diff --git a/.directory b/.directory new file mode 100644 index 0000000..4538186 --- /dev/null +++ b/.directory @@ -0,0 +1,6 @@ +[Dolphin] +Timestamp=2021,7,7,14,13,7 +Version=3 + +[Settings] +HiddenFilesShown=true diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..a86bdec --- /dev/null +++ b/.gitignore @@ -0,0 +1,18 @@ +__pycache__ +*.pyc +package.json +.vscode +tags +.idea + +notebooks/.ipynb_checkpoints +notebooks/junk +notebooks/dockstream.log +!notebooks/junk/desc.txt + +notebooks/_build + +Makefile +make.bat +*rst + diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..0f20bfb --- /dev/null +++ b/LICENSE @@ -0,0 +1,202 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2021 Molecular AI, AstraZeneca + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + diff --git a/README.md b/README.md new file mode 100644 index 0000000..8a91366 --- /dev/null +++ b/README.md @@ -0,0 +1,38 @@ +# `DockStreamCommunity` + +## Description +This repository is meant to store `Jupyter Notebook` tutorials and documentation for [DockStream](https://github.com/MolecularAI/DockStream). + +## Supported Backends +### Ligand Embedders +* **[`RDKit`](https://www.rdkit.org/docs/GettingStartedInPython.html#working-with-3d-molecules)** +* **[`Corina`](https://www.mn-am.com/products/corina)** +* **[OpenEye's `OMEGA`](https://www.eyesopen.com/omega)** +* **[Schrodinger's `LigPrep`](https://www.schrodinger.com/products/ligprep)** +* **[`TautEnum`](https://github.com/OpenEye-Contrib/TautEnum/blob/master/README)** + +### Docking Backends +* **[`AutoDock Vina`](http://vina.scripps.edu/index.html)** +* **[`rDock`](http://rdock.sourceforge.net)** +* **[OpenEye's `Hybrid`](https://www.eyesopen.com/oedocking-tk)** +* **[Schrodinger's `Glide`](https://www.schrodinger.com/glide)** +* **[CCDC's `GOLD`](https://www.ccdc.cam.ac.uk/solutions/csd-discovery/components/gold)** + +Note, that the `CCDC` package, the `OpenEye` toolkit and `Schrodinger`'s tools require you to obtain the respective software from those vendors. + +## Requirements +A Conda environment is provided: `DockStreamCommunity` via `environment.yml` to execute the `Jupyter Notebook` tutorials. +Some notebooks also require the `DockStream` and `DockStreamFull` environments to be installed +(see [DockStream README](https://github.com/MolecularAI/DockStream)). +``` +git clone +cd +conda env create -f environment.yml +conda activate DockStreamCommunity +jupyter notebook +``` + +## Contributors +Christian Margreitter (christian.margreitter@astrazeneca.com) +Jeff Guo (jeff.guo@astrazeneca.com) +Alexey Voronov (alexey.voronov1@astrazeneca.com) \ No newline at end of file diff --git a/data/.directory b/data/.directory new file mode 100644 index 0000000..948177f --- /dev/null +++ b/data/.directory @@ -0,0 +1,3 @@ +[Dolphin] +Timestamp=2021,7,7,14,13,10 +Version=3 diff --git a/data/1UYD/1UYD_apo.pdb b/data/1UYD/1UYD_apo.pdb new file mode 100644 index 0000000..a5b6528 --- /dev/null +++ b/data/1UYD/1UYD_apo.pdb @@ -0,0 +1,1925 @@ +ATOM 1 N GLU A 16 6.484 28.442 39.441 1.00 52.44 N +ATOM 2 CA GLU A 16 7.718 28.546 38.611 1.00 52.69 C +ATOM 3 C GLU A 16 7.625 27.706 37.277 1.00 50.43 C +ATOM 4 O GLU A 16 7.333 26.478 37.304 1.00 51.22 O +ATOM 5 CB GLU A 16 8.951 28.14 39.474 1.00 54.13 C +ATOM 6 CG GLU A 16 9.355 26.647 39.367 1.00 59.64 C +ATOM 7 CD GLU A 16 10.138 26.088 40.562 1.00 67.81 C +ATOM 8 OE1 GLU A 16 11.022 26.816 41.117 1.00 70.47 O +ATOM 9 OE2 GLU A 16 9.875 24.9 40.943 1.00 70.36 O +ATOM 10 N VAL A 17 7.856 28.355 36.137 1.00 46.65 N +ATOM 11 CA VAL A 17 8.11 27.634 34.889 1.00 43.37 C +ATOM 12 C VAL A 17 9.523 27.05 34.954 1.00 41.55 C +ATOM 13 O VAL A 17 10.499 27.794 35.209 1.00 41.48 O +ATOM 14 CB VAL A 17 7.967 28.556 33.636 1.00 44.16 C +ATOM 15 CG1 VAL A 17 8.234 27.763 32.31 1.00 41.94 C +ATOM 16 CG2 VAL A 17 6.598 29.245 33.609 1.00 42.58 C +ATOM 17 N GLU A 18 9.626 25.731 34.766 1.00 38.22 N +ATOM 18 CA GLU A 18 10.912 25.034 34.705 1.00 36.66 C +ATOM 19 C GLU A 18 11.363 24.695 33.258 1.00 35.43 C +ATOM 20 O GLU A 18 10.557 24.225 32.446 1.00 34.5 O +ATOM 21 CB GLU A 18 10.872 23.762 35.555 1.00 36.81 C +ATOM 22 CG GLU A 18 10.774 24.017 37.048 1.00 36.88 C +ATOM 23 CD GLU A 18 10.898 22.734 37.852 1.00 38.72 C +ATOM 24 OE1 GLU A 18 10.107 21.795 37.647 1.00 39.8 O +ATOM 25 OE2 GLU A 18 11.79 22.66 38.708 1.00 41.81 O +ATOM 26 N THR A 19 12.647 24.938 32.948 1.00 34.36 N +ATOM 27 CA THR A 19 13.198 24.7 31.604 1.00 32.13 C +ATOM 28 C THR A 19 14.224 23.534 31.576 1.00 31.87 C +ATOM 29 O THR A 19 15.151 23.461 32.428 1.00 31.61 O +ATOM 30 CB THR A 19 13.805 26.017 31.037 1.00 33.16 C +ATOM 31 OG1 THR A 19 12.81 27.053 31.025 1.00 31.93 O +ATOM 32 CG2 THR A 19 14.195 25.854 29.544 1.00 31.03 C +ATOM 33 N PHE A 20 14.035 22.614 30.627 1.00 29.7 N +ATOM 34 CA PHE A 20 14.869 21.419 30.513 1.00 28.8 C +ATOM 35 C PHE A 20 15.537 21.338 29.135 1.00 29.26 C +ATOM 36 O PHE A 20 14.967 21.825 28.124 1.00 27.33 O +ATOM 37 CB PHE A 20 14.001 20.177 30.739 1.00 27.51 C +ATOM 38 CG PHE A 20 13.246 20.21 32.039 1.00 28.58 C +ATOM 39 CD1 PHE A 20 12.014 20.881 32.13 1.00 26.8 C +ATOM 40 CD2 PHE A 20 13.795 19.633 33.203 1.00 23.76 C +ATOM 41 CE1 PHE A 20 11.31 20.926 33.338 1.00 26.39 C +ATOM 42 CE2 PHE A 20 13.11 19.69 34.389 1.00 25.72 C +ATOM 43 CZ PHE A 20 11.875 20.326 34.474 1.00 24.22 C +ATOM 44 N ALA A 21 16.701 20.682 29.084 1.00 29.14 N +ATOM 45 CA ALA A 21 17.311 20.357 27.789 1.00 30.67 C +ATOM 46 C ALA A 21 16.767 19.004 27.281 1.00 31.59 C +ATOM 47 O ALA A 21 16.676 18.084 28.053 1.00 31.93 O +ATOM 48 CB ALA A 21 18.861 20.376 27.872 1.00 28.44 C +ATOM 49 N PHE A 22 16.343 18.897 26.012 1.00 33.06 N +ATOM 50 CA PHE A 22 16.075 17.574 25.463 1.00 34.1 C +ATOM 51 C PHE A 22 17.406 16.832 25.453 1.00 34.96 C +ATOM 52 O PHE A 22 18.451 17.448 25.29 1.00 34.99 O +ATOM 53 CB PHE A 22 15.593 17.618 24.019 1.00 34.05 C +ATOM 54 CG PHE A 22 14.181 18.11 23.837 1.00 33.56 C +ATOM 55 CD1 PHE A 22 13.087 17.262 24.033 1.00 29.44 C +ATOM 56 CD2 PHE A 22 13.952 19.415 23.417 1.00 30.39 C +ATOM 57 CE1 PHE A 22 11.774 17.738 23.825 1.00 31.5 C +ATOM 58 CE2 PHE A 22 12.65 19.869 23.193 1.00 33.07 C +ATOM 59 CZ PHE A 22 11.559 19.02 23.406 1.00 29.49 C +ATOM 60 N GLN A 23 17.367 15.515 25.627 1.00 36.08 N +ATOM 61 CA GLN A 23 18.514 14.672 25.271 1.00 37.11 C +ATOM 62 C GLN A 23 18.997 15.044 23.863 1.00 36.71 C +ATOM 63 O GLN A 23 18.176 15.397 23.014 1.00 36.28 O +ATOM 64 CB GLN A 23 18.096 13.217 25.25 1.00 36.76 C +ATOM 65 CG GLN A 23 17.841 12.642 26.603 1.00 41.34 C +ATOM 66 CD GLN A 23 17.284 11.258 26.494 1.00 47.39 C +ATOM 67 OE1 GLN A 23 16.065 11.089 26.331 1.00 50.2 O +ATOM 68 NE2 GLN A 23 18.164 10.249 26.547 1.00 48.78 N +ATOM 69 N ALA A 24 20.305 14.96 23.608 1.00 36.31 N +ATOM 70 CA ALA A 24 20.855 15.317 22.299 1.00 36.44 C +ATOM 71 C ALA A 24 20.163 14.642 21.113 1.00 35.88 C +ATOM 72 O ALA A 24 19.94 15.27 20.072 1.00 36.86 O +ATOM 73 CB ALA A 24 22.372 15.039 22.257 1.00 37.28 C +ATOM 74 N GLU A 25 19.857 13.361 21.272 1.00 36.14 N +ATOM 75 CA GLU A 25 19.286 12.504 20.219 1.00 35.98 C +ATOM 76 C GLU A 25 17.876 12.943 19.863 1.00 35.35 C +ATOM 77 O GLU A 25 17.5 12.958 18.695 1.00 35.68 O +ATOM 78 CB GLU A 25 19.262 11.053 20.713 1.00 36.94 C +ATOM 79 CG GLU A 25 20.65 10.425 20.91 1.00 40.62 C +ATOM 80 CD GLU A 25 21.336 10.817 22.221 1.00 44.99 C +ATOM 81 OE1 GLU A 25 22.506 10.395 22.432 1.00 46.87 O +ATOM 82 OE2 GLU A 25 20.724 11.554 23.044 1.00 45.89 O +ATOM 83 N ILE A 26 17.097 13.304 20.882 1.00 34.6 N +ATOM 84 CA ILE A 26 15.763 13.914 20.708 1.00 34.78 C +ATOM 85 C ILE A 26 15.789 15.276 20.033 1.00 33.6 C +ATOM 86 O ILE A 26 15.024 15.514 19.123 1.00 33.95 O +ATOM 87 CB ILE A 26 14.98 14.017 22.061 1.00 34.33 C +ATOM 88 CG1 ILE A 26 14.985 12.652 22.774 1.00 35.81 C +ATOM 89 CG2 ILE A 26 13.54 14.531 21.797 1.00 33.73 C +ATOM 90 CD1 ILE A 26 14.059 11.678 22.175 1.00 36.2 C +ATOM 91 N ALA A 27 16.655 16.159 20.505 1.00 32.93 N +ATOM 92 CA ALA A 27 16.913 17.41 19.844 1.00 33.28 C +ATOM 93 C ALA A 27 17.27 17.137 18.366 1.00 33.67 C +ATOM 94 O ALA A 27 16.87 17.872 17.453 1.00 32.53 O +ATOM 95 CB ALA A 27 18.043 18.164 20.565 1.00 33.14 C +ATOM 96 N GLN A 28 18.013 16.069 18.125 1.00 34.48 N +ATOM 97 CA GLN A 28 18.519 15.858 16.772 1.00 36.17 C +ATOM 98 C GLN A 28 17.408 15.393 15.801 1.00 35.29 C +ATOM 99 O GLN A 28 17.267 15.961 14.697 1.00 36.01 O +ATOM 100 CB GLN A 28 19.733 14.933 16.797 1.00 37.01 C +ATOM 101 CG GLN A 28 20.247 14.578 15.405 1.00 43.74 C +ATOM 102 CD GLN A 28 21.365 13.541 15.433 1.00 50.85 C +ATOM 103 OE1 GLN A 28 21.519 12.799 16.414 1.00 52.59 O +ATOM 104 NE2 GLN A 28 22.157 13.501 14.352 1.00 54.02 N +ATOM 105 N LEU A 29 16.622 14.4 16.235 1.00 33.31 N +ATOM 106 CA LEU A 29 15.342 14.058 15.634 1.00 33.15 C +ATOM 107 C LEU A 29 14.416 15.279 15.307 1.00 32.99 C +ATOM 108 O LEU A 29 13.897 15.404 14.196 1.00 32.28 O +ATOM 109 CB LEU A 29 14.557 13.091 16.544 1.00 33.28 C +ATOM 110 CG LEU A 29 13.184 12.652 15.98 1.00 33.26 C +ATOM 111 CD1 LEU A 29 13.354 11.63 14.868 1.00 29.77 C +ATOM 112 CD2 LEU A 29 12.272 12.12 17.034 1.00 34.18 C +ATOM 113 N MET A 30 14.169 16.132 16.294 1.00 32.21 N +ATOM 114 CA MET A 30 13.326 17.31 16.086 1.00 31.48 C +ATOM 115 C MET A 30 13.897 18.193 14.993 1.00 32.09 C +ATOM 116 O MET A 30 13.148 18.581 14.115 1.00 33.08 O +ATOM 117 CB MET A 30 13.128 18.094 17.405 1.00 31.25 C +ATOM 118 CG MET A 30 12.438 17.298 18.477 1.00 29.26 C +ATOM 119 SD MET A 30 12.3 18.13 20.049 1.00 28.88 S +ATOM 120 CE MET A 30 10.942 19.245 19.81 1.00 29.95 C +ATOM 121 N SER A 31 15.207 18.5 15.004 1.00 32.81 N +ATOM 122 CA SER A 31 15.81 19.311 13.908 1.00 33.51 C +ATOM 123 C SER A 31 15.591 18.633 12.575 1.00 34.55 C +ATOM 124 O SER A 31 15.2 19.306 11.602 1.00 35.13 O +ATOM 125 CB SER A 31 17.326 19.576 14.038 1.00 33.62 C +ATOM 126 OG SER A 31 17.732 19.95 15.347 1.00 33.63 O +ATOM 127 N LEU A 32 15.837 17.321 12.514 1.00 34.82 N +ATOM 128 CA LEU A 32 15.576 16.54 11.276 1.00 35.73 C +ATOM 129 C LEU A 32 14.145 16.672 10.725 1.00 34.98 C +ATOM 130 O LEU A 32 13.954 16.794 9.534 1.00 35.76 O +ATOM 131 CB LEU A 32 15.923 15.077 11.492 1.00 36.11 C +ATOM 132 CG LEU A 32 15.981 14.083 10.326 1.00 39.06 C +ATOM 133 CD1 LEU A 32 17.26 14.294 9.535 1.00 44.56 C +ATOM 134 CD2 LEU A 32 15.972 12.683 10.9 1.00 42.16 C +ATOM 135 N ILE A 33 13.154 16.645 11.601 1.00 35.22 N +ATOM 136 CA ILE A 33 11.743 16.729 11.235 1.00 34.76 C +ATOM 137 C ILE A 33 11.402 18.135 10.741 1.00 34.92 C +ATOM 138 O ILE A 33 10.706 18.29 9.749 1.00 33.04 O +ATOM 139 CB ILE A 33 10.856 16.24 12.43 1.00 35.08 C +ATOM 140 CG1 ILE A 33 10.843 14.689 12.492 1.00 34.97 C +ATOM 141 CG2 ILE A 33 9.418 16.784 12.356 1.00 35.22 C +ATOM 142 CD1 ILE A 33 10.452 14.125 13.891 1.00 36.65 C +ATOM 143 N ILE A 34 11.932 19.145 11.436 1.00 35.65 N +ATOM 144 CA ILE A 34 11.849 20.546 11.014 1.00 36.2 C +ATOM 145 C ILE A 34 12.585 20.849 9.702 1.00 36.31 C +ATOM 146 O ILE A 34 12.059 21.574 8.879 1.00 36.98 O +ATOM 147 CB ILE A 34 12.312 21.478 12.165 1.00 36.41 C +ATOM 148 CG1 ILE A 34 11.228 21.514 13.255 1.00 36.11 C +ATOM 149 CG2 ILE A 34 12.68 22.906 11.627 1.00 35.74 C +ATOM 150 CD1 ILE A 34 11.667 21.954 14.572 1.00 31.96 C +ATOM 151 N ASN A 35 13.757 20.254 9.489 1.00 36.38 N +ATOM 152 CA ASN A 35 14.63 20.625 8.383 1.00 36.92 C +ATOM 153 C ASN A 35 14.516 19.794 7.106 1.00 36.79 C +ATOM 154 O ASN A 35 14.734 20.338 6.057 1.00 36.39 O +ATOM 155 CB ASN A 35 16.133 20.616 8.767 1.00 37.44 C +ATOM 156 CG ASN A 35 16.505 21.635 9.819 1.00 38.55 C +ATOM 157 OD1 ASN A 35 15.956 22.734 9.894 1.00 38.57 O +ATOM 158 ND2 ASN A 35 17.494 21.267 10.632 1.00 40.04 N +ATOM 159 N THR A 36 14.276 18.48 7.196 1.00 37 N +ATOM 160 CA THR A 36 14.091 17.643 5.979 1.00 37.4 C +ATOM 161 C THR A 36 12.96 18.147 5.053 1.00 36.94 C +ATOM 162 O THR A 36 11.897 18.571 5.536 1.00 36.43 O +ATOM 163 CB THR A 36 13.859 16.195 6.36 1.00 36.96 C +ATOM 164 OG1 THR A 36 14.951 15.781 7.174 1.00 40.38 O +ATOM 165 CG2 THR A 36 13.954 15.228 5.144 1.00 37.37 C +ATOM 166 N PHE A 37 13.204 18.119 3.739 1.00 35.96 N +ATOM 167 CA PHE A 37 12.125 18.382 2.795 1.00 35.2 C +ATOM 168 C PHE A 37 11.243 17.126 2.67 1.00 35.22 C +ATOM 169 O PHE A 37 11.721 16.005 2.325 1.00 35.78 O +ATOM 170 CB PHE A 37 12.632 18.825 1.426 1.00 35.57 C +ATOM 171 CG PHE A 37 11.519 19.104 0.439 1.00 34.95 C +ATOM 172 CD1 PHE A 37 10.914 20.361 0.399 1.00 35.81 C +ATOM 173 CD2 PHE A 37 11.056 18.099 -0.423 1.00 36.14 C +ATOM 174 CE1 PHE A 37 9.852 20.631 -0.49 1.00 35.16 C +ATOM 175 CE2 PHE A 37 9.993 18.35 -1.314 1.00 35.39 C +ATOM 176 CZ PHE A 37 9.4 19.629 -1.349 1.00 35.36 C +ATOM 177 N TYR A 38 9.962 17.3 2.983 1.00 33.53 N +ATOM 178 CA TYR A 38 9.043 16.165 3.038 1.00 32.71 C +ATOM 179 C TYR A 38 7.623 16.684 2.943 1.00 31.94 C +ATOM 180 O TYR A 38 7.104 17.252 3.895 1.00 31.51 O +ATOM 181 CB TYR A 38 9.239 15.357 4.319 1.00 32.62 C +ATOM 182 CG TYR A 38 8.324 14.136 4.433 1.00 34.51 C +ATOM 183 CD1 TYR A 38 8.575 12.993 3.679 1.00 32.98 C +ATOM 184 CD2 TYR A 38 7.201 14.144 5.273 1.00 35.05 C +ATOM 185 CE1 TYR A 38 7.78 11.92 3.763 1.00 34.91 C +ATOM 186 CE2 TYR A 38 6.377 13.038 5.354 1.00 34.94 C +ATOM 187 CZ TYR A 38 6.682 11.938 4.597 1.00 36.2 C +ATOM 188 OH TYR A 38 5.888 10.814 4.643 1.00 42.32 O +ATOM 189 N SER A 39 6.997 16.483 1.787 1.00 30.7 N +ATOM 190 CA SER A 39 5.676 17.051 1.542 1.00 30.12 C +ATOM 191 C SER A 39 4.487 16.4 2.284 1.00 29.08 C +ATOM 192 O SER A 39 3.482 17.089 2.595 1.00 27.85 O +ATOM 193 CB SER A 39 5.431 17.066 0.042 1.00 29.76 C +ATOM 194 OG SER A 39 6.441 17.882 -0.531 1.00 30.44 O +ATOM 195 N ASN A 40 4.586 15.093 2.532 1.00 27.64 N +ATOM 196 CA ASN A 40 3.462 14.327 3.114 1.00 28.95 C +ATOM 197 C ASN A 40 3.289 14.58 4.633 1.00 28.46 C +ATOM 198 O ASN A 40 3.277 13.666 5.437 1.00 28.79 O +ATOM 199 CB ASN A 40 3.609 12.853 2.733 1.00 28.48 C +ATOM 200 CG ASN A 40 2.511 11.965 3.271 1.00 30.36 C +ATOM 201 OD1 ASN A 40 2.8 10.859 3.752 1.00 36.5 O +ATOM 202 ND2 ASN A 40 1.275 12.369 3.117 1.00 30.16 N +ATOM 203 N LYS A 41 3.164 15.857 4.991 1.00 29.05 N +ATOM 204 CA LYS A 41 3.065 16.3 6.38 1.00 30.57 C +ATOM 205 C LYS A 41 1.758 15.844 7.091 1.00 30.49 C +ATOM 206 O LYS A 41 1.718 15.782 8.331 1.00 30.9 O +ATOM 207 CB LYS A 41 3.178 17.824 6.46 1.00 30.53 C +ATOM 208 CG LYS A 41 4.49 18.387 5.962 1.00 31.42 C +ATOM 209 CD LYS A 41 4.44 19.885 5.963 1.00 33.06 C +ATOM 210 CE LYS A 41 5.821 20.51 5.667 1.00 35.23 C +ATOM 211 NZ LYS A 41 6.693 20.731 6.867 1.00 35.61 N +ATOM 212 N GLU A 42 0.715 15.528 6.307 1.00 29.96 N +ATOM 213 CA GLU A 42 -0.612 15.223 6.858 1.00 30.55 C +ATOM 214 C GLU A 42 -0.658 14.045 7.848 1.00 30.33 C +ATOM 215 O GLU A 42 -1.556 13.976 8.715 1.00 30.62 O +ATOM 216 CB GLU A 42 -1.641 15.036 5.756 1.00 29.8 C +ATOM 217 CG GLU A 42 -1.533 13.687 5.111 1.00 32.83 C +ATOM 218 CD GLU A 42 -2.442 13.533 3.918 1.00 34.74 C +ATOM 219 OE1 GLU A 42 -2.797 14.532 3.287 1.00 34.48 O +ATOM 220 OE2 GLU A 42 -2.808 12.389 3.64 1.00 37 O +ATOM 221 N ILE A 43 0.3 13.13 7.728 1.00 29.77 N +ATOM 222 CA ILE A 43 0.392 11.995 8.652 1.00 29.9 C +ATOM 223 C ILE A 43 0.597 12.362 10.173 1.00 28.58 C +ATOM 224 O ILE A 43 0.564 11.499 11.034 1.00 28.94 O +ATOM 225 CB ILE A 43 1.449 10.918 8.14 1.00 29.89 C +ATOM 226 CG1 ILE A 43 2.904 11.412 8.25 1.00 31.99 C +ATOM 227 CG2 ILE A 43 1.186 10.524 6.69 1.00 31.24 C +ATOM 228 CD1 ILE A 43 3.571 11.296 9.628 1.00 29.2 C +ATOM 229 N PHE A 44 0.833 13.63 10.492 1.00 28.47 N +ATOM 230 CA PHE A 44 1.009 14.062 11.877 1.00 27.44 C +ATOM 231 C PHE A 44 -0.301 13.763 12.655 1.00 27.52 C +ATOM 232 O PHE A 44 -0.278 13.532 13.85 1.00 26.52 O +ATOM 233 CB PHE A 44 1.366 15.572 11.951 1.00 27.05 C +ATOM 234 CG PHE A 44 0.136 16.493 11.879 1.00 27.88 C +ATOM 235 CD1 PHE A 44 -0.31 16.99 10.641 1.00 26.56 C +ATOM 236 CD2 PHE A 44 -0.626 16.783 13.017 1.00 24 C +ATOM 237 CE1 PHE A 44 -1.488 17.774 10.535 1.00 23.23 C +ATOM 238 CE2 PHE A 44 -1.791 17.596 12.895 1.00 25.45 C +ATOM 239 CZ PHE A 44 -2.201 18.082 11.644 1.00 24.09 C +ATOM 240 N LEU A 45 -1.446 13.807 11.974 1.00 27.47 N +ATOM 241 CA LEU A 45 -2.721 13.738 12.696 1.00 27.88 C +ATOM 242 C LEU A 45 -3.029 12.297 13.127 1.00 28.5 C +ATOM 243 O LEU A 45 -3.389 12.065 14.288 1.00 29.36 O +ATOM 244 CB LEU A 45 -3.864 14.393 11.901 1.00 27.67 C +ATOM 245 CG LEU A 45 -5.2 14.499 12.659 1.00 27.63 C +ATOM 246 CD1 LEU A 45 -5.095 15.445 13.762 1.00 24.75 C +ATOM 247 CD2 LEU A 45 -6.339 14.886 11.728 1.00 27.64 C +ATOM 248 N ARG A 46 -2.823 11.315 12.251 1.00 28.56 N +ATOM 249 CA ARG A 46 -2.985 9.939 12.694 1.00 29.64 C +ATOM 250 C ARG A 46 -2.009 9.537 13.797 1.00 29.59 C +ATOM 251 O ARG A 46 -2.341 8.64 14.586 1.00 30.11 O +ATOM 252 CB ARG A 46 -2.901 8.927 11.557 1.00 30.49 C +ATOM 253 CG ARG A 46 -1.563 8.834 10.948 1.00 33.34 C +ATOM 254 CD ARG A 46 -1.055 7.401 10.651 1.00 36.23 C +ATOM 255 NE ARG A 46 0.29 7.541 10.087 1.00 37.88 N +ATOM 256 CZ ARG A 46 0.655 7.138 8.868 1.00 40.27 C +ATOM 257 NH1 ARG A 46 -0.217 6.541 8.063 1.00 38.09 N +ATOM 258 NH2 ARG A 46 1.902 7.34 8.454 1.00 39.5 N +ATOM 259 N GLU A 47 -0.852 10.203 13.897 1.00 28.19 N +ATOM 260 CA GLU A 47 0.129 9.848 14.923 1.00 27.31 C +ATOM 261 C GLU A 47 -0.405 10.29 16.287 1.00 28.07 C +ATOM 262 O GLU A 47 -0.351 9.546 17.266 1.00 26.68 O +ATOM 263 CB GLU A 47 1.507 10.509 14.663 1.00 28.44 C +ATOM 264 CG GLU A 47 2.327 10.017 13.438 1.00 29.25 C +ATOM 265 CD GLU A 47 2.387 8.486 13.301 1.00 32.52 C +ATOM 266 OE1 GLU A 47 2.544 7.847 14.341 1.00 29.14 O +ATOM 267 OE2 GLU A 47 2.264 7.922 12.168 1.00 33.92 O +ATOM 268 N LEU A 48 -0.922 11.526 16.332 1.00 28.44 N +ATOM 269 CA LEU A 48 -1.556 12.088 17.547 1.00 27.91 C +ATOM 270 C LEU A 48 -2.821 11.369 18.009 1.00 27.19 C +ATOM 271 O LEU A 48 -2.902 10.967 19.157 1.00 28.34 O +ATOM 272 CB LEU A 48 -1.777 13.599 17.385 1.00 27.71 C +ATOM 273 CG LEU A 48 -0.51 14.392 17.026 1.00 28.19 C +ATOM 274 CD1 LEU A 48 -0.898 15.859 16.885 1.00 27.71 C +ATOM 275 CD2 LEU A 48 0.65 14.227 18.009 1.00 25.34 C +ATOM 276 N ILE A 49 -3.775 11.165 17.11 1.00 27.97 N +ATOM 277 CA ILE A 49 -4.971 10.325 17.339 1.00 28 C +ATOM 278 C ILE A 49 -4.709 8.864 17.828 1.00 29.52 C +ATOM 279 O ILE A 49 -5.429 8.315 18.7 1.00 29.96 O +ATOM 280 CB ILE A 49 -5.811 10.303 16.021 1.00 28.17 C +ATOM 281 CG1 ILE A 49 -6.432 11.676 15.749 1.00 26.51 C +ATOM 282 CG2 ILE A 49 -6.862 9.155 16.034 1.00 28.44 C +ATOM 283 CD1 ILE A 49 -7.205 11.803 14.44 1.00 25.95 C +ATOM 284 N SER A 50 -3.707 8.236 17.252 1.00 29.25 N +ATOM 285 CA SER A 50 -3.272 6.93 17.657 1.00 30.63 C +ATOM 286 C SER A 50 -2.625 6.951 19.07 1.00 30.93 C +ATOM 287 O SER A 50 -2.867 6.028 19.873 1.00 30.12 O +ATOM 288 CB SER A 50 -2.318 6.388 16.587 1.00 30.79 C +ATOM 289 OG SER A 50 -1.198 5.792 17.193 1.00 36.65 O +ATOM 290 N ASN A 51 -1.826 7.992 19.374 1.00 30.49 N +ATOM 291 CA ASN A 51 -1.395 8.242 20.762 1.00 29.99 C +ATOM 292 C ASN A 51 -2.566 8.364 21.757 1.00 29.44 C +ATOM 293 O ASN A 51 -2.55 7.762 22.81 1.00 29.27 O +ATOM 294 CB ASN A 51 -0.5 9.491 20.841 1.00 29.99 C +ATOM 295 CG ASN A 51 0.891 9.265 20.218 1.00 31.74 C +ATOM 296 OD1 ASN A 51 1.25 8.116 19.865 1.00 31.05 O +ATOM 297 ND2 ASN A 51 1.685 10.358 20.09 1.00 28.85 N +ATOM 298 N SER A 52 -3.58 9.144 21.406 1.00 28.82 N +ATOM 299 CA SER A 52 -4.797 9.279 22.154 1.00 29.08 C +ATOM 300 C SER A 52 -5.543 7.94 22.354 1.00 30.15 C +ATOM 301 O SER A 52 -5.945 7.619 23.468 1.00 31.47 O +ATOM 302 CB SER A 52 -5.684 10.259 21.403 1.00 27.89 C +ATOM 303 OG SER A 52 -5.207 11.547 21.613 1.00 28.86 O +ATOM 304 N SER A 53 -5.749 7.202 21.266 1.00 30.8 N +ATOM 305 CA SER A 53 -6.338 5.877 21.268 1.00 31.8 C +ATOM 306 C SER A 53 -5.567 4.922 22.175 1.00 32.04 C +ATOM 307 O SER A 53 -6.164 4.202 22.948 1.00 33.3 O +ATOM 308 CB SER A 53 -6.444 5.328 19.826 1.00 32.27 C +ATOM 309 OG SER A 53 -6.985 3.994 19.781 1.00 33.19 O +ATOM 310 N ASP A 54 -4.242 4.886 22.09 1.00 32.23 N +ATOM 311 CA ASP A 54 -3.447 4.116 23.052 1.00 31.96 C +ATOM 312 C ASP A 54 -3.713 4.503 24.54 1.00 32.03 C +ATOM 313 O ASP A 54 -3.917 3.642 25.404 1.00 32.37 O +ATOM 314 CB ASP A 54 -1.972 4.306 22.744 1.00 31.89 C +ATOM 315 CG ASP A 54 -1.566 3.687 21.438 1.00 30.1 C +ATOM 316 OD1 ASP A 54 -2.328 2.897 20.875 1.00 31.06 O +ATOM 317 OD2 ASP A 54 -0.471 3.918 20.908 1.00 33.35 O +ATOM 318 N ALA A 55 -3.727 5.804 24.805 1.00 30.87 N +ATOM 319 CA ALA A 55 -4.021 6.378 26.112 1.00 31.35 C +ATOM 320 C ALA A 55 -5.445 6.11 26.693 1.00 31.26 C +ATOM 321 O ALA A 55 -5.638 6.045 27.929 1.00 30.81 O +ATOM 322 CB ALA A 55 -3.732 7.859 26.069 1.00 30 C +ATOM 323 N LEU A 56 -6.416 5.961 25.803 1.00 31.33 N +ATOM 324 CA LEU A 56 -7.785 5.658 26.178 1.00 31.42 C +ATOM 325 C LEU A 56 -7.917 4.15 26.427 1.00 32.41 C +ATOM 326 O LEU A 56 -8.601 3.745 27.357 1.00 32.37 O +ATOM 327 CB LEU A 56 -8.71 6.096 25.052 1.00 31.64 C +ATOM 328 CG LEU A 56 -8.911 7.598 24.938 1.00 28.09 C +ATOM 329 CD1 LEU A 56 -9.42 7.876 23.563 1.00 25.84 C +ATOM 330 CD2 LEU A 56 -9.911 8.022 25.99 1.00 26.94 C +ATOM 331 N ASP A 57 -7.222 3.331 25.618 1.00 32.25 N +ATOM 332 CA ASP A 57 -7.119 1.872 25.872 1.00 32.38 C +ATOM 333 C ASP A 57 -6.546 1.602 27.285 1.00 33.47 C +ATOM 334 O ASP A 57 -6.987 0.664 27.989 1.00 32.03 O +ATOM 335 CB ASP A 57 -6.171 1.173 24.892 1.00 30.24 C +ATOM 336 CG ASP A 57 -6.735 1.041 23.489 1.00 32.48 C +ATOM 337 OD1 ASP A 57 -7.947 1.312 23.221 1.00 29.17 O +ATOM 338 OD2 ASP A 57 -5.97 0.689 22.578 1.00 29.91 O +ATOM 339 N LYS A 58 -5.513 2.377 27.645 1.00 33.71 N +ATOM 340 CA LYS A 58 -4.815 2.171 28.915 1.00 34.58 C +ATOM 341 C LYS A 58 -5.72 2.429 30.103 1.00 34.23 C +ATOM 342 O LYS A 58 -5.817 1.615 31.005 1.00 35.9 O +ATOM 343 CB LYS A 58 -3.56 3.044 28.997 1.00 34.28 C +ATOM 344 CG LYS A 58 -2.589 2.486 30.007 1.00 38.51 C +ATOM 345 CD LYS A 58 -1.189 3.106 29.938 1.00 42.9 C +ATOM 346 CE LYS A 58 -0.368 2.602 31.139 1.00 45.91 C +ATOM 347 NZ LYS A 58 1.037 3.136 31.258 1.00 49.59 N +ATOM 348 N ILE A 59 -6.376 3.574 30.112 1.00 34.44 N +ATOM 349 CA ILE A 59 -7.287 3.883 31.19 1.00 34.74 C +ATOM 350 C ILE A 59 -8.453 2.877 31.275 1.00 35.17 C +ATOM 351 O ILE A 59 -8.777 2.385 32.386 1.00 34.54 O +ATOM 352 CB ILE A 59 -7.722 5.421 31.197 1.00 35.05 C +ATOM 353 CG1 ILE A 59 -8.337 5.815 32.553 1.00 36.22 C +ATOM 354 CG2 ILE A 59 -8.685 5.786 30.068 1.00 30.98 C +ATOM 355 CD1 ILE A 59 -7.574 5.357 33.801 1.00 37.22 C +ATOM 356 N ARG A 60 -9.032 2.527 30.124 1.00 35.31 N +ATOM 357 CA ARG A 60 -10.097 1.529 30.087 1.00 36.3 C +ATOM 358 C ARG A 60 -9.602 0.196 30.711 1.00 36.98 C +ATOM 359 O ARG A 60 -10.274 -0.413 31.567 1.00 37.15 O +ATOM 360 CB ARG A 60 -10.552 1.315 28.658 1.00 36.17 C +ATOM 361 CG ARG A 60 -11.722 0.353 28.529 1.00 38.66 C +ATOM 362 CD ARG A 60 -12.198 0.189 27.099 1.00 44.64 C +ATOM 363 NE ARG A 60 -11.154 -0.386 26.24 1.00 50 N +ATOM 364 CZ ARG A 60 -11.172 -0.384 24.901 1.00 51.74 C +ATOM 365 NH1 ARG A 60 -12.199 0.177 24.252 1.00 52.19 N +ATOM 366 NH2 ARG A 60 -10.159 -0.95 24.218 1.00 49.51 N +ATOM 367 N TYR A 61 -8.427 -0.26 30.308 1.00 36.5 N +ATOM 368 CA TYR A 61 -7.885 -1.484 30.877 1.00 36.98 C +ATOM 369 C TYR A 61 -7.65 -1.406 32.4 1.00 37.85 C +ATOM 370 O TYR A 61 -8.094 -2.28 33.156 1.00 37.85 O +ATOM 371 CB TYR A 61 -6.598 -1.853 30.156 1.00 37.23 C +ATOM 372 CG TYR A 61 -5.886 -3.029 30.778 1.00 37.53 C +ATOM 373 CD1 TYR A 61 -6.355 -4.321 30.572 1.00 34.85 C +ATOM 374 CD2 TYR A 61 -4.747 -2.84 31.599 1.00 38.49 C +ATOM 375 CE1 TYR A 61 -5.713 -5.403 31.117 1.00 35.22 C +ATOM 376 CE2 TYR A 61 -4.087 -3.936 32.177 1.00 36.1 C +ATOM 377 CZ TYR A 61 -4.585 -5.218 31.927 1.00 36.05 C +ATOM 378 OH TYR A 61 -3.959 -6.352 32.452 1.00 38.54 O +ATOM 379 N GLU A 62 -6.968 -0.354 32.843 1.00 37.96 N +ATOM 380 CA GLU A 62 -6.749 -0.131 34.262 1.00 39.5 C +ATOM 381 C GLU A 62 -8.031 -0.038 35.116 1.00 40.25 C +ATOM 382 O GLU A 62 -8.022 -0.39 36.3 1.00 40.1 O +ATOM 383 CB GLU A 62 -5.922 1.131 34.457 1.00 38.99 C +ATOM 384 CG GLU A 62 -4.425 0.9 34.521 1.00 41.81 C +ATOM 385 CD GLU A 62 -3.635 2.173 34.808 1.00 45.5 C +ATOM 386 OE1 GLU A 62 -4.122 3.018 35.589 1.00 47.05 O +ATOM 387 OE2 GLU A 62 -2.51 2.345 34.248 1.00 48.96 O +ATOM 388 N SER A 63 -9.132 0.416 34.522 1.00 41.25 N +ATOM 389 CA SER A 63 -10.392 0.562 35.28 1.00 42.25 C +ATOM 390 C SER A 63 -11.178 -0.722 35.511 1.00 42.82 C +ATOM 391 O SER A 63 -12.075 -0.743 36.363 1.00 43.26 O +ATOM 392 CB SER A 63 -11.31 1.578 34.645 1.00 42.25 C +ATOM 393 OG SER A 63 -11.722 1.114 33.357 1.00 38.45 O +ATOM 394 N LEU A 64 -10.849 -1.775 34.756 1.00 43.43 N +ATOM 395 CA LEU A 64 -11.424 -3.108 34.964 1.00 43.8 C +ATOM 396 C LEU A 64 -11.062 -3.661 36.325 1.00 44.35 C +ATOM 397 O LEU A 64 -11.905 -4.241 36.996 1.00 45.22 O +ATOM 398 CB LEU A 64 -10.945 -4.074 33.896 1.00 43.72 C +ATOM 399 CG LEU A 64 -11.345 -3.651 32.51 1.00 43.68 C +ATOM 400 CD1 LEU A 64 -10.838 -4.689 31.548 1.00 43.46 C +ATOM 401 CD2 LEU A 64 -12.867 -3.536 32.508 1.00 45.83 C +ATOM 402 N THR A 65 -9.817 -3.469 36.739 1.00 44.84 N +ATOM 403 CA THR A 65 -9.399 -3.894 38.063 1.00 45.39 C +ATOM 404 C THR A 65 -9.38 -2.751 39.059 1.00 45.62 C +ATOM 405 O THR A 65 -8.93 -2.922 40.18 1.00 46.5 O +ATOM 406 CB THR A 65 -8.017 -4.538 38.018 1.00 45.83 C +ATOM 407 OG1 THR A 65 -7.125 -3.663 37.318 1.00 45.97 O +ATOM 408 CG2 THR A 65 -8.022 -5.846 37.201 1.00 45.46 C +ATOM 409 N ASP A 66 -9.846 -1.574 38.667 1.00 45.97 N +ATOM 410 CA ASP A 66 -9.965 -0.449 39.614 1.00 45.63 C +ATOM 411 C ASP A 66 -10.981 0.576 39.125 1.00 45.33 C +ATOM 412 O ASP A 66 -10.61 1.625 38.607 1.00 44.22 O +ATOM 413 CB ASP A 66 -8.612 0.226 39.876 1.00 45.52 C +ATOM 414 CG ASP A 66 -8.668 1.184 41.037 1.00 47.02 C +ATOM 415 OD1 ASP A 66 -9.812 1.476 41.484 1.00 47.01 O +ATOM 416 OD2 ASP A 66 -7.632 1.68 41.581 1.00 47.88 O +ATOM 417 N PRO A 67 -12.266 0.261 39.27 1.00 45.26 N +ATOM 418 CA PRO A 67 -13.334 1.164 38.818 1.00 45.3 C +ATOM 419 C PRO A 67 -13.21 2.634 39.295 1.00 44.71 C +ATOM 420 O PRO A 67 -13.799 3.512 38.647 1.00 44.53 O +ATOM 421 CB PRO A 67 -14.6 0.476 39.366 1.00 45.27 C +ATOM 422 CG PRO A 67 -14.242 -0.975 39.24 1.00 45.42 C +ATOM 423 CD PRO A 67 -12.822 -0.984 39.835 1.00 45.47 C +ATOM 424 N SER A 68 -12.458 2.895 40.374 1.00 44.27 N +ATOM 425 CA SER A 68 -12.327 4.272 40.893 1.00 44.04 C +ATOM 426 C SER A 68 -11.469 5.186 39.986 1.00 43.56 C +ATOM 427 O SER A 68 -11.565 6.414 40.037 1.00 43.51 O +ATOM 428 CB SER A 68 -11.841 4.29 42.345 1.00 43.71 C +ATOM 429 OG SER A 68 -10.44 4.092 42.438 1.00 45.4 O +ATOM 430 N LYS A 69 -10.661 4.562 39.135 1.00 43.11 N +ATOM 431 CA LYS A 69 -9.876 5.253 38.109 1.00 42.44 C +ATOM 432 C LYS A 69 -10.709 6.073 37.121 1.00 41.75 C +ATOM 433 O LYS A 69 -10.176 6.995 36.493 1.00 41.95 O +ATOM 434 CB LYS A 69 -8.962 4.268 37.379 1.00 42.15 C +ATOM 435 CG LYS A 69 -7.66 4.136 38.129 1.00 43.58 C +ATOM 436 CD LYS A 69 -6.901 2.887 37.843 1.00 41.95 C +ATOM 437 CE LYS A 69 -5.696 2.915 38.737 1.00 37.82 C +ATOM 438 NZ LYS A 69 -4.705 2.022 38.152 1.00 39.79 N +ATOM 439 N LEU A 70 -12 5.757 36.996 1.00 40.49 N +ATOM 440 CA LEU A 70 -12.895 6.543 36.129 1.00 40.34 C +ATOM 441 C LEU A 70 -13.701 7.659 36.858 1.00 39.76 C +ATOM 442 O LEU A 70 -14.513 8.343 36.234 1.00 38.82 O +ATOM 443 CB LEU A 70 -13.822 5.629 35.319 1.00 39.45 C +ATOM 444 CG LEU A 70 -13.137 4.848 34.202 1.00 41.23 C +ATOM 445 CD1 LEU A 70 -14.137 3.88 33.503 1.00 41.48 C +ATOM 446 CD2 LEU A 70 -12.466 5.773 33.2 1.00 41.12 C +ATOM 447 N ASP A 71 -13.462 7.836 38.163 1.00 39.65 N +ATOM 448 CA ASP A 71 -14.099 8.938 38.897 1.00 40.16 C +ATOM 449 C ASP A 71 -13.715 10.304 38.343 1.00 40.41 C +ATOM 450 O ASP A 71 -14.393 11.297 38.605 1.00 39.9 O +ATOM 451 CB ASP A 71 -13.738 8.92 40.362 1.00 39.39 C +ATOM 452 CG ASP A 71 -14.237 7.699 41.077 1.00 40.1 C +ATOM 453 OD1 ASP A 71 -15.131 6.967 40.566 1.00 39 O +ATOM 454 OD2 ASP A 71 -13.76 7.404 42.187 1.00 38.79 O +ATOM 455 N SER A 72 -12.6 10.355 37.624 1.00 40.83 N +ATOM 456 CA SER A 72 -12.159 11.609 36.983 1.00 41.39 C +ATOM 457 C SER A 72 -12.871 11.83 35.622 1.00 41.77 C +ATOM 458 O SER A 72 -12.652 12.857 34.954 1.00 42.43 O +ATOM 459 CB SER A 72 -10.631 11.64 36.849 1.00 40.64 C +ATOM 460 OG SER A 72 -10.116 10.387 36.435 1.00 40.06 O +ATOM 461 N GLY A 73 -13.72 10.866 35.242 1.00 41.29 N +ATOM 462 CA GLY A 73 -14.561 10.975 34.068 1.00 42.3 C +ATOM 463 C GLY A 73 -14.724 9.649 33.343 1.00 42.89 C +ATOM 464 O GLY A 73 -13.729 8.997 33.001 1.00 43.03 O +ATOM 465 N LYS A 74 -15.973 9.233 33.123 1.00 43.41 N +ATOM 466 CA LYS A 74 -16.242 7.951 32.451 1.00 44.15 C +ATOM 467 C LYS A 74 -16.06 7.989 30.927 1.00 42.66 C +ATOM 468 O LYS A 74 -15.782 6.963 30.322 1.00 41.79 O +ATOM 469 CB LYS A 74 -17.657 7.426 32.777 1.00 45.01 C +ATOM 470 CG LYS A 74 -17.943 7.063 34.268 1.00 50.94 C +ATOM 471 CD LYS A 74 -18.064 5.52 34.498 1.00 57.78 C +ATOM 472 CE LYS A 74 -17.97 5.143 36.004 1.00 60.07 C +ATOM 473 NZ LYS A 74 -17.029 3.997 36.315 1.00 60.6 N +ATOM 474 N GLU A 75 -16.267 9.156 30.305 1.00 41.75 N +ATOM 475 CA GLU A 75 -16.387 9.24 28.835 1.00 40.72 C +ATOM 476 C GLU A 75 -15.01 9.097 28.175 1.00 39.42 C +ATOM 477 O GLU A 75 -14.088 9.788 28.544 1.00 39.22 O +ATOM 478 CB GLU A 75 -17.049 10.557 28.379 1.00 41.31 C +ATOM 479 CG GLU A 75 -18.508 10.756 28.802 1.00 42.91 C +ATOM 480 CD GLU A 75 -18.671 11.494 30.122 0.3 44.13 C +ATOM 481 OE1 GLU A 75 -17.651 11.856 30.769 0.3 43.63 O +ATOM 482 OE2 GLU A 75 -19.841 11.714 30.512 0.3 45.36 O +ATOM 483 N LEU A 76 -14.885 8.184 27.219 1.00 37.7 N +ATOM 484 CA LEU A 76 -13.623 7.925 26.538 1.00 36.02 C +ATOM 485 C LEU A 76 -13.737 8.359 25.086 1.00 35.19 C +ATOM 486 O LEU A 76 -14.388 7.698 24.269 1.00 35.54 O +ATOM 487 CB LEU A 76 -13.254 6.44 26.65 1.00 35.61 C +ATOM 488 CG LEU A 76 -13.163 5.879 28.078 1.00 34.97 C +ATOM 489 CD1 LEU A 76 -12.631 4.438 28.084 1.00 31.83 C +ATOM 490 CD2 LEU A 76 -12.295 6.784 28.907 1.00 35.05 C +ATOM 491 N HIS A 77 -13.11 9.469 24.755 1.00 34.1 N +ATOM 492 CA HIS A 77 -13.318 10.064 23.436 1.00 34.39 C +ATOM 493 C HIS A 77 -12.158 10.958 23.07 1.00 32.87 C +ATOM 494 O HIS A 77 -11.28 11.187 23.9 1.00 31.92 O +ATOM 495 CB HIS A 77 -14.638 10.859 23.377 1.00 34.53 C +ATOM 496 CG HIS A 77 -14.659 12.084 24.252 1.00 39.68 C +ATOM 497 ND1 HIS A 77 -15.382 12.154 25.425 1.00 44.18 N +ATOM 498 CD2 HIS A 77 -14.055 13.288 24.12 1.00 44.19 C +ATOM 499 CE1 HIS A 77 -15.226 13.346 25.968 1.00 44.17 C +ATOM 500 NE2 HIS A 77 -14.412 14.048 25.205 1.00 44.85 N +ATOM 501 N ILE A 78 -12.183 11.404 21.804 1.00 31.74 N +ATOM 502 CA ILE A 78 -11.198 12.268 21.135 1.00 31.07 C +ATOM 503 C ILE A 78 -11.94 13.434 20.447 1.00 30.97 C +ATOM 504 O ILE A 78 -12.913 13.21 19.702 1.00 31.27 O +ATOM 505 CB ILE A 78 -10.39 11.48 20.069 1.00 30.07 C +ATOM 506 CG1 ILE A 78 -9.774 10.231 20.682 1.00 28.92 C +ATOM 507 CG2 ILE A 78 -9.306 12.413 19.399 1.00 30.85 C +ATOM 508 CD1 ILE A 78 -8.808 9.494 19.751 1.00 24.55 C +ATOM 509 N ASN A 79 -11.485 14.664 20.709 1.00 30.81 N +ATOM 510 CA ASN A 79 -11.982 15.892 20.057 1.00 30.4 C +ATOM 511 C ASN A 79 -10.856 16.53 19.254 1.00 30.53 C +ATOM 512 O ASN A 79 -9.712 16.578 19.736 1.00 29.49 O +ATOM 513 CB ASN A 79 -12.489 16.908 21.102 1.00 30.27 C +ATOM 514 CG ASN A 79 -13.711 16.388 21.892 1.00 29.32 C +ATOM 515 OD1 ASN A 79 -13.93 16.755 23.067 1.00 30.35 O +ATOM 516 ND2 ASN A 79 -14.531 15.563 21.238 1.00 23.92 N +ATOM 517 N LEU A 80 -11.173 16.925 18.02 1.00 29.67 N +ATOM 518 CA LEU A 80 -10.29 17.741 17.181 1.00 30.31 C +ATOM 519 C LEU A 80 -10.841 19.134 17.086 1.00 29.86 C +ATOM 520 O LEU A 80 -11.939 19.343 16.628 1.00 30.62 O +ATOM 521 CB LEU A 80 -10.071 17.132 15.781 1.00 30 C +ATOM 522 CG LEU A 80 -9.792 15.629 15.767 1.00 30.48 C +ATOM 523 CD1 LEU A 80 -9.898 15.086 14.356 1.00 33.98 C +ATOM 524 CD2 LEU A 80 -8.468 15.282 16.425 1.00 27.7 C +ATOM 525 N ILE A 81 -10.085 20.093 17.572 1.00 31.05 N +ATOM 526 CA ILE A 81 -10.567 21.455 17.673 1.00 31.18 C +ATOM 527 C ILE A 81 -9.667 22.425 16.918 1.00 31.79 C +ATOM 528 O ILE A 81 -8.612 22.791 17.443 1.00 31.57 O +ATOM 529 CB ILE A 81 -10.654 21.915 19.155 1.00 31.53 C +ATOM 530 CG1 ILE A 81 -11.371 20.871 20.002 1.00 29.58 C +ATOM 531 CG2 ILE A 81 -11.338 23.371 19.262 1.00 30.56 C +ATOM 532 CD1 ILE A 81 -11.437 21.238 21.43 1.00 29.19 C +ATOM 533 N PRO A 82 -10.126 22.903 15.745 1.00 31.73 N +ATOM 534 CA PRO A 82 -9.431 23.946 15.007 1.00 32.5 C +ATOM 535 C PRO A 82 -9.836 25.338 15.508 1.00 33.34 C +ATOM 536 O PRO A 82 -10.987 25.587 15.85 1.00 33.79 O +ATOM 537 CB PRO A 82 -9.958 23.749 13.567 1.00 32.23 C +ATOM 538 CG PRO A 82 -11.246 23.11 13.712 1.00 30.8 C +ATOM 539 CD PRO A 82 -11.376 22.523 15.065 1.00 31.35 C +ATOM 540 N ASN A 83 -8.881 26.249 15.519 1.00 35.03 N +ATOM 541 CA ASN A 83 -9.155 27.645 15.822 1.00 35.6 C +ATOM 542 C ASN A 83 -8.323 28.591 14.965 1.00 37.12 C +ATOM 543 O ASN A 83 -7.127 28.813 15.26 1.00 35.4 O +ATOM 544 CB ASN A 83 -8.912 27.967 17.283 1.00 35.41 C +ATOM 545 CG ASN A 83 -9.417 29.374 17.651 1.00 34.27 C +ATOM 546 OD1 ASN A 83 -9.06 30.353 17.01 1.00 31.65 O +ATOM 547 ND2 ASN A 83 -10.255 29.459 18.675 1.00 28.51 N +ATOM 548 N LYS A 84 -8.972 29.185 13.951 1.00 38.95 N +ATOM 549 CA LYS A 84 -8.268 30.013 12.955 1.00 41.47 C +ATOM 550 C LYS A 84 -7.704 31.248 13.591 1.00 42.83 C +ATOM 551 O LYS A 84 -6.645 31.712 13.194 1.00 43.63 O +ATOM 552 CB LYS A 84 -9.155 30.393 11.757 1.00 41.23 C +ATOM 553 CG LYS A 84 -9.184 29.35 10.611 1.00 44.12 C +ATOM 554 CD LYS A 84 -10.564 29.203 9.966 1.00 49.13 C +ATOM 555 CE LYS A 84 -10.617 28.047 8.968 1.00 52.34 C +ATOM 556 NZ LYS A 84 -10.127 28.466 7.603 1.00 56.34 N +ATOM 557 N GLN A 85 -8.405 31.761 14.594 1.00 45.03 N +ATOM 558 CA GLN A 85 -8.03 32.999 15.25 1.00 47.16 C +ATOM 559 C GLN A 85 -6.741 32.87 16.076 1.00 47.17 C +ATOM 560 O GLN A 85 -5.938 33.818 16.095 1.00 48.23 O +ATOM 561 CB GLN A 85 -9.191 33.552 16.094 1.00 48.76 C +ATOM 562 CG GLN A 85 -8.828 34.813 16.938 1.00 54.54 C +ATOM 563 CD GLN A 85 -10.051 35.563 17.516 1.00 62.05 C +ATOM 564 OE1 GLN A 85 -11.154 34.993 17.665 1.00 64.41 O +ATOM 565 NE2 GLN A 85 -9.849 36.843 17.849 1.00 63.26 N +ATOM 566 N ASP A 86 -6.534 31.733 16.757 1.00 46 N +ATOM 567 CA ASP A 86 -5.255 31.5 17.432 1.00 44.5 C +ATOM 568 C ASP A 86 -4.287 30.742 16.552 1.00 42.71 C +ATOM 569 O ASP A 86 -3.134 30.577 16.949 1.00 43.18 O +ATOM 570 CB ASP A 86 -5.395 30.725 18.747 1.00 45.11 C +ATOM 571 CG ASP A 86 -6.498 31.248 19.629 1.00 47.2 C +ATOM 572 OD1 ASP A 86 -6.769 32.476 19.638 1.00 49.02 O +ATOM 573 OD2 ASP A 86 -7.149 30.479 20.357 1.00 48.46 O +ATOM 574 N ARG A 87 -4.767 30.27 15.392 1.00 40.15 N +ATOM 575 CA ARG A 87 -4.039 29.371 14.499 1.00 37.33 C +ATOM 576 C ARG A 87 -3.599 28.097 15.208 1.00 35.73 C +ATOM 577 O ARG A 87 -2.431 27.764 15.19 1.00 34.16 O +ATOM 578 CB ARG A 87 -2.813 30.073 13.913 1.00 38.17 C +ATOM 579 CG ARG A 87 -2.439 29.588 12.535 1.00 39.09 C +ATOM 580 CD ARG A 87 -1.024 29.781 12.216 1.00 42.06 C +ATOM 581 NE ARG A 87 -0.749 31.111 11.68 1.00 46.02 N +ATOM 582 CZ ARG A 87 0.454 31.713 11.756 1.00 50.34 C +ATOM 583 NH1 ARG A 87 1.503 31.117 12.35 1.00 48.86 N +ATOM 584 NH2 ARG A 87 0.614 32.921 11.229 1.00 51.64 N +ATOM 585 N THR A 88 -4.532 27.404 15.85 1.00 34.17 N +ATOM 586 CA THR A 88 -4.198 26.208 16.584 1.00 33.25 C +ATOM 587 C THR A 88 -5.071 25.08 16.134 1.00 33.12 C +ATOM 588 O THR A 88 -6.222 25.298 15.786 1.00 33.74 O +ATOM 589 CB THR A 88 -4.362 26.385 18.114 1.00 33.32 C +ATOM 590 OG1 THR A 88 -5.691 26.811 18.436 1.00 34.95 O +ATOM 591 CG2 THR A 88 -3.407 27.504 18.719 1.00 30.84 C +ATOM 592 N LEU A 89 -4.511 23.877 16.144 1.00 31.54 N +ATOM 593 CA LEU A 89 -5.302 22.663 16.097 1.00 30.69 C +ATOM 594 C LEU A 89 -5.02 21.895 17.387 1.00 30.4 C +ATOM 595 O LEU A 89 -3.863 21.645 17.743 1.00 30.38 O +ATOM 596 CB LEU A 89 -4.961 21.815 14.858 1.00 29.98 C +ATOM 597 CG LEU A 89 -5.7 20.488 14.716 1.00 28.41 C +ATOM 598 CD1 LEU A 89 -7.168 20.789 14.482 1.00 24.6 C +ATOM 599 CD2 LEU A 89 -5.094 19.66 13.594 1.00 25.29 C +ATOM 600 N THR A 90 -6.083 21.525 18.09 1.00 30.29 N +ATOM 601 CA THR A 90 -5.952 20.884 19.378 1.00 29.11 C +ATOM 602 C THR A 90 -6.561 19.516 19.292 1.00 29.05 C +ATOM 603 O THR A 90 -7.641 19.328 18.711 1.00 27.7 O +ATOM 604 CB THR A 90 -6.609 21.73 20.448 1.00 29.37 C +ATOM 605 OG1 THR A 90 -5.934 23.009 20.565 1.00 31.77 O +ATOM 606 CG2 THR A 90 -6.428 21.089 21.828 1.00 29.36 C +ATOM 607 N ILE A 91 -5.802 18.553 19.79 1.00 29.52 N +ATOM 608 CA ILE A 91 -6.188 17.123 19.84 1.00 30.06 C +ATOM 609 C ILE A 91 -6.407 16.774 21.304 1.00 29.7 C +ATOM 610 O ILE A 91 -5.451 16.667 22.082 1.00 28.21 O +ATOM 611 CB ILE A 91 -5.098 16.181 19.208 1.00 29.94 C +ATOM 612 CG1 ILE A 91 -4.952 16.462 17.697 1.00 30.98 C +ATOM 613 CG2 ILE A 91 -5.443 14.685 19.457 1.00 28.84 C +ATOM 614 CD1 ILE A 91 -4.133 17.719 17.365 1.00 30.04 C +ATOM 615 N VAL A 92 -7.684 16.619 21.634 1.00 29.94 N +ATOM 616 CA VAL A 92 -8.151 16.358 22.994 1.00 30.84 C +ATOM 617 C VAL A 92 -8.593 14.906 23.215 1.00 30.34 C +ATOM 618 O VAL A 92 -9.399 14.341 22.432 1.00 29.2 O +ATOM 619 CB VAL A 92 -9.358 17.261 23.353 1.00 30.96 C +ATOM 620 CG1 VAL A 92 -9.722 17.109 24.873 1.00 31.17 C +ATOM 621 CG2 VAL A 92 -9.062 18.718 22.979 1.00 31.08 C +ATOM 622 N ASP A 93 -8.13 14.323 24.328 1.00 30.35 N +ATOM 623 CA ASP A 93 -8.598 12.987 24.702 1.00 29.22 C +ATOM 624 C ASP A 93 -8.859 12.887 26.196 1.00 30.13 C +ATOM 625 O ASP A 93 -8.323 13.688 26.973 1.00 30.11 O +ATOM 626 CB ASP A 93 -7.653 11.885 24.17 1.00 29 C +ATOM 627 CG ASP A 93 -6.321 11.848 24.882 1.00 28.03 C +ATOM 628 OD1 ASP A 93 -6.301 11.431 26.053 1.00 24.05 O +ATOM 629 OD2 ASP A 93 -5.25 12.211 24.343 1.00 29.23 O +ATOM 630 N THR A 94 -9.694 11.926 26.612 1.00 29.86 N +ATOM 631 CA THR A 94 -9.865 11.653 28.074 1.00 29.49 C +ATOM 632 C THR A 94 -9.213 10.336 28.485 1.00 29.69 C +ATOM 633 O THR A 94 -9.776 9.534 29.275 1.00 28.79 O +ATOM 634 CB THR A 94 -11.336 11.624 28.404 1.00 29.93 C +ATOM 635 OG1 THR A 94 -11.973 10.692 27.504 1.00 28.44 O +ATOM 636 CG2 THR A 94 -11.967 12.969 28.023 1.00 27.4 C +ATOM 637 N GLY A 95 -8.019 10.121 27.934 1.00 29.89 N +ATOM 638 CA GLY A 95 -7.232 8.922 28.183 1.00 30.11 C +ATOM 639 C GLY A 95 -6.444 9.029 29.473 1.00 30.79 C +ATOM 640 O GLY A 95 -6.666 9.931 30.276 1.00 31.33 O +ATOM 641 N ILE A 96 -5.501 8.125 29.677 1.00 30.13 N +ATOM 642 CA ILE A 96 -4.841 8.026 30.981 1.00 29.89 C +ATOM 643 C ILE A 96 -4.055 9.285 31.467 1.00 30.56 C +ATOM 644 O ILE A 96 -3.865 9.484 32.669 1.00 30.05 O +ATOM 645 CB ILE A 96 -3.932 6.766 31.01 1.00 28.78 C +ATOM 646 CG1 ILE A 96 -3.578 6.4 32.452 1.00 28.35 C +ATOM 647 CG2 ILE A 96 -2.705 6.94 30.078 1.00 26.75 C +ATOM 648 CD1 ILE A 96 -3.514 4.919 32.637 1.00 27.67 C +ATOM 649 N GLY A 97 -3.592 10.108 30.53 1.00 30.51 N +ATOM 650 CA GLY A 97 -2.804 11.268 30.882 1.00 31.17 C +ATOM 651 C GLY A 97 -1.38 10.888 31.21 1.00 31.3 C +ATOM 652 O GLY A 97 -0.982 9.715 31.168 1.00 31.22 O +ATOM 653 N MET A 98 -0.606 11.917 31.507 1.00 32.17 N +ATOM 654 CA MET A 98 0.796 11.757 31.864 1.00 32.59 C +ATOM 655 C MET A 98 1.13 12.458 33.188 1.00 32.77 C +ATOM 656 O MET A 98 0.589 13.545 33.523 1.00 32.69 O +ATOM 657 CB MET A 98 1.692 12.283 30.735 1.00 32.78 C +ATOM 658 CG MET A 98 1.514 11.608 29.411 1.00 31.24 C +ATOM 659 SD MET A 98 2.323 12.442 28.046 1.00 28.77 S +ATOM 660 CE MET A 98 1.031 13.622 27.487 1.00 31.02 C +ATOM 661 N THR A 99 2.017 11.826 33.95 1.00 31.94 N +ATOM 662 CA THR A 99 2.559 12.422 35.159 1.00 31.55 C +ATOM 663 C THR A 99 3.748 13.343 34.874 1.00 31.87 C +ATOM 664 O THR A 99 4.315 13.336 33.807 1.00 32.42 O +ATOM 665 CB THR A 99 3.029 11.333 36.098 1.00 31.35 C +ATOM 666 OG1 THR A 99 4.06 10.569 35.443 1.00 29.95 O +ATOM 667 CG2 THR A 99 1.922 10.373 36.36 1.00 29.87 C +ATOM 668 N LYS A 100 4.156 14.113 35.87 1.00 32.74 N +ATOM 669 CA LYS A 100 5.352 14.949 35.737 1.00 32.19 C +ATOM 670 C LYS A 100 6.595 14.147 35.351 1.00 31.69 C +ATOM 671 O LYS A 100 7.406 14.598 34.515 1.00 28.97 O +ATOM 672 CB LYS A 100 5.615 15.698 37.037 1.00 32.39 C +ATOM 673 CG LYS A 100 6.605 16.814 36.869 1.00 35.27 C +ATOM 674 CD LYS A 100 6.949 17.449 38.166 1.00 38.51 C +ATOM 675 CE LYS A 100 7.612 18.775 37.919 1.00 44.24 C +ATOM 676 NZ LYS A 100 8.458 19.196 39.123 1.00 48.05 N +ATOM 677 N ALA A 101 6.759 12.965 35.97 1.00 31.85 N +ATOM 678 CA ALA A 101 7.93 12.114 35.642 1.00 32.04 C +ATOM 679 C ALA A 101 7.895 11.615 34.195 1.00 31.89 C +ATOM 680 O ALA A 101 8.939 11.452 33.558 1.00 32.04 O +ATOM 681 CB ALA A 101 8.074 10.94 36.625 1.00 32.36 C +ATOM 682 N ASP A 102 6.692 11.39 33.663 1.00 32.01 N +ATOM 683 CA ASP A 102 6.552 11.081 32.231 1.00 31.31 C +ATOM 684 C ASP A 102 7.18 12.2 31.396 1.00 31.61 C +ATOM 685 O ASP A 102 8.089 11.982 30.543 1.00 30.22 O +ATOM 686 CB ASP A 102 5.061 10.939 31.874 1.00 31.2 C +ATOM 687 CG ASP A 102 4.483 9.533 32.222 1.00 31.42 C +ATOM 688 OD1 ASP A 102 5.273 8.543 32.326 1.00 27.47 O +ATOM 689 OD2 ASP A 102 3.245 9.344 32.398 1.00 30.01 O +ATOM 690 N LEU A 103 6.673 13.409 31.676 1.00 31.91 N +ATOM 691 CA LEU A 103 6.946 14.622 30.902 1.00 32.01 C +ATOM 692 C LEU A 103 8.42 14.96 30.897 1.00 32.5 C +ATOM 693 O LEU A 103 8.948 15.292 29.858 1.00 31.59 O +ATOM 694 CB LEU A 103 6.138 15.816 31.43 1.00 30.99 C +ATOM 695 CG LEU A 103 4.643 15.846 31.222 1.00 31.7 C +ATOM 696 CD1 LEU A 103 4.014 17.161 31.84 1.00 26.35 C +ATOM 697 CD2 LEU A 103 4.295 15.61 29.705 1.00 30.45 C +ATOM 698 N ILE A 104 9.058 14.875 32.068 1.00 34.39 N +ATOM 699 CA ILE A 104 10.44 15.319 32.264 1.00 35.25 C +ATOM 700 C ILE A 104 11.432 14.213 31.945 1.00 37.2 C +ATOM 701 O ILE A 104 12.41 14.458 31.203 1.00 38.13 O +ATOM 702 CB ILE A 104 10.628 15.831 33.7 1.00 35 C +ATOM 703 CG1 ILE A 104 9.816 17.117 33.872 1.00 34.37 C +ATOM 704 CG2 ILE A 104 12.139 15.988 34.072 1.00 34.75 C +ATOM 705 CD1 ILE A 104 9.688 17.565 35.26 1.00 33.14 C +ATOM 706 N ASN A 105 11.182 13.003 32.476 1.00 38.64 N +ATOM 707 CA ASN A 105 12.106 11.877 32.298 1.00 39.37 C +ATOM 708 C ASN A 105 11.653 10.646 31.511 1.00 38.65 C +ATOM 709 O ASN A 105 12.294 10.288 30.522 1.00 36.93 O +ATOM 710 CB ASN A 105 12.609 11.39 33.652 1.00 41.1 C +ATOM 711 CG ASN A 105 13.436 12.415 34.338 1.00 46.36 C +ATOM 712 OD1 ASN A 105 14.376 12.949 33.744 1.00 53.07 O +ATOM 713 ND2 ASN A 105 13.088 12.737 35.593 1.00 51.01 N +ATOM 714 N ASN A 106 10.621 9.964 32.015 1.00 37.98 N +ATOM 715 CA ASN A 106 10.245 8.619 31.54 1.00 38.63 C +ATOM 716 C ASN A 106 10.113 8.512 30.046 1.00 37.72 C +ATOM 717 O ASN A 106 10.662 7.604 29.443 1.00 36.82 O +ATOM 718 CB ASN A 106 8.903 8.135 32.1 1.00 38.93 C +ATOM 719 CG ASN A 106 8.88 7.977 33.626 1.00 41.89 C +ATOM 720 OD1 ASN A 106 9.928 7.898 34.299 1.00 41.66 O +ATOM 721 ND2 ASN A 106 7.64 7.886 34.181 1.00 44.67 N +ATOM 722 N LEU A 107 9.359 9.438 29.457 1.00 37.43 N +ATOM 723 CA LEU A 107 8.982 9.348 28.045 1.00 37.34 C +ATOM 724 C LEU A 107 10.17 9.52 27.11 1.00 37.01 C +ATOM 725 O LEU A 107 10.269 8.807 26.132 1.00 37.49 O +ATOM 726 CB LEU A 107 7.835 10.315 27.706 1.00 36.42 C +ATOM 727 CG LEU A 107 6.515 9.975 28.395 1.00 36.11 C +ATOM 728 CD1 LEU A 107 5.486 11.007 28.094 1.00 35.91 C +ATOM 729 CD2 LEU A 107 6.011 8.589 27.995 1.00 36.79 C +ATOM 730 N GLY A 108 11.053 10.476 27.422 1.00 38.15 N +ATOM 731 CA GLY A 108 12.285 10.723 26.676 1.00 37.76 C +ATOM 732 C GLY A 108 13.285 9.569 26.81 1.00 38.84 C +ATOM 733 O GLY A 108 14.035 9.318 25.865 1.00 37.63 O +ATOM 734 N THR A 109 13.309 8.871 27.955 1.00 39.47 N +ATOM 735 CA THR A 109 14.166 7.683 28.081 1.00 41.66 C +ATOM 736 C THR A 109 13.778 6.54 27.142 1.00 41.35 C +ATOM 737 O THR A 109 14.662 5.962 26.515 1.00 41.72 O +ATOM 738 CB THR A 109 14.279 7.151 29.525 1.00 41.51 C +ATOM 739 OG1 THR A 109 15.393 6.242 29.602 1.00 46.82 O +ATOM 740 CG2 THR A 109 13.192 6.19 29.827 1.00 45.06 C +ATOM 741 N ILE A 110 12.474 6.201 27.063 1.00 41.53 N +ATOM 742 CA ILE A 110 11.977 5.262 26.04 1.00 40.68 C +ATOM 743 C ILE A 110 12.284 5.835 24.648 1.00 39.88 C +ATOM 744 O ILE A 110 12.678 5.108 23.749 1.00 39.12 O +ATOM 745 CB ILE A 110 10.451 5.101 26.11 1.00 41.28 C +ATOM 746 CG1 ILE A 110 9.901 4.867 27.514 1.00 42.93 C +ATOM 747 CG2 ILE A 110 9.941 4.043 25.116 1.00 42.55 C +ATOM 748 CD1 ILE A 110 8.312 5.037 27.504 1.00 45.68 C +ATOM 749 N ALA A 111 12.059 7.141 24.48 1.00 38.53 N +ATOM 750 CA ALA A 111 12.097 7.755 23.159 1.00 38.7 C +ATOM 751 C ALA A 111 13.505 7.666 22.583 1.00 38.76 C +ATOM 752 O ALA A 111 13.673 7.653 21.362 1.00 39.22 O +ATOM 753 CB ALA A 111 11.602 9.225 23.194 1.00 37.38 C +ATOM 754 N LYS A 112 14.507 7.579 23.457 1.00 38.61 N +ATOM 755 CA LYS A 112 15.891 7.563 23.005 1.00 39.09 C +ATOM 756 C LYS A 112 16.22 6.354 22.088 1.00 38.02 C +ATOM 757 O LYS A 112 16.831 6.525 21.054 1.00 37.74 O +ATOM 758 CB LYS A 112 16.857 7.714 24.2 1.00 40.25 C +ATOM 759 CG LYS A 112 18.345 7.905 23.819 1.00 43.11 C +ATOM 760 CD LYS A 112 19.272 7.686 25.042 1.00 50.85 C +ATOM 761 CE LYS A 112 20.763 7.806 24.667 1.00 51.91 C +ATOM 762 NZ LYS A 112 21.607 8.008 25.89 1.00 54.33 N +ATOM 763 N SER A 113 15.789 5.15 22.428 1.00 37.56 N +ATOM 764 CA SER A 113 16.055 4.025 21.526 1.00 37.8 C +ATOM 765 C SER A 113 15.215 4.001 20.212 1.00 37.99 C +ATOM 766 O SER A 113 15.737 3.621 19.139 1.00 38.4 O +ATOM 767 CB SER A 113 15.982 2.688 22.265 1.00 38.3 C +ATOM 768 OG SER A 113 14.665 2.361 22.677 1.00 37.84 O +ATOM 769 N GLY A 114 13.93 4.387 20.291 1.00 37.75 N +ATOM 770 CA GLY A 114 13.086 4.516 19.115 1.00 36.78 C +ATOM 771 C GLY A 114 13.655 5.532 18.157 1.00 37.26 C +ATOM 772 O GLY A 114 13.541 5.396 16.942 1.00 37.64 O +ATOM 773 N THR A 115 14.249 6.581 18.712 1.00 37.82 N +ATOM 774 CA THR A 115 14.944 7.617 17.922 1.00 38.58 C +ATOM 775 C THR A 115 16.14 7.097 17.11 1.00 38.71 C +ATOM 776 O THR A 115 16.217 7.347 15.9 1.00 38.9 O +ATOM 777 CB THR A 115 15.355 8.807 18.803 1.00 37.5 C +ATOM 778 OG1 THR A 115 14.194 9.347 19.422 1.00 37.99 O +ATOM 779 CG2 THR A 115 15.826 9.929 17.943 1.00 39.64 C +ATOM 780 N LYS A 116 17.048 6.386 17.78 1.00 39.84 N +ATOM 781 CA LYS A 116 18.173 5.688 17.126 1.00 41.27 C +ATOM 782 C LYS A 116 17.695 4.785 15.971 1.00 40.97 C +ATOM 783 O LYS A 116 18.078 5.007 14.822 1.00 41.47 O +ATOM 784 CB LYS A 116 19.005 4.904 18.162 1.00 41.92 C +ATOM 785 CG LYS A 116 20.367 4.281 17.674 1.00 46.16 C +ATOM 786 CD LYS A 116 21.107 3.626 18.88 1.00 52.74 C +ATOM 787 CE LYS A 116 22.558 3.176 18.577 1.00 55.51 C +ATOM 788 NZ LYS A 116 22.664 1.832 17.909 1.00 55.5 N +ATOM 789 N ALA A 117 16.865 3.782 16.282 1.00 40.61 N +ATOM 790 CA ALA A 117 16.207 2.947 15.261 1.00 39.99 C +ATOM 791 C ALA A 117 15.554 3.751 14.095 1.00 40.31 C +ATOM 792 O ALA A 117 15.828 3.464 12.932 1.00 40.16 O +ATOM 793 CB ALA A 117 15.192 1.991 15.9 1.00 38.94 C +ATOM 794 N PHE A 118 14.698 4.731 14.402 1.00 39.74 N +ATOM 795 CA PHE A 118 14.102 5.585 13.366 1.00 40.87 C +ATOM 796 C PHE A 118 15.195 6.217 12.5 1.00 42.04 C +ATOM 797 O PHE A 118 15.163 6.151 11.279 1.00 41.51 O +ATOM 798 CB PHE A 118 13.221 6.692 13.989 1.00 39.61 C +ATOM 799 CG PHE A 118 12.493 7.563 12.974 1.00 39.44 C +ATOM 800 CD1 PHE A 118 11.793 7 11.889 1.00 38.18 C +ATOM 801 CD2 PHE A 118 12.476 8.938 13.115 1.00 37.96 C +ATOM 802 CE1 PHE A 118 11.073 7.799 10.968 1.00 38.52 C +ATOM 803 CE2 PHE A 118 11.769 9.754 12.186 1.00 40.61 C +ATOM 804 CZ PHE A 118 11.073 9.173 11.107 1.00 36.94 C +ATOM 805 N MET A 119 16.183 6.823 13.145 1.00 44.51 N +ATOM 806 CA MET A 119 17.225 7.512 12.401 1.00 46.29 C +ATOM 807 C MET A 119 18.017 6.58 11.507 1.00 46.77 C +ATOM 808 O MET A 119 18.239 6.888 10.339 1.00 45.6 O +ATOM 809 CB MET A 119 18.047 8.438 13.304 1.00 46.66 C +ATOM 810 CG MET A 119 17.406 9.804 13.159 1.00 50.62 C +ATOM 811 SD MET A 119 17.851 11.068 14.259 1.00 63.04 S +ATOM 812 CE MET A 119 18.896 12.204 13.031 1.00 62.29 C +ATOM 813 N GLU A 120 18.338 5.404 12.039 1.00 48.08 N +ATOM 814 CA GLU A 120 18.941 4.316 11.259 1.00 49.45 C +ATOM 815 C GLU A 120 18.115 3.858 10.068 1.00 49.36 C +ATOM 816 O GLU A 120 18.674 3.618 8.994 1.00 50.17 O +ATOM 817 CB GLU A 120 19.237 3.104 12.146 1.00 49.37 C +ATOM 818 CG GLU A 120 20.403 3.337 13.075 1.00 53.34 C +ATOM 819 CD GLU A 120 20.806 2.092 13.835 1.00 59.11 C +ATOM 820 OE1 GLU A 120 19.956 1.164 14.024 1.00 60.53 O +ATOM 821 OE2 GLU A 120 21.989 2.052 14.246 1.00 62.71 O +ATOM 822 N ALA A 121 16.806 3.683 10.268 1.00 49.32 N +ATOM 823 CA ALA A 121 15.901 3.22 9.202 1.00 48.31 C +ATOM 824 C ALA A 121 15.781 4.219 8.064 1.00 48.2 C +ATOM 825 O ALA A 121 15.66 3.79 6.926 1.00 48.01 O +ATOM 826 CB ALA A 121 14.557 2.9 9.742 1.00 47.98 C +ATOM 827 N LEU A 122 15.806 5.53 8.363 1.00 48.23 N +ATOM 828 CA LEU A 122 15.781 6.593 7.332 1.00 49.03 C +ATOM 829 C LEU A 122 16.982 6.557 6.366 1.00 50.76 C +ATOM 830 O LEU A 122 16.827 6.812 5.159 1.00 51.53 O +ATOM 831 CB LEU A 122 15.645 7.979 7.938 1.00 48.1 C +ATOM 832 CG LEU A 122 14.347 8.249 8.709 1.00 45.67 C +ATOM 833 CD1 LEU A 122 14.496 9.519 9.463 1.00 43.35 C +ATOM 834 CD2 LEU A 122 13.141 8.318 7.801 1.00 42.43 C +ATOM 835 N GLN A 123 18.16 6.22 6.889 1.00 52.48 N +ATOM 836 CA GLN A 123 19.288 5.803 6.058 1.00 54.19 C +ATOM 837 C GLN A 123 19.227 4.287 5.85 1.00 53.87 C +ATOM 838 O GLN A 123 19.704 3.496 6.666 1.00 55.9 O +ATOM 839 CB GLN A 123 20.591 6.207 6.709 1.00 55.01 C +ATOM 840 CG GLN A 123 20.411 7.331 7.673 1.00 61.17 C +ATOM 841 CD GLN A 123 21.74 7.846 8.175 1.00 69.11 C +ATOM 842 OE1 GLN A 123 22.317 7.267 9.116 1.00 71.73 O +ATOM 843 NE2 GLN A 123 22.244 8.929 7.55 1.00 69.59 N +ATOM 844 N ALA A 124 18.627 3.896 4.74 1.00 52.84 N +ATOM 845 CA ALA A 124 18.224 2.536 4.426 1.00 51.66 C +ATOM 846 C ALA A 124 16.955 2.832 3.655 1.00 50.81 C +ATOM 847 O ALA A 124 16.317 1.947 3.1 1.00 51.36 O +ATOM 848 CB ALA A 124 17.941 1.676 5.696 1.00 51.55 C +ATOM 849 N GLY A 125 16.615 4.122 3.625 1.00 49.72 N +ATOM 850 CA GLY A 125 15.511 4.647 2.848 1.00 48.19 C +ATOM 851 C GLY A 125 14.109 4.295 3.317 1.00 47.27 C +ATOM 852 O GLY A 125 13.24 3.995 2.477 1.00 47.91 O +ATOM 853 N ALA A 126 13.883 4.281 4.637 1.00 45.18 N +ATOM 854 CA ALA A 126 12.528 4.377 5.182 1.00 43.66 C +ATOM 855 C ALA A 126 12.179 5.862 5.061 1.00 42.81 C +ATOM 856 O ALA A 126 13.064 6.701 4.796 1.00 42.48 O +ATOM 857 CB ALA A 126 12.501 3.949 6.596 1.00 43.11 C +ATOM 858 N ASP A 127 10.914 6.222 5.195 1.00 41.76 N +ATOM 859 CA ASP A 127 10.629 7.66 5.095 1.00 41.45 C +ATOM 860 C ASP A 127 9.96 8.154 6.38 1.00 39.7 C +ATOM 861 O ASP A 127 9.566 7.351 7.244 1.00 39.42 O +ATOM 862 CB ASP A 127 9.804 7.99 3.839 1.00 41.47 C +ATOM 863 CG ASP A 127 8.4 7.55 3.987 1.00 43.63 C +ATOM 864 OD1 ASP A 127 8.2 6.324 3.978 1.00 50.71 O +ATOM 865 OD2 ASP A 127 7.455 8.309 4.241 1.00 44.27 O +ATOM 866 N ILE A 128 9.827 9.471 6.497 1.00 38.6 N +ATOM 867 CA ILE A 128 9.325 10.073 7.728 1.00 37.06 C +ATOM 868 C ILE A 128 7.961 9.525 8.136 1.00 36.34 C +ATOM 869 O ILE A 128 7.681 9.445 9.335 1.00 36.12 O +ATOM 870 CB ILE A 128 9.385 11.642 7.682 1.00 37.84 C +ATOM 871 CG1 ILE A 128 10.835 12.117 7.893 1.00 36.48 C +ATOM 872 CG2 ILE A 128 8.434 12.311 8.73 1.00 35.42 C +ATOM 873 CD1 ILE A 128 11.138 13.485 7.28 1.00 38.7 C +ATOM 874 N SER A 129 7.138 9.097 7.163 1.00 35.63 N +ATOM 875 CA SER A 129 5.765 8.633 7.485 1.00 35.39 C +ATOM 876 C SER A 129 5.733 7.422 8.408 1.00 34.94 C +ATOM 877 O SER A 129 4.713 7.157 9.023 1.00 34.82 O +ATOM 878 CB SER A 129 4.894 8.397 6.242 1.00 34.45 C +ATOM 879 OG SER A 129 5.275 7.233 5.546 1.00 35.73 O +ATOM 880 N MET A 130 6.878 6.736 8.533 1.00 35.14 N +ATOM 881 CA MET A 130 7.027 5.523 9.334 1.00 35.17 C +ATOM 882 C MET A 130 7.434 5.74 10.801 1.00 34.33 C +ATOM 883 O MET A 130 7.577 4.779 11.583 1.00 33.72 O +ATOM 884 CB MET A 130 7.989 4.565 8.616 1.00 36.01 C +ATOM 885 CG MET A 130 7.387 4.013 7.315 1.00 38.56 C +ATOM 886 SD MET A 130 8.637 3.523 6.122 1.00 45.53 S +ATOM 887 CE MET A 130 9.209 2.026 6.921 1.00 45.1 C +ATOM 888 N ILE A 131 7.584 7.012 11.17 1.00 33.86 N +ATOM 889 CA ILE A 131 7.971 7.433 12.535 1.00 33.03 C +ATOM 890 C ILE A 131 7.225 6.71 13.655 1.00 33.54 C +ATOM 891 O ILE A 131 7.817 6.403 14.697 1.00 32.27 O +ATOM 892 CB ILE A 131 7.853 9.011 12.653 1.00 31.59 C +ATOM 893 CG1 ILE A 131 8.514 9.535 13.919 1.00 27.7 C +ATOM 894 CG2 ILE A 131 6.411 9.439 12.469 1.00 31.14 C +ATOM 895 CD1 ILE A 131 8.514 11.148 14.089 1.00 23.33 C +ATOM 896 N GLY A 132 5.94 6.411 13.397 1.00 35.74 N +ATOM 897 CA GLY A 132 5.029 5.773 14.338 1.00 38.01 C +ATOM 898 C GLY A 132 5.444 4.381 14.811 1.00 40.28 C +ATOM 899 O GLY A 132 5.184 4.004 15.981 1.00 40.86 O +ATOM 900 N GLN A 133 6.065 3.614 13.906 1.00 41.28 N +ATOM 901 CA GLN A 133 6.602 2.274 14.225 1.00 42.85 C +ATOM 902 C GLN A 133 7.788 2.307 15.168 1.00 41.98 C +ATOM 903 O GLN A 133 8.282 1.272 15.568 1.00 42.89 O +ATOM 904 CB GLN A 133 7.036 1.557 12.956 1.00 43.35 C +ATOM 905 CG GLN A 133 5.839 1.301 12.015 1.00 48.85 C +ATOM 906 CD GLN A 133 6.249 0.726 10.681 1.00 52.68 C +ATOM 907 OE1 GLN A 133 6.698 -0.427 10.612 1.00 54.82 O +ATOM 908 NE2 GLN A 133 6.088 1.519 9.608 1.00 53.45 N +ATOM 909 N PHE A 134 8.258 3.502 15.493 1.00 41.17 N +ATOM 910 CA PHE A 134 9.438 3.643 16.309 1.00 39.76 C +ATOM 911 C PHE A 134 9.119 4.326 17.657 1.00 39.58 C +ATOM 912 O PHE A 134 10.035 4.7 18.414 1.00 39.02 O +ATOM 913 CB PHE A 134 10.524 4.381 15.516 1.00 39.73 C +ATOM 914 CG PHE A 134 10.922 3.696 14.193 1.00 39.75 C +ATOM 915 CD1 PHE A 134 11.938 2.718 14.162 1.00 40 C +ATOM 916 CD2 PHE A 134 10.302 4.048 12.986 1.00 38.5 C +ATOM 917 CE1 PHE A 134 12.307 2.108 12.952 1.00 38 C +ATOM 918 CE2 PHE A 134 10.65 3.43 11.778 1.00 36.78 C +ATOM 919 CZ PHE A 134 11.648 2.467 11.765 1.00 37.75 C +ATOM 920 N GLY A 135 7.813 4.465 17.954 1.00 39.23 N +ATOM 921 CA GLY A 135 7.329 5.006 19.231 1.00 38.44 C +ATOM 922 C GLY A 135 7.591 6.497 19.459 1.00 37.81 C +ATOM 923 O GLY A 135 7.398 7.044 20.561 1.00 39.35 O +ATOM 924 N VAL A 136 8.024 7.18 18.41 1.00 36.35 N +ATOM 925 CA VAL A 136 8.331 8.608 18.506 1.00 34.2 C +ATOM 926 C VAL A 136 7.416 9.483 17.592 1.00 33.52 C +ATOM 927 O VAL A 136 7.806 10.578 17.177 1.00 33.32 O +ATOM 928 CB VAL A 136 9.837 8.844 18.221 1.00 34.04 C +ATOM 929 CG1 VAL A 136 10.664 8.339 19.383 1.00 32.79 C +ATOM 930 CG2 VAL A 136 10.27 8.158 16.933 1.00 32.14 C +ATOM 931 N GLY A 137 6.217 8.979 17.268 1.00 31.74 N +ATOM 932 CA GLY A 137 5.338 9.649 16.355 1.00 31.56 C +ATOM 933 C GLY A 137 4.87 10.999 16.854 1.00 32.13 C +ATOM 934 O GLY A 137 4.488 11.812 16.05 