMOL} program on your system (i.e. how is \sQuote{PYMOL} invoked). } } \details{ This function generates a PyMOL (python) script that will draw colored lines between (anti)correlated residues. The PyMOL script file is stored in the working directory with filename \dQuote{corr.py}, with coordinates in PDB format with filename \dQuote{corr.inpcrd.pdb}. PyMOL will only be launched when using argument \sQuote{launch=TRUE}. Alternatively a PDB file with CONECT records will be generated (when argument \code{type='pdb'}). For the PyMOL version, PyMOL CGO objects are generated - each object representing a range of correlation values (corresponding to the actual correlation values as found in the correlation matrix). E.g. the PyMOL object with name \dQuote{cor_-1_-08} would display all pairs of correlations with values between -1 and -0.8. } \value{ Called for its action. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{nma}}, \code{\link{dccm}} } \examples{ \dontrun{ ## Fetch stucture pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) ## Calculate normal modes modes <- nma(pdb) ## Calculate correlation matrix cm <- dccm.nma(modes) view.dccm(cm, modes$xyz) } } \keyword{ utilities } bio3d/man/filter.rmsd.Rd0000644000176200001440000000405412632622153014552 0ustar liggesusers\name{filter.rmsd} \alias{filter.rmsd} \title{ RMSD Filter } \description{ Identify and filter subsets of conformations at a given RMSD cutoff. } \usage{ filter.rmsd(xyz = NULL, rmsd.mat = NULL, cutoff = 0.5, fit = TRUE, verbose = TRUE, inds = NULL, \dots) } \arguments{ \item{xyz}{ a numeric matrix or list object containing multiple coordinates for pairwise comparison, such as that obtained from \code{\link{read.fasta.pdb}}. Not used if \code{rmsd.mat} is given. } \item{rmsd.mat}{ an optional matrix of RMSD values obtained from \code{\link{rmsd}}. } \item{cutoff}{ a numeric rmsd cutoff value. } \item{fit}{ logical, if TRUE coordinate superposition is performed prior to RMSD calculation. } \item{verbose}{ logical, if TRUE progress details are printed. } \item{inds}{ a vector of indices that selects the elements of \code{xyz} upon which the calculation should be based. By default, all the non-gap sites in \code{xyz}. } \item{\dots}{ additional arguments passed to and from functions. } } \details{ This function performs hierarchical cluster analysis of a given matrix of RMSD values \sQuote{rmsd.mat}, or an RMSD matrix calculated from a given coordinate matrix \sQuote{xyz}, to identify conformers that fall below a given RMSD cutoff value \sQuote{cutoff}. } \value{ Returns a list object with components: \item{ind}{indices of the conformers (rows) below the cutoff value.} \item{tree}{an object of class \code{"hclust"}, which describes the tree produced by the clustering process. } \item{rmsd.mat}{a numeric matrix with all pairwise RMSD values.} } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \seealso{ \code{\link{rmsd}}, \code{\link{read.pdb}}, \code{\link{read.fasta.pdb}}, \code{\link{read.dcd}} } \examples{ \dontrun{ data(kinesin) attach(kinesin, warn.conflicts=FALSE) k <- filter.rmsd(xyz=pdbs,cutoff=0.5) pdbs$id[k$ind] plot(k$tree, ylab="RMSD") abline(h=0.5, col="gray") detach(kinesin) } } \keyword{ utilities } bio3d/man/atom.index.Rd0000644000176200001440000000142612526367344014401 0ustar liggesusers\name{atom.index} \alias{atom.index} \docType{data} \title{ Atom Names/Types } \description{ This data set gives for various atom names/types the corresponding atomic symbols. } \usage{ atom.index } \format{ A data frame with the following components. \describe{ \item{\code{name}}{a character vector containing atom names/types.} \item{\code{symb}}{a character vector containing atomic symbols.} } } \seealso{ \code{\link{elements}}, \code{\link{atom.index}}, \code{\link{atom2ele}} } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \examples{ data(atom.index) atom.index # Get the atomic symbol of some atoms atom.names <- c("CA", "O", "N", "OXT") atom.index[match(atom.names, atom.index$name), "symb"] } \keyword{datasets} bio3d/man/clean.pdb.Rd0000644000176200001440000000356212544562303014154 0ustar liggesusers% Generated by roxygen2 (4.1.0): do not edit by hand % Please edit documentation in R/clean.pdb.R \name{clean.pdb} \alias{clean.pdb} \title{Inspect And Clean Up A PDB Object} \usage{ clean.pdb(pdb, consecutive = TRUE, force.renumber = FALSE, fix.chain = FALSE, fix.aa = FALSE, rm.wat = FALSE, rm.lig = FALSE, rm.h = FALSE, verbose = FALSE) } \arguments{ \item{pdb}{an object of class \code{pdb} as obtained from function \code{\link{read.pdb}}.} \item{consecutive}{logical, if TRUE renumbering will result in consecutive residue numbers spanning all chains. Otherwise new residue numbers will begin at 1 for each chain.} \item{force.renumber}{logical, if TRUE atom and residue records are renumbered even if no 'insert' code is found in the \code{pdb} object.} \item{fix.chain}{logical, if TRUE chains are relabeled based on chain breaks detected.} \item{fix.aa}{logical, if TRUE non-standard amino acid names are converted into equivalent standard names.} \item{rm.wat}{logical, if TRUE water atoms are removed.} \item{rm.lig}{logical, if TRUE ligand atoms are removed.} \item{rm.h}{logical, if TRUE hydrogen atoms are removed.} \item{verbose}{logical, if TRUE details of the conversion process are printed.} } \value{ a 'pdb' object with an additional \code{$log} component storing all the processing messages. } \description{ Inspect alternative coordinates, chain breaks, bad residue numbering, non-standard/unknow amino acids, etc. Return a 'clean' pdb object with fixed residue numbering and optionally relabeled chain IDs, corrected amino acid names, removed water, ligand, or hydrogen atoms. All changes are recorded in a log in the returned object. } \details{ call for its effects. } \examples{ \donttest{ # PDB server connection required - testing excluded pdb <- read.pdb("1a7l") clean.pdb(pdb) } } \author{ Xin-Qiu Yao & Barry Grant } \seealso{ \code{\link{read.pdb}} } bio3d/man/motif.find.Rd0000644000176200001440000000206712544562303014362 0ustar liggesusers\name{motif.find} \alias{motif.find} \title{ Find Sequence Motifs. } \description{ Return Position Indices of a Short Sequence Motif Within a Larger Sequence. } \usage{ motif.find(motif, sequence) } \arguments{ \item{motif}{ a character vector of the short sequence motif. } \item{sequence}{ a character vector of the larger sequence. } } \details{ The sequence and the motif can be given as a either a multiple or single element character vector. The dot character and other valid \code{regexpr} characters are allowed in the motif, see examples. } \value{ Returns a vector of position indices within the sequence where the motif was found, see examples. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \seealso{ \code{\link{regexpr}}, \code{\link{read.fasta}}, \code{\link{pdbseq}} } \examples{ \donttest{ # PDB server connection required - testing excluded aa.seq <- pdbseq( read.pdb( get.pdb("4q21", URLonly=TRUE) ) ) motif = c("G....GKS") motif.find(motif, aa.seq) } } \keyword{ utilities } bio3d/man/pdb.annotate.Rd0000644000176200001440000000323712632622153014700 0ustar liggesusers\name{pdb.annotate} \alias{pdb.annotate} \title{ Get Customizable Annotations From PDB } \description{ Get customizable annotations for query results from PDB. } \usage{ pdb.annotate(ids, anno.terms = NULL, unique = FALSE, verbose = FALSE) } \arguments{ \item{ids}{ A charater vector of one or more 4-letter PDB codes/identifiers of the files for query. } \item{anno.terms}{ Terms can be used for query. The "anno.terms" can be "structureId", "experimentalTechnique", "resolution", "chainId", "ligandId", "ligandName", "source", "scopDomain", "classification", "compound","title", "citation", "citationAuthor", "journalName", "publicationYear". If anno.terms=NULL, all information would be returned. } \item{unique}{ logical, if TRUE only unique PDB entries are returned. Alternatively data for each chain ID is provided. } \item{verbose}{ logical, if TRUE details of the RCurl \code{postForm} routine is printed. } } \details{ Given a list of PDB IDs and query terms, this function will download the required information from PDB, and return a data frame of query results. } \value{ Returns a data frame of query results with a row for each PDB record, and annotation terms column-wise. } \author{ Hongyang Li, Barry Grant, Lars Skjaerven} \examples{ \donttest{ # PDB server connection required - testing excluded # Fetch all annotation terms ids <- c("6Q21_B", "1NVW", "1P2U_A") anno <- pdb.annotate(ids) # Access terms, e.g. ligand names: anno$ligandName ## only unique PDB IDs anno <- pdb.annotate(ids, unique=TRUE) # Fetch only specific terms pdb.annotate(ids, anno.terms = c("ligandId", "citation")) } } \keyword{ utilities } bio3d/man/torsion.xyz.Rd0000644000176200001440000000614112544562303014650 0ustar liggesusers\name{torsion.xyz} \alias{torsion.xyz} \title{ Calculate Torsion/Dihedral Angles } \description{ Defined from the Cartesian coordinates of four successive atoms (A-B-C-D) the torsion or dihedral angle is calculated about an axis defined by the middle pair of atoms (B-C). } \usage{ torsion.xyz(xyz, atm.inc = 4) } \arguments{ \item{xyz}{ a numeric vector of Cartisean coordinates. } \item{atm.inc}{ a numeric value indicating the number of atoms to increment by between successive torsion evaluations (see below). } } \details{ The conformation of a polypeptide or nucleotide chain can be usefully described in terms of angles of internal rotation around its constituent bonds. If a system of four atoms A-B-C-D is projected onto a plane normal to bond B-C, the angle between the projection of A-B and the projection of C-D is described as the torsion angle of A and D about bond B-C. By convention angles are measured in the range -180 to +180, rather than from 0 to 360, with positive values defined to be in the clockwise direction. With \code{atm.inc=1}, torsion angles are calculated for each set of four successive atoms contained in \code{xyz} (i.e. moving along one atom, or three elements of \code{xyz}, between sucessive evaluations). With \code{atm.inc=4}, torsion angles are calculated for each set of four successive non-overlapping atoms contained in \code{xyz} (i.e. moving along four atoms, or twelve elements of \code{xyz}, between sucessive evaluations). } \value{ A numeric vector of torsion angles. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Karim ElSawy } \note{ Contributions from Barry Grant. } \seealso{ \code{\link{torsion.pdb}}, \code{\link{pca.tor}}, \code{\link{wrap.tor}}, \code{\link{read.pdb}}, \code{\link{read.dcd}}. } \examples{ ## Calculate torsions for cis & trans conformers xyz <- rbind(c(0,-0.5,0,1,0,0,1,1,0,0,1.5,0), c(0,-0.5,0,1,0,0,1,1,0,2,1.5,0)-3) cis.tor <- torsion.xyz( xyz[1,] ) trans.tor <- torsion.xyz( xyz[2,] ) apply(xyz, 1, torsion.xyz) plot(range(xyz), range(xyz), xlab="", ylab="", typ="n", axes=FALSE) apply(xyz, 1, function(x){ lines(matrix(x, ncol=3, byrow=TRUE), lwd=4) points(matrix(x, ncol=3, byrow=TRUE), cex=2.5, bg="white", col="black", pch=21) } ) text( t(apply(xyz, 1, function(x){ apply(matrix(x, ncol=3, byrow=TRUE)[c(2,3),], 2, mean) })), labels=c(0,180), adj=-0.5, col="red") \donttest{ # PDB server connection required - testing excluded ##-- PDB torsion analysis pdb <- read.pdb("1bg2") tor <- torsion.pdb(pdb) ## basic Ramachandran plot plot(tor$phi, tor$psi) ## torsion analysis of a single coordinate vector inds <- atom.select(pdb,"calpha") tor.ca <- torsion.xyz(pdb$xyz[inds$xyz], atm.inc=3) ##-- Compare two PDBs to highlight interesting residues aln <- read.fasta(system.file("examples/kif1a.fa",package="bio3d")) m <- read.fasta.pdb(aln) a <- torsion.xyz(m$xyz[1,],1) b <- torsion.xyz(m$xyz[2,],1) ## Note the periodicity of torsion angles d <- wrap.tor(a-b) plot(m$resno[1,],d, typ="h") } } \keyword{ utilities } bio3d/man/write.pqr.Rd0000644000176200001440000000565612544562303014267 0ustar liggesusers\name{write.pqr} \alias{write.pqr} \title{ Write PQR Format Coordinate File } \description{ Write a PQR file for a given \sQuote{xyz} Cartesian coordinate vector or matrix. } \usage{ write.pqr(pdb = NULL, xyz = pdb$xyz, resno = NULL, resid = NULL, eleno = NULL, elety = NULL, chain = NULL, o = NULL, b = NULL, het = FALSE, append = FALSE, verbose = FALSE, chainter = FALSE, file = "R.pdb") } \arguments{ \item{pdb}{ a PDB structure object obtained from \code{\link{read.pdb}}. } \item{xyz}{ Cartesian coordinates as a vector or 3xN matrix. } \item{resno}{ vector of residue numbers of length equal to length(xyz)/3. } \item{resid}{ vector of residue types/ids of length equal to length(xyz)/3. } \item{eleno}{ vector of element/atom numbers of length equal to length(xyz)/3. } \item{elety}{ vector of element/atom types of length equal to length(xyz)/3. } \item{chain}{ vector of chain identifiers with length equal to length(xyz)/3. } \item{o}{ vector of occupancy values of length equal to length(xyz)/3. } \item{b}{ vector of B-factors of length equal to length(xyz)/3. } \item{het}{ logical, if TRUE \sQuote{HETATM} records from \code{pdb} object are written to the output PDB file. } \item{append}{ logical, if TRUE output is appended to the bottom of an existing file (used primarly for writing multi-model files). } \item{verbose}{ logical, if TRUE progress details are printed. } \item{chainter}{ logical, if TRUE a TER line is inserted between chains. } \item{file}{ the output file name. } } \details{ Only the \code{xyz} argument is strictly required. Other arguments assume a default poly-ALA C-alpha structure with a blank chain id, occupancy values of 1.00 and B-factors equal to 0.00. If the input argument \code{xyz} is a matrix then each row is assumed to be a different structure/frame to be written to a \dQuote{multimodel} PDB file, with frames separated by \dQuote{END} records. } \value{ Called for its effect. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. For a description of PDB format (version3.3) see:\cr \url{http://www.wwpdb.org/documentation/format33/v3.3.html}. } \author{ Barry Grant with contributions from Joao Martins. } \note{ Check that: (1) \code{chain} is one character long e.g. \dQuote{A}, and (2) \code{resno} and \code{eleno} do not exceed \dQuote{9999}. } \seealso{ \code{\link{read.pdb}}, \code{\link{read.dcd}}, \code{\link{read.fasta.pdb}}, \code{\link{read.fasta}} } \examples{ \donttest{ # PDB server connection required - testing excluded # Read a PDB file pdb <- read.pdb( "1bg2" ) # Renumber residues nums <- as.numeric(pdb$atom[,"resno"]) nums <- nums - (nums[1] - 1) # Write out renumbered PDB file outfile = file.path(tempdir(), "eg.pdb") write.pdb(pdb=pdb, resno = nums, file = outfile) invisible( cat("\nSee the output file:", outfile, sep = "\n") ) } } \keyword{ IO } bio3d/man/aa2mass.Rd0000644000176200001440000000400412526367344013655 0ustar liggesusers\name{aa2mass} \alias{aa2mass} \title{ Amino Acid Residues to Mass Converter } \description{ Convert a sequence of amino acid residue names to mass. } \usage{ aa2mass(pdb, inds=NULL, mass.custom=NULL, addter=TRUE, mmtk=FALSE) } \arguments{ \item{pdb}{ a character vector containing the atom names to convert to atomic masses. Alternatively, a object of type \code{pdb} can be provided. } \item{inds}{ atom and xyz coordinate indices obtained from \code{atom.select} that selects the elements of \code{pdb} upon which the calculation should be based. } \item{mass.custom}{ a list of amino acid residue names and their corresponding masses. } \item{addter}{ logical, if TRUE terminal atoms are added to final masses. } \item{mmtk}{ logical, if TRUE use the exact aminoacid residue masses as provided with the MMTK database (for testing purposes). } } \details{ This function converts amino acid residue names to their corresponding masses. In the case of a non-standard amino acid residue name \code{mass.custom} can be used to map the residue to the correct mass. User-defined amino acid masses (with argument \code{mass.custom}) will override mass entries obtained from the database. See examples for more details. } \note{ When object of type \code{pdb} is provided, non-calpha atom records are omitted from the selection. } \value{ Returns a numeric vector of masses. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{atom.index}}, \code{\link{atom2mass}}, \code{\link{aa.index}}} \examples{ resi.names <- c("LYS", "ALA", "CYS", "HIS") masses <- aa2mass(resi.names, addter=FALSE) \dontrun{ ## Fetch atomic masses in a PDB object pdb <- read.pdb("3dnd") masses <- aa2mass(pdb) ## or masses <- aa2mass(pdb$atom[1:10,"resid"]) ## Dealing with unconventional residues #pdb <- read.pdb("1xj0") #mass.cust <- list("CSX"=122.166) #masses <- aa2mass(pdb, mass.custom=mass.cust) } } \keyword{ utilities } bio3d/man/conserv.Rd0000644000176200001440000000561212544562303014003 0ustar liggesusers\name{conserv} \alias{conserv} \title{ Score Residue Conservation At Each Position in an Alignment } \description{ Quantifies residue conservation in a given protein sequence alignment by calculating the degree of amino acid variability in each column of the alignment. } \usage{ conserv(x, method = c("similarity","identity","entropy22","entropy10"), sub.matrix = c("bio3d", "blosum62", "pam30", "other"), matrix.file = NULL, normalize.matrix = TRUE) } \arguments{ \item{x}{ an alignment list object with \code{id} and \code{ali} components, similar to that generated by \code{\link{read.fasta}}. } \item{method}{ the conservation assesment method. } \item{sub.matrix}{ a matrix to score conservation. } \item{matrix.file}{ a file name of an arbitary user matrix. } \item{normalize.matrix}{ logical, if TRUE the matrix is normalized pior to assesing conservation. } } \details{ To assess the level of sequence conservation at each position in an alignment, the \dQuote{similarity}, \dQuote{identity}, and \dQuote{entropy} per position can be calculated. The \dQuote{similarity} is defined as the average of the similarity scores of all pairwise residue comparisons for that position in the alignment, where the similarity score between any two residues is the score value between those residues in the chosen substitution matrix \dQuote{sub.matrix}. The \dQuote{identity} i.e. the preference for a specific amino acid to be found at a certain position, is assessed by averaging the identity scores resulting from all possible pairwise comparisons at that position in the alignment, where all identical residue comparisons are given a score of 1 and all other comparisons are given a value of 0. \dQuote{Entropy} is based on Shannons information entropy. See the \code{\link{entropy}} function for further details. Note that the returned scores are normalized so that conserved columns score 1 and diverse columns score 0. } \value{ Returns a numeric vector of scores } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. Grant, B.J. et al. (2007) \emph{J. Mol. Biol.} \bold{368}, 1231--1248. } \author{ Barry Grant } \note{ Each of these conservation scores has particular strengths and weaknesses. For example, entropy elegantly captures amino acid diversity but fails to account for stereochemical similarities. By employing a combination of scores and taking the union of their respective conservation signals we expect to achieve a more comprehensive analysis of sequence conservation (Grant, 2007). } \seealso{ \code{\link{read.fasta}}, \code{\link{read.fasta.pdb}} } \examples{ ## Read an example alignment aln <- read.fasta(system.file("examples/hivp_xray.fa",package="bio3d")) ## Score conservation conserv(x=aln$ali, method="similarity", sub.matrix="bio3d") ##conserv(x=aln$ali,method="entropy22", sub.matrix="other") } \keyword{ utilities } bio3d/man/pfam.Rd0000644000176200001440000000276512632622153013253 0ustar liggesusers\name{pfam} \alias{pfam} \title{ Download Pfam FASTA Sequence Alignment } \description{ Downloads FASTA sequence alignment from the Pfam database. } \usage{ pfam(id, alignment = "seed", verbose = FALSE) } \arguments{ \item{id}{ the Pfam familiy identifier (e.g \sQuote{Piwi}) or accession (e.g. \sQuote{PF02171}). } \item{alignment}{ the alignment type. Allowed values are: \sQuote{seed}, \sQuote{ncbi}, \sQuote{full}, \sQuote{metagenomics}. } \item{verbose}{ logical, if TRUE details of the download process is printed. } } \details{ This is a basic function to download a multiple sequence alignment for a protein family from the Pfam database. } \value{ A \sQuote{fasta} object with the following components: \item{ali }{ an alignment character matrix with a row per sequence and a column per equivalent aminoacid/nucleotide. } \item{ids }{ sequence names as identifiers. } \item{call }{ the matched call. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \note{ Full more information on the Pfam database:\cr \url{http://pfam.xfam.org} } \seealso{ \code{\link{read.fasta}}, \code{\link{hmmer}}, \code{\link{get.seq}}, \code{\link{uniprot}} } \examples{ \dontrun{ # PFAM server connection required - testing excluded aln <- pfam("piwi") aln <- pfam("PF02171") seq <- get.seq("1rx2_A", outfile = tempfile()) hmm <- hmmer(seq, type="hmmscan", db="pfam") aln <- pfam(hmm$acc[1]) } } \keyword{ utilities } bio3d/man/read.pdcBD.Rd0000644000176200001440000000702712544562303014214 0ustar liggesusers\name{read.pdcBD} \alias{read.pdcBD} \title{ Read PQR output from pdcBD File } \description{ Read a pdcBD PQR coordinate file. } \usage{ read.pdcBD(file, maxlines = 50000, multi = FALSE, rm.insert = FALSE, rm.alt = TRUE, verbose = TRUE) } \arguments{ \item{file}{ the name of the pdcBD PQR file to be read. } \item{maxlines}{ the maximum number of lines to read before giving up with large files. Default is 50,000 lines. } \item{multi}{ logical, if TRUE multiple ATOM records are read for all models in multi-model files. } \item{rm.insert}{ logical, if TRUE PDB insert records are ignored. } \item{rm.alt}{ logical, if TRUE PDB alternate records are ignored. } \item{verbose}{ print details of the reading process. } } \details{ \code{maxlines} may require increasing for some large multi-model files. The preferred means of reading such data is via binary DCD format trajectory files (see the \code{\link{read.dcd}} function). } \value{ Returns a list of class \code{"pdb"} with the following components: \item{atom}{ a character matrix containing all atomic coordinate ATOM data, with a row per ATOM and a column per record type. See below for details of the record type naming convention (useful for accessing columns). } \item{het }{ a character matrix containing atomic coordinate records for atoms within \dQuote{non-standard} HET groups (see \code{atom}). } \item{helix }{ \sQuote{start}, \sQuote{end} and \sQuote{length} of H type sse, where start and end are residue numbers \dQuote{resno}. } \item{sheet }{ \sQuote{start}, \sQuote{end} and \sQuote{length} of E type sse, where start and end are residue numbers \dQuote{resno}. } \item{seqres }{ sequence from SEQRES field. } \item{xyz }{ a numeric vector of ATOM coordinate data. } \item{calpha }{ logical vector with length equal to \code{nrow(atom)} with TRUE values indicating a C-alpha \dQuote{elety}. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. For a description of PDB format (version3.3) see:\cr \url{http://www.wwpdb.org/documentation/format33/v3.3.html}. } \author{ Barry Grant } \note{ For both \code{atom} and \code{het} list components the column names can be used as a convenient means of data access, namely: Atom serial number \dQuote{eleno} , Atom type \dQuote{elety}, Alternate location indicator \dQuote{alt}, Residue name \dQuote{resid}, Chain identifier \dQuote{chain}, Residue sequence number \dQuote{resno}, Code for insertion of residues \dQuote{insert}, Orthogonal coordinates \dQuote{x}, Orthogonal coordinates \dQuote{y}, Orthogonal coordinates \dQuote{z}, Occupancy \dQuote{o}, and Temperature factor \dQuote{b}. See examples for further details. } \seealso{ \code{\link{atom.select}}, \code{\link{write.pdb}}, \code{\link{read.dcd}}, \code{\link{read.fasta.pdb}}, \code{\link{read.fasta}} } \examples{ \donttest{ # PDB server connection required - testing excluded # Read a PDB file pdb <- read.pdb( "1bg2" ) # Print data for the first atom pdb$atom[1,] # Look at the first het atom pdb$het[1,] # Print some coordinate data pdb$atom[1:20, c("x","y","z")] # Print C-alpha coordinates (can also use 'atom.select') ##pdb$xyz[pdb$calpha, c("resid","x","y","z")] # Print SSE data (for helix and sheet) pdb$helix pdb$sheet$start # Print SEQRES data pdb$seqres # Renumber residues nums <- as.numeric(pdb$atom[,"resno"]) pdb$atom[,"resno"] <- nums - (nums[1] - 1) # Write out renumbered PDB file #write.pdb(pdb=pdb,file="eg.pdb") } } \keyword{ IO } bio3d/man/is.select.Rd0000644000176200001440000000145212526367344014223 0ustar liggesusers\name{is.select} \alias{is.select} \title{Is an Object of Class \sQuote{select}?} \description{ Checks whether its argument is an object of class \sQuote{select}. } \usage{ is.select(x) } \arguments{ \item{x}{an R object to be tested.} } \details{ Tests if x is an object of class \sQuote{select}, i.e. if x has a \dQuote{class} attribute equal to \code{select}. } \value{ TRUE if x is an object of class \sQuote{select} and FALSE otherwise } \author{ Julien Ide } \seealso{ \code{\link{atom.select}} } \examples{ # Read a PDB file pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) # Print structure summary atom.select(pdb) # Select all C-alpha atoms with residues numbers between 43 and 54 ca.inds <- atom.select(pdb, "calpha", resno=43:54) is.select(ca.inds) } \keyword{ classes } bio3d/man/dccm.nma.Rd0000644000176200001440000000346512544562303014010 0ustar liggesusers\name{dccm.nma} \alias{dccm.nma} \title{ Dynamic Cross-Correlation from Normal Modes Analysis } \description{ Calculate the cross-correlation matrix from Normal Modes Analysis. } \usage{ \method{dccm}{nma}(x, nmodes = NULL, ncore = NULL, \dots) } \arguments{ \item{x}{ an object of class \code{nma} as obtained from function \code{nma}. } \item{nmodes}{ numerical, number of modes to consider. } \item{ncore }{ number of CPU cores used to do the calculation. \code{ncore>1} requires package \sQuote{parallel} installed. } \item{\dots}{ additional arguments ? } } \details{ This function calculates the cross-correlation matrix from Normal Modes Analysis (NMA) obtained from \code{nma} of a protein structure. It returns a matrix of residue-wise cross-correlations whose elements, Cij, may be displayed in a graphical representation frequently termed a dynamical cross-correlation map, or DCCM. If Cij = 1 the fluctuations of residues i and j are completely correlated (same period and same phase), if Cij = -1 the fluctuations of residues i and j are completely anticorrelated (same period and opposite phase), and if Cij = 0 the fluctuations of i and j are not correlated. } \value{ Returns a cross-correlation matrix. } \references{ Wynsberghe. A.W.V, Cui, Q. \emph{Structure} \bold{14}, 1647--1653. Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{nma}}, \code{\link{plot.dccm}} } \examples{ ## Fetch stucture pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) ## Calculate normal modes modes <- nma(pdb) ## Calculate correlation matrix cm <- dccm.nma(modes) ## Plot correlation map plot(cm, sse = pdb, contour = FALSE, col.regions = bwr.colors(20), at = seq(-1, 1, 0.1)) } \keyword{ analysis } bio3d/man/is.xyz.Rd0000644000176200001440000000131612526367344013575 0ustar liggesusers\name{is.xyz} \alias{is.xyz} \alias{as.xyz} \title{ Is an Object of Class \sQuote{xyz}? } \description{ Checks whether its argument is an object of class \sQuote{xyz}. } \usage{ is.xyz(x) as.xyz(x) } \arguments{ \item{x}{ an R object to be tested } } \details{ Tests if x is an object of class \sQuote{xyz}, i.e. if x has a \dQuote{class} attribute equal to \code{xyz}. } \value{ TRUE if x is an object of class \sQuote{xyz} and FALSE otherwise } \seealso{ \code{\link{read.pdb}}, \code{\link{read.ncdf}}, \code{\link{read.dcd}}, \code{\link{fit.xyz}} } \examples{ # Read a PDB file pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) is.xyz(pdb$xyz) } \keyword{ classes } bio3d/man/geostas.Rd0000644000176200001440000001727312544562303013777 0ustar liggesusers\name{geostas} \alias{geostas} \alias{geostas.default} \alias{geostas.xyz} \alias{geostas.nma} \alias{geostas.enma} \alias{geostas.pdb} \alias{geostas.pdbs} \alias{amsm.xyz} \alias{print.geostas} \title{ GeoStaS Domain Finder } \description{ Identifies geometrically stable domains in biomolecules } \usage{ geostas(\dots) \method{geostas}{default}(\dots) \method{geostas}{xyz}(xyz, amsm = NULL, k = 3, pairwise = TRUE, clustalg = "kmeans", fit = TRUE, ncore = NULL, verbose=TRUE, \dots) \method{geostas}{nma}(nma, m.inds = 7:11, verbose=TRUE, \dots) \method{geostas}{enma}(enma, pdbs = NULL, m.inds = 1:5, verbose=TRUE, \dots) \method{geostas}{pdb}(pdb, inds = NULL, verbose=TRUE, \dots) \method{geostas}{pdbs}(pdbs, verbose=TRUE, \dots) amsm.xyz(xyz, ncore = NULL) \method{print}{geostas}(x, \dots) } \arguments{ \item{...}{ arguments passed to and from functions, such as \code{\link{kmeans}}, and \code{\link{hclust}} which are called internally in \code{geostas.xyz}. } \item{xyz}{ numeric matrix of xyz coordinates as obtained e.g. by \code{\link{read.ncdf}}, \code{\link{read.dcd}}, or \code{\link{mktrj}}. } \item{amsm}{ a numeric matrix as obtained by \code{\link{amsm.xyz}} (convenient e.g. for re-doing only the clustering analysis of the \sQuote{AMSM} matrix). } \item{k }{ an integer scalar or vector with the desired number of groups. } \item{pairwise }{ logical, if TRUE use pairwise clustering of the atomic movement similarity matrix (AMSM), else columnwise. } \item{clustalg}{ a character string specifing the clustering algorithm. Allowed values are \sQuote{kmeans} and \sQuote{hclust}. } \item{fit}{ logical, if TRUE coordinate superposition on identified core atoms is performed prior to the calculation of the AMS matrix. } \item{ncore }{ number of CPU cores used to do the calculation. \code{ncore>1} requires package \sQuote{parallel} installed. } \item{verbose}{ logical, if TRUE details of the geostas calculations are printed to screen. } \item{nma}{ an \sQuote{nma} object as obtained from function \code{\link{nma}}. Function \code{\link{mktrj}} is used internally to generate a trajectory based on the normal modes. } \item{m.inds}{ the mode number(s) along which trajectory should be made (see function \code{\link{mktrj}}). } \item{enma}{ an \sQuote{enma} object as obtained from function \code{\link{nma.pdbs}}. Function \code{\link{mktrj}} is used internally to generate a trajectory based on the normal modes. } \item{pdbs}{ a \sQuote{pdbs} object as obtained from function \code{\link{pdbaln}} or \code{\link{read.fasta.pdb}}. } \item{pdb}{ a \sQuote{pdb} object as obtained from function \code{\link{read.pdb}}. } \item{inds}{ a \sQuote{select} object as obtained from function \code{\link{atom.select}} giving the atomic indices at which the calculation should be based. By default the function will attempt to locate C-alpha atoms using function \code{\link{atom.select}}. } \item{x}{ a \sQuote{geostas} object as obtained from function \code{\link{geostas}}. } } \details{ This function attempts to identify rigid domains in a protein (or nucleic acid) structure based on an structural ensemble, e.g. obtained from NMR experiments, molecular dynamics simulations, or normal mode analysis. The algorithm is based on a geometric approach for comparing pairwise traces of atomic motion and the search for their best superposition using a quaternion representation of rotation. The result is stored in a NxN atomic movement similarity matrix (AMSM) describing the correspondence between all pairs of atom motion. Rigid domains are obtained by clustering the elements of the AMS matrix (\code{pairwise=TRUE}), or alternatively, the columns similarity (\code{pairwise=FALSE}), using either K-means (\code{\link{kmeans}}) or hierarchical (\code{\link{hclust}}) clustering. Compared to the conventional cross-correlation matrix (see function \code{\link{dccm}}) the \dQuote{geostas} approach provide functionality to also detect domains involved in rotational motions (i.e. two atoms located on opposite sides of a rotating domain will appear as anti-correlated in the cross-correlation matrix, but should obtain a high similarity coefficient in the AMS matrix). See examples for more details. } \note{ The current implementation in Bio3D uses a different fitting and clustering approach than the original Java implementation. The results will therefore differ. } \value{ Returns a list object of type \sQuote{geostas} with the following components: \item{amsm }{ a numeric matrix of atomic movement similarity (AMSM). } \item{fit.inds }{ a numeric vector of xyz indices used for fitting. } \item{grps }{ a numeric vector containing the domain assignment per residue. } \item{atomgrps }{ a numeric vector containing the domain assignment per atom (only provided for \code{geostas.pdb}). } \item{inds }{ a list of atom \sQuote{select} objects with indices to corresponding to the identified domains. } } \references{ Romanowska, J. et al. (2012) \emph{JCTC} \bold{8}, 2588--2599. Skjaerven, L. et al. (2014) \emph{BMC Bioinformatics} \bold{15}, 399. Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Julia Romanowska and Lars Skjaerven } \seealso{ \code{\link{plot.geostas}}, \code{\link{read.pdb}}, \code{\link{mktrj}}, \code{\link{read.ncdf}}, \code{\link{read.dcd}}, \code{\link{nma}}, \code{\link{dccm}}. } \examples{ \donttest{ # PDB server connection required - testing excluded #### NMR-ensemble example ## Read a multi-model PDB file pdb <- read.pdb("1d1d", multi=TRUE) ## Find domains and write PDB gs <- geostas(pdb, fit=TRUE) ## Plot a atomic movement similarity matrix plot.geostas(gs, contour=FALSE) ## Fit all frames to the 'first' domain domain.inds <- gs$inds[[1]] xyz <- pdbfit(pdb, inds=domain.inds) #write.pdb(pdb, xyz=xyz, chain=gs$atomgrps) } \dontrun{ #### NMA example ## Fetch stucture pdb <- read.pdb("1crn") ## Calculate (vibrational) normal modes modes <- nma(pdb) ## Find domains gs <- geostas(modes, k=2) ## Write NMA trajectory with domain assignment mktrj(modes, mode=7, chain=gs$grps) ## Redo geostas domain clustering gs <- geostas(modes, amsm=gs$amsm, k=5) #### Trajectory example ## Read inn DCD trajectory file, fit coordinates dcdfile <- system.file("examples/hivp.dcd", package = "bio3d") trj <- read.dcd(dcdfile) xyz <- fit.xyz(trj[1,], trj) ## Find domains gs <- geostas(xyz, k=3, fit=FALSE) ## Principal component analysis pc.md <- pca.xyz(xyz) ## Visualize PCs with colored domains (chain ID) mktrj(pc.md, pc=1, chain=gs$grps) #### X-ray ensemble GroEL subunits # Define the ensemble PDB-ids ids <- c("1sx4_[A,B,H,I]", "1xck_[A-B]", "1sx3_[A-B]", "4ab3_[A-B]") # Download and split PDBs by chain ID raw.files <- get.pdb(ids, path = "raw_pdbs", gzip = TRUE) files <- pdbsplit(raw.files, ids, path = "raw_pdbs/split_chain/") # Align structures pdbs <- pdbaln(files) # Find domains gs <- geostas(pdbs, k=4, fit=TRUE) # Superimpose to core region pdbs$xyz <- pdbfit(pdbs, inds=gs$fit.inds) # Principal component analysis pc.xray <- pca(pdbs) # Visualize PCs with colored domains (chain ID) mktrj(pc.xray, pc=1, chain=gs$grps) ##- Same, but more manual approach gaps.pos <- gap.inspect(pdbs$xyz) # Find core region core <- core.find(pdbs) # Fit to core region xyz <- fit.xyz(pdbs$xyz[1, gaps.pos$f.inds], pdbs$xyz[, gaps.pos$f.inds], fixed.inds=core$xyz, mobile.inds=core$xyz) # Find domains gs <- geostas(xyz, k=4, fit=FALSE) # Perform PCA pc.xray <- pca.xyz(xyz) # Make trajectory mktrj(pc.xray, pc=1, chain=gs$grps) } } \keyword{ analysis } bio3d/man/write.crd.Rd0000644000176200001440000000413712526367344014236 0ustar liggesusers\name{write.crd} \alias{write.crd} \title{ Write CRD File } \description{ Write a CHARMM CARD (CRD) coordinate file. } \usage{ write.crd(pdb = NULL, xyz = pdb$xyz, resno = NULL, resid = NULL, eleno = NULL, elety = NULL, segid = NULL, resno2 = NULL, b = NULL, verbose = FALSE, file = "R.crd") } \arguments{ \item{pdb}{ a structure object obtained from \code{\link{read.pdb}} or \code{\link{read.crd}}. } \item{xyz}{ Cartesian coordinates as a vector or 3xN matrix. } \item{resno}{ vector of residue numbers of length equal to length(xyz)/3. } \item{resid}{ vector of residue types/ids of length equal to length(xyz)/3. } \item{eleno}{ vector of element/atom numbers of length equal to length(xyz)/3. } \item{elety}{ vector of element/atom types of length equal to length(xyz)/3. } \item{segid}{ vector of segment identifiers with length equal to length(xyz)/3. } \item{resno2}{ vector of alternate residue numbers of length equal to length(xyz)/3. } \item{b}{ vector of weighting factors of length equal to length(xyz)/3. } \item{verbose}{ logical, if TRUE progress details are printed. } \item{file}{ the output file name. } } \details{ Only the \code{xyz} argument is strictly required. Other arguments assume a default poly-ALA C-alpha structure with a blank segid and B-factors equal to 0.00. } \value{ Called for its effect. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. For a description of CHARMM CARD (CRD) format see:\cr \url{http://www.charmmtutorial.org/index.php/CHARMM:The_Basics}. } \author{ Barry Grant } \note{ Check that \code{resno} and \code{eleno} do not exceed \dQuote{9999}. } \seealso{ \code{\link{read.crd}}, \code{\link{read.pdb}}, \code{\link{atom.select}}, \code{\link{write.pdb}}, \code{\link{read.dcd}}, \code{\link{read.fasta.pdb}}, \code{\link{read.fasta}} } \examples{ \dontrun{ # Read a PDB file pdb <- read.pdb( "1bg2" ) summary(pdb) # Convert to CHARMM format new <- convert.pdb(pdb, type="charmm") summary(new) # Write a CRD file write.crd(new, file="4charmm.crd") } } \keyword{ IO } bio3d/man/rgyr.Rd0000644000176200001440000000334312544562303013306 0ustar liggesusers\name{rgyr} \alias{rgyr} \title{ Radius of Gyration} \description{ Calculate the radius of gyration of coordinate sets. } \usage{ rgyr(xyz, mass=NULL, ncore=1, nseg.scale=1) } \arguments{ \item{xyz}{ a numeric vector, matrix or list object with an \code{xyz} component, containing one or more coordinate sets.} \item{mass}{ a numeric vector of atomic masses (unit a.m.u.), or a PDB object with masses stored in the "B-factor" column. If \code{mass==NULL}, all atoms are assumed carbon.} \item{ncore }{ number of CPU cores used to do the calculation. \code{ncore>1} requires package \sQuote{parallel} installed. } \item{nseg.scale }{ split input data into specified number of segments prior to running multiple core calculation. See \code{\link{fit.xyz}}. } } \details{ Radius of gyration is a standard measure of overall structural change of macromolecules. } \value{ Returns a numeric vector of radius of gyration. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Xin-Qiu Yao & Pete Kekenes-Huskey} \seealso{ \code{\link{fit.xyz}}, \code{\link{rmsd}}, \code{\link{read.pdb}}, \code{\link{read.fasta.pdb}} } \examples{ \donttest{ # PDB server connection required - testing excluded # -- Calculate Rog of single structure pdb <- read.pdb("1bg2") mass <- rep(12, length(pdb$xyz)/3) mass[substr(pdb$atom[,"elety"], 1, 1) == "N"] <- 14 mass[substr(pdb$atom[,"elety"], 1, 1) == "H"] <- 1 mass[substr(pdb$atom[,"elety"], 1, 1) == "O"] <- 16 mass[substr(pdb$atom[,"elety"], 1, 1) == "S"] <- 32 rgyr(pdb, mass) } \dontrun{ # -- Calculate Rog of a trajectory xyz <- read.dcd(system.file("examples/hivp.dcd", package="bio3d")) rg <- rgyr(xyz) rg[1:10] } } \keyword{ utilities } bio3d/man/atom2xyz.Rd0000644000176200001440000000123712412623040014106 0ustar liggesusers\name{atom2xyz} \alias{atom2xyz} \alias{xyz2atom} \title{ Convert Between Atom and xyz Indices } \description{ Basic functions to convert between xyz and their corresponding atom indices. } \usage{ atom2xyz(num) xyz2atom(xyz.ind) } \arguments{ \item{num}{ a numeric vector of atom indices. } \item{xyz.ind}{ a numeric vector of xyz indices. } } \value{ A numeric vector of either xyz or atom indices. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \seealso{ \code{\link{atom.select}}, \code{\link{read.pdb}} } \examples{ xyz.ind <- atom2xyz(c(1,10,15)) xyz2atom( xyz.ind ) } \keyword{ utilities } bio3d/man/read.pqr.Rd0000644000176200001440000000703512544562303014041 0ustar liggesusers\name{read.pqr} \alias{read.pqr} \title{ Read PQR File } \description{ Read a PQR coordinate file. } \usage{ read.pqr(file, maxlines = -1, multi = FALSE, rm.insert = FALSE, rm.alt = TRUE, verbose = TRUE) } \arguments{ \item{file}{ the name of the PQR file to be read. } \item{maxlines}{ the maximum number of lines to read before giving up with large files. By default if will read up to the end of input on the connection. } \item{multi}{ logical, if TRUE multiple ATOM records are read for all models in multi-model files. } \item{rm.insert}{ logical, if TRUE PDB insert records are ignored. } \item{rm.alt}{ logical, if TRUE PDB alternate records are ignored. } \item{verbose}{ print details of the reading process. } } \details{ \code{maxlines} may require increasing for some large multi-model files. The preferred means of reading such data is via binary DCD format trajectory files (see the \code{\link{read.dcd}} function). } \value{ Returns a list of class \code{"pdb"} with the following components: \item{atom}{ a character matrix containing all atomic coordinate ATOM data, with a row per ATOM and a column per record type. See below for details of the record type naming convention (useful for accessing columns). } \item{het }{ a character matrix containing atomic coordinate records for atoms within \dQuote{non-standard} HET groups (see \code{atom}). } \item{helix }{ \sQuote{start}, \sQuote{end} and \sQuote{length} of H type sse, where start and end are residue numbers \dQuote{resno}. } \item{sheet }{ \sQuote{start}, \sQuote{end} and \sQuote{length} of E type sse, where start and end are residue numbers \dQuote{resno}. } \item{seqres }{ sequence from SEQRES field. } \item{xyz }{ a numeric vector of ATOM coordinate data. } \item{calpha }{ logical vector with length equal to \code{nrow(atom)} with TRUE values indicating a C-alpha \dQuote{elety}. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. For a description of PDB format (version3.3) see:\cr \url{http://www.wwpdb.org/documentation/format33/v3.3.html}. } \author{ Barry Grant } \note{ For both \code{atom} and \code{het} list components the column names can be used as a convenient means of data access, namely: Atom serial number \dQuote{eleno} , Atom type \dQuote{elety}, Alternate location indicator \dQuote{alt}, Residue name \dQuote{resid}, Chain identifier \dQuote{chain}, Residue sequence number \dQuote{resno}, Code for insertion of residues \dQuote{insert}, Orthogonal coordinates \dQuote{x}, Orthogonal coordinates \dQuote{y}, Orthogonal coordinates \dQuote{z}, Occupancy \dQuote{o}, and Temperature factor \dQuote{b}. See examples for further details. } \seealso{ \code{\link{atom.select}}, \code{\link{write.pdb}}, \code{\link{read.dcd}}, \code{\link{read.fasta.pdb}}, \code{\link{read.fasta}} } \examples{ \donttest{ # PDB server connection required - testing excluded # Read a PDB file pdb <- read.pdb( "4q21" ) # Print data for the first atom pdb$atom[1,] # Look at the first het atom pdb$het[1,] # Print some coordinate data pdb$atom[1:20, c("x","y","z")] # Print C-alpha coordinates (can also use 'atom.select') ##pdb$xyz[pdb$calpha, c("resid","x","y","z")] # Print SSE data (for helix and sheet) pdb$helix pdb$sheet$start # Print SEQRES data pdb$seqres # Renumber residues nums <- as.numeric(pdb$atom[,"resno"]) pdb$atom[,"resno"] <- nums - (nums[1] - 1) # Write out renumbered PDB file #write.pdb(pdb=pdb,file="eg.pdb") } } \keyword{ IO } bio3d/man/get.seq.Rd0000644000176200001440000000347212632622153013672 0ustar liggesusers\name{get.seq} \alias{get.seq} \title{ Download FASTA Sequence Files } \description{ Downloads FASTA sequence files from the NR, or SWISSPROT/UNIPROT databases. } \usage{ get.seq(ids, outfile = "seqs.fasta", db = "nr") } \arguments{ \item{ids}{ A character vector of one or more appropriate database codes/identifiers of the files to be downloaded. } \item{outfile}{ A single element character vector specifying the name of the local file to which sequences will be written. } \item{db}{ A single element character vector specifying the database from which sequences are to be obtained. } } \details{ This is a basic function to automate sequence file download from the NR and SWISSPROT/UNIPROT databases. } \value{ If all files are successfully downloaded a list object with two components is returned: \item{ali }{ an alignment character matrix with a row per sequence and a column per equivalent aminoacid/nucleotide. } \item{ids }{ sequence names as identifiers.} This is similar to that returned by \code{\link{read.fasta}}. However, if some files were not successfully downloaded then a vector detailing which ids were not found is returned. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \note{ For a description of FASTA format see: \url{http://www.ncbi.nlm.nih.gov/BLAST/blastcgihelp.shtml}. When reading alignment files, the dash \sQuote{-} is interpreted as the gap character. } \seealso{ \code{\link{blast.pdb}}, \code{\link{read.fasta}}, \code{\link{read.fasta.pdb}}, \code{\link{get.pdb}} } \examples{ \dontrun{ ## Sequence identifiers (GI or PDB codes e.g. from blast.pdb etc.) get.seq( c("P01112", "Q61411", "P20171") ) #aa <-get.seq( c("4q21", "5p21") ) #aa$id #aa$ali } } \keyword{ IO } \keyword{ utilities } bio3d/man/read.crd.charmm.Rd0000644000176200001440000000425112526367344015262 0ustar liggesusers\name{read.crd.charmm} \alias{read.crd.charmm} \title{ Read CRD File } \description{ Read a CHARMM CARD (CRD) coordinate file. } \usage{ \method{read.crd}{charmm}(file, ext = TRUE, verbose = TRUE, ...) } \arguments{ \item{file}{ the name of the CRD file to be read. } \item{ext}{logical, if TRUE assume expanded CRD format. } \item{verbose}{ print details of the reading process. } \item{\dots}{ arguments going nowhere. } } \details{ See the function \code{\link{read.pdb}} for more details. } \value{ Returns a list with the following components: \item{atom}{ a character matrix containing all atomic coordinate data, with a row per atom and a column per record type. See below for details of the record type naming convention (useful for accessing columns). } \item{xyz }{ a numeric vector of coordinate data. } \item{calpha }{ logical vector with length equal to \code{nrow(atom)} with TRUE values indicating a C-alpha \dQuote{elety}. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. For a description of CHARMM CARD (CRD) format see:\cr \url{http://www.charmmtutorial.org/index.php/CHARMM:The_Basics}. } \author{ Barry Grant } \note{ Similar to the output of \code{\link{read.pdb}}, the column names of \code{atom} can be used as a convenient means of data access, namely: Atom serial number \dQuote{eleno}, Atom type \dQuote{elety}, Alternate location indicator \dQuote{alt}, Residue name \dQuote{resid}, Residue sequence number \dQuote{resno}, Code for insertion of residues \dQuote{insert}, Orthogonal coordinates \dQuote{x}, Orthogonal coordinates \dQuote{y}, Orthogonal coordinates \dQuote{z}, Weighting factor \dQuote{b}. See examples for further details. } \seealso{ \code{\link{write.crd}}, \code{\link{read.pdb}}, \code{\link{atom.select}}, \code{\link{write.pdb}}, \code{\link{read.dcd}}, \code{\link{read.fasta.pdb}}, \code{\link{read.fasta}} } \examples{ \dontrun{ pdb <- read.pdb("1bg2") crdfile <- tempfile() write.crd(pdb, file=crdfile) crd <- read.crd(crdfile) ca.inds <- which(crd$calpha) crd$atom[ca.inds[1:20],c("x","y","z")] # write.pdb(crd, file=tempfile()) } } \keyword{ IO } bio3d/man/dccm.xyz.Rd0000644000176200001440000001117612544562303014065 0ustar liggesusers\name{dccm.xyz} \alias{dccm.xyz} \alias{cov2dccm} \title{ DCCM: Dynamical Cross-Correlation Matrix } \description{ Determine the cross-correlations of atomic displacements. } \usage{ \method{dccm}{xyz}(x, reference = NULL, grpby=NULL, ncore=1, nseg.scale=1, \dots) cov2dccm(vcov, method = c("pearson", "lmi"), ncore = NULL) } \arguments{ \item{x}{ a numeric matrix of Cartesian coordinates with a row per structure/frame. } \item{reference}{ The reference structure about which displacements are analysed. } \item{grpby}{ a vector counting connective duplicated elements that indicate the elements of \code{xyz} that should be considered as a group (e.g. atoms from a particular residue). } \item{ncore }{ number of CPU cores used to do the calculation. \code{ncore>1} requires package \sQuote{parallel} installed. } \item{nseg.scale }{ split input data into specified number of segments prior to running multiple core calculation. See \code{\link{fit.xyz}}. } \item{\dots}{ hmm. } \item{vcov}{ numeric variance-covariance matrix. } \item{method}{ method to calculate the cross-correlation. } } \details{ The extent to which the atomic fluctuations/displacements of a system are correlated with one another can be assessed by examining the magnitude of all pairwise cross-correlation coefficients (see McCammon and Harvey, 1986). This function returns a matrix of all atom-wise cross-correlations whose elements, Cij, may be displayed in a graphical representation frequently termed a dynamical cross-correlation map, or DCCM. If Cij = 1 the fluctuations of atoms i and j are completely correlated (same period and same phase), if Cij = -1 the fluctuations of atoms i and j are completely anticorrelated (same period and opposite phase), and if Cij = 0 the fluctuations of i and j are not correlated. Typical characteristics of DCCMs include a line of strong cross-correlation along the diagonal, cross-correlations emanating from the diagonal, and off-diagonal cross-correlations. The high diagonal values occur where i = j, where Cij is always equal to 1.00. Positive correlations emanating from the diagonal indicate correlations between contiguous residues, typically within a secondary structure element or other tightly packed unit of structure. Typical secondary structure patterns include a triangular pattern for helices and a plume for strands. Off-diagonal positive and negative correlations may indicate potentially interesting correlations between domains of non-contiguous residues. \code{cov2dccm} function calculates the N-by-N cross-correlation matrix directly from a 3N-by-3N variance-covariance matrix. If \code{method = "pearson"}, the conventional Pearson's inner-product correlaiton calculation will be invoked, in which only the diagnol of each residue-residue covariance sub-matrix is considered. If \code{method = "lmi"}, then the linear mutual information cross-correlation will be calculated. \sQuote{LMI} considers both diagnol and off-diagnol entries in sub-matrices, and so even grabs the correlation of residues moving on orthognal directions. (See more details in \code{\link{lmi}}.) } \value{ Returns a cross-correlation matrix. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. McCammon, A. J. and Harvey, S. C. (1986) \emph{Dynamics of Proteins and Nucleic Acids}, Cambridge University Press, Cambridge. Lange, O.F. and Grubmuller, H. (2006) \emph{PROTEINS: Structure, Function, and Bioinformatics} \bold{62}:1053--1061. } \author{ Xin-Qiu Yao and Gisle Saelensminde } \seealso{ \code{\link{cor}} for examining xyz cross-correlations, \code{\link{dccm}}, \code{\link{dccm.nma}}, \code{\link{dccm.pca}}, \code{\link{lmi}}, \code{\link{dccm.enma}}. } \examples{ \dontrun{ ##-- Read example trajectory file trtfile <- system.file("examples/hivp.dcd", package="bio3d") trj <- read.dcd(trtfile) ## Read the starting PDB file to determine atom correspondence pdbfile <- system.file("examples/hivp.pdb", package="bio3d") pdb <- read.pdb(pdbfile) ## select residues 24 to 27 and 85 to 90 in both chains inds <- atom.select(pdb, resno=c(24:27,85:90), elety='CA') ## lsq fit of trj on pdb xyz <- fit.xyz(pdb$xyz, trj, fixed.inds=inds$xyz, mobile.inds=inds$xyz) ## DCCM (slow to run so restrict to Calpha) cij <- dccm(xyz) ## Plot DCCM plot(cij) ## Or library(lattice) contourplot(cij, region = TRUE, labels=FALSE, col="gray40", at=c(-1, -0.75, -0.5, -0.25, 0.25, 0.5, 0.75, 1), xlab="Residue No.", ylab="Residue No.", main="DCCM: dynamic cross-correlation map") } } \keyword{ utilities } bio3d/man/vmd.colors.Rd0000644000176200001440000000235112430771420014403 0ustar liggesusers\name{vmd.colors} \alias{vmd.colors} \title{ VMD Color Palette } \description{ This function creates a character vector of the colors used by the VMD molecular graphics program. } \usage{ vmd.colors(n=33, picker=FALSE, ...) } \arguments{ \item{n}{ The number of desired colors chosen in sequence from the VMD color palette (>=1) } \item{picker}{ Logical, if TRUE a color wheel plot will be produced to aid with color choice. } \item{\dots}{ Extra arguments passed to the \code{rgb} function, including alpha transparency. } } \details{ The function uses the underlying 33 RGB color codes from VMD, See \url{http://www.ks.uiuc.edu/Research/vmd/}. Note that colors will be recycled if \dQuote{n} > 33 with a warning issued. When \sQuote{picker} is set to \dQuote{TRUE} a color wheel of the requested colors will be plotted to the currently active device. } \value{ Returns a character vector with color names. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. \url{http://www.ks.uiuc.edu/Research/vmd/} } \author{ Barry Grant } \seealso{ \code{\link{bwr.colors}} } \examples{ ## Generate a vector of 10 colors clrs <- vmd.colors(10) vmd.colors(4, picker=TRUE) } \keyword{utility} bio3d/man/sip.Rd0000644000176200001440000000334612526367344013131 0ustar liggesusers\name{sip} \alias{sip} \alias{sip.default} \alias{sip.nma} \alias{sip.enma} \title{ Square Inner Product } \description{ Calculate the correlation between two atomic fluctuation vectors. } \usage{ sip(...) \method{sip}{nma}(a, b, ...) \method{sip}{enma}(enma, ncore=NULL, ...) \method{sip}{default}(v, w, ...) } \arguments{ \item{enma}{ an object of class \code{"enma"} obtained from function \code{nma.pdbs}. } \item{ncore }{ number of CPU cores used to do the calculation. \code{ncore>1} requires package \sQuote{parallel} installed. } \item{a}{ an \sQuote{nma} object as object from function \code{nma} to be compared to \code{b}. } \item{b}{ an \sQuote{nma} object as object from function \code{nma} to be compared to \code{a}. } \item{v}{ a numeric vector containing the atomic fluctuation values. } \item{w}{ a numeric vector containing the atomic fluctuation values. } \item{\dots}{ arguments passed to associated functions. } } \details{ SIP is a measure for the similarity of atomic fluctuations of two proteins, e.g. experimental b-factors, theroetical RMSF values, or atomic fluctuations obtained from NMA. } \value{ Returns the similarity coefficient(s). } \references{ Skjaerven, L. et al. (2014) \emph{BMC Bioinformatics} \bold{15}, 399. Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. Fuglebakk, E. et al. (2013) \emph{JCTC} \bold{9}, 5618--5628. } \author{ Lars Skjaerven } \seealso{Other similarity measures: \code{\link{covsoverlap}}, \code{\link{bhattacharyya}}, \code{\link{rmsip}}. } \examples{ pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) a <- nma(pdb) b <- nma(pdb, ff="anm") sip(a$fluctuations, b$fluctuations) } \keyword{ utilities } bio3d/man/read.fasta.Rd0000644000176200001440000000303412526367344014340 0ustar liggesusers\name{read.fasta} \alias{read.fasta} \title{ Read FASTA formated Sequences } \description{ Read aligned or un-aligned sequences from a FASTA format file. } \usage{ read.fasta(file, rm.dup = TRUE, to.upper = FALSE, to.dash=TRUE) } \arguments{ \item{file}{ input sequence file. } \item{rm.dup}{ logical, if TRUE duplicate sequences (with the same names/ids) will be removed. } \item{to.upper}{ logical, if TRUE residues are forced to uppercase. } \item{to.dash}{ logical, if TRUE \sQuote{.} gap characters are converted to \sQuote{-} gap characters. } } \value{ A list with two components: \item{ali }{ an alignment character matrix with a row per sequence and a column per equivalent aminoacid/nucleotide. } \item{ids }{ sequence names as identifers.} \item{call}{ the matched call. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \note{ For a description of FASTA format see: \url{http://www.ncbi.nlm.nih.gov/BLAST/blastcgihelp.shtml}. When reading alignment files, the dash \sQuote{-} is interpreted as the gap character. } \seealso{ \code{\link{read.fasta.pdb}} } \examples{ # Read alignment aln <- read.fasta(system.file("examples/hivp_xray.fa",package="bio3d")) # Print alignment overview aln # Sequence names/ids head( aln$id ) # Alignment positions 335 to 339 head( aln$ali[,33:39] ) # Sequence d2a4f_b aa123( aln$ali["d2a4f_b",] ) # Write out positions 33 to 45 only #aln$ali=aln$ali[,30:45] #write.fasta(aln, file="eg2.fa") } \keyword{ IO } bio3d/man/example.data.Rd0000644000176200001440000000526412526367344014702 0ustar liggesusers\name{example.data} \docType{data} \alias{example.data} \alias{kinesin} \alias{transducin} \alias{pdbs} \alias{core} \alias{annotation} \alias{hivp} \title{Bio3d Example Data} \description{ These data sets contain the results of running various Bio3D functions on example kinesin and transducin structural data, and on a short coarse-grained MD simulation data for HIV protease. The main purpose of including this data (which may be generated by the user by following the extended examples documented within the various Bio3D functions) is to speed up example execution. It should allow users to more quickly appreciate the capabilities of functions that would otherwise require raw data download, input and processing before execution. Note that related datasets formed the basis of the work described in (Grant, 2007) and (Yao & Grant, 2013) for \code{kinesin} and \code{transducin} examples, respectively. } \usage{ data(kinesin) data(transducin) data(hivp) } \format{ Three objects from analysis of the \code{kinesin} and \code{transducin} sequence and structure data: \enumerate{ \item{pdbs}{ is a list of class \code{"pdbs"} containing aligned PDB structure data. In the case of transducin this is the output of running \code{\link{pdbaln}} on a set of 47 kinesin structures from the SCOP database (again see \code{pdbs$id} for details). In both cases the coordinates are fitted onto the first structure based on \code{"core"} positions obtained from \code{\link{core.find}} and superposed using the function \code{\link{pdbfit}}. } \item{core}{ is a list of class \code{"core"} obtained by running the function \code{\link{core.find}} on the \code{pdbs} object as described above.} \item{annotation}{ is a character matrix describing the nucleotide state and bound ligand species for each structure in \code{pdbs} as obtained from the function \code{\link{pdb.annotate}}.} } One object named \code{net} in the hivp example data stores the correlation network obtained from the analysis of the MD simulation trajectory of HIV protease using the \code{cna} function. The original trajectory file can be accessed by the command \sQuote{system.file("examples/hivp.dcd", package="bio3d")}. } \source{ A related but more extensive dataset formed the basis of the work described in (Grant, 2007) and (Yao & Grant, 2013) for \code{kinesin} and \code{transducin} examples, respectively. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. Grant, B.J. et al. (2007) \emph{J. Mol. Biol.} \bold{368}, 1231--1248. Yao, X.Q. et al. (2013) \emph{Biophys. J.} \bold{105}, L08--L10. } \keyword{datasets} bio3d/man/trim.pdbs.Rd0000644000176200001440000000403412544562303014223 0ustar liggesusers\name{trim.pdbs} \alias{trim.pdbs} \title{ Filter or Trim a PDBs Object } \description{ Trim residues and/or filter out structures from a PDBs object. } \usage{ \method{trim}{pdbs}(pdbs, row.inds=NULL, col.inds=NULL, \dots) } \arguments{ \item{pdbs}{ an object of class \code{pdbs} as obtained from function \code{pdbaln} or \code{read.fasta.pdb}; a xyz matrix containing the cartesian coordinates of C-alpha atoms. } \item{row.inds }{ a numeric vector of indices pointing to the PDB structures to keep (rows in the \code{pdbs$ali} matrix). } \item{col.inds }{ a numeric vector of indices pointing to the alignment columns to keep (columns in the \code{pdbs$ali} matrix). } \item{\dots}{ additional arguments passed to and from functions. } } \details{ Utility function to remove structures, or trim off columns, in a \sQuote{pdbs} object. } \value{ Returns an updated \sQuote{pdbs} object with the following components: \item{xyz}{numeric matrix of aligned C-alpha coordinates.} \item{resno}{character matrix of aligned residue numbers.} \item{b}{numeric matrix of aligned B-factor values.} \item{chain}{character matrix of aligned chain identifiers.} \item{id}{character vector of PDB sequence/structure names.} \item{ali}{character matrix of aligned sequences.} \item{call}{ the matched call. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{pdbaln}}, \code{\link{gap.inspect}}, \code{\link{read.fasta}},\code{\link{read.fasta.pdb}}, \code{\link{trim.pdb}}, } \examples{ \dontrun{ ## Fetch PDB files and split to chain A only PDB files ids <- c("1a70_A", "1czp_A", "1frd_A", "1fxi_A", "1iue_A", "1pfd_A") raw.files <- get.pdb(ids, path = "raw_pdbs") files <- pdbsplit(raw.files, ids, path = "raw_pdbs/split_chain") ## Sequence Alignement, and connectivity check pdbs <- pdbaln(files) cons <- inspect.connectivity(pdbs) ## omit files with missing residues trim.pdbs(pdbs, row.inds=which(cons)) } } \keyword{ utilities }bio3d/man/covsoverlap.Rd0000644000176200001440000000317712544562303014673 0ustar liggesusers\name{covsoverlap} \alias{covsoverlap} \alias{covsoverlap.enma} \alias{covsoverlap.nma} \title{ Covariance Overlap } \description{ Calculate the covariance overlap obtained from NMA. } \usage{ covsoverlap(...) \method{covsoverlap}{enma}(enma, ncore=NULL, ...) \method{covsoverlap}{nma}(a, b, subset=NULL, ...) } \arguments{ \item{enma}{ an object of class \code{"enma"} obtained from function \code{nma.pdbs}. } \item{ncore }{ number of CPU cores used to do the calculation. \code{ncore>1} requires package \sQuote{parallel} installed. } \item{a}{ a list object with elements \sQuote{U} and \sQuote{L} (e.g. as obtained from function \code{nma}) containing the eigenvectors and eigenvalues, respectively, to be compared with \code{b}. } \item{b}{ a list object with elements \sQuote{U} and \sQuote{L} (e.g. as obtained from function \code{nma}) containing the eigenvectors and eigenvalues, respectively, to be compared with \code{a}. } \item{subset}{ the number of modes to consider. } \item{\dots}{ arguments passed to associated functions. } } \details{ Covariance overlap is a measure for the similarity between two covariance matrices, e.g. obtained from NMA. } \value{ Returns the similarity coefficient(s). } \references{ Skjaerven, L. et al. (2014) \emph{BMC Bioinformatics} \bold{15}, 399. Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. Romo, T.D. et al. (2011) \emph{Proteins} \bold{79}, 23--34. } \author{ Lars Skjaerven } \seealso{ Other similarity measures: \code{\link{sip}}, \code{\link{covsoverlap}}, \code{\link{bhattacharyya}}. } \keyword{ utilities } bio3d/man/overlap.Rd0000644000176200001440000000560412544562303013775 0ustar liggesusers\name{overlap} \alias{overlap} \title{ Overlap analysis } \description{ Calculate the squared overlap between sets of vectors. } \usage{ overlap(modes, dv, nmodes=20) } \arguments{ \item{modes}{ an object of class \code{"pca"} or \code{"nma"} as obtained from function \code{pca.xyz} or \code{nma}. Alternatively a 3NxM matrix of eigenvectors can be provided. } \item{dv}{ a displacement vector of length 3N. } \item{nmodes}{ the number of modes in which the calculation should be based. } } \details{ Squared overlap (or dot product) is used to measure the similiarity between a displacement vector (e.g. a difference vector between two conformational states) and mode vectors obtained from principal component or normal modes analysis. By definition the cumulative sum of the overlap values equals to one. Structure \code{modes$U} (or alternatively, the 3NxM matrix of eigenvectors) should be of same length (3N) as \code{dv}. } \value{ Returns a list with the following components: \item{overlap}{ a numeric vector of the squared dot products (overlap values) between the (normalized) vector (\code{dv}) and each mode in \code{mode}. } \item{overlap.cum}{ a numeric vector of the cumulative squared overlap values. } } \references{ Skjaerven, L. et al. (2011) \emph{Proteins} \bold{79}, 232--243. Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{rmsip}}, \code{\link{pca.xyz}}, \code{\link{nma}}, \code{\link{difference.vector}} } \examples{ data(kinesin) attach(kinesin, warn.conflicts=FALSE) # Ignore gap containing positions ##gaps.res <- gap.inspect(pdbs$ali) gaps.pos <- gap.inspect(pdbs$xyz) #-- Do PCA pc.xray <- pca.xyz(pdbs$xyz[, gaps.pos$f.inds]) # Define a difference vector between two structural states diff.inds <- c(grep("d1v8ja", pdbs$id), grep("d1goja", pdbs$id)) dv <- difference.vector( pdbs$xyz[diff.inds,], gaps.pos$f.inds ) # Calculate the squared overlap between the PCs and the difference vector o <- overlap(pc.xray, dv) o <- overlap(pc.xray$U, dv) # Plot results plot(o$overlap, type='h', ylim=c(0,1)) points(o$overlap) lines(o$overlap.cum, type='b', col='red') detach(kinesin) \dontrun{ ## Calculate overlap from NMA pdb.a <- read.pdb("1cmk") pdb.b <- read.pdb("3dnd") ## Fetch CA coordinates sele.a <- atom.select(pdb.a, chain='E', resno=c(15:350), elety='CA') sele.b <- atom.select(pdb.b, chain='A', resno=c(1:350), elety='CA') xyz <- rbind(pdb.a$xyz[sele.a$xyz], pdb.b$xyz[sele.b$xyz]) ## Superimpose xyz[2,] <- fit.xyz(xyz[1,], xyz[2,], 1:ncol(xyz)) ## The difference between the two conformations dv <- difference.vector( xyz ) ## Calculate normal modes modes <- nma(pdb.a, inds=sele.a) # Calculate the squared overlap between the normal modes # and the difference vector o <- overlap(modes, dv) } } \keyword{ utilities } bio3d/man/sdENM.Rd0000644000176200001440000000225712526367344013304 0ustar liggesusers\name{sdENM} \alias{sdENM} \title{ Index for the sdENM ff } \description{ A dictonary of spring force constants for the sdENM force field. } \usage{ data(sdENM) } \format{ An array of 27 matrices containg the spring force constants for the \sQuote{sdENM} force field (see Dehouch et al for more information). Each matrix in the array holds the force constants for all amino acid pairs for a specific distance range. See examples for more details. } \source{ Dehouck Y. & Mikhailov A.S. (2013) \emph{PLoS Comput Biol} \bold{9}:e1003209. } \references{ Skjaerven, L. et al. (2014) \emph{BMC Bioinformatics} \bold{15}, 399. Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. Dehouck Y. et al. (2013) \emph{PLoS Comput Biol} \bold{9}:e1003209. } \examples{ ## Load force constant data data(sdENM) ## force constants for amino acids A, C, D, E, and F ## in distance range [4, 4.5) sdENM[1:5, 1:5, 1] ## and distance range [4.5, 5) sdENM[1:5, 1:5, 2] ## amino acid pair A-P, at distance 4.2 sdENM["A", "P", 1] \dontrun{ ## for use in NMA pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) modes <- nma(pdb, ff="sdenm") } } \keyword{datasets} bio3d/man/com.Rd0000644000176200001440000000513612544562303013103 0ustar liggesusers\name{com} \alias{com} \alias{com.pdb} \alias{com.xyz} \title{ Center of Mass } \description{ Calculate the center of mass of a PDB object. } \usage{ com(...) \method{com}{pdb}(pdb, inds=NULL, use.mass=TRUE, ...) \method{com}{xyz}(xyz, mass=NULL, ...) } \arguments{ \item{pdb}{ an object of class \code{pdb} as obtained from function \code{read.pdb}. } \item{inds}{ atom and xyz coordinate indices obtained from \code{atom.select} that selects the elements of \code{pdb} upon which the calculation should be based.} \item{use.mass}{ logical, if TRUE the calculation will be mass weighted (center of mass). } \item{...}{ additional arguments to \code{atom2mass}. } \item{xyz}{ a numeric vector or matrix of Cartesian coordinates (e.g. an object of type \code{xyz}). } \item{mass}{ a numeric vector containing the masses of each atom in \code{xyz}. } } \details{ This function calculates the center of mass of the provided PDB structure / Cartesian coordiantes. Atom names found in standard amino acids in the PDB are mapped to atom elements and their corresponding relative atomic masses. In the case of an unknown atom name \code{elety.custom} and \code{mass.custom} can be used to map an atom to the correct atomic mass. See examples for more details. Alternatively, the atom name will be mapped automatically to the element corresponding to the first character of the atom name. Atom names starting with character \code{H} will be mapped to hydrogen atoms. } \value{ Returns the Cartesian coordinates at the center of mass. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{read.pdb}}, \code{\link{atom2mass}} } \examples{ \donttest{ # PDB server connection required - testing excluded ## Stucture of PKA: pdb <- read.pdb("3dnd") ## Center of mass: com(pdb) ## Center of mass of a selection inds <- atom.select(pdb, chain="I") com(pdb, inds) ## using XYZ Cartesian coordinates xyz <- pdb$xyz[, inds$xyz] com.xyz(xyz) ## with mass weighting com.xyz(xyz, mass=atom2mass(pdb$atom[inds$atom, "elety"]) ) } \dontrun{ ## Unknown atom names pdb <- read.pdb("3dnd") inds <- atom.select(pdb, resid="LL2") mycom <- com(pdb, inds, rescue=TRUE) #warnings() ## Map atom names manually pdb <- read.pdb("3RE0") inds <- atom.select(pdb, resno=201) myelety <- data.frame(name = c("CL2","PT1","N1","N2"), symb = c("Cl","Pt","N","N")) mymasses <- data.frame(symb = c("Cl","Pt"), mass = c(35.45, 195.08)) mycom <- com(pdb, inds, elety.custom=myelety, mass.custom=mymasses) } } \keyword{ utilities } bio3d/man/pdbs2pdb.Rd0000644000176200001440000000312612524171274014023 0ustar liggesusers\name{pdbs2pdb} \alias{pdbs2pdb} \title{ PDBs to PDB Converter } \description{ Convert a list of PDBs from an \code{"pdbs"} object to a list of \code{pdb} objects. } \usage{ pdbs2pdb(pdbs, inds = NULL, rm.gaps = FALSE) } \arguments{ \item{pdbs}{ a list of class \code{"pdbs"} containing PDB file data, as obtained from \code{read.fasta.pdb} or \code{pdbaln}. } \item{inds}{ a vector of indices that selects the PDB structures to convert. } \item{rm.gaps}{ logical, if TRUE atoms in gap containing columns are removed in the output \code{pdb} objects. } } \details{ This function will generate a list of \code{pdb} objects from a \code{"pdbs"} class. See examples for more details/ } \value{ Returns a list of \code{pdb} objects. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{read.pdb}}, \code{\link{pdbaln}}, \code{\link{read.fasta.pdb}}. } \examples{ \dontrun{ ## Fetch PDBs pdb.ids <- c("1YX5_B", "3NOB", "1P3Q_U") #outdir <- paste(tempdir(), "/raw_pdbs", sep="") outdir = "raw_pdbs" raw.files <- get.pdb(pdb.ids, path = outdir) ## Split PDBs by chain ID and multi-model records all.files <- pdbsplit(raw.files, pdb.ids, path =paste(outdir, "/split_chain", sep="")) ## Align and fit pdbs <- pdbaln(all.files, fit=TRUE) ## Convert back to PDB objects all.pdbs <- pdbs2pdb(pdbs) ## Access the first PDB object ## all.pdbs[[1]] ## Return PDB objects consisting of only ## atoms in non-gap positions all.pdbs <- pdbs2pdb(pdbs, rm.gaps=TRUE) } } \keyword{ utilities } bio3d/man/setup.ncore.Rd0000644000176200001440000000115312526367344014575 0ustar liggesusers\name{setup.ncore} \alias{setup.ncore} \title{ Setup for Running Bio3D Functions using Multiple CPU Cores } \description{ Internally used in parallelized Bio3D functions. } \usage{ setup.ncore(ncore, bigmem = FALSE) } \arguments{ \item{ncore}{ User set (or default) value of \sQuote{ncore}. } \item{bigmem}{ logical, if TRUE also check the availability of \sQuote{bigmemory} package. } } \details{ Check packages and set correct value of \sQuote{ncore}. } \value{ The actual value of \sQuote{ncore}. } \examples{ setup.ncore(NULL) setup.ncore(1) # setup.ncore(2) } \keyword{ utilities } bio3d/man/plot.nma.Rd0000644000176200001440000000300312526367344014054 0ustar liggesusers\name{plot.nma} \alias{plot.nma} \title{ Plot NMA Results } \description{ Produces eigenvalue/frequency spectrum plots and an atomic fluctuations plot. } \usage{ \method{plot}{nma}(x, pch = 16, col = par("col"), cex=0.8, mar=c(6, 4, 2, 2),...) } \arguments{ \item{x}{ the results of normal modes analysis obtained with \code{\link{nma}}. } \item{pch}{ a vector of plotting characters or symbols: see \code{\link{points}}. } \item{col}{ a character vector of plotting colors. } \item{cex}{ a numerical single element vector giving the amount by which plotting text and symbols should be magnified relative to the default. } \item{mar}{ A numerical vector of the form c(bottom, left, top, right) which gives the number of lines of margin to be specified on the four sides of the plot.} \item{\dots}{ extra plotting arguments passed to \code{\link{plot.bio3d}} that effect the atomic fluctuations plot only. } } \details{ \code{plot.nma} produces an eigenvalue (or frequency) spectrum plot together with a plot of the atomic fluctuations. } \value{ Called for its effect. } \references{ Skjaerven, L. et al. (2014) \emph{BMC Bioinformatics} \bold{15}, 399. Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{nma}}, \code{\link{plot.bio3d}} } \examples{ ## Fetch structure pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) ## Calculate modes modes <- nma(pdb) plot(modes, sse=pdb) } \keyword{ hplot } bio3d/man/seqaln.Rd0000644000176200001440000001262212544562303013606 0ustar liggesusers\name{seqaln} \alias{seqaln} \title{ Sequence Alignment with MUSCLE} \description{ Create multiple alignments of amino acid or nucleotide sequences according to the method of Edgar. } \usage{ seqaln(aln, id=NULL, profile=NULL, exefile="muscle", outfile="aln.fa", protein=TRUE, seqgroup=FALSE, refine=FALSE, extra.args="", verbose=FALSE) } \arguments{ \item{aln}{ a sequence character matrix, as obtained from \code{\link{seqbind}}, or an alignment list object as obtained from \code{\link{read.fasta}}. } \item{id}{ a vector of sequence names to serve as sequence identifers. } \item{profile}{ a profile alignment of class \sQuote{fasta} (e.g. obtained from \code{\link{read.fasta}}). The alignment \code{aln} will be added to the profile. } \item{exefile}{ file path to the \sQuote{MUSCLE} program on your system (i.e. how is \sQuote{MUSCLE} invoked). Alternatively, \sQuote{CLUSTALO} can be used. } \item{outfile}{ name of \sQuote{FASTA} output file to which alignment should be written. } \item{protein}{ logical, if TRUE the input sequences are assumed to be protein not DNA or RNA. } \item{seqgroup}{ logical, if TRUE similar sequences are grouped together in the output. } \item{refine}{ logical, if TRUE the input sequences are assumed to already be aligned, and only tree dependent refinement is performed. } \item{extra.args}{ a single character string containing extra command line arguments for the alignment program. } \item{verbose}{ logical, if TRUE \sQuote{MUSCLE} warning and error messages are printed. } } \details{ Sequence alignment attempts to arrange the sequences of protein, DNA or RNA, to highlight regions of shared similarity that may reflect functional, structural, and/or evolutionary relationships between the sequences. Aligned sequences are represented as rows within a matrix. Gaps (\sQuote{-}) are inserted between the aminoacids or nucleotides so that equivalent characters are positioned in the same column. This function calls the \sQuote{MUSCLE} program, to perform a multiple sequence alignment, which MUST BE INSTALLED on your system and in the search path for executables. If you have a large number of input sequences (a few thousand), or they are very long, the default settings may be too slow for practical use. A good compromise between speed and accuracy is to run just the first two iterations of the \sQuote{MUSCLE} algorithm by setting the \code{extra.args} argument to \dQuote{-maxiters 2}. You can set \sQuote{MUSCLE} to improve an existing alignment by setting \code{refine} to TRUE. To inspect the sequence clustering used by \sQuote{MUSCLE} to produce alignments, include \dQuote{-tree2 tree.out} in the \code{extra.args} argument. You can then load the \dQuote{tree.out} file with the \sQuote{read.tree} function from the \sQuote{ape} package. \sQuote{CLUSTALO} can be used as an alternative to \sQuote{MUSCLE} by specifiying \code{exefile='clustalo'}. This might be useful e.g. when adding several sequences to a profile alignment. } \value{ Returns a list of class \code{"fasta"} with the following components: \item{ali}{ an alignment character matrix with a row per sequence and a column per equivalent aminoacid/nucleotide. } \item{id}{ sequence names as identifers.} \item{call}{ the matched call. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. \sQuote{MUSCLE} is the work of Edgar: Edgar (2004) \emph{Nuc. Acid. Res.} \bold{32}, 1792--1797. Full details of the \sQuote{MUSCLE} algorithm, along with download and installation instructions can be obtained from:\cr \url{http://www.drive5.com/muscle}. } \author{ Barry Grant } \note{ A system call is made to the \sQuote{MUSCLE} program, which must be installed on your system and in the search path for executables. } \seealso{ \code{\link{read.fasta}}, \code{\link{read.fasta.pdb}}, \code{\link{get.seq}}, \code{\link{seqbind}}, \code{\link{pdbaln}}, \code{\link{plot.fasta}}, \code{\link{blast.pdb}} } \examples{ \dontrun{ ##-- Basic sequence alignemnt seqs <- get.seq(c("4q21_A", "1ftn_A")) aln <- seqaln(seqs) ##-- add a sequence to the (profile) alignment seq <- get.seq("1tnd_A") aln <- seqaln(seq, profile=aln) ##-- Read a folder/directory of PDB files #pdb.path <- "my_dir_of_pdbs" #files <- list.files(path=pdb.path , # pattern=".pdb", # full.names=TRUE) ##-- Use online files files <- get.pdb(c("4q21","1ftn"), URLonly=TRUE) ##-- Extract and store sequences raw <- NULL for(i in 1:length(files)) { pdb <- read.pdb(files[i]) raw <- seqbind(raw, pdbseq(pdb) ) } ##-- Align these sequences aln <- seqaln(raw, id=files, outfile="seqaln.fa") ##-- Read Aligned PDBs storing coordinate data pdbs <- read.fasta.pdb(aln) ## Sequence identity seqidentity(aln) ## Note that all the above can be done with the pdbaln() function: #pdbs <- pdbaln(files) ##-- For identical sequences with masking use a custom matrix aa <- seqbind(c("X","C","X","X","A","G","K"), c("C","-","A","X","G","X","X","K")) aln <- seqaln(aln=aln, id=c("a","b"), outfile="temp.fas", protein=TRUE, extra.args= paste("-matrix", system.file("matrices/custom.mat", package="bio3d"), "-gapopen -3.0 ", "-gapextend -0.5", "-center 0.0") ) } } \keyword{ utilities } bio3d/man/cnapath.Rd0000644000176200001440000001202312632622153013732 0ustar liggesusers\name{cnapath} \alias{cnapath} \alias{summary.cnapath} \alias{print.cnapath} \alias{view.cnapath} \title{ Suboptimal Path Analysis for Correlation Networks } \description{ Find k shortest paths between a pair of nodes, source and sink, in a correlation network. } \usage{ cnapath(cna, from, to, k = 10, ncore = NULL, \dots) \method{summary}{cnapath}(object, \dots, pdb = NULL, label = NULL, col = NULL, plot = FALSE, concise = FALSE, cutoff = 0.1, normalize = TRUE) \method{print}{cnapath}(x, \dots) view.cnapath(x, pdb, out.prefix = "view.cnapath", spline = FALSE, colors = c("blue", "red"), launch = FALSE, \dots) } \arguments{ \item{cna}{ A \sQuote{cna} object obtained from \code{\link{cna}}. } \item{from}{ Integer, node id for the source. } \item{to}{ Integer, node id for the sink. } \item{k}{ Integer, number of suboptimal paths to identify. } \item{ncore}{ Number of CPU cores used to do the calculation. By default (NULL), use all detected CPU cores. } \item{\dots}{ Additional arguments passed to igraph function \code{\link[igraph:get.shortest.paths]{get.shortest.paths}} (in the function \code{cnapath}), passed to \code{summary.cnapath} (in \code{print.cnapath}), as additional paths for comparison (in \code{summary.cnapath}), or passed to the function \code{colorRamp} (in \code{view.cnapath}). } \item{object}{ A \sQuote{cnapath} class of object obtained from \code{\link{cnapath}}. Multiple \sQuote{pa} input is allowed for comparative statistical analysis in \code{\link{summary.cnapath}}. } \item{pdb}{ A \sQuote{pdb} class of object obtained from \code{\link{read.pdb}} and is used as the reference for node residue ids or for molecular visulaization in VMD. } \item{label}{ Character, labels for paths identified from different networks. } \item{col}{ colors for plotting statistical resutls for paths identified from different networks. } \item{plot}{ logical, if TRUE path length distribution and node degeneracy will be plotted. } \item{concise}{ logical, if TRUE only \sQuote{on path} residues will be displayed. } \item{cutoff}{ numeric, degeneracy cutoff for displaying nodes on paths. } \item{normalize}{ logical, if TRUE node degeneracy is defined by the percentage of total number of paths. } \item{x}{ A \sQuote{cnapath} class of object obtained from \code{\link{cnapath}}. } \item{out.prefix}{ prefix for the names of output files, \sQuote{view.vmd} and \sQuote{view.pdb}. } \item{spline}{ logical, if TRUE all paths are displayed as spline curves. } \item{colors}{ character vector or integer scalar, define path colors. If a character vector, passed to \code{colorRamp} function to generate the color scales. If an integer, color all paths the same way with VMD color ID equal to the integer. } \item{launch}{ logical, if TRUE VMD will be launched. } } \value{ The function \code{\link{cnapath}} returns a \sQuote{cnapath} class of list containing following three components: \item{path}{ a list object containing all identified suboptimal paths. Each entry of the list is a sequence of node ids for the path. } \item{epath}{ a list object containing all identified suboptimal paths. Each entry of the list is a sequence of edge ids for the path. } \item{dist}{ a numeric vector of all path lengths. } The function \code{\link{summary.cnapath}} with return a matrix of (normalized) node degeneracy for \sQuote{on path} residues. } \references{ Yen, J.Y. (1971) \emph{Management Science} \bold{17}, 712--716. } \author{ Xin-Qiu Yao } \seealso{ \code{\link{cna}}, \code{\link{cna.dccm}}, \code{\link[igraph:get.shortest.paths]{get.shortest.paths}}. } \examples{ \donttest{ # Redundant testing excluded attach(transducin) inds = match(c("1TND_A", "1TAG_A"), pdbs$id) npdbs <- trim(pdbs, row.inds=inds) gaps.res <- gap.inspect(npdbs$ali) modes <- nma(npdbs) cij <- dccm(modes) net <- cna(cij, cutoff.cij=0.3) # get paths pa1 <- cnapath(net[[1]], from = 314, to=172, k=50) pa2 <- cnapath(net[[2]], from = 314, to=172, k=50) # print the information of a path pa1 # print two paths simultaneously pas <- list(pa1, pa2) names(pas) <- c("GTP", "GDP") print.cnapath(pas) # Or, for the same effect, # summary(pa1, pa2, label=c("GTP", "GDP")) # replace node numbers with residue name and residue number in the PDB file pdb <- read.pdb("1tnd") pdb <- trim.pdb(pdb, atom.select(pdb, chain="A", resno=npdbs$resno[1, gaps.res$f.inds])) print.cnapath(pas, pdb=pdb) # plot path length distribution and node degeneracy print.cnapath(pas, pdb = pdb, col=c("red", "darkgreen"), plot=TRUE) # View paths in 3D molecular graphic with VMD #view.cnapath(pa1, pdb, launch = TRUE) #view.cnapath(pa1, pdb, colors = 7, launch = TRUE) #view.cnapath(pa1, pdb, spline=TRUE, colors=c("pink", "red"), launch = TRUE) #pdb2 <- read.pdb("1tag") #pdb2 <- trim.pdb(pdb2, atom.select(pdb2, chain="A", resno=npdbs$resno[2, gaps.res$f.inds])) #view.cnapath(pa2, pdb2, launch = TRUE) detach(transducin) } } \keyword{ utilities } bio3d/man/bounds.Rd0000644000176200001440000000263412412623040013605 0ustar liggesusers\name{bounds} \alias{bounds} \title{ Bounds of a Numeric Vector } \description{ Find the \sQuote{bounds} (i.e. start, end and length) of consecutive numbers within a larger set of numbers in a given vector. } \usage{ bounds(nums, dup.inds=FALSE, pre.sort=TRUE) } \arguments{ \item{nums}{ a numeric vector. } \item{dup.inds}{ logical, if TRUE the bounds of consecutive duplicated elements are returned. } \item{pre.sort}{ logical, if TRUE the input vector is ordered prior to bounds determination. } } \details{ This is a simple utility function useful for summarizing the contents of a numeric vector. For example: find the start position, end position and lengths of secondary structure elements given a vector of residue numbers obtained from a DSSP secondary structure prediction. By setting \sQuote{dup.inds} to TRUE then the indices of the first (start) and last (end) duplicated elements of the vector are returned. For example: find the indices of atoms belonging to a particular residue given a vector of residue numbers (see below). } \value{ Returns a three column matrix listing starts, ends and lengths. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \examples{ test <- c(seq(1,5,1),8,seq(10,15,1)) bounds(test) test <- rep(c(1,2,4), times=c(2,3,4)) bounds(test, dup.ind=TRUE) } \keyword{ utilities } bio3d/man/print.core.Rd0000644000176200001440000000250112412623040014367 0ustar liggesusers\name{print.core} \alias{print.core} \title{ Printing Core Positions and Returning Indices } \description{ Print method for core.find objects. } \usage{ \method{print}{core}(x, vol = NULL, ...) } \arguments{ \item{x}{ a list object obtained with the function \code{\link{core.find}}. } \item{vol}{ the maximal cumulative volume value at which core positions are detailed. } \item{...}{ additional arguments to \sQuote{print}. } } \value{ Returns a three component list of indices: \item{atom}{atom indices of core positions} \item{xyz}{xyz indices of core positions} \item{resno}{residue numbers of core positions} } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \note{ The produced \code{\link{plot.core}} function can be useful for deciding on the core/non-core boundary. } \seealso{ \code{\link{core.find}}, \code{\link{plot.core}}} \examples{ \dontrun{ ##-- Generate a small kinesin alignment and read corresponding structures pdbfiles <- get.pdb(c("1bg2","2ncd","1i6i","1i5s"), URLonly=TRUE) pdbs <- pdbaln(pdbfiles) ##-- Find 'core' positions core <- core.find(pdbs) plot(core) ##-- Fit on these relatively invarient subset of positions core.inds <- print(core, vol=0.5) print(core, vol=0.7) print(core, vol=1.0) } } \keyword{ utilities } bio3d/man/hmmer.Rd0000644000176200001440000001345012632622153013431 0ustar liggesusers\name{hmmer} \alias{hmmer} \title{ HMMER Sequence Search } \description{ Perform a HMMER search against the PDB, NR, swissprot or other sequence and structure databases. } \usage{ hmmer(seq, type="phmmer", db = NULL, verbose = TRUE, timeout = 90) } \arguments{ \item{seq}{ a multi-element character vector containing the query sequence. Alternatively a \sQuote{fasta} object as obtained from functions \code{get.seq} or \code{read.fasta} can be provided. } \item{type}{ character string specifying the \sQuote{HMMER} job type. Current options are \sQuote{phmmer}, \sQuote{hmmscan}, \sQuote{hmmsearch}, and \sQuote{jackhmmer}. } \item{db}{ character string specifying the database to search. Current options are \sQuote{pdb}, \sQuote{nr}, \sQuote{swissprot}, \sQuote{pfam}, etc. See \sQuote{details} for a complete list. } \item{verbose}{ logical, if TRUE details of the download process is printed. } \item{timeout}{ integer specifying the number of seconds to wait for the blast reply before a time out occurs. } } \details{ This function employs direct HTTP-encoded requests to the HMMER web server. HMMER can be used to search sequence databases for homolog protein sequences. The HMMER server implements methods using probabilistic models called profile hidden Markov models (profile HMMs). There are currently four types of HMMER search to perform: - \sQuote{phmmer}: protein sequence vs protein sequence database.\cr (input argument \code{seq} must be a sequence). Allowed options for \code{type} includes: \sQuote{env_nr}, \sQuote{nr}, \sQuote{refseq}, \sQuote{pdb}, \sQuote{rp15}, \sQuote{rp35}, \sQuote{rp55}, \sQuote{rp75}, \sQuote{swissprot}, \sQuote{unimes}, \sQuote{uniprotkb}, \sQuote{uniprotrefprot}, \sQuote{pfamseq}. - \sQuote{hmmscan}: protein sequence vs profile-HMM database.\cr (input argument \code{seq} must be a sequence). Allowed options for \code{type} includes: \sQuote{pfam}, \sQuote{gene3d}, \sQuote{superfamily}, \sQuote{tigrfam}. - \sQuote{hmmsearch}: protein alignment/profile-HMM vs protein sequence database.\cr (input argument \code{seq} must be an alignment). Allowed options for \code{type} includes: \sQuote{pdb}, \sQuote{swissprot}. - \sQuote{jackhmmer}: iterative search vs protein sequence database.\cr (input argument \code{seq} must be an alignment). \sQuote{jackhmmer} functionality incomplete!! Allowed options for \code{type} includes: \sQuote{env_nr}, \sQuote{nr}, \sQuote{refseq}, \sQuote{pdb}, \sQuote{rp15}, \sQuote{rp35}, \sQuote{rp55}, \sQuote{rp75}, \sQuote{swissprot}, \sQuote{unimes}, \sQuote{uniprotkb}, \sQuote{uniprotrefprot}, \sQuote{pfamseq}. More information can be found at the HMMER website:\cr \url{http://hmmer.janelia.org} } \value{ A data frame with multiple components depending on the selected job \sQuote{type}. Frequently reported fields include: \item{name}{ a character vector containg the name of the target. } \item{acc}{ a character vector containg the accession identifier of the target. } \item{acc2}{ a character vector containg secondary accession of the target. } \item{id}{ a character vector containg Identifier of the target } \item{desc}{ a character vector containg entry description. } \item{score}{ a numeric vector containg bit score of the sequence (all domains, without correction). } \item{pvalue}{ a numeric vector containg the P-value of the score. } \item{evalue}{ a numeric vector containg the E-value of the score. } \item{nregions}{ a numeric vector containg Number of regions evaluated. } \item{nenvelopes}{ a numeric vector containg the number of envelopes handed over for domain definition, null2, alignment, and scoring. } \item{ndom}{ a numeric vector containg the total number of domains identified in this sequence. } \item{nreported}{ a numeric vector containg the number of domains satisfying reporting thresholding. } \item{nincluded}{ a numeric vector containg the number of domains satisfying inclusion thresholding. } \item{taxid}{ a character vector containg The NCBI taxonomy identifier of the target (if applicable). } \item{species}{ a character vector containg the species name. } \item{kg}{ a character vector containg the kingdom of life that the target belongs to - based on placing in the NCBI taxonomy tree. } More details can be found at the HMMER website:\cr \url{http://www.ebi.ac.uk/Tools/hmmer/help/api} } \note{ Note that the chained \sQuote{pdbs} HMMER field (used for redundant PDBs) is included directly into the result list (applies only when \code{db='pdb'}). In this case, the \sQuote{name} component of the target contains the parent (non redundant) entry, and the \sQuote{acc} component the chained PDB identiers. The search results will therefore provide duplicated PDB identiers for component \code{$name}, while \code{$acc} should be unique. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. Finn, R.D. et al. (2011) \emph{Nucl. Acids Res.} \bold{39}, 29--37. Eddy, S.R. (2011) \emph{PLoS Comput Biol} \bold{7}(10): e1002195. See also the \sQuote{HMMER} website:\cr \url{http://hmmer.janelia.org} } \author{ Lars Skjaerven } \note{ Online access is required to query HMMER services. } \seealso{ \code{\link{seqaln}}, \code{\link{get.seq}} \code{\link{blast.pdb}}, \code{\link{pfam}} \code{\link{uniprot}} } \examples{ \dontrun{ # HMMER server connection required - testing excluded ##- PHMMER seq <- get.seq("2abl_A", outfile=tempfile()) res <- hmmer(seq, db="pdb") ##- HMMSCAN fam <- hmmer(seq, type="hmmscan", db="pfam") pfam.aln <- pfam(fam$acc[1]) ##- HMMSEARCH hmm <- hmmer(pfam.aln, type="hmmsearch", db="pdb") unique(hmm$species) hmm$acc } } \keyword{ utilities } bio3d/man/pdbaln.Rd0000644000176200001440000000453512632622153013565 0ustar liggesusers\name{pdbaln} \alias{pdbaln} \title{ Sequence Alignment of PDB Files } \description{ Create multiple sequences alignments from a list of PDB files returning aligned sequence and structure records. } \usage{ pdbaln(files, fit = FALSE, pqr = FALSE, ncore = 1, nseg.scale = 1, ...) } \arguments{ \item{files}{ a character vector of PDB file names. } \item{fit}{ logical, if TRUE coordinate superposition is performed on the input structures. } \item{pqr}{ logical, if TRUE the input structures are assumed to be in PQR format. } \item{ncore }{ number of CPU cores used to do the calculation. \code{ncore>1} requires package \sQuote{parallel} installed. } \item{nseg.scale }{ split input data into specified number of segments prior to running multiple core calculation. See \code{\link{fit.xyz}}. } \item{\dots}{ extra arguments passed to \code{seqaln} function. } } \details{ This wrapper function calls the underlying functions \code{read.pdb}, \code{pdbseq}, \code{seqaln} and \code{read.fasta.pdb} returning a list of class \code{"pdbs"} similar to that returned by \code{read.fasta.pdb}. As these steps are often error prone it is recomended for most cases that the individual underlying functions are called in sequence with checks made on the valadity of their respective outputs to ensure sensible results. } \value{ Returns a list of class \code{"pdbs"} with the following five components: \item{xyz}{numeric matrix of aligned C-alpha coordinates.} \item{resno}{character matrix of aligned residue numbers.} \item{b}{numeric matrix of aligned B-factor values.} \item{chain}{character matrix of aligned chain identifiers.} \item{id}{character vector of PDB sequence/structure names.} \item{ali}{character matrix of aligned sequences.} \item{call}{ the matched call. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \note{ See recommendation in details section above. } \seealso{ \code{\link{read.pdb}}, \code{\link{pdbseq}}, \code{\link{seqaln}}, \code{\link{read.fasta}},\code{\link{read.fasta.pdb}}, \code{\link{core.find}}, \code{\link{fit.xyz}}, \code{\link{read.all}} } \examples{ \dontrun{ #files <- get.pdb(c("4q21","5p21"), URLonly=TRUE) files <- get.pdb(c("4q21","5p21"), path=tempdir(), overwrite=TRUE) pdbaln(files) } } \keyword{ utilities } bio3d/man/write.pdb.Rd0000644000176200001440000000705612632622153014224 0ustar liggesusers\name{write.pdb} \alias{write.pdb} \title{ Write PDB Format Coordinate File } \description{ Write a Protein Data Bank (PDB) file for a given \sQuote{xyz} Cartesian coordinate vector or matrix. } \usage{ write.pdb(pdb = NULL, file = "R.pdb", xyz = pdb$xyz, type = NULL, resno = NULL, resid = NULL, eleno = NULL, elety = NULL, chain = NULL, insert = NULL, alt = NULL, o = NULL, b = NULL, segid = NULL, elesy = NULL, charge = NULL, append = FALSE, verbose = FALSE, chainter = FALSE, end = TRUE, print.segid = FALSE) } \arguments{ \item{pdb}{ a PDB structure object obtained from \code{\link{read.pdb}}. } \item{file}{ the output file name. } \item{xyz}{ Cartesian coordinates as a vector or 3xN matrix. } \item{type}{ vector of record types, i.e. "ATOM" or "HETATM", with length equal to length(xyz)/3. } \item{resno}{ vector of residue numbers of length equal to length(xyz)/3. } \item{resid}{ vector of residue types/ids of length equal to length(xyz)/3. } \item{eleno}{ vector of element/atom numbers of length equal to length(xyz)/3. } \item{elety}{ vector of element/atom types of length equal to length(xyz)/3. } \item{chain}{ vector of chain identifiers with length equal to length(xyz)/3. } \item{insert}{ vector of insertion code with length equal to length(xyz)/3. } \item{alt}{ vector of alternate record with length equal to length(xyz)/3. } \item{o}{ vector of occupancy values of length equal to length(xyz)/3. } \item{b}{ vector of B-factors of length equal to length(xyz)/3. } \item{segid}{ vector of segment id of length equal to length(xyz)/3. } \item{elesy}{ vector of element symbol of length equal to length(xyz)/3. } \item{charge}{ vector of atomic charge of length equal to length(xyz)/3. } \item{append}{ logical, if TRUE output is appended to the bottom of an existing file (used primarly for writing multi-model files). } \item{verbose}{ logical, if TRUE progress details are printed. } \item{chainter}{ logical, if TRUE a TER line is inserted at termination of a chain. } \item{end}{ logical, if TRUE END line is written. } \item{print.segid}{ logical, if FALSE segid will not be written. } } \details{ Only the \code{xyz} argument is strictly required. Other arguments assume a default poly-ALA C-alpha structure with a blank chain id, occupancy values of 1.00 and B-factors equal to 0.00. If the input argument \code{xyz} is a matrix then each row is assumed to be a different structure/frame to be written to a \dQuote{multimodel} PDB file, with frames separated by \dQuote{END} records. } \value{ Called for its effect. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. For a description of PDB format (version3.3) see:\cr \url{http://www.wwpdb.org/documentation/format33/v3.3.html}. } \author{ Barry Grant with contributions from Joao Martins. } \note{ Check that: (1) \code{chain} is one character long e.g. \dQuote{A}, and (2) \code{resno} and \code{eleno} do not exceed \dQuote{9999}. } \seealso{ \code{\link{read.pdb}}, \code{\link{read.dcd}}, \code{\link{read.fasta.pdb}}, \code{\link{read.fasta}} } \examples{ \donttest{ # PDB server connection required - testing excluded # Read a PDB file pdb <- read.pdb( "1bg2" ) # Renumber residues nums <- as.numeric(pdb$atom[,"resno"]) nums <- nums - (nums[1] - 1) # Write out renumbered PDB file outfile = file.path(tempdir(), "eg.pdb") write.pdb(pdb=pdb, resno = nums, file = outfile) invisible( cat("\nSee the output file:", outfile, sep = "\n") ) } } \keyword{ IO } bio3d/man/inspect.connectivity.Rd0000644000176200001440000000256412544562303016511 0ustar liggesusers\name{inspect.connectivity} \alias{inspect.connectivity} \title{ Check the Connectivity of Protein Structures } \description{ Investigate protein coordinates to determine if the structure has missing residues. } \usage{ inspect.connectivity(pdbs, cut=4.) } \arguments{ \item{pdbs}{ an object of class \code{3daling} as obtained from function \code{pdbaln} or \code{read.fasta.pdb}; a xyz matrix containing the cartesian coordinates of C-alpha atoms; or a \sQuote{pdb} object as obtained from function \code{read.pdb}. } \item{cut }{ cutoff value to determine residue connectvitiy. } } \details{ Utility function for checking if the PDB structures in a \sQuote{pdbs} object contains missing residues inside the structure. } \value{ Returns a vector. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{dm}}, \code{\link{gap.inspect}} } \examples{ \dontrun{ ## Fetch PDB files and split to chain A only PDB files ids <- c("1a70_A", "1czp_A", "1frd_A", "1fxi_A", "1iue_A", "1pfd_A") raw.files <- get.pdb(ids, path = "raw_pdbs") files <- pdbsplit(raw.files, ids, path = "raw_pdbs/split_chain") ## Sequence Alignement, and connectivity check pdbs <- pdbaln(files) cons <- inspect.connectivity(pdbs) ## omit files with missing residues files = files[cons] } } \keyword{ analysis } bio3d/man/mktrj.Rd0000644000176200001440000000645412632622153013456 0ustar liggesusers\name{mktrj} \alias{mktrj} \alias{mktrj.pca} \alias{mktrj.nma} \alias{mktrj.enma} \title{ PCA / NMA Atomic Displacement Trajectory } \description{ Make a trajectory of atomic displacments along a given principal component / normal mode. } \usage{ mktrj(...) \method{mktrj}{pca}(pca = NULL, pc = 1, mag = 1, step = 0.125, file = NULL, ...) \method{mktrj}{nma}(nma = NULL, mode = 7, mag = 10, step = 1.25, file = NULL, ...) \method{mktrj}{enma}(enma = NULL, pdbs = NULL, s.inds = NULL, m.inds = NULL, mag = 10, step = 1.25, file = NULL, rock = TRUE, ncore = NULL, ...) } \arguments{ \item{pca}{ an object of class \code{"pca"} as obtained with function \code{\link{pca.xyz}} or \code{\link{pca}}.} \item{nma}{ an object of class \code{"nma"} as obtained with function \code{\link{nma.pdb}}.} \item{enma}{ an object of class \code{"enma"} as obtained with function \code{\link{nma.pdbs}}.} \item{pc}{ the PC number along which displacements should be made.} \item{mag}{ a magnification factor for scaling the displacements. } \item{step}{ the step size by which to increment along the pc/mode. } \item{file}{ a character vector giving the output PDB file name. } \item{mode}{ the mode number along which displacements should be made.} \item{pdbs}{ a list object of class \code{"pdbs"} (obtained with \code{\link{pdbaln}} or \code{\link{read.fasta.pdb}}) which corresponds to the \code{"enma"} object.} \item{s.inds}{ index or indices pointing to the structure(s) in the \code{enma} object for which the trajectory shall be generated. } \item{m.inds}{ the mode number(s) along which displacements should be made. } \item{rock}{ logical, if TRUE the trajectory rocks. } \item{ncore }{ number of CPU cores used to do the calculation. \code{ncore>1} requires package \sQuote{parallel} installed. } \item{\dots}{ additional arguments passed to and from functions (e.g. to function \code{\link{write.pdb}}). } } \details{ Trajectory frames are built from reconstructed Cartesian coordinates produced by interpolating from the mean structure along a given \code{pc} or \code{mode}, in increments of \code{step}. An optional magnification factor can be used to amplify displacements. This involves scaling by \code{mag}-times the standard deviation of the conformer distribution along the given \code{pc} (i.e. the square root of the associated eigenvalue). } \note{ Molecular graphics software such as VMD or PyMOL is useful for viewing trajectories see e.g: \cr \url{http://www.ks.uiuc.edu/Research/vmd/}. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant, Lars Skjaerven } \seealso{ \code{\link{pca}}, \code{\link{nma}}, \code{\link{nma.pdbs}}, \code{\link{view.modes}}. } \examples{ \dontrun{ ##- PCA example attach(transducin) # Calculate principal components pc.xray <- pca(pdbs, fit=TRUE) # Write PC trajectory of pc=1 outfile = tempfile() a <- mktrj(pc.xray, file = outfile) outfile detach(transducin) ##- NMA example ## Fetch stucture pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) ## Calculate (vibrational) normal modes modes <- nma(pdb) ## Visualize modes outfile = file.path(tempdir(), "mode_7.pdb") mktrj(modes, mode=7, file = outfile) outfile } } \keyword{ utilities } bio3d/man/trim.xyz.Rd0000644000176200001440000000264512544562303014133 0ustar liggesusers\name{trim.xyz} \alias{trim.xyz} \title{ Trim a XYZ Object of Cartesian Coordinates. } \description{ Produce a new smaller XYZ object, containing a subset of atoms. } \usage{ \method{trim}{xyz}(xyz, row.inds = NULL, col.inds = NULL, \dots) } \arguments{ \item{xyz}{ a XYZ object containing Cartesian coordinates, e.g. obtained from \code{\link{read.pdb}}, \code{\link{read.ncdf}}. } \item{row.inds}{ a numeric vector specifying which rows of the xyz matrix to return. } \item{col.inds}{ a numeric vector specifying which columns of the xyz matrix to return. } \item{\dots}{ additional arguments passed to and from functions. } } \details{ This function provides basic functionality for subsetting a matrix of class \sQuote{xyz} while also maintaining the class attribute. } \value{ Returns an object of class \code{xyz} with the Cartesian coordinates stored in a matrix object with dimensions M x 3N, where N is the number of atoms, and M number of frames. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{read.pdb}}, \code{\link{as.xyz}}. } \examples{ \dontrun{ ## Read a PDB file from the RCSB online database pdb <- read.pdb("1bg2") ## Select calpha atoms sele <- atom.select(pdb, "calpha") ## Trim XYZ trim(pdb$xyz, col.inds=sele$xyz) ## Equals to pdb$xyz[, sele$xyz, drop=FALSE] } } \keyword{ utilities } bio3d/man/angle.xyz.Rd0000644000176200001440000000302712544562303014241 0ustar liggesusers\name{angle.xyz} \alias{angle.xyz} \title{ Calculate the Angle Between Three Atoms } \description{ A function for basic bond angle determination. } \usage{ angle.xyz(xyz, atm.inc = 3) } \arguments{ \item{xyz}{ a numeric vector of Cartisean coordinates. } \item{atm.inc}{ a numeric value indicating the number of atoms to increment by between successive angle evaluations (see below). } } \value{ Returns a numeric vector of angles. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \note{ With \code{atm.inc=1}, angles are calculated for each set of three successive atoms contained in \code{xyz} (i.e. moving along one atom, or three elements of \code{xyz}, between sucessive evaluations). With \code{atm.inc=3}, angles are calculated for each set of three successive non-overlapping atoms contained in \code{xyz} (i.e. moving along three atoms, or nine elements of \code{xyz}, between sucessive evaluations). } \seealso{ \code{\link{torsion.pdb}}, \code{\link{torsion.xyz}}, \code{\link{read.pdb}}, \code{\link{read.dcd}}. } \examples{ ## Read a PDB file pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) ## Angle between N-CA-C atoms of residue four inds <- atom.select(pdb, resno=4, elety=c("N","CA","C")) angle.xyz(pdb$xyz[inds$xyz]) ## Basic stats of all N-CA-C bound angles inds <- atom.select(pdb, elety=c("N","CA","C")) summary( angle.xyz(pdb$xyz[inds$xyz]) ) #hist( angle.xyz(pdb$xyz[inds$xyz]), xlab="Angle" ) } \keyword{ utilities } bio3d/man/plot.cna.Rd0000644000176200001440000001016112544562303014035 0ustar liggesusers\name{plot.cna} \alias{plot.cna} \title{ Protein Structure Network Plots in 2D and 3D. } \description{ Plot a protein dynamic network as obtained from the \emph{cna} function. } \usage{ \method{plot}{cna}(x, pdb = NULL, weights=NULL, vertex.size=NULL, layout=NULL, col=NULL, full=FALSE, scale=TRUE, color.edge = FALSE, ...) } \arguments{ \item{x}{ A protein network graph object as obtained from the \sQuote{cna} function. } \item{pdb}{ A PDB structure object obtained from \sQuote{read.pdb}. If supplied this will be used to guide the network plot \sQuote{layout}, see \sQuote{layout.cna} for details. } \item{weights}{ A numeric vector containing the edge weights for the network. } \item{vertex.size}{ A numeric vector of node/community sizes. If NULL the size will be taken from the input network graph object \sQuote{x}. Typically for \sQuote{full=TRUE} nodes will be of an equal size and for \sQuote{full=FALSE} community node size will be proportional to the residue membership of each community. } \item{layout}{ Either a function or a numeric matrix. It specifies how the vertices will be placed on the plot. See \sQuote{layout.cna}. } \item{col}{ A vector of colors used for node/vertex rendering. If NULL these values are taken from the input network \sQuote{V(x$community.network)$color}. } \item{full}{ Logical, if TRUE the full all-atom network rather than the clustered community network will be plotted. } \item{scale}{ Logical, if TRUE weights are scaled with respect to the network. } \item{color.edge}{ Logical, if TRUE edges are colored with respect to their weights. } \item{\dots}{ Additional graphical parameters for \sQuote{plot.igraph}. } } \details{ This function calls \sQuote{plot.igraph} from the igraph package to plot cna networks the way we like them. The plot layout is user settable, we like the options of: \sQuote{layout.cna}, \sQuote{layout.fruchterman.reingold}, \sQuote{layout.mds} or \sQuote{layout.svd}. Note that first of these uses PDB structure information to produce a more meaningful layout. Extensive plot modifications are possible by setting additional graphical parameters (\dots). These options are detailed in \sQuote{igraph.plotting}. Common parameters to alter include: \describe{ \item{vertex.label:}{Node labels, \code{V(x$network)$name}. Use NA to omit.} \item{vertex.label.color:}{Node label colors, see also \code{vertex.label.cex} etc. } \item{edge.color:}{Edge colors, \code{E(x$network)$color}. } \item{mark.groups:}{Community highlighting, a community list object, see also \code{mark.col} etc.} } } \value{ Produces a network plot on the active graphics device. Also returns the plot layout coordinates silently, which can be passed to the \sQuote{identify.cna} function. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant and Guido Scarabelli } \note{ Be sure to check the correspondence of your \sQuote{pdb} object with your network object \sQuote{x}, as few internal checks are currently performed by the \sQuote{layout.cna} function. } \seealso{ \code{\link[igraph:plot.igraph]{plot.igraph}}, \code{\link[igraph:plot.communities]{plot.communities}}, \code{\link[igraph:igraph.plotting]{igraph.plotting}} } \examples{ \donttest{ # PDB server connection required - testing excluded require(igraph) ##-- Build a CNA object pdb <- read.pdb("4Q21") modes <- nma(pdb) cij <- dccm(modes) net <- cna(cij, cutoff.cij=0.2) # Plot coarse grain network based on dynamically coupled communities xy <- plot.cna(net) #plot.dccm(cij, margin.segments=net$communities$membership) # Chose a different PDB informed layout for plot plot.cna(net, pdb) # Play with plot layout and colors... plot.cna(net, layout=layout.mds(net$community.network), col=c("blue","green") ) } \dontrun{ # Plot full residue network colored by communities - will be slow due to number of edges!! plot.cna(net, pdb, full=TRUE) # Alter plot settings plot.cna(net, pdb, full=TRUE, vertex.size=3, weights=1, vertex.label=NA) } } \keyword{ hplot } bio3d/man/pdb2aln.Rd0000644000176200001440000000611212526367344013652 0ustar liggesusers\name{pdb2aln} \alias{pdb2aln} \title{ Align a PDB structure to an existing alignment } \description{ Extract sequence from a PDB object and align it to an existing multiple sequence alignment that you wish keep intact. } \usage{ pdb2aln(aln, pdb, id="seq.pdb", aln.id=NULL, file="pdb2aln.fa", \dots) } \arguments{ \item{aln}{ an alignment list object with \code{id} and \code{ali} components, similar to that generated by \code{\link{read.fasta}}, \code{\link{read.fasta.pdb}}, and \code{\link{seqaln}}. } \item{pdb}{ the PDB object to be added to \code{aln}. } \item{id}{ name for the PDB sequence in the generated new alignment. } \item{aln.id}{ id of the sequence in \code{aln} that is close to the sequence from \code{pdb}. } \item{file}{ output file name for writing the generated new alignment. } \item{\dots}{ additional arguments passed to \code{\link{seqaln}}. } } \details{ The basic effect of this function is to add a PDB sequence to an existing alignement. In this case, the function is simply a wrapper of \code{\link{seq2aln}}. The more advanced (and also more useful) effect is giving complete mappings from the column indices of the original alignment (\code{aln$ali}) to atomic indices of equivalent C-alpha atoms in the \code{pdb}. These mappings are stored in the output list (see below 'Value' section). This feature is better illustrated in the function \code{\link{pdb2aln.ind}}, which calls \code{pdb2aln} and directly returns atom selections given a set of alignment positions. (See \code{\link{pdb2aln.ind}} for details. ) When \code{aln.id} is provided, the function will do pairwise alignment between the sequence from \code{pdb} and the sequence in \code{aln} with id matching \code{aln.id}. This is the best way to use the function if the protein has an identical or very similar sequence to one of the sequences in \code{aln}. } \value{ Return a list object of the class 'fasta' containing three components: \item{id}{ sequence names as identifers.} \item{ali}{ an alignment character matrix with a row per sequence and a column per equivalent aminoacid/nucleotide. } \item{ref}{ an integer 2xN matrix, where N is the number of columns of the new alignment \code{ali}. The first row contains the column indices of the original alignment \code{aln$ali}. The second row contains atomic indices of equivalent C-alpha atoms in \code{pdb}. Gaps in the new alignement are indicated by NAs. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Xin-Qiu Yao & Barry Grant } \seealso{ \code{\link{seqaln}}, \code{\link{seq2aln}}, \code{\link{seqaln.pair}}, \code{\link{pdb2aln.ind}}} \examples{ \dontrun{ ##--- Read aligned PDB coordinates (CA only) aln <- read.fasta(system.file("examples/kif1a.fa",package="bio3d")) pdbs <- read.fasta.pdb(aln) ##--- Read PDB coordinate for a new structure (all atoms) id <- get.pdb("2kin", URLonly=TRUE) pdb <- read.pdb(id) # add pdb to the alignment naln <- pdb2aln(aln=pdbs, pdb=pdb, id=id) naln } } \keyword{ utilities } bio3d/man/bwr.colors.Rd0000644000176200001440000000321712544562303014415 0ustar liggesusers\name{bwr.colors} \alias{bwr.colors} \alias{mono.colors} \title{ Color Palettes } \description{ Create a vector of \sQuote{n} \dQuote{contiguous} colors forming either a Blue-White-Red or a White-Gray-Black color palette. } \usage{ bwr.colors(n) mono.colors(n) } \arguments{ \item{n}{ the number of colors in the palette (>=1). } } \details{ The function \code{bwr.colors} returns a vector of \code{n} color names that range from blue through white to red. The function \code{mono.colors} returns color names ranging from white to black. Note: the first element of the returned vector will be NA. } \value{ Returns a character vector, \code{cv}, of color names. This can be used either to create a user-defined color palette for subsequent graphics with \code{palette(cv)}, or as a \code{col=} specification in graphics functions and \code{par}. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. The \code{bwr.colors} function is derived from the \code{gplots} package function \code{colorpanel} by Gregory R. Warnes. } \author{ Barry Grant } \seealso{ \code{\link{vmd.colors}}, \code{\link{cm.colors}}, \code{\link{colors}}, \code{\link{palette}}, \code{\link{hsv}}, \code{\link{rgb}}, \code{\link{gray}}, \code{\link{col2rgb}} } \examples{ \donttest{ # Redundant testing excluded # Color a distance matrix pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) d <- dm(pdb,"calpha") plot(d, color.palette=bwr.colors) plot(d, resnum.1 = pdb$atom[pdb$calpha,"resno"], color.palette = mono.colors, xlab="Residue Number", ylab="Residue Number") } } \keyword{ utilities } bio3d/man/read.pdb.Rd0000644000176200001440000001311512632622153013776 0ustar liggesusers\name{read.pdb} \alias{read.pdb} \alias{print.pdb} \alias{summary.pdb} \title{ Read PDB File} \description{ Read a Protein Data Bank (PDB) coordinate file. } \usage{ read.pdb(file, maxlines = -1, multi = FALSE, rm.insert = FALSE, rm.alt = TRUE, ATOM.only = FALSE, verbose = TRUE) \method{print}{pdb}(x, printseq=TRUE, \dots) \method{summary}{pdb}(object, printseq=FALSE, \dots) } \arguments{ \item{file}{ a single element character vector containing the name of the PDB file to be read, or the four letter PDB identifier for online file access. } \item{maxlines}{ the maximum number of lines to read before giving up with large files. By default if will read up to the end of input on the connection. } \item{multi}{ logical, if TRUE multiple ATOM records are read for all models in multi-model files and their coordinates returned. } \item{rm.insert}{ logical, if TRUE PDB insert records are ignored. } \item{rm.alt}{ logical, if TRUE PDB alternate records are ignored. } \item{ATOM.only}{ logical, if TRUE only ATOM/HETATM records are stored. Useful for speed enhancements with large files where secondary structure, biological unit and other remark records are not required. } \item{verbose}{ print details of the reading process. } \item{x}{ a PDB structure object obtained from \code{\link{read.pdb}}. } \item{object}{ a PDB structure object obtained from \code{\link{read.pdb}}. } \item{printseq}{ logical, if TRUE the PDB ATOM sequence will be printed to the screen. See also \code{\link{pdbseq}}. } \item{...}{ additional arguments to \sQuote{print}. } } \details{ \code{maxlines} may be set so as to restrict the reading to a portion of input files. Note that the preferred means of reading large multi-model files is via binary DCD or NetCDF format trajectory files (see the \code{\link{read.dcd}} and \code{\link{read.ncdf}} functions). } \value{ Returns a list of class \code{"pdb"} with the following components: \item{atom}{ a data.frame containing all atomic coordinate ATOM and HETATM data, with a row per ATOM/HETATM and a column per record type. See below for details of the record type naming convention (useful for accessing columns). } \item{helix }{ \sQuote{start}, \sQuote{end} and \sQuote{length} of H type sse, where start and end are residue numbers \dQuote{resno}. } \item{sheet }{ \sQuote{start}, \sQuote{end} and \sQuote{length} of E type sse, where start and end are residue numbers \dQuote{resno}. } \item{seqres }{ sequence from SEQRES field. } \item{xyz }{ a numeric matrix of class \code{"xyz"} containing the ATOM and HETATM coordinate data. } \item{calpha }{ logical vector with length equal to \code{nrow(atom)} with TRUE values indicating a C-alpha \dQuote{elety}. } \item{remark }{ a list object containing information taken from 'REMARK' records of a \code{"pdb"}. It can be used for building biological units (See \code{\link{biounit}}). } \item{call }{ the matched call. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. For a description of PDB format (version3.3) see:\cr \url{http://www.wwpdb.org/documentation/format33/v3.3.html}. } \author{ Barry Grant } \note{ For both \code{atom} and \code{het} list components the column names can be used as a convenient means of data access, namely: Atom serial number \dQuote{eleno} , Atom type \dQuote{elety}, Alternate location indicator \dQuote{alt}, Residue name \dQuote{resid}, Chain identifier \dQuote{chain}, Residue sequence number \dQuote{resno}, Code for insertion of residues \dQuote{insert}, Orthogonal coordinates \dQuote{x}, Orthogonal coordinates \dQuote{y}, Orthogonal coordinates \dQuote{z}, Occupancy \dQuote{o}, and Temperature factor \dQuote{b}. See examples for further details. } \seealso{ \code{\link{atom.select}}, \code{\link{write.pdb}}, \code{\link{trim.pdb}}, \code{\link{cat.pdb}}, \code{\link{read.prmtop}}, \code{\link{as.pdb}}, \code{\link{read.dcd}}, \code{\link{read.ncdf}}, \code{\link{read.fasta.pdb}}, \code{\link{read.fasta}}, \code{\link{biounit}} } \examples{ ## Read a PDB file from the RCSB online database #pdb <- read.pdb("4q21") ## Read a PDB file from those included with the package pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) ## Print a brief composition summary pdb ## Examine the storage format (or internal *str*ucture) str(pdb) ## Print data for the first four atom pdb$atom[1:4,] ## Print some coordinate data head(pdb$atom[, c("x","y","z")]) ## Or coordinates as a numeric vector head(pdb$xyz) ## Print C-alpha coordinates (can also use 'atom.select' function) head(pdb$atom[pdb$calpha, c("resid","elety","x","y","z")]) inds <- atom.select(pdb, elety="CA") head( pdb$atom[inds$atom, ] ) ## The atom.select() function returns 'indices' (row numbers) ## that can be used for accessing subsets of PDB objects, e.g. inds <- atom.select(pdb,"ligand") pdb$atom[inds$atom,] pdb$xyz[inds$xyz] ## See the help page for atom.select() function for more details. \dontrun{ ## Print SSE data for helix and sheet, ## see also dssp() and stride() functions print.sse(pdb) pdb$helix pdb$sheet$start ## Print SEQRES data pdb$seqres ## SEQRES as one letter code aa321(pdb$seqres) ## Where is the P-loop motif in the ATOM sequence inds.seq <- motif.find("G....GKT", pdbseq(pdb)) pdbseq(pdb)[inds.seq] ## Where is it in the structure inds.pdb <- atom.select(pdb,resno=inds.seq, elety="CA") pdb$atom[inds.pdb$atom,] pdb$xyz[inds.pdb$xyz] ## View in interactive 3D mode #view(pdb) } } \keyword{ IO } bio3d/man/print.fasta.Rd0000644000176200001440000000367312526367344014572 0ustar liggesusers\name{print.fasta} \alias{print.fasta} \alias{.print.fasta.ali} \title{ Printing Sequence Alignments } \description{ Print method for fasta and pdbs sequence alignment objects. } \usage{ \method{print}{fasta}(x, alignment=TRUE, ...) .print.fasta.ali(x, width = NULL, col.inds = NULL, numbers = TRUE, conservation=TRUE, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{ a sequence alignment object as obtained from the functions \code{\link{read.fasta}}, \code{\link{read.fasta.pdb}}, \code{\link{pdbaln}}, \code{\link{seqaln}}, etc. } \item{alignment}{ logical, if TRUE the sequence alignment will be printed to screen. } \item{width}{ a single numeric value giving the number of residues per printed sequence block. By default this is determined from considering alignment identifier widths given a standard 85 column terminal window. } \item{col.inds}{ an optional numeric vector that can be used to select subsets of alignment positions/columns for printing. } \item{numbers}{ logical, if TRUE position numbers and a tick-mark every 10 positions are printed above and below sequence blocks. } \item{conservation}{ logical, if TRUE conserved and semi-conserved columns in the alignment are marked with an \sQuote{*} and \sQuote{^}, respectively. } \item{\dots}{ additional arguments to \sQuote{.print.fasta.ali}. } } \value{ Called mostly for its effect but also silently returns block divided concatenated sequence strings as a matrix. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696.} \author{ Barry Grant } \seealso{ \code{\link{read.fasta}}, \code{\link{read.fasta.pdb}}, \code{\link{pdbaln}}, \code{\link{seqaln}} } \examples{ file <- system.file("examples/kif1a.fa",package="bio3d") aln <- read.fasta(file) print(aln) # print(aln, col.inds=30:100, numbers=FALSE) } \keyword{ utilities } bio3d/man/get.pdb.Rd0000644000176200001440000000433612544562303013651 0ustar liggesusers\name{get.pdb} \alias{get.pdb} \title{ Download PDB Coordinate Files } \description{ Downloads PDB coordinate files from the RCSB Protein Data Bank. } \usage{ get.pdb(ids, path = ".", URLonly=FALSE, overwrite = FALSE, gzip = FALSE, split = FALSE, verbose = TRUE, ncore = 1, ...) } \arguments{ \item{ids}{ A character vector of one or more 4-letter PDB codes/identifiers or 6-letter PDB-ID_Chain-ID of the files to be downloaded, or a \sQuote{blast} object containing \sQuote{pdb.id}. } \item{path}{ The destination path/directory where files are to be written. } \item{URLonly}{ logical, if TRUE a character vector containing the URL path to the online file is returned and files are not downloaded. If FALSE the files are downloaded. } \item{overwrite}{ logical, if FALSE the file will not be downloaded if it alread exist. } \item{gzip}{ logical, if TRUE the gzipped PDB will be downloaded and extracted locally. } \item{split}{ logical, if TRUE \code{\link{pdbsplit}} funciton will be called to split pdb files into separated chains. } \item{verbose}{ print details of the reading process. } \item{ncore}{ number of CPU cores used to do the calculation. \code{ncore>1} requires package \sQuote{parallel} installed. } \item{\dots}{ extra arguments passed to \code{\link{pdbsplit}} function. } } \details{ This is a basic function to automate file download from the PDB. } \value{ Returns a list of successfully downloaded files. Or optionally if URLonly is TRUE a list of URLs for said files. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. For a description of PDB format (version3.3) see:\cr \url{http://www.wwpdb.org/documentation/format33/v3.3.html}. } \author{ Barry Grant } \seealso{ \code{\link{read.pdb}}, \code{\link{write.pdb}}, \code{\link{atom.select}}, \code{\link{read.fasta.pdb}}, \code{\link{read.fasta}}, \code{\link{pdbsplit}} } \examples{ \donttest{ # PDB server connection required - testing excluded ## PDB file paths get.pdb( c("1poo", "1moo"), URLonly=TRUE ) ## These URLs can be used by 'read.pdb' pdb <- read.pdb( get.pdb("5p21", URL=TRUE) ) summary(pdb) ## Download PDB file ## get.pdb("5p21") } } \keyword{utilities} bio3d/man/layout.cna.Rd0000644000176200001440000000426712544562303014406 0ustar liggesusers\name{layout.cna} \alias{layout.cna} \title{ Protein Structure Network Layout } \description{ Determine protein structure network layout in 2D and 3D from the geometric center of each community. } \usage{ layout.cna(x, pdb, renumber=TRUE, k=2, full=FALSE) } \arguments{ \item{x}{ A protein structure network object as obtained from the \sQuote{cna} function. } \item{pdb}{ A pdb class object as obtained from the \sQuote{read.pdb} function. } \item{renumber}{ Logical, if TRUE the input \sQuote{pdb} will be re-numbered starting at residue number one before community coordinate averages are calculated. } \item{k}{ A single element numeric vector between 1 and 3 specifying the returned coordinate dimensions. } \item{full}{ Logical, if TRUE the full all-Calpha atom network coordinates will be returned rather than the default clustered network community coordinates. } } \details{ This function calculates the geometric center for each community from the atomic position of it's Calpha atoms taken from a corresponding PDB file. Care needs to be taken to ensure the PDB residue numbers and the community vector names/length match. The community residue membership are typically taken from the input network object but can be supplied as a list object with 'x$communities$membership'. } \value{ A numeric matrix of Nxk, where N is the number of communities and k the number of dimensions requested. } \author{ Guido Scarabelli and Barry Grant } \seealso{ \code{\link{plot.cna}}, \code{\link[igraph:plot.communities]{plot.communities}}, \code{\link[igraph:igraph.plotting]{igraph.plotting}}, \code{\link[igraph:plot.igraph]{plot.igraph}}} \examples{ # Load the correlation network attach(hivp) # Read the starting PDB file to determine atom correspondence pdbfile <- system.file("examples/hivp.pdb", package="bio3d") pdb <- read.pdb(pdbfile) # Plot will be slow #xy <- plot.cna(net) #plot3d.cna(net, pdb) layout.cna(net, pdb, k=3) layout.cna(net, pdb) # can be used as input to plot.cna and plot3d.cna.... # plot.cna( net, layout=layout.cna(net, pdb) ) # plot3d.cna(net, pdb, layout=layout.cna(net, pdb, k=3)) detach(hivp) } \keyword{ utility } bio3d/man/mustang.Rd0000644000176200001440000000576712544562303014015 0ustar liggesusers\name{mustang} \alias{mustang} \title{ Structure-based Sequence Alignment with MUSTANG } \description{ Create a multiple sequence alignment from a bunch of PDB files. } \usage{ mustang(files, exefile="mustang", outfile="aln.mustang.fa", cleanpdb=FALSE, cleandir="mustangpdbs", verbose=TRUE) } \arguments{ \item{files}{ a character vector of PDB file names. } \item{exefile}{ file path to the \sQuote{MUSTANG} program on your system (i.e. how is \sQuote{MUSTANG} invoked). } \item{outfile}{ name of \sQuote{FASTA} output file to which alignment should be written. } \item{cleanpdb}{ logical, if TRUE iterate over the PDB files and map non-standard residues to standard residues (e.g. SEP->SER..) to produce \sQuote{clean} PDB files. } \item{cleandir}{ character string specifying the directory in which the \sQuote{clean} PDB files should be written. } \item{verbose}{ logical, if TRUE \sQuote{MUSTANG} warning and error messages are printed. } } \details{ Structure-based sequence alignment with \sQuote{MUSTANG} attempts to arrange and align the sequences of proteins based on their 3D structure. This function calls the \sQuote{MUSTANG} program, to perform a multiple structure alignment, which MUST BE INSTALLED on your system and in the search path for executables. Note that non-standard residues are mapped to \dQuote{Z} in MUSTANG. As a workaround the bio3d \sQuote{mustang} function will attempt to map any non-standard residues to standard residues (e.g. SEP->SER, etc). To avoid this behaviour use \sQuote{cleanpdb=FALSE}. } \value{ A list with two components: \item{ali}{ an alignment character matrix with a row per sequence and a column per equivalent aminoacid. } \item{ids}{ sequence names as identifers.} } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. \sQuote{MUSTANG} is the work of Konagurthu et al: Konagurthu, A.S. et al. (2006) \emph{Proteins} \bold{64}(3):559--74. More details of the \sQuote{MUSTANG} algorithm, along with download and installation instructions can be obtained from:\cr \url{http://www.csse.monash.edu.au/~karun/Site/mustang.html}. } \author{ Lars Skjaerven } \note{ A system call is made to the \sQuote{MUSTANG} program, which must be installed on your system and in the search path for executables. } \seealso{ \code{\link{read.fasta}}, \code{\link{read.fasta.pdb}}, \code{\link{pdbaln}}, \code{\link{plot.fasta}}, \code{\link{seqaln}} } \examples{ \dontrun{ ## Fetch PDB files and split to chain A only PDB files ids <- c("1a70_A", "1czp_A", "1frd_A") files <- get.pdb(ids, split = TRUE, path = tempdir()) ##-- Or, read a folder/directory of existing PDB files #pdb.path <- "my_dir_of_pdbs" #files <- list.files(path=pdb.path , # pattern=".pdb", # full.names=TRUE) ##-- Align these PDB sequences aln <- mustang(files) ##-- Read Aligned PDBs storing coordinate data pdbs <- read.fasta.pdb(aln) } } \keyword{ utilities } bio3d/man/as.select.Rd0000644000176200001440000000177412544562303014212 0ustar liggesusers\name{as.select} \alias{as.select} \title{ Convert Atomic Indices to a Select Object } \description{ Convert atomic indices to a select object with \sQuote{atom} and \sQuote{xyz} components. } \usage{ as.select(x, \dots) } \arguments{ \item{x}{ a numeric vector containing atomic indices to be converted to a \sQuote{select} object. Alternatively, a logical vector can be provided. } \item{\dots}{ arguments passed to and from functions. } } \details{ Convert atomic indices to a select object with \sQuote{atom} and \sQuote{xyz} components. } \value{ Returns a list of class \code{"select"} with the following components: \item{atom}{ a numeric matrix of atomic indices. } \item{xyz }{ a numeric matrix of xyz indices. } \item{call }{ the matched call. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{atom.select}}, \code{\link{read.pdb}} } \examples{ as.select(c(1,2,3)) } \keyword{ utilities } bio3d/man/inner.prod.Rd0000644000176200001440000000227612526367344014415 0ustar liggesusers\name{inner.prod} \alias{inner.prod} \title{ Mass-weighted Inner Product } \description{ Inner product of vectors (mass-weighted if requested). } \usage{ inner.prod(x, y, mass=NULL) } \arguments{ \item{x}{ a numeric vector or matrix. } \item{y}{ a numeric vector or matrix. } \item{mass}{ a numeric vector containing the atomic masses for weighting. } } \details{ This function calculates the inner product between two vectors, or alternatively, the column-wise vector elements of matrices. If atomic masses are provided, the dot products will be mass-weighted. See examples for more details. } \value{ Returns the inner product(s). } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{nma} }, \code{\link{normalize.vector} } } \examples{ ## Matrix operations x <- 1:3 y <- diag(x) z <- matrix(1:9, ncol = 3, nrow = 3) inner.prod(x,y) inner.prod(y,z) ## Application to normal modes pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) ## Calculate (vibrational) normal modes modes <- nma(pdb) ## Check for orthogonality inner.prod(modes$U[,7], modes$U[,8]) } \keyword{ utilities } bio3d/man/pdbfit.Rd0000644000176200001440000000467112632622153013576 0ustar liggesusers\name{pdbfit} \alias{pdbfit} \alias{pdbfit.pdb} \alias{pdbfit.pdbs} \title{ PDB File Coordinate Superposition } \description{ Protein Databank Bank file coordinate superposition with the Kabsch algorithm. } \usage{ pdbfit(...) \method{pdbfit}{pdb}(pdb, inds = NULL, ...) \method{pdbfit}{pdbs}(pdbs, inds = NULL, outpath = NULL, ...) } \arguments{ \item{pdb}{ a multi-model pdb object of class \code{"pdb"}, as obtained from \code{read.pdb}. } \item{pdbs}{ a list of class \code{"pdbs"} containing PDB file data, as obtained from \code{read.fasta.pdb} or \code{pdbaln}. } \item{inds}{ a list object with a \sQuote{xyz} component with indices that selects the coordinate positions (in terms of x, y and z elements) upon which fitting should be based. This defaults to all equivalent non-gap positions for function \code{pdbfit.pdbs}, and to all calpha atoms for function \code{pdbfit.pdb}. } \item{outpath}{ character string specifing the output directory for optional coordinate file output. Note that full files (i.e. all atom files) are written, seebelow. } \item{\dots}{ extra arguments passed to \code{fit.xyz} function. } } \details{ The function \code{pdbfit} is a wrapper for the function \code{fit.xyz}, wherein full details of the superposition procedure are documented. Input to \code{pdbfit.pdbs} should be a list object obtained with the function \code{\link{read.fasta.pdb}} or \code{\link{pdbaln}}. See the examples below. For function \code{pdbfit.pdb} the input should be a multi-model \code{pdb} object with multiple (>1) frames in the \sQuote{xyz} component. The reference frame for supperposition (i.e. the fixed structure to which others are superposed) is the first entry in the input \code{"pdbs"} object. For finer control use \code{\link{fit.xyz}}. } \value{ Returns moved coordinates. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. Kabsch \emph{Acta Cryst} (1978) \bold{A34}, 827--828. } \author{ Barry Grant } \seealso{ \code{\link{pdbaln}}, \code{\link{read.fasta.pdb}}, \code{\link{fit.xyz}}, \code{\link{rmsd}}, \code{\link{read.pdb}} } \examples{ \dontrun{ #files <- get.pdb(c("4q21","5p21"), URLonly=TRUE) files <- get.pdb(c("4q21","5p21"), path=tempdir(), overwrite=TRUE) pdbs <- pdbaln(files) xyz <- pdbfit(pdbs) # Superpose again this time outputing PDBs #xyz <- pdbaln( files, outpath="fitted" ) } } \keyword{ utilities } bio3d/man/read.fasta.pdb.Rd0000644000176200001440000000623712632622153015102 0ustar liggesusers\name{read.fasta.pdb} \alias{read.fasta.pdb} \title{ Read Aligned Structure Data } \description{ Read aligned PDB structures and store their C-alpha atom data, including xyz coordinates, residue numbers, residue type and B-factors. } \usage{ read.fasta.pdb(aln, prefix = "", pdbext = "", fix.ali = FALSE, ncore = 1, nseg.scale = 1, ...) } \arguments{ \item{aln}{ an alignment data structure obtained with \code{\link{read.fasta}}. } \item{prefix}{ prefix to aln$id to locate PDB files. } \item{pdbext}{ the file name extention of the PDB files. } \item{fix.ali}{ logical, if TRUE check consistence between \code{$ali} and \code{$resno}, and correct \code{$ali} if they don't match. } \item{ncore }{ number of CPU cores used to do the calculation. \code{ncore>1} requires package \sQuote{parallel} installed. } \item{nseg.scale }{ split input data into specified number of segments prior to running multiple core calculation. See \code{\link{fit.xyz}}. } \item{\dots}{ other parameters for \code{\link{read.pdb}}. } } \details{ The input \code{aln}, produced with \code{\link{read.fasta}}, must have identifers (i.e. sequence names) that match the PDB file names. For example the sequence corresponding to the structure \dQuote{1bg2.pdb} should have the identifer \sQuote{1bg2}. See examples below. Sequence miss-matches will generate errors. Thus, care should be taken to ensure that the sequences in the alignment match the sequences in their associated PDB files. } \value{ Returns a list of class \code{"pdbs"} with the following five components: \item{xyz}{numeric matrix of aligned C-alpha coordinates.} \item{resno}{character matrix of aligned residue numbers.} \item{b}{numeric matrix of aligned B-factor values.} \item{chain}{character matrix of aligned chain identifiers.} \item{id}{character vector of PDB sequence/structure names.} \item{ali}{character matrix of aligned sequences.} \item{resid}{character matrix of aligned 3-letter residue names.} \item{sse}{character matrix of aligned helix and strand secondary structure elements as defined in each PDB file.} \item{call}{ the matched call. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \note{ The sequence character \sQuote{X} is useful for masking unusual or unknown residues, as it can match any other residue type. } \seealso{ \code{\link{read.fasta}}, \code{\link{read.pdb}}, \code{\link{core.find}}, \code{\link{fit.xyz}}, \code{\link{read.all}} } \examples{ \donttest{ # Redundant testing excluded # Read sequence alignment file <- system.file("examples/kif1a.fa",package="bio3d") aln <- read.fasta(file) # Read aligned PDBs pdbs <- read.fasta.pdb(aln) # Structure/sequence names/ids basename( pdbs$id ) # Alignment positions 335 to 339 pdbs$ali[,335:339] pdbs$resid[,335:339] pdbs$resno[,335:339] pdbs$b[,335:339] # Alignment C-alpha coordinates for these positions pdbs$xyz[, atom2xyz(335:339)] # See 'fit.xyz()' function for actual coordinate superposition # e.g. fit to first structure # xyz <- fit.xyz(pdbs$xyz[1,], pdbs) # xyz[, atom2xyz(335:339)] } } \keyword{ IO } bio3d/man/print.xyz.Rd0000644000176200001440000000137612526367344014324 0ustar liggesusers\name{print.xyz} \alias{print.xyz} \title{ Printing XYZ coordinates } \description{ Print method for objects of class \sQuote{xyz}. } \usage{ \method{print}{xyz}(x, ...) } \arguments{ \item{x}{ a \sQuote{xyz} object indicating 3-D coordinates of biological molecules. } \item{\dots}{ additional arguments passed to \sQuote{print}. } } \value{ Called for its effect. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696.} \author{ Barry Grant } \seealso{ \code{\link{is.xyz}}, \code{\link{read.ncdf}}, \code{\link{read.pdb}}, \code{\link{read.dcd}}, \code{\link{fit.xyz}} } \examples{ # Read a PDB file pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) print(pdb$xyz) } \keyword{ utilities } bio3d/man/plot.cmap.Rd0000644000176200001440000001015012526367344014222 0ustar liggesusers\name{plot.cmap} \alias{plot.cmap} \title{ Plot Contact Matrix } \description{ Plot a contact matrix with optional secondary structure in the marginal regions. } \usage{ \method{plot}{cmap}(x, col=2, pch=16, main="Contact map", sub="", xlim=NULL, ylim=NULL, xlab = "Residue index", ylab = xlab, axes=TRUE, ann=par("ann"), sse=NULL, sse.type="classic", sse.min.length=5, bot=TRUE, left=TRUE, helix.col="gray20", sheet.col="gray80", sse.border=FALSE, add=FALSE, ...) } \arguments{ \item{x}{ a numeric matrix of residue contacts as obtained from function \code{cmap}. } \item{col}{ color code or name, see \code{par}. } \item{pch}{ plotting \sQuote{character}, i.e., symbol to use. This can either be a single character or an integer code for one of a set of graphics symbols. See \code{points}. } \item{main}{ a main title for the plot, see also \sQuote{title}. } \item{sub}{ a sub-title for the plot. } \item{xlim}{ the x limits (x1,x2) of the plot. Note that x1 > x2 is allowed and leads to a reversed axis. } \item{ylim}{ the y limits of the plot. } \item{xlab}{ a label for the x axis, defaults to a description of \sQuote{x}. } \item{ylab}{ a label for the y axis, defaults to a description of \sQuote{y}. } \item{axes}{ a logical value indicating whether both axes should be drawn on the plot. Use graphical parameter \sQuote{xaxt} or \sQuote{yaxt} to suppress just one of the axes. } \item{ann}{ a logical value indicating whether the default annotation (title and x and y axis labels) should appear on the plot. } \item{sse}{ secondary structure object as returned from \code{\link{dssp}}, \code{\link{stride}} or in certain cases \code{\link{read.pdb}}. } \item{sse.type}{ single element character vector that determines the type of secondary structure annotation drawn. The following values are possible, \sQuote{classic} and \sQuote{fancy}. See details and examples below. } \item{sse.min.length}{ a single numeric value giving the length below which secondary structure elements will not be drawn. This is useful for the exclusion of short helix and strand regions that can often crowd these forms of plots. } \item{left}{ logical, if TRUE rectangles for each sse are drawn towards the left of the plotting region. } \item{bot}{ logical, if TRUE rectangles for each sse are drawn towards the bottom of the plotting region. } \item{helix.col}{ The colors for rectangles representing alpha helices. } \item{sheet.col}{ The colors for rectangles representing beta strands. } \item{sse.border}{ The border color for all sse rectangles. } \item{add}{ logical, specifying if the contact map should be added to an already existing plot. Note that when \sQuote{TRUE} only points are plotted (no annotation). } \item{\dots}{ other graphical parameters. } } \details{ This function is useful for plotting a residue-residue contact data for a given protein structure along with a schematic representation of major secondary structure elements. Two forms of secondary structure annotation are available: so called \sQuote{classic} and \sQuote{fancy}. The former draws marginal rectangles and has been available within Bio3D from version 0.1. The later draws more \sQuote{fancy} (and distracting) 3D like helices and arrowed strands. } \value{ Called for its effect. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven, Barry Grant } \note{ Be sure to check the correspondence of your \sQuote{sse} object with the \sQuote{x} values being plotted as no internal checks are performed. } \seealso{ \code{\link{cmap}}, \code{\link{dm}}, \code{\link{plot.dmat}}, \code{\link{plot.default}}, \code{\link{plot.bio3d}}, \code{\link{dssp}}, \code{\link{stride}} } \examples{ ##- Read PDB file pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) ##- Calcualte contact map cm <- cmap(pdb) ##- Plot contact map plot.cmap(cm, sse=pdb) ##- Add to plot plot.cmap(t(cm), col=3, pch=17, add=TRUE) } \keyword{ hplot } bio3d/man/atom.select.Rd0000644000176200001440000001412612632622153014540 0ustar liggesusers\name{atom.select} \alias{atom.select} \alias{atom.select.pdb} \alias{atom.select.prmtop} \alias{print.select} \title{ Atom Selection from PDB and PRMTOP Structure Objects } \description{ Return the \sQuote{atom} and \sQuote{xyz} coordinate indices of \sQuote{pdb} or \sQuote{prmtop} structure objects corresponding to the intersection of a hierarchical selection. } \usage{ atom.select(\dots) \method{atom.select}{pdb}(pdb, string = NULL, type = NULL, eleno = NULL, elety = NULL, resid = NULL, chain = NULL, resno = NULL, segid = NULL, operator = "AND", inverse = FALSE, value = FALSE, verbose=FALSE, \dots) \method{atom.select}{prmtop}(prmtop, ...) \method{print}{select}(x, \dots) } \arguments{ \item{\dots}{ arguments passed to \code{atom.select.pdb}, \code{atom.select.prmtop}, or \code{print}. } \item{pdb}{ a structure object of class \code{"pdb"}, obtained from \code{\link{read.pdb}}. } \item{string}{ a single selection keyword from \code{calpha} \code{cbeta} \code{backbone} \code{protein} \code{nucleic} \code{ligand} \code{water} \code{h} or \code{noh}. } \item{type}{ a single element character vector for selecting \sQuote{ATOM} or \sQuote{HETATM} record types. } \item{eleno}{ a numeric vector of element numbers. } \item{elety}{ a character vector of atom names. } \item{resid}{ a character vector of residue name identifiers. } \item{chain}{ a character vector of chain identifiers. } \item{resno}{ a numeric vector of residue numbers. } \item{segid}{ a character vector of segment identifiers. } \item{operator}{ a single element character specifying either the AND or OR operator by which individual selection components should be combined. Allowed values are \sQuote{"AND"} and \sQuote{"OR"}. } \item{verbose}{ logical, if TRUE details of the selection are printed. } \item{inverse}{ logical, if TRUE the inversed selection is retured (i.e. all atoms NOT in the selection). } \item{value}{ logical, if FALSE, vectors containing the (integer) indices of the matches determined by \code{atom.select} are returned, and if TRUE, a \code{pdb} object containing the matching atoms themselves is returned. } \item{prmtop}{ a structure object of class \code{"prmtop"}, obtained from \code{\link{read.prmtop}}. } \item{x}{ a atom.select object as obtained from \code{\link{atom.select}}. } } \details{ This function allows for the selection of atom and coordinate data corresponding to the intersection of various input criteria. Input selection criteria include selection \code{string} keywords (such as \code{"calpha"}, \code{"backbone"}, \code{"protein"}, \code{"nucleic"}, \code{"ligand"}, etc.) and individual named selection components (including \sQuote{chain}, \sQuote{resno}, \sQuote{resid}, \sQuote{elety} etc.). For example, \code{atom.select(pdb, "calpha")} will return indices for all C-alpha (CA) atoms found in protein residues in the \code{pdb} object, \code{atom.select(pdb, "backbone")} will return indices for all protein N,CA,C,O atoms, and \code{atom.select(pdb, "cbeta")} for all protein N,CA,C,O,CB atoms. Note that keyword \code{string} shortcuts can be combined with individual selection components, e.g. \code{atom.select(pdb, "protein", chain="A")} will select all protein atoms found in chain A. Selection criteria are combined according to the provided \code{operator} argument. The default operator \code{AND} (or \code{&}) will combine by intersection while \code{OR} (or \code{|}) will take the union. For example, \code{atom.select(pdb, "protein", elety=c("N", "CA", "C"), resno=65:103)} will select the N, CA, C atoms in the protein residues 65 through 103, while \code{atom.select(pdb, "protein", resid="ATP", operator="OR")} will select all protein atoms as well as any ATP residue(s). Other \code{string} shortcuts include: \code{"calpha"}, \code{"back"}, \code{"backbone"}, \code{"cbeta"}, \code{"protein"}, \code{"notprotein"}, \code{"ligand"}, \code{"water"}, \code{"notwater"}, \code{"h"}, \code{"noh"}, \code{"nucleic"}, and \code{"notnucleic"}. In addition, the \code{\link{combine.select}} function can further combine atom selections using \sQuote{AND}, \sQuote{OR}, or \sQuote{NOT} logical operations. } \note{ Protein atoms are defined as any atom in a residue matching the residue name in the attached \code{aa.table} data frame. See \code{aa.table$aa3} for a complete list of residue names. Nucleic atoms are defined as all atoms found in residues with names A, U, G, C, T, I, DA, DU, DG, DC, DT, or DI. Water atoms/residues are defined as those with residue names H2O, OH2, HOH, HHO, OHH, SOL, WAT, TIP, TIP, TIP3, or TIP4. } \value{ Returns a list of class \code{"select"} with the following components: \item{atom}{ a numeric matrix of atomic indices. } \item{xyz }{ a numeric matrix of xyz indices. } \item{call }{ the matched call. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant, Lars Skjaerven } \seealso{ \code{\link{read.pdb}}, \code{\link{as.select}}, \code{\link{combine.select}}, \code{\link{trim.pdb}}, \code{\link{write.pdb}}, \code{\link{read.prmtop}}, \code{\link{read.crd}}, \code{\link{read.dcd}}, \code{\link{read.ncdf}}. } \examples{ ##- PDB example # Read a PDB file pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) # Select protein atoms of chain A atom.select(pdb, "protein", chain="A") # Select all atoms except from the protein atom.select(pdb, "protein", inverse=TRUE, verbose=TRUE) # Select all C-alpha atoms with residues numbers between 43 and 54 sele <- atom.select(pdb, "calpha", resno=43:54, verbose=TRUE) # Access the PDB data with the selection indices print( pdb$atom[ sele$atom, "resid" ] ) print( pdb$xyz[ sele$xyz ] ) # Trim PDB to selection ca.pdb <- trim.pdb(pdb, sele) \dontrun{ ##- PRMTOP example prmtop <- read.prmtop("prot_solvated.prmtop") ## Atom selection ca.inds <- atom.select(prmtop, "calpha") } } \keyword{utilities} bio3d/man/seq2aln.Rd0000644000176200001440000000413612526367344013701 0ustar liggesusers\name{seq2aln} \alias{seq2aln} \title{ Add a Sequence to an Existing Alignmnet } \description{ Add one or more sequences to an existing multiple alignment that you wish to keep intact. } \usage{ seq2aln(seq2add, aln, id = "seq", file = "aln.fa", \dots) } \arguments{ \item{seq2add}{ an sequence character vector or an alignment list object with \code{id} and \code{ali} components, similar to that generated by \code{\link{read.fasta}} and \code{\link{seqaln}}. } \item{aln}{ an alignment list object with \code{id} and \code{ali} components, similar to that generated by \code{\link{read.fasta}} and \code{\link{seqaln}}. } \item{id}{ a vector of sequence names to serve as sequence identifers. } \item{file}{ name of \sQuote{FASTA} output file to which alignment should be written. } \item{\dots}{ additional arguments passed to \code{\link{seqaln}}. } } \details{ This function calls the \sQuote{MUSCLE} program, to perform a profile profile alignment, which MUST BE INSTALLED on your system and in the search path for executables. } \value{ A list with two components: \item{ali}{ an alignment character matrix with a row per sequence and a column per equivalent aminoacid/nucleotide. } \item{id}{ sequence names as identifers.} } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. \sQuote{MUSCLE} is the work of Edgar: Edgar (2004) \emph{Nuc. Acid. Res.} \bold{32}, 1792--1797. Full details of the \sQuote{MUSCLE} algorithm, along with download and installation instructions can be obtained from:\cr \url{http://www.drive5.com/muscle}. } \author{ Barry Grant } \note{ A system call is made to the \sQuote{MUSCLE} program, which must be installed on your system and in the search path for executables. } \seealso{ \code{\link{seqaln}}, \code{\link{read.fasta}}, \code{\link{read.fasta.pdb}}, \code{\link{seqbind}} } \examples{ \dontrun{ aa.1 <- pdbseq( read.pdb("1bg2") ) aa.2 <- pdbseq( read.pdb("3dc4") ) aa.3 <- pdbseq( read.pdb("1mkj") ) aln <- seqaln( seqbind(aa.1,aa.2) ) seq2aln(aa.3, aln) } } \keyword{ utilities } bio3d/man/binding.site.Rd0000644000176200001440000000644712544562303014710 0ustar liggesusers\name{binding.site} \alias{binding.site} \title{ Binding Site Residues } \description{ Determines the interacting residues between two PDB entities. } \usage{ binding.site(a, b=NULL, a.inds=NULL, b.inds=NULL, cutoff=5, hydrogens=TRUE, byres=TRUE, verbose=FALSE) } \arguments{ \item{a}{ an object of class \code{pdb} as obtained from function \code{read.pdb}. } \item{b}{ an object of class \code{pdb} as obtained from function \code{read.pdb}. } \item{a.inds}{ atom and xyz coordinate indices obtained from \code{atom.select} that selects the elements of \code{a} upon which the calculation should be based.} \item{b.inds}{ atom and xyz coordinate indices obtained from \code{atom.select} that selects the elements of \code{b} upon which the calculation should be based.} \item{cutoff}{ distance cutoff } \item{hydrogens}{ logical, if FALSE hydrogen atoms are omitted from the calculation. } \item{byres}{ logical, if TRUE all atoms in a contacting residue is returned. } \item{verbose}{ logical, if TRUE details of the selection are printed. } } \details{ This function reports the residues of \code{a} closer than a cutoff to \code{b}. This is a wrapper function calling the underlying function \code{dist.xyz}. If \code{b=NULL} then \code{b.inds} should be elements of \code{a} upon which the calculation is based (typically chain A and B of the same PDB file). If \code{b=a.inds=b.inds=NULL} the function will use \code{\link{atom.select}} with arguments \code{"protein"} and \code{"ligand"} to determine receptor and ligand, respectively. } \value{ Returns a list with the following components: \item{inds}{ object of class \code{select} with \code{atom} and \code{xyz} components. } \item{inds$atom}{ atom indices of \code{a}. } \item{inds$xyz}{ xyz indices of \code{a}. } \item{resnames}{ a character vector of interacting residues. } \item{resno}{ a numeric vector of interacting residues numbers. } \item{chain}{ a character vector of the associated chain identifiers of \code{"resno"}. } \item{call}{ the matched call. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{read.pdb}}, \code{\link{atom.select}}, \code{\link{dm}} } \examples{ \donttest{ # PDB server connection required - testing excluded pdb <- read.pdb('3dnd') ## automatically identify 'protein' and 'ligand' bs <- binding.site(pdb) bs$resnames #pdb$atom[bs$inds$atom, ] # provide indices rec.inds <- atom.select(pdb, chain="A", resno=1:350) lig.inds <- atom.select(pdb, chain="A", resno=351) bs <- binding.site(pdb, a.inds=rec.inds, b.inds=lig.inds) } \dontrun{ # Interaction between peptide and protein rec.inds <- atom.select(pdb, chain='A', resno=c(1:350)) lig.inds <- atom.select(pdb, chain='I', resno=c(5:24)) bs <- binding.site(pdb, a.inds=rec.inds, b.inds=lig.inds) } \donttest{ # Redundant testing excluded # Interaction between two PDB entities #rec <- read.pdb("receptor.pdb") #lig <- read.pdb("ligand.pdb") rec <- trim.pdb(pdb, inds=rec.inds) lig <- trim.pdb(pdb, inds=lig.inds) bs <- binding.site(rec, lig, hydrogens=FALSE) } } \keyword{ utilities } bio3d/man/seqaln.pair.Rd0000644000176200001440000000412312526367344014545 0ustar liggesusers\name{seqaln.pair} \alias{seqaln.pair} \title{ Sequence Alignment of Identical Protein Sequences } \description{ Create multiple alignments of amino acid sequences according to the method of Edgar. } \usage{ seqaln.pair(aln, \dots) } \arguments{ \item{aln}{ a sequence character matrix, as obtained from \code{\link{seqbind}}, or an alignment list object as obtained from \code{\link{read.fasta}}. } \item{\dots}{ additional arguments for the function \code{\link{seqaln}}. } } \details{ This function is intended for the alignment of identical sequences only. For standard alignment see the related function \code{\link{seqaln}}. This function is useful for determining the equivalences between sequences and structures. For example in aligning a PDB sequence to an existing multiple sequence alignment, where one would first mask the alignment sequences and then run the alignment to determine equivalences. } \value{ A list with two components: \item{ali}{ an alignment character matrix with a row per sequence and a column per equivalent aminoacid/nucleotide. } \item{ids}{ sequence names as identifers.} } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. \sQuote{MUSCLE} is the work of Edgar: Edgar (2004) \emph{Nuc. Acid. Res.} \bold{32}, 1792--1797. Full details of the \sQuote{MUSCLE} algorithm, along with download and installation instructions can be obtained from:\cr \url{http://www.drive5.com/muscle}. } \author{ Barry Grant } \note{ A system call is made to the \sQuote{MUSCLE} program, which must be installed on your system and in the search path for executables. } \seealso{ \code{\link{seqaln}}, \code{\link{read.fasta}}, \code{\link{read.fasta.pdb}}, \code{\link{seqbind}} } \examples{ ## NOTE: FOLLOWING EXAMPLE NEEDS MUSCLE INSTALLED if(check.utility("muscle")) { ##- Aligning a PDB sequence to an existing sequence alignment ##- Simple example aln <- seqbind(c("X","C","X","X","A","G","K"), c("C","-","A","X","G","X","X","K")) seqaln.pair(aln, outfile = tempfile()) } } \keyword{ utilities } bio3d/man/store.atom.Rd0000644000176200001440000000142412412623040014402 0ustar liggesusers\name{store.atom} \alias{store.atom} \title{ Store all-atom data from a PDB object } \description{ Not intended for public usage } \usage{ store.atom(pdb) } \arguments{ \item{pdb}{ A pdb object as obtained from read.pdb } } \details{ This function was requested by a user and has not been extensively tested. Hence it is not yet recommended for public usage. } \value{ Returns a matrix of all-atom data } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \note{ This function is still in development and is NOT part of the offical bio3d package } \seealso{ \code{\link{read.fasta.pdb}} } \examples{ \dontrun{ pdb <- read.pdb( get.pdb("5p21", URLonly=TRUE) ) a <- store.atom(pdb) a[,,1:2] } } \keyword{ utilities } bio3d/man/pca.tor.Rd0000644000176200001440000000302112526367344013672 0ustar liggesusers\name{pca.tor} \alias{pca.tor} \title{ Principal Component Analysis } \description{ Performs principal components analysis (PCA) on torsion angle \code{data}. } \usage{ \method{pca}{tor}(data, \dots) } \arguments{ \item{data}{ numeric matrix of torsion angles with a row per structure. } \item{\dots}{ additional arguments passed to the method \code{pca.xyz}. } } \value{ Returns a list with the following components: \item{L }{eigenvalues.} \item{U }{eigenvectors (i.e. the variable loadings).} \item{z.u }{scores of the supplied \code{data} on the pcs.} \item{sdev }{the standard deviations of the pcs.} \item{mean }{the means that were subtracted.} } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant and Karim ElSawy } \seealso{ \code{\link{torsion.xyz}}, \code{\link{plot.pca}}, \code{\link{plot.pca.loadings}}, \code{\link{pca.xyz}} } \examples{ ##-- PCA on torsion data for multiple PDBs data(kinesin) attach(kinesin, warn.conflicts=FALSE) gaps.pos <- gap.inspect(pdbs$xyz) tor <- t(apply( pdbs$xyz[, gaps.pos$f.inds], 1, torsion.xyz, atm.inc=1)) pc.tor <- pca.tor(tor[,-c(1,218,219,220)]) #plot(pc.tor) plot.pca.loadings(pc.tor) detach(kinesin) \dontrun{ ##-- PCA on torsion data from an MD trajectory trj <- read.dcd( system.file("examples/hivp.dcd", package="bio3d") ) tor <- t(apply(trj, 1, torsion.xyz, atm.inc=1)) gaps <- gap.inspect(tor) pc.tor <- pca.tor(tor[,gaps$f.inds]) plot.pca.loadings(pc.tor) } } \keyword{ utilities } \keyword{ multivariate } bio3d/man/var.xyz.Rd0000644000176200001440000000220012524171274013734 0ustar liggesusers\name{var.xyz} \alias{var.xyz} \alias{var.pdbs} \title{ Pairwise Distance Variance in Cartesian Coordinates } \description{ Calculate the variance of all pairwise distances in an ensemble of Cartesian coordinates. } \usage{ var.xyz(xyz, weights=TRUE) var.pdbs(pdbs, ...) } \arguments{ \item{xyz}{ an object of class \code{"xyz"} containing Cartesian coordinates in a matrix. } \item{weights }{ logical, if TRUE weights are calculated based on the pairwise distance variance. } \item{pdbs}{ a \sQuote{pdbs} object as object from function \code{pdbaln}. } \item{\dots}{ arguments passed to associated functions. } } \details{ This function calculates the variance of all pairwise distances in an ensemble of Cartesian coordinates. The primary use of this function is to calculate weights to scale the pair force constant for NMA. } \value{ Returns the a matrix of the pairwise distance variance, formated as weights if \sQuote{weights=TRUE}. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{nma.pdbs}} } \keyword{ utilities } bio3d/man/atom2ele.Rd0000644000176200001440000000430312544562303014030 0ustar liggesusers\name{atom2ele} \alias{atom2ele} \alias{atom2ele.default} \alias{atom2ele.pdb} \title{ Atom Names/Types to Atomic Symbols Converter } \description{ Convert atom names/types into atomic symbols } \usage{ atom2ele(\dots) \method{atom2ele}{default}(x, elety.custom=NULL, rescue=TRUE, \dots) \method{atom2ele}{pdb}(pdb, inds, elety.custom=NULL, rescue=TRUE, \dots) } \arguments{ \item{x}{a character vector containing atom names/types to be converted.} \item{elety.custom}{a customized data.frame containing atom names/types and corresponding atomic symbols.} \item{rescue}{logical, if TRUE the atomic symbols will be mapped to the first character of the atom names/types.} \item{pdb}{an object of class \sQuote{pdb} for which \code{elety} will be converted.} \item{inds}{an object of class \sQuote{select} indicating a subset of the \code{pdb} object to be used (see \code{\link{atom.select}} and \code{\link{trim.pdb}}).} \item{\dots}{further arguments passed to or from other methods.} } \details{ The default method searchs for the atom names/types in the \code{\link{atom.index}} data set and returns their corresponding atomic symbols. If \code{elety.custom} is specified it is combined with \code{\link{atom.index}} (using \code{rbind}) before searching. Therefore, \code{elety.custom} must contains columns named \code{name} and \code{symb}. The S3 method for object of class \sQuote{pdb}, pass \code{pdb$atom[,"elety"]} to the default method. } \value{Return a character vector of atomic symbols} \author{Julien Ide, Lars Skjaerven} \seealso{ \code{\link{atom.index}}, \code{\link{elements}}, \code{\link{read.pdb}}, \code{\link{atom2mass}}, \code{\link{formula2mass}} } \examples{ atom.names <- c("CA", "O", "N", "OXT") atom2ele(atom.names) \donttest{ # PDB server connection required - testing excluded ## Get atomic symbols from a PDB object with a customized data set pdb <- read.pdb("3RE0",verbose=FALSE) inds <- atom.select(pdb, resno=201, verbose=FALSE) ## maps CL2 to C atom2ele(pdb, inds, elety.custom = NULL) ## map CL2 to Cl manually myelety <- data.frame(name = "CL2", symb = "Cl") atom2ele(pdb, inds, elety.custom = myelety) } } \keyword{ utilities } bio3d/man/plot.dmat.Rd0000644000176200001440000000763612544562303014236 0ustar liggesusers\name{plot.dmat} \alias{plot.dmat} \title{ Plot Distance Matrix } \description{ Plot a distance matrix (DM) or a difference distance matrix (DDM). } \usage{ \method{plot}{dmat}(x, key = TRUE, resnum.1 = c(1:ncol(x)), resnum.2 = resnum.1, axis.tick.space = 20, zlim = range(x, finite = TRUE), nlevels = 20, levels = pretty(zlim, nlevels), color.palette = bwr.colors, col = color.palette(length(levels) - 1), axes = TRUE, key.axes, xaxs = "i", yaxs = "i", las = 1, grid = TRUE, grid.col = "yellow", grid.nx = floor(ncol(x)/30), grid.ny = grid.nx, center.zero = TRUE, flip=TRUE, ...) } \arguments{ \item{x}{ a numeric distance matrix generated by the function \code{\link{dm}}. } \item{key}{ logical, if TRUE a color key is plotted. } \item{resnum.1}{ a vector of residue numbers for annotating the x axis. } \item{resnum.2}{ a vector of residue numbers for annotating the y axis. } \item{axis.tick.space}{ the separation between each axis tick mark. } \item{zlim}{ z limits for the distances to be plotted. } \item{nlevels}{ if \code{levels} is not specified, the range of 'z' values is divided into approximately this many levels. } \item{levels}{ a set of levels used to partition the range of 'z'. Must be *strictly* increasing (and finite). Areas with 'z' values between consecutive levels are painted with the same color. } \item{color.palette}{ a color palette function, used to assign colors in the plot. } \item{col}{ an explicit set of colors to be used in the plot. This argument overrides any palette function specification. } \item{axes}{ logical, if TRUE plot axes are drawn. } \item{key.axes}{ statements which draw axes on the plot key. It overrides the default axis. } \item{xaxs}{ the x axis style. The default is to use internal labeling. } \item{yaxs}{ the y axis style. The default is to use internal labeling. } \item{las}{ the style of labeling to be used. The default is to use horizontal labeling. } \item{grid}{ logical, if TRUE overlaid grid is drawn. } \item{grid.col}{ color of the overlaid grid. } \item{grid.nx}{ number of grid cells in the x direction. } \item{grid.ny}{ number of grid cells in the y direction. } \item{center.zero}{ logical, if TRUE levels are forced to be equidistant around zero, assuming that zlim ranges from less than to more than zero. } \item{flip}{ logical, indicating whether the second axis should be fliped. } \item{\dots}{ additional graphical parameters for image. } } \value{ Called for its effect. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696.T Much of this function is based on the \code{filled.contour} function by Ross Ihaka. } \author{ Barry Grant } \note{ This function is based on the \code{layout} and legend key code in the function \code{filled.contour} by Ross Ihaka. As with \code{filled.contour} the output is a combination of two plots: the legend and (in this case) \code{image} (rather than a contour plot). } \seealso{ \code{\link{dm}}, \code{\link{filled.contour}}, \code{\link{contour}}, \code{\link{image}} } \examples{ # Read PDB file pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) # DM d <- dm(pdb,"calpha") # Plot DM ##filled.contour(d, nlevels = 4) ##plot(d) plot(d, resnum.1 = pdb$atom[pdb$calpha,"resno"], color.palette = mono.colors, xlab="Residue Number", ylab="Residue Number") \dontrun{ # Download and align two PDB files pdbs <- pdbaln( get.pdb( c( "4q21", "521p"), path=tempdir(), overwrite=TRUE)) # Get distance matrix a <- dm.xyz(pdbs$xyz[1,]) b <- dm.xyz(pdbs$xyz[2,]) # Calculate DDM c <- a - b # Plot DDM plot(c,key=FALSE, grid=FALSE) plot(c, axis.tick.space=10, resnum.1=pdbs$resno[1,], resnum.2=pdbs$resno[2,], grid.col="black", xlab="Residue No. (4q21)", ylab="Residue No. (521p)") } } \keyword{ hplot } bio3d/man/combine.select.Rd0000644000176200001440000000536612544562303015224 0ustar liggesusers\name{combine.select} \alias{combine.select} \title{ Combine Atom Selections From PDB Structure } \description{ Do "and", "or", or "not" set operations between two or more atom selections made by \code{\link{atom.select}} } \usage{ combine.select(sel1=NULL, sel2=NULL, \dots, operator="AND", verbose=TRUE) } \arguments{ \item{sel1}{ an atom selection object of class \code{"select"}, obtained from \code{\link{atom.select}}. } \item{sel2}{ a second atom selection object of class \code{"select"}, obtained from \code{\link{atom.select}}. } \item{\dots}{ more select objects for the set operation. } \item{operator}{ name of the set operation. } \item{verbose}{ logical, if TRUE details of the selection combination are printed. } } \details{ The value of \code{operator} should be one of following: (1) "AND", "and", or "&" for set intersect, (2) "OR", "or", "|", or "+" for set union, (3) "NOT", "not", "!", or "-" for set difference \code{sel1 - sel2 - sel3 ...}. } \value{ Returns a list of class \code{"select"} with components: \item{atom }{atom indices of selected atoms.} \item{xyz }{xyz indices of selected atoms.} \item{call }{the matched call.} } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Xin-Qiu Yao } \seealso{ \code{\link{atom.select}}, \code{\link{as.select}} \code{\link{read.pdb}}, \code{\link{trim.pdb}} } \examples{ # Read a PDB file pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) ## - Build atom selections to be operated # Select C-alpha atoms of entire system ca.global.inds <- atom.select(pdb, "calpha") # Select C-beta atoms of entire protein cb.global.inds <- atom.select(pdb, "protein", elety="CB") # Select backbone atoms of entire system bb.global.inds <- atom.select(pdb, "backbone") # Select all atoms with residue number from 46 to 50 aa.local.inds <- atom.select(pdb, resno=46:50) # Do set intersect: # - Return C-alpha atoms with residue number from 46 to 50 ca.local.inds <- combine.select(ca.global.inds, aa.local.inds) print( pdb$atom[ ca.local.inds$atom, ] ) # Do set subtract: # - Return side-chain atoms with residue number from 46 to 50 sc.local.inds <- combine.select(aa.local.inds, bb.global.inds, operator="-") print( pdb$atom[ sc.local.inds$atom, ] ) # Do set union: # - Return C-alpha and side-chain atoms with residue number from 46 to 50 casc.local.inds <- combine.select(ca.local.inds, sc.local.inds, operator="+") print( pdb$atom[ casc.local.inds$atom, ] ) # More than two selections: # - Return side-chain atoms (but not C-beta) with residue number from 46 to 50 sc2.local.inds <- combine.select(aa.local.inds, bb.global.inds, cb.global.inds, operator="-") print( pdb$atom[ sc2.local.inds$atom, ] ) } \keyword{utilities} bio3d/man/read.crd.Rd0000644000176200001440000000316712526367344014021 0ustar liggesusers\name{read.crd} \alias{read.crd} \title{ Read Coordinate Data from Amber or Charmm } \description{ Read a CHARMM CARD (CRD) or AMBER coordinate file. } \usage{ read.crd(file, ...) } \arguments{ \item{file}{ the name of the coordinate file to be read. } \item{\dots}{ additional arguments passed to the methods \code{read.crd.charmm} or \code{read.crd.amber}. } } \details{ \code{read.crd} is a generic function calling the corresponding function determined by the class of the input argument \code{x}. Use \code{methods("read.crd")} to get all the methods for \code{read.crd} generic: \code{\link{read.crd.charmm}} will be used for file extension \sQuote{.crd}. \code{\link{read.crd.amber}} will be used for file extension \sQuote{.rst} or \sQuote{.inpcrd}. See examples for each corresponding function for more details. } \value{ See the \sQuote{value} section for the corresponding functions for more details. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant and Lars Skjaerven} \seealso{ \code{\link{read.crd.amber}}, \code{\link{read.crd.charmm}}, \code{\link{write.crd}}, \code{\link{read.prmtop}}, \code{\link{read.pdb}}, \code{\link{write.pdb}}, \code{\link{atom.select}}, \code{\link{read.dcd}}, \code{\link{read.ncdf}} } \examples{ \dontrun{ ## Read a PRMTOP file prmtop <- read.prmtop("prot_solvated.prmtop") print(prmtop) ## Read a Amber CRD file crds <- read.crd("prot_solvated.inpcrd") ## Atom selection ca.inds <- atom.select(prmtop, "calpha") ## Convert to PDB format pdb <- as.pdb(prmtop, crds, inds=ca.inds) } } \keyword{ IO } bio3d/man/prune.cna.Rd0000644000176200001440000000360012632622153014206 0ustar liggesusers\name{prune.cna} \alias{prune.cna} \title{ Prune A cna Network Object } \description{ Remove nodes and their associated edges from a cna network graph. } \usage{ prune.cna(x, edges.min = 1, size.min = 1) } \arguments{ \item{x}{ A protein network graph object as obtained from the \sQuote{cna} function.} \item{edges.min}{ A single element numeric vector specifying the minimum number of edges that retained nodes should have. Nodes with less than \sQuote{edges.min} will be pruned. } \item{size.min}{ A single element numeric vector specifying the minimum node size that retained nodes should have. Nodes with less composite residues than \sQuote{size.min} will be pruned. } } \details{ This function is useful for cleaning up cna network plots by removing, for example, small isolated nodes. The output is a new cna object minus the pruned nodes and their associated edges. Node naming is preserved. } \value{ A cna class object, see function \code{\link{cna}} for details. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \note{ Some improvements to this function are required, including a better effort to preserve the original community structure rather than calculating a new one. Also may consider removing nodes form the raw.network object that is returned also. } \seealso{ \code{\link{cna}}, \code{\link{summary.cna}}, \code{\link{view.cna}}, \code{\link{plot.cna}} } \examples{ # Load the correlation network attach(hivp) # Read the starting PDB file to determine atom correspondence pdbfile <- system.file("examples/hivp.pdb", package="bio3d") pdb <- read.pdb(pdbfile) # Plot coarse grain network based on dynamically coupled communities par(mfcol=c(1,2), mar=c(0,0,0,0)) plot.cna(net) # Prune network dnet <- prune.cna(net, edges.min = 1) plot(dnet) detach(hivp) } \keyword{ utility } bio3d/man/basename.pdb.Rd0000644000176200001440000000241312526367344014647 0ustar liggesusers\name{basename.pdb} \alias{basename.pdb} \title{ Manipulate PDB File Names } \description{ Removes all of the path up to and including the last path separator (if any) and the final \sQuote{.pdb} extension. } \usage{ basename.pdb(x, mk4 = FALSE) } \arguments{ \item{x}{ character vector of PDB file names, containing path and extensions.} \item{mk4}{ logical, if TRUE the output will be truncated to the first 4 characters of the basename. This is frequently convenient for matching RCSB PDB identifier conventions (see examples below). } } \details{ This is a simple utility function for the common task of PDB file name manipulation. It is used internally in several bio3d functions and van be thought of as basename for PDB files. } \value{ A character vector of the same length as the input \sQuote{x}. Paths not containing any separators are taken to be in the current directory. If an element of input is \sQuote{x} is \sQuote{NA}, so is the result. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \seealso{ \code{\link{basename}}, \code{\link{dirname}} } \examples{ basename.pdb("/somedir/somewhere/1bg2_myfile.pdb") basename.pdb("/somedir/somewhere/1bg2_myfile.pdb", TRUE) } \keyword{ utilities } bio3d/man/dccm.Rd0000644000176200001440000000357312632622153013234 0ustar liggesusers\name{dccm} \alias{dccm} \title{ DCCM: Dynamical Cross-Correlation Matrix } \description{ Determine the cross-correlations of atomic displacements. } \usage{ dccm(x, ...) } \arguments{ \item{x}{ a numeric matrix of Cartesian coordinates with a row per structure/frame which will br passed to \code{dccm.xyz()}. Alternatively, an object of class \code{nma} as obtained from function \code{nma} that will be passed to the \code{dccm.nma()} function, see below for examples. } \item{\dots}{ additional arguments passed to the methods \code{dccm.xyz}, \code{dccm.pca}, \code{dccm.nma}, and \code{dccm.enma}. } } \details{ \code{dccm} is a generic function calling the corresponding function determined by the class of the input argument \code{x}. Use \code{methods("dccm")} to get all the methods for \code{dccm} generic: \code{\link{dccm.xyz}} will be used when \code{x} is a numeric matrix containing Cartesian coordinates (e.g. trajectory data). \code{\link{dccm.pca}} will calculate the cross-correlations based on an \code{pca} object. \code{\link{dccm.nma}} will calculate the cross-correlations based on an \code{nma} object. Similarly, \code{\link{dccm.enma}} will calculate the correlation matrices based on an ensemble of \code{nma} objects (as obtained from function \code{nma.pdbs}). \code{\link{plot.dccm}} and \code{\link{view.dccm}} provides convenient functionality to plot a correlation map, and visualize the correlations in the structure, respectively. See examples for each corresponding function for more details. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant, Lars Skjaerven } \seealso{ \code{\link{dccm.xyz}}, \code{\link{dccm.nma}}, \code{\link{dccm.enma}}, \code{\link{dccm.pca}}, \code{\link{plot.dccm}}, \code{\link{view.dccm}}. } \keyword{ utilities } bio3d/man/plot.fasta.Rd0000644000176200001440000000520112430771420014365 0ustar liggesusers\name{plot.fasta} \alias{plot.fasta} \title{ Plot a Multiple Sequence Alignment } \description{ Produces a schematic representation of a multiple sequence alignment. } \usage{ \method{plot}{fasta}(x, plot.labels = TRUE, plot.bars = TRUE, plot.lines = FALSE, plot.axis = TRUE, seq.index = NULL, color.conserved = FALSE, cutoff=0.5, col=NULL, bars.scale=2, row.spacing=0.5, aln.col="grey50", cex.text=1, add=FALSE, ...) } \arguments{ \item{x}{ multiple sequence alignement of class \sQuote{fasta} as obtained from \code{\link{seqaln}}. } \item{plot.labels}{ logical, if TRUE labels will be printed next to the sequence bar. } \item{plot.bars}{ logical, if TRUE an additional bar representing sequence conservation will be plotted. } \item{plot.lines}{ logical, if TRUE sequence conservation will be represented with a plot. } \item{plot.axis}{ logical, if TRUE x-axis will be plotted. } \item{seq.index}{ printed tick labels will correspond to the sequence of the provided index. } \item{color.conserved}{ logical, if TRUE conserved residues will be colored according to \dQuote{clustal} coloring scheme. } \item{cutoff}{ conservation \sQuote{cutoff} value below which alignment columns are not colored. } \item{col}{ character vector with color codes for the conservation bars. By default, \code{heat.colors} will be used. } \item{bars.scale }{ scaling factor for the height of the conservation bar when \sQuote{plot.bars=TRUE}. } \item{row.spacing }{ space between the sequence bars. } \item{aln.col }{ color of the alignment bars. } \item{cex.text }{ scaling factor for the labels. } \item{add }{ logical, if TRUE \code{plot.new()} will not be called. } \item{\dots}{ additional arguments not paseed anywhere. } } \details{ \code{plot.fasta} is a utility function for producting a schematic representation of a multiple sequence alignment. } \value{ Called for its effect. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{seqaln}}, \code{\link{read.fasta}}, \code{\link{entropy}}, \code{\link{aln2html}}. } \examples{ # Read alignment aln<-read.fasta(system.file("examples/kif1a.fa",package="bio3d")) ## alignment plot plot(aln) \dontrun{ infile <- "http://pfam.sanger.ac.uk/family/PF00071/alignment/seed/format?format=fasta" aln <- read.fasta( infile ) plot(aln) } } \keyword{ hplot } bio3d/man/deformation.nma.Rd0000644000176200001440000000417512544562303015410 0ustar liggesusers\name{deformation.nma} \alias{deformation.nma} \title{ Deformation Analysis } \description{ Calculate deformation energies from Normal Mode Analysis. } \usage{ deformation.nma(nma, mode.inds = NULL, pfc.fun = NULL, ncore = NULL) } \arguments{ \item{nma}{ a list object of class \code{"nma"} (obtained with \code{\link{nma}}).} \item{mode.inds}{ a numeric vector of mode indices in which the calculation should be based. } \item{pfc.fun}{ customized pair force constant (\sQuote{pfc}) function. The provided function should take a vector of distances as an argument to return a vector of force constants. See \code{nma} for examples. } \item{ncore }{ number of CPU cores used to do the calculation. \code{ncore>1} requires package \sQuote{parallel} installed. } } \details{ Deformation analysis provides a measure for the amount of local flexibility of the protein structure - i.e. atomic motion relative to neighbouring atoms. It differs from \sQuote{fluctuations} (e.g. RMSF values) which provide amplitudes of the absolute atomic motion. Deformation energies are calculated based on the \code{nma} object. By default the first 20 non-trivial modes are included in the calculation. See examples for more details. } \value{ Returns a list with the following components: \item{ei }{ numeric matrix containing the energy contribution (E) from each atom (i; row-wise) at each mode index (column-wise). } \item{sums }{ deformation energies corresponding to each mode. } } \references{ Hinsen, K. (1998) \emph{Proteins} \bold{33}, 417--429. Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{nma} } } \examples{ ## Fetch stucture pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) ## Calculate (vibrational) normal modes modes <- nma(pdb) ## Calculate deformation energies def.energies <- deformation.nma(modes) \dontrun{ ## Fluctuations of first non-trivial mode def.energies <- deformation.nma(modes, mode.inds=seq(7, 16)) write.pdb(pdb=NULL, xyz=modes$xyz, b=def.energies$ei[,1]) } } \keyword{ analysis } bio3d/man/check.utility.Rd0000644000176200001440000000135512526367344015113 0ustar liggesusers\name{check.utility} \alias{check.utility} \title{ Check on Missing Utility Programs } \description{ Internally used in examples, tests, or vignettes. } \usage{ check.utility(x = c("muscle", "dssp", "stride", "mustang", "makeup"), quiet = TRUE) } \arguments{ \item{x}{ Names of one or more utility programs to check. } \item{quiet}{ logical, if TRUE no warning or message printed. } } \details{ Check if requested utility programs are availabe or not. } \value{ logical, TRUE if programs are available and FALSE if any one of them is missing. } \examples{ check.utility(c("muscle", "dssp"), quiet=FALSE) if(!check.utility("mustang")) cat(" The utility program, MUSTANG, is missing on your system\n") } \keyword{ utilities } bio3d/man/aa.table.Rd0000644000176200001440000000232012544562303013764 0ustar liggesusers\name{aa.table} \alias{aa.table} \docType{data} \title{ Table of Relevant Amino Acids } \description{ This data set provides the atomic masses of a selection of amino acids regularly occuring in proteins. } \usage{ aa.table } \format{ A data frame with the following components. \describe{ \item{\code{aa3}}{a character vector containing three-letter amino acid code.} \item{\code{aa1}}{a character vector containing one-letter amino acid code.} \item{\code{mass}}{a numeric vector containing the mass of the respective amino acids. } \item{\code{formula}}{a character vector containing the formula of the amino acid in which the mass calculat was based. } \item{\code{name}}{a character vector containing the full names of the respective amino acids. } } } \seealso{ \code{\link{aa2mass}}, \code{\link{aa.index}}, \code{\link{atom.index}}, \code{\link{elements}}, } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \examples{ data(aa.table) aa.table ## table look up aa.table["HIS", ] ## read PDB, and fetch residue masses pdb <- read.pdb(system.file("examples/1hel.pdb", package="bio3d")) aa2mass(pdb) } \keyword{datasets} bio3d/man/write.fasta.Rd0000644000176200001440000000376012544562303014555 0ustar liggesusers\name{write.fasta} \alias{write.fasta} \title{ Write FASTA Formated Sequences } \description{ Write aligned or un-aligned sequences to a FASTA format file. } \usage{ write.fasta(alignment=NULL, ids=NULL, seqs=alignment$ali, file, append = FALSE) } \arguments{ \item{alignment}{ an alignment list object with \code{id} and \code{ali} components, similar to that generated by \code{\link{read.fasta}}. } \item{ids}{ a vector of sequence names to serve as sequence identifers } \item{seqs}{ an sequence or alignment character matrix or vector with a row per sequence } \item{file}{ name of output file. } \item{append}{ logical, if TRUE output will be appended to \code{file}; otherwise, it will overwrite the contents of \code{file}. } } \value{ Called for its effect. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \note{ For a description of FASTA format see: \url{http://www.ncbi.nlm.nih.gov/BLAST/blastcgihelp.shtml}. } \seealso{ \code{\link{read.fasta}}, \code{\link{read.fasta.pdb}} } \examples{ \donttest{ # PDB server connection required - testing excluded ## Read a PDB file pdb <- read.pdb("1bg2") ## Extract sequence from PDB file s <- aa321(pdb$seqres) # SEQRES a <- aa321(pdb$atom[pdb$calpha,"resid"]) # ATOM ## Write simple fasta file #write.fasta( seqs=seqbind(s,a), file="eg.fa") #write.fasta( ids=c("seqres","atom"), seqs=seqbind(s,a), file="eg.fa" ) outfile1 = file.path(tempdir(), "eg.fa") write.fasta(list( id=c("seqres"),ali=s ), file = outfile1) write.fasta(list( id=c("atom"),ali=a ), file = outfile1, append=TRUE) ## Align seqres and atom records #seqaln(seqbind(s,a)) ## Read alignment aln<-read.fasta(system.file("examples/kif1a.fa",package="bio3d")) ## Cut all but positions 130 to 245 aln$ali=aln$ali[,130:245] outfile2 = file.path(tempdir(), "eg2.fa") write.fasta(aln, file = outfile2) invisible( cat("\nSee the output files:", outfile1, outfile2, sep="\n") ) } } \keyword{ IO } bio3d/man/lmi.Rd0000644000176200001440000000423512544562303013105 0ustar liggesusers\name{lmi} \alias{lmi} \title{ LMI: Linear Mutual Information Matrix } \description{ Calculate the linear mutural information correlations of atomic displacements. } \usage{ lmi(trj, grpby=NULL, ncore=1) } \arguments{ \item{trj}{ a numeric matrix of Cartesian coordinates with a row per structure/frame. } \item{grpby}{ a vector counting connective duplicated elements that indicate the elements of \code{trj} that should be considered as a group (e.g. atoms from a particular residue). } \item{ncore}{ number of CPU cores used to do the calculation. \code{ncore>1} requires package \sQuote{parallel} installed. } } \details{ The correlation of the atomic fluctuations of a system can be assessed by the Linear Mutual Information (LMI) and the LMI has no unwanted dependency on the relative orientation of the fluctuations which the Pearson coefficient suffers from. This function returns a matrix of all atom-wise linear mutual information whose elements are denoted as Cij. If Cij = 1, the fluctuations of atoms i and j are completely correlated and if Cij = 0, the fluctuations of atoms i and j are not correlated. } \value{ Returns a linear mutual information matrix. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. Lange, O.F. and Grubmuller, H. (2006) \emph{PROTEINS: Structure, Function, and Bioinformatics} \bold{62}:1053--1061. } \author{ Hongyang Li & Barry Grant} \examples{ \donttest{ # Redundant testing excluded ##-- Read example trajectory file trtfile <- system.file("examples/hivp.dcd", package="bio3d") trj <- read.dcd(trtfile) ## Read the starting PDB file to determine atom correspondence pdbfile <- system.file("examples/hivp.pdb", package="bio3d") pdb <- read.pdb(pdbfile) ## select residues 24 to 27 and 85 to 90 in both chains inds <- atom.select(pdb, resno=c(24:27,85:90), elety='CA') ## lsq fit of trj on pdb xyz <- fit.xyz(pdb$xyz, trj, fixed.inds=inds$xyz, mobile.inds=inds$xyz) ## LMI matrix (slow to run so restrict to Calpha) cij <- lmi(xyz) ## Plot LMI matrix #plot(cij) col.scale <- colorRampPalette(c("gray95", "cyan"))(5) plot(cij, at=seq(0.4,1, length=5), col.regions=col.scale) } } bio3d/man/fit.xyz.Rd0000644000176200001440000001212712544562303013736 0ustar liggesusers\name{fit.xyz} \alias{fit.xyz} \alias{rot.lsq} \title{ Coordinate Superposition } \description{ Coordinate superposition with the Kabsch algorithm. } \usage{ fit.xyz(fixed, mobile, fixed.inds = NULL, mobile.inds = NULL, verbose=FALSE, prefix= "", pdbext = "", outpath = "fitlsq", full.pdbs=FALSE, ncore = 1, nseg.scale = 1, ...) rot.lsq(xx, yy, xfit = rep(TRUE, length(xx)), yfit = xfit, verbose = FALSE) } \arguments{ \item{fixed }{ numeric vector of xyz coordinates.} \item{mobile}{ numeric vector, numeric matrix, or an object with an \code{xyz} component containing one or more coordinate sets. } \item{fixed.inds}{ a vector of indices that selects the elements of \code{fixed} upon which fitting should be based.} \item{mobile.inds}{ a vector of indices that selects the elements of \code{mobile} upon which fitting should be based.} \item{full.pdbs}{ logical, if TRUE \dQuote{full} coordinate files (i.e. all atoms) are written to the location specified by \code{outpath}. } \item{prefix}{ prefix to mobile$id to locate \dQuote{full} input PDB files. Only required if \code{full.pdbs} is TRUE. } \item{pdbext}{ the file name extension of the input PDB files. } \item{outpath}{ character string specifing the output directory when \code{full.pdbs} is TRUE. } \item{xx}{ numeric vector corresponding to the moving \sQuote{subject} coordinate set. } \item{yy}{ numeric vector corresponding to the fixed \sQuote{target} coordinate set. } \item{xfit}{ logical vector with the same length as \code{xx}, with TRUE elements corresponding to the subset of positions upon which fitting is to be performed. } \item{yfit}{ logical vector with the same length as \code{yy}, with TRUE elements corresponding to the subset of positions upon which fitting is to be performed. } \item{verbose}{ logical, if TRUE more details are printed. } \item{\dots}{ other parameters for \code{\link{read.pdb}}. } \item{ncore }{ number of CPU cores used to do the calculation. \code{ncore>1} requires package \sQuote{parallel} installed. } \item{nseg.scale }{ split input data into specified number of segments prior to running multiple core calculation. } } \details{ The function \code{fit.xyz} is a wrapper for the function \code{rot.lsq}, which performs the actual coordinate superposition. The function \code{rot.lsq} is an implementation of the Kabsch algorithm (Kabsch, 1978) and evaluates the optimal rotation matrix to minimize the RMSD between two structures. Since the Kabsch algorithm assumes that the number of points are the same in the two input structures, care should be taken to ensure that consistent atom sets are selected with \code{fixed.inds} and \code{mobile.inds}. Optionally, \dQuote{full} PDB file superposition and output can be accomplished by setting \cr \code{full.pdbs=TRUE}. In that case, the input (\code{mobile}) passed to \code{fit.xyz} should be a list object obtained with the function \code{\link{read.fasta.pdb}}, since the components \code{id}, \code{resno} and \code{xyz} are required to establish correspondences. See the examples below. In dealing with large vector and matrix, running on multiple cores, especially when \code{ncore>>1}, may ask for a large portion of system memory. To avoid the overuse of memory, input data is first split into segments (for xyz matrix, the splitting is along the row). The number of data segments is equal to \code{nseg.scale*nseg.base}, where \code{nseg.base } is an integer determined by the dimension of the data. } \value{ Returns moved coordinates. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. Kabsch \emph{Acta Cryst} (1978) \bold{A34}, 827--828. } \author{ Barry Grant with \code{rot.lsq} contributions from Leo Caves } \seealso{ \code{\link{rmsd}}, \code{\link{read.pdb}}, \code{\link{read.fasta.pdb}}, \code{\link{read.dcd}} } \examples{ \donttest{ # PDB server connection required - testing excluded ##--- Read an alignment & Fit aligned structures aln <- read.fasta(system.file("examples/kif1a.fa",package="bio3d")) pdbs <- read.fasta.pdb(aln) gaps <- gap.inspect(pdbs$xyz) xyz <- fit.xyz( fixed = pdbs$xyz[1,], mobile = pdbs$xyz, fixed.inds = gaps$f.inds, mobile.inds = gaps$f.inds ) #rmsd( xyz[, gaps$f.inds] ) #rmsd( pdbs$xyz[, gaps$f.inds] ) ##-- Superpose again this time outputing PDBs xyz <- fit.xyz( fixed = pdbs$xyz[1,], mobile = pdbs, fixed.inds = gaps$f.inds, mobile.inds = gaps$f.inds, outpath = "rough_fit", full.pdbs = TRUE) ##--- Fit two PDBs A <- read.pdb("1bg2") A.ind <- atom.select(A, resno=c(256:269), elety='CA') B <- read.pdb("2kin") B.ind <- atom.select(B, resno=c(257:270), elety='CA') xyz <- fit.xyz(fixed=A$xyz, mobile=B$xyz, fixed.inds=A.ind$xyz, mobile.inds=B.ind$xyz) # Write out moved PDB C <- B; C$xyz = xyz write.pdb(pdb=C, file = "moved.pdb") } } \keyword{ utilities } bio3d/man/plot.hmmer.Rd0000644000176200001440000000640312632622153014406 0ustar liggesusers\name{plot.hmmer} \alias{plot.hmmer} \title{ Plot a Summary of HMMER Hit Statistics. } \description{ Produces a number of basic plots that should facilitate hit selection from the match statistics of a HMMER result. } \usage{ \method{plot}{hmmer}(x, cutoff = NULL, cut.seed=NULL, cluster=TRUE, mar=c(2, 5, 1, 1), cex=1.1, ...) } \arguments{ \item{x}{ HMMER results as obtained from the function \code{\link{hmmer}}. } \item{cutoff}{ A numeric cutoff value, in terms of minus the log of the evalue, for returned hits. If null then the function will try to find a suitable cutoff near \sQuote{cut.seed} which can be used as an initial guide (see below). } \item{cut.seed}{ A numeric seed cutoff value, used for initial cutoff estimation. If null then a seed position is set to the point of largest drop-off in normalized scores (i.e. the biggest jump in E-values). } \item{cluster}{ Logical, if TRUE (and \sQuote{cutoff} is null) a clustering of normalized scores is performed to partition hits in groups by similarity to query. If FALSE the partition point is set to the point of largest drop-off in normalized scores. } \item{mar}{ A numerical vector of the form c(bottom, left, top, right) which gives the number of lines of margin to be specified on the four sides of the plot.} \item{cex}{ a numerical single element vector giving the amount by which plot labels should be magnified relative to the default. } \item{\dots}{ extra plotting arguments. } } \details{ Examining plots of HMMER scores, E-values and normalized scores (-log(E-Value), see \sQuote{hmmer} function) can aid in the identification sensible hit similarity thresholds. If a \sQuote{cutoff} value is not supplied then a basic hierarchical clustering of normalized scores is performed with initial group partitioning implemented at a hopefully sensible point in the vicinity of \sQuote{h=cut.seed}. Inspection of the resultant plot can then be use to refine the value of \sQuote{cut.seed} or indeed \sQuote{cutoff}. As the \sQuote{cutoff} value can vary depending on the desired application and indeed the properties of the system under study it is envisaged that \sQuote{plot.hmmer} will be called multiple times to aid selection of a suitable \sQuote{cutoff} value. See the examples below for further details. } \value{ Produces a plot on the active graphics device and returns a three component list object: \item{hits}{ an ordered matrix detailing the subset of hits with a normalized score above the chosen cutoff. Database identifiers are listed along with their cluster group number. } \item{acc}{ a character vector containing the database accession identifier of each hit above the chosen threshold. } \item{inds}{ a numeric vector containing the indices of the hits relative to the input hmmer object.} } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant, Lars Skjaerven } \note{ TO BE IMPROVED. } \seealso{ \code{\link{hmmer}}, \code{\link{blast.pdb}} } \examples{ \dontrun{ # HMMER server connection required - testing excluded ##- PHMMER seq <- get.seq("2abl_A", outfile = tempfile()) res <- hmmer(seq, db="pdb") plot.hmmer(res) } } \keyword{ hplot } bio3d/man/torsion.pdb.Rd0000644000176200001440000000544612544562303014572 0ustar liggesusers\name{torsion.pdb} \alias{torsion.pdb} \title{ Calculate Mainchain and Sidechain Torsion/Dihedral Angles } \description{ Calculate all torsion angles for a given protein PDB structure object. } \usage{ torsion.pdb(pdb) } \arguments{ \item{pdb}{ a PDB structure object as obtained from function \code{read.pdb}. } } \details{ The conformation of a polypeptide chain can be usefully described in terms of angles of internal rotation around its constituent bonds. See the related \code{torsion.xyz} function, which is called by this function, for details. } \value{ Returns a list object with the following components: \item{phi}{ main chain torsion angle for atoms C,N,CA,C. } \item{psi}{ main chain torsion angle for atoms N,CA,C,N. } \item{omega}{ main chain torsion angle for atoms CA,C,N,CA. } \item{alpha}{ virtual torsion angle between consecutive C-alpha atoms. } \item{chi1}{ side chain torsion angle for atoms N,CA,CB,*G. } \item{chi2}{ side chain torsion angle for atoms CA,CB,*G,*D. } \item{chi3}{ side chain torsion angle for atoms CB,*G,*D,*E. } \item{chi4}{ side chain torsion angle for atoms *G,*D,*E,*Z. } \item{chi5}{ side chain torsion angle for atoms *D,*E,*Z, NH1. } \item{coords}{ numeric matrix of \sQuote{justified} coordinates. } \item{tbl}{ a numeric matrix of psi, phi and chi torsion angles. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \note{ For the protein backbone, or main-chain atoms, the partial double-bond character of the peptide bond between \sQuote{C=N} atoms severely restricts internal rotations. In contrast, internal rotations around the single bonds between \sQuote{N-CA} and \sQuote{CA-C} are only restricted by potential steric collisions. Thus, to a good approximation, the backbone conformation of each residue in a given polypeptide chain can be characterised by the two angles phi and psi. Sidechain conformations can also be described by angles of internal rotation denoted chi1 up to chi5 moving out along the sidechain. } \seealso{ \code{\link{torsion.xyz}}, \code{\link{read.pdb}}, \code{\link{dssp}}, \code{\link{stride}}. } \examples{ \donttest{ # PDB server connection required - testing excluded ##-- PDB torsion analysis pdb <- read.pdb( "1bg2" ) tor <- torsion.pdb(pdb) head(tor$tbl) ## basic Ramachandran plot plot(tor$phi, tor$psi) ## torsion analysis of a single coordinate vector #inds <- atom.select(pdb,"calpha") #tor.ca <- torsion.xyz(pdb$xyz[inds$xyz], atm.inc=1) ##-- Compare two PDBs to highlight interesting residues aln <- read.fasta(system.file("examples/kif1a.fa",package="bio3d")) m <- read.fasta.pdb(aln) a <- torsion.xyz(m$xyz[1,],1) b <- torsion.xyz(m$xyz[2,],1) d <- wrap.tor(a-b) plot(m$resno[1,],d, typ="h") } } \keyword{ utilities } bio3d/man/pairwise.Rd0000644000176200001440000000111212412623040014124 0ustar liggesusers\name{pairwise} \alias{pairwise} \title{ Pair Indices } \description{ A utility function to determine indices for pairwise comparisons. } \usage{ pairwise(N) } \arguments{ \item{N}{ a single numeric value representing the total number of things to undergo pairwise comparison. } } \value{ Returns a two column numeric matrix giving the indices for all pairs. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \seealso{ \code{\link{seqidentity}} } \examples{ pairwise(3) pairwise(20) } \keyword{ utilities } bio3d/man/pca.xyz.Rd0000644000176200001440000001132412544562303013715 0ustar liggesusers\name{pca.xyz} \alias{pca.xyz} \alias{print.pca} \title{ Principal Component Analysis } \description{ Performs principal components analysis (PCA) on a \code{xyz} numeric data matrix. } \usage{ \method{pca}{xyz}(xyz, subset = rep(TRUE, nrow(as.matrix(xyz))), use.svd = FALSE, rm.gaps=FALSE, mass = NULL, \dots) \method{print}{pca}(x, nmodes=6, \dots) } \arguments{ \item{xyz}{ numeric matrix of Cartesian coordinates with a row per structure. } \item{subset}{ an optional vector of numeric indices that selects a subset of rows (e.g. experimental structures vs molecular dynamics trajectory structures) from the full \code{xyz} matrix. Note: the full \code{xyz} is projected onto this subspace.} \item{use.svd}{ logical, if TRUE singular value decomposition (SVD) is called instead of eigenvalue decomposition. } \item{rm.gaps}{ logical, if TRUE gap positions (with missing coordinate data in any input structure) are removed before calculation. This is equivalent to removing NA cols from xyz. } \item{x}{ an object of class \code{pca}, as obtained from function \code{pca.xyz}. } \item{nmodes}{ numeric, number of modes to be printed. } \item{mass}{ a \sQuote{pdb} object or numeric vector of residue/atom masses. By default (\code{mass=NULL}), mass is ignored. If provided with a \sQuote{pdb} object, masses of all amino acids obtained from \code{\link{aa2mass}} are used. } \item{\dots}{ additional arguments to \code{\link{fit.xyz}} (for \code{pca.xyz}) or to \code{print} (for \code{print.pca}). } } \note{ If \code{mass} is provided, mass weighted coordinates will be considered, and iteration of fitting onto the mean structure is performed internally. The extra fitting process is to remove external translation and rotation of the whole system. With this option, a direct comparison can be made between PCs from \code{\link{pca.xyz}} and vibrational modes from \code{\link{nma.pdb}}, with the fact that \deqn{A=k_BTF^{-1}}{A=k[B]TF^-1}, where \eqn{A} is the variance-covariance matrix, \eqn{F} the Hessian matrix, \eqn{k_B}{k[B]} the Boltzmann's constant, and \eqn{T} the temperature. } \value{ Returns a list with the following components: \item{L }{eigenvalues.} \item{U }{eigenvectors (i.e. the x, y, and z variable loadings).} \item{z }{scores of the supplied \code{xyz} on the pcs.} \item{au }{atom-wise loadings (i.e. xyz normalized eigenvectors).} \item{sdev }{the standard deviations of the pcs.} \item{mean }{the means that were subtracted.} } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \seealso{ \code{\link{pca}}, \code{\link{pca.pdbs}}, \code{\link{plot.pca}}, \code{\link{mktrj.pca}}, \code{\link{pca.tor}}, \code{\link{project.pca}} } \examples{ \dontrun{ #-- Read transducin alignment and structures aln <- read.fasta(system.file("examples/transducin.fa",package="bio3d")) pdbs <- read.fasta.pdb(aln) # Find core core <- core.find(pdbs, #write.pdbs = TRUE, verbose=TRUE) rm(list=c("pdbs", "core")) } #-- OR for demo purposes just read previously saved transducin data attach(transducin) # Previously fitted coordinates based on sub 1.0A^3 core. See core.find() function. xyz <- pdbs$xyz #-- Do PCA ignoring gap containing positions pc.xray <- pca.xyz(xyz, rm.gaps=TRUE) # Plot results (conformer plots & scree plot overview) plot(pc.xray, col=annotation[, "color"]) # Plot a single conformer plot of PC1 v PC2 plot(pc.xray, pc.axes=1:2, col=annotation[, "color"]) ## Plot atom wise loadings plot.bio3d(pc.xray$au[,1], ylab="PC1 (A)") \donttest{ # PDB server connection required - testing excluded ## Plot loadings in relation to reference structure 1TAG pdb <- read.pdb("1tag") ind <- grep("1TAG", pdbs$id) ## location in alignment resno <- pdbs$resno[ind, !is.gap(pdbs)] ## non-gap residues tpdb <- trim.pdb(pdb, resno=resno) op <- par(no.readonly=TRUE) par(mfrow = c(3, 1), cex = 0.6, mar = c(3, 4, 1, 1)) plot.bio3d(pc.xray$au[,1], resno, ylab="PC1 (A)", sse=tpdb) plot.bio3d(pc.xray$au[,2], resno, ylab="PC2 (A)", sse=tpdb) plot.bio3d(pc.xray$au[,3], resno, ylab="PC3 (A)", sse=tpdb) par(op) } \dontrun{ # Write PC trajectory resno = pdbs$resno[1, !is.gap(pdbs)] resid = aa123(pdbs$ali[1, !is.gap(pdbs)]) a <- mktrj.pca(pc.xray, pc=1, file="pc1.pdb", resno=resno, resid=resid ) b <- mktrj.pca(pc.xray, pc=2, file="pc2.pdb", resno=resno, resid=resid ) c <- mktrj.pca(pc.xray, pc=3, file="pc3.pdb", resno=resno, resid=resid ) } detach(transducin) } \keyword{ utilities } \keyword{ multivariate } bio3d/man/consensus.Rd0000644000176200001440000000356512412623040014337 0ustar liggesusers\name{consensus} \alias{consensus} \title{ Sequence Consensus for an Alignment } \description{ Determines the consensus sequence for a given alignment at a given identity cutoff value. } \usage{ consensus(alignment, cutoff = 0.6) } \arguments{ \item{alignment}{ an \code{alignment} object created by the \code{\link{read.fasta}} function or an alignment character matrix. } \item{cutoff}{ a numeric value beteen 0 and 1, indicating the minimum sequence identity threshold for determining a consensus amino acid. Default is 0.6, or 60 percent residue identity. } } \value{ A vector containing the consensus sequence, where \sQuote{-} represents positions with no consensus (i.e. under the \code{cutoff}) } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \seealso{ \code{\link{read.fasta}} } \examples{ #-- Read HIV protease alignment aln <- read.fasta(system.file("examples/hivp_xray.fa",package="bio3d")) # Generate consensus con <- consensus(aln) print(con$seq) # Plot residue frequency matrix ##png(filename = "freq.png", width = 1500, height = 780) col <- mono.colors(32) aa <- rev(rownames(con$freq)) image(x=1:ncol(con$freq), y=1:nrow(con$freq), z=as.matrix(rev(as.data.frame(t(con$freq)))), col=col, yaxt="n", xaxt="n", xlab="Alignment Position", ylab="Residue Type") # Add consensus along the axis axis(side=1, at=seq(0,length(con$seq),by=5)) axis(side=2, at=c(1:22), labels=aa) axis(side=3, at=c(1:length(con$seq)), labels =con$seq) axis(side=4, at=c(1:22), labels=aa) grid(length(con$seq), length(aa)) box() # Add consensus sequence for(i in 1:length(con$seq)) { text(i, which(aa==con$seq[i]),con$seq[i],col="white") } # Add lines for residue type separation abline(h=c(2.5,3.5, 4.5, 5.5, 3.5, 7.5, 9.5, 12.5, 14.5, 16.5, 19.5), col="gray") } \keyword{ utilities } bio3d/man/rle2.Rd0000644000176200001440000000260112526367344013173 0ustar liggesusers\name{rle2} \title{Run Length Encoding with Indices} \alias{rle2} \alias{print.rle2} \concept{runs} \description{ Compute the lengths, values and indices of runs of equal values in a vector. This is a modifed version of base function \code{rle()}. } \usage{ rle2(x) \method{print}{rle2}(x, digits = getOption("digits"), prefix = "", \dots) } \arguments{ \item{x}{an atomic vector for \code{rle()}; an object of class \code{"rle"} for \code{inverse.rle()}.} \item{\dots}{further arguments; ignored here.} \item{digits}{number of significant digits for printing, see \code{\link{print.default}}.} \item{prefix}{character string, prepended to each printed line.} } \details{ Missing values are regarded as unequal to the previous value, even if that is also missing. \code{inverse.rle()} is the inverse function of \code{rle2()} and \code{rle()}, reconstructing \code{x} from the runs. } \value{ \code{rle()} returns an object of class \code{"rle"} which is a list with components: \item{lengths}{an integer vector containing the length of each run.} \item{values}{a vector of the same length as \code{lengths} with the corresponding values.} } \examples{ x <- rev(rep(6:10, 1:5)) rle(x) ## lengths [1:5] 5 4 3 2 1 ## values [1:5] 10 9 8 7 6 rle2(x) ## lengths: int [1:5] 5 4 3 2 1 ## values : int [1:5] 10 9 8 7 6 ## indices: int [1:5] 5 9 12 14 15 } \keyword{manip} bio3d/man/write.pir.Rd0000644000176200001440000000460012544562303014243 0ustar liggesusers\name{write.pir} \alias{write.pir} \title{ Write PIR Formated Sequences } \description{ Write aligned or un-aligned sequences to a PIR format file. } \usage{ write.pir(alignment=NULL, ids=NULL, seqs=alignment$ali, pdb.file = NULL, chain.first = NULL, resno.first = NULL, chain.last = NULL, resno.last = NULL, file, append = FALSE) } \arguments{ \item{alignment}{ an alignment list object with \code{id} and \code{ali} components, similar to that generated by \code{\link{read.fasta}}. } \item{ids}{ a vector of sequence names to serve as sequence identifers } \item{seqs}{ an sequence or alignment character matrix or vector with a row per sequence } \item{pdb.file}{ a vector of pdb filenames; For sequence, provide "". } \item{chain.first}{ a vector of chain id for the first residue. } \item{resno.first}{ a vector of residue number for the first residue. } \item{chain.last}{ a vector of chain id for the last residue. } \item{resno.last}{ a vector of residue number for the last residue. } \item{file}{ name of output file. } \item{append}{ logical, if TRUE output will be appended to \code{file}; otherwise, it will overwrite the contents of \code{file}. } } \value{ Called for its effect. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Xin-Qiu Yao } \note{ PIR is required format for input alignment file to use Modeller. For a description of PIR format see: \url{https://salilab.org/modeller/manual/node488.html}. } \seealso{ \code{\link{read.fasta}}, \code{\link{read.fasta.pdb}}, \code{\link{write.fasta}} } \examples{ \donttest{ # Needs MUSCLE installed - testing excluded if(check.utility("muscle")) { ## Generate an input file for structural modeling of ## transducin G-alpha subunit using the template 3SN6_A ## Read transducin alpha subunit sequence seq <- get.seq("P04695", db = "uniprot", outfile = tempfile()) ## Read structure template path = tempdir() pdb.file <- get.pdb("3sn6_A", path = path, split = TRUE) pdb <- read.pdb(pdb.file) ## Build an alignment between template and target aln <- seqaln(seqbind(pdbseq(pdb), seq), id = c("3sn6_A", seq$id), outfile = tempfile()) ## Write PIR format alignment file outfile = file.path(tempdir(), "eg.pir") write.pir(aln, pdb.file = c(pdb.file, ""), file = outfile) invisible( cat("\nSee the output file:", outfile, sep = "\n") ) } } } \keyword{ IO } bio3d/man/fluct.nma.Rd0000644000176200001440000000220312526367344014214 0ustar liggesusers\name{fluct.nma} \alias{fluct.nma} \title{ NMA Fluctuations } \description{ Calculates the atomic fluctuations from normal modes analysis. } \usage{ fluct.nma(nma, mode.inds=NULL) } \arguments{ \item{nma}{ a list object of class \code{"nma"} (obtained with \code{\link{nma}}).} \item{mode.inds}{ a numeric vector containing the the mode numbers in which the calculation should be based. } } \details{ Atomic fluctuations are calculated based on the \code{nma} object. By default all modes are included in the calculation. See examples for more details. } \value{ Returns a numeric vector of atomic fluctuations. } \references{ Hinsen, K. et al. (2000) \emph{Chemical Physics} \bold{261}, 25--37. Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{nma} } } \examples{ ## Fetch stucture pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) ## Calculate (vibrational) normal modes modes <- nma(pdb) ## Fluctuations f <- fluct.nma(modes) ## Fluctuations of first non-trivial mode f <- fluct.nma(modes, mode.inds=c(7,8)) } \keyword{ analysis } bio3d/man/normalize.vector.Rd0000644000176200001440000000236312526367344015635 0ustar liggesusers\name{normalize.vector} \alias{normalize.vector} \title{ Mass-Weighted Normalized Vector } \description{ Normalizes a vector (mass-weighted if requested). } \usage{ normalize.vector(x, mass=NULL) } \arguments{ \item{x}{ a numeric vector or matrix to be normalized. } \item{mass}{ a numeric vector containing the atomic masses for weighting. } } \details{ This function normalizes a vector, or alternatively, the column-wise vector elements of a matrix. If atomic masses are provided the vector is mass-weigthed. See examples for more details. } \value{ Returns the normalized vector(s). } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{nma} }, \code{\link{inner.prod} } } \examples{ x <- 1:3 y <- matrix(1:9, ncol = 3, nrow = 3) normalize.vector(x) normalize.vector(y) ## Application to normal modes pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) ## Calculate (vibrational) normal modes modes <- nma(pdb) ## Returns a vector nv <- normalize.vector(modes$modes[,7]) ## Returns a matrix nv <- normalize.vector(modes$modes[,7:10]) ## Mass-weighted nv <- normalize.vector(modes$modes[,7], mass=modes$mass) } \keyword{ utilities } bio3d/man/is.gap.Rd0000644000176200001440000000364612526367344013522 0ustar liggesusers\name{is.gap} \alias{is.gap} \title{ Gap Characters } \description{ Test for the presence of gap characters. } \usage{ is.gap(x, gap.char = c("-", ".")) } \arguments{ \item{x}{ an R object to be tested. Typically a sequence vector or sequence/structure alignment object as returned from \code{seqaln}, \code{pdbaln} etc. } \item{gap.char}{ a character vector containing the gap character types to test for. } } \value{ Returns a logical vector with the same length as the input vector, or the same length as the number of columns present in an alignment input object \sQuote{x}. In the later case TRUE elements corresponding to \sQuote{gap.char} matches in any alignment column (i.e. gap containing columns). } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \note{ During alignment, gaps are introduced into sequences that are believed to have undergone deletions or insertions with respect to other sequences in the alignment. These gaps, often referred to as indels, can be represented with \sQuote{NA}, \sQuote{-} or \sQuote{.} characters. This function provides a simple test for the presence of such characters, or indeed any set of user defined characters set by the \sQuote{gap.char} argument. } \seealso{ \code{\link{gap.inspect}}, \code{\link{read.fasta}}, \code{\link{read.fasta.pdb}}, \code{\link{seqaln}}, \code{\link{pdbaln}} } \examples{ is.gap( c("G",".","X","-","G","K","S","T") ) \dontrun{ aln <- read.fasta( system.file("examples/kif1a.fa", package = "bio3d") ) ##- Print only non-gap positions (i.e. no gaps in any sequence) aln$ali[, !is.gap(aln) ] ##- Mask any existing gaps with an "X" xaln <- aln xaln$ali[ is.gap(xaln$ali) ]="X" ##- Read a new PDB and align its sequence to the existing masked alignment pdb <- read.pdb( "1mkj" ) seq2aln(pdbseq(pdb), xaln, id = "1mkj") } } \keyword{ utilities } bio3d/man/pca.array.Rd0000644000176200001440000000173212526367344014213 0ustar liggesusers\name{pca.array} \alias{pca.array} \title{ Principal Component Analysis of an arrary of matrices } \description{ Calculate the principal components of an array of correlation or covariance matrices. } \usage{ \method{pca}{array}(x, use.svd = TRUE, ...) } \arguments{ \item{x}{ an array of matrices, e.g. correlation or covariance matrices as obtained from functions \code{dccm} or \code{enma2covs}. } \item{use.svd}{ logical, if TRUE singular value decomposition (SVD) is called instead of eigenvalue decomposition. } \item{\dots}{ . } } \details{ This function performs an PCA of residue-residue cross-correlations or covariance matrices derived from ensemble NMA of M structures. } \value{ Returns a list with components equivalent to the output from \code{pca.xyz}. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Xin-Qiu Yao, Lars Skjaerven } \seealso{ \code{\link{pca.xyz}} } \keyword{ utilities } bio3d/man/as.fasta.Rd0000644000176200001440000000237212526367344014034 0ustar liggesusers\name{as.fasta} \alias{as.fasta} \title{ Alignment to FASTA object } \description{ Convert alignment/sequence in matrix/vector format to FASTA object. } \usage{ as.fasta(x, id=NULL, \dots) } \arguments{ \item{x}{ a sequence character matrix/vector (e.g obtained from \code{\link{get.seq}} or \code{\link{seqbind}}). } \item{id}{ a vector of sequence names to serve as sequence identifers. By default the function will use the row names of the alignment if they exists, otherwise ids will be generated. } \item{\dots}{ arguments passed to and from functions. } } \details{ This function provides basic functionality to convert a sequence character matrix/vector to a FASTA object. } \value{ Returns a list of class \code{"fasta"} with the following components: \item{ali}{ an alignment character matrix with a row per sequence and a column per equivalent aminoacid/nucleotide. } \item{id}{ sequence names as identifers.} \item{call }{ the matched call. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{get.seq}}, \code{\link{seqaln}}, \code{\link{seqbind}}, \code{\link{pdbaln}} } \examples{ as.fasta(c("A", "C", "D")) } \keyword{ utilities } bio3d/man/pca.pdbs.Rd0000644000176200001440000000323212544562303014012 0ustar liggesusers\name{pca.pdbs} \alias{pca.pdbs} \title{ Principal Component Analysis } \description{ Performs principal components analysis (PCA) on an ensemble of PDB structures. } \usage{ \method{pca}{pdbs}(pdbs, core.find = FALSE, fit = FALSE, \dots) } \arguments{ \item{pdbs}{ an object of class \code{pdbs} as obtained from function \code{pdbaln} or \code{read.fasta.pdb}. } \item{core.find}{ logical, if TRUE core.find() function will be called to find core positions and coordinates of PDB structures will be fitted based on cores. } \item{fit}{ logical, if TRUE coordinates of PDB structures will be fitted based on all CA atoms. } \item{\dots}{ additional arguments passed to the method \code{pca.xyz}. } } \details{ The function \code{pca.pdbs} is a wrapper for the function \code{\link{pca.xyz}}, wherein more details of the PCA procedure are documented. } \value{ Returns a list with the following components: \item{L }{eigenvalues.} \item{U }{eigenvectors (i.e. the variable loadings).} \item{z.u }{scores of the supplied \code{data} on the pcs.} \item{sdev }{the standard deviations of the pcs.} \item{mean }{the means that were subtracted.} } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant, Lars Skjaerven and Xin-Qiu Yao} \seealso{ \code{\link{pca}}, \code{\link{pca.xyz}}, \code{\link{pdbaln}}, \code{\link{nma}}. } \examples{ attach(transducin) #-- Do PCA ignoring gap containing positions pc.xray <- pca(pdbs) # Plot results (conformer plots & scree plot) plot(pc.xray, col=annotation[, "color"]) detach(transducin) } \keyword{ utilities } \keyword{ multivariate } bio3d/man/view.cna.Rd0000644000176200001440000000625312632622153014036 0ustar liggesusers\name{view.cna} \alias{view.cna} \title{ View CNA Protein Structure Network Community Output in VMD } \description{ This function generates a VMD scene file and a PDB file that can be read and rendered by the VMD molecular viewer. Chose \sQuote{color by chain} to see corresponding regions of structure colored by community along with the community protein structure network. } \usage{ view.cna(x, pdb, layout = layout.cna(x, pdb, k=3), col.sphere=NULL, col.lines = "silver", weights = NULL, radius = table(x$communities$membership)/5, alpha = 1, vmdfile = "network.vmd", pdbfile = "network.pdb", launch = FALSE) } \arguments{ \item{x}{A 'cna' class object such as obtained from \sQuote{cna} function/ } \item{pdb}{A 'pdb' class object such as obtained from \sQuote{read.pdb} function. } \item{layout}{ A numeric matrix of Nx3 XYZ coordinate matrix, where N is the number of community spheres to be drawn. } \item{col.sphere}{ A numeric vector containing the sphere colors. } \item{col.lines}{ A character object specifying the color of the edges (default 'silver'). Must use VMD colors names. } \item{weights}{ A numeric vector specifying the edge width. Default is taken from E(x$community.network)$weight. } \item{radius}{ A numeric vector containing the sphere radii. Default is taken from the number of community members divided by 5. } \item{alpha}{ A single element numeric vector specifying the VMD alpha transparency parameter. Default is set to 1. } \item{vmdfile}{ A character element specifying the output VMD scene file name that will be loaded in VMD. } \item{pdbfile}{ A character element specifying the output pdb file name to be loaded in VMD. } \item{launch}{ Logical. If TRUE, a VMD session will be started with the output of \sQuote{view.cna}. } } \details{ This function generates a scaled sphere (communities) and stick (edges) representation of the community network along with the corresponding protein structure divided into chains, one chain for each community. The sphere radii are proportional to the number of community members and the edge widths correspond to network edge weights. } \value{ Two files are generated as output. A pdb file with the residue chains assigned according to the community and a text file containing The drawing commands for the community representation. } \references{ Humphrey, W., Dalke, A. and Schulten, K., ``VMD - Visual Molecular Dynamics'' J. Molec. Graphics 1996, 14.1, 33-38. } \author{ Barry Grant} \examples{ \dontrun{ # Load the correlation network from MD data attach(hivp) # Read the starting PDB file to determine atom correspondence pdbfile <- system.file("examples/hivp.pdb", package="bio3d") pdb <- read.pdb(pdbfile) # View cna view.cna(net, pdb, launch=FALSE) ## within VMD set 'coloring method' to 'Chain' and 'Drawing method' to Tube ##-- From NMA pdb.gdi = read.pdb("1KJY") pdb.gdi = trim.pdb(pdb.gdi, inds=atom.select(pdb.gdi, chain="A", elety="CA")) modes.gdi = nma(pdb.gdi) cij.gdi = dccm(modes.gdi) net.gdi = cna(cij.gdi, cutoff.cij=0.35) #view.cna(net.gdi, pdb.gdi, alpha = 0.7, launch=TRUE) detach(hivp) } } \keyword{ utility } bio3d/man/diag.ind.Rd0000644000176200001440000000167412412623040013773 0ustar liggesusers\name{diag.ind} \alias{diag.ind} \title{ Diagonal Indices of a Matrix } \description{ Returns a matrix of logicals the same size of a given matrix with entries 'TRUE' in the upper triangle close to the diagonal. } \usage{ diag.ind(x, n = 1, diag = TRUE) } \arguments{ \item{x}{ a matrix. } \item{n}{ the number of elements from the diagonal to include. } \item{diag}{ logical. Should the diagonal be included? } } \details{ Basic function useful for masking elements close to the diagonal of a given matrix. } \value{ Returns a matrix of logicals the same size of a given matrix with entries 'TRUE' in the upper triangle close to the diagonal. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \seealso{ \code{\link{diag}}, \code{\link{lower.tri}}, \code{\link{upper.tri}}, \code{\link{matrix}} } \examples{ diag.ind( matrix(,ncol=5,nrow=5), n=3 ) } \keyword{ utilities } bio3d/man/aa123.Rd0000644000176200001440000000277512544562303013142 0ustar liggesusers\name{aa123} \alias{aa123} \alias{aa321} \title{ Convert Between 1-letter and 3-letter Aminoacid Codes } \description{ Convert between one-letter IUPAC aminoacid codes and three-letter PDB style aminoacid codes. } \usage{ aa123(aa) aa321(aa) } \arguments{ \item{aa}{ a character vector of individual aminoacid codes. } } \details{ Standard conversions will map \sQuote{A} to \sQuote{ALA}, \sQuote{G} to \sQuote{GLY}, etc. Non-standard codes in \code{aa} will generate a warning and return \sQuote{UNK} or \sQuote{X}. } \value{ A character vector of aminoacid codes. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. For a description of IUPAC one-letter codes see:\cr \url{http://www.chem.qmul.ac.uk/iupac/AminoAcid/} For more information on PDB residue codes see:\cr \url{http://ligand-expo.rcsb.org/ld-search.html} } \author{ Barry Grant } \seealso{ \code{\link{read.pdb}}, \code{\link{read.fasta}}, \code{\link{pdbseq}} } \examples{ # Simple conversion aa123(c("D","L","A","G","S","H")) aa321(c("ASP", "LEU", "ALA", "GLY", "SER", "HIS")) \dontrun{ # Extract sequence from a PDB file's ATOM and SEQRES cards pdb <- read.pdb("1BG2") s <- aa321(pdb$seqres) # SEQRES a <- aa321(pdb$atom[pdb$calpha,"resid"]) # ATOM # Write both sequences to a fasta file write.fasta(alignment=seqbind(s,a), id=c("seqres","atom"), file="eg2.fa") # Alternative approach for ATOM sequence extraction pdbseq(pdb) pdbseq(pdb, aa1=FALSE ) } } \keyword{ utilities } bio3d/man/filter.identity.Rd0000644000176200001440000000424012544562303015435 0ustar liggesusers\name{filter.identity} \alias{filter.identity} \title{ Percent Identity Filter } \description{ Identify and filter subsets of sequences at a given sequence identity cutoff. } \usage{ filter.identity(aln = NULL, ide = NULL, cutoff = 0.6, verbose = TRUE, \dots) } \arguments{ \item{aln}{ sequence alignment list, obtained from \code{\link{seqaln}} or \code{\link{read.fasta}}, or an alignment character matrix. Not used if \sQuote{ide} is given.} \item{ide}{ an optional identity matrix obtained from \code{\link{seqidentity}}. } \item{cutoff}{ a numeric identity cutoff value ranging between 0 and 1. } \item{verbose}{ logical, if TRUE print details of the clustering process. } \item{\dots}{ additional arguments passed to and from functions. } } \details{ This function performs hierarchical cluster analysis of a given sequence identity matrix \sQuote{ide}, or the identity matrix calculated from a given alignment \sQuote{aln}, to identify sequences that fall below a given identity cutoff value \sQuote{cutoff}. } \value{ Returns a list object with components: \item{ind}{indices of the sequences below the cutoff value.} \item{tree}{an object of class \code{"hclust"}, which describes the tree produced by the clustering process. } \item{ide}{a numeric matrix with all pairwise identity values.} } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \seealso{ \code{\link{read.fasta}}, \code{\link{seqaln}}, \code{\link{seqidentity}}, \code{\link{entropy}}, \code{\link{consensus}} } \examples{ data(kinesin) attach(kinesin, warn.conflicts=FALSE) ide.mat <- seqidentity(pdbs) # Histogram of pairwise identity values op <- par(no.readonly=TRUE) par(mfrow=c(2,1)) hist(ide.mat[upper.tri(ide.mat)], breaks=30,xlim=c(0,1), main="Sequence Identity", xlab="Identity") k <- filter.identity(ide=ide.mat, cutoff=0.6) ide.cut <- seqidentity(pdbs$ali[k$ind,]) hist(ide.cut[upper.tri(ide.cut)], breaks=10, xlim=c(0,1), main="Sequence Identity", xlab="Identity") #plot(k$tree, axes = FALSE, ylab="Sequence Identity") #print(k$ind) # selected par(op) detach(kinesin) } \keyword{ utilities } bio3d/man/view.modes.Rd0000644000176200001440000000322112632622153014374 0ustar liggesusers\name{view.modes} \alias{view.modes} \title{ Vector Field Visualization of Modes } \description{ Structural visualization of mode vectors obtained from PCA or NMA. } \usage{ view.modes(modes, mode=NULL, outprefix="mode_vecs", scale=5, dual=FALSE, launch=FALSE, exefile="pymol") } \arguments{ \item{modes}{ an object of class \code{nma} or \code{pca} as obtained from functions \code{nma} or \code{pca.xyz}. } \item{mode}{ the mode number for which the vector field should be made. } \item{outprefix}{ character string specifying the file prefix. If \code{NULL} the temp directory will be used. } \item{scale}{ global scaling factor. } \item{dual}{ logical, if TRUE mode vectors are also drawn in both direction. } \item{launch}{ logical, if TRUE PyMol will be launched. } \item{exefile}{ file path to the \sQuote{PYMOL} program on your system (i.e. how is \sQuote{PYMOL} invoked). } } \details{ This function generates a PyMOL (python) script for drawing mode vectors on a PDB structure. The PyMOL script file is stored in the working directory with filename \dQuote{mode_vecs.py}, with coordinates in PDB format \dQuote{mode_vecs.inpcrd.pdb}. PyMOL will only be launched when using argument \sQuote{launch=TRUE}. } \value{ Called for its action. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{nma}}, \code{\link{pca.xyz}} } \examples{ \dontrun{ ## Fetch stucture pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) ## Calculate normal modes modes <- nma(pdb) view.modes(modes, mode=7) } } \keyword{ utilities } bio3d/man/vec2resno.Rd0000644000176200001440000000240012412623040014210 0ustar liggesusers\name{vec2resno} \alias{vec2resno} \title{ Replicate Per-residue Vector Values } \description{ Replicate values in one vector based on consecutive entries in a second vector. Useful for adding per-residue data to all-atom PDB files. } \usage{ vec2resno(vec, resno) } \arguments{ \item{vec}{ a vector of values to be replicated.} \item{resno}{ a reference vector or a PDB structure object, obtained from \code{\link{read.pdb}}, upon which replication is based. } } \details{ This function can aid in mapping data to PDB structure files. For example, residue conservation per position (or any other one value per residue data) can be replicated to fit the B-factor field of an all atom PDB file which can then be rendered according to this field in a molecular viewer. A basic check is made to ensure that the number of consecutively unique entries in the reference vector equals the length of the vector to be replicated. } \value{ Returns a vector of replicated values. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \seealso{ \code{\link{read.pdb}}, \code{\link{atom.select}}, \code{\link{write.pdb}} } \examples{ vec2resno(c("a","b"), c(1,1,1,1,2,2)) } \keyword{ utilities } bio3d/man/hclustplot.Rd0000644000176200001440000000532512544562303014526 0ustar liggesusers\name{hclustplot} \alias{hclustplot} \title{ Dendrogram with Clustering Annotation } \description{ Draw a standard dendrogram with clustering annotation in the marginal regions and colored labels. } \usage{ hclustplot(hc, k = NULL, h = NULL, colors = NULL, labels = NULL, fillbox = FALSE, heights = c(1, .3), mar = c(1, 1, 0, 1), ...) } \arguments{ \item{hc}{ an object of the type produced by \code{hclust}. } \item{k}{ an integer scalar or vector with the desired number of groups. Redirected to function \code{cutree}. } \item{h}{ numeric scalar or vector with heights where the tree should be cut. Redirected to function \code{cutree}. At least one of \sQuote{k} or \sQuote{h} must be specified. } \item{colors}{ a numerical or character vector with the same length as \sQuote{hc} specifying the colors of the labels. } \item{labels}{ a character vector with the same length as \sQuote{hc} containing the labels to be written. } \item{fillbox}{ logical, if TRUE clustering annotation will be drawn as filled boxes below the dendrogram. } \item{heights}{ numeric vector of length two specifying the values for the heights of rows on the device. See function \code{layout}. } \item{mar}{ a numerical vector of the form \sQuote{c(bottom, left, top, right)} which gives the number of lines of margin to be specified on the four sides of the plot. If left at default the margins will be adjusted upon adding arguments \sQuote{main}, \sQuote{ylab}, etc. } \item{\dots}{ other graphical parameters passed to functions \code{plot.dendrogram}, \code{mtext}, and \code{par}. Note that certain arguments will be ignored. } } \details{ This function adds extended visualization of cluster membership to a standard dendrogram. If \sQuote{k} or \sQuote{h} is provided a call to \code{cutree} will provide cluster membership information. Alternatively a vector of colors or cluster membership information can be provided through argument \sQuote{colors}. See examples for further details on usage. } \note{ Argument \sQuote{horiz=TRUE} currently not supported. } \value{ Called for its effect. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{plot.hclust}}, \code{\link{plot.dendrogram}}, \code{\link{hclust}}, \code{\link{cutree}}. } \examples{ \donttest{ # Redundant testing excluded attach(transducin) ##- perform RMSD clustering rd <- rmsd(pdbs, fit=TRUE) hc <- hclust(as.dist(rd)) ##- draw dendrogram hclustplot(hc, k=3) ##- draw dendrogram with manual clustering annotation #hclustplot(hc, colors=annotation[, "color"], labels=pdbs$id) detach(transducin) } } \keyword{ hplot } bio3d/man/read.crd.amber.Rd0000644000176200001440000000303012526367344015073 0ustar liggesusers\name{read.crd.amber} \alias{read.crd.amber} \title{ Read AMBER Coordinate files } \description{ Read coordinate data from an AMBER coordinate / restart file. } \usage{ \method{read.crd}{amber}(file, ...) } \arguments{ \item{file}{ name of crd file to read. } \item{\dots}{ arguments passed to and from functions. } } \details{ Read a AMBER Coordinate format file. } \value{ A list object of type \sQuote{amber} and \sQuote{crd} with the following components: \item{xyz}{ a numeric matrix of class \sQuote{xyz} containing the Cartesian coordinates. } \item{velocities}{ a numeric vector containg the atom velocities. } \item{time}{ numeric, length of the simulation (applies to Amber restart coordinate files). } \item{natoms}{ total number of atoms in the coordinate file. } \item{box}{ dimensions of the box. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. \url{http://ambermd.org/formats.html} } \author{ Lars Skjaerven } \note{ See AMBER documentation for Coordinate format description. } \seealso{ \code{\link{read.prmtop}}, \code{\link{read.ncdf}}, \code{\link{as.pdb}}, \code{\link{atom.select}}, \code{\link{read.pdb}}, \code{\link{read.crd.charmm}} } \examples{ \dontrun{ ## Read a PRMTOP file prmtop <- read.prmtop("prot_solvated.prmtop") print(prmtop) ## Read a Amber CRD file crds <- read.crd("prot_solvated.inpcrd") ## Atom selection ca.inds <- atom.select(prmtop, "calpha") ## Convert to PDB format pdb <- as.pdb(prmtop, crds, inds=ca.inds) } } \keyword{ IO } bio3d/man/as.pdb.Rd0000644000176200001440000001470312544562303013474 0ustar liggesusers\name{as.pdb} \alias{as.pdb} \alias{as.pdb.mol2} \alias{as.pdb.prmtop} \alias{as.pdb.default} \title{ Convert to PDB format } \description{ Convert Tripos Mol2 format, or Amber parameter/topology and coordinate data to PDB format. } \usage{ as.pdb(\dots) \method{as.pdb}{mol2}(mol2, \dots) \method{as.pdb}{prmtop}(prmtop, crd=NULL, inds=NULL, inds.crd=inds, ncore=NULL, \dots) \method{as.pdb}{default}(pdb=NULL, xyz=NULL, type=NULL, resno=NULL, resid=NULL, eleno=NULL, elety=NULL, chain=NULL, insert=NULL, alt=NULL, o=NULL, b=NULL, segid=NULL, elesy=NULL, charge=NULL, verbose=TRUE, \dots) } \arguments{ \item{\dots}{ arguments passed to and from functions. } \item{mol2}{ a list object of type \code{"mol2"} (obtained with \code{\link{read.mol2}}). } \item{prmtop}{ a list object of type \code{"prmtop"} (obtained with \code{\link{read.prmtop}}). } \item{crd}{ a list object of type \code{"crd"} (obtained with \code{\link{read.crd.amber}}). } \item{inds}{ a list object of type \code{"select"} as obtained from \code{\link{atom.select}}. The indices points to which atoms in the PRMTOP object to convert. } \item{inds.crd}{ same as the \sQuote{inds} argument, but pointing to the atoms in CRD object to convert. By default, this argument equals to \sQuote{inds}, assuming the same number and sequence of atoms in the PRMTOP and CRD objects. } \item{ncore }{ number of CPU cores used to do the calculation. \code{ncore>1} requires package \sQuote{parallel} installed. } \item{pdb}{ an object of class \sQuote{pdb} as obtained from \code{\link{read.pdb}}. } \item{xyz}{ a numeric vector/matrix of Cartesian coordinates. If provided, the number of atoms in the new PDB object will be set to \code{ncol(as.xyz(xyz))/3} (see \code{\link{as.xyz}}). If \code{xyz} is not provided the number of atoms will be based on the length of \code{eleno}, \code{resno}, or \code{resid} (in that order). } \item{type}{ a character vector of record types, i.e. "ATOM" or "HETATM", with length equal to \code{ncol(as.xyz(xyz))/3}. Alternatively, a single element character vector can be provided which will be repeated to match the number of atoms. } \item{resno}{ a numeric vector of residue numbers of length equal to \code{ncol(as.xyz(xyz))/3}. } \item{resid}{ a character vector of residue types/ids of length equal to \code{ncol(as.xyz(xyz))/3}. Alternatively, a single element character vector can be provided which will be repeated to match the number of atoms. } \item{eleno}{ a numeric vector of element/atom numbers of length equal to \code{ncol(as.xyz(xyz))/3}. } \item{elety}{ a character vector of element/atom types of length equal to \code{ncol(as.xyz(xyz))/3}. Alternatively, a single element character vector can be provided which will be repeated to match the number of atoms. } \item{chain}{ a character vector of chain identifiers with length equal to \code{ncol(as.xyz(xyz))/3}. Alternatively, a single element character vector can be provided which will be repeated to match the number of atoms. } \item{insert}{ a character vector of insertion code with length equal to \code{ncol(as.xyz(xyz))/3}. } \item{alt}{ a character vector of alternate record with length equal to \code{ncol(as.xyz(xyz))/3}. } \item{o}{ a numeric vector of occupancy values of length equal to \code{ncol(as.xyz(xyz))/3}. Alternatively, a single element numeric vector can be provided which will be repeated for to match the number of atoms. } \item{b}{ a numeric vector of B-factors of length equal to \code{ncol(as.xyz(xyz))/3}. Alternatively, a single element numeric vector can be provided which will be repeated to match the number of atoms.} \item{segid}{ a character vector of segment id of length equal to \code{ncol(as.xyz(xyz))/3}. Alternatively, a single element character vector can be provided which will be repeated to match the number of atoms. } \item{elesy}{ a character vector of element symbol of length equal to \code{ncol(as.xyz(xyz))/3}. Alternatively, a single element character vector can be provided which will be repeated to match the number of atoms. } \item{charge}{ a numeric vector of atomic charge of length equal to \code{ncol(as.xyz(xyz))/3}. } \item{verbose}{ logical, if TRUE details of the PDB generation process is printed to screen. } } \details{ This function converts Tripos Mol2 format, Amber formatted parameter/topology (PRMTOP) and coordinate objects, and vector data to a PDB object. While \code{as.pdb.mol2} and \code{as.pdb.prmtop} converts specific objects to a PDB object, \code{as.pdb.default} provides basic functionality to convert raw data such as vectors of e.g. residue numbers, residue identifiers, Cartesian coordinates, etc to a PDB object. When \code{pdb} is provided the returned PDB object is built from the input object with fields replaced by any input vector arguments. e.g. \code{as.pdb(pdb, xyz=crd)} will return the same PDB object, with only the Cartesian coordinates changed to \code{crd}. } \value{ Returns a list of class \code{"pdb"} with the following components: \item{atom}{ a data.frame containing all atomic coordinate ATOM data, with a row per ATOM and a column per record type. See below for details of the record type naming convention (useful for accessing columns). } \item{xyz }{ a numeric matrix of ATOM coordinate data of class \code{xyz}. } \item{calpha }{ logical vector with length equal to \code{nrow(atom)} with TRUE values indicating a C-alpha \dQuote{elety}. } \item{call }{ the matched call. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. \url{http://ambermd.org/formats.html} } \author{ Lars Skjaerven } \seealso{ \code{\link{read.crd}}, \code{\link{read.ncdf}}, \code{\link{atom.select}}, \code{\link{read.pdb}} } \examples{ ## Vector(s) to PDB object pdb <- as.pdb(resno=1:6, elety="CA", resid="ALA", chain="A") pdb \dontrun{ ## Read a PRMTOP file prmtop <- read.prmtop("prot_solvated.prmtop") ## Atom selection ca.inds <- atom.select(prmtop, "calpha") ## Read a Amber CRD file crds <- read.crd("prot_solvated.inpcrd") ## Convert to PDB format pdb <- as.pdb(prmtop, crds, inds=ca.inds) ## Convert to PDB format trj <- read.ncdf("traj.nc", at.sel=ca.inds) pdb <- as.pdb(prmtop, trj[20,], inds=ca.inds, inds.crd=NULL) } } \keyword{ IO } bio3d/man/elements.Rd0000644000176200001440000000367312524171274014146 0ustar liggesusers\name{elements} \alias{elements} \docType{data} \title{Periodic Table of the Elements} \description{This data set gives various information on chemical elements.} \usage{elements} \format{ A data frame containing for each chemical element the following information. \describe{ \item{\code{num}}{atomic number} \item{\code{symb}}{elemental symbol} \item{\code{areneg}}{Allred and Rochow electronegativity (0.0 if unknown)} \item{\code{rcov}}{covalent radii (in Angstrom) (1.6 if unknown)} \item{\code{rbo}}{"bond order" radii} \item{\code{rvdw}}{van der Waals radii (in Angstrom) (2.0 if unknown)} \item{\code{maxbnd}}{maximum bond valence (6 if unknown)} \item{\code{mass}}{IUPAC recommended atomic masses (in amu)} \item{\code{elneg}}{Pauling electronegativity (0.0 if unknown)} \item{\code{ionization}}{ionization potential (in eV) (0.0 if unknown)} \item{\code{elaffinity}}{electron affinity (in eV) (0.0 if unknown)} \item{\code{red}}{red value for visualization} \item{\code{green}}{green value for visualization} \item{\code{blue}}{blue value for visualization} \item{\code{name}}{element name} } } \source{ Open Babel (2.3.1) file: element.txt\cr Created from the Blue Obelisk Cheminformatics Data Repository\cr Direct Source: http://www.blueobelisk.org/\cr http://www.blueobelisk.org/repos/blueobelisk/elements.xml includes furhter bibliographic citation information\cr - Allred and Rochow Electronegativity from http://www.hull.ac.uk/chemistry/electroneg.php?type=Allred-Rochow\cr - Covalent radii from http://dx.doi.org/10.1039/b801115j\cr - Van der Waals radii from http://dx.doi.org/10.1021/jp8111556\cr } \examples{ data(elements) elements # Get the mass of some elements symb <- c("C","O","H") elements[match(symb,elements[,"symb"]),"mass"] # Get the van der Waals radii of some elements symb <- c("C","O","H") elements[match(symb,elements[,"symb"]),"rvdw"] } \keyword{datasets} bio3d/man/is.pdb.Rd0000644000176200001440000000142112526367344013505 0ustar liggesusers\name{is.pdb} \alias{is.pdb} \alias{is.pdbs} \title{ Is an Object of Class \sQuote{pdb(s)}? } \description{ Checks whether its argument is an object of class \sQuote{pdb} or \sQuote{pdbs}. } \usage{ is.pdb(x) is.pdbs(x) } \arguments{ \item{x}{ an R object. } } \details{ Tests if the object \sQuote{x} is of class \sQuote{pdb} (\code{is.pdb}) or \sQuote{pdbs} (\code{is.pdbs}), i.e. if \sQuote{x} has a \dQuote{class} attribute equal to \code{pdb} or \code{pdbs}. } \value{ TRUE if x is an object of class \sQuote{pdb(s)} and FALSE otherwise } \seealso{ \code{\link{read.pdb}}, \code{\link{read.fasta.pdb}}, \code{\link{pdbaln}} } \examples{ # Read a PDB file pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) is.pdb(pdb) } \keyword{ classes } bio3d/man/blast.pdb.Rd0000644000176200001440000001635112632622153014175 0ustar liggesusers\name{blast.pdb} \alias{blast.pdb} \alias{get.blast} \alias{plot.blast} \title{ NCBI BLAST Sequence Search and Summary Plot of Hit Statistics} \description{ Run NCBI blastp, on a given sequence, against the PDB, NR and swissprot sequence databases. Produce plots that facilitate hit selection from the match statistics of a BLAST result. } \usage{ blast.pdb(seq, database = "pdb", time.out = NULL, chain.single=TRUE) get.blast(urlget, time.out = NULL, chain.single=TRUE) \method{plot}{blast}(x, cutoff = NULL, cut.seed=NULL, cluster=TRUE, mar=c(2, 5, 1, 1), cex=1.5, ...) } \arguments{ \item{seq}{ a single element or multi-element character vector containing the query sequence. Alternatively a \sQuote{fasta} object from function \code{get.seq} can be provided. } \item{database}{ a single element character vector specifying the database against which to search. Current options are \sQuote{pdb}, \sQuote{nr} and \sQuote{swissprot}. } \item{time.out}{ integer specifying the number of seconds to wait for the blast reply before a time out occurs. } \item{urlget}{ the URL to retrieve BLAST results; Usually it is returned by blast.pdb if time.out is set and met. } \item{chain.single}{ logical, if TRUE double NCBI character PDB database chain identifiers are simplified to lowercase '1WF4_GG' > '1WF4_g'. If FALSE no conversion to match RCSB PDB files is performed. } \item{x}{ BLAST results as obtained from the function \code{\link{blast.pdb}}. } \item{cutoff}{ A numeric cutoff value, in terms of minus the log of the evalue, for returned hits. If null then the function will try to find a suitable cutoff near \sQuote{cut.seed} which can be used as an initial guide (see below). } \item{cut.seed}{ A numeric seed cutoff value, used for initial cutoff estimation. If null then a seed position is set to the point of largest drop-off in normalized scores (i.e. the biggest jump in E-values). } \item{cluster}{ Logical, if TRUE (and \sQuote{cutoff} is null) a clustering of normalized scores is performed to partition hits in groups by similarity to query. If FALSE the partition point is set to the point of largest drop-off in normalized scores. } \item{mar}{ A numerical vector of the form c(bottom, left, top, right) which gives the number of lines of margin to be specified on the four sides of the plot.