1.00 33.24 O +ATOM 935 N PHE A 138 4.881 11.233 18.164 1.00 31.8 N +ATOM 936 CA PHE A 138 4.594 12.553 18.727 1.00 32.13 C +ATOM 937 C PHE A 138 5.379 13.679 18.067 1.00 31.74 C +ATOM 938 O PHE A 138 4.839 14.748 17.822 1.00 32.69 O +ATOM 939 CB PHE A 138 4.805 12.603 20.262 1.00 31.21 C +ATOM 940 CG PHE A 138 4.73 14.01 20.833 1.00 30.3 C +ATOM 941 CD1 PHE A 138 3.473 14.667 20.982 1.00 30.4 C +ATOM 942 CD2 PHE A 138 5.899 14.705 21.159 1.00 28.08 C +ATOM 943 CE1 PHE A 138 3.386 15.978 21.463 1.00 28.19 C +ATOM 944 CE2 PHE A 138 5.823 16.04 21.651 1.00 29.52 C +ATOM 945 CZ PHE A 138 4.564 16.672 21.797 1.00 28.63 C +ATOM 946 N TYR A 139 6.652 13.461 17.768 1.00 32.09 N +ATOM 947 CA TYR A 139 7.449 14.544 17.182 1.00 30.92 C +ATOM 948 C TYR A 139 7.086 14.91 15.726 1.00 30.88 C +ATOM 949 O TYR A 139 7.455 15.996 15.224 1.00 29.92 O +ATOM 950 CB TYR A 139 8.917 14.253 17.362 1.00 32.19 C +ATOM 951 CG TYR A 139 9.323 14.169 18.835 1.00 32.62 C +ATOM 952 CD1 TYR A 139 9.474 15.342 19.588 1.00 35.11 C +ATOM 953 CD2 TYR A 139 9.546 12.923 19.478 1.00 30.82 C +ATOM 954 CE1 TYR A 139 9.826 15.293 20.955 1.00 35.69 C +ATOM 955 CE2 TYR A 139 9.897 12.867 20.832 1.00 32.7 C +ATOM 956 CZ TYR A 139 10.052 14.061 21.555 1.00 35.18 C +ATOM 957 OH TYR A 139 10.408 14.067 22.882 1.00 40.28 O +ATOM 958 N SER A 140 6.31 14.05 15.064 1.00 29.44 N +ATOM 959 CA SER A 140 5.746 14.402 13.754 1.00 28.96 C +ATOM 960 C SER A 140 4.885 15.658 13.824 1.00 29.5 C +ATOM 961 O SER A 140 4.613 16.282 12.795 1.00 29.53 O +ATOM 962 CB SER A 140 4.901 13.256 13.184 1.00 29.49 C +ATOM 963 OG SER A 140 3.707 13.109 13.915 1.00 24.45 O +ATOM 964 N ALA A 141 4.461 16.024 15.047 1.00 29.93 N +ATOM 965 CA ALA A 141 3.787 17.3 15.294 1.00 30.18 C +ATOM 966 C ALA A 141 4.602 18.478 14.777 1.00 30.74 C +ATOM 967 O ALA A 141 4.014 19.461 14.322 1.00 31.95 O +ATOM 968 CB ALA A 141 3.453 17.489 16.787 1.00 29.87 C +ATOM 969 N TYR A 142 5.935 18.362 14.791 1.00 30.08 N +ATOM 970 CA TYR A 142 6.815 19.449 14.34 1.00 29.72 C +ATOM 971 C TYR A 142 6.927 19.618 12.81 1.00 29.55 C +ATOM 972 O TYR A 142 7.618 20.534 12.303 1.00 28.78 O +ATOM 973 CB TYR A 142 8.215 19.371 15.043 1.00 29.68 C +ATOM 974 CG TYR A 142 8.073 19.755 16.481 1.00 30.15 C +ATOM 975 CD1 TYR A 142 8.052 21.118 16.853 1.00 30.2 C +ATOM 976 CD2 TYR A 142 7.795 18.785 17.458 1.00 29.63 C +ATOM 977 CE1 TYR A 142 7.866 21.493 18.184 1.00 27.47 C +ATOM 978 CE2 TYR A 142 7.579 19.147 18.773 1.00 26.81 C +ATOM 979 CZ TYR A 142 7.609 20.503 19.123 1.00 28.08 C +ATOM 980 OH TYR A 142 7.374 20.882 20.429 1.00 27.06 O +ATOM 981 N LEU A 143 6.263 18.723 12.085 1.00 28.62 N +ATOM 982 CA LEU A 143 6.073 18.895 10.639 1.00 28.3 C +ATOM 983 C LEU A 143 5.178 20.103 10.353 1.00 27.7 C +ATOM 984 O LEU A 143 5.355 20.811 9.344 1.00 26.7 O +ATOM 985 CB LEU A 143 5.476 17.63 10.015 1.00 28.25 C +ATOM 986 CG LEU A 143 6.259 16.319 10.044 1.00 27.87 C +ATOM 987 CD1 LEU A 143 5.314 15.213 9.658 1.00 30.32 C +ATOM 988 CD2 LEU A 143 7.51 16.338 9.077 1.00 26.25 C +ATOM 989 N VAL A 144 4.196 20.32 11.228 1.00 28.54 N +ATOM 990 CA VAL A 144 3.178 21.348 10.993 1.00 28.52 C +ATOM 991 C VAL A 144 3.108 22.476 12.045 1.00 29.39 C +ATOM 992 O VAL A 144 2.566 23.543 11.739 1.00 29.28 O +ATOM 993 CB VAL A 144 1.757 20.741 10.78 1.00 28.95 C +ATOM 994 CG1 VAL A 144 1.706 19.75 9.558 1.00 25.57 C +ATOM 995 CG2 VAL A 144 1.233 20.104 12.041 1.00 27.53 C +ATOM 996 N ALA A 145 3.628 22.227 13.263 1.00 28.78 N +ATOM 997 CA ALA A 145 3.543 23.142 14.412 1.00 28.39 C +ATOM 998 C ALA A 145 4.838 23.854 14.661 1.00 28.45 C +ATOM 999 O ALA A 145 5.875 23.204 14.687 1.00 28.2 O +ATOM 1000 CB ALA A 145 3.158 22.374 15.67 1.00 28.17 C +ATOM 1001 N GLU A 146 4.803 25.184 14.843 1.00 29.47 N +ATOM 1002 CA GLU A 146 6.025 25.925 15.223 1.00 29.86 C +ATOM 1003 C GLU A 146 6.304 25.817 16.731 1.00 29.64 C +ATOM 1004 O GLU A 146 7.453 25.985 17.164 1.00 27.93 O +ATOM 1005 CB GLU A 146 6.026 27.383 14.752 1.00 30.38 C +ATOM 1006 CG GLU A 146 5.213 28.31 15.648 1.00 37.24 C +ATOM 1007 CD GLU A 146 4.85 29.66 15.016 1.00 46.42 C +ATOM 1008 OE1 GLU A 146 5.481 30.059 14 1.00 49.72 O +ATOM 1009 OE2 GLU A 146 3.925 30.351 15.551 1.00 48.96 O +ATOM 1010 N LYS A 147 5.243 25.5 17.497 1.00 29.85 N +ATOM 1011 CA LYS A 147 5.285 25.273 18.942 1.00 29.74 C +ATOM 1012 C LYS A 147 4.155 24.281 19.271 1.00 29.66 C +ATOM 1013 O LYS A 147 3.045 24.411 18.74 1.00 29.34 O +ATOM 1014 CB LYS A 147 5.052 26.592 19.717 1.00 31.48 C +ATOM 1015 CG LYS A 147 5.038 26.435 21.261 1.00 28.96 C +ATOM 1016 CD LYS A 147 5.052 27.779 21.976 1.00 33.55 C +ATOM 1017 CE LYS A 147 3.761 28.485 21.703 1.00 36.44 C +ATOM 1018 NZ LYS A 147 3.766 29.823 22.284 1.00 37.98 N +ATOM 1019 N VAL A 148 4.439 23.308 20.144 1.00 29.02 N +ATOM 1020 CA VAL A 148 3.432 22.365 20.641 1.00 28.93 C +ATOM 1021 C VAL A 148 3.339 22.581 22.143 1.00 29.4 C +ATOM 1022 O VAL A 148 4.378 22.613 22.82 1.00 29.21 O +ATOM 1023 CB VAL A 148 3.81 20.872 20.309 1.00 28.84 C +ATOM 1024 CG1 VAL A 148 2.791 19.879 20.91 1.00 26.66 C +ATOM 1025 CG2 VAL A 148 3.973 20.681 18.774 1.00 26.56 C +ATOM 1026 N THR A 149 2.099 22.722 22.636 1.00 29.21 N +ATOM 1027 CA THR A 149 1.773 22.844 24.04 1.00 28.4 C +ATOM 1028 C THR A 149 0.978 21.622 24.508 1.00 28.69 C +ATOM 1029 O THR A 149 -0.039 21.272 23.921 1.00 29.17 O +ATOM 1030 CB THR A 149 0.965 24.146 24.284 1.00 29.51 C +ATOM 1031 OG1 THR A 149 1.617 25.279 23.65 1.00 27.73 O +ATOM 1032 CG2 THR A 149 0.928 24.495 25.829 1.00 28.9 C +ATOM 1033 N VAL A 150 1.44 20.937 25.542 1.00 28.01 N +ATOM 1034 CA VAL A 150 0.731 19.718 25.939 1.00 27.55 C +ATOM 1035 C VAL A 150 0.149 19.927 27.317 1.00 28.17 C +ATOM 1036 O VAL A 150 0.924 20.084 28.265 1.00 27.85 O +ATOM 1037 CB VAL A 150 1.64 18.435 25.933 1.00 27.57 C +ATOM 1038 CG1 VAL A 150 0.847 17.227 26.458 1.00 23.81 C +ATOM 1039 CG2 VAL A 150 2.163 18.153 24.511 1.00 25.36 C +ATOM 1040 N ILE A 151 -1.192 19.932 27.436 1.00 28.65 N +ATOM 1041 CA ILE A 151 -1.847 20.009 28.76 1.00 29.04 C +ATOM 1042 C ILE A 151 -2.301 18.61 29.187 1.00 29.41 C +ATOM 1043 O ILE A 151 -2.966 17.891 28.422 1.00 29.65 O +ATOM 1044 CB ILE A 151 -3.045 21.024 28.778 1.00 29.45 C +ATOM 1045 CG1 ILE A 151 -2.748 22.28 27.937 1.00 30.28 C +ATOM 1046 CG2 ILE A 151 -3.467 21.394 30.249 1.00 27.76 C +ATOM 1047 CD1 ILE A 151 -1.923 23.263 28.606 1.00 31.29 C +ATOM 1048 N THR A 152 -1.931 18.183 30.389 1.00 28.98 N +ATOM 1049 CA THR A 152 -2.21 16.793 30.726 1.00 29.16 C +ATOM 1050 C THR A 152 -2.483 16.599 32.188 1.00 29.34 C +ATOM 1051 O THR A 152 -1.881 17.272 33.031 1.00 30.02 O +ATOM 1052 CB THR A 152 -1.082 15.826 30.195 1.00 29.02 C +ATOM 1053 OG1 THR A 152 -1.489 14.469 30.374 1.00 24.77 O +ATOM 1054 CG2 THR A 152 0.243 15.945 31.036 1.00 29.52 C +ATOM 1055 N LYS A 153 -3.394 15.678 32.477 1.00 30.36 N +ATOM 1056 CA LYS A 153 -3.778 15.347 33.869 1.00 31.22 C +ATOM 1057 C LYS A 153 -3.88 13.834 34.047 1.00 33 C +ATOM 1058 O LYS A 153 -4.732 13.194 33.44 1.00 33.02 O +ATOM 1059 CB LYS A 153 -5.089 16.044 34.263 1.00 30.45 C +ATOM 1060 CG LYS A 153 -5.673 15.725 35.693 1.00 29.63 C +ATOM 1061 CD LYS A 153 -4.956 16.412 36.84 1.00 27.95 C +ATOM 1062 CE LYS A 153 -5.591 16.089 38.2 1.00 28.11 C +ATOM 1063 NZ LYS A 153 -5.356 14.633 38.62 1.00 26.93 N +ATOM 1064 N HIS A 154 -2.976 13.27 34.853 1.00 34.19 N +ATOM 1065 CA HIS A 154 -3.02 11.879 35.267 1.00 35.68 C +ATOM 1066 C HIS A 154 -3.676 11.799 36.652 1.00 37.51 C +ATOM 1067 O HIS A 154 -3.492 12.691 37.486 1.00 37.82 O +ATOM 1068 CB HIS A 154 -1.596 11.341 35.297 1.00 35.44 C +ATOM 1069 CG HIS A 154 -1.495 9.846 35.35 1.00 36.11 C +ATOM 1070 ND1 HIS A 154 -1.798 9.117 36.482 1.00 35.75 N +ATOM 1071 CD2 HIS A 154 -1.058 8.949 34.433 1.00 34.81 C +ATOM 1072 CE1 HIS A 154 -1.594 7.833 36.249 1.00 33.76 C +ATOM 1073 NE2 HIS A 154 -1.144 7.706 35.013 1.00 37.78 N +ATOM 1074 N ASN A 155 -4.473 10.76 36.88 1.00 39.71 N +ATOM 1075 CA ASN A 155 -5.048 10.469 38.195 1.00 42.44 C +ATOM 1076 C ASN A 155 -4.104 10.623 39.418 1.00 43.85 C +ATOM 1077 O ASN A 155 -4.519 11.11 40.482 1.00 43.8 O +ATOM 1078 CB ASN A 155 -5.607 9.043 38.223 1.00 43 C +ATOM 1079 CG ASN A 155 -6.996 8.917 37.616 1.00 43.2 C +ATOM 1080 OD1 ASN A 155 -7.743 9.903 37.372 1.00 43.33 O +ATOM 1081 ND2 ASN A 155 -7.367 7.68 37.395 1.00 43.9 N +ATOM 1082 N ASP A 156 -2.858 10.195 39.275 1.00 44.82 N +ATOM 1083 CA ASP A 156 -1.97 10.167 40.433 1.00 47.35 C +ATOM 1084 C ASP A 156 -1.267 11.507 40.743 1.00 47.17 C +ATOM 1085 O ASP A 156 -0.537 11.626 41.751 1.00 48.12 O +ATOM 1086 CB ASP A 156 -0.934 9.043 40.265 1.00 48.7 C +ATOM 1087 CG ASP A 156 -1.55 7.634 40.45 1.00 52.35 C +ATOM 1088 OD1 ASP A 156 -2.629 7.495 41.107 1.00 55.55 O +ATOM 1089 OD2 ASP A 156 -1.007 6.604 39.987 1.00 55.48 O +ATOM 1090 N ASP A 157 -1.513 12.511 39.9 1.00 45.35 N +ATOM 1091 CA ASP A 157 -0.663 13.684 39.818 1.00 43.43 C +ATOM 1092 C ASP A 157 -1.474 14.99 39.607 1.00 42.18 C +ATOM 1093 O ASP A 157 -2.714 14.972 39.569 1.00 41.95 O +ATOM 1094 CB ASP A 157 0.364 13.448 38.708 1.00 43.37 C +ATOM 1095 CG ASP A 157 1.718 14.059 39.012 1.00 45.54 C +ATOM 1096 OD1 ASP A 157 1.781 15.107 39.7 1.00 43.95 O +ATOM 1097 OD2 ASP A 157 2.794 13.583 38.572 1.00 50.53 O +ATOM 1098 N GLU A 158 -0.773 16.117 39.522 1.00 40.46 N +ATOM 1099 CA GLU A 158 -1.361 17.426 39.241 1.00 39.72 C +ATOM 1100 C GLU A 158 -1.529 17.616 37.737 1.00 37.36 C +ATOM 1101 O GLU A 158 -1.024 16.811 36.952 1.00 36.97 O +ATOM 1102 CB GLU A 158 -0.429 18.527 39.784 1.00 41.39 C +ATOM 1103 CG GLU A 158 -0.317 18.614 41.324 1.00 48.98 C +ATOM 1104 CD GLU A 158 -1.69 18.73 41.982 1.00 59.72 C +ATOM 1105 OE1 GLU A 158 -2.049 17.884 42.868 1.00 63.62 O +ATOM 1106 OE2 GLU A 158 -2.44 19.673 41.584 1.00 64.67 O +ATOM 1107 N GLN A 159 -2.228 18.673 37.326 1.00 35.21 N +ATOM 1108 CA GLN A 159 -2.298 19.049 35.921 1.00 34.44 C +ATOM 1109 C GLN A 159 -1.052 19.847 35.566 1.00 34.58 C +ATOM 1110 O GLN A 159 -0.66 20.746 36.321 1.00 35.99 O +ATOM 1111 CB GLN A 159 -3.563 19.835 35.636 1.00 33.67 C +ATOM 1112 CG GLN A 159 -3.86 20.146 34.19 1.00 34.31 C +ATOM 1113 CD GLN A 159 -5.304 20.6 33.983 1.00 32.97 C +ATOM 1114 OE1 GLN A 159 -6.21 19.893 34.388 1.00 34.54 O +ATOM 1115 NE2 GLN A 159 -5.515 21.745 33.331 1.00 29.63 N +ATOM 1116 N TYR A 160 -0.444 19.524 34.416 1.00 34.28 N +ATOM 1117 CA TYR A 160 0.772 20.188 33.91 1.00 33.64 C +ATOM 1118 C TYR A 160 0.646 20.654 32.488 1.00 33.95 C +ATOM 1119 O TYR A 160 -0.138 20.117 31.702 1.00 33.69 O +ATOM 1120 CB TYR A 160 1.993 19.26 33.931 1.00 33.47 C +ATOM 1121 CG TYR A 160 2.355 18.77 35.32 1.00 34.62 C +ATOM 1122 CD1 TYR A 160 3.114 19.569 36.213 1.00 35.25 C +ATOM 1123 CD2 TYR A 160 1.891 17.529 35.755 1.00 32.48 C +ATOM 1124 CE1 TYR A 160 3.416 19.102 37.503 1.00 35.64 C +ATOM 1125 CE2 TYR A 160 2.183 17.068 36.979 1.00 35.45 C +ATOM 1126 CZ TYR A 160 2.921 17.824 37.858 1.00 37.86 C +ATOM 1127 OH TYR A 160 3.147 17.239 39.078 1.00 40.76 O +ATOM 1128 N ALA A 161 1.451 21.668 32.188 1.00 33.01 N +ATOM 1129 CA ALA A 161 1.672 22.151 30.836 1.00 31.57 C +ATOM 1130 C ALA A 161 3.15 21.969 30.513 1.00 30.56 C +ATOM 1131 O ALA A 161 4.014 22.499 31.231 1.00 29.42 O +ATOM 1132 CB ALA A 161 1.247 23.632 30.695 1.00 30.82 C +ATOM 1133 N TRP A 162 3.377 21.16 29.471 1.00 29.17 N +ATOM 1134 CA TRP A 162 4.632 21.017 28.738 1.00 28.67 C +ATOM 1135 C TRP A 162 4.552 21.871 27.46 1.00 28.13 C +ATOM 1136 O TRP A 162 3.505 21.94 26.814 1.00 29.25 O +ATOM 1137 CB TRP A 162 4.824 19.537 28.375 1.00 27.97 C +ATOM 1138 CG TRP A 162 6.008 19.134 27.536 1.00 25.38 C +ATOM 1139 CD1 TRP A 162 7.121 18.505 27.971 1.00 25.77 C +ATOM 1140 CD2 TRP A 162 6.147 19.241 26.093 1.00 27.93 C +ATOM 1141 NE1 TRP A 162 7.972 18.233 26.915 1.00 27.52 N +ATOM 1142 CE2 TRP A 162 7.409 18.68 25.751 1.00 26.45 C +ATOM 1143 CE3 TRP A 162 5.341 19.779 25.054 1.00 25.59 C +ATOM 1144 CZ2 TRP A 162 7.884 18.614 24.422 1.00 25.29 C +ATOM 1145 CZ3 TRP A 162 5.824 19.711 23.73 1.00 28.07 C +ATOM 1146 CH2 TRP A 162 7.084 19.134 23.436 1.00 27.23 C +ATOM 1147 N GLU A 163 5.64 22.534 27.097 1.00 27.48 N +ATOM 1148 CA GLU A 163 5.66 23.285 25.837 1.00 28 C +ATOM 1149 C GLU A 163 7.089 23.562 25.507 1.00 27.1 C +ATOM 1150 O GLU A 163 7.888 23.714 26.395 1.00 26.58 O +ATOM 1151 CB GLU A 163 4.79 24.538 25.947 1.00 27.42 C +ATOM 1152 CG GLU A 163 5.161 25.638 24.967 1.00 30.23 C +ATOM 1153 CD GLU A 163 4.258 26.85 25.083 1.00 32.37 C +ATOM 1154 OE1 GLU A 163 4.65 27.819 25.764 1.00 39.66 O +ATOM 1155 OE2 GLU A 163 3.159 26.83 24.491 1.00 41.27 O +ATOM 1156 N SER A 164 7.137 23.165 23.955 1.00 27.64 N +ATOM 1157 CA SER A 164 8.406 23.292 23.261 1.00 28.44 C +ATOM 1158 C SER A 164 8.265 23.828 21.806 1.00 30.01 C +ATOM 1159 O SER A 164 7.358 23.439 21.042 1.00 29.27 O +ATOM 1160 CB SER A 164 9.189 21.964 23.258 1.00 28.35 C +ATOM 1161 OG SER A 164 10.411 22.171 22.549 1.00 27.35 O +ATOM 1162 N SER A 165 9.174 24.74 21.459 1.00 30.57 N +ATOM 1163 CA SER A 165 9.322 25.25 20.121 1.00 32.12 C +ATOM 1164 C SER A 165 10.542 24.634 19.453 1.00 31.92 C +ATOM 1165 O SER A 165 11.108 25.227 18.551 1.00 32.06 O +ATOM 1166 CB SER A 165 9.474 26.784 20.137 1.00 32.28 C +ATOM 1167 OG SER A 165 8.291 27.41 20.578 1.00 34.14 O +ATOM 1168 N ALA A 166 10.886 23.415 19.858 1.00 32.53 N +ATOM 1169 CA ALA A 166 12.181 22.801 19.596 1.00 32.9 C +ATOM 1170 C ALA A 166 13.351 23.754 20.051 1.00 33.09 C +ATOM 1171 O ALA A 166 13.261 24.393 21.119 1.00 33.08 O +ATOM 1172 CB ALA A 166 12.281 22.382 18.138 1.00 32.79 C +ATOM 1173 N GLY A 167 14.393 23.879 19.246 1.00 32.77 N +ATOM 1174 CA GLY A 167 15.573 24.649 19.628 1.00 33 C +ATOM 1175 C GLY A 167 16.348 24.073 20.818 1.00 32.7 C +ATOM 1176 O GLY A 167 17.202 24.758 21.42 1.00 33.77 O +ATOM 1177 N GLY A 168 16.023 22.831 21.164 1.00 31.3 N +ATOM 1178 CA GLY A 168 16.782 22.051 22.118 1.00 31.04 C +ATOM 1179 C GLY A 168 16.303 22.082 23.546 1.00 30.8 C +ATOM 1180 O GLY A 168 16.919 21.435 24.395 1.00 30.86 O +ATOM 1181 N SER A 169 15.237 22.852 23.81 1.00 30.66 N +ATOM 1182 CA SER A 169 14.651 22.96 25.146 1.00 30.17 C +ATOM 1183 C SER A 169 13.151 22.783 25.18 1.00 30.39 C +ATOM 1184 O SER A 169 12.438 22.965 24.15 1.00 29.34 O +ATOM 1185 CB SER A 169 15.005 24.283 25.836 1.00 29.62 C +ATOM 1186 OG SER A 169 14.353 25.376 25.217 1.00 31.48 O +ATOM 1187 N PHE A 170 12.693 22.448 26.394 1.00 28.7 N +ATOM 1188 CA PHE A 170 11.288 22.362 26.702 1.00 29.2 C +ATOM 1189 C PHE A 170 10.985 22.915 28.102 1.00 29.53 C +ATOM 1190 O PHE A 170 11.88 22.943 28.982 1.00 28.98 O +ATOM 1191 CB PHE A 170 10.731 20.9 26.472 1.00 28.5 C +ATOM 1192 CG PHE A 170 11.22 19.877 27.454 1.00 29.19 C +ATOM 1193 CD1 PHE A 170 10.496 19.617 28.617 1.00 27.5 C +ATOM 1194 CD2 PHE A 170 12.387 19.107 27.197 1.00 27.9 C +ATOM 1195 CE1 PHE A 170 10.952 18.594 29.524 1.00 27.29 C +ATOM 1196 CE2 PHE A 170 12.833 18.086 28.115 1.00 26.9 C +ATOM 1197 CZ PHE A 170 12.116 17.841 29.257 1.00 23.73 C +ATOM 1198 N THR A 171 9.745 23.361 28.31 1.00 29.27 N +ATOM 1199 CA THR A 171 9.329 23.774 29.645 1.00 29.73 C +ATOM 1200 C THR A 171 8.232 22.894 30.21 1.00 30.98 C +ATOM 1201 O THR A 171 7.419 22.315 29.47 1.00 30.05 O +ATOM 1202 CB THR A 171 8.839 25.206 29.657 1.00 29.62 C +ATOM 1203 OG1 THR A 171 7.661 25.327 28.824 1.00 30.5 O +ATOM 1204 CG2 THR A 171 9.908 26.194 29.049 1.00 31.82 C +ATOM 1205 N VAL A 172 8.206 22.828 31.54 1.00 32.92 N +ATOM 1206 CA VAL A 172 7.108 22.253 32.321 1.00 35.13 C +ATOM 1207 C VAL A 172 6.724 23.222 33.485 1.00 36.76 C +ATOM 1208 O VAL A 172 7.596 23.75 34.183 1.00 34.77 O +ATOM 1209 CB VAL A 172 7.529 20.864 32.925 1.00 35.21 C +ATOM 1210 CG1 VAL A 172 6.46 20.343 33.886 1.00 35.59 C +ATOM 1211 CG2 VAL A 172 7.852 19.835 31.806 1.00 35.27 C +ATOM 1212 N ARG A 173 5.428 23.464 33.656 1.00 38.61 N +ATOM 1213 CA ARG A 173 4.898 24.164 34.823 1.00 40.96 C +ATOM 1214 C ARG A 173 3.619 23.436 35.24 1.00 42.5 C +ATOM 1215 O ARG A 173 2.955 22.766 34.409 1.00 42.58 O +ATOM 1216 CB ARG A 173 4.576 25.617 34.482 1.00 40.95 C +ATOM 1217 CG ARG A 173 3.473 25.75 33.419 1.00 43.02 C +ATOM 1218 CD ARG A 173 3.117 27.18 32.931 1.00 44.91 C +ATOM 1219 NE ARG A 173 2.147 27.127 31.807 1.00 45.58 N +ATOM 1220 CZ ARG A 173 0.805 27.006 31.932 1.00 43.99 C +ATOM 1221 NH1 ARG A 173 0.238 26.926 33.135 0.5 43.11 N +ATOM 1222 NH2 ARG A 173 0.041 26.956 30.843 0.5 42.45 N +ATOM 1223 N THR A 174 3.248 23.56 36.507 1.00 44.23 N +ATOM 1224 CA THR A 174 1.898 23.154 36.879 1.00 46.66 C +ATOM 1225 C THR A 174 0.864 24.11 36.206 1.00 47.74 C +ATOM 1226 O THR A 174 1.042 25.347 36.169 1.00 48.06 O +ATOM 1227 CB THR A 174 1.701 23.1 38.398 1.00 46.37 C +ATOM 1228 OG1 THR A 174 1.757 24.429 38.888 1.00 48.51 O +ATOM 1229 CG2 THR A 174 2.847 22.388 39.106 1.00 45.58 C +ATOM 1230 N ASP A 175 -0.195 23.529 35.643 1.00 48.93 N +ATOM 1231 CA ASP A 175 -1.191 24.317 34.919 1.00 49.79 C +ATOM 1232 C ASP A 175 -2.283 24.776 35.893 1.00 51 C +ATOM 1233 O ASP A 175 -2.967 23.964 36.554 1.00 50.28 O +ATOM 1234 CB ASP A 175 -1.776 23.521 33.748 1.00 49.17 C +ATOM 1235 CG ASP A 175 -2.626 24.378 32.797 1.00 49.28 C +ATOM 1236 OD1 ASP A 175 -2.222 25.511 32.451 1.00 48.18 O +ATOM 1237 OD2 ASP A 175 -3.713 23.987 32.328 1.00 48.34 O +ATOM 1238 N THR A 176 -2.425 26.091 35.987 1.00 52.94 N +ATOM 1239 CA THR A 176 -3.52 26.665 36.759 1.00 54.9 C +ATOM 1240 C THR A 176 -4.727 27.064 35.861 1.00 55.17 C +ATOM 1241 O THR A 176 -5.738 27.556 36.386 1.00 56.36 O +ATOM 1242 CB THR A 176 -3.032 27.85 37.667 1.00 55.51 C +ATOM 1243 OG1 THR A 176 -2.494 28.9 36.846 1.00 56.02 O +ATOM 1244 CG2 THR A 176 -1.862 27.427 38.592 1.00 55.19 C +ATOM 1245 N GLY A 177 -4.628 26.837 34.541 1.00 54.49 N +ATOM 1246 CA GLY A 177 -5.755 26.984 33.627 1.00 53.7 C +ATOM 1247 C GLY A 177 -6.997 26.168 34.002 1.00 53.37 C +ATOM 1248 O GLY A 177 -7.087 25.609 35.109 1.00 52.68 O +ATOM 1249 N GLU A 178 -7.967 26.111 33.085 1.00 53 N +ATOM 1250 CA GLU A 178 -9.157 25.265 33.255 1.00 52.85 C +ATOM 1251 C GLU A 178 -8.771 23.873 33.873 1.00 52.24 C +ATOM 1252 O GLU A 178 -7.954 23.14 33.285 1.00 52.08 O +ATOM 1253 CB GLU A 178 -9.877 25.079 31.919 1.00 52.67 C +ATOM 1254 CG GLU A 178 -11.19 24.323 32.022 1.00 55.62 C +ATOM 1255 CD GLU A 178 -12.297 25.159 32.633 1.00 56.77 C +ATOM 1256 OE1 GLU A 178 -12.436 26.337 32.244 1.00 63.72 O +ATOM 1257 OE2 GLU A 178 -13.026 24.637 33.502 1.00 65.08 O +ATOM 1258 N PRO A 179 -9.166 23.254 34.735 1.00 51.39 N +ATOM 1259 CA PRO A 179 -8.962 21.849 35.105 1.00 50.61 C +ATOM 1260 C PRO A 179 -9.594 20.94 34.08 1.00 49.8 C +ATOM 1261 O PRO A 179 -10.675 21.295 33.633 1.00 50.14 O +ATOM 1262 CB PRO A 179 -9.732 21.711 36.429 1.00 50.77 C +ATOM 1263 CG PRO A 179 -9.842 23.072 36.977 1.00 50.87 C +ATOM 1264 CD PRO A 179 -9.841 24.003 35.811 1.00 51.75 C +ATOM 1265 N MET A 180 -8.985 19.796 33.754 1.00 48.58 N +ATOM 1266 CA MET A 180 -9.489 18.963 32.656 1.00 47.66 C +ATOM 1267 C MET A 180 -10.053 17.575 33.032 1.00 46.54 C +ATOM 1268 O MET A 180 -10.582 16.82 32.165 1.00 47.48 O +ATOM 1269 CB MET A 180 -8.472 18.896 31.501 1.00 47.67 C +ATOM 1270 CG MET A 180 -7.141 18.182 31.798 1.00 48.43 C +ATOM 1271 SD MET A 180 -5.989 18.54 30.424 1.00 48.84 S +ATOM 1272 CE MET A 180 -6.844 17.68 29.131 1.00 48.81 C +ATOM 1273 N GLY A 181 -9.984 17.239 34.316 1.00 44.37 N +ATOM 1274 CA GLY A 181 -10.573 15.994 34.761 1.00 41.51 C +ATOM 1275 C GLY A 181 -9.552 14.864 34.702 1.00 39.61 C +ATOM 1276 O GLY A 181 -9.088 14.413 35.748 1.00 39.53 O +ATOM 1277 N ARG A 182 -9.242 14.393 33.492 1.00 36.99 N +ATOM 1278 CA ARG A 182 -8.197 13.367 33.204 1.00 34.11 C +ATOM 1279 C ARG A 182 -8.053 13.364 31.693 1.00 33.48 C +ATOM 1280 O ARG A 182 -9.073 13.43 30.988 1.00 33.52 O +ATOM 1281 CB ARG A 182 -8.599 11.943 33.708 1.00 34.28 C +ATOM 1282 CG ARG A 182 -7.554 10.823 33.531 1.00 28.96 C +ATOM 1283 CD ARG A 182 -8.126 9.418 33.501 1.00 28.4 C +ATOM 1284 NE ARG A 182 -9.121 9.278 32.438 1.00 30.92 N +ATOM 1285 CZ ARG A 182 -10.436 9.118 32.61 1.00 31.5 C +ATOM 1286 NH1 ARG A 182 -10.956 9.073 33.827 1.00 32.71 N +ATOM 1287 NH2 ARG A 182 -11.251 9.034 31.537 1.00 32.74 N +ATOM 1288 N GLY A 183 -6.82 13.275 31.188 1.00 31.84 N +ATOM 1289 CA GLY A 183 -6.601 13.324 29.758 1.00 30.04 C +ATOM 1290 C GLY A 183 -5.431 14.184 29.332 1.00 29.33 C +ATOM 1291 O GLY A 183 -4.543 14.489 30.122 1.00 29.82 O +ATOM 1292 N THR A 184 -5.447 14.57 28.06 1.00 28.17 N +ATOM 1293 CA THR A 184 -4.352 15.243 27.388 1.00 25.93 C +ATOM 1294 C THR A 184 -4.931 16.126 26.298 1.00 25.83 C +ATOM 1295 O THR A 184 -5.819 15.693 25.564 1.00 24.49 O +ATOM 1296 CB THR A 184 -3.36 14.202 26.782 1.00 26.09 C +ATOM 1297 OG1 THR A 184 -2.825 13.382 27.826 1.00 25.19 O +ATOM 1298 CG2 THR A 184 -2.118 14.89 26.102 1.00 24.78 C +ATOM 1299 N LYS A 185 -4.421 17.365 26.216 1.00 26.16 N +ATOM 1300 CA LYS A 185 -4.635 18.267 25.058 1.00 26.82 C +ATOM 1301 C LYS A 185 -3.275 18.514 24.454 1.00 26.85 C +ATOM 1302 O LYS A 185 -2.322 18.89 25.162 1.00 26.2 O +ATOM 1303 CB LYS A 185 -5.214 19.632 25.46 1.00 26.39 C +ATOM 1304 CG LYS A 185 -6.435 19.543 26.305 1.00 30.42 C +ATOM 1305 CD LYS A 185 -6.913 20.867 26.939 1.00 38.85 C +ATOM 1306 CE LYS A 185 -7.179 21.983 25.92 1.00 46.18 C +ATOM 1307 NZ LYS A 185 -6.814 23.332 26.506 1.00 50.94 N +ATOM 1308 N VAL A 186 -3.177 18.294 23.151 1.00 27.37 N +ATOM 1309 CA VAL A 186 -1.976 18.666 22.433 1.00 27.44 C +ATOM 1310 C VAL A 186 -2.341 19.848 21.546 1.00 28.56 C +ATOM 1311 O VAL A 186 -3.131 19.704 20.61 1.00 26.92 O +ATOM 1312 CB VAL A 186 -1.45 17.507 21.572 1.00 28.36 C +ATOM 1313 CG1 VAL A 186 -0.304 18.015 20.673 1.00 24.71 C +ATOM 1314 CG2 VAL A 186 -1.059 16.328 22.486 1.00 27.66 C +ATOM 1315 N ILE A 187 -1.798 21.02 21.882 1.00 29.24 N +ATOM 1316 CA ILE A 187 -2.144 22.236 21.163 1.00 29.91 C +ATOM 1317 C ILE A 187 -1.071 22.503 20.101 1.00 30.14 C +ATOM 1318 O ILE A 187 0.055 22.835 20.431 1.00 30.51 O +ATOM 1319 CB ILE A 187 -2.312 23.437 22.137 1.00 30.5 C +ATOM 1320 CG1 ILE A 187 -3.293 23.077 23.247 1.00 29.6 C +ATOM 1321 CG2 ILE A 187 -2.748 24.738 21.373 1.00 27.92 C +ATOM 1322 CD1 ILE A 187 -3.299 24.077 24.326 1.00 29.6 C +ATOM 1323 N LEU A 188 -1.419 22.299 18.835 1.00 30.72 N +ATOM 1324 CA LEU A 188 -0.483 22.519 17.75 1.00 30.08 C +ATOM 1325 C LEU A 188 -0.62 23.97 17.364 1.00 31.98 C +ATOM 1326 O LEU A 188 -1.678 24.359 16.867 1.00 33.63 O +ATOM 1327 CB LEU A 188 -0.778 21.631 16.552 1.00 28.88 C +ATOM 1328 CG LEU A 188 -0.715 20.135 16.783 1.00 27.09 C +ATOM 1329 CD1 LEU A 188 -1.256 19.448 15.57 1.00 27.54 C +ATOM 1330 CD2 LEU A 188 0.71 19.678 17.138 1.00 23.21 C +ATOM 1331 N HIS A 189 0.423 24.775 17.641 1.00 32.16 N +ATOM 1332 CA HIS A 189 0.524 26.145 17.112 1.00 32.46 C +ATOM 1333 C HIS A 189 1.122 26.091 15.716 1.00 32.28 C +ATOM 1334 O HIS A 189 2.321 25.923 15.538 1.00 32.64 O +ATOM 1335 CB HIS A 189 1.397 27.011 18.007 1.00 32.1 C +ATOM 1336 CG HIS A 189 0.942 27.052 19.44 1.00 33.53 C +ATOM 1337 ND1 HIS A 189 0.04 27.983 19.913 1.00 34.83 N +ATOM 1338 CD2 HIS A 189 1.256 26.269 20.495 1.00 33.63 C +ATOM 1339 CE1 HIS A 189 -0.165 27.783 21.202 1.00 35.52 C +ATOM 1340 NE2 HIS A 189 0.568 26.751 21.582 1.00 35.62 N +ATOM 1341 N LEU A 190 0.275 26.219 14.711 1.00 32.37 N +ATOM 1342 CA LEU A 190 0.697 25.911 13.346 1.00 32.33 C +ATOM 1343 C LEU A 190 1.639 26.947 12.73 1.00 33.28 C +ATOM 1344 O LEU A 190 1.574 28.168 13.041 1.00 32 O +ATOM 1345 CB LEU A 190 -0.533 25.714 12.459 1.00 31.49 C +ATOM 1346 CG LEU A 190 -1.495 24.553 12.773 1.00 31.53 C +ATOM 1347 CD1 LEU A 190 -2.674 24.591 11.847 1.00 29.86 C +ATOM 1348 CD2 LEU A 190 -0.815 23.169 12.665 1.00 30.9 C +ATOM 1349 N LYS A 191 2.523 26.442 11.859 1.00 34.33 N +ATOM 1350 CA LYS A 191 3.329 27.288 11.006 1.00 35.66 C +ATOM 1351 C LYS A 191 2.389 28.019 10.061 1.00 37.47 C +ATOM 1352 O LYS A 191 1.381 27.433 9.595 1.00 37.74 O +ATOM 1353 CB LYS A 191 4.3 26.451 10.19 1.00 34.47 C +ATOM 1354 CG LYS A 191 5.39 25.729 10.977 1.00 36.66 C +ATOM 1355 CD LYS A 191 6.216 24.824 10.043 1.00 39.06 C +ATOM 1356 CE LYS A 191 6.796 23.628 10.78 1.00 41.1 C +ATOM 1357 NZ LYS A 191 8.001 23.028 10.127 1.00 43.72 N +ATOM 1358 N GLU A 192 2.75 29.264 9.738 1.00 38.73 N +ATOM 1359 CA GLU A 192 2.039 30.124 8.767 1.00 40.53 C +ATOM 1360 C GLU A 192 1.515 29.474 7.487 1.00 39.59 C +ATOM 1361 O GLU A 192 0.41 29.788 7.023 1.00 39.54 O +ATOM 1362 CB GLU A 192 2.942 31.292 8.375 1.00 41.85 C +ATOM 1363 CG GLU A 192 2.326 32.355 7.453 1.00 48.2 C +ATOM 1364 CD GLU A 192 3.204 33.596 7.42 1.00 56.86 C +ATOM 1365 OE1 GLU A 192 4.284 33.513 6.798 1.00 61.81 O +ATOM 1366 OE2 GLU A 192 2.859 34.623 8.06 1.00 60.48 O +ATOM 1367 N ASP A 193 2.311 28.579 6.907 1.00 39.99 N +ATOM 1368 CA ASP A 193 1.945 27.94 5.651 1.00 39.7 C +ATOM 1369 C ASP A 193 1.312 26.556 5.864 1.00 38.34 C +ATOM 1370 O ASP A 193 1.19 25.766 4.916 1.00 37.86 O +ATOM 1371 CB ASP A 193 3.164 27.907 4.693 1.00 41.58 C +ATOM 1372 CG ASP A 193 4.344 27.079 5.245 1.00 44.08 C +ATOM 1373 OD1 ASP A 193 4.309 26.676 6.438 1.00 48.43 O +ATOM 1374 OD2 ASP A 193 5.357 26.781 4.558 1.00 47.59 O +ATOM 1375 N GLN A 194 0.887 26.285 7.099 1.00 37.04 N +ATOM 1376 CA GLN A 194 0.232 25.012 7.453 1.00 35.15 C +ATOM 1377 C GLN A 194 -1.253 25.142 7.878 1.00 35.27 C +ATOM 1378 O GLN A 194 -1.851 24.188 8.384 1.00 34.9 O +ATOM 1379 CB GLN A 194 1.031 24.295 8.535 1.00 34.02 C +ATOM 1380 CG GLN A 194 2.411 23.79 8.135 1.00 33.56 C +ATOM 1381 CD GLN A 194 2.524 23.177 6.706 1.00 33.41 C +ATOM 1382 OE1 GLN A 194 3.406 23.561 5.945 1.00 37.09 O +ATOM 1383 NE2 GLN A 194 1.685 22.225 6.382 1.00 29.82 N +ATOM 1384 N THR A 195 -1.845 26.32 7.654 1.00 35.16 N +ATOM 1385 CA THR A 195 -3.233 26.604 8.024 1.00 34.6 C +ATOM 1386 C THR A 195 -4.281 25.768 7.297 1.00 34.4 C +ATOM 1387 O THR A 195 -5.47 25.829 7.639 1.00 33.95 O +ATOM 1388 CB THR A 195 -3.571 28.086 7.903 1.00 35 C +ATOM 1389 OG1 THR A 195 -3.348 28.514 6.559 1.00 36.56 O +ATOM 1390 CG2 THR A 195 -2.592 28.937 8.719 1.00 36.43 C +ATOM 1391 N GLU A 196 -3.857 24.933 6.357 1.00 34.02 N +ATOM 1392 CA GLU A 196 -4.811 24.015 5.74 1.00 34.85 C +ATOM 1393 C GLU A 196 -5.399 23.022 6.759 1.00 34.77 C +ATOM 1394 O GLU A 196 -6.515 22.494 6.582 1.00 34.32 O +ATOM 1395 CB GLU A 196 -4.166 23.293 4.554 1.00 34.56 C +ATOM 1396 CG GLU A 196 -3.26 22.149 4.939 1.00 34.93 C +ATOM 1397 CD GLU A 196 -2.479 21.594 3.75 1.00 35.78 C +ATOM 1398 OE1 GLU A 196 -1.485 22.217 3.365 1.00 35.57 O +ATOM 1399 OE2 GLU A 196 -2.854 20.533 3.198 1.00 36.39 O +ATOM 1400 N TYR A 197 -4.642 22.764 7.824 1.00 34.39 N +ATOM 1401 CA TYR A 197 -5.073 21.816 8.851 1.00 34.77 C +ATOM 1402 C TYR A 197 -6.069 22.414 9.838 1.00 35.55 C +ATOM 1403 O TYR A 197 -6.462 21.782 10.806 1.00 36.38 O +ATOM 1404 CB TYR A 197 -3.858 21.205 9.528 1.00 34.23 C +ATOM 1405 CG TYR A 197 -2.996 20.51 8.503 1.00 32.18 C +ATOM 1406 CD1 TYR A 197 -3.448 19.358 7.861 1.00 26.35 C +ATOM 1407 CD2 TYR A 197 -1.752 21.037 8.142 1.00 28.39 C +ATOM 1408 CE1 TYR A 197 -2.663 18.725 6.88 1.00 28.46 C +ATOM 1409 CE2 TYR A 197 -0.948 20.419 7.168 1.00 29.64 C +ATOM 1410 CZ TYR A 197 -1.419 19.263 6.538 1.00 31.89 C +ATOM 1411 OH TYR A 197 -0.645 18.639 5.596 1.00 29.73 O +ATOM 1412 N LEU A 198 -6.494 23.636 9.554 1.00 36.73 N +ATOM 1413 CA LEU A 198 -7.568 24.301 10.269 1.00 