} \item{cex}{ a numerical single element vector giving the amount by which plot labels should be magnified relative to the default. } \item{\dots}{ extra plotting arguments. } } \details{ The \code{blast.pdb} function employs direct HTTP-encoded requests to the NCBI web server to run BLASTP, the protein search algorithm of the BLAST software package. BLAST, currently the most popular pairwise sequence comparison algorithm for database searching, performs gapped local alignments via a heuristic strategy: it identifies short nearly exact matches or hits, bidirectionally extends non-overlapping hits resulting in ungapped extended hits or high-scoring segment pairs(HSPs), and finally extends the highest scoring HSP in both directions via a gapped alignment (Altschul et al., 1997) For each pairwise alignment BLAST reports the raw score, bitscore and an E-value that assess the statistical significance of the raw score. Note that unlike the raw score E-values are normalized with respect to both the substitution matrix and the query and database lengths. Here we also return a corrected normalized score (mlog.evalue) that in our experience is easier to handle and store than conventional E-values. In practice, this score is equivalent to minus the natural log of the E-value. Note that, unlike the raw score, this score is independent of the substitution matrix and and the query and database lengths, and thus is comparable between BLASTP searches. Examining plots of BLAST alignment lengths, scores, E-values and normalized scores (-log(E-Value) from the \code{blast.pdb} function can aid in the identification sensible hit similarity thresholds. This is facilitated by the \code{plot.blast} function. If a \sQuote{cutoff} value is not supplied then a basic hierarchical clustering of normalized scores is performed with initial group partitioning implemented at a hopefully sensible point in the vicinity of \sQuote{h=cut.seed}. Inspection of the resultant plot can then be use to refine the value of \sQuote{cut.seed} or indeed \sQuote{cutoff}. As the \sQuote{cutoff} value can vary depending on the desired application and indeed the properties of the system under study it is envisaged that \sQuote{plot.blast} will be called multiple times to aid selection of a suitable \sQuote{cutoff} value. See the examples below for further details. } \value{ The function \code{blast.pdb} returns a list with the first eight components below. The function \code{plot.blast} produces a plot on the active graphics device and returns a three component list object with \code{hits}, \code{pdb.id} and \code{gi.id} see below: \item{bitscore }{ a numeric vector containing the raw score for each alignment. } \item{evalue }{ a numeric vector containing the E-value of the raw score for each alignment. } \item{mlog.evalue }{ a numeric vector containing minus the natural log of the E-value. } \item{gi.id }{ a character vector containing the gi database identifier of each hit. } \item{pdb.id }{ a character vector containing the PDB database identifier of each hit. } \item{hit.tbl }{ a character matrix summarizing BLAST results for each reported hit, see below. } \item{raw }{ a data frame summarizing BLAST results, note multiple hits may appear in the same row. } \item{url }{ a single element character vector with the NCBI result URL and RID code. This can be passed to the get.blast function. } \item{hits}{ an ordered matrix detailing the subset of hits with a normalized score above the chosen cutoff. Database identifiers are listed along with their cluster group number. } \item{pdb.id}{ a character vector containing the PDB database identifier of each hit above the chosen threshold. } \item{gi.id}{ a character vector containing the gi database identifier of each hit above the chosen threshold. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. \sQuote{BLAST} is the work of Altschul et al.: Altschul, S.F. et al. (1990) \emph{J. Mol. Biol.} \bold{215}, 403--410. Full details of the \sQuote{BLAST} algorithm, along with download and installation instructions can be obtained from:\cr \url{http://www.ncbi.nlm.nih.gov/BLAST/}. } \author{ Barry Grant } \note{ Online access is required to query NCBI blast services. } \seealso{ \code{\link{plot.blast}}, \code{\link{hmmer}}, \code{\link{seqaln}}, \code{\link{get.pdb}} } \examples{ \dontrun{ pdb <- read.pdb("4q21") blast <- blast.pdb( pdbseq(pdb) ) head(blast$hit.tbl) top.hits <- plot(blast) head(top.hits$hits) ## Use 'get.blast()' to retrieve results at a later time. #x <- get.blast(blast$url) #head(x$hit.tbl) # Examine and download 'best' hits top.hits <- plot.blast(blast, cutoff=188) head(top.hits$hits) #get.pdb(top.hits) } } \keyword{ utilities } \keyword{ hplot } bio3d/man/dssp.Rd0000644000176200001440000001301312544562303013267 0ustar liggesusers\name{dssp} \alias{dssp} \alias{dssp.pdb} \alias{dssp.pdbs} \alias{dssp.xyz} \alias{stride} \alias{print.sse} \title{ Secondary Structure Analysis with DSSP or STRIDE } \description{ Secondary structure assignment according to the method of Kabsch and Sander (DSSP) or the method of Frishman and Argos (STRIDE). } \usage{ dssp(\dots) \method{dssp}{pdb}(pdb, exefile = "dssp", resno=TRUE, full=FALSE, verbose=FALSE, \dots) \method{dssp}{pdbs}(pdbs, \dots) \method{dssp}{xyz}(xyz, pdb, \dots) stride(pdb, exefile = "stride", resno=TRUE) \method{print}{sse}(x, \dots) } \arguments{ \item{pdb}{ a structure object of class \code{"pdb"}, obtained from \code{\link{read.pdb}}. } \item{exefile}{ file path to the \sQuote{DSSP} or \sQuote{STRIDE} program on your system (i.e. how is \sQuote{DSSP} or \sQuote{STRIDE} invoked). } \item{resno}{ logical, if TRUE output is in terms of residue numbers rather than residue index (position in sequence). } \item{full}{ logical, if TRUE bridge pairs and hbonds columns are parsed. } \item{verbose}{ logical, if TRUE \sQuote{DSSP} warning and error messages are printed. } \item{pdbs}{ a list object of class \code{"pdbs"} (obtained with \code{\link{pdbaln}} or \code{\link{read.fasta.pdb}}). } \item{xyz}{ a trajectory object of class \code{"xyz"}, obtained from \code{\link{read.ncdf}}, \code{\link{read.dcd}}, \code{\link{read.crd}}. } \item{x}{ an \code{sse} object obtained from \code{\link{dssp.pdb}} or \code{\link{stride}}. } \item{...}{ additional arguments to and from functions. } } \details{ This function calls the \sQuote{DSSP} or \sQuote{STRIDE} program to define secondary structure and psi and phi torsion angles. } \value{ Returns a list with the following components: \item{helix}{ \sQuote{start}, \sQuote{end}, \sQuote{length}, \sQuote{chain} and \sQuote{type} of helix, where start and end are residue numbers or residue index positions depending on the value of \dQuote{resno} input argument. } \item{sheet}{ \sQuote{start}, \sQuote{end} and \sQuote{length} of E type sse, where start and end are residue numbers \dQuote{resno}. } \item{turn}{ \sQuote{start}, \sQuote{end} and \sQuote{length} of T type sse, where start and end are residue numbers \dQuote{resno}. } \item{phi}{ a numeric vector of phi angles. } \item{psi}{ a numeric vector of psi angles. } \item{acc}{ a numeric vector of solvent accessibility. } \item{sse}{ a character vector of secondary structure type per residue. } \item{hbonds}{ a 10 or 16 column matrix containing the bridge pair records as well as backbone NH-->O and O-->NH H-bond records. (Only available for \code{\link{dssp}} } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. \sQuote{DSSP} is the work of Kabsch and Sander: Kabsch and Sander (1983) \emph{Biopolymers.} \bold{12}, 2577--2637. For information on obtaining \sQuote{DSSP}, see:\cr \url{http://swift.cmbi.ru.nl/gv/dssp/}. \sQuote{STRIDE} is the work of Frishman and Argos: Frishman and Argos (1995) \emph{Proteins.} \bold{3}, 566--579. For information on obtaining the \sQuote{STRIDE} program, see:\cr \url{http://webclu.bio.wzw.tum.de/stride/}, or copy it from an installation of VMD. } \author{ Barry Grant, Lars Skjaerven (dssp.pdbs) } \note{ A system call is made to the \sQuote{DSSP} or \sQuote{STRIDE} program, which must be installed on your system and in the search path for executables. For the \code{hbonds} list component the column names can be used as a convenient means of data access, namely:\cr Bridge pair 1 \dQuote{BP1},\cr Bridge pair 2 \dQuote{BP2},\cr Backbone H-bond (NH-->O) \dQuote{NH-O.1},\cr H-bond energy of NH-->O \dQuote{E1},\cr Backbone H-bond (O-->NH) \dQuote{O-HN.1},\cr H-bond energy of O-->NH \dQuote{E2},\cr Backbone H-bond (NH-->O) \dQuote{NH-O.2},\cr H-bond energy of NH-->O \dQuote{E3},\cr Backbone H-bond (O-->NH) \dQuote{O-HN.2},\cr H-bond energy of O-->NH \dQuote{E4}.\cr If \sQuote{resno=TRUE} the following additional columns are included:\cr Chain ID of resno \dQuote{BP1}: \dQuote{ChainBP1},\cr Chain ID of resno \dQuote{BP2}: \dQuote{ChainBP2},\cr Chain ID of resno \dQuote{O-HN.1}: \dQuote{Chain1},\cr Chain ID of resno \dQuote{NH-O.2}: \dQuote{Chain2},\cr Chain ID of resno \dQuote{O-HN.1}: \dQuote{Chain3},\cr Chain ID of resno \dQuote{NH-O.2}: \dQuote{Chain4}. } \seealso{ \code{\link{read.pdb}}, \code{\link{torsion.pdb}}, \code{\link{torsion.xyz}}, \code{\link{plot.bio3d}}, \code{\link{read.ncdf}}, \code{\link{read.dcd}}, \code{\link{read.prmtop}}, \code{\link{read.crd}}, } \examples{ \dontrun{ ##- PDB example # Read a PDB file pdb <- read.pdb("1bg2") sse <- dssp(pdb) sse2 <- stride(pdb) ## Short summary sse sse2 # Helix data sse$helix # Precent SSE content sum(sse$helix$length)/sum(pdb$calpha) * 100 sum(sse$sheet$length)/sum(pdb$calpha) * 100 ##- PDBs example aln <- read.fasta( system.file("examples/kif1a.fa",package="bio3d") ) pdbs <- read.fasta.pdb( aln ) ## Aligned PDB defined secondary structure pdbs$sse ## Aligned DSSP defined secondary structure sse <- dssp(pdbs) ##- XYZ Trajectory pdb <- read.pdb("2mda", multi=TRUE) dssp.xyz(pdb$xyz, pdb) ## Note. for large MD trajectories you may want to skip some frames, e.g. xyz <- rbind(pdb$xyz, pdb$xyz) ## dummy trajectory frames <- seq(1, to=nrow(xyz), by=4) ## frame numbers to examine ss <- dssp.xyz(xyz[frames, ], pdb) ## matrix of sse frame x residue } } \keyword{ utilities } bio3d/man/pdbseq.Rd0000644000176200001440000000254712544562303013606 0ustar liggesusers\name{pdbseq} \alias{pdbseq} \title{ Extract The Aminoacid Sequence From A PDB Object } \description{ Return a vector of the one-letter IUPAC or three-letter PDB style aminoacid codes from a given PDB object. } \usage{ pdbseq(pdb, inds = NULL, aa1 = TRUE) } \arguments{ \item{pdb}{a PDB structure object obtained from \code{\link{read.pdb}}. } \item{inds}{ a list object of ATOM and XYZ indices as obtained from \code{\link{atom.select}}. } \item{aa1}{ logical, if TRUE then the one-letter IUPAC sequence is returned. IF FALSE then the three-letter PDB style sequence is returned.} } \details{ See the examples below and the functions \code{\link{atom.select}} and \code{\link{aa321}} for further details. } \value{ A character vector of aminoacid codes. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. For a description of IUPAC one-letter codes see:\cr \url{http://www.chem.qmul.ac.uk/iupac/AminoAcid/} For more information on PDB residue codes see:\cr \url{http://ligand-expo.rcsb.org/ld-search.html} } \author{ Barry Grant } \seealso{ \code{\link{read.pdb}}, \code{\link{atom.select}}, \code{\link{aa321}}, \code{\link{read.fasta}} } \examples{ \dontrun{ pdb <- read.pdb( "5p21" ) pdbseq(pdb) #pdbseq(pdb, inds=atom.select(pdb, resno=5:15, elety="CA"), aa1=FALSE) } } \keyword{ utilities } bio3d/man/nma.Rd0000644000176200001440000000506512544562303013101 0ustar liggesusers\name{nma} \alias{nma} \title{ Normal Mode Analysis } \description{ Perform normal mode analysis (NMA) on either a single or an ensemble of protein structures. } \usage{ nma(...) } \arguments{ \item{\dots}{ arguments passed to the methods \code{\link{nma.pdb}}, or \code{\link{nma.pdbs}}. For function \code{\link{nma.pdb}} this will include an object of class \code{pdb} as obtained from function \code{\link{read.pdb}}. For function \code{\link{nma.pdbs}} an object of class \code{pdbs} as obtained from function \code{\link{pdbaln}} or \code{\link{read.fasta.pdb}}. } } \details{ Normal mode analysis (NMA) is a computational approach for studying and characterizing protein flexibility. Current functionality entails normal modes calculation on either a single protein structure or an ensemble of aligned protein structures. This generic \code{\link{nma}} function calls the corresponding methods for the actual calculation, which is determined by the class of the input argument: Function \code{\link{nma.pdb}} will be used when the input argument is of class \code{pdb}. The function calculates the normal modes of a C-alpha model of a protein structure. Function \code{\link{nma.pdbs}} will be used when the input argument is of class \code{pdbs}. The function will perform normal mode analysis of each PDB structure stored in the \code{pdbs} object (\sQuote{ensemble NMA}). See documentation and examples for each corresponding function for more details. } \references{ Skjaerven, L. et al. (2014) \emph{BMC Bioinformatics} \bold{15}, 399. Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{nma.pdb}}, \code{\link{nma.pdbs}}, \code{\link{pca}}. } \examples{ ##- Singe structure NMA ## Fetch stucture pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) ## Calculate normal modes modes <- nma(pdb) ## Print modes print(modes) ## Plot modes plot(modes) ## Visualize modes #m7 <- mktrj.nma(modes, mode=7, file="mode_7.pdb") \donttest{ ## Needs MUSCLE installed - testing excluded ##- Ensemble NMA if(check.utility("muscle")) { ## Fetch PDB files and split to chain A only PDB files ids <- c("1a70_A", "1czp_A", "1frd_A", "1fxi_A", "1iue_A", "1pfd_A") files <- get.pdb(ids, split = TRUE, path = tempdir()) ## Sequence Alignement pdbs <- pdbaln(files, outfile = tempfile()) ## Normal mode analysis on aligned data modes <- nma(pdbs, rm.gaps=FALSE) ## Plot fluctuation data plot(modes, pdbs=pdbs) } } } \keyword{ analysis } bio3d/man/load.enmff.Rd0000644000176200001440000001144412526367344014345 0ustar liggesusers\name{load.enmff} \alias{load.enmff} \alias{ff.calpha} \alias{ff.calphax} \alias{ff.anm} \alias{ff.pfanm} \alias{ff.sdenm} \alias{ff.reach} \title{ ENM Force Field Loader } \description{ Load force field for elastic network normal mode calculation. } \usage{ load.enmff(ff = 'calpha') ff.calpha(r, rmin=2.9, ...) ff.anm(r, cutoff=15, gamma=1, ...) ff.pfanm(r, cutoff=NULL, ...) ff.calphax(r, atom.id, ssdat=NULL, verbose=FALSE, ...) ff.sdenm(r, atom.id, ssdat=NULL, ...) ff.reach(r, atom.id, ssdat=NULL, ...) } \arguments{ \item{ff}{ a character string specifying the force field to use: \sQuote{calpha}, \sQuote{anm}, \sQuote{pfanm}, \sQuote{calphax}, \sQuote{reach}, or \sQuote{sdenm}. } \item{r}{ a numeric vector of c-alpha distances. } \item{rmin}{ lowest allowed atom-atom distance for the force constant calculation. The default of 2.9A is based on an evaluation of 24 high-resolution X-ray structures (< 1A). } \item{cutoff}{ numerical, cutoff for pair-wise interactions. } \item{gamma}{ numerical, global scaling factor. } \item{atom.id}{ atomic index. } \item{ssdat}{ sequence and structure data. } \item{verbose}{ logical, if TRUE interaction details are printed. } \item{...}{ additional arguments (for technical reasons). } } \details{ This function provides a collection of elastic network model (ENM) force fields for normal modes analysis (NMA) of protein structures. It returns a function for calculating the residue-residue spring force constants. The \sQuote{calpha} force field - originally developed by Konrad Hinsen - is the recommended one for most applications. It employs a spring force constant differentiating between nearest-neighbour pairs along the backbone and all other pairs. The force constant function was parameterized by fitting to a local minimum of a crambin model using the AMBER94 force field. The implementation of the \sQuote{ANM} (Anisotropic Network Model) force field originates from the lab of Ivet Bahar. It uses a simplified (step function) spring force constant based on the pair-wise distance. A variant of this from the Jernigan lab is the so-called \sQuote{pfANM} (parameter free ANM) with interactions that fall off with the square of the distance. The \sQuote{calphax} force field is an extension of the original \sQuote{calpha} force field by Hinsen. In this implementation we have included specific force constants for disulfide bridges, helix 1-4 interactions, and beta sheet bridges. The \sQuote{sdENM} (by Dehouck and Mikhailov) employs residue specific spring force constants. It has been parameterized through a statistical analysis of a total of 1500 NMR ensembles. The \sQuote{REACH} force field (by Moritsugu and Smith) is parameterized based on variance-covariance matrices obtained from MD simulations. It employs force constants that fall off exponentially with distance for non-bonded pairs. See references for more details on the individual force fields. } \note{ The arguments \sQuote{atom.id} and \sQuote{ssdat} are used from within function \sQuote{build.hessian} for functions that are not simply a function of the pair-wise distance. e.g. the force constants in the \sQuote{calphax} model is a function of c-alpha distances, SSE information and SS-bonds, while the \sQuote{sdENM} force field computes the force constants based on a function of the residue types and calpha distance. } \value{ \sQuote{load.enmff} returns a function for calculating the spring force constants. The \sQuote{ff} functions returns a numeric vector of residue-residue spring force constants. } \references{ Skjaerven, L. et al. (2014) \emph{BMC Bioinformatics} \bold{15}, 399. Hinsen, K. et al. (2000) \emph{Chemical Physics} \bold{261}, 25--37. Atilgan, A.R. et al. (2001) \emph{Biophysical Journal} \bold{80}, 505--515. Dehouck Y. & Mikhailov A.S. (2013) \emph{PLoS Comput Biol} \bold{9}:e1003209. Moritsugu K. & Smith J.C. (2008) \emph{Biophysical Journal} \bold{95}, 1639--1648. Yang, L. et al. (2009) \emph{PNAS} \bold{104}, 12347-52. Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{nma}}, \code{\link{build.hessian}} } \examples{ ## Load the c-alpha force field pfc.fun <- load.enmff('calpha') ## Calculate the pair force constant for a set of C-alpha distances force.constants <- pfc.fun( seq(4,8, by=0.5) ) ## Calculate the complete spring force constant matrix ## Fetch PDB pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) ## Fetch only c-alpha coordinates ca.inds <- atom.select(pdb, 'calpha') xyz <- pdb$xyz[ca.inds$xyz] ## Calculate distance matrix dists <- dm.xyz(xyz, mask.lower=FALSE) ## all pair-wise spring force constants fc.matrix <- apply(dists, 1, pfc.fun) } \keyword{ utilities } bio3d/man/dccm.pca.Rd0000644000176200001440000000405412544562303013773 0ustar liggesusers\name{dccm.pca} \alias{dccm.pca} \title{ Dynamic Cross-Correlation from Principal Component Analysis } \description{ Calculate the cross-correlation matrix from principal component analysis (PCA). } \usage{ \method{dccm}{pca}(x, pc = NULL, ncore = NULL, \dots) } \arguments{ \item{x}{ an object of class \code{pca} as obtained from function \code{pca.xyz}. } \item{pc}{ numerical, indices of PCs to be included in the calculation. If all negative, PCs complementary to \code{abs(pc)} are included. } \item{ncore }{ number of CPU cores used to do the calculation. By default (\code{ncore = NULL}), use all available cores detected. } \item{\dots}{ additional arguments to \code{cov2dccm}. } } \details{ This function calculates the cross-correlation matrix from principal component analysis (PCA) obtained from \code{pca.xyz} of a set of protein structures. It is an alternative way to calculate correlation in addition to the conventional way from xyz coordinates directly. But, in this new way one can freely chooses the PCs to be included in the calculation (e.g. filter PCs with small eigenvalues). } \value{ Returns a cross-correlation matrix. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Xin-Qiu Yao} \seealso{ \code{\link{pca.xyz}}, \code{\link{plot.dccm}} } \examples{ \dontrun{ ##-- Read example trajectory file trtfile <- system.file("examples/hivp.dcd", package="bio3d") trj <- read.dcd(trtfile) ## Read the starting PDB file to determine atom correspondence pdbfile <- system.file("examples/hivp.pdb", package="bio3d") pdb <- read.pdb(pdbfile) ## Select residues 24 to 27 and 85 to 90 in both chains inds <- atom.select(pdb, resno=c(24:27,85:90), elety='CA') ## lsq fit of trj on pdb xyz <- fit.xyz(pdb$xyz, trj, fixed.inds=inds$xyz, mobile.inds=inds$xyz) ## Do PCA pca <- pca.xyz(xyz) ## DCCM: only use first 10 PCs cij <- dccm(pca, pc = c(1:10)) ## Plot DCCM plot(cij) ## DCCM: remove first 10 PCs cij <- dccm(pca, pc = -c(1:10)) ## Plot DCCM plot(cij) } } \keyword{ analysis } bio3d/man/pca.Rd0000644000176200001440000000345312526367344013100 0ustar liggesusers\name{pca} \alias{pca} \title{ Principal Component Analysis } \description{ Performs principal components analysis (PCA) on biomolecular structure data. } \usage{ pca(...) } \arguments{ \item{\dots}{ arguments passed to the methods \code{pca.xyz}, \code{pca.pdbs}, etc. Typically this includes either a numeric matrix of Cartesian coordinates with a row per structure/frame (function \code{pca.xyz()}), or an object of class \code{pdbs} as obtained from function \code{pdbaln} or \code{read.fasta.pdb} (function \code{pca.pdbs()}). } } \details{ Principal component analysis can be performed on any structure dataset of equal or unequal sequence composition to capture and characterize inter-conformer relationships. This generic \code{pca} function calls the corresponding methods function for actual calculation, which is determined by the class of the input argument \code{x}. Use \code{methods("pca")} to list all the current methods for \code{pca} generic. These will include: \code{\link{pca.xyz}}, which will be used when \code{x} is a numeric matrix containing Cartesian coordinates (e.g. trajectory data). \code{\link{pca.pdbs}}, which will perform PCA on the Cartesian coordinates of a input \code{pdbs} object (as obtained from the \sQuote{read.fasta.pdb} or \sQuote{pdbaln} functions). Currently, function \code{\link{pca.tor}} should be called explicitly as there are currently no defined \sQuote{tor} object classes. See the documentation and examples for each individual function for more details and worked examples. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant, Lars Skjaerven } \seealso{ \code{\link{pca.xyz}}, \code{\link{pca.pdbs}}, \code{\link{pdbaln}}. } \keyword{ utilities } bio3d/man/identify.cna.Rd0000644000176200001440000000470712544562303014703 0ustar liggesusers\name{identify.cna} \alias{identify.cna} \title{ Identify Points in a CNA Protein Structure Network Plot } \description{ \sQuote{identify.cna} reads the position of the graphics pointer when the (first) mouse button is pressed. It then searches the coordinates given in \sQuote{x} for the point closest to the pointer. If this point is close enough to the pointer, its index and community members will be returned as part of the value of the call and the community members will be added as labels to the plot. } \usage{ \method{identify}{cna}(x, labels=NULL, cna=NULL, ...) } \arguments{ \item{x}{ A numeric matrix with Nx2 dimensions, where N is equal to the number of objects in a 2D CNA plot such as obtained from the \sQuote{plot.cna} and various \sQuote{layout} functions. } \item{labels}{ An optional character vector giving labels for the points. Will be coerced using \sQuote{as.character}, and recycled if necessary to the length of \sQuote{x}. Excess labels will be discarded, with a warning. } \item{cna}{ A network object as returned from the \sQuote{cna} function. } \item{\dots}{ Extra options passed to \sQuote{identify} function. } } \details{ This function calls the \sQuote{identify} and \sQuote{summary.cna} functions to query and label 2D CNA protein structure network plots produced by the \sQuote{plot.cna} function. Clicking with the mouse on plot points will add the corresponding labels and them to the plot and returned list object. A click with the right mouse button will stop the function. } \value{ If \sQuote{labels} or \sQuote{cna} inputs are provided then a membership vector will be returned with the selected community ids and their members. Otherwise a vector with the ids of the selected communities will be returned. } \author{ Guido Scarabelli and Barry Grant } \seealso{ \code{\link{plot.cna}}, \code{\link{identify}}, \code{\link[igraph:plot.igraph]{plot.igraph}}, \code{\link[igraph:plot.communities]{plot.communities}}, \code{\link[igraph:igraph.plotting]{igraph.plotting}} } \examples{ \dontrun{ attach(hivp) # Read the starting PDB file to determine atom correspondence pdbfile <- system.file("examples/hivp.pdb", package="bio3d") pdb <- read.pdb(pdbfile) # Plot the network xy <- plot.cna(net) # Use identify.cna on the communities d <- identify.cna(xy, cna=net) # Right click to end the function... ## d <- identify(xy, summary(net)$members) detach(hivp) } } \keyword{ utility } bio3d/man/nma.pdbs.Rd0000644000176200001440000001146412632622153014026 0ustar liggesusers\name{nma.pdbs} \alias{nma.pdbs} \alias{print.enma} \title{ Ensemble Normal Mode Analysis } \description{ Perform normal mode analysis (NMA) on an ensemble of aligned protein structures. } \usage{ \method{nma}{pdbs}(pdbs, fit = TRUE, full = FALSE, subspace = NULL, rm.gaps = TRUE, varweight=FALSE, outpath = NULL, ncore = 1, \dots) \method{print}{enma}(x, \dots) } \arguments{ \item{pdbs}{ a numeric matrix of aligned C-alpha xyz Cartesian coordinates. For example an alignment data structure obtained with \code{\link{read.fasta.pdb}} or \code{\link{pdbaln}}. } \item{fit}{ logical, if TRUE coordinate superposition is performed prior to normal mode calculations. } \item{full}{ logical, if TRUE return the complete, full structure, \sQuote{nma} objects. } \item{subspace}{ number of eigenvectors to store for further analysis. } \item{rm.gaps}{ logical, if TRUE obtain the hessian matrices for only atoms in the aligned positions (non-gap positions in all aligned structures). Thus, gap positions are removed from output. } \item{varweight}{ logical, if TRUE perform weighing of the pair force constants. Alternatively, provide a NxN matrix containing the weights. See function \code{\link{var.xyz}}. } \item{outpath}{ character string specifing the output directory to which the PDB structures should be written. } \item{ncore }{ number of CPU cores used to do the calculation. \code{ncore>1} requires package \sQuote{parallel} installed. } \item{x}{ an \code{enma} object obtained from \code{\link{nma.pdbs}}. } \item{...}{ additional arguments to \code{nma}, \code{\link{aa2mass}}, and \code{\link{print}}. } } \details{ This function performs normal mode analysis (NMA) on a set of aligned protein structures obtained with function \code{\link{read.fasta.pdb}} or \code{\link{pdbaln}}. The main purpose is to provide aligned atomic fluctuations and mode vectors in an automated fashion. The normal modes are calculated on the full structures as provided by object \sQuote{pdbs}. With the input argument \sQuote{full=TRUE} the full \sQuote{nma} objects are returned together with output \sQuote{U.subs} providing the aligned mode vectors. When \sQuote{rm.gaps=TRUE} the unaligned atoms are ommited from output. With default arguments \sQuote{rmsip} provides RMSIP values for all pairwise structures. See examples for more details. } \value{ Returns an \sQuote{enma} object with the following components: \item{fluctuations }{ a numeric matrix containing aligned atomic fluctuations with one row per input structure. } \item{rmsip}{ a numeric matrix of pair wise RMSIP values (only the ten lowest frequency modes are included in the calculation). } \item{U.subspace }{ a three-dimensional array with aligned eigenvectors (corresponding to the subspace defined by the first N non-trivial eigenvectors (\sQuote{U}) of the \sQuote{nma} object). } \item{L}{ numeric matrix containing the raw eigenvalues with one row per input structure. } \item{xyz }{ an object of class \sQuote{xyz} containing the Cartesian coordinates in which the calculation was performed. Coordinates are superimposed to the first structure of the \code{pdbs} object when \sQuote{fit=TRUE}. } \item{full.nma }{ a list with a \code{nma} object for each input structure. } } \references{ Skjaerven, L. et al. (2014) \emph{BMC Bioinformatics} \bold{15}, 399. Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ For normal mode analysis on single structure PDB: \code{\link{nma.pdb}} For the analysis of the resulting \sQuote{eNMA} object: \code{\link{mktrj.enma}}, \code{\link{dccm.enma}}, \code{\link{plot.enma}}, \code{\link{cov.enma}}. Similarity measures: \code{\link{sip}}, \code{\link{covsoverlap}}, \code{\link{bhattacharyya}}, \code{\link{rmsip}}. Related functionality: \code{\link{pdbaln}}, \code{\link{read.fasta.pdb}}. } \examples{ \donttest{ # Needs MUSCLE installed - testing excluded if(check.utility("muscle")) { ## Fetch PDB files and split to chain A only PDB files ids <- c("1a70_A", "1czp_A", "1frd_A", "1fxi_A", "1iue_A", "1pfd_A") files <- get.pdb(ids, split = TRUE, path = tempdir()) ## Sequence Alignement pdbs <- pdbaln(files, outfile = tempfile()) ## Normal mode analysis on aligned data modes <- nma(pdbs, rm.gaps=FALSE) ## Plot fluctuation data plot(modes, pdbs=pdbs) ## Cluster on Fluctuation similariy sip <- sip(modes) hc <- hclust(dist(sip)) col <- cutree(hc, k=3) ## Plot fluctuation data plot(modes, pdbs=pdbs, col=col) ## Remove gaps from output modes <- nma(pdbs, rm.gaps=TRUE) ## RMSIP is pre-calculated heatmap(1-modes$rmsip) ## Bhattacharyya coefficient bc <- bhattacharyya(modes) heatmap(1-bc) } } } \keyword{ analysis } bio3d/man/difference.vector.Rd0000644000176200001440000000313212544562303015712 0ustar liggesusers\name{difference.vector} \alias{difference.vector} \title{ Difference Vector } \description{ Define a difference vector between two conformational states. } \usage{ difference.vector(xyz, xyz.inds=NULL, normalize=FALSE) } \arguments{ \item{xyz}{ numeric matrix of Cartesian coordinates with a row per structure. } \item{xyz.inds}{ a vector of indices that selects the elements of columns upon which the calculation should be based. } \item{normalize}{ logical, if TRUE the difference vector is normalized. } } \details{ Squared overlap (or dot product) is used to measure the similiarity between a displacement vector (e.g. a difference vector between two conformational states) and mode vectors obtained from principal component or normal modes analysis. } \value{ Returns a numeric vector of the structural difference (normalized if desired). } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{overlap}} } \examples{ data(kinesin) attach(kinesin, warn.conflicts=FALSE) # Ignore gap containing positions gaps.pos <- gap.inspect(pdbs$xyz) #-- Do PCA pc.xray <- pca.xyz(pdbs$xyz[, gaps.pos$f.inds]) # Define a difference vector between two structural states diff.inds <- c(grep("d1v8ja", pdbs$id), grep("d1goja", pdbs$id)) ## Calculate the difference vector dv <- difference.vector( pdbs$xyz[diff.inds,], gaps.pos$f.inds ) # Calculate the squared overlap between the PCs and the difference vector o <- overlap(pc.xray, dv) detach(kinesin) } \keyword{ utilities } bio3d/man/pdb2sse.Rd0000644000176200001440000000175512544562303013672 0ustar liggesusers% Generated by roxygen2 (4.1.0): do not edit by hand % Please edit documentation in R/pdb2sse.R \name{pdb2sse} \alias{pdb2sse} \title{Obtain An SSE Sequence Vector From A PDB Object} \usage{ pdb2sse(pdb, verbose = TRUE) } \arguments{ \item{pdb}{an object of class \code{pdb} as obtained from function \code{\link{read.pdb}}.} \item{verbose}{logical, if TRUE warnings and other messages will be printed.} } \value{ a character vector indicating SSE elements for each amino acide residue. The 'names' attribute of the vector contains 'resno', 'chain', 'insert', and 'SSE segment number', seperated by the character '_'. } \description{ Results are similar to that returned by stride(pdb)$sse and dssp(pdb)$sse. } \details{ call for its effects. } \examples{ \donttest{ # PDB server connection required - testing excluded pdb <- read.pdb("1a7l") sse <- pdb2sse(pdb) sse } } \author{ Barry Grant & Xin-Qiu Yao } \seealso{ \code{\link{dssp}}, \code{\link{stride}}, \code{\link{bounds.sse}} } bio3d/man/dccm.enma.Rd0000644000176200001440000000432712544562303014153 0ustar liggesusers\name{dccm.enma} \alias{dccm.enma} \title{ Cross-Correlation for Ensemble NMA (eNMA) } \description{ Calculate the cross-correlation matrices from an ensemble of NMA objects. } \usage{ \method{dccm}{enma}(x, ncore = NULL, na.rm=FALSE, \dots) } \arguments{ \item{x}{ an object of class \code{enma} as obtained from function \code{nma.pdbs}. } \item{ncore }{ number of CPU cores used to do the calculation. \code{ncore>1} requires package \sQuote{parallel} installed. } \item{na.rm }{ logical, if FALSE the DCCM might containt NA values (applies only when the \code{enma} object is calculated with argument \sQuote{rm.gaps=FALSE}). } \item{\dots}{ additional arguments passed to \code{dccm.nma}. } } \details{ This is a wrapper function for calling \code{dccm.nma} on a collection of \sQuote{nma} objects as obtained from function \code{nma.pdbs}. See examples for more details. } \value{ Returns a list with the following components: \item{all.dccm }{ an array or list containing the correlation matrices for each \sQuote{nma} object. An array is returned when the \sQuote{enma} object is calculated with \sQuote{rm.gaps=TRUE}, and a list is used when \sQuote{rm.gaps=FALSE}. } \item{avg.dccm }{ a numeric matrix containing the average correlation matrix. The average is only calculated when the \sQuote{enma} object is calculated with \sQuote{rm.gaps=TRUE}. } } \references{ Wynsberghe. A.W.V, Cui, Q. \emph{Structure} \bold{14}, 1647--1653. Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{nma}}, \code{\link{dccm.nma}}, \code{\link{plot.dccm}} } \examples{ \donttest{ ## Needs MUSCLE installed - testing excluded if(check.utility("muscle")) { ## Fetch PDB files and split to chain A only PDB files ids <- c("1a70_A", "1czp_A", "1frd_A", "1fxi_A", "1iue_A", "1pfd_A") files <- get.pdb(ids, split = TRUE, path = tempdir()) ## Sequence/Structure Alignement pdbs <- pdbaln(files, outfile = tempfile()) ## Normal mode analysis on aligned data modes <- nma(pdbs) ## Calculate all 6 correlation matrices cij <- dccm(modes) ## Plot correlations for first structure plot.dccm(cij$all.dccm[,,1]) } } } \keyword{ analysis } bio3d/man/formula2mass.Rd0000644000176200001440000000132012524171274014730 0ustar liggesusers\name{formula2mass} \alias{formula2mass} \title{Chemical Formula to Mass Converter} \description{Compute the molar mass associated to a chemical formula.} \usage{ formula2mass(form, sum.mass = TRUE) } \arguments{ \item{form}{a character string containing a chemical formula on the form: 'C3 H5 N O1'.} \item{sum.mass}{logical, should the mass of each element be summed.} } \details{ Compute the molar mass (in g.mol-1) associated to a chemical formula. } \value{ Return a single element numeric vector containing the mass corresponding to a given chemical formula. } \author{Lars Skjaerven} \seealso{ \code{\link{atom2ele}}, \code{\link{atom2mass}} } \examples{ #formula2mass("C5 H6 N O3") } \keyword{ utilities } bio3d/man/plot.enma.Rd0000644000176200001440000000650312526367344014231 0ustar liggesusers\name{plot.enma} \alias{plot.enma} \title{ Plot eNMA Results } \description{ Produces a plot of atomic fluctuations of aligned normal modes. } \usage{ \method{plot}{enma}(x, pdbs = NULL, conservation = NULL, variance = FALSE, spread = FALSE, offset = 1, col = NULL, signif = FALSE, pcut = 0.005, qcut = 0.04, xlab = "Alignment Position", ylab=c("Fluctuations", "Fluct.variance", "Seq.conservation"), xlim = NULL, ylim = NULL, mar = c(4, 5, 2, 2), ...) } \arguments{ \item{x}{ the results of ensemble NMA obtained with \code{\link{nma.pdbs}}. Alternatively, a matrix in the similar format as \code{enma$fluctuations} can be provided. } \item{pdbs}{ an object of class \sQuote{pdbs} in which the \sQuote{enma} object \code{x} was obtained from. If provided SSE data of the first structure of \code{pdbs} will drawn. } \item{conservation}{ logical, if TRUE sequence conservation is plotted. Alternatively, provide the conservation assement method (\sQuote{similarity}, \sQuote{identity}, \sQuote{entropy22}, or \sQuote{entropy10}). A numeric vector of residue conservation values are also allowed. } \item{variance}{ logical, if TRUE fluctuation variance is plotted. } \item{spread}{ logical, if TRUE the fluctuation profiles are spread - i.e. not on top of each other. } \item{offset}{ numerical offset value in use when \sQuote{spread=TRUE}. } \item{col}{ a character vector of plotting colors. Used also to group fluctuation profiles when \sQuote{spread=TRUE}. NA values in col will omit the corresponding fluctuation profile in the plot. } \item{signif}{ logical, if TRUE significance of difference is plotted. } \item{pcut}{ P-value cutoff for the significance. } \item{qcut}{ Cutoff for the minimal difference of mean fluctuation to plot the significance. } \item{xlab}{ a label for the x axis. } \item{ylab}{ labels for the y axes. } \item{mar}{ a numerical vector of the form c(bottom, left, top, right) which gives the number of lines of margin to be specified on the four sides of the plot.} \item{xlim}{ the x limits of the plot. } \item{ylim}{ the y limits of the plot. } \item{\dots}{ extra plotting arguments passed to \code{plot.bio3d} that effect the atomic fluctuations plot only. } } \details{ \code{plot.enma} produces a fluctuation plot of aligned \code{nma} objects. } \value{ Called for its effect. } \references{ Skjaerven, L. et al. (2014) \emph{BMC Bioinformatics} \bold{15}, 399. Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven, Barry Grant } \seealso{ \code{\link{nma.pdbs}}, \code{\link{nma}}, \code{\link{plot.bio3d}}, \code{\link{entropy}}. } \examples{ \dontrun{ ids <- c("1a70_A", "1czp_A", "1frd_A", "1fxi_A", "1iue_A", "1pfd_A") raw.files <- get.pdb(ids, path = "raw_pdbs") files <- pdbsplit(raw.files, ids, path = "raw_pdbs/split_chain") ## Sequence Alignement pdbs <- pdbaln(files) ## Normal mode analysis on aligned data all.modes <- nma.pdbs(pdbs, rm.gaps=TRUE) ## Plot fluctuations plot.enma(all.modes, pdbs=pdbs, conservation=TRUE) ## group and spread fluctuation profiles grps <- rep(NA, length(pdbs$id)) grps[c(2,3)]=1 grps[c(4,5)]=2 plot.enma(all.modes, pdbs=pdbs, col=grps, spread=TRUE) } } \keyword{ hplot } bio3d/man/network.amendment.Rd0000644000176200001440000000303312544562303015757 0ustar liggesusers\name{network.amendment} \alias{network.amendment} \title{ Amendment of a CNA Network According To A Input Community Membership Vector. } \description{ This function changes the \sQuote{communities} attribute of a \sQuote{cna} class object to match a given membership vector. } \usage{ network.amendment(x, membership, minus.log=TRUE) } \arguments{ \item{x}{ A protein network graph object as obtained from the \sQuote{cna} function. } \item{membership}{ A numeric vector containing the new community membership. } \item{minus.log}{ Logical. Whether to use the minus.log on the cij values. } } \value{ Returns a \sQuote{cna} class object with the attributes changed according to the membership vector provided. } \details{ This function is useful, in combination with \sQuote{community.tree}, for inspecting different community partitioning options of a input \sQuote{cna} object. See examples. } \author{ Guido Scarabelli } \seealso{ \code{\link{cna}}, \code{\link{community.tree}}, \code{\link{summary.cna}} } \examples{ \donttest{ # PDB server connection required - testing excluded ##-- Build a CNA object pdb <- read.pdb("4Q21") modes <- nma(pdb) cij <- dccm(modes) net <- cna(cij, cutoff.cij=0.2) ##-- Community membership vector for each clustering step tree <- community.tree(net, rescale=TRUE) ## Produce a new k=7 membership vector and CNA network memb.k7 <- tree$tree[ tree$num.of.comms == 7, ] net.7 <- network.amendment(net, memb.k7) plot(net.7, pdb) print(net) print(net.7) } } \keyword{utility} bio3d/man/chain.pdb.Rd0000644000176200001440000000301312544562303014143 0ustar liggesusers\name{chain.pdb} \alias{chain.pdb} \title{ Find Possible PDB Chain Breaks } \description{ Find possible chain breaks based on connective Calpha atom separation. } \usage{ chain.pdb(pdb, ca.dist = 4, blank = "X", rtn.vec = TRUE) } \arguments{ \item{pdb}{ a PDB structure object obtained from \code{\link{read.pdb}}. } \item{ca.dist}{ the maximum distance that separates Calpha atoms considered to be in the same chain. } \item{blank}{ a character to assign non-protein atoms. } \item{rtn.vec}{ logical, if TRUE then the one-letter chain vector consisting of the 26 upper-case letters of the Roman alphabet is returned. } } \details{ This is a basic function for finding possible chain breaks in PDB structure files, i.e. connective Calpha atoms that are further than \code{ca.dist} apart. } \value{ Prints basic chain information and if \code{rtn.vec} is TRUE returns a character vector of chain ids consisting of the 26 upper-case letters of the Roman alphabet plus possible \code{blank} entries for non-protein atoms. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \seealso{ \code{\link{read.pdb}}, \code{\link{atom.select}}, \code{\link{trim.pdb}}, \code{\link{write.pdb}} } \examples{ \donttest{ # PDB server connection required - testing excluded full.pdb <- read.pdb( get.pdb("5p21", URLonly=TRUE) ) inds <- atom.select(full.pdb, resno=c(10:20,30:33)) cut.pdb <- trim.pdb(full.pdb, inds) chain.pdb(cut.pdb) } } \keyword{ utilities } bio3d/man/bio3d.package.Rd0000644000176200001440000001046112632664234014720 0ustar liggesusers\name{bio3d-package} \alias{bio3d-package} \alias{bio3d} \docType{package} \title{ Biological Structure Analysis } \description{ Utilities for the analysis of protein structure and sequence data. } \details{ \tabular{ll}{ Package: \tab bio3d\cr Type: \tab Package\cr Version: \tab 2.2-4\cr Date: \tab 2015-12-11\cr License: \tab GPL version 2 or newer\cr URL: \tab \url{http://thegrantlab.org/bio3d/}\cr } Features include the ability to read and write structure (\code{\link{read.pdb}}, \code{\link{write.pdb}}, \code{\link{read.fasta.pdb}}), sequence (\code{\link{read.fasta}}, \code{\link{write.fasta}}) and dynamics trajectory data (\code{\link{read.dcd}}, \code{\link{read.ncdf}}, \code{\link{write.ncdf}}). Perform sequence and structure database searches (\code{\link{blast.pdb}}, \code{\link{hmmer}}), atom summaries (\code{\link{summary.pdb}}), atom selection (\code{\link{atom.select}}), alignment (\code{\link{pdbaln}}, \code{\link{seqaln}}, \code{\link{mustang}}) superposition (\code{\link{rot.lsq}}, \code{\link{fit.xyz}}), \code{\link{pdbfit}}), rigid core identification (\code{\link{core.find}}, \code{\link{plot.core}}, \code{\link{fit.xyz}}), dynamic domain analysis (\code{\link{geostas}}), torsion/dihedral analysis (\code{\link{torsion.pdb}}, \code{\link{torsion.xyz}}), clustering (via \code{\link{hclust}}), principal component analysis (\code{\link{pca.xyz}}, \code{\link{pca.pdbs}}, \code{\link{pca.tor}}, \code{\link{plot.pca}}, \code{\link{plot.pca.loadings}}, \code{\link{mktrj.pca}}), dynamical cross-correlation analysis (\code{\link{dccm}}, \code{\link{lmi}}, \code{\link{plot.dccm}}) and correlation network analysis (\code{\link{cna}}, \code{\link{plot.cna}}, \code{\link{cnapath}}) of structure data. Perform conservation analysis of sequence (\code{\link{seqaln}}, \code{\link{conserv}}, \code{\link{seqidentity}}, \code{\link{entropy}}, \code{\link{consensus}}) and structural (\code{\link{pdbaln}}, \code{\link{rmsd}}, \code{\link{rmsf}}, \code{\link{core.find}}) data. Perform normal mode analysis (\code{\link{nma}}, \code{\link{build.hessian}}), ensemble normal mode analysis (\code{\link{nma.pdbs}}), mode comparison (\code{\link{rmsip}}) and (\code{\link{overlap}}), atomic fluctuation prediction (\code{\link{fluct.nma}}), cross-correlation analysis (\code{\link{dccm.nma}}), cross-correlation visualization (\code{\link{view.dccm}}), deformation analysis (\code{\link{deformation.nma}}), and mode visualization (\code{\link{view.modes}}, \code{\link{mktrj.nma}}). In addition, various utility functions are provided to facilitate manipulation and analysis of biological sequence and structural data (e.g. \code{\link{get.pdb}}, \code{\link{get.seq}}, \code{\link{aa123}}, \code{\link{aa321}}, \code{\link{pdbseq}}, \code{\link{aln2html}}, \code{\link{atom.select}}, \code{\link{rot.lsq}}, \code{\link{fit.xyz}}, \code{\link{is.gap}}, \code{\link{gap.inspect}}, \code{\link{orient.pdb}}, \code{\link{pairwise}}, \code{\link{plot.bio3d}}, \code{\link{plot.nma}}, \code{\link{plot.blast}}, \code{\link{biounit}}, etc.). } \note{ The latest version, package vignettes and documentation with worked example outputs can be obtained from the bio3d website:\cr \url{http://thegrantlab.org/bio3d/}.\cr \url{http://thegrantlab.org/bio3d/html/}.\cr \url{http://bitbucket.org/Grantlab/bio3d}. } \author{ Barry Grant Xin-Qiu Yao Lars Skjaerven Julien Ide } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. Skjaerven, L. et al. (2014) \emph{BMC Bioinformatics} \bold{15}, 399. } \keyword{ documentation } \examples{ help(package="bio3d") # list the functions within the package #lbio3d() # list bio3d function names only ## Or visit: ## http://thegrantlab.org/bio3d/html/ ## See the individual functions for further documentation and examples, e.g. #help(read.pdb) ## Or online: ## http://thegrantlab.org/bio3d/html/read.pdb.html \dontrun{ ##-- See the list of Bio3D demos demo(package="bio3d") ## Try some out, e.g: demo(pdb) # PDB Reading, Manipulation, Searching and Alignment demo(pca) # Principal Component Analysis demo(md) # Molecular Dynamics Trajectory Analysis demo(nma) # Normal Mode Analysis ## See package vignettes and tutorals online: ## http://thegrantlab.org/bio3d/tutorials } } bio3d/man/read.dcd.Rd0000644000176200001440000000632212544562303013767 0ustar liggesusers\name{read.dcd} \alias{read.dcd} \title{ Read CHARMM/X-PLOR/NAMD Binary DCD files } \description{ Read coordinate data from a binary DCD trajectory file. } \usage{ read.dcd(trjfile, big=FALSE, verbose = TRUE, cell = FALSE) } \arguments{ \item{trjfile}{ name of trajectory file to read. A vector if treat a batch of files } \item{big}{ logical, if TRUE attempt to read large files into a big.matrix object } \item{verbose}{ logical, if TRUE print details of the reading process. } \item{cell}{logical, if TRUE return cell information only. Otherwise, return coordinates.} } \details{ Reads a CHARMM or X-PLOR/NAMD binary trajectory file with either big- or little-endian storage formats. Reading is accomplished with two different sub-functions: \code{dcd.header}, which reads header info, and \code{dcd.frame}, which takes header information and reads atoms frame by frame producing an nframes/natom*3 matrix of cartesian coordinates or an nframes/6 matrix of cell parameters. } \value{ A numeric matrix of xyz coordinates with a frame/structure per row and a Cartesian coordinate per column or a numeric matrix of cell information with a frame/structure per row and lengths and angles per column. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \note{ See CHARMM documentation for DCD format description. If you experience problems reading your trajectory file with read.dcd() consider first reading your file into VMD and from there exporting a new DCD trajectory file with the 'save coordinates' option. This new file should be easily read with read.dcd(). Error messages beginning 'cannot allocate vector of size' indicate a failure to obtain memory, either because the size exceeded the address-space limit for a process or, more likely, because the system was unable to provide the memory. Note that on a 32-bit OS there may well be enough free memory available, but not a large enough contiguous block of address space into which to map it. In such cases try setting the input option 'big' to TRUE. This is an experimental option that results in a 'big.matrix' object. } \seealso{ \code{\link{read.pdb}}, \code{\link{write.pdb}}, \code{\link{atom.select}} } \examples{ \donttest{ # Redundant testing excluded ##-- Read cell parameters from example trajectory file trtfile <- system.file("examples/hivp.dcd", package="bio3d") trj <- read.dcd(trtfile, cell = TRUE) ##-- Read coordinates from example trajectory file trj <- read.dcd(trtfile) ## Read the starting PDB file to determine atom correspondence pdbfile <- system.file("examples/hivp.pdb", package="bio3d") pdb <- read.pdb(pdbfile) ## select residues 24 to 27 and 85 to 90 in both chains inds <- atom.select(pdb, resno=c(24:27,85:90), elety='CA') ## lsq fit of trj on pdb xyz <- fit.xyz(pdb$xyz, trj, fixed.inds=inds$xyz, mobile.inds=inds$xyz) ##-- RMSD of trj frames from PDB r1 <- rmsd(a=pdb, b=xyz) } \dontrun{ # Pairwise RMSD of trj frames for positions 47 to 54 flap.inds <- atom.select(pdb, resno=c(47:54), elety='CA') p <- rmsd(xyz[,flap.inds$xyz]) # plot highlighting flap opening? plot.dmat(p, color.palette = mono.colors) } } \keyword{ IO } bio3d/man/rmsip.Rd0000644000176200001440000000476612526367344013477 0ustar liggesusers\name{rmsip} \alias{rmsip} \alias{rmsip.default} \alias{rmsip.enma} \title{ Root Mean Square Inner Product } \description{ Calculate the RMSIP between two mode subspaces. } \usage{ rmsip(...) \method{rmsip}{enma}(enma, ncore=NULL, subset=10, ...) \method{rmsip}{default}(modes.a, modes.b, subset=10, row.name="a", col.name="b", ...) } \arguments{ \item{enma}{ an object of class \code{"enma"} obtained from function \code{nma.pdbs}. } \item{ncore }{ number of CPU cores used to do the calculation. \code{ncore>1} requires package \sQuote{parallel} installed. } \item{subset}{ the number of modes to consider. } \item{modes.a}{ an object of class \code{"pca"} or \code{"nma"} as obtained from functions \code{pca.xyz} or \code{nma}. } \item{modes.b}{ an object of class \code{"pca"} or \code{"nma"} as obtained from functions \code{pca.xyz} or \code{nma}. } \item{row.name}{ prefix name for the rows. } \item{col.name}{ prefix name for the columns. } \item{\dots}{ arguments passed to associated functions. } } \details{ RMSIP is a measure for the similarity between two set of modes obtained from principal component or normal modes analysis. } \value{ Returns an \code{rmsip} object with the following components: \item{overlap}{ a numeric matrix containing pairwise (squared) dot products between the modes. } \item{rmsip}{ a numeric RMSIP value. } For function \code{rmsip.enma} a numeric matrix containing all pairwise RMSIP values of the modes stored in the \code{enma} object. } \references{ Skjaerven, L. et al. (2014) \emph{BMC Bioinformatics} \bold{15}, 399. Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. Amadei, A. et al. (1999) \emph{Proteins} \bold{36}, 19--424. } \author{ Lars Skjaerven } \seealso{ \code{\link{pca}}, \code{\link{nma}}, \code{\link{overlap}}. Other similarity measures: \code{\link{sip}}, \code{\link{covsoverlap}}, \code{\link{bhattacharyya}}. } \examples{ \dontrun{ # Load data for HIV example trj <- read.dcd(system.file("examples/hivp.dcd", package="bio3d")) pdb <- read.pdb(system.file("examples/hivp.pdb", package="bio3d")) # Do PCA on simulation data xyz.md <- fit.xyz(pdb$xyz, trj, fixed.inds=1:ncol(trj)) pc.sim <- pca.xyz(xyz.md) # NMA modes <- nma(pdb) # Calculate the RMSIP between the MD-PCs and the NMA-MODEs r <- rmsip(modes, pc.sim, subset=10, row.name="NMA", col.name="PCA") # Plot pairwise overlap values plot(r, xlab="NMA", ylab="PCA") } } \keyword{ utilities } bio3d/man/aa2index.Rd0000644000176200001440000000370412412623040014005 0ustar liggesusers\name{aa2index} \alias{aa2index} \title{ Convert an Aminoacid Sequence to AAIndex Values } \description{ Converts sequences to aminoacid indeces from the \sQuote{AAindex} database. } \usage{ aa2index(aa, index = "KYTJ820101", window = 1) } \arguments{ \item{aa}{ a protein sequence character vector. } \item{index}{ an index name or number (default: \dQuote{KYTJ820101}, hydropathy index by Kyte-Doolittle, 1982). } \item{window}{ a positive numeric value, indicating the window size for smoothing with a sliding window average (default: 1, i.e. no smoothing). } } \details{ By default, this function simply returns the index values for each amino acid in the sequence. It can also be set to perform a crude sliding window average through the \code{window} argument. } \value{ Returns a numeric vector. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. \sQuote{AAIndex} is the work of Kanehisa and co-workers: Kawashima and Kanehisa (2000) \emph{Nucleic Acids Res.} \bold{28}, 374; Tomii and Kanehisa (1996) \emph{Protein Eng.} \bold{9}, 27--36; Nakai, Kidera and Kanehisa (1988) \emph{Protein Eng.} \bold{2}, 93--100. For a description of the \sQuote{AAindex} database see:\cr \url{http://www.genome.jp/aaindex/} or the \code{\link{aa.index}} documentation. } \author{ Ana Rodrigues } \seealso{ \code{\link{aa.index}}, \code{\link{read.fasta}} } \examples{ ## Residue hydropathy values seq <- c("R","S","D","X","-","X","R","H","Q","V","L") aa2index(seq) \dontrun{ ## Use a sliding window average aa2index(aa=seq, index=22, window=3) ## Use an alignment aln <- read.fasta(system.file("examples/hivp_xray.fa",package="bio3d")) prop <- t(apply(aln$ali, 1, aa2index, window=1)) ## find and use indices for volume calculations i <- which(sapply(aa.index, function(x) length(grep("volume", x$D, ignore.case=TRUE)) != 0)) sapply(i, function(x) aa2index(aa=seq, index=x, window=5)) } } \keyword{ utilities } bio3d/man/struct.aln.Rd0000644000176200001440000000712212544562303014417 0ustar liggesusers\name{struct.aln} \alias{struct.aln} \title{ Structure Alignment Of Two PDB Files } \description{ Performs a sequence and structural alignment of two PDB entities. } \usage{ struct.aln(fixed, mobile, fixed.inds=NULL, mobile.inds=NULL, write.pdbs=TRUE, outpath = "fitlsq", prefix=c("fixed", "mobile"), max.cycles=10, cutoff=0.5, ... ) } \arguments{ \item{fixed}{ an object of class \code{pdb} as obtained from function \code{read.pdb}. } \item{mobile}{ an object of class \code{pdb} as obtained from function \code{read.pdb}. } \item{fixed.inds}{ atom and xyz coordinate indices obtained from \code{atom.select} that selects the elements of \code{fixed} upon which the calculation should be based.} \item{mobile.inds}{ atom and xyz coordinate indices obtained from \code{atom.select} that selects the elements of \code{mobile} upon which the calculation should be based.} \item{write.pdbs}{ logical, if TRUE the aligned structures are written to PDB files.} \item{outpath}{ character string specifing the output directory when \code{write.pdbs} is TRUE. } \item{prefix}{ a character vector of length 2 containing the filename prefix in which the fitted structures should be written. } \item{max.cycles}{ maximum number of refinement cycles.} \item{cutoff}{ standard deviation of the pairwise distances for aligned residues at which the fitting refinement stops.} \item{\dots}{ extra arguments passed to \code{seqaln} function.} } \details{ This function performs a sequence alignment followed by a structural alignment of the two PDB entities. Cycles of refinement steps of the structural alignment are performed to improve the fit by removing atoms with a high structural deviation. The primary purpose of the function is to allow rapid structural alignment (and RMSD analysis) for protein structures with unequal, but related sequences. The function reports the residues of \code{fixed} and \code{mobile} included in the final structural alignment, as well as the related RMSD values. This function makes use of the underlying functions \code{seqaln}, \code{rot.lsq}, and \code{rmsd}. } \value{ Returns a list with the following components: \item{a.inds}{ atom and xyz indices of \code{fixed}. } \item{b.inds}{ atom and xyz indices of \code{mobile}. } \item{xyz}{ fitted xyz coordinates of \code{mobile}. } \item{rmsd}{ a numeric vector of RMSD values after each cycle of refinement. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjarven } \seealso{ \code{\link{rmsd}}, \code{\link{rot.lsq}}, \code{\link{seqaln}}, \code{\link{pdbaln}} } \examples{ \donttest{ # Needs MUSCLE installed - testing excluded if(check.utility("muscle")) { ## Stucture of PKA: a <- read.pdb("1cmk") ## Stucture of PKB: b <- read.pdb("2jdo") ## Align and fit b on to a: path = file.path(tempdir(), "struct.aln") aln <- struct.aln(a, b, outpath = path, outfile = tempfile()) ## Should be the same as aln$rmsd (when using aln$a.inds and aln$b.inds) rmsd(a$xyz, b$xyz, aln$a.inds$xyz, aln$b.inds$xyz, fit=TRUE) invisible( cat("\nSee the output files:", list.files(path, full.names = TRUE), sep="\n") ) } } \dontrun{ ## Align two subunits of GroEL (open and closed states) a <- read.pdb("1sx4") b <- read.pdb("1xck") ## Select chain A only a.inds <- atom.select(a, chain="A") b.inds <- atom.select(b, chain="A") ## Align and fit: aln <- struct.aln(a,b, a.inds, b.inds) } } \keyword{ utilities } bio3d/man/cna.Rd0000644000176200001440000001677712632622153013101 0ustar liggesusers\name{cna} \alias{cna} \alias{cna.dccm} \alias{cna.ensmb} \title{ Protein Dynamic Correlation Network Construction and Community Analysis. } \description{ This function builds both residue-based and community-based undirected weighted network graphs from an input correlation matrix, as obtained from the functions \sQuote{dccm}, \sQuote{dccm.nma}, and \sQuote{dccm.enma}. Community detection/clustering is performed on the initial residue based network to determine the community organization and network structure of the community based network. } \usage{ cna(cij, \dots) \method{cna}{dccm}(cij, cutoff.cij=0.4, cm=NULL, vnames=colnames(cij), cluster.method="btwn", collapse.method="max", cols=vmd.colors(), minus.log=TRUE, \dots) \method{cna}{ensmb}(cij, \dots, ncore = NULL) } \arguments{ \item{cij}{ A numeric array with 2 dimensions (nXn) containing atomic correlation values, where "n" is the residue number. The matrix elements should be in between 0 and 1 (atomic correlations). Can be also a set of correlation matrices for ensemble network analysis. See \sQuote{dccm} function in bio3d package for further details. } \item{\dots}{ Additional arguments passed to the methods \code{cna.dccm} and \code{cna.ensmb}. } \item{cutoff.cij}{ Numeric element specifying the cutoff on cij matrix values. Coupling below cutoff.cij are set to 0. } \item{cm}{ (optinal) A numeric array with 2 dimensions (nXn) containing binary contact values, where "n" is the residue number. The matrix elements should be 1 if two residues are in contact and 0 if not in contact. See the \sQuote{cmap} function in bio3d package for further details. } \item{vnames}{ A vector of names for each column in the input cij. This will be used for referencing residues in a similar way to residue numbers in later analysis. } \item{cluster.method}{ A character string specifying the method for community determination. Supported methods are:\cr btwn="Girvan-Newman betweenness"\cr walk="Random walk"\cr greed="Greedy algorithm for modularity optimization"\cr } \item{collapse.method}{ A single element character vector specifing the \sQuote{cij} collapse method, can be one of \sQuote{max}, \sQuote{median}, \sQuote{mean}, or \sQuote{trimmed}. By defualt the \sQuote{max} method is used to collapse the input residue based \sQuote{cij} matrix into a smaller community based network by taking the maximium \sQuote{abs(cij)} value between communities as the comunity-to-community cij value for clustered network construction. } \item{cols}{ A vector of colors assigned to network nodes. } \item{minus.log}{ Logical, indicating whether \sQuote{-log(abs(cij))} values should be used for network construction. } \item{ncore}{ Number of CPU cores used to do the calculation. By default, use all available cores. } } \value{ Returns a list object that includes igraph network and community objects with the following components: \item{network}{ An igraph residue-wise graph object. See below for more details.} \item{communities}{ An igraph residue-wise community object. See below for more details. } \item{communitiy.network}{ An igraph community-wise graph object. See below for more details. } \item{community.cij}{ Numeric square matrix containing the absolute values of the atomic correlation input matrix for each community as obtained from \sQuote{cij} via application of \sQuote{collapse.method}. } \item{cij}{ Numeric square matrix containing the absolute values of the atomic correlation input matrix. } } \details{ The input to this function should be a correlation matrix as obtained from the \sQuote{dccm}, \sQuote{dccm.mean} or \sQuote{dccm.nma} and related functions. Optionally, a contact map \sQuote{cm} may also given as input to filter the correlation matrix resulting in the exclusion of network edges between non-contacting atom pairs (as defined in the contact map). Internally this function calls the igraph package functions \sQuote{graph.adjacency}, \sQuote{edge.betweenness.community}, \sQuote{walktrap.community}, \sQuote{fastgreedy.community}. The first constructs an undirected weighted network graph. The second performs Girvan-Newman style clustering by calculating the edge betweenness of the graph, removing the edge with the highest edge betweenness score, calculates modularity (i.e. the difference between the current graph partition and the partition of a random graph, see Newman and Girvan, Physical Review E (2004), Vol 69, 026113), then recalculating edge betweenness of the edges and again removing the one with the highest score, etc. The returned community partition is the one with the highest overall modularity value. \sQuote{walktrap.community} implements the Pons and Latapy algorithm based on the idea that random walks on a graph tend to get "trapped" into densely connected parts of it, i.e. a community. The random walk process is used to determine a distance between nodes. Nodes with low distance values are joined in the same community. \sQuote{fastgreedy.community} instead determines the community structure based on the optimization of the modularity. In the starting state each node is isolated and belongs to a separated community. Communities are then joined together (according to the network edges) in pairs and the modularity is calculated. At each step the join resulting in the highest increase of modularity is chosen. This process is repeated until a single community is obtained, then the partitioning with the highest modularity score is selected. } \author{ Guido Scarabelli and Barry Grant } \seealso{ \code{\link{plot.cna}}, \code{\link{summary.cna}}, \code{\link{view.cna}}, \code{\link[igraph:graph.adjacency]{graph.adjacency}}, \code{\link[igraph:edge.betweenness.community]{edge.betweenness.community}}, \code{\link[igraph:walktrap.community]{walktrap.community}}, \code{\link[igraph:fastgreedy.community]{fastgreedy.community}} } \examples{ \donttest{ # PDB server connection required - testing excluded ##-- Build a correlation network from NMA results ## Read example PDB pdb <- read.pdb("4Q21") ## Perform NMA modes <- nma(pdb) #plot(modes, sse=pdb) ## Calculate correlations cij <- dccm(modes) #plot(cij, sse=pdb) ## Build, and betweenness cluster, a network graph net <- cna(cij, cutoff.cij=0.35) #plot(net, pdb) ## within VMD set 'coloring method' to 'Chain' and 'Drawing method' to Tube #view.cna(net, trim.pdb(pdb, atom.select(pdb,"calpha")), launch=TRUE ) ##-- Build a correlation network from MD results ## Read example trajectory file trtfile <- system.file("examples/hivp.dcd", package="bio3d") trj <- read.dcd(trtfile) ## Read the starting PDB file to determine atom correspondence pdbfile <- system.file("examples/hivp.pdb", package="bio3d") pdb <- read.pdb(pdbfile) ## select residues 24 to 27 and 85 to 90 in both chains inds <- atom.select(pdb, resno=c(24:27,85:90), elety='CA') ## lsq fit of trj on pdb xyz <- fit.xyz(pdb$xyz, trj, fixed.inds=inds$xyz, mobile.inds=inds$xyz) ## calculate dynamical cross-correlation matrix cij <- dccm(xyz) ## Build, and betweenness cluster, a network graph net <- cna(cij) # Plot coarse grained network based on dynamically coupled communities xy <- plot.cna(net) plot.dccm(cij, margin.segments=net$communities$membership) ##-- Begin to examine network structure - see CNA vignette for more details net summary(net) attributes(net) table( net$communities$members ) } } \keyword{analysis} bio3d/man/dm.Rd0000644000176200001440000001003012544562303012712 0ustar liggesusers\name{dm} \alias{dm} \alias{dm.pdb} \alias{dm.xyz} \title{ Distance Matrix Analysis } \description{ Construct a distance matrix for a given protein structure. } \usage{ dm(\dots) \method{dm}{pdb}(pdb, inds = NULL, grp = TRUE, verbose=TRUE, \dots) \method{dm}{xyz}(xyz, grpby = NULL, scut = NULL, mask.lower = TRUE, ncore=1, \dots) } \arguments{ \item{pdb}{ a \code{pdb} structure object as returned by \code{\link{read.pdb}} or a numeric vector of \sQuote{xyz} coordinates.} \item{inds}{ atom and xyz coordinate indices obtained from \code{atom.select} that selects the elements of \code{pdb} upon which the calculation should be based. } \item{grp}{ logical, if TRUE atomic distances will be grouped according to their residue membership. See \sQuote{grpby}. } \item{verbose}{ logical, if TRUE possible warnings are printed. } \item{xyz}{ a numeric vector or matrix of Cartesian coordinates.} \item{grpby}{ a vector counting connective duplicated elements that indicate the elements of \code{xyz} that should be considered as a group (e.g. atoms from a particular residue). } \item{scut}{ a cutoff neighbour value which has the effect of excluding atoms, or groups, that are sequentially within this value.} \item{mask.lower}{ logical, if TRUE the lower matrix elements (i.e. those below the diagonal) are returned as NA.} \item{ncore }{ number of CPU cores used to do the calculation. \code{ncore>1} requires package \sQuote{parallel} installed. } \item{\dots}{ arguments passed to and from functions. } } \details{ Distance matrices, also called distance plots or distance maps, are an established means of describing and comparing protein conformations (e.g. Phillips, 1970; Holm, 1993). A distance matrix is a 2D representation of 3D structure that is independent of the coordinate reference frame and, ignoring chirality, contains enough information to reconstruct the 3D Cartesian coordinates (e.g. Havel, 1983). } \value{ Returns a numeric matrix of class \code{"dmat"}, with all N by N distances, where N is the number of selected atoms. With multiple frames the output is provided in a three dimensional array. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. Phillips (1970) \emph{Biochem. Soc. Symp.} \bold{31}, 11--28. Holm (1993) \emph{J. Mol. Biol.} \bold{233}, 123--138. Havel (1983) \emph{Bull. Math. Biol.} \bold{45}, 665--720. } \author{ Barry Grant } \note{ The input \code{selection} can be any character string or pattern interpretable by the function \code{\link{atom.select}}. For example, shortcuts \code{"calpha"}, \code{"back"}, \code{"all"} and selection strings of the form \code{/segment/chain/residue number/residue name/element number/element name/}; see \code{\link{atom.select}} for details. If a coordinate vector is provided as input (rather than a \code{pdb} object) the \code{selection} option is redundant and the input vector should be pruned instead to include only desired positions. } \seealso{ \code{\link{plot.dmat}}, \code{\link{read.pdb}}, \code{\link{atom.select}} } \examples{ \donttest{ # PDB server connection required - testing excluded ##--- Distance Matrix Plot pdb <- read.pdb( "4q21" ) k <- dm(pdb,selection="calpha") filled.contour(k, nlevels = 10) ## NOTE: FOLLOWING EXAMPLE NEEDS MUSCLE INSTALLED if(check.utility("muscle")) { ##--- DDM: Difference Distance Matrix # Downlaod and align two PDB files pdbs <- pdbaln( get.pdb( c( "4q21", "521p"), path = tempdir() ), outfile = tempfile() ) # Get distance matrix a <- dm.xyz(pdbs$xyz[1,]) b <- dm.xyz(pdbs$xyz[2,]) # Calculate DDM c <- a - b # Plot DDM plot(c,key=FALSE, grid=FALSE) plot(c, axis.tick.space=10, resnum.1=pdbs$resno[1,], resnum.2=pdbs$resno[2,], grid.col="black", xlab="Residue No. (4q21)", ylab="Residue No. (521p)") } } \dontrun{ ##-- Residue-wise distance matrix based on the ## minimal distance between all available atoms l <- dm.xyz(pdb$xyz, grpby=pdb$atom[,"resno"], scut=3) } } \keyword{ utilities } bio3d/man/aa.index.Rd0000644000176200001440000000403712544562303014013 0ustar liggesusers\name{aa.index} \alias{aa.index} \docType{data} \title{ AAindex: Amino Acid Index Database } \description{ A collection of published indices, or scales, of numerous physicochemical and biological properties of the 20 standard aminoacids (Release 9.1, August 2006). } \usage{data(aa.index)} \format{ A list of 544 named indeces each with the following components: \enumerate{ \item{H}{ character vector: Accession number. } \item{D}{ character vector: Data description. } \item{R}{ character vector: LITDB entry number. } \item{A}{ character vector: Author(s). } \item{T}{ character vector: Title of the article. } \item{J}{ character vector: Journal reference. } \item{C}{ named numeric vector: Correlation coefficients of similar indeces (with coefficients of 0.8/-0.8 or more/less). The correlation coefficient is calculated with zeros filled for missing values. } \item{I}{ named numeric vector: Amino acid index data. } } } \source{ \sQuote{AAIndex} was obtained from:\cr \url{http://www.genome.jp/aaindex/}\cr For a description of the \sQuote{AAindex} database see:\cr \url{http://www.genome.jp/aaindex/aaindex_help.html}. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. \sQuote{AAIndex} is the work of Kanehisa and co-workers:\cr Kawashima and Kanehisa (2000) \emph{Nucleic Acids Res.} \bold{28}, 374;\cr Tomii and Kanehisa (1996) \emph{Protein Eng.} \bold{9}, 27--36;\cr Nakai, Kidera and Kanehisa (1988) \emph{Protein Eng.} \bold{2}, 93--100. } \examples{ ## Load AAindex data data(aa.index) ## Find all indeces described as "volume" ind <- which(sapply(aa.index, function(x) length(grep("volume", x$D, ignore.case=TRUE)) != 0)) ## find all indeces with author "Kyte" ind <- which(sapply(aa.index, function(x) length(grep("Kyte", x$A)) != 0)) ## examine the index aa.index[[ind]]$I ## find indeces which correlate with it all.ind <- names(which(Mod(aa.index[[ind]]$C) >= 0.88)) ## examine them all sapply(all.ind, function (x) aa.index[[x]]$I) } \keyword{datasets} bio3d/man/write.ncdf.Rd0000644000176200001440000000364312632622153014367 0ustar liggesusers\name{write.ncdf} \alias{write.ncdf} \title{ Write AMBER Binary netCDF files } \description{ Write coordinate data to a binary netCDF trajectory file. } \usage{ write.ncdf(x, trjfile = "R.ncdf", cell = NULL) } \arguments{ \item{x}{ A numeric matrix of xyz coordinates with a frame/structure per row and a Cartesian coordinate per column. } \item{trjfile}{ name of the output trajectory file. } \item{cell}{ A numeric matrix of cell information with a frame/structure per row and a cell length or angle per column. If NULL cell will not be written. } } \details{ Writes an AMBER netCDF (Network Common Data Form) format trajectory file with the help of David W. Pierce's (UCSD) ncdf4 package available from CRAN. } \value{ Called for its effect. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. \url{http://www.unidata.ucar.edu/software/netcdf/} \url{http://cirrus.ucsd.edu/~pierce/ncdf/} \url{http://ambermd.org/formats.html#netcdf} } \author{ Barry Grant } \note{ See AMBER documentation for netCDF format description. NetCDF binary trajectory files are supported by the AMBER modules sander, pmemd and ptraj. Compared to formatted trajectory files, the binary trajectory files are smaller, higher precision and significantly faster to read and write. NetCDF provides for file portability across architectures, allows for backwards compatible extensibility of the format and enables the files to be self-describing. Support for this format is available in VMD. } \seealso{ \code{\link{read.dcd}}, \code{\link{read.ncdf}}, \code{\link{read.pdb}}, \code{\link{write.pdb}}, \code{\link{atom.select}} } \examples{ \dontrun{ ##-- Read example trajectory file trtfile <- system.file("examples/hivp.dcd", package="bio3d") trj <- read.dcd(trtfile) ## Write to netCDF format write.ncdf(trj, "newtrj.nc") ## Read trj trj <- read.ncdf("newtrj.nc") } } \keyword{ IO } bio3d/man/dist.xyz.Rd0000644000176200001440000000337612544562303014125 0ustar liggesusers\name{dist.xyz} \alias{dist.xyz} \title{ Calculate the Distances Between the Rows of Two Matrices } \description{ Compute the pairwise euclidean distances between the rows of two matrices. } \usage{ dist.xyz(a, b = NULL, all.pairs=TRUE, ncore=1, nseg.scale=1) } \arguments{ \item{a}{ a numeric data matrix or vector } \item{b}{ an optional second data matrix or vector } \item{all.pairs}{ logical, if TRUE all pairwise distances between the rows of \sQuote{a} and all rows of \sQuote{b} are computed, if FALSE only the distances between coresponding rows of \sQuote{a} and \sQuote{b} are computed. } \item{ncore }{ number of CPU cores used to do the calculation. \code{ncore>1} requires package \sQuote{parallel} installed. } \item{nseg.scale }{ split input data into specified number of segments prior to running multiple core calculation. See \code{\link{fit.xyz}}. } } \details{ This function returns a matrix of euclidean distances between each row of \sQuote{a} and all rows of \sQuote{b}. Input vectors are coerced to three dimensional matrices (representing the Cartesian coordinates x, y and z) prior to distance computation. If \sQuote{b} is not provided then the pairwise distances between all rows of \sQuote{a} are computed. } \value{ Returns a matrix of pairwise euclidean distances between each row of \sQuote{a} and all rows of \sQuote{b}. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \note{ This function will choke if \sQuote{b} has too many rows. } \seealso{ \code{\link{dm}}, \code{\link{dist}} } \examples{ dist.xyz( c(1,1,1, 3,3,3), c(3,3,3, 2,2,2, 1,1,1)) dist.xyz( c(1,1,1, 3,3,3), c(3,3,3, 2,2,2, 1,1,1), all.pairs=FALSE) } \keyword{ utilities } bio3d/man/filter.cmap.Rd0000644000176200001440000000422512544562303014527 0ustar liggesusers\name{filter.cmap} \alias{filter.cmap} \title{ Contact Map Consensus Filtering } \description{ This function filters a tridimensional contact matrix (nXnXz, where n is the residue number and z is the simulation number) selecting only the contact present in at least p simulations, where p<=z. } \usage{ filter.cmap(cm, cutoff.sims = dim(cm)[3]) } \arguments{ \item{cm}{ A numeric array with 3 dimensions (nXnXz) containing binary contact values. "n" is the residue number, "z" the simulation number. The matrix elements should be 1 if two residues are in contact and 0 if they are not in contact. } \item{cutoff.sims}{A single element numeric vector corresponding to the minimum number of simulations a contact between two residues must be present. If not, it will be set to 0 in the output matrix. } } \value{ The output matrix is a nXn binary matrix (n = residue number). Elements equal to 1 correspond to residues in contact, elements equal to 0 to residues not in contact. } \seealso{ \code{\link{cmap}} } \examples{ \dontrun{ ## need abind package if(!require(abind)) { install.packages("abind") require(abind) } ## load example data pdbfile <- system.file("examples/hivp.pdb", package="bio3d") pdb <- read.pdb(pdbfile) trtfile <- system.file("examples/hivp.dcd", package="bio3d") trj <- read.dcd(trtfile, verbose=FALSE) ## split the trj example in two num.of.frames <- dim(trj)[1] trj1 <- trj[1:(num.of.frames/2),] trj2 <- trj[((num.of.frames/2)+1):num.of.frames,] ## Lets work with Calpha atoms only ca.inds <- atom.select(pdb, "calpha") #noh.inds <- atom.select(pdb, "noh") ## calculate single contact map matrices cm.1 <- cmap(trj1[,ca.inds$xyz], pcut=0.3, scut=0, dcut=7, mask.lower=FALSE) cm.2 <- cmap(trj2[,ca.inds$xyz], pcut=0.3, scut=0, dcut=5, mask.lower=FALSE) ## create a 3D contact matrix from 3 simulations cm.all <- abind(cm.1, cm.2, along=3) ## calculate average contact matrix cm.filter <- filter.cmap(cm=cm.all, cutoff.sims=2) ## plot the result par(pty="s", mfcol=c(1,3)) image(cm.1, col=c(NA,"black")) image(cm.2, col=c(NA,"black")) image(cm.filter, col=c(NA,"black")) } } \keyword{analysis} bio3d/man/wrap.tor.Rd0000644000176200001440000000204512412623040014063 0ustar liggesusers\name{wrap.tor} \alias{wrap.tor} \title{ Wrap Torsion Angle Data } \description{ Adjust angular data so that the absolute difference of any of the observations from its mean is not greater than 180 degrees. } \usage{ wrap.tor(data, wrapav=TRUE, avestruc=NULL) } \arguments{ \item{data}{ a numeric vector or matrix of torsion angle data as obtained from \code{torsion.xyz}. } \item{wrapav}{ logical, if TRUE average structure is also \sQuote{wrapped} } \item{avestruc}{ a numeric vector corresponding to the average structure } } \details{ This is a basic utility function for coping with the periodicity of torsion angle data, by \sQuote{wraping} angular data such that the absolute difference of any of the observations from its column-wise mean is not greater than 180 degrees. } \value{ A numeric vector or matrix of wrapped torsion angle data. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Karim ElSawy } \seealso{ \code{\link{torsion.xyz}} } \keyword{ utilities } bio3d/man/lbio3d.Rd0000644000176200001440000000055612412623040013470 0ustar liggesusers\name{lbio3d} \alias{lbio3d} \title{ List all Functions in the bio3d Package } \description{ A simple shortcut for ls("package:bio3d"). } \usage{ lbio3d() } \value{ A character vector of function names from the bio3d package. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \keyword{ utilities } bio3d/man/sse.bridges.Rd0000644000176200001440000000227012526367344014541 0ustar liggesusers\name{sse.bridges} \alias{sse.bridges} \title{ SSE Backbone Hydrogen Bonding } \description{ Determine backbone C=O to N-H hydrogen bonding in secondary structure elements. } \usage{ sse.bridges(sse, type="helix", hbond=TRUE, energy.cut=-1.0) } \arguments{ \item{sse}{ an sse object as obtained with \code{dssp}. } \item{type}{ character string specifying \sQuote{helix} or \sQuote{sheet}. } \item{hbond}{ use hbond records in the dssp output. } \item{energy.cut}{ cutoff for the dssp hbond energy. } } \details{ Simple functionality to parse the \sQuote{BP} and \sQuote{hbond} records of the DSSP output. Requires input from function \code{dssp} with arguments \code{resno=FALSE} and \code{full=TRUE}. } \value{ Returns a numeric matrix of two columns containing the residue ids of the paired residues. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{read.pdb}}, \code{\link{dssp}} } \examples{ \dontrun{ # Read a PDB file pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) sse <- dssp(pdb, resno=FALSE, full=TRUE) sse.bridges(sse, type="helix") } } \keyword{ utilities } bio3d/man/cmap.Rd0000644000176200001440000000616112632340443013241 0ustar liggesusers\name{cmap} \alias{cmap} \alias{cmap.default} \alias{cmap.xyz} \alias{cmap.pdb} \title{ Contact Map } \description{ Construct a Contact Map for Given Protein Structure(s).} \usage{ cmap(\dots) \method{cmap}{default}(\dots) \method{cmap}{xyz}(xyz, grpby = NULL, dcut = 4, scut = 3, pcut=1, mask.lower = TRUE, ncore=1, nseg.scale=1, \dots) \method{cmap}{pdb}(pdb, inds = NULL, verbose = FALSE, \dots) } \arguments{ \item{xyz}{ numeric vector of xyz coordinates or a numeric matrix of coordinates with a row per structure/frame. } \item{grpby}{ a vector counting connective duplicated elements that indicate the elements of \code{xyz} that should be considered as a group (e.g. atoms from a particular residue). } \item{dcut}{ a cutoff distance value below which atoms are considered in contact. } \item{scut}{ a cutoff neighbour value which has the effect of excluding atoms that are sequentially within this value. } \item{pcut}{ a cutoff probability of structures/frames showing a contact, above which atoms are considered in contact with respect to the ensemble} \item{mask.lower}{ logical, if TRUE the lower matrix elements (i.e. those below the diagonal) are returned as NA.} \item{ncore }{ number of CPU cores used to do the calculation. \code{ncore>1} requires package \sQuote{parallel} installed. } \item{nseg.scale }{ split input data into specified number of segments prior to running multiple core calculation. See \code{\link{fit.xyz}}. } \item{pdb}{ a structure object of class \code{"pdb"}, obtained from \code{\link{read.pdb}}. } \item{inds}{ a list object of ATOM and XYZ indices as obtained from \code{\link{atom.select}}. } \item{verbose}{ logical, if TRUE details of the selection are printed. } \item{\dots}{ arguments passed to and from functions. } } \details{ A contact map is a simplified distance matrix. See the distance matrix function \code{\link{dm}} for further details. Function \code{"cmap.pdb"} is a wrapper for \code{"cmap.xyz"} which selects all \sQuote{notwater} atoms and calculates the contact matrix grouped by residue number. } \value{ Returns a N by N numeric matrix composed of zeros and ones, where one indicates a contact between selected atoms. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \seealso{ \code{\link{dm}}, \code{\link{dccm}}, \code{\link{dist}}, \code{\link{dist.xyz}} } \examples{ ##- Read PDB file pdb <- read.pdb( system.file("examples/hivp.pdb", package="bio3d") ) ## Atom Selection indices inds <- atom.select(pdb, "calpha") ## Reference contact map ref.cont <- cmap( pdb$xyz[inds$xyz], dcut=6, scut=3 ) plot.cmap(ref.cont) \dontrun{ ##- Read Traj file trj <- read.dcd( system.file("examples/hivp.dcd", package="bio3d") ) ## For each frame of trajectory sum.cont <- NULL for(i in 1:nrow(trj)) { ## Contact map for frame 'i' cont <- cmap(trj[i,inds$xyz], dcut=6, scut=3) ## Product with reference prod.cont <- ref.cont * cont sum.cont <- c(sum.cont, sum(prod.cont,na.rm=TRUE)) } plot(sum.cont, typ="l") } } \keyword{ utilities } bio3d/man/rmsf.Rd0000644000176200001440000000151412632622153013266 0ustar liggesusers\name{rmsf} \alias{rmsf} \title{ Atomic RMS Fluctuations } \description{ Calculate atomic root mean squared fluctuations. } \usage{ rmsf(xyz, average=FALSE) } \arguments{ \item{xyz}{ numeric matrix of coordinates with each row corresponding to an individual conformer. } \item{average}{ logical, if TRUE averaged over atoms. } } \details{ An often used measure of conformational variance. } \value{ Returns a numeric vector of RMSF values. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \seealso{ \code{\link{read.dcd}}, \code{\link{fit.xyz}}, \code{\link{read.fasta.pdb}} } \examples{ attach(transducin) # Ignore Gaps gaps <- gap.inspect(pdbs$ali) r <- rmsf(pdbs$xyz) plot(r[gaps$f.inds], typ="h", ylab="RMSF (A)") detach(transducin) } \keyword{ utilities } bio3d/man/plot.rmsip.Rd0000644000176200001440000000246512526367344014446 0ustar liggesusers\name{plot.rmsip} \alias{plot.rmsip} \title{ Plot RMSIP Results } \description{ Produces a heat plot of RMSIP (Root mean square inner product) for the visualization of modes similarity. } \usage{ \method{plot}{rmsip}(x, xlab = NULL, ylab = NULL, col = gray(50:0/50), zlim=c(0,1), \dots) } \arguments{ \item{x}{ an object of class \code{rmsip}. } \item{xlab}{ a label for the x axis, defaults to \sQuote{a}. } \item{ylab}{ a label for the y axis, defaults to \sQuote{b}. } \item{col}{ a vector of colors for the RMSIP map (or overlap values). } \item{zlim}{ the minimum and maximum \sQuote{z} values for which colors should be plotted. } \item{\dots}{ additional arguments to function \code{image}. } } \details{ \code{plot.rmsip} produces a color image with the function \code{image}. } \value{ Called for its effect. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{rmsip}}, \code{\link{overlap}}, \code{\link{nma}}, \code{\link{image}}. } \examples{ ## Read PDB structure pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) ## Perform NMA modes.a <- nma(pdb, ff="calpha") modes.b <- nma(pdb, ff="anm") ## Calculate and plot RMSIP r <- rmsip(modes.a, modes.b) plot(r) } \keyword{ hplot } bio3d/man/filter.dccm.Rd0000644000176200001440000000653712632622153014523 0ustar liggesusers\name{filter.dccm} \alias{filter.dccm} \title{ Filter for Cross-correlation Matrices (Cij) } \description{ This function builds various cij matrix for correlation network analysis } \usage{ filter.dccm(x, cutoff.cij = 0.4, cmap = NULL, xyz = NULL, fac = NULL, cutoff.sims = NULL, collapse = TRUE, extra.filter = NULL, ...) } \arguments{ \item{x}{ A matrix (nXn), a numeric array with 3 dimensions (nXnXm), a list with m cells each containing nXn matrix, or a list with \sQuote{all.dccm} component, containing atomic correlation values, where "n" is the number of residues and "m" the number of calculations. The matrix elements should be in between -1 and 1. See \sQuote{dccm} function in bio3d package for further details. } \item{cutoff.cij}{ Threshold for each individual correlation value. See below for details. } \item{cmap}{ logical, if TRUE both correlation values and contact map are inspected. } \item{xyz}{ XYZ coordinates for distance matrix calculation. } \item{fac}{ factor indicating distinct categories of input correlation matrices. } \item{cutoff.sims}{ Threshold for the number of simulations with observed correlation value above \code{cutoff.cij} for the same residue/atomic pairs. See below for details. } \item{collapse}{ logical, if TRUE the mean matrix will be returned. } \item{extra.filter}{ Filter to apply in addition to the model chosen. } \item{\dots}{ extra arguments passed to function \code{cmap}. } } \value{ Returns a matrix of class "dccm" or a 3D array of filtered cross-correlations. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Xin-Qiu Yao, Guido Scarabelli & Barry Grant } \details{ If cmap=TRUE, the function inspects a set of cross-correlation matrices, or DCCM, and decides edges for correlation network analysis based on: 1. min(abs(cij)) >= cutoff.cij, or 2. max(abs(cij)) >= cutoff.cij && residues contact each other based on results from \code{cmap}. If cmap=FALSE, the function filters DCCMs with \code{cutoff.cij} and return the mean of correlations present in at least \code{cutoff.sims} calculated matrices. } \seealso{ \code{\link{cna}}, \code{\link{dccm}}, \code{\link{dccm.nma}}, \code{\link{dccm.xyz}}, \code{\link{cmap}}, \code{\link{plot.dccm}} } \examples{ \dontrun{ # Example of transducin attach(transducin) gaps.pos <- gap.inspect(pdbs$xyz) modes <- nma.pdbs(pdbs, full=TRUE) dccms <- dccm.enma(modes) cij <- filter.dccm(dccms, xyz=pdbs) # Example protein kinase # Select Protein Kinase PDB IDs ids <- c("4b7t_A", "2exm_A", "1opj_A", "4jaj_A", "1a9u_A", "1tki_A", "1csn_A", "1lp4_A") # Download and split by chain ID files <- get.pdb(ids, path = "raw_pdbs", split=TRUE) # Alignment of structures pdbs <- pdbaln(files) # Sequence identity summary(c(seqidentity(pdbs))) # NMA on all structures modes <- nma.pdbs(pdbs, full = TRUE) # Calculate correlation matrices for each structure cij <- dccm(modes) # Set DCCM plot panel names for combined figure dimnames(cij$all.dccm) = list(NULL, NULL, ids) plot.dccm(cij$all.dccm) # Filter to display only correlations present in all structures cij.all <- filter.dccm(cij, cutoff.sims = 8, cutoff.cij = 0) plot.dccm(cij.all, main = "Consensus Residue Cross Correlation") detach(transducin) } } \keyword{analysis} bio3d/man/nma.pdb.Rd0000644000176200001440000001741412544562303013646 0ustar liggesusers\name{nma.pdb} \alias{nma.pdb} \alias{build.hessian} \alias{print.nma} \title{ Normal Mode Analysis } \description{ Perform elastic network model (ENM) C-alpha normal modes calculation of a protein structure. } \usage{ \method{nma}{pdb}(pdb, inds = NULL, ff = 'calpha', pfc.fun = NULL, mass = TRUE, temp = 300.0, keep = NULL, hessian = NULL, outmodes = NULL, \dots ) build.hessian(xyz, pfc.fun, fc.weights = NULL, sequ = NULL, sse = NULL, ss.bonds = NULL, \dots) \method{print}{nma}(x, nmodes=6, \dots) } \arguments{ \item{pdb}{ an object of class \code{pdb} as obtained from function \code{\link{read.pdb}}. } \item{inds}{ atom and xyz coordinate indices obtained from \code{\link{atom.select}} that selects the elements of \code{pdb} upon which the calculation should be based. If not provided the function will attempt to select the calpha atoms automatically (based on function \code{\link{atom.select}}). } \item{ff}{ character string specifying the force field to use: \sQuote{calpha}, \sQuote{anm}, \sQuote{pfanm}, \sQuote{calphax}, \sQuote{reach}, or \sQuote{sdenm}. } \item{pfc.fun}{ customized pair force constant (\sQuote{pfc}) function. The provided function should take a vector of distances as an argument to return a vector of force constants. If provided, 'pfc.fun' will override argument \code{ff}. See examples below. } \item{mass}{ logical, if TRUE the Hessian will be mass-weighted. } \item{temp}{ numerical, temperature for which the amplitudes for scaling the atomic displacement vectors are calculated. Set \sQuote{temp=NULL} to avoid scaling. } \item{keep}{ numerical, final number of modes to be stored. Note that all subsequent analyses are limited to this subset of modes. This option is useful for very large structures and cases where memory may be limiting. } \item{hessian}{ hessian matrix as obtained from \code{\link{build.hessian}}. For internal purposes and generally not intended for public use. } \item{outmodes}{ atom indices as obtained from \code{\link{atom.select}}) specifying the atoms to include in the resulting mode object. } \item{xyz}{ a numeric vector of Cartesian coordinates. } \item{fc.weights}{ a numeric matrix of size NxN (where N is the number of calpha atoms) containg scaling factors for the pariwise force constants. See examples below. } \item{sse}{ secondary structure elements as obtained from \code{dssp}. } \item{sequ}{ a character vector of the amino acid sequence. } \item{ss.bonds}{ a numeric two-column matrix containing the residue numbers of the disulfide bridges in the structure. } \item{x}{ an \code{nma} object obtained from \code{\link{nma.pdb}}. } \item{nmodes}{ numeric, number of modes to be printed. } \item{...}{ additional arguments to \code{\link{build.hessian}}, \code{\link{aa2mass}}, \code{pfc.fun}, and \code{\link{print}}. One useful option here for dealing with unconventional residues is \sQuote{mass.custom}, see the \code{\link{aa2mass}} function for details. } } \details{ This function calculates the normal modes of a C-alpha model of a protein structure. A number of force fields are implemented all of whhich employ the elastic network model (ENM). The \sQuote{calpha} force field - originally developed by Konrad Hinsen - is the recommended one for most applications. It employs a spring force constant differentiating between nearest-neighbour pairs along the backbone and all other pairs. The force constant function was parameterized by fitting to a local minimum of a crambin model using the AMBER94 force field. See \code{\link{load.enmff}} for details of the different force fields. By default \code{\link{nma.pdb}} will diagonalize the mass-weighted Hessian matrix. The resulting mode vectors are moreover scaled by the thermal fluctuation amplitudes. The implementation under default arguments reproduces the calculation of normal modes (VibrationalModes) in the Molecular Modeling Toolkit (MMTK) package. To reproduce ANM modes set \code{ff='anm'}, \code{mass=FALSE}, and \code{temp=NULL}. } \value{ Returns an object of class \sQuote{nma} with the following components: \item{modes}{ numeric matrix with columns containing the normal mode vectors. Mode vectors are converted to unweighted Cartesian coordinates when \code{mass=TRUE}. Note that the 6 first trivial eigenvectos appear in columns one to six. } \item{frequencies}{ numeric vector containing the vibrational frequencies corresponding to each mode (for \code{mass=TRUE}). } \item{force.constants}{ numeric vector containing the force constants corresponding to each mode (for \code{mass=FALSE)}). } \item{fluctuations}{ numeric vector of atomic fluctuations. } \item{U}{ numeric matrix with columns containing the raw eigenvectors. Equals to the \code{modes} component when \code{mass=FALSE} and \code{temp=NULL}. } \item{L}{ numeric vector containing the raw eigenvalues. } \item{xyz}{ numeric matrix of class \code{xyz} containing the Cartesian coordinates in which the calculation was performed. } \item{mass}{ numeric vector containing the residue masses used for the mass-weighting. } \item{temp}{ numerical, temperature for which the amplitudes for scaling the atomic displacement vectors are calculated. } \item{triv.modes}{ number of trivial modes. } \item{natoms}{ number of C-alpha atoms. } \item{call}{ the matched call. } } \note{ The current version provides an efficent implementation of NMA with execution time comparable to similar software (when the entire Hessian is diagonalized). The main (speed related) bottleneck is currently the diagonalization of the Hessian matrix which is performed with the core R function \code{\link{eigen}}. For computing a few (5-20) approximate modes the user can consult package \sQuote{irlba}. NMA is memory extensive and users should be cautions when running larger proteins (>3000 residues). Use \sQuote{keep} to reduce the amount of memory needed to store the final \sQuote{nma} object (the full 3Nx3N Hessian matrix still needs to be allocated). We thank Edvin Fuglebakk for valuable discussions on the implementation as well as for contributing with testing. } \references{ Skjaerven, L. et al. (2014) \emph{BMC Bioinformatics} \bold{15}, 399. Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. Hinsen, K. et al. (2000) \emph{Chemical Physics} \bold{261}, 25--37. } \author{ Lars Skjaerven } \seealso{ \code{\link{fluct.nma}}, \code{\link{mktrj.nma}}, \code{\link{dccm.nma}}, \code{\link{overlap}}, \code{\link{rmsip}}, \code{\link{load.enmff}}. } \examples{ ## Fetch stucture pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) ## Calculate normal modes modes <- nma(pdb) ## Print modes print(modes) ## Plot modes plot(modes) ## Visualize modes #m7 <- mktrj.nma(modes, mode=7, file="mode_7.pdb") \dontrun{ ## Use Anisotropic Network Model modes <- nma(pdb, ff="anm", mass=FALSE, temp=NULL, cutoff=15) ## Use SSE information and SS-bonds sse <- dssp(pdb, resno=FALSE, full=TRUE) ss.bonds <- matrix(c(76,94, 64,80, 30,115, 6,127), ncol=2, byrow=TRUE) modes <- nma(pdb, ff="calphax", sse=sse, ss.bonds=ss.bonds) ## User defined energy function ## Note: Must take a vector of distances "my.ff" <- function(r) { ifelse( r>15, 0, 1 ) } ## Modes with a user defined energy function modes <- nma(pdb, pfc.fun=my.ff) ## A more manual approach sele <- atom.select(pdb, chain='A', elety='CA') xyz <- pdb$xyz[sele$xyz] hessian <- build.hessian(xyz, my.ff) modes <- eigen(hessian) ## Dealing with unconventional residues pdb <- read.pdb("1xj0") ## nma(pdb) modes <- nma(pdb, mass.custom=list(CSX=121.166)) } } \keyword{ analysis } bio3d/man/read.mol2.Rd0000644000176200001440000000726712544562303014117 0ustar liggesusers\name{read.mol2} \alias{read.mol2} \alias{print.mol2} \title{ Read MOL2 File } \description{ Read a Sybyl MOL2 file } \usage{ read.mol2(file, maxlines = -1L) \method{print}{mol2}(x, \dots) } \arguments{ \item{file}{ a single element character vector containing the name of the MOL2 file to be read. } \item{maxlines}{ the maximum number of lines to read before giving up with large files. Default is all lines. } \item{x}{ an object as obtained from \code{read.sdf}. } \item{...