38.78 C +ATOM 1414 C LEU A 198 -8.874 24.222 9.48 1.00 39.36 C +ATOM 1415 O LEU A 198 -9.906 24.668 9.956 1.00 40.84 O +ATOM 1416 CB LEU A 198 -7.218 25.789 10.499 1.00 39.55 C +ATOM 1417 CG LEU A 198 -6.016 26.209 11.373 1.00 40.31 C +ATOM 1418 CD1 LEU A 198 -5.775 27.696 11.268 1.00 36.21 C +ATOM 1419 CD2 LEU A 198 -6.206 25.798 12.819 1.00 43.67 C +ATOM 1420 N GLU A 199 -8.833 23.693 8.265 1.00 39.82 N +ATOM 1421 CA GLU A 199 -10.013 23.661 7.405 1.00 40.76 C +ATOM 1422 C GLU A 199 -10.749 22.351 7.602 1.00 40.25 C +ATOM 1423 O GLU A 199 -10.133 21.257 7.662 1.00 39.91 O +ATOM 1424 CB GLU A 199 -9.608 23.763 5.933 1.00 41.36 C +ATOM 1425 CG GLU A 199 -9.537 25.154 5.323 1.00 46.28 C +ATOM 1426 CD GLU A 199 -8.73 25.094 4.024 1.00 57.58 C +ATOM 1427 OE1 GLU A 199 -9.058 24.242 3.117 1.00 60.32 O +ATOM 1428 OE2 GLU A 199 -7.732 25.865 3.915 1.00 62.41 O +ATOM 1429 N GLU A 200 -12.069 22.448 7.649 1.00 40.26 N +ATOM 1430 CA GLU A 200 -12.88 21.303 8.052 1.00 40.64 C +ATOM 1431 C GLU A 200 -12.77 20.094 7.09 1.00 38.95 C +ATOM 1432 O GLU A 200 -12.565 18.949 7.513 1.00 38.94 O +ATOM 1433 CB GLU A 200 -14.329 21.747 8.212 1.00 41.43 C +ATOM 1434 CG GLU A 200 -15.309 20.586 8.32 1.00 45.15 C +ATOM 1435 CD GLU A 200 -16.696 21.061 8.616 1.00 52.21 C +ATOM 1436 OE1 GLU A 200 -17.093 22.138 8.081 1.00 54.26 O +ATOM 1437 OE2 GLU A 200 -17.378 20.356 9.397 1.00 56.67 O +ATOM 1438 N ARG A 201 -12.927 20.379 5.807 1.00 38.09 N +ATOM 1439 CA ARG A 201 -12.861 19.385 4.747 1.00 38.76 C +ATOM 1440 C ARG A 201 -11.508 18.675 4.747 1.00 36.97 C +ATOM 1441 O ARG A 201 -11.435 17.477 4.467 1.00 36.7 O +ATOM 1442 CB ARG A 201 -13.137 20.046 3.387 1.00 39.16 C +ATOM 1443 CG ARG A 201 -12.137 21.172 3.114 1.00 45.4 C +ATOM 1444 CD ARG A 201 -12.683 22.428 2.449 1.00 52.46 C +ATOM 1445 NE ARG A 201 -11.596 23.281 1.959 1.00 56.96 N +ATOM 1446 CZ ARG A 201 -11.611 23.912 0.786 1.00 59.54 C +ATOM 1447 NH1 ARG A 201 -12.652 23.792 -0.034 1.00 58.61 N +ATOM 1448 NH2 ARG A 201 -10.576 24.661 0.428 1.00 61.96 N +ATOM 1449 N ARG A 202 -10.457 19.424 5.091 1.00 35.59 N +ATOM 1450 CA ARG A 202 -9.085 18.912 5.148 1.00 33.69 C +ATOM 1451 C ARG A 202 -8.959 17.893 6.263 1.00 32.7 C +ATOM 1452 O ARG A 202 -8.464 16.792 6.02 1.00 31.66 O +ATOM 1453 CB ARG A 202 -8.058 20.067 5.301 1.00 32.79 C +ATOM 1454 CG ARG A 202 -6.595 19.697 5.12 1.00 30.88 C +ATOM 1455 CD ARG A 202 -6.251 18.804 3.909 1.00 31.92 C +ATOM 1456 NE ARG A 202 -4.802 18.57 3.782 1.00 28.94 N +ATOM 1457 CZ ARG A 202 -4.234 17.402 3.482 1.00 32.43 C +ATOM 1458 NH1 ARG A 202 -4.978 16.328 3.242 1.00 32.78 N +ATOM 1459 NH2 ARG A 202 -2.91 17.302 3.395 1.00 31.92 N +ATOM 1460 N ILE A 203 -9.411 18.26 7.47 1.00 32.36 N +ATOM 1461 CA ILE A 203 -9.326 17.376 8.649 1.00 32.1 C +ATOM 1462 C ILE A 203 -10.137 16.092 8.433 1.00 33.56 C +ATOM 1463 O ILE A 203 -9.594 14.994 8.69 1.00 33.53 O +ATOM 1464 CB ILE A 203 -9.686 18.107 10.007 1.00 32.08 C +ATOM 1465 CG1 ILE A 203 -8.801 19.338 10.232 1.00 31.33 C +ATOM 1466 CG2 ILE A 203 -9.582 17.153 11.2 1.00 31.39 C +ATOM 1467 CD1 AILE A 203 -9.411 20.395 11.153 0.5 30.26 C +ATOM 1468 CD1 BILE A 203 -7.302 19.028 10.193 0.5 33.01 C +ATOM 1469 N LYS A 204 -11.389 16.232 7.938 1.00 33.56 N +ATOM 1470 CA LYS A 204 -12.281 15.108 7.616 1.00 34.28 C +ATOM 1471 C LYS A 204 -11.587 14.151 6.663 1.00 33.98 C +ATOM 1472 O LYS A 204 -11.546 12.938 6.899 1.00 32.94 O +ATOM 1473 CB LYS A 204 -13.54 15.591 6.878 1.00 34.96 C +ATOM 1474 CG LYS A 204 -14.759 15.911 7.688 1.00 39.81 C +ATOM 1475 CD LYS A 204 -16.021 15.874 6.811 1.00 45.14 C +ATOM 1476 CE LYS A 204 -17.196 16.493 7.559 1.00 48.99 C +ATOM 1477 NZ LYS A 204 -17.194 17.979 7.449 1.00 53.39 N +ATOM 1478 N GLU A 205 -11.077 14.716 5.558 1.00 33.57 N +ATOM 1479 CA GLU A 205 -10.288 13.958 4.597 1.00 33.94 C +ATOM 1480 C GLU A 205 -9.183 13.075 5.269 1.00 33.45 C +ATOM 1481 O GLU A 205 -9.138 11.855 5.086 1.00 32.75 O +ATOM 1482 CB GLU A 205 -9.706 14.909 3.552 1.00 33.81 C +ATOM 1483 CG GLU A 205 -8.987 14.194 2.432 1.00 34.04 C +ATOM 1484 CD GLU A 205 -8.306 15.169 1.516 1.00 32.78 C +ATOM 1485 OE1 GLU A 205 -7.708 16.135 2.041 1.00 30 O +ATOM 1486 OE2 GLU A 205 -8.401 14.963 0.277 1.00 33.41 O +ATOM 1487 N ILE A 206 -8.323 13.71 6.066 1.00 33.46 N +ATOM 1488 CA ILE A 206 -7.215 13.011 6.744 1.00 33.18 C +ATOM 1489 C ILE A 206 -7.707 11.953 7.756 1.00 32.79 C +ATOM 1490 O ILE A 206 -7.222 10.814 7.782 1.00 30.95 O +ATOM 1491 CB ILE A 206 -6.313 14.04 7.44 1.00 33.23 C +ATOM 1492 CG1 ILE A 206 -5.707 14.999 6.407 1.00 35.67 C +ATOM 1493 CG2 ILE A 206 -5.202 13.347 8.201 1.00 33.74 C +ATOM 1494 CD1 ILE A 206 -5.09 16.288 7.036 1.00 38.3 C +ATOM 1495 N VAL A 207 -8.68 12.339 8.595 1.00 32.41 N +ATOM 1496 CA VAL A 207 -9.261 11.385 9.531 1.00 32.19 C +ATOM 1497 C VAL A 207 -9.813 10.197 8.793 1.00 33.1 C +ATOM 1498 O VAL A 207 -9.553 9.082 9.177 1.00 32.92 O +ATOM 1499 CB VAL A 207 -10.284 12.01 10.498 1.00 32.48 C +ATOM 1500 CG1 VAL A 207 -11.01 10.906 11.334 1.00 29.37 C +ATOM 1501 CG2 VAL A 207 -9.586 13.044 11.381 1.00 29.36 C +ATOM 1502 N LYS A 208 -10.517 10.43 7.696 1.00 35 N +ATOM 1503 CA LYS A 208 -11.107 9.334 6.95 1.00 37.22 C +ATOM 1504 C LYS A 208 -10.036 8.442 6.281 1.00 37.55 C +ATOM 1505 O LYS A 208 -10.187 7.226 6.202 1.00 36.61 O +ATOM 1506 CB LYS A 208 -12.107 9.883 5.929 1.00 37.2 C +ATOM 1507 CG LYS A 208 -12.557 8.853 4.906 1.00 42.63 C +ATOM 1508 CD LYS A 208 -13.692 9.431 4.022 1.00 52.47 C +ATOM 1509 CE LYS A 208 -13.995 8.505 2.807 1.00 56.46 C +ATOM 1510 NZ LYS A 208 -15.484 8.309 2.617 1.00 59.81 N +ATOM 1511 N LYS A 209 -8.967 9.077 5.794 1.00 38.6 N +ATOM 1512 CA LYS A 209 -7.86 8.411 5.097 1.00 39.11 C +ATOM 1513 C LYS A 209 -6.956 7.533 6.001 1.00 38.82 C +ATOM 1514 O LYS A 209 -6.557 6.428 5.606 1.00 37.98 O +ATOM 1515 CB LYS A 209 -7.051 9.481 4.34 1.00 39.59 C +ATOM 1516 CG LYS A 209 -5.703 9.069 3.788 1.00 42.63 C +ATOM 1517 CD LYS A 209 -5.163 10.141 2.825 1.00 47.3 C +ATOM 1518 CE LYS A 209 -4.092 9.531 1.873 1.00 52 C +ATOM 1519 NZ LYS A 209 -3.273 10.538 1.042 1.00 52.16 N +ATOM 1520 N HIS A 210 -6.661 8.013 7.214 1.00 38.47 N +ATOM 1521 CA HIS A 210 -5.632 7.414 8.072 1.00 38.41 C +ATOM 1522 C HIS A 210 -6.141 6.928 9.403 1.00 38.55 C +ATOM 1523 O HIS A 210 -5.452 6.14 10.069 1.00 38.63 O +ATOM 1524 CB HIS A 210 -4.513 8.424 8.374 1.00 38.74 C +ATOM 1525 CG HIS A 210 -3.761 8.89 7.164 1.00 39.66 C +ATOM 1526 ND1 HIS A 210 -3.04 8.031 6.352 1.00 41.26 N +ATOM 1527 CD2 HIS A 210 -3.602 10.127 6.637 1.00 40.04 C +ATOM 1528 CE1 HIS A 210 -2.468 8.725 5.382 1.00 39.6 C +ATOM 1529 NE2 HIS A 210 -2.802 9.995 5.525 1.00 40.5 N +ATOM 1530 N SER A 211 -7.318 7.4 9.811 1.00 38.36 N +ATOM 1531 CA SER A 211 -7.788 7.168 11.182 1.00 38.98 C +ATOM 1532 C SER A 211 -9.214 6.648 11.262 1.00 39.51 C +ATOM 1533 O SER A 211 -9.891 6.827 12.276 1.00 38.52 O +ATOM 1534 CB SER A 211 -7.634 8.443 12.034 1.00 38.5 C +ATOM 1535 OG SER A 211 -6.276 8.836 12.134 1.00 38.2 O +ATOM 1536 N GLN A 212 -9.657 5.992 10.196 1.00 40.48 N +ATOM 1537 CA GLN A 212 -11.07 5.585 10.068 1.00 41.71 C +ATOM 1538 C GLN A 212 -11.509 4.476 11.014 1.00 40.7 C +ATOM 1539 O GLN A 212 -12.672 4.377 11.298 1.00 40.94 O +ATOM 1540 CB GLN A 212 -11.413 5.226 8.618 1.00 42.2 C +ATOM 1541 CG GLN A 212 -10.749 3.948 8.115 1.00 48.07 C +ATOM 1542 CD GLN A 212 -9.452 4.135 7.278 1.00 54.59 C +ATOM 1543 OE1 GLN A 212 -8.344 4.364 7.825 1.00 58.9 O +ATOM 1544 NE2 GLN A 212 -9.583 3.974 5.962 1.00 55.73 N +ATOM 1545 N PHE A 213 -10.577 3.678 11.543 1.00 40.31 N +ATOM 1546 CA PHE A 213 -10.929 2.537 12.394 1.00 39.19 C +ATOM 1547 C PHE A 213 -10.567 2.681 13.871 1.00 38.14 C +ATOM 1548 O PHE A 213 -10.378 1.697 14.583 1.00 37.48 O +ATOM 1549 CB PHE A 213 -10.333 1.26 11.796 1.00 39.07 C +ATOM 1550 CG PHE A 213 -10.953 0.897 10.484 1.00 40.96 C +ATOM 1551 CD1 PHE A 213 -12.353 0.765 10.384 1.00 40.66 C +ATOM 1552 CD2 PHE A 213 -10.162 0.74 9.343 1.00 40.19 C +ATOM 1553 CE1 PHE A 213 -12.976 0.445 9.159 1.00 44.6 C +ATOM 1554 CE2 PHE A 213 -10.753 0.427 8.111 1.00 42.34 C +ATOM 1555 CZ PHE A 213 -12.175 0.277 8.007 1.00 42.99 C +ATOM 1556 N ILE A 214 -10.463 3.921 14.323 1.00 36.76 N +ATOM 1557 CA ILE A 214 -10.191 4.179 15.73 1.00 35.02 C +ATOM 1558 C ILE A 214 -11.442 3.829 16.589 1.00 34.98 C +ATOM 1559 O ILE A 214 -12.563 4.175 16.207 1.00 36.29 O +ATOM 1560 CB ILE A 214 -9.687 5.632 15.895 1.00 34.7 C +ATOM 1561 CG1 ILE A 214 -8.361 5.819 15.107 1.00 31.52 C +ATOM 1562 CG2 ILE A 214 -9.629 6.054 17.4 1.00 31.51 C +ATOM 1563 CD1 ILE A 214 -7.081 5.33 15.834 1.00 29.48 C +ATOM 1564 N GLY A 215 -11.256 3.128 17.707 1.00 33.5 N +ATOM 1565 CA GLY A 215 -12.378 2.627 18.49 1.00 33.93 C +ATOM 1566 C GLY A 215 -12.975 3.518 19.571 1.00 34.01 C +ATOM 1567 O GLY A 215 -13.419 3.03 20.644 1.00 34.21 O +ATOM 1568 N TYR A 216 -12.971 4.826 19.313 1.00 32.64 N +ATOM 1569 CA TYR A 216 -13.522 5.816 20.227 1.00 31.96 C +ATOM 1570 C TYR A 216 -14.132 6.822 19.305 1.00 31.35 C +ATOM 1571 O TYR A 216 -13.684 6.959 18.163 1.00 31.68 O +ATOM 1572 CB TYR A 216 -12.405 6.457 21.08 1.00 31.5 C +ATOM 1573 CG TYR A 216 -11.697 5.417 21.929 1.00 32.7 C +ATOM 1574 CD1 TYR A 216 -12.247 5.01 23.164 1.00 30.99 C +ATOM 1575 CD2 TYR A 216 -10.518 4.774 21.472 1.00 30.91 C +ATOM 1576 CE1 TYR A 216 -11.642 4.03 23.931 1.00 28.85 C +ATOM 1577 CE2 TYR A 216 -9.904 3.775 22.232 1.00 27.36 C +ATOM 1578 CZ TYR A 216 -10.475 3.416 23.473 1.00 30.43 C +ATOM 1579 OH TYR A 216 -9.906 2.441 24.265 1.00 24.4 O +ATOM 1580 N PRO A 217 -15.154 7.532 19.757 1.00 31.33 N +ATOM 1581 CA PRO A 217 -15.707 8.596 18.92 1.00 31.13 C +ATOM 1582 C PRO A 217 -14.663 9.71 18.809 1.00 31.01 C +ATOM 1583 O PRO A 217 -14.001 10.058 19.784 1.00 30.26 O +ATOM 1584 CB PRO A 217 -16.977 9.044 19.68 1.00 32.68 C +ATOM 1585 CG PRO A 217 -16.987 8.305 21.006 1.00 29.33 C +ATOM 1586 CD PRO A 217 -15.827 7.421 21.069 1.00 31.25 C +ATOM 1587 N ILE A 218 -14.467 10.181 17.586 1.00 31.5 N +ATOM 1588 CA ILE A 218 -13.636 11.327 17.261 1.00 30.96 C +ATOM 1589 C ILE A 218 -14.542 12.45 16.748 1.00 31.47 C +ATOM 1590 O ILE A 218 -15.064 12.371 15.635 1.00 31.1 O +ATOM 1591 CB ILE A 218 -12.641 10.969 16.152 1.00 31.08 C +ATOM 1592 CG1 ILE A 218 -11.762 9.77 16.549 1.00 29.82 C +ATOM 1593 CG2 ILE A 218 -11.852 12.231 15.651 1.00 30.28 C +ATOM 1594 CD1 ILE A 218 -10.824 9.348 15.419 1.00 25.38 C +ATOM 1595 N THR A 219 -14.693 13.514 17.533 1.00 32.16 N +ATOM 1596 CA THR A 219 -15.546 14.632 17.117 1.00 32.39 C +ATOM 1597 C THR A 219 -14.735 15.773 16.502 1.00 33.06 C +ATOM 1598 O THR A 219 -13.888 16.398 17.166 1.00 32.8 O +ATOM 1599 CB THR A 219 -16.314 15.117 18.313 1.00 32.9 C +ATOM 1600 OG1 THR A 219 -17.034 13.998 18.865 1.00 33.71 O +ATOM 1601 CG2 THR A 219 -17.418 16.193 17.932 1.00 33.1 C +ATOM 1602 N LEU A 220 -14.991 16.048 15.221 1.00 33.22 N +ATOM 1603 CA LEU A 220 -14.387 17.202 14.59 1.00 32.9 C +ATOM 1604 C LEU A 220 -15.249 18.465 14.808 1.00 33.33 C +ATOM 1605 O LEU A 220 -16.263 18.688 14.125 1.00 33.18 O +ATOM 1606 CB LEU A 220 -14.074 16.914 13.109 1.00 32.99 C +ATOM 1607 CG LEU A 220 -13.577 18.142 12.302 1.00 33.45 C +ATOM 1608 CD1 LEU A 220 -12.25 18.801 12.845 1.00 29.85 C +ATOM 1609 CD2 LEU A 220 -13.507 17.839 10.826 1.00 34.27 C +ATOM 1610 N PHE A 221 -14.83 19.277 15.781 1.00 34.37 N +ATOM 1611 CA PHE A 221 -15.433 20.571 16.079 1.00 34.55 C +ATOM 1612 C PHE A 221 -15.277 21.5 14.868 1.00 36.71 C +ATOM 1613 O PHE A 221 -14.238 21.495 14.191 1.00 36.16 O +ATOM 1614 CB PHE A 221 -14.726 21.255 17.261 1.00 32.62 C +ATOM 1615 CG PHE A 221 -15.111 20.761 18.64 1.00 31.8 C +ATOM 1616 CD1 PHE A 221 -15.183 19.383 18.953 1.00 29.05 C +ATOM 1617 CD2 PHE A 221 -15.287 21.681 19.679 1.00 28.36 C +ATOM 1618 CE1 PHE A 221 -15.497 18.954 20.256 1.00 29.7 C +ATOM 1619 CE2 PHE A 221 -15.588 21.264 20.975 1.00 28.14 C +ATOM 1620 CZ PHE A 221 -15.716 19.897 21.27 1.00 29.68 C +ATOM 1621 N VAL A 222 -16.293 22.324 14.611 1.00 39.23 N +ATOM 1622 CA VAL A 222 -16.212 23.301 13.531 1.00 42.36 C +ATOM 1623 C VAL A 222 -16.122 24.734 14.097 1.00 43.95 C +ATOM 1624 O VAL A 222 -16.483 24.967 15.232 1.00 44.45 O +ATOM 1625 CB VAL A 222 -17.369 23.118 12.521 1.00 42.33 C +ATOM 1626 CG1 VAL A 222 -17.476 21.681 12.113 1.00 43.39 C +ATOM 1627 CG2 VAL A 222 -18.668 23.536 13.095 1.00 43.17 C +ATOM 1628 N GLU A 223 -15.619 25.69 13.33 1.00 46.7 N +ATOM 1629 CA GLU A 223 -15.616 27.088 13.785 1.00 48.98 C +ATOM 1630 C GLU A 223 -16.912 27.809 13.411 1.00 50.28 C +ATOM 1631 O GLU A 223 -17.365 27.664 12.274 1.00 51.03 O +ATOM 1632 CB GLU A 223 -14.45 27.765 13.143 1.00 49.32 C +ATOM 1633 CG GLU A 223 -13.156 27.273 13.733 1.00 51.67 C +ATOM 1634 CD GLU A 223 -12.051 28.292 13.569 1.00 52.76 C +ATOM 1635 OE1 GLU A 223 -12.191 29.432 14.068 1.00 54.3 O +ATOM 1636 OE2 GLU A 223 -11.062 27.934 12.915 1.00 52.94 O +ATOM 1637 N LYS A 224 -17.505 28.648 14.271 1.00 51.92 N +ATOM 1638 CA LYS A 224 -16.885 29.44 15.328 1.00 53.33 C +ATOM 1639 C LYS A 224 -17.851 29.626 16.544 1.00 53.43 C +ATOM 1640 O LYS A 224 -17.476 29.652 17.739 1.00 53.09 O +ATOM 1641 CB LYS A 224 -16.523 30.812 14.719 1.00 53.99 C +ATOM 1642 CG LYS A 224 -17.281 31.147 13.409 1.00 56.86 C +ATOM 1643 CD LYS A 224 -16.362 31.743 12.34 1.00 61.95 C +ATOM 1644 CE LYS A 224 -16.928 31.544 10.938 1.00 65.16 C +ATOM 1645 NZ LYS A 224 -17.737 32.741 10.567 1.00 68.63 N +TER 1646 LYS A 224 +HETATM 1675 O HOH A2001 4.778 29.512 37.657 1.00 54.43 O +HETATM 1676 O HOH A2002 12.988 28.364 33.758 1.00 35 O +HETATM 1677 O HOH A2003 11.954 19.365 38.208 1.00 49.01 O +HETATM 1678 O HOH A2004 7.896 22.666 36.69 1.00 47.22 O +HETATM 1679 O HOH A2005 12.472 28.761 29.185 1.00 49.37 O +HETATM 1680 O HOH A2006 21.069 17.158 26.967 1.00 42.15 O +HETATM 1681 O HOH A2007 8.951 29.79 27.688 1.00 49.9 O +HETATM 1682 O HOH A2008 14.352 14.367 26.438 1.00 21.37 O +HETATM 1683 O HOH A2009 17.637 8.274 28.623 1.00 62.81 O +HETATM 1684 O HOH A2010 14.976 13.929 25.643 1.00 54.75 O +HETATM 1685 O HOH A2011 21.704 17.297 19.307 1.00 53.57 O +HETATM 1686 O HOH A2012 21.846 14.362 25.838 1.00 45.3 O +HETATM 1687 O HOH A2013 21.449 19.049 22.151 1.00 47.18 O +HETATM 1688 O HOH A2014 24.152 10.961 25.48 1.00 84.86 O +HETATM 1689 O HOH A2015 21.287 11.19 25.729 1.00 50.5 O +HETATM 1690 O HOH A2016 16.164 20.546 18.117 1.00 41.56 O +HETATM 1691 O HOH A2017 19.13 16.583 12.799 1.00 35.17 O +HETATM 1692 O HOH A2018 10.833 21.983 3.687 1.00 67.05 O +HETATM 1693 O HOH A2019 15.996 19.235 -0.248 1.00 43.11 O +HETATM 1694 O HOH A2020 7.102 21.165 2.242 1.00 40.87 O +HETATM 1695 O HOH A2021 -10.516 22.938 24.74 1.00 54.67 O +HETATM 1696 O HOH A2022 10.194 17.165 7.273 1.00 34.62 O +HETATM 1697 O HOH A2023 8.95 20.352 8.786 1.00 53.9 O +HETATM 1698 O HOH A2024 -1.109 4.738 13.481 1.00 42.05 O +HETATM 1699 O HOH A2025 10.22 23.215 8.502 1.00 58.77 O +HETATM 1700 O HOH A2026 18.647 18.276 10.596 1.00 40.11 O +HETATM 1701 O HOH A2027 -0.647 6.105 26.836 1.00 35.14 O +HETATM 1702 O HOH A2028 -0.489 2.162 14.886 1.00 47.46 O +HETATM 1703 O HOH A2029 -0.358 1.243 17.523 1.00 80.49 O +HETATM 1704 O HOH A2030 -0.833 -0.613 28.526 1.00 67.66 O +HETATM 1705 O HOH A2031 10.445 20.73 5.962 1.00 46.86 O +HETATM 1706 O HOH A2032 -5.87 -2.878 25.987 1.00 42.58 O +HETATM 1707 O HOH A2033 -5.285 -1.029 18.112 1.00 68.61 O +HETATM 1708 O HOH A2034 -7.052 -3.416 23.234 1.00 47.91 O +HETATM 1709 O HOH A2035 15.871 17.512 2.73 1.00 48.69 O +HETATM 1710 O HOH A2036 0.549 3.645 26.292 1.00 37.03 O +HETATM 1711 O HOH A2037 8.086 18.406 6.168 1.00 42.68 O +HETATM 1712 O HOH A2038 9.03 19.846 3.612 1.00 38.35 O +HETATM 1713 O HOH A2039 5.372 9.342 2.355 1.00 50.4 O +HETATM 1714 O HOH A2040 -15.745 1.778 27.329 1.00 44.93 O +HETATM 1715 O HOH A2041 -4.125 -2.574 35.456 1.00 50.2 O +HETATM 1716 O HOH A2042 0.387 16.601 3.168 1.00 27.19 O +HETATM 1717 O HOH A2043 5.973 20.507 -0.38 1.00 32.43 O +HETATM 1718 O HOH A2044 3.074 19.618 2.66 1.00 28.19 O +HETATM 1719 O HOH A2045 1.847 17.366 0.103 1.00 40.03 O +HETATM 1720 O HOH A2046 -0.654 10.404 3.454 1.00 41.08 O +HETATM 1721 O HOH A2047 1.109 8.142 4.238 1.00 40.27 O +HETATM 1722 O HOH A2048 8.191 22.748 6.787 1.00 45.12 O +HETATM 1723 O HOH A2049 -2.85 11.536 9.304 1.00 45.46 O +HETATM 1724 O HOH A2050 -2.544 14.965 0.229 1.00 45.27 O +HETATM 1725 O HOH A2051 -17.988 11.45 21.853 1.00 56.55 O +HETATM 1726 O HOH A2052 -11.345 17.007 28.556 1.00 54.67 O +HETATM 1727 O HOH A2053 -13.576 15.242 30.504 1.00 77.05 O +HETATM 1728 O HOH A2054 -11.137 20.856 25.299 1.00 58.04 O +HETATM 1729 O HOH A2055 -4.219 26.459 27.41 1.00 53.76 O +HETATM 1730 O HOH A2056 -9.154 26.071 20.731 1.00 33.94 O +HETATM 1731 O HOH A2057 -6.642 30.38 8.521 1.00 53.04 O +HETATM 1732 O HOH A2058 -15.066 7.151 7.617 1.00 51.14 O +HETATM 1733 O HOH A2059 1.315 5.529 14.331 1.00 42.88 O +HETATM 1734 O HOH A2060 2.342 7.7 17.038 1.00 37.97 O +HETATM 1735 O HOH A2061 -3.283 28.168 22.787 1.00 59.13 O +HETATM 1736 O HOH A2062 -9.135 24.293 23.245 1.00 44.71 O +HETATM 1737 O HOH A2063 -2.425 13.103 21.146 1.00 23.38 O +HETATM 1738 O HOH A2064 -9.411 19.344 28.268 1.00 64.51 O +HETATM 1739 O HOH A2065 0.577 5.674 18.958 1.00 28.87 O +HETATM 1740 O HOH A2066 -3.783 3.367 16.749 1.00 50.98 O +HETATM 1741 O HOH A2067 1.807 4.635 16.776 1.00 36.39 O +HETATM 1742 O HOH A2068 -0.188 7.263 24.146 1.00 27.16 O +HETATM 1743 O HOH A2069 7.422 13.532 39.634 1.00 59.16 O +HETATM 1744 O HOH A2070 -5.058 2.351 20.664 1.00 36.64 O +HETATM 1745 O HOH A2071 1.191 5.297 22.486 1.00 39.51 O +HETATM 1746 O HOH A2072 -1.99 1.629 18.051 1.00 75.12 O +HETATM 1747 O HOH A2073 -2.646 0.813 25.907 1.00 52.42 O +HETATM 1748 O HOH A2074 19.258 1.837 21.178 1.00 48.8 O +HETATM 1749 O HOH A2075 17.614 0.3 24.241 1.00 72.51 O +HETATM 1750 O HOH A2076 19.881 4.514 22.7 1.00 47.16 O +HETATM 1751 O HOH A2077 12.214 1.106 17.894 1.00 39.45 O +HETATM 1752 O HOH A2078 -6.607 -1.759 20.826 1.00 46.61 O +HETATM 1753 O HOH A2079 -8.326 -1.232 26.85 1.00 41.3 O +HETATM 1754 O HOH A2080 -9.483 0.862 20.918 1.00 36.92 O +HETATM 1755 O HOH A2081 -3.385 -0.294 22.806 1.00 45.91 O +HETATM 1756 O HOH A2082 1.806 1.703 33.59 1.00 48.78 O +HETATM 1757 O HOH A2083 0.738 5.41 32.144 1.00 49.47 O +HETATM 1758 O HOH A2084 2.742 0.915 29.766 1.00 60.82 O +HETATM 1759 O HOH A2085 1.911 3.853 28.932 1.00 52.41 O +HETATM 1760 O HOH A2086 -13.976 -1.719 25.627 1.00 68.82 O +HETATM 1761 O HOH A2087 -14.376 1.299 24.947 1.00 41.39 O +HETATM 1762 O HOH A2088 10.81 11.311 1.445 1.00 42.89 O +HETATM 1763 O HOH A2089 13.889 11.094 5.028 1.00 50.68 O +HETATM 1764 O HOH A2090 -6.646 -3.774 34.676 1.00 45.1 O +HETATM 1765 O HOH A2091 -1.672 0.417 32.828 1.00 57.5 O +HETATM 1766 O HOH A2092 -4.626 5.905 36.491 1.00 37.16 O +HETATM 1767 O HOH A2093 -5.874 -0.821 37.686 1.00 27.03 O +HETATM 1768 O HOH A2094 -1.168 0.26 37.144 1.00 51.9 O +HETATM 1769 O HOH A2095 -14.246 0.36 32.112 1.00 74.84 O +HETATM 1770 O HOH A2096 9.836 25.317 13.448 1.00 50.2 O +HETATM 1771 O HOH A2097 9.881 28.657 16.368 1.00 58.35 O +HETATM 1772 O HOH A2098 2.144 6.106 36.347 1.00 56.01 O +HETATM 1773 O HOH A2099 -11.579 11.978 40.658 1.00 53.35 O +HETATM 1774 O HOH A2100 15.504 28.725 20.531 1.00 80.32 O +HETATM 1775 O HOH A2101 21.741 25.776 19.244 1.00 74.98 O +HETATM 1776 O HOH A2102 -18.196 11.09 33.541 1.00 81.34 O +HETATM 1777 O HOH A2103 -16.337 4.476 30.526 1.00 43.33 O +HETATM 1778 O HOH A2104 -14.205 11.607 30.829 1.00 39.54 O +HETATM 1779 O HOH A2105 -17.815 13.868 28.191 1.00 59.76 O +HETATM 1780 O HOH A2106 -16.253 5.75 24.002 1.00 45.33 O +HETATM 1781 O HOH A2107 -17.103 6.525 26.454 1.00 43.05 O +HETATM 1782 O HOH A2108 -14.661 15.886 27.684 1.00 89.3 O +HETATM 1783 O HOH A2109 -18.02 11.371 25.118 1.00 45.67 O +HETATM 1784 O HOH A2110 -12.975 19.239 24.657 1.00 38.33 O +HETATM 1785 O HOH A2111 -17.423 15.611 22.543 1.00 43.01 O +HETATM 1786 O HOH A2112 -13.209 17.222 26.526 1.00 47.11 O +HETATM 1787 O HOH A2113 -16.662 16.84 24.883 1.00 42.07 O +HETATM 1788 O HOH A2114 -1.702 26.882 27.141 1.00 60.86 O +HETATM 1789 O HOH A2115 -12.782 26.619 17.849 1.00 28.98 O +HETATM 1790 O HOH A2116 -2.986 26.126 2.597 1.00 40.02 O +HETATM 1791 O HOH A2117 -13.132 30.209 20.658 1.00 59.91 O +HETATM 1792 O HOH A2118 -11.561 27.177 20.09 1.00 35.1 O +HETATM 1793 O HOH A2119 -7.465 28.197 7.037 1.00 65.79 O +HETATM 1794 O HOH A2120 -5.144 30.958 11.118 1.00 42 O +HETATM 1795 O HOH A2121 -16.644 15.393 10.629 1.00 43.31 O +HETATM 1796 O HOH A2122 -10.128 36.87 14.896 1.00 64.05 O +HETATM 1797 O HOH A2123 -4.638 35.03 12.944 1.00 72.43 O +HETATM 1798 O HOH A2124 -11.743 32.252 17.792 1.00 61.63 O +HETATM 1799 O HOH A2125 -15.069 21.264 1.726 1.00 89.77 O +HETATM 1800 O HOH A2126 -16.037 12.155 7.128 1.00 62.54 O +HETATM 1801 O HOH A2127 -13.234 8.665 9.5 1.00 75.82 O +HETATM 1802 O HOH A2128 -14.045 13.837 11.322 1.00 46.35 O +HETATM 1803 O HOH A2129 -12.122 12.653 1.5 1.00 44.57 O +HETATM 1804 O HOH A2130 -9.964 8.053 2.16 1.00 51.03 O +HETATM 1805 O HOH A2131 -14.082 9.226 12.04 1.00 41.6 O +HETATM 1806 O HOH A2132 -14.292 1.128 13.558 1.00 52.75 O +HETATM 1807 O HOH A2133 -7.397 24.931 18.895 1.00 25.45 O +HETATM 1808 O HOH A2134 -5.857 27.621 20.946 1.00 49.26 O +HETATM 1809 O HOH A2135 -16.555 4.661 14.232 1.00 61.78 O +HETATM 1810 O HOH A2136 -15.541 0.409 19.181 1.00 42.65 O +HETATM 1811 O HOH A2137 -16.901 1.137 22.056 1.00 54.1 O +HETATM 1812 O HOH A2138 -6.667 24.475 23.05 1.00 51.17 O +HETATM 1813 O HOH A2139 -4.269 14.474 22.999 1.00 34.23 O +HETATM 1814 O HOH A2140 -9.3 15.761 28.521 1.00 44.92 O +HETATM 1815 O HOH A2141 -4.73 8.541 34.911 1.00 26.61 O +HETATM 1816 O HOH A2142 5.4 8.298 36.25 1.00 25.38 O +HETATM 1817 O HOH A2143 10.093 17.424 39.666 1.00 41.38 O +HETATM 1818 O HOH A2144 5.993 20.588 39.799 1.00 58.23 O +HETATM 1819 O HOH A2145 5.537 12.293 38.542 1.00 32.02 O +HETATM 1820 O HOH A2146 1.334 8.021 30.981 1.00 29.11 O +HETATM 1821 O HOH A2147 10.142 16.334 27.558 1.00 35.56 O +HETATM 1822 O HOH A2148 11.433 11.365 37.64 1.00 71.75 O +HETATM 1823 O HOH A2149 10.378 8.367 37.404 1.00 58.49 O +HETATM 1824 O HOH A2150 10.525 12.569 29.18 1.00 29.65 O +HETATM 1825 O HOH A2151 14.583 8.316 30.611 1.00 40.46 O +HETATM 1826 O HOH A2152 18.028 5.276 29.037 1.00 66.18 O +HETATM 1827 O HOH A2153 16.995 4.262 31.616 1.00 60.7 O +HETATM 1828 O HOH A2154 10.997 3.614 29.142 1.00 81.46 O +HETATM 1829 O HOH A2155 10.849 3.841 21.568 1.00 56.09 O +HETATM 1830 O HOH A2156 19.007 8.048 19.975 1.00 48.53 O +HETATM 1831 O HOH A2157 15.427 3.581 25.132 1.00 66.37 O +HETATM 1832 O HOH A2158 17.434 1.467 19.076 1.00 37.41 O +HETATM 1833 O HOH A2159 13.342 1.183 20.16 1.00 48.16 O +HETATM 1834 O HOH A2160 18.157 3.726 24.77 1.00 56.57 O +HETATM 1835 O HOH A2161 21.124 7.748 15.936 1.00 61.9 O +HETATM 1836 O HOH A2162 16.191 0.663 12.401 1.00 40.86 O +HETATM 1837 O HOH A2163 19.138 0.63 16.937 1.00 73.28 O +HETATM 1838 O HOH A2164 20.295 0.264 8.797 1.00 59.88 O +HETATM 1839 O HOH A2165 20.375 5.401 3.906 1.00 61.16 O +HETATM 1840 O HOH A2166 11.237 10.888 4.65 1.00 42.1 O +HETATM 1841 O HOH A2167 3.805 5.751 11.244 1.00 34.31 O +HETATM 1842 O HOH A2168 4.325 5.061 6.29 1.00 57.26 O +HETATM 1843 O HOH A2169 3.017 1.717 14.843 1.00 56 O +HETATM 1844 O HOH A2170 6.075 -0.401 6.827 1.00 60.4 O +HETATM 1845 O HOH A2171 7.472 -1.507 15.744 1.00 44.85 O +HETATM 1846 O HOH A2172 3.964 3.59 9.142 1.00 55 O +HETATM 1847 O HOH A2173 6.584 -2.092 13.412 1.00 56.47 O +HETATM 1848 O HOH A2174 5.034 9.041 20.463 1.00 32.5 O +HETATM 1849 O HOH A2175 8.816 1.334 19.474 1.00 47.71 O +HETATM 1850 O HOH A2176 4.574 6.927 18.116 1.00 25.83 O +HETATM 1851 O HOH A2177 10.409 12.601 24.727 1.00 43.66 O +HETATM 1852 O HOH A2178 8.523 23.324 13.494 1.00 44.37 O +HETATM 1853 O HOH A2179 5.532 30.088 10.921 1.00 37.43 O +HETATM 1854 O HOH A2180 1.101 29.529 15.784 1.00 29.83 O +HETATM 1855 O HOH A2181 9.788 26.215 16 1.00 41.07 O +HETATM 1856 O HOH A2182 -6.972 12.937 37.116 1.00 33.97 O +HETATM 1857 O HOH A2183 -0.103 5.414 34.602 1.00 54.95 O +HETATM 1858 O HOH A2184 -7.173 11.698 40.927 1.00 47.23 O +HETATM 1859 O HOH A2185 -4.984 6.057 39.81 1.00 55.79 O +HETATM 1860 O HOH A2186 -4.237 8.841 42.533 1.00 44.3 O +HETATM 1861 O HOH A2187 -1.407 4.495 38.452 1.00 55.3 O +HETATM 1862 O HOH A2188 -3.305 13.313 42.212 1.00 84.7 O +HETATM 1863 O HOH A2189 1.168 15.264 42.789 1.00 49.48 O +HETATM 1864 O HOH A2190 -0.847 14.662 35.616 1.00 23.71 O +HETATM 1865 O HOH A2191 -4.319 19.801 39.3 1.00 37.47 O +HETATM 1866 O HOH A2192 -7.616 18.74 36.584 1.00 65.86 O +HETATM 1867 O HOH A2193 3.465 19.296 40.383 1.00 63.3 O +HETATM 1868 O HOH A2194 1.333 28.87 24.378 1.00 70.72 O +HETATM 1869 O HOH A2195 4.343 29.914 25.155 1.00 45.06 O +HETATM 1870 O HOH A2196 13.5 27.231 18.679 1.00 41.76 O +HETATM 1871 O HOH A2197 8.705 27.905 23.069 1.00 41.5 O +HETATM 1872 O HOH A2198 7.533 29.537 19.012 1.00 39.28 O +HETATM 1873 O HOH A2199 11.591 25.62 23.008 1.00 32.13 O +HETATM 1874 O HOH A2200 12.803 27.841 21.861 1.00 60.84 O +HETATM 1875 O HOH A2201 17.685 27.156 20.327 1.00 50.94 O +HETATM 1876 O HOH A2202 18.397 24.545 24.254 1.00 38.18 O +HETATM 1877 O HOH A2203 19.748 24.259 21.511 1.00 64.77 O +HETATM 1878 O HOH A2204 15.259 22.84 16.185 1.00 32.83 O +HETATM 1879 O HOH A2205 14.456 20.727 20.14 1.00 26.26 O +HETATM 1880 O HOH A2206 18.418 21.782 19.045 1.00 40.53 O +HETATM 1881 O HOH A2207 19.103 19.982 23.917 1.00 51.51 O +HETATM 1882 O HOH A2208 15.893 26.745 23.675 1.00 53.76 O +HETATM 1883 O HOH A2209 11.496 26.089 25.805 1.00 50.31 O +HETATM 1884 O HOH A2210 5.484 24.7 30.127 1.00 30.61 O +HETATM 1885 O HOH A2211 4.861 27.584 30.356 1.00 73.52 O +HETATM 1886 O HOH A2212 4.842 25.338 37.848 1.00 53.99 O +HETATM 1887 O HOH A2213 6.059 22.771 38.637 1.00 48.54 O +HETATM 1888 O HOH A2214 -5.85 23.339 36.686 1.00 43.19 O +HETATM 1889 O HOH A2215 -13.194 23.124 34.147 1.00 53.36 O +HETATM 1890 O HOH A2216 -11.994 27.472 34.604 1.00 45.84 O +HETATM 1891 O HOH A2217 -9.294 15.166 38.329 1.00 53.86 O +HETATM 1892 O HOH A2218 -11.922 13.104 31.578 1.00 40.46 O +HETATM 1893 O HOH A2219 -7.039 23.366 29.914 1.00 45.47 O +HETATM 1894 O HOH A2220 -0.966 27.632 24.198 1.00 43.61 O +HETATM 1895 O HOH A2221 -1.157 24.886 4.273 1.00 32.49 O +HETATM 1896 O HOH A2222 6.393 25.353 6.878 1.00 57.5 O +HETATM 1897 O HOH A2223 6.179 28.541 7.462 1.00 53.96 O +HETATM 1898 O HOH A2224 1.144 20.465 4.752 1.00 32.64 O +HETATM 1899 O HOH A2225 4.367 21.926 3.168 1.00 52.65 O +HETATM 1900 O HOH A2226 -1.161 28.427 5.221 1.00 49.82 O +HETATM 1901 O HOH A2227 -2.726 20.501 0.424 1.00 50.06 O +HETATM 1902 O HOH A2228 -3.365 23.86 0.752 1.00 42 O +HETATM 1903 O HOH A2229 -6.845 22.399 2.059 1.00 41.96 O +HETATM 1904 O HOH A2230 -6.113 24.922 1.965 1.00 66.72 O +HETATM 1905 O HOH A2231 -13.152 25.391 7.669 1.00 38.85 O +HETATM 1906 O HOH A2232 -20.021 20.375 9.667 1.00 51.9 O +HETATM 1907 O HOH A2233 -16.857 17.997 11.227 1.00 47.1 O +HETATM 1908 O HOH A2234 -19.579 19.193 11.567 1.00 37.43 O +HETATM 1909 O HOH A2235 -13.433 16.374 3.186 1.00 40.55 O +HETATM 1910 O HOH A2236 -13.753 23.192 5.045 1.00 54.04 O +HETATM 1911 O HOH A2237 -14.296 22.088 -0.407 1.00 54.2 O +HETATM 1912 O HOH A2238 -13.858 11.668 8.502 1.00 39.59 O +HETATM 1913 O HOH A2239 -10.487 10.971 2.541 1.00 49.91 O +HETATM 1914 O HOH A2240 -6.136 13.324 -0.689 1.00 29.55 O +HETATM 1915 O HOH A2241 -10.77 14.051 -0.516 1.00 29.47 O +HETATM 1916 O HOH A2242 -13.547 13.749 3.438 1.00 46.27 O +HETATM 1917 O HOH A2243 -6.164 10.274 -0.652 1.00 50.21 O +HETATM 1918 O HOH A2244 -4.414 13.331 0.98 1.00 54.74 O +HETATM 1919 O HOH A2245 -4.495 4.748 12.906 1.00 59.29 O +HETATM 1920 O HOH A2246 -2.868 5.01 6.854 1.00 65.33 O +HETATM 1921 O HOH A2247 -4.848 7.465 13.817 1.00 33.29 O +HETATM 1922 O HOH A2248 -5.806 10.937 10.441 1.00 33.85 O +HETATM 1923 O HOH A2249 -12.434 7.222 13.248 1.00 44.31 O +HETATM 1924 O HOH A2250 -12.292 3.228 6.254 1.00 46.56 O +HETATM 1925 O HOH A2251 -12.933 0.653 15.493 1.00 55.76 O +HETATM 1926 O HOH A2252 -7.425 3.651 11.393 1.00 49.4 O +HETATM 1927 O HOH A2253 -15.667 2.353 16.537 1.00 48.83 O +HETATM 1928 O HOH A2254 -14.203 4.023 13.793 1.00 55.29 O +HETATM 1929 O HOH A2255 -8.91 2.256 18.8 1.00 27.19 O +HETATM 1930 O HOH A2256 -12.111 0.937 21.508 1.00 42.14 O +HETATM 1931 O HOH A2257 -15.196 3.337 22.794 1.00 56.3 O +HETATM 1932 O HOH A2258 -14.02 6.692 15.272 1.00 36.03 O +HETATM 1933 O HOH A2259 -15.82 8.635 15.258 1.00 48.89 O +HETATM 1934 O HOH A2260 -14.436 11.474 12.847 1.00 53.76 O +HETATM 1935 O HOH A2261 -17.055 12.102 13.987 1.00 75.78 O +HETATM 1936 O HOH A2262 -15.582 12.969 20.948 1.00 36.01 O +HETATM 1937 O HOH A2263 -19.588 14.668 20.348 1.00 42.41 O +HETATM 1938 O HOH A2264 -18.771 11.921 16.656 1.00 59.12 O +HETATM 1939 O HOH A2265 -18.998 19.038 14.123 1.00 25.46 O +HETATM 1940 O HOH A2266 -17.162 15.167 13.855 1.00 33.99 O +HETATM 1941 O HOH A2267 -18.412 16.582 12.221 1.00 75.59 O +HETATM 1942 O HOH A2268 -13.488 21.839 11.276 1.00 44.02 O +HETATM 1943 O HOH A2269 -15.43 25.662 17.661 1.00 23.79 O +HETATM 1944 O HOH A2270 -19.669 27.838 11.043 1.00 55.23 O +HETATM 1945 O HOH A2271 -13.97 28.921 16.85 1.00 43.7 O +HETATM 1946 O HOH A2272 -14.122 24.194 10.828 1.00 46.83 O +HETATM 1947 O HOH A2273 -15.641 29.536 19.646 1.00 44.88 O +HETATM 1948 O HOH A2274 -15.315 34.381 13.316 1.00 73.75 O +HETATM 1949 O HOH A2275 -4.07 10.974 27.786 1.00 24.3 O +HETATM 1950 O HOH A2276 0.252 12.61 21.263 1.00 35.14 O +HETATM 1951 O HOH A2277 1.253 7.731 28.429 1.00 29.26 O +TER 1952 HOH A2277 +END \ No newline at end of file diff --git a/data/1UYD/PU8.pdb b/data/1UYD/PU8.pdb new file mode 100644 index 0000000..22f5e58 --- /dev/null +++ b/data/1UYD/PU8.pdb @@ -0,0 +1,28 @@ +HETATM 1647 C19 PU8 A1224 4.403 15.528 26.579 1.00 43.34 C +HETATM 1648 O3 PU8 A1224 5.122 15.084 25.453 1.00 38.5 O +HETATM 1649 C5 PU8 A1224 5.091 13.786 24.846 1.00 38.08 C +HETATM 1650 C6 PU8 A1224 3.959 13.178 24.291 1.00 36.1 C +HETATM 1651 C4 PU8 A1224 6.324 13.153 24.774 1.00 37.95 C +HETATM 1652 O1 PU8 A1224 7.437 13.8 25.363 1.00 41.22 O +HETATM 1653 C7 PU8 A1224 7.968 14.982 24.74 1.00 44.16 C +HETATM 1654 C3 PU8 A1224 6.465 11.928 24.166 1.00 36.97 C +HETATM 1655 O2 PU8 A1224 7.738 11.276 24.146 1.00 36.02 O +HETATM 1656 C8 PU8 A1224 8.125 10.375 23.092 1.00 36.99 C +HETATM 1657 C2 PU8 A1224 5.35 11.326 23.613 1.00 35.51 C +HETATM 1658 C1 PU8 A1224 4.106 11.922 23.656 1.00 35.3 C +HETATM 1659 C9 PU8 A1224 2.981 11.134 22.997 1.00 34.84 C +HETATM 1660 C10 PU8 A1224 1.799 10.898 23.897 1.00 34.9 C +HETATM 1661 N1 PU8 A1224 0.569 11.343 23.665 1.00 31.78 N +HETATM 1662 C11 PU8 A1224 -0.186 10.943 24.666 1.00 32.61 C +HETATM 1663 C13 PU8 A1224 -1.534 11.121 24.945 1.00 30.23 C +HETATM 1664 N5 PU8 A1224 -2.439 11.829 24.033 1.00 30.89 N +HETATM 1665 C12 PU8 A1224 0.625 10.27 25.567 1.00 36.14 C +HETATM 1666 N4 PU8 A1224 0.118 9.78 26.717 1.00 30.6 N +HETATM 1667 C14 PU8 A1224 -1.195 9.985 26.947 1.00 31.1 C +HETATM 1668 N3 PU8 A1224 -2.004 10.644 26.089 1.00 29.53 N +HETATM 1669 N2 PU8 A1224 1.854 10.251 25.056 1.00 37.25 N +HETATM 1670 C15 PU8 A1224 3.053 9.635 25.638 1.00 39.92 C +HETATM 1671 C16 PU8 A1224 2.913 8.131 25.636 1.00 43.4 C +HETATM 1672 C17 PU8 A1224 3.644 7.266 24.612 1.00 47.59 C +HETATM 1673 C18 PU8 A1224 4.665 7.894 23.678 1.00 49.42 C +HETATM 1674 CL PU8 A1224 2.419 14.003 24.367 1.00 31.27 CL diff --git a/data/1UYD/ligands_smiles.txt b/data/1UYD/ligands_smiles.txt new file mode 100644 index 0000000..a87ee7f --- /dev/null +++ b/data/1UYD/ligands_smiles.txt @@ -0,0 +1,15 @@ +C#CCCCn1c(Cc2cc(OC)c(OC)c(OC)c2Cl)nc2c(N)ncnc21 +CCCCn1c(Cc2cc(OC)c(OC)c(OC)c2)nc2c(N)ncnc21 +CCCCn1c(Cc2cc(OC)ccc2OC)nc2c(N)ncnc21 +CCCCn1c(Cc2cccc(OC)c2)nc2c(N)ncnc21 +C#CCCCn1c(Cc2cc(OC)c(OC)c(OC)c2Cl)nc2c(N)nc(F)nc21 +CCCCn1c(Cc2ccc(OC)cc2)nc2c(N)ncnc21 +CCCCn1c(Cc2ccc3c(c2)OCO3)nc2c(N)ncnc21 +CCCCn1c(Cc2cc(OC)ccc2OC)nc2c(N)nc(F)nc21 +CCCCn1c(Cc2ccc3c(c2)OCO3)nc2c(N)nc(F)nc21 +C#CCCCn1c(Cc2cc(OC)ccc2OC)nc2c(N)nc(F)nc21 +CC(C)NCCCn1c(Cc2cc3c(cc2I)OCO3)nc2c(N)nc(F)nc21 +CC(C)NCCCn1c(Sc2cc3c(cc2Br)OCO3)nc2c(N)ncnc21 +CC(C)NCCCn1c(Sc2cc3c(cc2I)OCO3)nc2c(N)ncnc21 +COc1ccc(OC)c(Cc2nc3nc(F)nc(N)c3[nH]2)c1 +Nc1nccn2c(NCc3ccccc3)c(Cc3cc4c(cc3Br)OCO4)nc12 diff --git a/data/Analysis_Script/Correlation/DockStream_Output/Urokinase_GOLD_Docking_Data.csv b/data/Analysis_Script/Correlation/DockStream_Output/Urokinase_GOLD_Docking_Data.csv new file mode 100644 index 0000000..48136eb --- /dev/null +++ b/data/Analysis_Script/Correlation/DockStream_Output/Urokinase_GOLD_Docking_Data.csv @@ -0,0 +1,36 @@ +ligand_number,enumeration,conformer_number,name,score,smiles,lowest_conformer +0,0,0,0:0:0,58.0203,COC(=O)Nc1cccc2c1cc(cc2)C(=[NH2+])N,True +1,0,0,1:0:0,66.1756,CC(C)n1cc(cn1)c2c(ccc3c2cc(cc3)C(=[NH2+])N)OC,True +2,0,0,2:0:0,65.7353,c1ccc(cc1)NC(=O)Nc2ccc3cc(ccc3c2)C(=[NH2+])N,True +3,0,0,3:0:0,66.8972,COc1ccc2ccc(cc2c1c3cnn(c3)S(=O)(=O)C)C(=[NH2+])N,True +4,0,0,4:0:0,57.6422,c1ccc(cc1)S(=O)(=O)Nc2ccc3cc(ccc3c2)C(=[NH2+])N,True +5,0,0,5:0:0,54.9542,c1cc2ccc(cc2c(c1)N)C(=[NH2+])N,True +6,0,0,6:0:0,78.7894,COC(=O)Nc1cc(cc2c1cc(cc2)C(=[NH2+])N)C(=O)Nc3ccc(cc3)C[NH3+],True +7,0,0,7:0:0,76.0345,c1cc2cc(ccc2cc1C#Cc3ccc4c(c3)CC[NH2+]C4)C(=[NH2+])N,True +8,0,0,8:0:0,70.9389,c1cc(cc(c1)/C=C/c2ccc3cc(ccc3c2)C(=[NH2+])N)CCO,True +9,0,0,9:0:0,56.2542,COc1cc2ccc(cc2c(c1OC)OC)C(=[NH2+])N,True +10,0,0,10:0:0,83.759,CS(=O)(=O)n1cc(cn1)c2cc(cc3c2cc(cc3)C(=[NH2+])N)C(=O)Nc4ccccc4,True +11,0,0,11:0:0,75.904,c1ccc(cc1)NC(=O)c2cc3ccc(cc3c(c2)c4ncccn4)C(=[NH2+])N,True +12,1,0,12:1:0,71.4485,c1c(cc(cc1Cl)Cl)CNc2c(nc(c(n2)N)C(=O)NC(=[NH2+])N)Cl,True 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100644 index 0000000..5bb56ce --- /dev/null +++ b/data/Analysis_Script/Correlation/DockStream_Output/Urokinase_Hybrid_Docking_Data.csv @@ -0,0 +1,35 @@ +ligand_number,enumeration,conformer_number,name,score,smiles,lowest_conformer +0,0,0,0:0:0,-9.497414,COC(=O)Nc1cccc2c1cc(cc2)C(=[NH2+])N,True +1,0,0,1:0:0,-10.227792,CC(C)n1cc(cn1)c2c(ccc3c2cc(cc3)C(=[NH2+])N)OC,True +2,0,0,2:0:0,-9.293001,c1ccc(cc1)NC(=O)Nc2ccc3cc(ccc3c2)C(=[NH2+])N,True +3,0,0,3:0:0,-12.296974,COc1ccc2ccc(cc2c1c3cnn(c3)S(=O)(=O)C)C(=[NH2+])N,True +4,0,0,4:0:0,-9.061113,c1ccc(cc1)S(=O)(=O)Nc2ccc3cc(ccc3c2)C(=[NH2+])N,True +6,0,0,6:0:0,-12.548185,COC(=O)Nc1cc(cc2c1cc(cc2)C(=[NH2+])N)C(=O)Nc3ccc(cc3)C[NH3+],True +7,0,0,7:0:0,-13.230839,c1cc2cc(ccc2cc1C#Cc3ccc4c(c3)CC[NH2+]C4)C(=[NH2+])N,True +8,0,0,8:0:0,-10.143242,c1cc(cc(c1)/C=C/c2ccc3cc(ccc3c2)C(=[NH2+])N)CCO,True +9,0,0,9:0:0,-13.406862,COc1cc2ccc(cc2c(c1OC)OC)C(=[NH2+])N,True 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+19,3080,3.48855071650044 +20,18,1.25527250510331 +21,1490,3.17318626841227 +22,777,2.89042101880091 +23,21,1.32221929473392 +24,637,2.80413943233535 +25,223,2.34830486304816 +26,42,1.6232492903979 +27,94,1.9731278535997 +28,9,0.954242509439325 +29,1250,3.09691001300806 +30,7280,3.86213137931304 +31,5910,3.77158748088125 +32,628,2.7979596437372 +33,610,2.78532983501077 +34,40,1.60205999132796 +35,40000,4.60205999132796 +36,50000,4.69897000433602 +37,10000,4 +38,100000,5 +39,100000,5 +40,100000,5 +41,100000,5 diff --git a/data/Analysis_Script/Enrichment/Active/COX2_Glide_LigPrep.csv b/data/Analysis_Script/Enrichment/Active/COX2_Glide_LigPrep.csv new file mode 100644 index 0000000..a703831 --- /dev/null +++ b/data/Analysis_Script/Enrichment/Active/COX2_Glide_LigPrep.csv @@ -0,0 +1,38 @@ +ligand_number,enumeration,conformer_number,name,score,smiles,lowest_conformer +0,0,0,0:0:0,-10.5418,[H]c1c([H])c(-c2c(-c3c([H])c([H])c(S(=O)(=O)N([H])[H])c([H])c3[H])n([H])c3c([H])c([H])c([H])c([H])c23)c([H])c([H])c1OC([H])([H])[H],True +1,0,0,1:0:0,-10.5469,[H]c1c([H])c(-c2c(-c3c([H])c([H])c(S(=O)(=O)C([H])([H])[H])c([H])c3[H])n([H])c3c([H])c([H])c([H])c([H])c23)c([H])c([H])c1OC([H])([H])[H],True +3,0,0,3:0:0,-10.3447,[H]c1c(-c2c([H])c([H])c(S(=O)(=O)C([H])([H])[H])c([H])c2[H])nc(N([H])C([H])([H])c2c([H])c([H])c(F)c([H])c2[H])nc1C(F)(F)F,True +5,0,0,5:0:0,-10.6696,[H]c1nn2nc(-c3c([H])c([H])c(OC([H])([H])C([H])([H])[H])c([H])c3[H])c(-c3c([H])c([H])c(S(=O)(=O)N([H])[H])c([H])c3[H])c2c([H])c1[H],True +6,0,0,6:0:0,-10.149,[H]c1c([H])c(C2=C(c3c([H])c([H])c(S(=O)(=O)C([H])([H])[H])c([H])c3[H])C([H])([H])C3(C2([H])[H])C([H])([H])C3([H])[H])c([H])c([H])c1Cl,True +7,0,0,7:0:0,-10.4958,[H]c1c([H])c(C2=C(c3c([H])c([H])c(S(=O)(=O)N([H])[H])c([H])c3[H])C([H])([H])C3(C2([H])[H])C([H])([H])C3([H])[H])c([H])c([H])c1OC([H])([H])[H],True 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/dev/null and b/data/Benchmarking_Script/1UYD_grid.oeb differ diff --git a/data/Benchmarking_Script/1UYD_grid.zip b/data/Benchmarking_Script/1UYD_grid.zip new file mode 100644 index 0000000..f2c95b7 Binary files /dev/null and b/data/Benchmarking_Script/1UYD_grid.zip differ diff --git a/data/Benchmarking_Script/OpenEye_target_preparation.json b/data/Benchmarking_Script/OpenEye_target_preparation.json new file mode 100644 index 0000000..e43c511 --- /dev/null +++ b/data/Benchmarking_Script/OpenEye_target_preparation.json @@ -0,0 +1,39 @@ +{ + "target_preparation": + { + "header": { + "environment":{ + "export": [ + {"key": "OE_LICENSE", "value": "/opt/scp/software/oelicense/1.0/oe_license.seq1"} + ] + } + }, + "input_path": "/1UYD_apo.pdb", + "fixer": { + "enabled": true, + "standardize": true, + "remove_heterogens": false, + "fix_missing_heavy_atoms": true, + "fix_missing_hydrogens": true, + "fix_missing_loops": true, + "add_water_box": false + }, + "runs": [ + { + "backend": "OpenEye", + "output": { + "receptor_path": "/1UYD_Grid.oeb" + }, + "parameters": { + "pH": 7.4 + }, + "cavity": { + "method": "reference_ligand", + "reference_ligand_path": "/PU8.pdb", + "reference_ligand_format": "PDB" + } + } + ] + } +} + diff --git a/data/Benchmarking_Script/ligands_smiles.smi b/data/Benchmarking_Script/ligands_smiles.smi new file mode 100644 index 0000000..a87ee7f --- /dev/null +++ b/data/Benchmarking_Script/ligands_smiles.smi @@ -0,0 +1,15 @@ +C#CCCCn1c(Cc2cc(OC)c(OC)c(OC)c2Cl)nc2c(N)ncnc21 +CCCCn1c(Cc2cc(OC)c(OC)c(OC)c2)nc2c(N)ncnc21 +CCCCn1c(Cc2cc(OC)ccc2OC)nc2c(N)ncnc21 +CCCCn1c(Cc2cccc(OC)c2)nc2c(N)ncnc21 +C#CCCCn1c(Cc2cc(OC)c(OC)c(OC)c2Cl)nc2c(N)nc(F)nc21 +CCCCn1c(Cc2ccc(OC)cc2)nc2c(N)ncnc21 +CCCCn1c(Cc2ccc3c(c2)OCO3)nc2c(N)ncnc21 +CCCCn1c(Cc2cc(OC)ccc2OC)nc2c(N)nc(F)nc21 +CCCCn1c(Cc2ccc3c(c2)OCO3)nc2c(N)nc(F)nc21 +C#CCCCn1c(Cc2cc(OC)ccc2OC)nc2c(N)nc(F)nc21 +CC(C)NCCCn1c(Cc2cc3c(cc2I)OCO3)nc2c(N)nc(F)nc21 +CC(C)NCCCn1c(Sc2cc3c(cc2Br)OCO3)nc2c(N)ncnc21 +CC(C)NCCCn1c(Sc2cc3c(cc2I)OCO3)nc2c(N)ncnc21 +COc1ccc(OC)c(Cc2nc3nc(F)nc(N)c3[nH]2)c1 +Nc1nccn2c(NCc3ccccc3)c(Cc3cc4c(cc3Br)OCO4)nc12 diff --git a/data/Glide/1UYD_grid.zip b/data/Glide/1UYD_grid.zip new file mode 100644 index 0000000..f2c95b7 Binary files /dev/null and b/data/Glide/1UYD_grid.zip differ diff --git a/data/rDock/rbcavity_1UYD.prm b/data/rDock/rbcavity_1UYD.prm new file mode 100644 index 0000000..c41c849 --- /dev/null +++ b/data/rDock/rbcavity_1UYD.prm @@ -0,0 +1,27 @@ +RBT_PARAMETER_FILE_V1.00 +TITLE rDock_default_cavity_reference_ligand + +RECEPTOR_FILE +RECEPTOR_FLEX 3.0 + +################################################################## +### CAVITY DEFINITION: REFERENCE LIGAND METHOD +################################################################## +SECTION MAPPER + SITE_MAPPER RbtLigandSiteMapper + REF_MOL + RADIUS 6.0 + SMALL_SPHERE 1.0 + MIN_VOLUME 100 + MAX_CAVITIES 1 + VOL_INCR 0.0 + GRIDSTEP 0.5 +END_SECTION + +################################# +#CAVITY RESTRAINT PENALTY +################################# +SECTION CAVITY + SCORING_FUNCTION RbtCavityGridSF + WEIGHT 1.0 +END_SECTION diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000..21ca077 --- /dev/null +++ b/environment.yml @@ -0,0 +1,18 @@ +name: DockStreamCommunity +channels: + - conda-forge + - omnia + - plotly + - pytorch + - openeye + - rdkit +dependencies: + - jupyter>=1.0.0 + - numpy>=1.18.4 + - matplotlib + - seaborn>=0.11.0 + - pandas>=1.0.3 + - pip + - scikit-learn + - python>=3.7 + diff --git a/notebooks/_static/modifications.css b/notebooks/_static/modifications.css new file mode 100644 index 0000000..b006b28 --- /dev/null +++ b/notebooks/_static/modifications.css @@ -0,0 +1,7 @@ +.wy-nav-content { + max-width: 1200px !important; +} + +html { + font-size: 1.2em; +} diff --git a/notebooks/advanced_features.ipynb b/notebooks/advanced_features.ipynb new file mode 100644 index 0000000..638a5b1 --- /dev/null +++ b/notebooks/advanced_features.ipynb @@ -0,0 +1,155 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Advanced features\n", + "Some complex features are available for multiple (or all) docking backends. This notebook describes how to use them and gives some background information on them.\n", + "\n", + "## Write-out details\n", + "For writing out the poses (as `SDF`) and scores (as `CSV`), the user can set the parameter `overwrite` to `false` (note that in `JSON`, there is no capital 'F'), which will cause a check whether the file specified already exists. If so, the first free numbering is attached as a prefix, e.g. \"0000_\" (or, if this is already taken as well, \"0001_\").\n", + "\n", + "```\n", + "\"output\":\n", + "{\n", + " \"poses\": { \"poses_path\": , \"overwrite\": false},\n", + " \"scores\": { \"scores_path\": , \"overwrite\": false}\n", + "}\n", + "```\n", + "\n", + "Another option for `poses` is `mode`, which can be set to `best_per_enumeration`: even when `N` poses are calculated by the backend, only the top-scoring one is saved (per enumeration, see below).\n", + "\n", + "```\n", + "\"output\":\n", + "{\n", + " \"poses\": { \"poses_path\": , \"mode\": \"best_per_enumeration\"}\n", + "}\n", + "```\n", + "\n", + "In order to make it easier to match the `CSV` and `SDF` outputs, the following to tags are added to the poses:\n", + "* `original_smiles`: The `SMILES` that was used as input\n", + "* `smiles`: The actual `SMILES` used for embedding and docking. If no enumerations were calculated, this is always identical to `original_smiles`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compound naming scheme\n", + "While it is possible to retain molecules' names throughout the process (when using `CSV` or `SDF` input, see respective notebook), the following scheme is used internally to uniquely identify any compound:\n", + "\n", + "`::` (all starting with '0').\n", + "\n", + "The ligand ID refers to the input number, e.g. the first compound in a `smi` file will have '0', the second '1' and so on. If there are no enumerations (see below), there will be only one per ligand (i.e. all will have the enumeration ID '0'). The conformer ID refers to the pose number. The poses are order from \"best\" to \"worst\" and their number can be specified in the configuration file.\n", + "\n", + "![](img/naming_scheme.png)\n", + "\n", + "## Logging\n", + "As described in the notebook on the command-line scripts, you may want to set your own logging configuration (also check on the `-debug` flag available). See folder `config/logging` for examples on different \"levels\". By default, the output logging file is called \"dockstream_run.log\", but you can set it in the header region of configuration files as such:\n", + "\n", + "```\n", + "\"header\":\n", + "{\n", + " \"logging\": {\n", + " \"logfile\": \n", + " }\n", + "}\n", + "```\n", + "\n", + "## Prefix execution\n", + "For external programs, it might be necessary to set specific steps (e.g. loading set modules) prior to using them. In those cases, the optional parameter `prefix_execution` is joined to the binary call with `&&`. For example, if `rDock` is available in the module \"rDock\", you can load it like this at the docking stage:\n", + "\n", + "```\n", + "...\n", + " {\n", + " \"backend\": \"rDock\",\n", + " \"run_id\": \"rDock\",\n", + " ...\n", + " \"parameters\": {\n", + " \"prefix_execution\": \"module load rDock\",\n", + " ...\n", + " },\n", + " ...\n", + " }\n", + "```\n", + "\n", + "If this is omitted, the binaries should be available in your `PATH` variable. You can explicitly export environment variables via the respective block in the header region:\n", + "\n", + "```\n", + "...\n", + " \"header\": {\n", + " \"environment\": {\n", + " \"export\": [{\n", + " \"key\": \"OE_LICENSE\", \"value\": \"/home/user/oelicense/1.0/oe_license.seq1\"\n", + " }]\n", + " }\n", + " }\n", + "...\n", + "```\n", + "\n", + "## Tautomer and protonation enumeration: `TautEnum`\n", + "Some molecules have different protonation and tautomeric states. `DockStream` allows you to use [`TautEnum`](https://github.com/OpenEye-Contrib/TautEnum) to enumerate all \"meaningful\" states, embed and dock them all and then report the individual scores and poses for them. The following optional block should be inserted into the `input` block of the `ligand embedding`:\n", + "\n", + "```\n", + "...\n", + " \"ligand_preparation\": {\n", + " \"embedding_pools\": [\n", + " {\n", + " \"input\": {\n", + " \"use_taut_enum\": {\n", + " \"prefix_execution\": \"module load taut_enum\",\n", + " \"enumerate_protonation\": true\n", + " },\n", + " ...\n", + " }]\n", + " }\n", + "...\n", + "```\n", + "Use `enumerate_protonation` (a `boolean` flag) to specify whether you want to do just tautomerizations or all protonations as well.\n", + "\n", + "![](img/enumeration.png)\n", + "\n", + "## `SMIRKS`\n", + "Sometimes, we want to enforce a certain state on a (sub-)molecule. `DockStream` offers the possibility to use `transformations` in the embedding pools (see example below). At the moment, only `OpenEye`'s implementation of `SMIRKS` are supported.\n", + "\n", + "```\n", + "...\n", + " \"input\": {\n", + " \"transformations\": \n", + " [{\n", + " \"type\": \"smirks\",\n", + " \"backend\": \"OpenEye\",\n", + " \"smirks\": \"[c:1]1[n:2][c:3]2[c:4]([n:5][c:6][n;X2:7][c:8]2[n:9]1)>>[c:1]1[n:2][c:3]2[c:4]([n:5][c:6][n+:7>([H])[c:8]2[n:9]1)\",\n", + " \"fail_action\": \"keep\"\n", + " }]\n", + " }\n", + "...\n", + "```\n", + "\n", + "![](img/SMIRKS.png)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/code/score_transformation.py b/notebooks/code/score_transformation.py new file mode 100644 index 0000000..dea3168 --- /dev/null +++ b/notebooks/code/score_transformation.py @@ -0,0 +1,269 @@ +import numpy as np +import math + +import matplotlib.pyplot as plt +import seaborn as sns + + +class ComponentSpecificParametersEnum: + __LOW = "low" + __HIGH = "high" + __K = "k" + __TRANSFORMATION = "transformation" + __SCIKIT = "scikit" + __CLAB_INPUT_FILE = "clab_input_file" + __COEF_DIV = "coef_div" + __COEF_SI = "coef_si" + __COEF_SE = "coef_se" + __TRANSFORMATION_TYPE = "transformation_type" + __DESCRIPTOR_TYPE = "descriptor_type" + + @property + def LOW(self): + return self.__LOW + + @LOW.setter + def LOW(self, value): + raise ValueError("Do not assign value to a ComponentSpecificParametersEnum field") + + @property + def HIGH(self): + return self.__HIGH + + @HIGH.setter + def HIGH(self, value): + raise ValueError("Do not assign value to a ComponentSpecificParametersEnum field") + + @property + def K(self): + return self.__K + + @K.setter + def K(self, value): + raise ValueError("Do not assign value to a ComponentSpecificParametersEnum field") + + @property + def TRANSFORMATION(self): + return self.__TRANSFORMATION + + @TRANSFORMATION.setter + def TRANSFORMATION(self, value): + raise ValueError("Do not assign value to a ComponentSpecificParametersEnum field") + + @property + def SCIKIT(self): + return self.__SCIKIT + + @SCIKIT.setter + def SCIKIT(self, value): + raise ValueError("Do not assign value to a ComponentSpecificParametersEnum field") + + @property + def CLAB_INPUT_FILE(self): + return self.__CLAB_INPUT_FILE + + @CLAB_INPUT_FILE.setter + def CLAB_INPUT_FILE(self, value): + raise ValueError("Do not assign value to a ComponentSpecificParametersEnum field") + + @property + def COEF_DIV(self): + return self.__COEF_DIV + + @COEF_DIV.setter + def COEF_DIV(self, value): + raise ValueError("Do not assign value to a ComponentSpecificParametersEnum field") + + @property + def COEF_SI(self): + return self.__COEF_SI + + @COEF_SI.setter + def COEF_SI(self, value): + raise ValueError("Do not assign value to a ComponentSpecificParametersEnum field") + + @property + def COEF_SE(self): + return self.__COEF_SE + + @COEF_SE.setter + def COEF_SE(self, value): + raise ValueError("Do not assign value to a ComponentSpecificParametersEnum field") + + @property + def TRANSFORMATION_TYPE(self): + return self.__TRANSFORMATION_TYPE + + @TRANSFORMATION_TYPE.setter + def TRANSFORMATION_TYPE(self, value): + raise ValueError("Do not assign value to a ComponentSpecificParametersEnum field") + + @property + def DESCRIPTOR_TYPE(self): + return self.__DESCRIPTOR_TYPE + + @DESCRIPTOR_TYPE.setter + def DESCRIPTOR_TYPE(self, value): + raise ValueError("Do not assign value to a ComponentSpecificParametersEnum field") + + +class TransformationTypeEnum: + __DOUBLE_SIGMOID = "double_sigmoid" + __SIGMOID = "sigmoid" + __REVERSE_SIGMOID = "reverse_sigmoid" + __RIGHT_STEP = "right_step" + __STEP = "step" + __NO_TRANSFORMATION = "no_transformation" + + @property + def DOUBLE_SIGMOID(self): + return self.__DOUBLE_SIGMOID + + @DOUBLE_SIGMOID.setter + def DOUBLE_SIGMOID(self, value): + raise ValueError("Do not assign value to a TransformationTypeEnum field") + + @property + def SIGMOID(self): + return self.__SIGMOID + + @SIGMOID.setter + def SIGMOID(self, value): + raise ValueError("Do not assign value to a TransformationTypeEnum field") + + @property + def REVERSE_SIGMOID(self): + return self.__REVERSE_SIGMOID + + @REVERSE_SIGMOID.setter + def REVERSE_SIGMOID(self, value): + raise ValueError("Do not assign value to a TransformationTypeEnum field") + + @property + def RIGHT_STEP(self): + return self.__RIGHT_STEP + + @RIGHT_STEP.setter + def RIGHT_STEP(self, value): + raise ValueError("Do not assign value to a TransformationTypeEnum field") + + @property + def STEP(self): + return self.__STEP + + @STEP.setter + def STEP(self, value): + raise ValueError("Do not assign value to a TransformationTypeEnum field") + + @property + def NO_TRANSFORMATION(self): + return self.__NO_TRANSFORMATION + + @NO_TRANSFORMATION.setter + def NO_TRANSFORMATION(self, value): + raise ValueError("Do not assign value to a TransformationTypeEnum field") + + +class TransformationFactory: + + def __init__(self): + self._csp_enum = ComponentSpecificParametersEnum() + self._transformation_function_registry = self._default_transformation_function_registry() + + def _default_transformation_function_registry(self) -> dict: + enum = TransformationTypeEnum() + transformation_list = { + enum.SIGMOID: self.sigmoid_transformation, + enum.REVERSE_SIGMOID: self.reverse_sigmoid_transformation, + enum.DOUBLE_SIGMOID: self.double_sigmoid, + enum.NO_TRANSFORMATION: self.no_transformation, + enum.RIGHT_STEP: self.right_step, + enum.STEP: self.step + } + return transformation_list + + def get_transformation_function(self, parameters: dict): + transformation_type = parameters[self._csp_enum.TRANSFORMATION_TYPE] + transformation_function = self._transformation_function_registry[transformation_type] + return transformation_function + + def no_transformation(self, predictions: list, parameters: dict) -> np.array: + return np.array(predictions, dtype=np.float32) + + def right_step(self, predictions, parameters) -> np.array: + _low = parameters[self._csp_enum.LOW] + + def _right_step_formula(value, low): + if value >= low: + return 1 + return 0 + + transformed = [_right_step_formula(value, _low) for value in predictions] + return np.array(transformed, dtype=np.float32) + + def step(self, predictions, parameters) -> np.array: + _low = parameters[self._csp_enum.LOW] + _high = parameters[self._csp_enum.HIGH] + + def _right_step_formula(value, low, high): + if low <= value <= high: + return 1 + return 0 + + transformed = [_right_step_formula(value, _low, _high) for value in predictions] + return np.array(transformed, dtype=np.float32) + + def sigmoid_transformation(self, predictions: list, parameters: dict) -> np.array: + _low = parameters[self._csp_enum.LOW] + _high = parameters[self._csp_enum.HIGH] + _k = parameters[self._csp_enum.K] + + def _exp(pred_val, low, high, k) -> float: + return math.pow(10, (10 * k * (pred_val - (low + high) * 0.5) / (low - high))) + + transformed = [1 / (1 + _exp(pred_val, _low, _high, _k)) for pred_val in predictions] + return np.array(transformed, dtype=np.float32) + + def reverse_sigmoid_transformation(self, predictions: list, parameters: dict) -> np.array: + _low = parameters[self._csp_enum.LOW] + _high = parameters[self._csp_enum.HIGH] + _k = parameters[self._csp_enum.K] + + def _reverse_sigmoid_formula(value, low, high, k) -> float: + try: + return 1 / (1 + 10 ** (k * (value - (high + low) / 2) * 10 / (high - low))) + except: + return 0 + + transformed = [_reverse_sigmoid_formula(pred_val, _low, _high, _k) for pred_val in predictions] + return np.array(transformed, dtype=np.float32) + + def double_sigmoid(self, predictions: list, parameters: dict) -> np.array: + _low = parameters[self._csp_enum.LOW] + _high = parameters[self._csp_enum.HIGH] + _coef_div = parameters[self._csp_enum.COEF_DIV] + _coef_si = parameters[self._csp_enum.COEF_SI] + _coef_se = parameters[self._csp_enum.COEF_SE] + + def _double_sigmoid_formula(value, low, high, coef_div=100., coef_si=150., coef_se=150.): + try: + A = 10 ** (coef_se * (value / coef_div)) + B = (10 ** (coef_se * (value / coef_div)) + 10 ** (coef_se * (low / coef_div))) + C = (10 ** (coef_si * (value / coef_div)) / ( + 10 ** (coef_si * (value / coef_div)) + 10 ** (coef_si * (high / coef_div)))) + return (A / B) - C + except: + return 0 + + transformed = [_double_sigmoid_formula(pred_val, _low, _high, _coef_div, _coef_si, _coef_se) for pred_val in + predictions] + return np.array(transformed, dtype=np.float32) + + +def render_curve(title, x, y): + plt.figure(figsize=(16, 10), dpi=80) + plt.xlabel("input_score") + plt.ylabel("transformed_score") + plt.title(title, fontsize=18) + sns.lineplot(x=x, y=y) + plt.show() diff --git a/notebooks/command-line_interfaces.ipynb b/notebooks/command-line_interfaces.ipynb new file mode 100644 index 0000000..0af714a --- /dev/null +++ b/notebooks/command-line_interfaces.ipynb @@ -0,0 +1,310 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "How to run this notebook?