}{ additional arguments to \sQuote{print}. } } \details{ Basic functionality to parse a MOL2 file. The current version reads and stores \code{@MOLECULE}, \code{@ATOM} and \code{@BOND} records. In the case of a multi-molecule MOL2 file, each molecule will be stored as an individual object in a list. Conversely, if the multi-molecule MOL2 file contains identical molecules in different conformations (typically a dockin run), then the output will be one object with an \code{atom} and \code{xyz} component (xyz in matrix representation; row-wise coordinates). See examples for further details. } \value{ Returns a list of molecules containing the following components: \item{atom}{ a data frame containing all atomic coordinate ATOM data, with a row per ATOM and a column per record type. See below for details of the record type naming convention (useful for accessing columns). } \item{bond}{ a data frame containing all atomic bond information. } \item{xyz}{ a numeric matrix of ATOM coordinate data. } \item{info}{ a numeric vector of MOL2 info data. } \item{name}{ a single element character vector containing the molecule name. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. For a description of the MOL2 format see:\cr \url{http://www.tripos.com/data/support/mol2.pdf}. } \author{ Lars Skjaerven } \note{ For \code{atom} list components the column names can be used as a convenient means of data access, namely: Atom serial number \dQuote{eleno}, Atom name \dQuote{elena}, Orthogonal coordinates \dQuote{x}, Orthogonal coordinates \dQuote{y}, Orthogonal coordinates \dQuote{z}, Atom type \dQuote{elety}, Residue name \dQuote{resid}, Atom charge \dQuote{charge}, Status bit \dQuote{statbit}, For \code{bond} list components the column names are: Bond identifier \dQuote{id}, number of the atom at one end of the bond\dQuote{origin}, number of the atom at the other end of the bond \dQuote{target}, the SYBYL bond type \dQuote{type}. See examples for further details. } \seealso{ \code{\link{atom.select}}, \code{\link{read.pdb}} } \examples{ cat("\n") \dontrun{ ## Read a single entry MOL2 file ## (returns a single object) mol <- read.mol2("single.mol2") ## Short summary of the molecule print(mol) ## ATOM records mol$atom ## BOND records mol$bond ## Print some coordinate data head(mol$atom[, c("x","y","z")]) ## Or coordinates as a numeric vector head(mol$xyz) ## Print atom charges head(mol$atom[, "charge"]) ## Read a multi-molecule MOL2 file ## (returns a list of objects) multi.mol <- read.mol2("zinc.mol2") ## Number of molecules described in file length(multi.mol) ## Access ATOM records for the first molecule multi.mol[[1]]$atom ## Or coordinates for the second molecule multi.mol[[2]]$xyz ## Process output from docking (e.g. DOCK) ## (typically one molecule with many conformations) ## (returns one object, but xyz in matrix format) dock <- read.mol2("dock.mol2") ## Reference PDB file (e.g. X-ray structure) pdb <- read.pdb("dock_ref.pdb") ## Calculate RMSD of docking modes sele <- atom.select(dock, "noh") rmsd(pdb$xyz, dock$xyz, b.inds=sele$xyz) } } \keyword{ IO } bio3d/man/project.pca.Rd0000644000176200001440000000333712526367344014546 0ustar liggesusers\name{project.pca} \alias{project.pca} \alias{z2xyz.pca} \alias{xyz2z.pca} \title{ Project Data onto Principal Components } \description{ Projects data onto principal components. } \usage{ project.pca(data, pca, angular = FALSE, fit = FALSE, ...) z2xyz.pca(z.coord, pca) xyz2z.pca(xyz.coord, pca) } \arguments{ \item{data}{ a numeric vector or row-wise matrix of data to be projected. } \item{pca}{ an object of class \code{"pca"} as obtained from functions \code{pca.xyz} or \code{pca.tor}. } \item{angular}{ logical, if TRUE the data to be projected is treated as torsion angle data. } \item{fit}{ logical, if TRUE the data is first fitted to \code{pca$mean}. } \item{\dots}{ other parameters for \code{\link{fit.xyz}}. } \item{xyz.coord}{ a numeric vector or row-wise matrix of data to be projected. } \item{z.coord}{ a numeric vector or row-wise matrix of PC scores (i.e. the z-scores which are centered and rotated versions of the origional data projected onto the PCs) for conversion to xyz coordinates. } } \value{ A numeric vector or matrix of projected PC scores. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Karim ElSawy and Barry Grant} \seealso{ \code{\link{pca.xyz}}, \code{\link{pca.tor}}, \code{\link{fit.xyz}} } \examples{ \dontrun{ data(transducin) attach(transducin, warn.conflicts=FALSE) gaps.pos <- gap.inspect(pdbs$xyz) #-- Do PCA without structures 2 and 7 pc.xray <- pca.xyz(pdbs$xyz[-c(2,7), gaps.pos$f.inds]) #-- Project structures 2 and 7 onto the PC space d <- project.pca(pdbs$xyz[c(2,7), gaps.pos$f.inds], pc.xray) plot(pc.xray$z[,1], pc.xray$z[,2],col="gray") points(d[,1],d[,2], col="red") detach(transducin) } } \keyword{ utilities } bio3d/man/cat.pdb.Rd0000644000176200001440000000234012544562303013632 0ustar liggesusers\name{cat.pdb} \alias{cat.pdb} \title{ Concatenate Multiple PDB Objects } \description{ Produce a new concatenated PDB object from two or more smaller PDB objects. } \usage{ cat.pdb(\dots, renumber=FALSE, rechain=TRUE) } \arguments{ \item{\dots}{ two or more PDB structure objects obtained from \code{\link{read.pdb}}. } \item{renumber}{ logical, if \sQuote{TRUE} residues will be renumbered. } \item{rechain}{ logical, if \sQuote{TRUE} molecules will be assigned new chain identifiers. } } \details{ This is a basic utility function for creating a concatenated PDB object based on multipe smaller PDB objects. } \value{ Returns an object of class \code{"pdb"}. See \code{\link{read.pdb}} for further details. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Lars Skjaerven } \seealso{ \code{\link{read.pdb}}, \code{\link{atom.select}}, \code{\link{write.pdb}}, \code{\link{trim.pdb}} } \examples{ \dontrun{ ## Read a PDB file from the RCSB online database pdb1 <- read.pdb("1etl") pdb2 <- read.pdb("1hel") ## Concat new.pdb <- cat.pdb(pdb1, pdb2, pdb1, rechain=TRUE, renumber=TRUE) ## Write to file write.pdb(new.pdb, file="concat.pdb") } } \keyword{ utilities } bio3d/man/gap.inspect.Rd0000644000176200001440000000421312412623040014521 0ustar liggesusers\name{gap.inspect} \alias{gap.inspect} \title{ Alignment Gap Summary } \description{ Report the number of gaps per sequence and per position for a given alignment. } \usage{ gap.inspect(x) } \arguments{ \item{x}{ a matrix or an alignment data structure obtained from \code{\link{read.fasta}} or \code{\link{read.fasta.pdb}}.} } \details{ Reports the number of gap characters per row (i.e. sequence) and per column (i.e. position) for a given \code{alignment}. In addition, the indices for gap and non-gap containing coloums are returned along with a binary matrix indicating the location of gap positions. } \value{ Returns a list object with the following components: \item{row }{a numeric vector detailing the number of gaps per row (i.e. sequence).} \item{col }{a numeric vector detailing the number of gaps per column (i.e. position).} \item{t.inds }{ indices for gap containing coloums } \item{f.inds }{ indices for non-gap containing coloums} \item{bin }{a binary numeric matrix with the same dimensions as the \code{alignment}, with 0 at non-gap positions and 1 at gap positions.} } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \note{ During alignment, gaps are introduced into sequences that are believed to have undergone deletions or insertions with respect to other sequences in the alignment. These gaps, often referred to as indels, can be represented with \sQuote{NA}, a \sQuote{-} or \sQuote{.} character. This function gives an overview of gap occurrence and may be useful when considering positions or sequences that could/should be excluded from further analysis. } \seealso{ \code{\link{read.fasta}}, \code{\link{read.fasta.pdb}}} \examples{ aln <- read.fasta( system.file("examples/hivp_xray.fa", package = "bio3d") ) gap.stats <- gap.inspect(aln$ali) gap.stats$row # Gaps per sequence gap.stats$col # Gaps per position ##gap.stats$bin # Binary matrix (1 for gap, 0 for aminoacid) ##aln[,gap.stats$f.inds] # Alignment without gap positions plot(gap.stats$col, typ="h", ylab="No. of Gaps") } \keyword{ utilities } bio3d/man/convert.pdb.Rd0000644000176200001440000000772512544562303014557 0ustar liggesusers\name{convert.pdb} \alias{convert.pdb} \title{Renumber and Convert Between Various PDB formats} \description{ Renumber and convert between CHARMM, Amber, Gromacs and Brookhaven PDB formats. } \usage{ convert.pdb(pdb, type=c("original", "pdb", "charmm", "amber", "gromacs"), renumber = FALSE, first.resno = 1, first.eleno = 1, consecutive=TRUE, rm.h = TRUE, rm.wat = FALSE, verbose=TRUE) } \arguments{ \item{pdb}{ a structure object of class \code{"pdb"}, obtained from \code{\link{read.pdb}}. } \item{type}{ output format, one of \sQuote{original}, \sQuote{pdb}, \sQuote{charmm}, \sQuote{amber}, or \sQuote{gromacs}. The default option of \sQuote{original} results in no conversion. } \item{renumber}{ logical, if TRUE atom and residue records are renumbered using \sQuote{first.resno} and \sQuote{first.eleno}. } \item{first.resno}{ first residue number to be used if \sQuote{renumber} is TRUE. } \item{first.eleno}{ first element number to be used if \sQuote{renumber} is TRUE. } \item{consecutive}{ logical, if TRUE renumbering will result in consecutive residue numbers spanning all chains. Otherwise new residue numbers will begin at \sQuote{first.resno} for each chain. } \item{rm.h}{ logical, if TRUE hydrogen atoms are removed. } \item{rm.wat}{ logical, if TRUE water atoms are removed. } \item{verbose}{ logical, if TRUE details of the conversion process are printed. } } \details{ Convert atom names and residue names, renumber atom and residue records, strip water and hydrogen atoms from \code{pdb} objects. Format \code{type} can be one of \dQuote{ori}, \dQuote{pdb}, \dQuote{charmm}, \dQuote{amber} or \dQuote{gromacs}. } \value{ Returns a list of class \code{"pdb"}, with the following components: \item{atom}{ a character matrix containing all atomic coordinate ATOM data, with a row per ATOM and a column per record type. See below for details of the record type naming convention (useful for accessing columns). } \item{het}{ a character matrix containing atomic coordinate records for atoms within \dQuote{non-standard} HET groups (see \code{atom}). } \item{helix}{ \sQuote{start}, \sQuote{end} and \sQuote{length} of H type sse, where start and end are residue numbers \dQuote{resno}. } \item{sheet}{ \sQuote{start}, \sQuote{end} and \sQuote{length} of E type sse, where start and end are residue numbers \dQuote{resno}. } \item{seqres }{ sequence from SEQRES field. } \item{xyz}{ a numeric vector of ATOM coordinate data. } \item{calpha}{ logical vector with length equal to \code{nrow(atom)} with TRUE values indicating a C-alpha \dQuote{elety}. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. For a description of PDB format (version3.3) see:\cr \url{http://www.wwpdb.org/documentation/format33/v3.3.html}. } \author{ Barry Grant } \note{ For both \code{atom} and \code{het} list components the column names can be used as a convenient means of data access, namely: Atom serial number \dQuote{eleno} , Atom type \dQuote{elety}, Alternate location indicator \dQuote{alt}, Residue name \dQuote{resid}, Chain identifier \dQuote{chain}, Residue sequence number \dQuote{resno}, Code for insertion of residues \dQuote{insert}, Orthogonal coordinates \dQuote{x}, Orthogonal coordinates \dQuote{y}, Orthogonal coordinates \dQuote{z}, Occupancy \dQuote{o}, and Temperature factor \dQuote{b}. See examples for further details. } \seealso{ \code{\link{atom.select}}, \code{\link{write.pdb}}, \code{\link{read.dcd}}, \code{\link{read.fasta.pdb}}, \code{\link{read.fasta}} } \examples{ \dontrun{ # Read a PDB file pdb <- read.pdb("4q21") pdb head( pdb$atom[pdb$calpha,"resno"] ) # Convert to CHARMM format new <- convert.pdb(pdb, type="amber", renumber=TRUE, first.resno=22 ) head( new$atom[new$calpha,"resno"] ) # Write a PDB file #write.pdb(new, file="tmp4amber.pdb") } } \keyword{ utilities } bio3d/man/uniprot.Rd0000644000176200001440000000233112632622153014015 0ustar liggesusers\name{uniprot} \alias{uniprot} \title{ Fetch UniProt Entry Data. } \description{ Fetch protein sequence and functional information from the UniProt database. } \usage{ uniprot(accid) } \arguments{ \item{accid}{ UniProt accession id. } } \details{ This is a basic utility function for downloading information from the UniProt database. UniProt contains protein sequence and functional information. } \value{ Returns a list object with the following components: \item{accession}{ a character vector with UniProt accession id's. } \item{name}{ abbreviated name. } \item{fullName}{ full recommended protein name. } \item{shortName }{ short protein name. } \item{sequence}{ protein sequence. } \item{gene}{ gene names. } \item{organism}{ organism. } \item{taxon}{ taxonomic lineage. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. See also the UniProt web-site for more information:\cr \url{http://www.uniprot.org/}. } \author{ Lars Skjaerven } \seealso{ \code{\link{blast.pdb}}, \code{\link{get.seq}} } \examples{ \dontrun{ # UNIPROT server connection required - testing excluded prot <- uniprot('PH4H_HUMAN') prot$fullName prot$sequence } } \keyword{ utilities } bio3d/man/plot.bio3d.Rd0000644000176200001440000001427012544562303014301 0ustar liggesusers\name{plot.bio3d} \alias{plot.bio3d} \alias{plotb3} \title{ Plots with marginal SSE annotation } \description{ Draw a standard scatter plot with optional secondary structure in the marginal regions. } \usage{ plotb3(x, resno = NULL, rm.gaps = FALSE, type = "h", main = "", sub = "", xlim = NULL, ylim = NULL, ylim2zero = TRUE, xlab = "Residue", ylab = NULL, axes = TRUE, ann = par("ann"), col = par("col"), sse = NULL, sse.type="classic", sse.min.length=5, top = TRUE, bot = TRUE, helix.col = "gray20", sheet.col = "gray80", sse.border = FALSE, ...) \method{plot}{bio3d}(...) } \arguments{ \item{x}{ a numeric vector of values to be plotted. Any reasonable way of defining these plot coordinates is acceptable. See the function \sQuote{xy.coords} for details. } \item{resno}{ an optional vector with length equal to that of \sQuote{x} that will be used to annotate the xaxis. This is typically a vector of residue numbers. If NULL residue positions from 1 to the length of \sQuote{x} will be used. See examples below. } \item{rm.gaps}{ logical, if TRUE gaps in \code{x}, indicated by NA values, will be removed from plot. } \item{type}{ one-character string giving the type of plot desired. The following values are possible, (for details, see \sQuote{plot}): \sQuote{p} for points, \sQuote{l} for lines, \sQuote{o} for over-plotted points and lines, \sQuote{b}, \sQuote{c}) for points joined by lines, \sQuote{s} and \sQuote{S} for stair steps and \sQuote{h} for histogram-like vertical lines. Finally, \sQuote{n} does not produce any points or lines. } \item{main}{ a main title for the plot, see also \sQuote{title}. } \item{sub}{ a sub-title for the plot. } \item{xlim}{ the x limits (x1,x2) of the plot. Note that x1 > x2 is allowed and leads to a reversed axis. } \item{ylim}{ the y limits of the plot. } \item{ylim2zero}{ logical, if TRUE the y-limits are forced to start at zero. } \item{xlab}{ a label for the x axis, defaults to a description of \sQuote{x}. } \item{ylab}{ a label for the y axis, defaults to a description of \sQuote{y}. } \item{axes}{ a logical value indicating whether both axes should be drawn on the plot. Use graphical parameter \sQuote{xaxt} or \sQuote{yaxt} to suppress just one of the axes. } \item{ann}{ a logical value indicating whether the default annotation (title and x and y axis labels) should appear on the plot. } \item{col}{ The colors for lines and points. Multiple colors can be specified so that each point is given its own color. If there are fewer colors than points they are recycled in the standard fashion. Lines are plotted in the first color specified. } \item{sse}{ secondary structure object as returned from \code{\link{dssp}}, \code{\link{stride}} or in certain cases \code{\link{read.pdb}}. } \item{sse.type}{ single element character vector that determines the type of secondary structure annotation drawn. The following values are possible, \sQuote{classic} and \sQuote{fancy}. See details and examples below. } \item{sse.min.length}{ a single numeric value giving the length below which secondary structure elements will not be drawn. This is useful for the exclusion of short helix and strand regions that can often crowd these forms of plots. } \item{top}{ logical, if TRUE rectangles for each sse are drawn towards the top of the plotting region. } \item{bot}{ logical, if TRUE rectangles for each sse are drawn towards the bottom of the plotting region. } \item{helix.col}{ The colors for rectangles representing alpha helices. } \item{sheet.col}{ The colors for rectangles representing beta strands. } \item{sse.border}{ The border color for all sse rectangles. } \item{\dots}{ other graphical parameters. } } \details{ This function is useful for plotting per-residue numeric vectors for a given protein structure (e.g. results from RMSF, PCA, NMA etc.) along with a schematic representation of major secondary structure elements. Two forms of secondary structure annotation are available: so called \sQuote{classic} and \sQuote{fancy}. The former draws marginal rectangles and has been available within Bio3D from version 0.1. The later draws more \sQuote{fancy} (and distracting) 3D like helices and arrowed strands. See the functions \sQuote{plot.default}, \code{\link{dssp}} and \code{\link{stride}} for further details. } \value{ Called for its effect. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \note{ Be sure to check the correspondence of your \sQuote{sse} object with the \sQuote{x} values being plotted as no internal checks are performed. } \seealso{ \code{\link{plot.default}}, \code{\link{dssp}}, \code{\link{stride}} } \examples{ \donttest{ # PDB server connection required - testing excluded ## Plot of B-factor values along with secondary structure from PDB pdb <- read.pdb( "1bg2" ) bfac <- pdb$atom[pdb$calpha,"b"] plot.bio3d(bfac, sse=pdb, ylab="B-factor", col="gray") points(bfac, typ="l") } \dontrun{ ## Use PDB residue numbers and include short secondary structure elements plot.bio3d(pdb$atom[pdb$calpha,"b"], sse=pdb, resno=pdb, ylab="B-factor", typ="l", lwd=1.5, col="blue", sse.min.length=0) ## Calculate secondary structure using stride() or dssp() #sse <- stride(pdb) sse <- dssp(pdb) ## Plot of B-factor values along with calculated secondary structure plot.bio3d(pdb$atom[pdb$calpha,"b"], sse=sse, ylab="B-factor", typ="l", col="blue", lwd=2) } ## Plot 'aligned' data respecting gap positions attach(transducin) pdb = read.pdb("1tnd") ## Reference PDB see: pdbs$id[1] pdb = trim.pdb(pdb, inds=atom.select(pdb, chain="A")) ## Plot of B-factor values with gaps plot.bio3d(pdbs$b, resno=pdb, sse=pdb, ylab="B-factor") ## Plot of B-factor values after removing all gaps plot.bio3d(pdbs$b, rm.gaps=TRUE, resno = pdb, sse=pdb, ylab="B-factor") detach(transducin) ## Fancy secondary structure elements ##plot.bio3d(pdb$atom[pdb$calpha,"b"], sse=pdb, ssetype="fancy") ## Currently not implemented } \keyword{ hplot } bio3d/man/plot.pca.Rd0000644000176200001440000000721512526367344014055 0ustar liggesusers\name{plot.pca} \alias{plot.pca} \alias{plot.pca.score} \alias{plot.pca.scree} \title{ Plot PCA Results } \description{ Produces a z-score plot (conformer plot) and an eigen spectrum plot (scree plot). } \usage{ \method{plot}{pca}(x, pc.axes=NULL, pch=16, col=par("col"), cex=0.8, mar=c(4, 4, 1, 1),...) \method{plot}{pca.scree}(x, y = NULL, type = "o", pch = 18, main = "", sub = "", xlim = c(0, 20), ylim = NULL, ylab = "Proporton of Variance (\%)", xlab = "Eigenvalue Rank", axes = TRUE, ann = par("ann"), col = par("col"), lab = TRUE, ...) \method{plot}{pca.score}(x, inds=NULL, col=rainbow(nrow(x)), lab = "", ...) } \arguments{ \item{x}{ the results of principal component analysis obtained with \code{\link{pca.xyz}}. } \item{pc.axes}{ an optional numeric vector of length two specifying the principal components to be plotted. A NULL value will result in an overview plot of the first three PCs and a scree plot. See examples. } \item{pch}{ a vector of plotting characters or symbols: see \sQuote{points}. } \item{col}{ a character vector of plotting colors. } \item{cex}{ a numerical single element vector giving the amount by which plotting text and symbols should be magnified relative to the default. } \item{mar}{ A numerical vector of the form c(bottom, left, top, right) which gives the number of lines of margin to be specified on the four sides of the plot.} \item{inds}{ row indices of the conformers to label. } \item{lab}{ a character vector of plot labels. } \item{y}{ the y coordinates for the scree plot. } \item{type}{ one-character string giving the type of plot desired.} \item{main}{ a main title for the plot, see also 'title'.} \item{sub}{ a sub-title for the plot.} \item{xlim}{ the x limits of the plot. } \item{ylim}{ the y limits of the plot.} \item{ylab}{ a label for the y axis.} \item{xlab}{ a label for the x axis.} \item{axes}{ a logical value indicating whether both axes should be drawn.} \item{ann}{ a logical value indicating whether the default annotation (title and x and y axis labels) should appear on the plot. } \item{\dots}{ extra plotting arguments. } } \details{ \code{plot.pca} is a wrapper calling both \code{plot.pca.score} and \code{plot.pca.scree} resulting in a 2x2 plot with three score plots and one scree plot. } \value{ Produces a plot of PCA results in the active graphics device and invisibly returns the plotted \sQuote{z} coordinates along the requested \sQuote{pc.axes}. See examples section where these coordinates are used to identify plotted points. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \seealso{ \code{\link{pca.xyz}}, \code{\link{plot.bio3d}} } \examples{ data(transducin) attach(transducin, warn.conflicts=FALSE) pc.xray <- pca(pdbs$xyz, rm.gaps=TRUE) plot(pc.xray) ## Color plot by nucleotide state vcolors <- annotation[, "color"] plot(pc.xray, col=vcolors) ## Focus on a single plot of PC1 vs PC2 x <- plot(pc.xray, pc.axes=1:2, col=vcolors) ## Identify points interactively with mouse clicks #identify(x, labels=basename.pdb(pdbs$id)) ## Add labels to select points inds <- c(1,10,37) text(x[inds,], labels=basename.pdb(pdbs$id[inds]), col="blue") ## Alternative labeling method #labs <- rownames(annotation) #inds <- c(2,7) #plot.pca.score(pc.xray, inds=inds, col=vcolors, lab=labs) ## color by seq identity groupings #ide <- seqidentity(pdbs$ali) #hc <- hclust(as.dist(1-ide)) #grps <- cutree(hc, h=0.2) #vcolors <- rainbow(max(grps))[grps] #plot.pca.score(pc.xray, inds=inds, col=vcolors, lab=labs) detach(transducin) } \keyword{ hplot } bio3d/man/plot.fluct.Rd0000644000176200001440000001043212632622153014410 0ustar liggesusers\name{plot.fluct} \alias{plot.fluct} \title{ Plot Fluctuations } \description{ Produces a plot of atomic fluctuations obtained from ensemble normal mode analysis or molecular dynamics simulations. } \usage{ \method{plot}{fluct}(x, col = NULL, signif = FALSE, p.cutoff = 0.005, q.cutoff = 0.04, s.cutoff = 5, n.cutoff = 2, mean = FALSE, polygon = FALSE, ncore = NULL, ...) } \arguments{ \item{x}{ a numeric vector or matrix containing atomic fluctuation data obtained from e.g. \code{\link{nma.pdbs}} or \code{\link{rmsf}}. } \item{col}{ a character vector of plotting colors. Used also to group fluctuation profiles. NA values in col will omit the corresponding fluctuation profile in the plot. } \item{signif}{ logical, if TRUE significance of fluctuation difference is calculated and annotated for each atomic position. } \item{p.cutoff}{ Cutoff of p-value to define significance. } \item{q.cutoff}{ Cutoff of the mean fluctuation difference to define significance. } \item{s.cutoff}{ Cutoff of sample size in each group to calculate the significance. } \item{n.cutoff}{ Cutoff of consecutive residue positions with significant fluctuation difference. If the actual number is less than the cutoff, correponding postions will not be annotated. } \item{mean}{ logical, if TRUE plot mean fluctuations of each group. Significance is still calculated with the original data. } \item{polygon}{ logical, if TRUE a nicer plot with area under the line for the first row of \code{x} are filled with polygons. } \item{ncore }{ number of CPU cores used to do the calculation. By default (\code{ncore=NULL}), use all available CPU cores. The argument is only used when \code{signif=TRUE}. } \item{\dots}{ extra plotting arguments passed to \code{plot.bio3d}. } } \details{ The significance calculation is performed when \code{signif=TRUE} and there are at least two groups with sample size larger than or equal to \code{s.cutoff}. A "two-sided" student's t-test is performed for each atomic position (each column of \code{x}). If \code{x} contains gaps, indicated by \code{NA}s, only non-gapped positions are considered. The position is considered significant if both \code{p-value <= p.cutoff} and the mean value difference of the two groups, q, satisfies \code{q >= q.cutoff}. If more than two groups are available, every pair of groups are subjected to the t-test calculation and the minimal p-value along with the q-value for the corresponding pair are used for the significance evaluation. } \value{ If significance is calculated, return a vector indicating significant positions. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Xin-Qiu Yao, Lars Skjaerven, Barry Grant } \seealso{ \code{\link{plot.bio3d}}, \code{\link{rmsf}}, \code{\link{nma.pdbs}}, \code{\link[stats:t.test]{t.test}}, \code{\link[graphics:polygon]{polygon}}. } \examples{ \dontrun{ ## load transducin example data attach(transducin) ## remove flexible termina for a full-length NMA calculation inds = 30:(ncol(pdbs$ali)-8) npdbs = trim(pdbs, col.inds = inds) gaps.res = gap.inspect(npdbs$ali) ## reference PDB for secondary structure annotation ref.pdb_id = substr(npdbs$id[1], 1, 4) ref.chain_id = substr(npdbs$id[1], 6, 6) ref.resno = npdbs$resno[1, !is.na(npdbs$resno[1, ])] pdb = read.pdb(ref.pdb_id) pdb = trim(pdb, resno=ref.resno, chain=ref.chain_id) ## eNMA calculation and obtain modes of motion including atomic fluctuations modes <- nma(npdbs, rm.gaps=FALSE, full=FALSE, ncore=NULL) x = modes$fluctuation ## simple line plot with SSE annotation plot.fluct(x, sse = pdb, resno = pdb) ## group data by specifying colors of each fluctuation line; same color indicates ## same group. Also do significance calculation and annotation col = annotation[, "color"] col[annotation[, "state3"] == "GDI"] = "blue" plot.fluct(x, col=col, signif = TRUE, sse = pdb, resno = pdb) ## show only line of mean values for each group. Gapped positions are removed. Nicer ## plot with area shaded for the first group. plot.fluct(x, col=col, signif = TRUE, sse = pdb, resno = pdb, mean=TRUE, polygon=TRUE, rm.gaps=TRUE) detach(transducin) } } \keyword{ hplot } bio3d/man/print.cna.Rd0000644000176200001440000000341312544562303014215 0ustar liggesusers\name{print.cna} \alias{print.cna} \alias{summary.cna} \title{ Summarize and Print Features of a cna Network Graph } \description{ These functions attempt to summarize and print a cna network graph to the terminal in a human readable form. } \usage{ \method{print}{cna}(x, ...) \method{summary}{cna}(object, verbose=TRUE, ...) } \arguments{ \item{x}{ A cna network and community object as obtained from the function \sQuote{cna}.} \item{object}{ A cna network and community object as obtained from the function \sQuote{cna}.} \item{verbose}{ Logical, if TRUE a community summary table is prited to screen.} \item{\dots}{ Extra arguments passed to the \sQuote{write.table} function. } } \details{ Simple summary and print methods for protein dynamic networks. } \value{ The function summary.cna returns a list with the following components: \item{id}{ A community number/identifier vector with an element for each community. } \item{size}{ A numeric community size vector, with elements giving the number of nodes within each community. } \item{members}{ A lst detailing the nodes within each community. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Guido Scarabelli and Barry Grant } \seealso{ \code{\link{cna}}, \code{\link[igraph:print.igraph]{print.igraph}}, \code{\link[igraph:str.igraph]{str.igraph}}, \code{\link[igraph:igraph.plotting]{igraph.plotting}} } \examples{ ## Load the correlation network attach(hivp) ## Read the starting PDB file to determine atom correspondence pdbfile <- system.file("examples/hivp.pdb", package="bio3d") pdb <- read.pdb(pdbfile) ## Examine network composition print(net) x<- summary(net) x$members[[2]] detach(hivp) } \keyword{ utilities } bio3d/man/rmsd.Rd0000644000176200001440000000501612544562303013267 0ustar liggesusers\name{rmsd} \alias{rmsd} \title{ Root Mean Square Deviation } \description{ Calculate the RMSD between coordinate sets. } \usage{ rmsd(a, b=NULL, a.inds=NULL, b.inds=NULL, fit=FALSE, ncore=1, nseg.scale=1) } \arguments{ \item{a}{ a numeric vector containing the reference coordinate set for comparison with the coordinates in \code{b}. Alternatively, if \code{b=NULL} then \code{a} can be a matrix or list object containing multiple coordinates for pairwise comparison. } \item{b}{ a numeric vector, matrix or list object with an \code{xyz} component, containing one or more coordinate sets to be compared with \code{a}. } \item{a.inds }{ a vector of indices that selects the elements of \code{a} upon which the calculation should be based. } \item{b.inds }{ a vector of indices that selects the elements of \code{b} upon which the calculation should be based. } \item{fit }{logical, if TRUE coordinate superposition is performed prior to RMSD calculation. } \item{ncore }{ number of CPU cores used to do the calculation. \code{ncore>1} requires package \sQuote{parallel} installed. } \item{nseg.scale }{ split input data into specified number of segments prior to running multiple core calculation. See \code{\link{fit.xyz}}. } } \details{ RMSD is a standard measure of structural distance between coordinate sets. Structure \code{a[a.inds]} and \code{b[b.inds]} should have the same length. A least-squares fit is performed prior to RMSD calculation by setting \code{fit=TRUE}. See the function \code{fit.xyz} for more details of the fitting process. } \value{ Returns a numeric vector of RMSD value(s). } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \seealso{ \code{\link{fit.xyz}}, \code{\link{rot.lsq}}, \code{\link{read.pdb}}, \code{\link{read.fasta.pdb}} } \examples{ \donttest{ # Redundant testing excluded # -- Calculate RMSD between two or more structures aln <- read.fasta(system.file("examples/kif1a.fa",package="bio3d")) pdbs <- read.fasta.pdb(aln) # Gap positions inds <- gap.inspect(pdbs$xyz) # Superposition before pairwise RMSD rmsd(pdbs$xyz, fit=TRUE) # RMSD between structure 1 and structures 2 and 3 rmsd(a=pdbs$xyz[1,], b=pdbs$xyz[2:3,], a.inds=inds$f.inds, b.inds=inds$f.inds, fit=TRUE) # RMSD between structure 1 and all structures in alignment rmsd(a=pdbs$xyz[1,], b=pdbs, a.inds=inds$f.inds, b.inds=inds$f.inds, fit=TRUE) # RMSD without superposition rmsd(pdbs$xyz) } } \keyword{ utilities } bio3d/man/plot.core.Rd0000644000176200001440000000514212526367344014237 0ustar liggesusers\name{plot.core} \alias{plot.core} \title{ Plot Core Fitting Progress } \description{ Plots the total ellipsoid volume of core positions versus core size at each iteration of the core finding process. } \usage{ \method{plot}{core}(x, y = NULL, type = "h", main = "", sub = "", xlim = NULL, ylim = NULL, xlab = "Core Size (Number of Residues)", ylab = "Total Ellipsoid Volume (Angstrom^3)", axes = TRUE, ann = par("ann"), col = par("col"), ...) } \arguments{ \item{x}{ a list object obtained with the function \code{\link{core.find}} from which the \sQuote{volume} component is taken as the x coordinates for the plot. } \item{y}{ the y coordinates for the plot. } \item{type}{ one-character string giving the type of plot desired. } \item{main}{ a main title for the plot, see also \sQuote{title}. } \item{sub}{ a sub-title for the plot. } \item{xlim}{ the x limits of the plot. } \item{ylim}{ the y limits of the plot. } \item{xlab}{ a label for the x axis. } \item{ylab}{ a label for the y axis. } \item{axes}{ a logical value indicating whether both axes should be drawn. } \item{ann}{ a logical value indicating whether the default annotation (title and x and y axis labels) should appear on the plot. } \item{col}{ The colors for lines and points. Multiple colours can be specified so that each point is given its own color. If there are fewer colors than points they are recycled in the standard fashion. } \item{\dots}{ extra plotting arguments. } } \value{ Called for its effect. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \note{ The produced plot can be useful for deciding on the core/non-core boundary. } \seealso{ \code{\link{core.find}}, \code{\link{print.core}}} \examples{ \dontrun{ ##-- Generate a small kinesin alignment and read corresponding structures pdbfiles <- get.pdb(c("1bg2","2ncd","1i6i","1i5s"), URLonly=TRUE) pdbs <- pdbaln(pdbfiles) ##-- Find 'core' positions core <- core.find(pdbs) plot(core) ##-- Fit on these relatively invarient subset of positions core.inds <- print(core) xyz <- pdbfit(pdbs, core.inds, outpath="corefit_structures") ##-- Compare to fitting on all equivalent positions xyz2 <- pdbfit(pdbs) ## Note that overall RMSD will be higher but RMSF will ## be lower in core regions, which may equate to a ## 'better fit' for certain applications gaps <- gap.inspect(pdbs$xyz) rmsd(xyz[,gaps$f.inds]) rmsd(xyz2[,gaps$f.inds]) plot(rmsf(xyz[,gaps$f.inds]), typ="l", col="blue", ylim=c(0,9)) points(rmsf(xyz2[,gaps$f.inds]), typ="l", col="red") } } \keyword{ hplot } bio3d/man/pdb2aln.ind.Rd0000644000176200001440000000704612526367344014432 0ustar liggesusers\name{pdb2aln.ind} \alias{pdb2aln.ind} \title{ Mapping from alignment positions to PDB atomic indices } \description{ Find the best alignment between a PDB structure and an existing alignment. Then, given a set of column indices of the original alignment, returns atom selections of equivalent C-alpha atoms in the PDB structure. } \usage{ pdb2aln.ind(aln, pdb, inds = NULL, ...) } \arguments{ \item{aln}{ an alignment list object with \code{id} and \code{ali} components, similar to that generated by \code{\link{read.fasta}}, \code{\link{read.fasta.pdb}}, \code{\link{pdbaln}}, and \code{\link{seqaln}}. } \item{pdb}{ the PDB object to be aligned to \code{aln}. } \item{inds}{ a numeric vector containing a subset of column indices of \code{aln}. If NULL, non-gap positions of \code{aln$ali} are used. } \item{\dots}{ additional arguments passed to \code{\link{pdb2aln}}. } } \details{ Call \code{\link{pdb2aln}} to align the sequence of \code{pdb} to \code{aln}. Then, find the atomic indices of C-alpha atoms in \code{pdb} that are equivalent to \code{inds}, the subset of column indices of \code{aln$ali}. The function is a rountine utility in a combined analysis of molecular dynamics (MD) simulation trajectories and crystallographic structures. For example, a typical post-analysis of MD simulation is to compare the principal components (PCs) derived from simulation trajectories with those derived from crystallographic structures. The C-alpha atoms used to fit trajectories and do PCA must be the same (or equivalent) to those used in the analysis of crystallographic structures, e.g. the 'non-gap' alignment positions. Call \code{pdb2aln.ind} with providing relevant alignment positions, one can easily get equivalent atom selections ('select' class objects) for the simulation topology (PDB) file and then do proper trajectory analysis. } \value{ Returns a list containing two "select" objects: \item{a}{ atom and xyz indices for the alignment. } \item{b}{ atom and xyz indices for the PDB. } Note that if any element of \code{inds} has no corresponding CA atom in the PDB, the output \code{a$atom} and \code{b$atom} will be shorter than \code{inds}, i.e. only indices having equivalent CA atoms are returned. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Xin-Qiu Yao, Lars Skjaerven & Barry Grant } \seealso{ \code{\link{seq2aln}}, \code{\link{seqaln.pair}}, \code{\link{pdb2aln}} } \examples{ \dontrun{ ##--- Read aligned PDB coordinates (CA only) aln <- read.fasta(system.file("examples/kif1a.fa",package="bio3d")) pdbs <- read.fasta.pdb(aln) ##--- Read the topology file of MD simulations ##--- For illustration, here we read another pdb file (all atoms) pdb <- read.pdb("2kin") #--- Map the non-gap positions to PDB C-alpha atoms #pc.inds <- gap.inspect(pdbs$ali) #npc.inds <- pdb2aln.ind(aln=pdbs, pdb=pdb, inds=pc.inds$f.inds) #npc.inds$a #npc.inds$b #--- Or, map the non-gap positions with a known close sequence in the alignment #npc.inds <- pdb2aln.ind(aln=pdbs, pdb=pdb, aln.id="1bg2", inds=pc.inds$f.inds) #--- Map core positions core <- core.find(pdbs) core.inds <- pdb2aln.ind(aln=pdbs, pdb=pdb, inds = core$c1A.atom) core.inds$a core.inds$b ##--- Fit simulation trajectories to one of the X-ray structures based on ##--- core positions #xyz <- fit.xyz(pdbs$xyz[1,], pdb$xyz, core.inds$a$xyz, core.inds$b$xyz) ##--- Do PCA of trajectories based on non-gap positions #pc.traj <- pca(xyz[, npc.inds$b$xyz]) } } \keyword{ utilities } bio3d/man/trim.pdb.Rd0000644000176200001440000000650512544562303014045 0ustar liggesusers\name{trim} \alias{trim} \alias{trim.pdb} \title{ Trim a PDB Object To A Subset of Atoms. } \description{ Produce a new smaller PDB object, containing a subset of atoms, from a given larger PDB object. } \usage{ trim(\dots) \method{trim}{pdb}(pdb, \dots, inds = NULL, sse = TRUE) } \arguments{ \item{pdb}{ a PDB structure object obtained from \code{\link{read.pdb}}. } \item{\dots}{ additional arguments passed to \code{\link{atom.select}}. If \code{inds} is also provided, these arguments will be ignored. } \item{inds}{ a list object of ATOM and XYZ indices as obtained from \code{\link{atom.select}}. If NULL, atom selection will be obtained from calling \code{atom.select(pdb, \dots)}. } \item{sse}{ logical, if \sQuote{FALSE} helix and sheet components are omitted from output. } } \details{ This is a basic utility function for creating a new PDB object based on a selection of atoms. } \value{ Returns a list of class \code{"pdb"} with the following components: \item{atom}{ a character matrix containing all atomic coordinate ATOM data, with a row per ATOM and a column per record type. See below for details of the record type naming convention (useful for accessing columns). } \item{het }{ a character matrix containing atomic coordinate records for atoms within \dQuote{non-standard} HET groups (see \code{atom}). } \item{helix }{ \sQuote{start}, \sQuote{end} and \sQuote{length} of H type sse, where start and end are residue numbers \dQuote{resno}. } \item{sheet }{ \sQuote{start}, \sQuote{end} and \sQuote{length} of E type sse, where start and end are residue numbers \dQuote{resno}. } \item{seqres }{ sequence from SEQRES field. } \item{xyz }{ a numeric vector of ATOM coordinate data. } \item{xyz.models }{ a numeric matrix of ATOM coordinate data for multi-model PDB files. } \item{calpha }{ logical vector with length equal to \code{nrow(atom)} with TRUE values indicating a C-alpha \dQuote{elety}. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. For a description of PDB format (version3.3) see:\cr \url{http://www.wwpdb.org/documentation/format33/v3.3.html}. . } \author{ Barry Grant, Lars Skjaerven } \note{ \code{het} and \code{seqres} list components are returned unmodified. For both \code{atom} and \code{het} list components the column names can be used as a convenient means of data access, namely: Atom serial number \dQuote{eleno}, Atom type \dQuote{elety}, Alternate location indicator \dQuote{alt}, Residue name \dQuote{resid}, Chain identifier \dQuote{chain}, Residue sequence number \dQuote{resno}, Code for insertion of residues \dQuote{insert}, Orthogonal coordinates \dQuote{x}, Orthogonal coordinates \dQuote{y}, Orthogonal coordinates \dQuote{z}, Occupancy \dQuote{o}, and Temperature factor \dQuote{b}. See examples for further details. } \seealso{ \code{\link{trim.pdbs}}, \code{\link{trim.xyz}}, \code{\link{read.pdb}}, \code{\link{atom.select}} } \examples{ \dontrun{ ## Read a PDB file from the RCSB online database pdb <- read.pdb("1bg2") ## Select calpha atoms sele <- atom.select(pdb, "calpha") ## Trim PDB new.pdb <- trim.pdb(pdb, inds=sele) ## Or, simply #new.pdb <- trim.pdb(pdb, "calpha") ## Write to file write.pdb(new.pdb, file="calpha.pdb") } } \keyword{ utilities } bio3d/man/plot.geostas.Rd0000644000176200001440000000267312544562303014752 0ustar liggesusers\name{plot.geostas} \alias{plot.geostas} \title{ Plot Geostas Results } \description{ Plot an atomic movement similarity matrix with domain annotation } \usage{ \method{plot}{geostas}(x, at=seq(0, 1, 0.1), main="AMSM with Domain Assignment", col.regions=rev(heat.colors(200)), margin.segments=x$grps, ...) } \arguments{ \item{x}{ an object of type \code{geostas} as obtained by the \sQuote{geostas} function. } \item{at}{ numeric vector specifying the levels to be colored. } \item{main}{ a main title for the plot. } \item{col.regions}{ color vector. See \code{contourplot} for more information. } \item{margin.segments}{ a numeric vector of cluster membership as obtained from cutree() or other community detection method. This will be used for bottom and left margin annotation. } \item{\dots}{ additional graphical parameters for \code{\link{plot.dccm}} and \code{contourplot}. } } \details{ This is a wrapper function for \code{\link{plot.dccm}} with appropriate adjustments for plotting atomic movement similarity matrix obtained from function \code{\link{geostas}}. See the \code{\link{plot.dccm}} for more details. } \value{ Called for its effect. } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant, Lars Skjaerven } \seealso{ \code{\link{plot.dccm}}, \code{\link{geostas}} } \keyword{ hplot } bio3d/man/read.prmtop.Rd0000644000176200001440000000506412526367344014570 0ustar liggesusers\name{read.prmtop} \alias{read.prmtop} \alias{print.prmtop} \title{ Read AMBER Parameter/Topology files } \description{ Read parameter and topology data from an AMBER PrmTop file. } \usage{ read.prmtop(file) \method{print}{prmtop}(x, printseq=TRUE, \dots) } \arguments{ \item{file}{ a single element character vector containing the name of the PRMTOP file to be read. } \item{x}{ a PRMTOP structure object obtained from \code{\link{read.prmtop}}. } \item{printseq}{ logical, if TRUE the residue sequence will be printed to the screen. See also \code{\link{pdbseq}}. } \item{...}{ additional arguments to \sQuote{print}. } } \details{ This function provides basic functionality to read and parse a AMBER PrmTop file. The resulting \sQuote{prmtop} object contains a complete list object of the information stored in the PrmTop file. See examples for further details. } \value{ Returns a list of class \sQuote{prmtop} (inherits class \sQuote{amber}) with components according to the flags present in the PrmTop file. See the AMBER documentation for a complete list of flags/components: \url{http://ambermd.org/formats.html}. Selected components: \item{ATOM_NAME}{ a character vector of atom names. } \item{ATOMS_PER_MOLECULE}{ a numeric vector containing the number of atoms per molecule. } \item{MASS}{ a numeric vector of atomic masses. } \item{RESIDUE_LABEL}{ a character vector of residue labels. } \item{RESIDUE_RESIDUE_POINTER}{ a numeric vector of pointers to the first atom in each residue. } \item{call }{ the matched call. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. \url{http://ambermd.org/formats.html} } \author{ Lars Skjaerven } \note{ See AMBER documentation for PrmTop format description:\cr \url{http://ambermd.org/formats.html}. } \seealso{ \code{\link{read.crd}}, \code{\link{read.ncdf}}, \code{\link{as.pdb}}, \code{\link{atom.select}}, \code{\link{read.pdb}} } \examples{ \dontrun{ ## Read a PRMTOP file prmtop <- read.prmtop("prot_solvated.prmtop") print(prmtop) ## Explore parameters prmtop$MASS prmtop$ATOM_NAME ## Atom selection ca.inds <- atom.select(prmtop, "calpha") ## Trajectory processing trj <- read.ncdf("traj.nc", at.sel=ca.inds) ## Read a Amber CRD file crds <- read.crd("prot_solvated.inpcrd") ## Convert to PDB format pdb <- as.pdb(prmtop, crds, inds=ca.inds) ## Convert to multimodel PDB format pdb <- as.pdb(prmtop, trj[1:20,], inds=ca.inds, inds.crd=NULL) ## RMSD of trajectory rd <- rmsd(crds$xyz[ca.inds$xyz], traj, fit=TRUE) } } \keyword{ IO } bio3d/man/read.all.Rd0000644000176200001440000000667012544562303014013 0ustar liggesusers\name{read.all} \alias{read.all} \title{ Read Aligned Structure Data} \description{ Read aligned PDB structures and store their equalvalent atom data, including xyz coordinates, residue numbers, residue type and B-factors. } \usage{ read.all(aln, prefix = "", pdbext = "", sel = NULL, ...) } \arguments{ \item{aln}{ an alignment data structure obtained with \code{\link{read.fasta}}. } \item{prefix}{ prefix to aln$id to locate PDB files. } \item{pdbext}{ the file name extention of the PDB files. } \item{sel}{ a selection string detailing the atom type data to store (see function store.atom) } \item{\dots}{ other parameters for \code{\link{read.pdb}}. } } \details{ The input \code{aln}, produced with \code{\link{read.fasta}}, must have identifers (i.e. sequence names) that match the PDB file names. For example the sequence corresponding to the structure file \dQuote{mypdbdir/1bg2.pdb} should have the identifer \sQuote{mypdbdir/1bg2.pdb} or \sQuote{1bg2} if input \sQuote{prefix} and \sQuote{pdbext} equal \sQuote{mypdbdir/} and \sQuote{pdb}. See the examples below. Sequence miss-matches will generate errors. Thus, care should be taken to ensure that the sequences in the alignment match the sequences in their associated PDB files. } \value{ Returns a list of class \code{"pdbs"} with the following five components: \item{xyz}{numeric matrix of aligned C-alpha coordinates.} \item{resno}{character matrix of aligned residue numbers.} \item{b}{numeric matrix of aligned B-factor values.} \item{chain}{character matrix of aligned chain identifiers.} \item{id}{character vector of PDB sequence/structure names.} \item{ali}{character matrix of aligned sequences.} \item{resid}{character matrix of aligned 3-letter residue names.} \item{all }{numeric matrix of aligned equalvelent atom coordinates. } \item{all.elety}{numeric matrix of aligned atom element types. } \item{all.resid}{numeric matrix of aligned three-letter residue codes. } \item{all.resno}{numeric matrix of aligned residue numbers. } } \references{ Grant, B.J. et al. (2006) \emph{Bioinformatics} \bold{22}, 2695--2696. } \author{ Barry Grant } \note{ This function is still in development and is NOT part of the offical bio3d package. The sequence character \sQuote{X} is useful for masking unusual or unknown residues, as it can match any other residue type. } \seealso{ \code{\link{read.fasta}}, \code{\link{read.pdb}}, \code{\link{core.find}}, \code{\link{fit.xyz}} } \examples{ # still working on speeding this guy up \dontrun{ ## Read sequence alignment file <- system.file("examples/kif1a.fa",package="bio3d") aln <- read.fasta(file) ## Read aligned PDBs storing all data for 'sel' sel <- c("N", "CA", "C", "O", "CB", "*G", "*D", "*E", "*Z") pdbs <- read.all(aln, sel=sel) atm <- colnames(pdbs$all) ca.ind <- which(atm == "CA") core <- core.find(pdbs) core.ind <- c( matrix(ca.ind, nrow=3)[,core$c0.5A.atom] ) ## Fit structures nxyz <- fit.xyz(pdbs$all[1,], pdbs$all, fixed.inds = core.ind, mobile.inds = core.ind) ngap.col <- gap.inspect(nxyz) #npc.xray <- pca.xyz(nxyz[ ,ngap.col$f.inds]) #a <- mktrj.pca(npc.xray, pc=1, file="pc1-all.pdb", # elety=pdbs$all.elety[1,unique( ceiling(ngap.col$f.inds/3) )], # resid=pdbs$all.resid[1,unique( ceiling(ngap.col$f.inds/3) )], # resno=pdbs$all.resno[1,unique( ceiling(ngap.col$f.inds/3) )] ) } } \keyword{ IO }