\n", + "
    \n", + "
  1. Install the DockStream environment: conda env create -f environment.yml in the DockStream directory
    2. \n", + "
  2. Activate the environment: conda activate DockStreamCommunity
    3. \n", + "
  3. Execute jupyter: jupyter notebook
    4. \n", + "
  4. Copy the link to a browser
    5. \n", + "
  5. Update variables dockstream_path and dockstream_env (the path to the environment DockStream) in the \n", + " first code block below
    6. \n", + "
\n", + "
\n", + "\n", + "# Command-Line Interfaces\n", + "While `DockStream` can be loaded as a library, the most common use-case is to call a command-line interface (e.g. implicitly when using `REINVENT`). The entry points described below are currently supported and are explained together with the available parameters. To execute them, we need to either load the `conda` environment before or specify the full path to the `python` version we want to use (here, we will make use of the latter option)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "import tempfile\n", + "\n", + "# update these paths to reflect your system's configuration\n", + "dockstream_path = os.path.expanduser(\"~/Desktop/ProjectData/DockStream\")\n", + "dockstream_env = os.path.expanduser(\"~/miniconda3/envs/DockStream\")\n", + "dockstreamcommunity_env = os.path.expanduser(\"~/miniconda3/envs/DockStreamCommunity\")\n", + "\n", + "# generate the paths to the entry points\n", + "target_preparator = os.path.join(dockstream_path, \"target_preparator.py\")\n", + "docker = os.path.join(dockstream_path, \"docker.py\")\n", + "sdf2smiles = os.path.join(dockstream_path, \"sdf2smiles.py\")\n", + "unit_tests = os.path.join(dockstream_path, \"unit_tests.py\")\n", + "benchmarking = os.path.join(dockstream_path, \"benchmarking.py\")\n", + "analysis = os.path.join(dockstream_path, \"analysis.py\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## General Options\n", + "Most command-line interfaces support the `-h` (or `--help`) flag, which will cause the script to ignore all other options set and print a usage description. Another common option is `-log_conf`, which is optional and allows to set the logging configuration (see `config/logging` subfolder of `DockStream` for examples). By default, only important messages (`Error` and `Warning` levels) are logged out, but by using the `-log_conf` parameter, users can utilize their own definitions. The recommended way, however, to obtain more detailed logging messages (for example when debugging a run), is to use the `-debug` flag.\n", + "\n", + "## `target_preparator.py`\n", + "For all backends we need to define the target in a specific format. Depending on the backend, these files hold information on the location of the binding site, constraintes, etc. - except for `Glide`, we can use this command line tool to generate the receptors (please see the respective notebooks for details). While the major input is a `JSON` file defining the preparation run (set by the only mandatory parameter, `-conf`), you can tweak the behaviour also by setting additional parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "usage: target_preparator.py [-h] [-conf CONF] [-validation VALIDATION]\r\n", + " [-silent SILENT] [-debug] [-log_conf LOG_CONF]\r\n", + "\r\n", + "Implements entry point for the target preparation for one or multiple\r\n", + "backends.\r\n", + "\r\n", + "optional arguments:\r\n", + " -h, --help show this help message and exit\r\n", + " -conf CONF A path to an preparation configuration file (JSON\r\n", + " dictionary) that is to be executed.\r\n", + " -validation VALIDATION\r\n", + " If set to False, this flag will prohibit a JSON Schema\r\n", + " validation.\r\n", + " -silent SILENT If set, the program will silently execute without\r\n", + " printing status updates.\r\n", + " -debug Set this flag to activate the inbuilt debug logging\r\n", + " mode (this will overwrite parameter \"-log_conf\", if\r\n", + " set).\r\n", + " -log_conf LOG_CONF Set absolute path to a logger configuration other than\r\n", + " the default stored at \"config/logging/default.json\".\r\n" + ] + } + ], + "source": [ + "# execute this in a command-line environment after replacing the parameters\n", + "!{dockstream_env}/bin/python {target_preparator} -h" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `unit_tests.py`\n", + "This entry point does not have any parameters but it might be a good idea to check any new installation by executing:\n", + "\n", + "```\n", + "/python unit_tests.py\n", + "```\n", + "\n", + "**Note:** Some of them might fail if they require a software dependency that is not found or not installed on your system." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `docker.py`\n", + "This is the main component of `DockStream` and performs its tasks based on the instruction `JSON` file provided with the `-conf` parameter. Note, that the `-validation` parameter is not yet functional." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "usage: docker.py [-h] -conf CONF [-validation VALIDATION] [-silent SILENT]\r\n", + " [-smiles SMILES] [-print_scores] [-print_all] [-debug]\r\n", + " [-log_conf LOG_CONF] [-output_prefix OUTPUT_PREFIX]\r\n", + " [-input_csv INPUT_CSV]\r\n", + " [-input_csv_smiles_column INPUT_CSV_SMILES_COLUMN]\r\n", + " [-input_csv_names_column INPUT_CSV_NAMES_COLUMN]\r\n", + "\r\n", + "Implements entry point for the docking using one or multiple backends.\r\n", + "\r\n", + "optional arguments:\r\n", + " -h, --help show this help message and exit\r\n", + " -conf CONF A path to an docking configuration file (JSON\r\n", + " dictionary) that is to be executed.\r\n", + " -validation VALIDATION\r\n", + " If set to False, this flag will prohibit a JSON Schema\r\n", + " validation.\r\n", + " -silent SILENT If set, the program will silently execute without\r\n", + " printing status updates.\r\n", + " -smiles SMILES Use this flag to hand over the input SMILES over the\r\n", + " command-line, separated by ';'.\r\n", + " -print_scores Set this flag to activate linewise print-outs of the\r\n", + " scores to the shell.\r\n", + " -print_all Set this flag (together with \"-print_scores\") to print\r\n", + " out the scores for all conformers, not just the best\r\n", + " one.\r\n", + " -debug Set this flag to activate the inbuilt debug logging\r\n", + " mode (this will overwrite parameter \"-log_conf\", if\r\n", + " set).\r\n", + " -log_conf LOG_CONF Set absolute path to a logger configuration other than\r\n", + " the default stored at \"config/logging/default.json\".\r\n", + " -output_prefix OUTPUT_PREFIX\r\n", + " If specified, this prefix will be added to all output\r\n", + " file names.\r\n", + " -input_csv INPUT_CSV If set (a path to a CSV file), this will overwrite any\r\n", + " input file specification in the configuration.\r\n", + " -input_csv_smiles_column INPUT_CSV_SMILES_COLUMN\r\n", + " If \"-input_csv\" is set, you need to specify the column\r\n", + " name with the smiles as well.\r\n", + " -input_csv_names_column INPUT_CSV_NAMES_COLUMN\r\n", + " Optional name of the name column, if \"-input_csv\" is\r\n", + " specified.\r\n" + ] + } + ], + "source": [ + "# execute this in a command-line environment after replacing the parameters\n", + "!{dockstream_env}/bin/python {docker} -h" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The interface to `REINVENT` is facilitated via the `-smiles` and `-print_scores` options (for input and output, respectively). In addition, `-output_prefix` is used to add the epoch number from `REINVENT` to the file names. Option `-print_scores` (without `-print_all` activated) causes `DockStream` to print the best score per ligand (not conformation nor enumeration) to `stdout`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `sdf2smiles.py`\n", + "This is a simple convenience script that takes an `SDF` file as input and produces a text file with one `SMILE` per line for each molecule. This can be directly used as input for `DockStream`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "usage: sdf2smiles.py [-h] [-sdf SDF] [-smi SMI] [-csv CSV] [-keep_stereo]\r\n", + " [-tags2columns TAGS2COLUMNS [TAGS2COLUMNS ...]]\r\n", + "\r\n", + "Implements simple translator taking an SDF file and spitting out SMILES.\r\n", + "\r\n", + "optional arguments:\r\n", + " -h, --help show this help message and exit\r\n", + " -sdf SDF A path a SDF file.\r\n", + " -smi SMI A path an output text file.\r\n", + " -csv CSV A path an output CSV file.\r\n", + " -keep_stereo If set, exported SMILES contain stereo-information.\r\n", + " -tags2columns TAGS2COLUMNS [TAGS2COLUMNS ...]\r\n", + " A list of strings for which tags should be transformed\r\n", + " into columns.\r\n" + ] + } + ], + "source": [ + "!{dockstream_env}/bin/python {sdf2smiles} -h" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `benchmarking.py`\n", + "This script facilitates batch execution of `DockStream`. A folder containing any number of configuration `JSONs` can be passed to the `-input_path` parameter. See `demo_Benchmarking_Script` for details." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "usage: benchmarking.py [-h] -input_path INPUT_PATH\r\n", + "\r\n", + "Facilitates batch DockStream execution.\r\n", + "\r\n", + "optional arguments:\r\n", + " -h, --help show this help message and exit\r\n", + " -input_path INPUT_PATH\r\n", + " The path to either a folder of DockStream json files\r\n", + " or a single json file.\r\n" + ] + } + ], + "source": [ + "# execute this in a command-line environment after replacing the parameters\n", + "!{dockstream_env}/bin/python {benchmarking} -h" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `analysis.py`\n", + "This script automates analysis of `DockStream` results. A configuration `JSON` can be passed to the `-input_json` parameter to denote the analysis mode. See `demo_Analysis_Script` for details." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "usage: analysis.py [-h] -input_json INPUT_JSON\r\n", + "\r\n", + "Implements entry point to DockStream output analysis.\r\n", + "\r\n", + "optional arguments:\r\n", + " -h, --help show this help message and exit\r\n", + " -input_json INPUT_JSON\r\n", + " Path to user provided json file which contains all the\r\n", + " paths/metrics for analysis.\r\n" + ] + } + ], + "source": [ + "# execute this in a command-line environment after replacing the parameters\n", + "!{dockstreamcommunity_env}/bin/python {analysis} -h" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/conf.py b/notebooks/conf.py new file mode 100644 index 0000000..f2ac84e --- /dev/null +++ b/notebooks/conf.py @@ -0,0 +1,115 @@ +# Configuration file for the Sphinx documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +# import os +# import sys +# sys.path.insert(0, os.path.abspath('.')) + + +# -- Project information ----------------------------------------------------- + +master_doc = 'index' + +project = 'DockStream' +copyright = '2021, Christian Margreitter' +author = 'Christian Margreitter' + +# The full version, including alpha/beta/rc tags +release = "1.0.0" + + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + "nbsphinx", + "sphinx.ext.mathjax" # for math equations +] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +exclude_patterns = ['_build'] + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] + +# -- Options for LaTeX output --------------------------------------------- + +# See https://www.sphinx-doc.org/en/master/latex.html +latex_elements = { + 'papersize': 'a4paper', + 'printindex': '', + 'sphinxsetup': r""" + %verbatimwithframe=false, + %verbatimwrapslines=false, + %verbatimhintsturnover=false, + VerbatimColor={HTML}{F5F5F5}, + VerbatimBorderColor={HTML}{E0E0E0}, + noteBorderColor={HTML}{E0E0E0}, + noteborder=1.5pt, + warningBorderColor={HTML}{E0E0E0}, + warningborder=1.5pt, + warningBgColor={HTML}{FBFBFB}, + """, + 'preamble': r""" +\usepackage[sc,osf]{mathpazo} +\linespread{1.05} % see http://www.tug.dk/FontCatalogue/urwpalladio/ +\renewcommand{\sfdefault}{pplj} % Palatino instead of sans serif +\IfFileExists{zlmtt.sty}{ + \usepackage[light,scaled=1.05]{zlmtt} % light typewriter font from lmodern +}{ + \renewcommand{\ttdefault}{lmtt} % typewriter font from lmodern +} +\usepackage{booktabs} % for Pandas dataframes +""", +} + +latex_documents = [ + (master_doc, 'nbsphinx.tex', project, author, 'howto'), +] + +latex_show_urls = 'footnote' +latex_show_pagerefs = True + +# -- Options for EPUB output ---------------------------------------------- + +# These are just defined to avoid Sphinx warnings related to EPUB: +version = release +suppress_warnings = [ + 'nbsphinx', + 'epub.unknown_project_files'] + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = 'sphinx_rtd_theme' +html_theme_options = { + 'navigation_with_keys': True, + 'collapse_navigation': False, +} +html_title = project + ' version ' + release + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] +html_css_files = [ + "modifications.css" +] diff --git a/notebooks/demo_Analysis_Script.ipynb b/notebooks/demo_Analysis_Script.ipynb new file mode 100644 index 0000000..1f9db9c --- /dev/null +++ b/notebooks/demo_Analysis_Script.ipynb @@ -0,0 +1,1675 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analysis Script Demo\n", + "\n", + "The purpose of the `Analysis Script` is to automate analysis of `DockStream` run outputs. The analysis includes calculating correlation metrics and generation of enrichment plots. This enables the results from different `DockStream` run configurations to be quantitatively and qualitatively compared. Therefore, the `Analysis Script` is particularly well suited for analyzing the data from batch executions of `DockStream` enabled by the `Benchmarking Script`. A demo for the `Benchmarking Script` can be found in the `demo_Benchmarking_Script` Jupyter notebook. This notebook focuses strictly on the `Analysis Script`. Specifics into the capabilities of the `Analysis Script` are explained in this notebook which is divided into 3 sections to reflect the 3 distinct use cases supported.\n", + "\n", + "\n", + "# Analysis Script Use Cases:\n", + " 1. Enrichment Analysis\n", + " 2. Correlation Analysis\n", + " 3. Thresholds Analysis \n", + "\n", + "__Note:__ By default, this notebook will deposit all files created into `~/Desktop/Analysis_demo`.\n", + "\n", + "The following imports / loadings are only necessary when executing this notebook. If you want to use `analysis.py` directly from the command-line, it is enough to execute the following with the appropriate input JSON:\n", + "\n", + "```\n", + "conda activate DockStream\n", + "python /path/to/DockStream/analysis.py -input_json \n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "import tempfile\n", + "import pandas as pd\n", + "import ipykernel\n", + "\n", + "# update these paths to reflect your system's configuration\n", + "dockstream_path = os.path.expanduser(\"~/Desktop/ProjectData/DockStream\")\n", + "dockstream_env = os.path.expanduser(\"~/miniconda3/envs/DockStreamCommunity\")\n", + "\n", + "# no changes are necessary beyond this point\n", + "# ---------\n", + "# get the notebook's root path\n", + "try: ipynb_path\n", + "except NameError: ipynb_path = os.getcwd()\n", + "\n", + "# generate the path to the analysis script entry point\n", + "analysis_script = os.path.join(dockstream_path, \"analysis.py\")\n", + "\n", + "# generate a folder with 3 subfolders to store the output\n", + "output_dir = os.path.expanduser(\"~/Desktop/Analysis_demo\")\n", + "enrichment_results_dir = os.path.join(output_dir, \"Enrichment_Output\")\n", + "correlation_results_dir = os.path.join(output_dir, \"Correlation_Output\")\n", + "thresholds_results_dir = os.path.join(output_dir, \"Thresholds_Output\")\n", + "try:\n", + " os.mkdir(output_dir)\n", + " os.mkdir(enrichment_results_dir)\n", + " os.mkdir(correlation_results_dir)\n", + " os.mkdir(thresholds_results_dir)\n", + "except FileExistsError:\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Enrichment Analysis\n", + "\n", + "Suppose you are involved in an early phase project and want to integrate an *in silico* docking component. You do not have any experimental data yet but perhaps there is in-house data or public data that has been compiled into a set of active ligands and inactive ligands (a *calibration dataset*). As a first effort, you are interested in identifying a docking protocol that can \"pick out\" an active ligand when some arbitrary large number of ligands is docked. In the perfect scenario, active ligands will score much better than inactive ligands (on average) when docked with some combination of backend + ligand embedder + settings. The `Benchmarking Script` was introduced to automate and streamline batch execution of `DockStream`. The `Analysis Script` and specfically its `Enrichment Analysis` functionality was introduced to automate generation of histograms, boxplots, and pROC curves to qualitatively and quantitatively compare enrichment between different `DockStream` runs. The dataset used in this section is from the DEKOIS 2.0 dataset. For more information:\n", + "\n", + "https://pubs.acs.org/doi/abs/10.1021/ci400115b\n", + "\n", + "Cyclooxygenase-2 (COX2) and Glutaminyl-peptide cyclotransferase (QPCT) from the DEKOIS 2.0 dataset were chosen to demonstrate `Enrichment Analysis`. These public datasets provide a set of active and inactive ligands which have been docked using `Glide`. The corresponding `DockStream` outputs were shipped with the `DockStream` codebase and is ready for use in this notebook. Note, `Glide` was chosen as the backend arbitrarily - `Enrichment Analysis` is compatible with any backend. Let's take a look at the COX2 active and inactive ligands `Glide` data." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# generate the paths to the COX2 actives and inactives data shipped with this implementation\n", + "COX2_ACTIVES_PATH = os.path.join(ipynb_path, \"../data/Analysis_Script/Enrichment/COX2_Glide_Actives.csv\")\n", + "COX2_INACTIVES_PATH = os.path.join(ipynb_path, \"../data/Analysis_Script/Enrichment/COX2_Glide_Inactives.csv\")\n", + "\n", + "# read the COX2 actives and inactives data to show its contents\n", + "COX2_ACTIVES = pd.read_csv(os.path.join(ipynb_path, \"../data/Analysis_Script/Enrichment/Active/COX2_Glide_LigPrep.csv\"))\n", + "COX2_INACTIVES = pd.read_csv(os.path.join(ipynb_path, \"../data/Analysis_Script/Enrichment/Inactive/COX2_Glide_LigPrep.csv\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ligand_numberenumerationconformer_numbernamescoresmileslowest_conformer
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" + ], + "text/plain": [ + " ligand_number enumeration conformer_number name score \\\n", + "0 0 0 0 0:0:0 -6.02393 \n", + "1 3 1 0 3:1:0 -4.61955 \n", + "2 15 0 0 15:0:0 -4.89755 \n", + "\n", + " smiles lowest_conformer \n", + "0 [H]c1c(Cl)c([H])n2c(Sc3nc(C([H])([H])[H])nc4sc... True \n", + "1 [H]C(c1c(N2C([H])([H])C([H])([H])OC([H])([H])C... True \n", + "2 [H]c1sc(-n2nnn(C([H])([H])C(=O)[N-]c3nc4c([H])... True " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# visualize the COX2 inactives data\n", + "COX2_INACTIVES.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above cells display the first 3 entries in the COX2 actives and inactives data. The important observation is that the `CSV` files provided are simply the raw and unmodified `DockStream` output `CSVs`.\n", + "\n", + "A quick glance at the data shows that the `Glide` scores for the active ligands are lower (more potent) than the `Glide` scores for the inactive ligands which is desirable. We will now demonstrate the generation of an enrichment histogram and boxplot using the `Analysis Script`. Similar to the `Benchmarking Script`, a configuration `JSON` is required as an input argument. Let's take a look at the `Analysis Script` configuration `JSON` as applied to `Enrichment Analysis`. \n", + "\n", + "**Note:** All the parameters of the configuration `JSON` will be covered in this notebook but for the purpose of each `Analysis Script` usage section, the relevant parameters will be marked using a comment while non-relevant parameters will have value `\"---\"`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "enrichment_json = {\n", + " \"input_docking_data\": {\n", + " \"data_path\": \"---\",\n", + " \"data_metric\": \"---\",\n", + " \"max_data_metric_best\": \"---\",\n", + " \"data_thresholds\": \"---\"\n", + " },\n", + " \"input_exp_data\": {\n", + " \"exp_data_path\": \"---\",\n", + " \"exp_metric\": \"---\",\n", + " \"max_exp_metric_best\": \"---\",\n", + " \"exp_thresholds\": \"---\"\n", + " },\n", + " \"input_enrichment_data\": { \n", + " \"data_path_actives\": COX2_ACTIVES_PATH, # path to the DockStream output for the active ligands\n", + " \"data_path_inactives\": COX2_INACTIVES_PATH, # path to the DockStream output for the inactive ligands\n", + " \"actives_data_metric\": \"score\", # active ligands activity metric\n", + " \"inactives_data_metric\": \"score\", # inactive ligands activity metric\n", + " \"max_metric_best\": \"False\" # denotes whether a greater value is better (ex. GOLD GoldScore)\n", + " },\n", + " \"plot_settings\": {\n", + " \"enrichment_analysis\": \"True\", # denotes to generate histograms, boxplots, and pROC curves only\n", + " \"pROC_overlay\": \"False\" # denotes whether to generate an overlay pROC curve only\n", + " },\n", + " \"output\": {\n", + " \"output_path\": enrichment_results_dir # desired output directory\n", + " }\n", + "}\n", + "\n", + "with open(os.path.join(output_dir, \"enrichment.json\"), \"w+\") as f:\n", + " json.dump(enrichment_json, f, indent=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "The relevant parameters for an `Enrichment Analysis` configuration JSON are elaborated below:\n", + "\n", + "* `\"data_path_actives\" / \"data_path_inactives\"`\n", + "\n", + "Path to a `CSV` file containing the active/inactive ligands data. This is typically the raw and unmodified `DockStream` output. It is perfectly fine for this `CSV` to be any `CSV` file as long as it contains a column that contains the activity data (e.g. for `DockStream` output, the activity data will be `\"score\"` which represents the docking score).\n", + "\n", + "* `\"actives_data_metric\" / \"inactives_data_metric\"`\n", + "\n", + "String that denotes the name of the activity metric for the active/inactive ligands in their corresponding `CSV` files. Typically, both of these parameters will be `\"score\"` as the most common use case will be inputting the raw and unmodified `DockStream` output for both active and inactive ligands.\n", + "\n", + "* `\"max_metric_best\"`\n", + "\n", + "Boolean which denotes whether a greater value is better for the actives / inactive data (e.g. for `Glide` docking scores, the lower the score, the greater the predicted binding affinity - `\"max_metric_best\"` = \"False\")\n", + "\n", + "* `\"enrichment_analysis\"`\n", + "\n", + "Boolean which denotes whether the user wants to perform `Enrichment Analysis`. This parameter takes only 2 possible values: `\"True\"` or `\"False\"`. If `\"True\"`, *only* enrichment histograms, boxplots, and pROC curves are generated. Importantly, this also causes the script to ignore all other parameters in the configuration `JSON` except the ones that are marked in the above code block by a comment. Therefore, it is this boolean parameter that allows executing the `Analysis Script` for `Enrichment Analysis`. If `\"False\"`, all the parameters marked in the above code block by a comment are ignored and denotes that the `Analysis Script` should perform either `Correlation Analysis` or `Thresholds Analysis` which will be introduced later.\n", + "\n", + "* `\"pROC_overlay\"`\n", + "\n", + "Boolean which denotes whether the user wants to generate **only** an overlay pROC curve. This parameter is only parsed if \"enrichment_analysis\" as defined above is set to `\"True\"`. Enrichment histograms and boxplots are not generated.\n", + "\n", + "* `\"output_path\"`\n", + "\n", + "Path to the desired output directory. This is where all the output of the `Analysis Script` execution will be saved.\n", + "\n", + "Let's now execute the `Analysis Script` with the configuration `JSON` above." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enrichment histogram, boxplot, and pROC curve constructed. Exiting script now.\r\n" + ] + } + ], + "source": [ + "# execute this in a command-line environment after replacing the parameters\n", + "!{dockstream_env}/bin/python {analysis_script} -input_json {os.path.join(output_dir, \"enrichment.json\")}" + ] + }, + { + "attachments": { + "COX2_Glide_LigPrep_enrichment_boxplot.png": { + "image/png": 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J2vnnnx+dOnVqZMc7rmHDhiXGkyZNqvPc3M3ZuHFjPPDAA/XO9UmVlZV1ft4XXnhhIzr9yKhRoxLj559/frPHtWnTJjHe9MMhAACa7txzz23wsbvuumv069cvUXvttdfS3FFyvb333rt2/O6779Z5ru8nvfjii/Hee+8lat/+9rez4gufH9v0Ov3++++PmpqarZ63atWqePLJJ+ud65Pmz5+f+P0pNzc3cUd2Q216zf/cc89t9rhN/4xc8wPA9tV664cAADTc/fffn3ieb0FBQRx77LH1ntOuXbv44he/mPgG+4QJE2L06NFbPOeFF15IjFu3bl3vBx4t0THHHBOdOnWq3YZv3bp18fDDD9f50GVTzz77bCxZsqR23Lp16xg8ePAWj585c2asX7++drzbbrs16Rv6u+++e/To0SMWLVoUERH/+c9/YunSpdG1a9fEcZ/80C8i4qGHHorRo0e7KwAAoJlat24dhx12WKPO6dWrV7z00ku144ZsAb2pVCoV//nPf2LevHmxZMmSqKysjDVr1mw2yPzkTj+pVCrmz5+/xe2kp02blhh36dJlq79btDRDhgyJ66+/PjZs2BAR/330y6a7AW3qoYceinXr1tWOO3XqFAMHDtzi8Zv+LA844IDo0qVLo/vdf//9Izc3t/Z3vn//+9+xYcOGxONrIiI+85nPJMb33ntvnHbaaVt95jMAkB4CYAAgrSZOnJgYn3jiiYlnw27JaaedlgiAP37+Va9evTZ7/MyZMxPjfffdNzp06ND4hndgbdq0iVNPPTWxlfLEiRO3GgBv+mdw9NFH17sV3KbP59pvv/0a3+z/r2vXrrUBcMRHIfCmAfAhhxwShYWFtXd7rFu3Ls4888wYMWJEDB06NPr27ZsVW/oBAGxve+65Z7Rv375R5+y6666J8erVqxt87muvvRb33XdfPPLII/XeyVuf+tbb9Dr1wAMPjNzc3Cats6Pq3LlzHHXUUYm7eSdOnLjVAHjTa/7BgwfX2Wnnkz4Z8kc0/Zo/Ly8vOnbsWPvs3zVr1kR5eXl07949cdyxxx4b1157be0XTcvLy+O0006L0aNHx2mnnRaf+9znmrQ+ANAwAmAAIG1mzZoVb7zxRqK2te2fP3booYdGz549Y+HChbW10tLSuPTSSzd7/CdDxoiI4uLiRnbbMgwbNiwRAL/88svx9ttv1/lG/cdWr14dTzzxRJ056rPptnpPPvlk7Lvvvk3sOGlzz3Ju165dfOc734mrrrqqtrZ+/fq499574957740uXbrEQQcdFAcccED0798/+vTpk3Uf9AEAbAuFhYWNPmfTL2tWV1dv9Zzq6ur4xS9+Effee2+DtiuuT32POFm8eHFi3Lt372attaMaNmxYIgB+4oknYvXq1XXC+Y/Nnz8/XnnllTpz1GfBggWJ8d133534PaM5Kioq6gTAPXv2jHPOOSfx3OGqqqq47bbb4rbbbovddtstcc2/7777+hIoAKSRZwADAGnzyTt4Iz7aEvjggw9u0Lk5OTl1til+4IEHEttJf9Kmdxg05cOulqC4uLjOt/M3/bb/Jz388MOJreCKiori6KOPrneNpt6t0RBb+kBv9OjR8Z3vfCdatap7Obps2bJ49NFH45prrokRI0bEwQcfHBdeeGE88MADsXbt2m3WKwBAS1ffHaDpUl1dHd/85jfjnnvuaXb4GxGJR5Fsame55h84cGB06tSpdvzhhx/GI488ssXjN/19YL/99ttqOL4tr/m3dBf3//7v/25x96IPPvggHnjggRgzZkycdtppceihh8Z3v/vdeOKJJ+r9dwIAaBh3AAMAaVFdXR0PP/xwolZQUBC/+tWvGjzHJ+/+jfjo+VdTp07dbIBZVVWVGDd2q7uWZOjQoXHttdfWju+///4thqebhvBb2wouov67Lpqrvg8FL7roojj66KPjpptuimeeeab2uWebqqqqiqeeeiqeeuqp6Ny5c1x44YVx9tlnu0MAACADbr311njmmWcStby8vBg0aFCUlJTEZz/72ejZs2d06NAh8vLyIi8vL3Hs2WefHS+++GKD1tpZrvnbtGkTgwcPjr/85S+1tdLS0vif//mfOsfW1NTE/fffn6ht7e7fiG17zb+lL+3m5ubGVVddFSeeeGLcfPPN8eKLL0YqldrssRUVFfHwww/Hww8/HLvvvntccsklMWTIkG3WMwBkOwEwAJAWkyZNqvOt8nnz5sW8efOaNe+ECRM2GwAXFBQkxmvWrGnWOjuyIUOGxPXXX1/7TfiFCxfGCy+8EIcddljiuHfeeSdefvnlRK0hHwZtuu1f//7946CDDmpm1x/ZZ5996n29T58+cfPNN0d5eXlMnjw5pk+fHi+99FKdbak/Vl5eHldffXVMnTo1fv/732+Xu1wAAPhIRUVF3HbbbYla//7947e//W2dLYC3pDE7uhQUFCR+x8jma/4zzjgjEQC//PLL8c4778Ree+2VOO75559PPA7n4/B4aza95j/qqKPS9tiX3Xbbrd7XBwwYEAMGDIj3338/nn766dpr/iVLlmz2+Pfffz8uvfTSmD59evzsZz9LS48AsLMRAAMAabHpnafpMmnSpFi1alV06NAhUe/YsWNivC23NMu0oqKiOOqoo2LSpEm1tdLS0joB8KZ/Br17966zffTmfHK7uYiIXr16xfe///1mdNx4nTt3jjPOOCPOOOOMiPjo7u+ZM2fGc889F08++WQsXbo0cfzTTz8dv/rVr+KHP/zhdu0TAGBn9vTTT8eHH35YO+7YsWP84Q9/iKKiogbP0Zg7UTt27Ji4zs/ma/799tsv9ttvv3jttddqaxMmTIjvfve7ieNKS0sT46OPPrpBP/9OnTrFBx98UDvu379/XHTRRc3sunF23333GD16dIwePToiPnou8YwZM2Lq1Knx9NNPx8qVKxPH//3vf4999tknzj777O3aJwBkA88ABgCabenSpTF16tRtMnd1dXU8+OCDdeo9evRIjMvKyrbJ+juKTe/kfeKJJ6KysrJ23NSt4CIiunXrlhi/9dZbTewyfbp16xYnnnhi/PSnP43JkyfH9ddfXyeovueee2LFihUZ6hAAYOczc+bMxPjEE09sVPj74YcfJkLIrdn0mn/u3LkNPrcl2vT6/YEHHkhsmVxZWRlPPPFEvedsyY54zb/HHnvEaaedFr/85S9jypQp8ZOf/CTatWuXOObmm29Oy7OmAWBnIwAGAJrt/vvvTzz3qV27dvHKK6/UbgHd2P+NHDkyMf/EiRPrrLnpFsXz5s3bps+1yrRNv9m/du3aeOSRR2rHL7zwQuIZym3atIlTTz21QXMfeOCBifHs2bPrfPs+k1q3bh2DBw+OW265JVFfv359TJs2LUNdAQDsfJYtW5YYf+5zn2vU+f/+979rH2vSEJte88+YMWOLz5ttjpycnLTP2RSDBw9OPOLkgw8+iBdeeKF2/MgjjyTuwO7cufNmH5ezOZte8z/77LM7VLDatm3bOOuss+Kaa65J1MvLy2POnDkZ6goAWi4BMADQbJsGtAMHDqzzze3GOOmkkxLjf//733W+oX7IIYckxhs2bKizHVq65OXlJcaN+dAqXTb3bK9P/tw33f554MCBDb4b49BDD0186LVhw4bNhu6Ztv/++8fee++dqDXmDhIAAJpn0+f3tm7duKfL/eMf/2jU8Zte8y9btiyefPLJRs3REJte70d8tBPR9lZUVFQn0P3kdf6m1/yDBw9u8J/Bpo+PWbFixTb5WTbXCSecEO3bt0/UXPMDQOMJgAGAZpk1a1a88cYbidrJJ5/crDkPOeSQ6NKlS6K26Ycde+65Z5SUlCRqt99++zbZErigoCAxztS2w5tu7/bSSy/Ff/7zn6isrIzHH3+83mPrU1RUFMcee2yi9sc//nGHfMZafn5+plsAANhpdezYMTF+++23G3zum2++GQ8//HCj1jvkkEPi05/+dKL2u9/9Lu3h7KbX+xE7zjX/v/71r6isrIz//Oc/MWPGjMRrQ4cObfC8xcXF0bt370TthhtuyEjQXZ/c3NzYZZddMt0GALR4AmAAoFk2DWbbt28fRx11VLPmzM3NjS9+8YuJ2qbbTEdEfO1rX0uMly5dGj/4wQ/S/iHGbrvtlhhnaguy/fbbL4qLixO1iRMnxqOPPpq4G6NLly6N/jP41re+lRiXl5fHt7/97Sb/LGtqamL58uWbfe29995r0pwrV66s82WDnj17NmkuAAAar1evXonxww8/XOeu4M2prKyMSy+9tNE76bRq1Sq++tWvJmqvv/56/OxnP0s8G7e52rVrF506dUrUMnXNf/TRR0fnzp1rx2vXro3HHnuszu9dffr0if3226/B8+bk5MQ3v/nNRO2NN96Iyy+/vMlbQVdXV2/xMTxNveafP39+nd8jXPMDQOMJgAGAJquurq7zLf5jjz02Ld/Y3vQu4iVLlsSzzz6bqB1xxBF1guKnn346vvrVr8aCBQsatM706dPjf//3f+s9pk+fPonx3XffHUuXLm3Q/Om26bf8J06cWGfr68ZsBfex4uLiGD16dKI2bdq0GDVqVLz55psNnmf58uVxzz33xIknnrjFOzyuvfbaOP3006O0tDRWr17doHnXrl0bl112WeKZZ23atInDDz+8wb0BANA8m37JsCFfwPzggw/ivPPOi7KysiatOWzYsPjCF76QqP3973+PSy65ZItfOPykmpqaePLJJ+PKK6+s97hNr/lvvfXWqKysbHzDzdS6des6j34pLS2N+++/P1FrzN2/Hzv++ONj4MCBido///nPOP/882PhwoUNnmfRokVxyy23xLHHHlvnruSPXXzxxfGlL30pHnvsscQ1fH2WL18el112WaLWtWvXOn82AMDWNe6TQQCAT5g0aVKdbYKbu/3zxw488MDo1q1bLFmypLY2YcKEOh86XXPNNTFv3rx49913a2svvvhinHjiiTF48OA45phjYt99941OnTp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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After executing the above cell block, an enrichment histogram, boxplot, and pROC curve is generated and saved into the specified output directory. Let's take a look at these plots.\n", + "\n", + "
\n", + "\n", + "
\n", + "\n", + "The enrichment histogram plots the docking scores and the frequency of occurence in each bin for both active and inactive ligands. It is evident that there is enrichment observed as the active ligands generally exhibit a better docking score than the inactive ligands. Therefore, the docking configuration above (`Glide` with `LigPrep` was used in this example) could be well suited for initial *in silico* design efforts for this specific protein target. Unfortunately, there is an imbalance in the dataset with many more inactive ligands than active ligands which is a common occurrence. Consequently, it can be difficult to gain an appreciation for the actual spread of the active ligands' docking scores as the height of some bins can be quite small (ex. the green bins at ~ -8 docking score). In situations like this, an enrichment boxplot can better illustrate enrichment. Let's take a look:\n", + "\n", + "
\n", + "\n", + "
\n", + "\n", + "The enrichment boxplot can better illustrate enrichment in imbalanced datasets. In contrast to the enrichment histogram above, the relatively small sample size of actives does not obscure visibility of its data spread. It is again evident that there is enrichment given the docking scores for the active ligands is generally better than the inactive ligands. Important features of a boxplot are listed below:\n", + "\n", + "* The orange line in the middle of each box is the median\n", + "* The 2 sides of the box on either side of the orange line represent Q1 (25th percentile) and Q3 (75th percentile)\n", + "* The interquartile range is defined as the difference between Q3 and Q1 --> IQR = Q3 - Q1\n", + "* The 2 endpoints of the boxplot are the points Q1 - 1.5 * IQR and Q3 + 1.5 * IQR --> most points are within these \"extremes\"\n", + "* Outliers are points outside the \"extremes\" (defined above) and are represented by circles\n", + "\n", + "The above listed metrics are all visible in text boxes pasted directly on the boxplot. `\"Count\"` is the number of data points in each ligand type (e.g. there are 37 active ligands).\n", + "\n", + "Next, a pROC curve and an accompanying `JSON` containing the area under the curve (AUC) is saved to the output folder. The pROC curve provides an additional qualitative evaluation of enrichment while the AUC facilitates quantitative comparison. Please see `Appendix: Analysis Metrics` at the end of the notebook for more details regarding the pROC curve and AUC. Let's take a look at the pROC curve.\n", + "\n", + "
\n", + "\n", + "
\n", + "\n", + "`\"Random\"` corresponds to a docking protocol randomly classifies active and inactive ligands. It is necessarily the case that the docking protocol must be better than this `\"Random\"` classifier. Any curve that is to the left of the random curve displays enrichment. The AUC can be used to quantitatively compare docking protocols. Let's take a look at the output `JSON` that reports this metric.\n", + "\n", + "`\n", + "{\n", + " \"Random\": 0.434,\n", + " \"COX2_Glide_LigPrep\": 2.412\n", + "}\n", + "`\n", + "\n", + "A random classifier always has a AUC of 0.434. Any value greater than 0.434 is considered enrichment.\n", + "\n", + "Finally, the Enrichment Factor for the top 5% ligands (EF 5%) is calculated. Please see `Appendix: Analysis Metrics` at the end of the notebook for more details. Any value greater than 0 means there are active ligands that do indeed score within the top 5% ligands (including decoys). Similar to the pROC AUC, the EF 5% is provided in the corresponding output `JSON`.\n", + "\n", + "`\n", + "{\n", + " \"Docking Experiment EF 5%\": 16.22\n", + "}\n", + "`\n", + "\n", + "**Note:** In the explanations above, active ligands were described as having a \"better\" docking score than inactive ligands. This is because depending on the backend + scoring function chosen, \"better\" can mean lower or higher. For example, the plots above are the result of `Glide` docking which follows the rule, \"lower scores are better\". In contrast, `GOLD` using its `GoldScore` scoring function follows the rule, \"higher scores are better\". In this case, nothing needs to be changed when running `Enrichment Analysis` but do take note of the \"scoring function rule\" when analyzing the enrichment histogram and boxplot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`Enrichment Analysis` supports batch execution - if you want to generate enrichment histograms, boxplots, pROC curves, and calculated pROC AUC for a batch of `DockStream` runs, simply provide the paths to the folders containing the `DockStream` output for the active and inactive ligands.\n", + "\n", + "**Important:** The active and inactive data are matched based on the file names (Ex. COX2_Glide_LigPrep.csv in the actives folder is matched to COX2_Glide_LigPrep.csv in the inactives folder). Therefore, it is recommended when executing `DockStream` for the purpose of batch `Enrichment Analysis` that the actives and inactives are run separately and the output saved to separate folders. Ensure the output `CSV` names are identical.\n", + "\n", + "COX2 and QPCT `Glide` docking data is provided. Previously, the configuration `JSON` was constructed by providing the absolute path to the COX2 active and inactive ligands data. This time, let's generate a configuration `JSON` by providing the absolute path to the folder which contains both the COX2 and QPCT active and inactive ligands data. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# generate the paths to the actives and inactives data folder shipped with this implementation\n", + "ACTIVES_PATH = os.path.join(ipynb_path, \"../data/Analysis_Script/Enrichment/Active\")\n", + "INACTIVES_PATH = os.path.join(ipynb_path, \"../data/Analysis_Script/Enrichment/Inactive\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "batch_enrichment_json = {\n", + " \"input_docking_data\": {\n", + " \"data_path\": \"---\",\n", + " \"data_metric\": \"---\",\n", + " \"max_data_metric_best\": \"---\",\n", + " \"data_thresholds\": \"---\"\n", + " },\n", + " \"input_exp_data\": {\n", + " \"exp_data_path\": \"---\",\n", + " \"exp_metric\": \"---\",\n", + " \"max_exp_metric_best\": \"---\",\n", + " \"exp_thresholds\": \"---\"\n", + " },\n", + " \"input_enrichment_data\": { \n", + " \"data_path_actives\": ACTIVES_PATH, # path to the DockStream output folder for the active ligands\n", + " \"data_path_inactives\": INACTIVES_PATH, # path to the DockStream output folder for the inactive ligands\n", + " \"actives_data_metric\": \"score\", # active ligands activity metric\n", + " \"inactives_data_metric\": \"score\", # inactive ligands activity metric\n", + " \"max_metric_best\": \"False\" # denotes whether a greater value is better (ex. GOLD GoldScore)\n", + " },\n", + " \"plot_settings\": {\n", + " \"enrichment_analysis\": \"True\", # denotes to generate histograms, boxplots, and pROC curves only\n", + " \"pROC_overlay\": \"False\" # denotes whether to generate an overlay pROC curve only\n", + " },\n", + " \"output\": {\n", + " \"output_path\": enrichment_results_dir # desired output directory\n", + " }\n", + "}\n", + "\n", + "with open(os.path.join(output_dir, \"batch_enrichment.json\"), \"w+\") as f:\n", + " json.dump(batch_enrichment_json, f, indent=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now execute the `Analysis Script` with the new configuration `JSON` above." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Batch enrichment histograms, boxplots, and pROC curves constructed. Exiting script now.\r\n" + ] + } + ], + "source": [ + "# execute this in a command-line environment after replacing the parameters\n", + "!{dockstream_env}/bin/python {analysis_script} -input_json {os.path.join(output_dir, \"batch_enrichment.json\")}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice in the output folder, there is now a histogram, boxplot, and pROC curve for both the COX2 and QPCT `Glide` data. For brevity, the new plots are not shown. Only the pROC AUC and EF 5% `JSONs` are shown." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**pROC AUC Values**\n", + "\n", + "`{\n", + " \"Random\": 0.434,\n", + " \"COX2_Glide_LigPrep\": 2.412,\n", + " \"QPCT_Glide_CTE\": 0.343\n", + "}`\n", + "\n", + "**EF 5%**\n", + "\n", + "`\n", + "{\n", + " \"COX2_Glide_LigPrep\": 16.22,\n", + " \"QPCT_Glide_CTE\": 0.0\n", + "}\n", + "`\n", + "\n", + "All pROC AUC and EF 5% values are stored in the output `JSONs` (there are now pROC values for COX2 **and** QPCT) and allows one to easily screen through many `Enrichment Analysis` runs.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, the last functionality available via `Enrichment Analysis` is the generation of overlay pROC curves. These plots can be especially useful to qualitatively assess a batch of `DockStream` results. The configuration `JSON` required is identical to the batch `Enrichment Analysis` `JSON` above except **`\"pROC_overlay\" = \"True\"`**. Let's generate the configuration `JSON`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "pROC_overlay_enrichment_json = {\n", + " \"input_docking_data\": {\n", + " \"data_path\": \"---\",\n", + " \"data_metric\": \"---\",\n", + " \"max_data_metric_best\": \"---\",\n", + " \"data_thresholds\": \"---\"\n", + " },\n", + " \"input_exp_data\": {\n", + " \"exp_data_path\": \"---\",\n", + " \"exp_metric\": \"---\",\n", + " \"max_exp_metric_best\": \"---\",\n", + " \"exp_thresholds\": \"---\"\n", + " },\n", + " \"input_enrichment_data\": { \n", + " \"data_path_actives\": ACTIVES_PATH, # path to the DockStream output folder for the active ligands\n", + " \"data_path_inactives\": INACTIVES_PATH, # path to the DockStream output folder for the inactive ligands\n", + " \"actives_data_metric\": \"score\", # active ligands activity metric\n", + " \"inactives_data_metric\": \"score\", # inactive ligands activity metric\n", + " \"max_metric_best\": \"False\" # denotes whether a greater value is better (ex. GOLD GoldScore)\n", + " },\n", + " \"plot_settings\": {\n", + " \"enrichment_analysis\": \"True\", # denotes to generate histograms, boxplots, and pROC curves only\n", + " \"pROC_overlay\": \"True\" # denotes whether to generate an overlay pROC curve only\n", + " },\n", + " \"output\": {\n", + " \"output_path\": enrichment_results_dir # desired output directory\n", + " }\n", + "}\n", + "\n", + "with open(os.path.join(output_dir, \"pROC_overlay_enrichment.json\"), \"w+\") as f:\n", + " json.dump(pROC_overlay_enrichment_json, f, indent=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice `\"data_path_actives\"` and `\"data_path_inactives\"` are paths to the folders containing *all* the active and inactive ligands data. The same guidelines for file names are relevant here and stated again below:\n", + "\n", + "**Important:** The active and inactive data are matched based on the file names (Ex. COX2_Glide_LigPrep.csv in the actives folder is matched to COX2_Glide_LigPrep.csv in the inactives folder). Therefore, it is recommended when executing `DockStream` for the purpose of generating overlay pROC curves that the actives and inactives are run separately and the output saved to separate folders. Ensure the output `CSV` names are identical.\n", + "\n", + "Let's now execute the `Analysis Script` to generate an overlay pROC curve." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overlay pROC curves plot generated. All pROC AUC calculated. Exiting script now.\r\n" + ] + } + ], + "source": [ + "# execute this in a command-line environment after replacing the parameters\n", + "!{dockstream_env}/bin/python {analysis_script} -input_json {os.path.join(output_dir, \"pROC_overlay_enrichment.json\")}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at the overlay pROC curve and the output pROC AUC `JSON`." + ] + }, + { + "attachments": { + "overlay_pROC_curve.png": { + "image/png": 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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "
\n", + "\n", + "The overlay pROC curve allows one to easily observe that COX2 displays significantly more enrichment than QPCT. This is reaffirmed quantitatively in the pROC AUC `JSON`:\n", + "\n", + "`{\n", + " \"Random\": 0.434,\n", + " \"COX2_Glide_LigPrep\": 2.412,\n", + " \"QPCT_Glide_CTE\": 0.343\n", + "}`\n", + "\n", + "Finally, the EF 5% values are also outputted as in the case of batch `Enrichment Analysis`.\n", + "\n", + "`\n", + "{\n", + " \"COX2_Glide_LigPrep\": 16.22,\n", + " \"QPCT_Glide_CTE\": 0.0\n", + "}\n", + "`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Correlation Analysis\n", + "\n", + "Suppose you are involved in a hit discovery and/or lead optimization project. You are interested in applying `DockStream` to help guide experimental efforts. Specifically, you want to determine the backend + ligand embedder + settings combination (e.g. `Glide with LigPrep` and `\"SP\"` precision) that performs \"best\" for your ligand-receptor system. \"Best\" here is defined as the configuration that yields the best correlation between docking scores and experimental data (ex. in the perfect scenario, a better docking score always equates to a more potent compound). One can imagine that the choice of backend + ligand embedder + settings (some sort of \"hyper-parameters\") leads to a combinatorial explosion of possibilities. The `Benchmarking script` was introduced to automate and streamline batch execution of `DockStream`. The `Analysis Script` and specfically its `Correlation Analysis` functionality was introduced to automate and streamline the quantitative correlation comparison between batch `DockStream` runs. The purpose is to allow the user to relatively easily compare and identify the best docking configuration for their project. This process is useful as a standalone *in silico* design component but also provides a good integration point for `REINVENT` where identifying the \"best\" docking configuration will structurally inform `REINVENT` runs. The dataset used in this section is from the D3R Grand Challenge which provides open access to experimental assay data. For more information see:\n", + "\n", + "https://drugdesigndata.org/\n", + "\n", + "The Urokinase dataset released by Abbott was chosen for `Correlation Analysis` demonstration. The dataset was sampled to take only the active ligands which were then docked using `GOLD` and `Hybrid` and the `DockStream` output is shipped with the `DockStream` codebase. Note that `GOLD` and `Hybrid` were chosen as backends arbitrarily - `Correlation Analysis` is compatible with any backend. Let's take a look at the `GOLD` and `Hybrid` docking data and the Urokinase experimental data." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# generate the paths to the Hybrid docking data, the folder containg both Hybrid and GOLD docking data, \n", + "# and the experimental data shipped with this implementation\n", + "HYBRID_PATH = os.path.join(ipynb_path, \"../data/Analysis_Script/Correlation/DockStream_Output/Urokinase_Hybrid_Docking_Data.csv\")\n", + "DOCKING_DATA_PATH = os.path.join(ipynb_path, \"../data/Analysis_Script/Correlation/DockStream_Output\")\n", + "EXP_PATH = os.path.join(ipynb_path, \"../data/Analysis_Script/Correlation/Urokinase_Exp_Data.csv\")\n", + "\n", + "# read the GOLD and Hybrid docking data and experimental data to show its contents\n", + "GOLD_DATA = pd.read_csv(os.path.join(ipynb_path, \"../data/Analysis_Script/Correlation/DockStream_Output/Urokinase_GOLD_Docking_Data.csv\"))\n", + "HYBRID_DATA = pd.read_csv(os.path.join(ipynb_path, \"../data/Analysis_Script/Correlation/DockStream_Output/Urokinase_Hybrid_Docking_Data.csv\"))\n", + "EXP_DATA = pd.read_csv(os.path.join(ipynb_path, \"../data/Analysis_Script/Correlation/Urokinase_Exp_Data.csv\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ligand_numberenumerationconformer_numbernamescoresmileslowest_conformer
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ligand_numberenumerationconformer_numbernamescoresmileslowest_conformer
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" + ], + "text/plain": [ + " ligand_number Ki (nM) Log Ki (nM)\n", + "0 0 40.0 1.602060\n", + "1 1 4500.0 3.653213\n", + "2 2 610.0 2.785330" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "EXP_DATA.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above cells display the first 3 entries in the Urokinase `GOLD` and `Hybrid` `DockStream` output. The important observation is that the `CSV` files provided are simply the raw and unmodified `DockStream` output `CSVs`.\n", + "\n", + "The first 3 entries in the Urokinase assay activity is also displayed. There have been some slight processing of the raw data. Notably, the `Ki (nM)` has been log transformed simply to create a better suited y-scale for plotting. In addition, the `Log Ki (nM)` is mapped to the ligand via `\"ligand_number\"` which is internally generated following `DockStream` execution (i.e. each ligand is given a `\"ligand_number\"` in the raw and unmodified `DockStream` output `CSVs`). Manually creating this experimental data file is unfortunately required but only needs to be done once. We will now demonstrate the generation of a scatter plot and an output `JSON` which contains relevant statistics metrics. Similar to the `Benchmarking Script`, a configuration `JSON` is required as an input argument. Let's take a look at the `Analysis Script` configuration `JSON` as applied to `Correlation Analysis`. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "correlation_json = {\n", + " \"input_docking_data\": {\n", + " \"data_path\": HYBRID_PATH, # path to the docking data\n", + " \"data_metric\": \"score\", # docked ligands activity metric\n", + " \"max_data_metric_best\": \"False\", # denotes whether a greater docking score = greater predicted affinity\n", + " \"data_thresholds\": \"N/A\" # must be \"N/A\" for correlation analysis\n", + " },\n", + " \"input_exp_data\": {\n", + " \"exp_data_path\": EXP_PATH, # path to the experimental data\n", + " \"exp_metric\": \"Log Ki (nM)\", # experimental data activity metric\n", + " \"max_exp_metric_best\": \"False\", # denotes whether a greater value = greater affinity/potency\n", + " \"exp_thresholds\": \"N/A\" # must be \"N/A\" for correlation analysis\n", + " },\n", + " \"input_enrichment_data\": { \n", + " \"data_path_actives\": \"---\", \n", + " \"data_path_inactives\": \"---\", \n", + " \"actives_data_metric\": \"---\", \n", + " \"inactives_data_metric\": \"---\", \n", + " \"max_metric_best\": \"---\" \n", + " },\n", + " \"plot_settings\": {\n", + " \"enrichment_analysis\": \"False\", # denotes to generate histograms, boxplots, and pROC curves only\n", + " \"pROC_overlay\": \"False\" # denotes whether to generate an overlay pROC curve only\n", + " },\n", + " \"output\": {\n", + " \"output_path\": correlation_results_dir # desired output directory\n", + " }\n", + "}\n", + "\n", + "with open(os.path.join(output_dir, \"correlation.json\"), \"w+\") as f:\n", + " json.dump(correlation_json, f, indent=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The relevant parameters for a `Correlation Analysis` configuration JSON are elaborated below:\n", + "\n", + "* `\"data_path\" / \"exp_data_path\"`\n", + "\n", + "Path to a `CSV` file or a folder containing `CSV` files (as we will see later) containing the docking/experimental data. The `CSV` files are typically the raw and unmodified `DockStream` output. It is perfectly fine for this `CSV` to be any `CSV` file as long as it contains a column that contains the activity data (e.g. for `DockStream` output, the activity data will be `\"score\"` which represents the docking score).\n", + "\n", + "* `\"data_metric\" / \"exp_metric\"`\n", + "\n", + "String that denotes the name of the activity metric for the docking/experimental data in their corresponding `CSV` files. Typically, `\"data_metric\"` will be `\"score\"` as the most common use case will be inputting the raw and unmodified `DockStream` output. The `\"exp_metric\"` will be project specific - the example in this section is `\"Log Ki (nM)\"`.\n", + "\n", + "* `\"max_data_metric_best\" / \"max_exp_metric_best\"`\n", + "\n", + "Boolean which denotes whether a greater value = greater affinity/potency (e.g. for `Glide` docking scores, the lower the score, the greater the predicted binding affinity - `\"max_data_metric_best\"` = \"False\"). Suppose you have `\"pIC50\"` values - this would require `\"max_exp_metric_best\"` = \"True\".\n", + "\n", + "* `\"data_thresholds\" / \"exp_thresholds\"`\n", + "\n", + "These parameters are relevant for `Threshold Analysis` which is introduced in the next section. For `Correlation Analysis`, these parameters must be set to `\"N/A\"`.\n", + "\n", + "* `\"enrichment_analysis\"`\n", + "\n", + "Boolean which denotes whether the user wants to perform `Enrichment Analysis`. This parameter takes only 2 possible values: `\"True\"` or `\"False\"`. For `Correlation Analysis`, it must be set to `\"False\"`.\n", + "\n", + "* `\"pROC_overlay\"`\n", + "\n", + "Boolean which denotes whether the user wants to generate *only* an overlay pROC curve. This parameter is only parsed if `\"enrichment_analysis\"` = `\"True\"`. Set to `\"False\"` for `Correlation Analysis`\n", + "\n", + "\n", + "* \"output_path\"\n", + "\n", + "Path to the desired output directory. This is where all the output of the `Analysis Script` execution will be saved.\n", + "\n", + "Let's now execute the `Analysis Script` with the configuration `JSON` above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# execute this in a command-line environment after replacing the parameters\n", + "!{dockstream_env}/bin/python {analysis_script} -input_json {os.path.join(output_dir, \"correlation.json\")}" + ] + }, + { + "attachments": { + "Urokinase_Hybrid_Docking_Data_scatplot.png": { + "image/png": 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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After executing the above cell block, a scatter plot and a `JSON` containing calculated metrics are saved into the specified output directory. Let's take a look at the scatter plot.\n", + "\n", + "
\n", + "\n", + "
\n", + "\n", + "The title of the scatter plot is extracted from the `CSV` file name. The x and y axes labels are extracted from the \"data_metric\" and `\"exp_metric\"` parameters in the configuration `JSON` described previously. The scatter plot shows the distribution of (docking score, experimental activity) pairs. Correlation metrics are also computed and are saved in a `JSON` file for readibility. Let's take a look at the `JSON`.\n", + "\n", + "`{\n", + " \"Urokinase_Hybrid_Docking_Data.csv\": {\n", + " \"coeff_determination\": 0.057778420203442704,\n", + " \"Spearman_coeff\": -0.09245110132393133,\n", + " \"Kendall_coeff\": -0.058928665383788846\n", + " }\n", + "}`\n", + "\n", + "The `JSON` displays the origin of the data in the header (e.g. \"Urokinase_Hybrid_Docking.csv\"). The 3 correlation metrics computed are The Coefficient of Determination ($R^{2}$) and Spearman ($\\rho$) and Kendall ($\\tau$) Correlation. Please see `Appendix: Analysis Metrics` at the end of this notebook for details regarding each metric. \n", + "\n", + "**Practical Note:** Do take note of repeated values in your dataset. A common occurrence of this is when the experimental assay caps output values at the limit of quantification, LOQ (e.g. pIC50 capping at 4 M). These \"inactive ligands\" will skew the correlation metrics and one may consider removing these data points. Doing so will generally improve the correlation metrics and may yield a more meaningful interpretation. However, the same can be said for retaining these data points; removing repeated metrics inherently causes loss of information which could be detrimental\n", + "\n", + "**Important Note:** \"Conventionally\", correlation scores are positive when an increase in the `x variable` is matched by an increase in the `y variable` and vice versa. Unfortunately, depending on the docking backend and the experimental assay, docking scores and experimental activity metrics may follow either the rule \"lower scores are better\" or \"higher scores are better\". Consider the case with `Glide` docking scores and accompanying `\"pIC50\"` values. Lower `Glide` scores are better and greater `\"pIC50\"` scores are better. Therefore, a negative correlation value is actually good! To avoid any ambiguities, these discrepancies are handled internally to follow the \"conventional\" interpretation: A positive correlation value **always** means a \"better\" docking score yields a \"better\" experimental activity value regardless of the metrics used as long as the `\"max_data_metric_best\"` and `\"max_exp_metric_best\"` are properly assigned in the configuration `JSON`. Therefore, if you observe that the correlation metric is negative, it does indeed mean that the docking scores do not exhibit positive correlation with the experimental activity values (i.e. the docking configuration is not well suited for the project)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now suppose you want to perform `Correlation Analysis` on many `DockStream` runs. In this section, we provided `GOLD` and `Hybrid` docking data. Previously, only `Hybrid` was analyzed. `Correlation Analysis` supports batch execution. Simply create a folder to hold all the `DockStream` output `CSV` files and pass this path as the `\"data_path\"` argument in the analysis configuration `JSON` - let's take a look. " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# note everything is the same as the JSON above except the \"data_path\" parameter whose value is now a folder\n", + "batch_correlation_json = {\n", + " \"input_docking_data\": {\n", + " \"data_path\": DOCKING_DATA_PATH, # path to the docking data folder\n", + " \"data_metric\": \"score\", # docked ligands activity metric\n", + " \"max_data_metric_best\": \"False\", # denotes whether a greater docking score = greater predicted affinity\n", + " \"data_thresholds\": \"N/A\" # must be \"N/A\" for correlation analysis\n", + " },\n", + " \"input_exp_data\": {\n", + " \"exp_data_path\": EXP_PATH, # path to the experimental data\n", + " \"exp_metric\": \"Log Ki (nM)\", # experimental data activity metric\n", + " \"max_exp_metric_best\": \"False\", # denotes whether a greater value = greater affinity/potency\n", + " \"exp_thresholds\": \"N/A\" # must be \"N/A\" for correlation analysis\n", + " },\n", + " \"input_enrichment_data\": { \n", + " \"data_path_actives\": \"---\", \n", + " \"data_path_inactives\": \"---\", \n", + " \"actives_data_metric\": \"---\", \n", + " \"inactives_data_metric\": \"---\", \n", + " \"max_metric_best\": \"---\" \n", + " },\n", + " \"plot_settings\": {\n", + " \"enrichment_analysis\": \"False\", # denotes to generate histograms, boxplots, and pROC curves only\n", + " \"pROC_overlay\": \"False\" # denotes whether to generate an overlay pROC curve only\n", + " },\n", + " \"output\": {\n", + " \"output_path\": correlation_results_dir # desired output directory\n", + " }\n", + "}\n", + "\n", + "with open(os.path.join(output_dir, \"batch_correlation.json\"), \"w+\") as f:\n", + " json.dump(batch_correlation_json, f, indent=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's execute batch `correlation analysis`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# execute this in a command-line environment after replacing the parameters\n", + "!{dockstream_env}/bin/python {analysis_script} -input_json {os.path.join(output_dir, \"batch_correlation.json\")}" + ] + }, + { + "attachments": { + "Urokinase_GOLD_Docking_Data_scatplot.png": { + "image/png": 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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After executing the above cell block, scatter plots for both the `Hybrid` and `GOLD` run and a `JSON` containing calculated metrics are saved into the specified output directory. There are now 2 scatter plots:\n", + "\n", + "
\n", + "\n", + "
\n", + "\n", + "The above scatter plot is the exact same plot as previously generated.\n", + "\n", + "
\n", + "\n", + "
\n", + "\n", + "The above scatter plot is now generated as well when `Analysis Script` is called. In practise, there is no limit to the number of scatter plots that can be generated. This is simply controlled by how many `DockStream` output `CSV` files are provided in the input folder. The correlation metrics are also computed for all runs and instead of 1 `JSON` per run, all the metrics are compiled into a single `JSON`. Let's take a look.\n", + "\n", + "`{\n", + " \"Urokinase_GOLD_Docking_Data.csv\": {\n", + " \"coeff_determination\": 0.07536299287923043,\n", + " \"Spearman_coeff\": -0.28606357302214785,\n", + " \"Kendall_coeff\": -0.20178603601115574\n", + " },\n", + " \"Urokinase_Hybrid_Docking_Data.csv\": {\n", + " \"coeff_determination\": 0.057778420203442704,\n", + " \"Spearman_coeff\": -0.09245110132393133,\n", + " \"Kendall_coeff\": -0.058928665383788846\n", + " }\n", + "}`\n", + "\n", + "The header once again displays the origins of the data with the corresponding correlation metrics. The `JSON` would simply be longer if there were more `DockStream` output `CSV` files analyzed.\n", + "\n", + "\n", + "**Warning:** Notice the ranked correlation metrics for `GOLD` and `Hybrid` are negative. This is indeed the case for `Hybrid`, but they should actually be positive for `GOLD` because the `GoldScore` scoring function was used (higher scores = better scores). The reason for this is because in the configuration JSON, `\"max_data_metric_best\"` was set to `False`. Therefore, when running batch `Correlation Analysis`, keep in the mind the nature of the scoring functions. In this case, please multiply the `GOLD` correlation metrics by -1. Conversely, if `\"max_data_metric_best\"` were set to `True`, the `GOLD` `GoldScore` correlation metrics would be correct but the `Hybrid` correlation metrics would need to be multiplied by -1. This discrepancy is most prevalent when running large batch `Correlation Analysis` with many \"higher scores = better scores\" and \"lower scores = better scores\" scoring functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Thresholds Analysis\n", + "\n", + "Suppose you are now later in a project such as at the lead optimization phase. You have previously applied `DockStream` to guide synthetic efforts and have now obtained some experimental activity data. You may now want to set-up another iteration of `DockStream` experiments but before doing so, you are interested in observing the distribution of \"active\" and \"inactive\" ligands. \"Active\" and \"inactive\" are in quotations here because you may want to consider a ligand \"active\" / \"inactive\" given a concrete threshold value (e.g. `Glide` docking scores <= -10 are considered docking active and `\"pIC50\"` values > 5.5 are considered experimentally active). Given these thresholds, what is the distribution of your ligand set? Thresholds that yield a favorable distribution may help guide further computational experiments (e.g. only consider docking scores better than a certain threshold).\n", + "\n", + "Alternatively, it may be the case that docking protocols fail to yield any observed correlation with experimental data. For instance, you may have applied `Correlation Analysis` to evaluate many `DockStream` runs but they all show extremely poor correlation. In this situation, it can be valuable to investigate whether setting thresholds can facilitate a general separation of your data.\n", + "\n", + "`Thresholds Analysis` addresses these questions and contains all the capabilities of `Correlation Analysis` (scatter plot generation and calculation of statistics metrics) but with added features. Notably, confusion matrices and stacked histograms are generated to facilitate qualitative and quantitative comparison of the distribution of `true positives` (docking active and experimentally active), `true negatives` (docking inactive and experimentally inactive), `false positives` (docking active and experimentally inactive), and `false negatives` (docking inactive and experimentally active). The ligands are classified into these labels given user specified thresholds. \n", + "\n", + "**Classification Example**: Consider the user provided thresholds pair of (`-7 Glide` docking score, `3 Log Kd`):\n", + "* `Glide` docking score <= -7 = docking active and `Glide` docking score > -7 = docking inactive\n", + "* `Log Kd` value <= 3 = experimentally active and `Log Kd` value > 3 = experimentally inactive\n", + "* `true positive` = docking active and experimentally active (ex. -8 `Glide` docking score and 2 `Log Kd`\n", + "\n", + "The dataset used in this section is from the D3R Grand Challenge which provides open access to experimental assay data. For more information:\n", + "\n", + "https://drugdesigndata.org/\n", + "\n", + "The CHK1 Kinase dataset released by Abbott was chosen for `Thresholds Analysis` demonstration. The dataset combines both active and inactive ligands which were then docked using `Glide` and the `DockStream` output is shipped with the `DockStream` codebase. Note that `Glide` was chosen as the backend arbitrarily - `Thresholds Analysis` is compatible with any backend. Let's take a look at the data file." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# generate the paths to the CHK1 Kinase Glide and experimental assay data\n", + "CHK1_DATA_PATH = os.path.join(ipynb_path, \"../data/Analysis_Script/Thresholds/CHK1_Data.csv\")\n", + "\n", + "# read the CHK1 Kinase Glide and experimental assay data to show its contents\n", + "CHK1_DATA = pd.read_csv(os.path.join(ipynb_path, \"../data/Analysis_Script/Thresholds/CHK1_Data.csv\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " ligand_number score Kd (nM) Log Kd (nM)\n", + "0 0 -7.74490 178.00 2.250420\n", + "1 1 -8.03648 15.40 1.187521\n", + "2 2 -7.68241 2.95 0.469822" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CHK1_DATA.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above cell displays the first 3 entries in the CHK1 data file which contains the `Glide` scores and the assay activity. There has been some slight processing of the raw data. Notably, the `Glide` docking scores and the experimental assay data has been compiled into a single file. This was done because the CHK1 raw data files separated the active and inactive ligands which are now compiled together. The experimental assay data was matched to each ligand via `\"ligand_number\"` which is internally generated following `DockStream` execution (i.e. each ligand is given a `\"ligand_number\"` in the raw and unmodified `DockStream` output `CSVs`). We will now demonstrate the functionalities of `Threshold Analysis`. Similar to the `Benchmarking Script`, a configuration `JSON` is required as an input argument. Let's take a look at the `Analysis Script` configuration `JSON` as applied to `Threshold Analysis`. " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "thresholds_json = {\n", + " \"input_docking_data\": {\n", + " \"data_path\": CHK1_DATA_PATH, # path to the docking data folder\n", + " \"data_metric\": \"score\", # docked ligands activity metric\n", + " \"max_data_metric_best\": \"False\", # denotes whether a greater docking score = greater predicted affinity\n", + " \"data_thresholds\": [-7] # a list that holds the threshold to separate active/inactive ligands based on docking score\n", + " },\n", + " \"input_exp_data\": {\n", + " \"exp_data_path\": CHK1_DATA_PATH, # path to the experimental data\n", + " \"exp_metric\": \"Log Kd (nM)\", # experimental data activity metric\n", + " \"max_exp_metric_best\": \"False\", # denotes whether a greater value = greater affinity/potency\n", + " \"exp_thresholds\": [3] # a list that holds the threshold to separate active/inactive ligands based on experimental data\n", + " },\n", + " \"input_enrichment_data\": { \n", + " \"data_path_actives\": \"---\", \n", + " \"data_path_inactives\": \"---\", \n", + " \"actives_data_metric\": \"---\", \n", + " \"inactives_data_metric\": \"---\", \n", + " \"max_metric_best\": \"---\" \n", + " },\n", + " \"plot_settings\": {\n", + " \"enrichment_analysis\": \"False\", # denotes to generate histograms, boxplots, and pROC curves only\n", + " \"pROC_overlay\": \"False\" # denotes whether to generate an overlay pROC curve only\n", + " },\n", + " \"output\": {\n", + " \"output_path\": thresholds_results_dir # desired output directory\n", + " }\n", + "}\n", + "\n", + "with open(os.path.join(output_dir, \"thresholds.json\"), \"w+\") as f:\n", + " json.dump(thresholds_json, f, indent=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The relevant parameters for a `thresholds analysis` configuration `JSON` are elaborated below:\n", + "\n", + "* `\"data_path\" / \"exp_data_path\"`\n", + "\n", + "Path to a `CSV` file or a folder containing `CSV` files (as we will see later) containing the docking/experimental data. The `CSV` files are typically the raw and unmodified `DockStream` output. It is perfectly fine for this `CSV` to be any `CSV` file as long as it contains a column that contains the activity data (e.g. for `DockStream` output, the activity data will be `\"score\"` which represents the docking score).\n", + "\n", + "* `\"data_metric\" / \"exp_metric\"`\n", + "\n", + "String that denotes the name of the activity metric for the docking/experimental data in their corresponding `CSV` files. Typically, \"data_metric\" will be `\"score\"` as the most common use case will be inputting the raw and unmodified `DockStream` output. The \"exp_metric\" will be project specific - the example in this section is `\"Log Ki (nM)\"`.\n", + "\n", + "* `\"max_data_metric_best\" / \"max_exp_metric_best\"`\n", + "\n", + "Boolean which denotes whether a greater value = greater affinity/potency (e.g. for `Glide` docking scores, the lower the score, the greater the predicted binding affinity - `\"max_data_metric_best\"` = `False`). Suppose you have `\"pIC50\"` values - this would require `\"max_exp_metric_best\"` = `True`.\n", + "\n", + "* `\"data_thresholds\" / \"exp_thresholds\"`\n", + "\n", + "This parameter takes a list of value(s) as input. The threshold values act as the hard boundary value in which the docking scores / experimental activity values are classified as \"active\" or \"inactive\".\n", + "\n", + "**Note:** One extremely important parameter to keep in mind is `\"max_data_metric_best\"` / `\"max_exp_metric_best\"` explained above. Their boolean values dictate the interpretation of the threshold value boundaries. For example, consider `Glide` docking data and accompanying `\"pIC50\"` data. `Glide` follows the rule \"lower scores, greater predicted binding affinity\" while a greater `\"pIC50\"` means a more potent ligand. In this case, `\"max_data_metric_best\"` = `False` and `\"max_exp_metric_best\"` = `True`. Now consider the thresholds pair (docking threshold, experimental threshold) as (-7, 3). In this case, `Glide` scores <= -7 will be considered \"docking active\" while `\"pIC50\"` values > 3 will be considered \"experimentally active\". Depending on how the ligands are classified, the generated plots would differ significantly and could be misleading. It is vital to be conscious of how your specific data metrics are to be interpreted.\n", + "\n", + "* `\"enrichment_analysis\"`\n", + "\n", + "Boolean which denotes whether the user wants to perform `Enrichment Analysis`. This parameter takes only 2 possible values: `\"True\"` or `\"False\"`. For `Thresholds Analysis`, it must be set to `\"False\"`.\n", + "\n", + "* `\"pROC_overlay\"`\n", + "\n", + "Boolean which denotes whether the user wants to generate *only* an overlay pROC curve. This parameter is only parsed if `\"enrichment_analysis\"` = `\"True\"`. Set to `\"False\"` for `Thresholds Analysis`\n", + "\n", + "* `\"output_path\"`\n", + "\n", + "Path to the desired output directory. This is where all the output of the `Analysis Script` execution will be saved.\n", + "\n", + "Let's now execute the `Analysis Script` with the configuration `JSON` above." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's execute `Thresholds Analysis`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# execute this in a command-line environment after replacing the parameters\n", + "!{dockstream_env}/bin/python {analysis_script} -input_json {os.path.join(output_dir, \"thresholds.json\")}" + ] + }, + { + "attachments": { + "CHK1_Data_histogram_%28-7,%203%29.png": { + "image/png": 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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### In the following section, `Thresholds Analysis` classifies ligands as follows: \n", + "* `True Positives` = Docking active and experimentally active\n", + "* `True Negatives` = Docking inactive and experimentally inactive\n", + "* `False Positives` = Docking active but experimentally inactive\n", + "* `False Negatives` = Docking inactive but experimentally active\n", + "\n", + "#### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", + "\n", + "After executing the above cell block, you will notice `Thresholds Analysis` generates many more plots. As mentioned before, `Thresholds Analysis` contains all the functionalities of `Correlation Analysis` with many additional features. Let's first take a look at the commonalities.\n", + "\n", + "
\n", + "\n", + "
\n", + "\n", + "Once again the title of the scatter plot is extracted from the `CSV` file name. The x and y axes labels are extracted from the `\"data_metric\"` and `\"exp_metric\"` parameters in the configuration `JSON` above. The scatter plot shows the distribution of (docking score, experimental activity) pairs. As with before, correlation metrics are calculated and saved into a `JSON` file.\n", + "\n", + "`{\n", + " \"CHK1_Data.csv\": {\n", + " \"coeff_determination\": 0.012665427425591846,\n", + " \"Spearman_coeff\": 0.0947266781245275,\n", + " \"Kendall_coeff\": 0.06257646699328888\n", + " },\n", + " \"MCC_for_CHK1_Data.csv_(-7, 3)\": -0.011353884997945918\n", + "}`\n", + "\n", + "The scatter plots makes it evident that there is extremely poor correlation. This is reaffirmed quantitatively by the correlation metrics (e.g. Spearman correlation = ~0). In this situtation, the thresholds provided can be used to investigate general separation. Let's take a look at the stacked histograms generated.\n", + "\n", + "
\n", + "\n", + "
\n", + "\n", + "The stacked histogram shows the distribution of `true positives`, `true negatives`, `false positives`, and `false negatives` once again classified based on the user defined thresholds. A separation is now visible - this of course is only possible due to the hard cut-off values enforced by the threshold. Valuable information that can be extracted from the stacked histograms are what sort of thresholds leads to a favorable enrichment on your project. For instance, one may want to consider only docking score < -8 to maximize `true positives` and minimize `false positives`.\n", + "\n", + "A pooled stacked histogram is also generated which \"pools\" the `true positives` and `false negatives` together and the `true negative` and `false positives` together. In essence, `pooled positives` represent all ligands that are experimentally active while `pooled negatives` represent all ligands that are experimentally inactive.\n", + "\n", + "
\n", + "\n", + "
\n", + "\n", + "**Note:** The histograms are \"stacked\" meaning the `\"absolute count\"` for bins that feature more than 1 data classification are meant to be counted from the height visible and not from the base (e.g. for the pooled histogram at ~ -5 docking score, there is 1 \"pooled positive\" and 1 \"pooled negative\" **and not** 1 \"pooled positive\" and 2 \"pooled negatives\")\n", + "\n", + "An accompanying plot to the stacked histograms is the confusion matrix.\n", + "\n", + "
\n", + "\n", + "
\n", + "\n", + "\n", + "The confusion matrix features binarized docking scores and experimental activity values which are classified as \"active\" (\"1\") and \"inactive\" (\"0\") based on the user specified thresholds. For instance, the bottom right region corresponds to a binarized docking score of \"1\" and a binarized experimental activity value of \"1\" - therefore, these are considered `true positives` (docking active and experimentally active). As illustrated in the plot, there are 39 `true positives` given the specified thresholds. One new addition to the output statistics `JSON` is the Matthews Correlation Coefficient (MCC) which for our purposes can be interpreted as, \"how many more/less `false positive` + `false negative` ligands are there compared to `true positive` + `true negative` ligands given the user specified thresholds?\" Please see `Appendix: Analysis Metrics` for details regarding the MCC. The output correlation `JSON` is displayed below again:\n", + "\n", + "`{\n", + " \"CHK1_Data.csv\": {\n", + " \"coeff_determination\": 0.012665427425591846,\n", + " \"Spearman_coeff\": 0.0947266781245275,\n", + " \"Kendall_coeff\": 0.06257646699328888\n", + " },\n", + " \"MCC_for_CHK1_Data.csv_(-7, 3)\": -0.011353884997945918\n", + "}`\n", + "\n", + "\n", + "The values pair in parentheses, (-7, 3) denotes that the MCC corresponds to a docking score threshold of -7 and an experimental activity threshold of 3. The MCC is best analyzed in combination with the confusion matrix. It was previously stated that the MCC can be interpreted as the proportion of `true positives` + `true negatives` (67 ligands) compared to `false positives` + `false negatives` (70 ligands). Given the specified thresholds, there are more `false positives` + `false negatives` than `true positives` + `true negatives` which is why the MCC is negative." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`Thresholds Analysis` supports batch execution to evaluate many thresholds pairs at once. Simply append threshold values to the list (without limit) and the script will run each `Thresholds Analysis` successively until the list has been iterated. Let's take a look at an example analysis configuration `JSON` with multiple thresholds." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# note everything is the same as the JSON above except the \"data thresholds\" and \"exp_thresholds\"\n", + "# parameters, whose values are now a list containing multiple values\n", + "batch_thresholds_json = {\n", + " \"input_docking_data\": {\n", + " \"data_path\": CHK1_DATA_PATH, # path to the docking data folder\n", + " \"data_metric\": \"score\", # docked ligands activity metric\n", + " \"max_data_metric_best\": \"False\", # denotes whether a greater docking score = greater predicted affinity\n", + " \"data_thresholds\": [-7, -8] # a list that holds the thresholds to separate active/inactive ligands based on docking score\n", + " },\n", + " \"input_exp_data\": {\n", + " \"exp_data_path\": CHK1_DATA_PATH, # path to the experimental data\n", + " \"exp_metric\": \"Log Kd (nM)\", # experimental data activity metric\n", + " \"max_exp_metric_best\": \"False\", # denotes whether a greater value = greater affinity/potency\n", + " \"exp_thresholds\": [3, 3] # a list that holds the thresholds to separate active/inactive ligands based on experimental data\n", + " },\n", + " \"input_enrichment_data\": { \n", + " \"data_path_actives\": \"---\", \n", + " \"data_path_inactives\": \"---\", \n", + " \"actives_data_metric\": \"---\", \n", + " \"inactives_data_metric\": \"---\", \n", + " \"max_metric_best\": \"---\" \n", + " },\n", + " \"plot_settings\": {\n", + " \"enrichment_analysis\": \"False\", # denotes to generate histograms, boxplots, and pROC curves only\n", + " \"pROC_overlay\": \"False\" # denotes whether to generate an overlay pROC curve only\n", + " },\n", + " \"output\": {\n", + " \"output_path\": thresholds_results_dir # desired output directory\n", + " }\n", + "}\n", + "\n", + "with open(os.path.join(output_dir, \"batch_thresholds.json\"), \"w+\") as f:\n", + " json.dump(batch_thresholds_json, f, indent=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`Thresholds Analysis` takes the thresholds pair (for docking data and experimental data) based on their list position (e.g. (-7, 3) followed by (-8, 3)). Let's execute batch `Threshold Analysis`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# execute this in a command-line environment after replacing the parameters\n", + "!{dockstream_env}/bin/python {analysis_script} -input_json {os.path.join(output_dir, \"batch_thresholds.json\")}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### All the expected plots (scatter plots, confusion matrices and stacked histograms) and correlation metrics are generated for each thresholds pair. For brevity, only the new correlation metrics `JSON` is shown below\n", + "\n", + "`{\n", + " \"CHK1_Data.csv\": {\n", + " \"coeff_determination\": 0.012665427425591846,\n", + " \"Spearman_coeff\": 0.0947266781245275,\n", + " \"Kendall_coeff\": 0.06257646699328888\n", + " },\n", + " \"MCC_for_CHK1_Data.csv_(-7, 3)\": -0.011353884997945918,\n", + " \"MCC_for_CHK1_Data.csv_(-8, 3)\": 0.2188082683889328\n", + "}`\n", + "\n", + "The MCC for the thresholds pair (-8, 3) is added to the correlation metrics `JSON`. Notice it has a positive value - based on the thresholds pair (-8, 3), there are now more `true positives` + `true negatives` than `false positives` + `false negatives`.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Appendix: Analysis Metrics\n", + "\n", + "## Enrichment Analysis\n", + "\n", + "**pROC AUC**\n", + "* The propensity of the docking protocol to distinguish between actives and inactives\n", + "* A `Random` docking protocol always has a pROC AUC value of 0.434 - a docking protocol displaying enrichment **must** possess pROC AUC > 0.434\n", + "\n", + "For more information (including equations), see:\n", + "\n", + "**R. D. Clark, et al., J. Comput. Aided Mol. Des. 2008, 22, 141-146.**\n", + "\n", + "**Enrichment Factor 5% (EF 5%)**\n", + "* The number of actives recovered in the top 5% ligands (sorted by docking scores) relative to the number of actives in the entire dataset\n", + "\n", + "For more information (including equations), see:\n", + "**A. Bender, et al., J. Chem. Inf. Model. 2005, 45(5), 1369-1375.\n", + "\n", + "## Correlation Analysis\n", + "\n", + "**Coefficient of Determination (\"R-squared\" - $R^{2}$)**\n", + "* Measure of linear correlation\n", + "* Values range from [-1,1]\n", + "* Interpreted as: \"How well can you predict the change in the experimental activity given a change in the docking score via a linear function?\"\n", + "* Value of 1 means the experimental activity can be perfectly predicted via a linear function given a change in docking score\n", + "\n", + "**Note:** The Coefficient of Determination is probably not the best metric to use to quantify the docking score correlation with experimental activity. The reason is inherent to the interpretation of the docking score. While it is true in most cases that the docking score is *\"meant\"* to be interpreted as the binding affinity (e.g. `Glide` docking scores have units of -kcal/mol), it is generally not accurate. Therefore, quantifying this correlation using the magnitudes of the docking score and experimental activity is not the best representation. However, the Coefficient of Determination was included because it acts firstly as a sanity check (there should still be correlation observed) and secondly, it can be interesting to observe how well a linear relationship can capture the data (there have been internal projects where a docking protocol displayed a $R^{2}$ > 0.9)\n", + "\n", + "In contrast, Spearman and Kendall Ranked Correlation measures the ranked correlation (e.g. Does the \"best\" docking score equate to the most potent ligand? Does the 3rd \"best\" docking score equate to the 3rd most potent ligand? Does the \"worst\" docking score equate to the least potent ligand?) \n", + "\n", + "#### Spearman and Kendall Correlation Shared Characteristics:\n", + "\n", + "* Measures ranked correlation and can assess non-linear monotonic relationships\n", + "\n", + "* Values range from [-1,1]\n", + "\n", + "* Value of -1 interpreted as: \"As the docking score gets \"worse\", experimental activity always increases\" \n", + " --> This is the exact opposite of what is desired \n", + " \n", + "* Value of 1 interpreted as: \"As the docking score gets \"better\", experimental activity always increases\" \n", + " --> This would mean the docking configuration is perfect \n", + "\n", + "**Spearman Correlation (\"Rho\" - $\\rho$)**\n", + "* Does not explicitly account for repeated values in the dataset - instead, the average ranking is taken \n", + "\n", + "For more information (including equations), see: \n", + "\n", + "**Zwillinger, D. and Kokoska, S. (2000). CRC Standard Probability and Statistics Tables and Formulae. Chapman & Hall: New York. 2000. Section 14.7**\n", + " \n", + "**Kendall Correlation (\"Tau-b\" - $\\tau$-b)**\n", + "* \"Tau-b\" is a variation of Kendall Correlation chosen because it accounts for ties in the data (Spearman does not)\n", + "* The general interpretation of Kendall Correlation is: \"How much more likely is one expected to observe\n", + " correctly ranked pairs (concordant) compared to incorrectly ranked pairs (discordant)?\"\n", + "* In the case of many ties in the docking and/or experimental data, Kendall Correlation is a better metric to use than Spearman Correlation\n", + "\n", + "For more information (including equations), see:\n", + "\n", + "**https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.kendalltau.html (tau_b)**\n", + "\n", + "**Kendall, M. G., Biometrika 1945, 33(3), 239-251**\n", + "\n", + "## Thresholds Analysis\n", + " \n", + "**Matthews Correlation Coefficient (MCC)**\n", + "* Traditionally used to evaluate the performance of binary classifiers\n", + "* Value ranges from [-1,1]\n", + "* Interpretation for our purposes is, \"how many more/less `false positive` + `false negative` ligands are there compared to `true positive` + `true negative` ligands given the user specified thresholds?\" Positive value means there are more `true positive` + `true negatives` and negative value means there are more `false positive` + `false negatives`\n", + "\n", + "For more information (including equations), see:\n", + "\n", + "**https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html**" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/demo_AutoDock_Vina.ipynb b/notebooks/demo_AutoDock_Vina.ipynb new file mode 100644 index 0000000..23ce719 --- /dev/null +++ b/notebooks/demo_AutoDock_Vina.ipynb @@ -0,0 +1,563 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "How to run this notebook?
\n", + "
    \n", + "
  1. Install the DockStream environment: conda env create -f environment.yml in the DockStream directory
    2. \n", + "
  2. Activate the environment: conda activate DockStreamCommunity
    3. \n", + "
  3. Execute jupyter: jupyter notebook
    4. \n", + "
  4. Copy the link to a browser
    5. \n", + "
  5. Update variables dockstream_path and dockstream_env (the path to the environment DockStream) in the \n", + " first code block below
    6. \n", + "
\n", + "
\n", + "\n", + "
\n", + " Caution:\n", + " Make sure, you have the AutoDock Vina binary available somewhere.\n", + "
\n", + "\n", + "# `AutoDock Vina` backend demo\n", + "This notebook will demonstrate how to **(a)** set up a `AutoDock Vina` backend run with `DockStream`, including the most important settings and **(b)** how to set up a `REINVENT` run with `AutoDock` docking enabled as one of the scoring function components.\n", + "\n", + "**Steps:**\n", + "* a: Set up `DockStream` run\n", + " 1. Prepare the receptor\n", + " 2. Prepare the input: SMILES and configuration file (JSON format)\n", + " 3. Execute the docking and parse the results\n", + "* b: Set up `REINVENT` run with a `DockStream` component\n", + " 1. Prepare the receptor (see *a.1*)\n", + " 2. Prepare the input (see *a.2*)\n", + " 3. Prepare the `REINVENT` configuration (JSON format)\n", + " 4. Execute `REINVENT`\n", + "\n", + "The following imports / loadings are only necessary when executing this notebook. If you want to use `DockStream` directly from the command-line, it is enough to execute the following with the appropriate configurations:\n", + "\n", + "```\n", + "conda activate DockStream\n", + "python /path/to/DockStream/target_preparator.py -conf target_prep.json\n", + "python /path/to/DockStream/docker.py -conf docking.json\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "import tempfile\n", + "\n", + "# update these paths to reflect your system's configuration\n", + "dockstream_path = os.path.expanduser(\"~/Desktop/ProjectData/DockStream\")\n", + "dockstream_env = os.path.expanduser(\"~/miniconda3/envs/DockStream\")\n", + "vina_binary_location = os.path.expanduser(\"~/Desktop/ProjectData/foreign/AutoDockVina/autodock_vina_1_1_2_linux_x86/bin\")\n", + "\n", + "# no changes are necessary beyond this point\n", + "# ---------\n", + "# get the notebook's root path\n", + "try: ipynb_path\n", + "except NameError: ipynb_path = os.getcwd()\n", + "\n", + "# generate the paths to the entry points\n", + "target_preparator = dockstream_path + \"/target_preparator.py\"\n", + "docker = dockstream_path + \"/docker.py\"\n", + "\n", + "# generate a folder to store the results\n", + "output_dir = os.path.expanduser(\"~/Desktop/AutoDock_Vina_demo\")\n", + "try:\n", + " os.mkdir(output_dir)\n", + "except FileExistsError:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# generate the paths to the files shipped with this implementation\n", + "apo_1UYD_path = ipynb_path + \"/../data/1UYD/1UYD_apo.pdb\"\n", + "reference_ligand_path = ipynb_path + \"/../data/1UYD/PU8.pdb\"\n", + "smiles_path = ipynb_path + \"/../data/1UYD/ligands_smiles.txt\"\n", + "\n", + "# generate output paths for the configuration file, the \"fixed\" PDB file and the \"Gold\" receptor\n", + "target_prep_path = output_dir + \"/ADV_target_prep.json\"\n", + "fixed_pdb_path = output_dir + \"/ADV_fixed_target.pdb\"\n", + "adv_receptor_path = output_dir + \"/ADV_receptor.pdbqt\"\n", + "log_file_target_prep = output_dir + \"/ADV_target_prep.log\"\n", + "log_file_docking = output_dir + \"/ADV_docking.log\"\n", + "\n", + "# generate output paths for the configuration file, embedded ligands, the docked ligands and the scores\n", + "docking_path = output_dir + \"/ADV_docking.json\"\n", + "ligands_conformers_path = output_dir + \"/ADV_embedded_ligands.sdf\"\n", + "ligands_docked_path = output_dir + \"/ADV_ligands_docked.sdf\"\n", + "ligands_scores_path = output_dir + \"/ADV_scores.csv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Target preparation\n", + "`AutoDock Vina` uses the `PDBQT` format for both the receptors and the (individual ligands). First, we will generate the receptor into which we want to dock the molecules. This is a semi-automated process and while `DockStream` has an entry point to help you setting this up, it might be wise to think about the details of this process beforehand, including:\n", + "* Is my target structure complete (e.g. has it missing loops in the area of interest)?\n", + "* Do I have a reference ligand in a complex (holo) structure or do I need to define the binding cleft (cavity) in a different manner?\n", + "* Do I want to keep the crystal water molecules, potential co-factors and such or not?\n", + "\n", + "This step has to be done once per project and target. Typically, we start from a PDB file with a holo-structure, that is, a protein with its ligand. Using a holo-structure as input is convenient for two reasons:\n", + "1. The cavity can be specified as being a certain area around the ligand in the protein (assuming the binding mode does not change too much).\n", + "2. One can align other ligands (often a series with considerable similarity is used in docking studies) to the \"reference ligand\", potentially improving the performance.\n", + "\n", + "![](img/target_preparation_template_method.png)\n", + "\n", + "For this notebook, it is assumed that you are able to\n", + "\n", + "1. download `1UYD` and\n", + "2. split it into `1UYD_apo.pdb` and `reference_ligand.pdb` (name is `PU8` in the file), respectively.\n", + "\n", + "We will now set up the JSON instruction file for the target preparator that will help us build a receptor suitable for `AutoDock Vina` docking later. We will also include a small section (internally using [PDBFixer](https://github.com/openmm/pdbfixer)) that will take care of minor problems of the input structure, such as missing hetero atoms - but of course you can address these things with a program of your choice as well. We will write the JSON to the output folder in order to load it with the `target_preparator.py` entry point of `DockStream`.\n", + "\n", + "Note, that we can use the (optional) `extract_box` block in the configuration to specify the cavity's box (the area where the algorithm will strive to optimize the poses). For this we simply specify a reference ligand and the algorithm will extract the center-of-geometry and the minimum and maximum values for all three axes. This information is printed to the log file and can be used to specify the cavity in the docking step." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# specify the target preparation JSON file as a dictionary and write it out\n", + "tp_dict = {\n", + " \"target_preparation\":\n", + " {\n", + " \"header\": { # general settings\n", + " \"logging\": { # logging settings (e.g. which file to write to)\n", + " \"logfile\": log_file_target_prep\n", + " }\n", + " },\n", + " \"input_path\": apo_1UYD_path, # this should be an absolute path\n", + " \"fixer\": { # based on \"PDBFixer\"; tries to fix common problems with PDB files\n", + " \"enabled\": True,\n", + " \"standardize\": True, # enables standardization of residues\n", + " \"remove_heterogens\": True, # remove hetero-entries\n", + " \"fix_missing_heavy_atoms\": True, # if possible, fix missing heavy atoms\n", + " \"fix_missing_hydrogens\": True, # add hydrogens, which are usually not present in PDB files\n", + " \"fix_missing_loops\": False, # add missing loops; CAUTION: the result is usually not sufficient\n", + " \"add_water_box\": False, # if you want to put the receptor into a box of water molecules\n", + " \"fixed_pdb_path\": fixed_pdb_path # if specified and not \"None\", the fixed PDB file will be stored here\n", + " },\n", + " \"runs\": [ # \"runs\" holds a list of backend runs; at least one is required\n", + " {\n", + " \"backend\": \"AutoDockVina\", # one of the backends supported (\"AutoDockVina\", \"OpenEye\", ...)\n", + " \"output\": {\n", + " \"receptor_path\": adv_receptor_path # the generated receptor file will be saved to this location\n", + " },\n", + " \"parameters\": {\n", + " \"pH\": 7.4, # sets the protonation states (NOT used in Vina)\n", + " \"extract_box\": { # in order to extract the coordinates of the pocket (see text)\n", + " \"reference_ligand_path\": reference_ligand_path, # path to the reference ligand\n", + " \"reference_ligand_format\": \"PDB\" # format of the reference ligand\n", + " }\n", + "}}]}}\n", + "\n", + "with open(target_prep_path, 'w') as f:\n", + " json.dump(tp_dict, f, indent=\" \")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "REMARK Name = /tmp/tmponfdi39q.pdb\r\n", + "REMARK x y z vdW Elec q Type\r\n", + "REMARK _______ _______ _______ _____ _____ ______ ____\r\n", + "ATOM 1 N GLU A 1 6.484 28.442 39.441 0.00 0.00 +0.386 N \r\n", + "ATOM 2 CA GLU A 1 7.718 28.546 38.611 0.00 0.00 -0.005 C \r\n", + "ATOM 3 C GLU A 1 7.625 27.706 37.277 0.00 0.00 +0.199 C \r\n", + "ATOM 4 O GLU A 1 7.333 26.478 37.304 0.00 0.00 -0.278 OA\r\n", + "ATOM 5 CB GLU A 1 8.951 28.140 39.474 0.00 0.00 -0.048 C \r\n", + "ATOM 6 CG GLU A 1 9.355 26.647 39.367 0.00 0.00 +0.048 C \r\n", + "ATOM 7 CD GLU A 1 10.138 26.088 40.562 0.00 0.00 +0.356 C \r\n", + "ATOM 8 OE1 GLU A 1 11.022 26.816 41.117 0.00 0.00 -0.246 OA\r\n", + "ATOM 9 OE2 GLU A 1 9.875 24.900 40.943 0.00 0.00 -0.246 OA\r\n", + "ATOM 10 N VAL A 2 7.856 28.355 36.137 0.00 0.00 -0.305 N \r\n", + "ATOM 11 CA VAL A 2 8.110 27.634 34.889 0.00 0.00 +0.102 C \r\n", + "ATOM 12 C VAL A 2 9.523 27.050 34.954 0.00 0.00 +0.234 C \r\n", + "ATOM 13 O VAL A 2 10.499 27.794 35.209 0.00 0.00 -0.274 OA\r\n", + "ATOM 14 CB VAL A 2 7.967 28.556 33.636 0.00 0.00 -0.020 C \r\n", + "ATOM 15 CG1 VAL A 2 8.234 27.763 32.310 0.00 0.00 -0.061 C \r\n", + "ATOM 16 CG2 VAL A 2 6.598 29.245 33.609 0.00 0.00 -0.061 C \r\n", + "ATOM 17 N GLU A 3 9.626 25.731 34.766 0.00 0.00 -0.302 N \r\n", + "ATOM 18 CA GLU A 3 10.912 25.034 34.705 0.00 0.00 +0.100 C \r\n", + "ATOM 19 C GLU A 3 11.363 24.695 33.258 0.00 0.00 +0.234 C \r\n", + "ATOM 20 O GLU A 3 10.557 24.225 32.446 0.00 0.00 -0.274 OA\r\n", + "ATOM 21 CB GLU A 3 10.872 23.762 35.555 0.00 0.00 -0.016 C \r\n", + "ATOM 22 CG GLU A 3 10.774 24.017 37.048 0.00 0.00 +0.051 C \r\n" + ] + } + ], + "source": [ + "# execute this in a command-line environment after replacing the parameters\n", + "!{dockstream_env}/bin/python {target_preparator} -conf {target_prep_path}\n", + "!head -n 25 {adv_receptor_path}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is it, now we have **(a)** fixed some minor issues with the input structure and **(b)** generated a reference ligand-based receptor and stored it in a binary file. For inspection later, we will write out the \"fixed\" PDB structure (parameter `fixed_pdb_path` in the `fixer` block above)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Docking\n", + "In this section we consider a case where we have just prepared the receptor and want to dock a bunch of ligands (molecules, compounds) into the binding cleft. Often, we only have the structure of the molecules in the form of `SMILES`, rather than a 3D structure so the first step will be to generate these conformers before proceeding. In `DockStream` you can embed your ligands with a variety of programs including `Corina`, `RDKit`, `OMEGA` and `LigPrep` and use them freely with any backend. Here, we will use `Corina` for the conformer embedding.\n", + "\n", + "But first, we will have a look at the ligands:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['C#CCCCn1c(Cc2cc(OC)c(OC)c(OC)c2Cl)nc2c(N)ncnc21', 'CCCCn1c(Cc2cc(OC)c(OC)c(OC)c2)nc2c(N)ncnc21', 'CCCCn1c(Cc2cc(OC)ccc2OC)nc2c(N)ncnc21', 'CCCCn1c(Cc2cccc(OC)c2)nc2c(N)ncnc21', 'C#CCCCn1c(Cc2cc(OC)c(OC)c(OC)c2Cl)nc2c(N)nc(F)nc21', 'CCCCn1c(Cc2ccc(OC)cc2)nc2c(N)ncnc21', 'CCCCn1c(Cc2ccc3c(c2)OCO3)nc2c(N)ncnc21', 'CCCCn1c(Cc2cc(OC)ccc2OC)nc2c(N)nc(F)nc21', 'CCCCn1c(Cc2ccc3c(c2)OCO3)nc2c(N)nc(F)nc21', 'C#CCCCn1c(Cc2cc(OC)ccc2OC)nc2c(N)nc(F)nc21', 'CC(C)NCCCn1c(Cc2cc3c(cc2I)OCO3)nc2c(N)nc(F)nc21', 'CC(C)NCCCn1c(Sc2cc3c(cc2Br)OCO3)nc2c(N)ncnc21', 'CC(C)NCCCn1c(Sc2cc3c(cc2I)OCO3)nc2c(N)ncnc21', 'COc1ccc(OC)c(Cc2nc3nc(F)nc(N)c3[nH]2)c1', 'Nc1nccn2c(NCc3ccccc3)c(Cc3cc4c(cc3Br)OCO4)nc12']\n" + ] + } + ], + "source": [ + "# load the smiles (just for illustrative purposes)\n", + "# here, 15 moleucles will be used\n", + "with open(smiles_path, 'r') as f:\n", + " smiles = [smile.strip() for smile in f.readlines()]\n", + "print(smiles)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While the embedding and docking tasks in `DockStream` are both specified in the same configuration file, they are handled independently. This means it is perfectly fine to either load conformers (from an `SDF` file) directly or to use a call of `docker.py` merely to generate conformers without doing the docking afterwards.\n", + "\n", + "`DockStream` uses the notion of (embedding) \"pool\"s, of which multiple can be specified and accessed via identifiers. Note, that while the way conformers are generated is highly backend specific, `DockStream` allows you to use the results interchangably. This allows to (a) re-use embedded molecules for multiple docking runs (e.g. different scoring functions), without the necessity to embed them more than once and (b) to combine embeddings and docking backends freely.\n", + "\n", + "One important feature is that you can also specify an `align` block for the pools, which will try to align the conformers produced to the reference ligand's coordinates. Alignment is especially useful if your molecules have a large common sub-structure, as it will potentially enhance the results. **Warning:** At the moment, this feature is a bit unstable at times (potentially crashes, if no overlap of a ligand with the reference ligand can be found).\n", + "\n", + "As mentioned at the target preparation stage, we need to specify the cavity (binding cleft) or search space for `AutoDock Vina`. As we have extracted the \"box\" (see print-out of the logging file below) using a reference ligand, this helps us deciding on the dimensions of the search space:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!cat {log_file_target_prep}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The three `mean` values will serve as the center of the search space and from the minimum and maximum values in all three dimensions, we decide to use 15 (for `x`) and 10 (for `y` and `z`, respectively). As larger ligands could be used, we will give the algorithm some leeway in each dimension." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# specify the embedding and docking JSON file as a dictionary and write it out\n", + "ed_dict = {\n", + " \"docking\": {\n", + " \"header\": { # general settings\n", + " \"logging\": { # logging settings (e.g. which file to write to)\n", + " \"logfile\": log_file_docking\n", + " }\n", + " },\n", + " \"ligand_preparation\": { # the ligand preparation part, defines how to build the pool\n", + " \"embedding_pools\": [\n", + " {\n", + " \"pool_id\": \"Corina_pool\", # here, we only have one pool\n", + " \"type\": \"Corina\",\n", + " \"parameters\": {\n", + " \"prefix_execution\": \"module load corina\" # only required, if a module needs to be loaded to execute \"Corina\"\n", + " },\n", + " \"input\": {\n", + " \"standardize_smiles\": False,\n", + " \"type\": \"smi\",\n", + " \"input_path\": smiles_path\n", + " },\n", + " \"output\": { # the conformers can be written to a file, but \"output\" is\n", + " # not required as the ligands are forwarded internally\n", + " \"conformer_path\": ligands_conformers_path, \n", + " \"format\": \"sdf\"\n", + " }\n", + " }\n", + " ]\n", + " },\n", + " \"docking_runs\": [\n", + " {\n", + " \"backend\": \"AutoDockVina\",\n", + " \"run_id\": \"AutoDockVina\",\n", + " \"input_pools\": [\"Corina_pool\"],\n", + " \"parameters\": {\n", + " \"binary_location\": vina_binary_location, # absolute path to the folder, where the \"vina\" binary\n", + " # can be found\n", + " \"parallelization\": {\n", + " \"number_cores\": 4\n", + " },\n", + " \"seed\": 42, # use this \"seed\" to generate reproducible results; if\n", + " # varied, slightly different results will be produced\n", + " \"receptor_pdbqt_path\": [adv_receptor_path], # paths to the receptor files\n", + " \"number_poses\": 2, # number of poses to be generated\n", + " \"search_space\": { # search space (cavity definition); see text\n", + " \"--center_x\": 3.3,\n", + " \"--center_y\": 11.5,\n", + " \"--center_z\": 24.8,\n", + " \"--size_x\": 15,\n", + " \"--size_y\": 10,\n", + " \"--size_z\": 10\n", + " }\n", + " },\n", + " \"output\": {\n", + " \"poses\": { \"poses_path\": ligands_docked_path },\n", + " \"scores\": { \"scores_path\": ligands_scores_path }\n", + "}}]}}\n", + "\n", + "with open(docking_path, 'w') as f:\n", + " json.dump(ed_dict, f, indent=2)\n", + "\n", + "# print out path to generated JSON\n", + "print(docking_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-9.2\r\n", + "-9.3\r\n", + "-9.3\r\n", + "-9.5\r\n", + "-9.6\r\n", + "-9.2\r\n", + "-10.1\r\n", + "-9.4\r\n", + "-10.3\r\n", + "-9.5\r\n", + "-9.3\r\n", + "-9.2\r\n", + "-9.2\r\n", + "-9.8\r\n", + "-11.0\r\n" + ] + } + ], + "source": [ + "# execute this in a command-line environment after replacing the parameters\n", + "!{dockstream_env}/bin/python {docker} -conf {docking_path} -print_scores" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note, that the scores are usually only outputted to a `CSV` file specified by the `scores` block, but that since we have used parameter `-print_scores` they will also be printed to `stdout` (line-by-line).\n", + "\n", + "These scores are associated with docking poses (see picture below for a couple of ligands overlaid in the binding pocket).\n", + "\n", + "![](img/docked_ligands_overlay_holo.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using `DockStream` as a scoring component in `REINVENT`\n", + "The *de novo* design platform `REINVENT` holds a recently added `DockStream` scoring function component (also check out our collection of notebooks in the [ReinventCommunity](https://github.com/MolecularAI/ReinventCommunity) repository). This means, provided that all necessary input files and configurations are available, you may run `REINVENT` and incorporate docking scores into the score of the compounds generated. Together with `FastROCS`, this represents the first step to integrate physico-chemical 3D information.\n", + "\n", + "While the docking scores are a very crude proxy for the actual binding affinity (at best), it does prove useful as a *geometric filter* (removing ligands that obviously do not fit the binding cavity). Furthermore, a severe limitation of knowledge-based predictions e.g. in activity models is the domain applicability. Docking, as a chemical space agnostic component, can enhance the ability of the agent for scaffold-hopping, i.e. to explore novel sub-areas in the chemical space.\n", + "\n", + "### The `REINVENT` configuration JSON\n", + "\n", + "While every docking backend has its own configuration (see section above), calling `DockStream`'s `docker.py` entry point ensures that they all follow the same external API. Thus the component that needs to be added to `REINVENT`'s JSON configuration (to the `scoring_function`->`parameters` list) looks as follows for `AutoDock Vina`:\n", + "\n", + "```\n", + "{\n", + " \"component_type\": \"dockstream\",\n", + " \"name\": \"dockstream\",\n", + " \"weight\": 1,\n", + " \"model_path\": \"\",\n", + " \"smiles\": [],\n", + " \"specific_parameters\": {\n", + " \"transformation\": true,\n", + " \"transformation_type\": \"reverse_sigmoid\",\n", + " \"low\": -12,\n", + " \"high\": -8,\n", + " \"k\": 0.25,\n", + " \"configuration_path\": \"/docking.json\",\n", + " \"docker_script_path\": \"/docker.py\",\n", + " \"environment_path\": \"/miniconda3/envs/DockStream/bin/python\"\n", + " }\n", + "}\n", + "```\n", + "\n", + "You will need to update `configuration_path`, `docker_script_path` and the link to the environment, `environment_path` to match your system's configuration. It might be, that the latter two are already set to meaningful defaults, but your `DockStream` configuration JSON file will be specific for each run. \n", + "\n", + "#### How to find an appropriate transformation?\n", + "We use a *reverse sigmoid* score transformation to bring the numeric, continuous value that was outputted by `DockStream` and fed back to `REINVENT` into a 0 to 1 regime. The parameters `low`, `high` and `k` are critical: their exact value naturally depends on the backend used, but also on the scoring function (make sure, \"more negative is better\" - otherwise you are looking for a *sigmoid* transformation) and potentially also the project used. The values reported here can be used as rule-of-thumb for an `AutoDock Vina` run. Below is a code snippet, that helps to find the appropriate parameters (excerpt of the `ReinventCommunity` notebook `Score_Transformations`)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# load the dependencies and classes used\n", + "%run code/score_transformation.py\n", + "\n", + "# set plotting parameters\n", + "small = 12\n", + "med = 16\n", + "large = 22\n", + "params = {\"axes.titlesize\": large,\n", + " \"legend.fontsize\": med,\n", + " \"figure.figsize\": (16, 10),\n", + " \"axes.labelsize\": med,\n", + " \"axes.titlesize\": med,\n", + " \"xtick.labelsize\": med,\n", + " \"ytick.labelsize\": med,\n", + " \"figure.titlesize\": large}\n", + "plt.rcParams.update(params)\n", + "plt.style.use(\"seaborn-whitegrid\")\n", + "sns.set_style(\"white\")\n", + "%matplotlib inline\n", + "\n", + "# set up Enums and factory\n", + "tt_enum = TransformationTypeEnum()\n", + "csp_enum = ComponentSpecificParametersEnum()\n", + "factory = TransformationFactory()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# sigmoid transformation\n", + "# ---------\n", + "values_list = np.arange(-14, -7, 0.25).tolist()\n", + "specific_parameters = {csp_enum.TRANSFORMATION: True,\n", + " csp_enum.LOW: -12,\n", + " csp_enum.HIGH: -8,\n", + " csp_enum.K: 0.25,\n", + " csp_enum.TRANSFORMATION_TYPE: tt_enum.REVERSE_SIGMOID}\n", + "transform_function = factory.get_transformation_function(specific_parameters)\n", + "transformed_scores = transform_function(predictions=values_list,\n", + " parameters=specific_parameters)\n", + "\n", + "# render the curve\n", + "render_curve(title=\" Reverse Sigmoid Transformation\", x=values_list, y=transformed_scores)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How to specify the `DockStream` configuration file?\n", + "In principle, all options that are supported in a \"normal\" `DockStream` run (see above) are supported for usage with `REINVENT` as well, with a few notable exceptions. First, as we report only one value per ligand (and a \"consensus score\" is not yet supported), you should only use **one** embedding / pool and **one** backend (as in the example above). Second, the prospective ligands are not supplied via a file but from `stdin`, thus we will need to change the `input` part of the pool definition. Also, we might not want to write-out all conformers, so we will remove the `output` block entirely. The updated section then looks as follows:\n", + "\n", + "```\n", + "{\n", + " \"pool_id\": \"Corina_pool\",\n", + " \"type\": \"Corina\",\n", + " \"parameters\": {\n", + " \"removeHs\": False\n", + " },\n", + " \"input\": {\n", + " \"standardize_smiles\": False\n", + " }\n", + "}\n", + "```\n", + "\n", + "Finally, we will update the docking run as well. Typically, we want to see the docked poses per epoch and maybe also the scores and the SMILES in a well-tabulated format. Thus, we might retain the `output` block here, but as every epoch generates each of the files, it would overwrite it by default. If parameter `overwrite` is set to `False`, each consecutive write-out will be appended by a number, e.g. first epoch *poses.sdf* and *scores.csv*, second epoch *0001_poses.sdf* and *0001_scores.csv*, third epoch *0002_poses.sdf* and *0002_scores.csv* and so on.\n", + "\n", + "```\n", + "{\n", + " \"backend\": \"AutoDockVina\",\n", + " \"run_id\": \"AutoDockVina\",\n", + " \"input_pools\": [\"Corina_pool\"],\n", + " ...\n", + " \"output\": {\n", + " \"poses\": { \"poses_path\": ligands_docked_path, \"overwrite\": False },\n", + " \"scores\": { \"scores_path\": ligands_scores_path, \"overwrite\": False }\n", + " }\n", + "}\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/demo_Benchmarking_Script.ipynb b/notebooks/demo_Benchmarking_Script.ipynb new file mode 100644 index 0000000..0c8fb4c --- /dev/null +++ b/notebooks/demo_Benchmarking_Script.ipynb @@ -0,0 +1,351 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "How to run this notebook?
\n", + "
    \n", + "
  1. Install the DockStream environment: conda env create -f environment.yml in the DockStream directory
    2. \n", + "
  2. Activate the environment: conda activate DockStreamCommunity
    3. \n", + "
  3. Execute jupyter: jupyter notebook
    4. \n", + "
  4. Copy the link to a browser
    5. \n", + "
  5. Update variables dockstream_path and dockstream_env (the path to the environment DockStream) in the \n", + " first code block below
    6. \n", + "
\n", + "
\n", + "\n", + "# Benchmarking Script Demo\n", + "\n", + "The purpose of the `benchmarking script` is to enable automated batch execution of `DockStream` runs. This will allow users to run multiple backends + ligand embedders (e.g. `Glide with LigPrep` and `Hybrid with Corina`) in a more streamlined manner to determine the best docking configuration for their specific application. A subsequent `analysis script` quantifies the differences between different docking configurations by automating calculations of relevant enrichment metrics and generating plots for visualization of `DockStream` run results. A demo for the `analysis script` can be found in the `demo_Analysis_Script` Jupyter notebook. This notebook focuses strictly on the `benchmarking script` and demonstrates the necessary preparatory steps for batch execution of `DockStream` runs. \n", + "\n", + "\n", + "**Benchmarking Script Steps:**\n", + " 1. Prepare the ligands file\n", + " 2. Prepare the receptors/grids\n", + " 3. Prepare `DockStream` configuration files (`JSON` format)\n", + " 4. Execute the script and parse the results\n", + "\n", + "\n", + "__Note:__ By default, this notebook will deposit all files created into `~/Desktop/Benchmarking_demo`.\n", + "\n", + "The following imports / loadings are only necessary when executing this notebook. If you want to use `benchmarking_v2.py` directly from the command-line, it is enough to execute the following with the appropriate input path (path to a folder containing `DockStream` configuration `JSONs`):\n", + "\n", + "```\n", + "conda activate DockStream (or DockStreamFull for GOLD docking)\n", + "python /path/to/DockStream/benchmarking.py -input_path \n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "import tempfile\n", + "\n", + "# update these paths to reflect your system's configuration\n", + "dockstream_path = os.path.expanduser(\"~/Desktop/ProjectData/DockStream\")\n", + "# note: DockStreamFull (as opposed to DockStream) is required to run GOLD\n", + "dockstream_env = os.path.expanduser(\"~/miniconda3/envs/DockStream\")\n", + "\n", + "# no changes are necessary beyond this point\n", + "# ---------\n", + "# get the notebook's root path\n", + "try: ipynb_path\n", + "except NameError: ipynb_path = os.getcwd()\n", + "\n", + "# generate the path to the benchmarking script entry point\n", + "benchmarking_script = os.path.join(dockstream_path, \"benchmarking.py\")\n", + "\n", + "# generate a folder to store the results\n", + "output_dir = os.path.expanduser(\"~/Desktop/Benchmarking_demo\")\n", + "try:\n", + " os.mkdir(output_dir)\n", + "except FileExistsError:\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Prepare the Ligands File\n", + "\n", + "A file containing all the ligands to be docked must be generated in either `SDF` or `SMI` or `CSV` format (see docking_input_types notebook for more details). Typically, `SMI` format is used for readability especially when there is a substantial number of ligands to be docked. In the `DockStream` codebase, there is a script called `sdf2smiles.py` which converts a ligands database in `SDF` format to `SMI` format. \n", + "\n", + "For the purpose of this notebook, a ligand `SMI` file is provided and shipped in the `DockStream` codebase. Let's generate the path to this file:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# generate the paths to the ligands smi file shipped with this implementation\n", + "ligands_path = ipynb_path + \"/../data/Benchmarking_Script/ligands_smiles.smi\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Prepare the Receptors/Grids\n", + "\n", + "As with any `DockStream` run, a receptor/grid must be prepared before the docking run. This process will be dependent on the backend(s) used. For example, receptor grids for `Glide` can be generated using Schrodinger's GUI, `Maestro` (see demo_Glide for more details). On the other hand, receptor grids for `Hybrid` can be generated using `target_preparator.py` which is a script shipped with the `DockStream` codebase (see demo_Hybrid for more details).\n", + "\n", + "For the purpose of this notebook, Glide and Hybrid receptor grids are provided and shipped with the `DockStream` codebase. Note that the benchmarking script is compatible with any backend and any ligand embedder. `Glide` and `Hybrid` were chosen in this notebook arbitrarily. Let's generate the paths to the relevant files:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# generate the paths to the receptor grids shipped with this implementation\n", + "glide_grid_path = os.path.join(ipynb_path, \"../data/Benchmarking_Script/1UYD_grid.zip\")\n", + "hybrid_grid_path = os.path.join(ipynb_path, \"../data/Benchmarking_Script/1UYD_grid.oeb\")\n", + "smiles_path = os.path.join(ipynb_path, \"../data/Benchmarking_Script/ligands_smiles.smi\")\n", + "\n", + "# generate output paths for the docked ligands and the scores\n", + "glide_docked_poses_path = os.path.join(output_dir, \"glide_docked_poses.sdf\")\n", + "glide_docked_scores_path = os.path.join(output_dir, \"glide_docked_scores.csv\")\n", + "hybrid_docked_poses_path = os.path.join(output_dir, \"hybrid_docked_poses.sdf\")\n", + "hybrid_docked_scores_path = os.path.join(output_dir, \"hybrid_docked_scores.csv\") " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice in the above code block the `benchmarking_run_jsons` path is a path to a folder rather than a single `DockStream` `JSON` configuration file. This is because the `benchmarking script` takes as input a folder containing 1 or more `DockStream` `JSON` configuration files and runs them all successively (single runs are supported too, in which case, the path to the single configuration `JSON` should be passed). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Prepare the DockStream Configuration Files\n", + "\n", + "Next, we need to generate the `DockStream` configuration files. This step is covered in the backend specific demos (e.g. `demo_Glide`) but will be briefly described again here as it is especially relevant in highlighting the utility of the benchmarking script. Let's first create a new subfolder to hold the `DockStream` run JSONs that will be generated later:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# generate output paths for the DockStream configuration files \n", + "benchmarking_conf_jsons = os.path.join(output_dir, \"benchmarking_conf_jsons\")\n", + "# create the benchmaking run jsons subfolder\n", + "try:\n", + " os.mkdir(benchmarking_conf_jsons)\n", + "except FileExistsError:\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each `DockStream` run will require its own configuration `JSON` (they need not be unique but that would simply run `DockStream` with the exact same configuration which is probably not desirable unless you are interested in observing the stochastic nature of some docking algorithms in select backends such as `GOLD`). An example `Glide with LigPrep` configuration `JSON` is shown in the below code block (for more details on `Glide`, see demo_Glide)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# specify the embedding and docking JSON file as a dictionary and write it out\n", + "glide_ligprep_conf_json = {\n", + " \"docking\": {\n", + " \"header\": { # general settings\n", + " \"environment\": {\n", + " }\n", + " },\n", + " \"ligand_preparation\": { # the ligand preparation part, defines how to build the pool\n", + " \"embedding_pools\": [\n", + " {\n", + " \"pool_id\": \"Ligprep\",\n", + " \"type\": \"Ligprep\",\n", + " \"parameters\": {\n", + " \"prefix_execution\": \"module load schrodinger/2019-4\",\n", + " \"use_epik\": {\n", + " \"target_pH\": 7.4, # LigPrep embeds ligands at a specified pH which is particularly\n", + " \"pH_tolerance\": 0.2 # relevant to ionization states --> this parameter can be tweaked\n", + " },\n", + " \"force_field\": \"OPLS3e\"\n", + " },\n", + " \"input\": {\n", + " \"standardize_smiles\": False,\n", + " \"input_path\": smiles_path,\n", + " \"type\": \"smi\" \n", + " }\n", + " }\n", + " ]\n", + " },\n", + " \"docking_runs\": [\n", + " {\n", + " \"backend\": \"Glide\",\n", + " \"run_id\": \"Glide\",\n", + " \"input_pools\": [\"Ligprep\"],\n", + " \"parameters\": {\n", + " \"prefix_execution\": \"module load schrodinger/2019-4\", # will be executed before a program call\n", + " \"parallelization\": { \n", + " \"number_cores\": 2\n", + " },\n", + " \"glide_flags\": { # all all command-line flags for Glide here \n", + " \"-HOST\": \"localhost\"\n", + " },\n", + " \"glide_keywords\": { # add all keywords for the \"input.in\" file here\n", + " \"EXPANDED_SAMPLING\": \"True\", # all these parameteres can be tweaked and/or\n", + " \"GRIDFILE\": [glide_grid_path], # included/omitted\n", + " \"NENHANCED_SAMPLING\": \"2\",\n", + " \"POSE_OUTTYPE\": \"ligandlib_sd\",\n", + " \"POSES_PER_LIG\": \"3\",\n", + " \"POSTDOCK_NPOSE\": \"15\",\n", + " \"POSTDOCKSTRAIN\": \"True\",\n", + " \"PRECISION\": \"HTVS\"\n", + " }\n", + " },\n", + " \"output\": {\n", + " \"poses\": { \"poses_path\": glide_docked_poses_path }, # output path to save docked poses\n", + " \"scores\": { \"scores_path\": glide_docked_scores_path } # output path to save docked scores \n", + " }\n", + " }\n", + " ]\n", + " }\n", + "}\n", + "\n", + "with open(os.path.join(benchmarking_conf_jsons, \"Glide_LigPrep.json\"), \"w+\") as f:\n", + " json.dump(glide_ligprep_conf_json, f, indent=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above cell block saves the `DockStream` `Glide` configuration `JSON` in the benchmarking `JSONs` folder. Notice the comments that highlight parameters that can be tweaked. For instance, `\"target_pH\"` can be tweaked if the user is interested in docking a set of ligands at different pH. This can have a significant impact on ligand activity as the ionization states will change. Moreover, one could envision changing parameters located under `\"glide_keywords\"`. For instance, the `\"PRECISION\"` can be changed to `\"SP\" (\"Standard Precision\")` which is generally more accurate than `\"HTVS\" (\"High Throughput Virtual Screening\")` which is only used in this notebook simply because it is much faster. One can change as many or as few parameters as they would like. It is evident that the combinations of parameters leads to a combinatorial explosion of docking configurations. In the event that the user wants to run many `DockStream` runs, it would be cumbersome to keep executing `docker.py`. The utility of the `benchmarking script` is to automate running all `DockStream` jobs so long as the configuration `JSON` is provided. Internally, the script calls `docker.py` and therefore no functionalities are lost in using the benchmarking script.\n", + "\n", + "As the purpose of this notebook is to demonstrate batch execution of `DockStream` runs, the below code block will generate an example `Hybrid with Corina` configuration `JSON` (for more details on `Hybrid`, see demo_Hybrid)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "hybrid_corina_conf_json = {\n", + " \"docking\": {\n", + " \"header\": {\n", + " \"environment\":{\n", + " }\n", + " },\n", + " \"ligand_preparation\": {\n", + " \"embedding_pools\": [\n", + " {\n", + " \"pool_id\": \"Corina\", # Corina is used here as the ligand embedder\n", + " \"type\": \"Corina\", # but this can be changed to LigPrep or RDKit\n", + " \"parameters\": {\n", + " \"prefix_execution\": \"module load corina\"\n", + " },\n", + " \"input\": {\n", + " \"standardize_smiles\": False,\n", + " \"input_path\": ligands_path,\n", + " \"type\": \"smi\"\n", + " }\n", + " }\n", + " ]\n", + " },\n", + " \"docking_runs\": [\n", + " {\n", + " \"backend\": \"Hybrid\",\n", + " \"run_id\": \"Hybrid\",\n", + " \"input_pools\": [\"Corina\"],\n", + " \"parameters\": {\n", + " \"prefix_execution\": \"module load oedocking\",\n", + " \"parallelization\": {\n", + " \"number_cores\": 2\n", + " },\n", + " \"receptor_paths\": [hybrid_grid_path]\n", + " },\n", + " \"output\": {\n", + " \"poses\": { \"poses_path\": hybrid_docked_poses_path }, # output path to save docked poses\n", + " \"scores\": { \"scores_path\": hybrid_docked_scores_path } # output path to save docked scores \n", + " }\n", + " }\n", + " ]\n", + " }\n", + "}\n", + "\n", + "with open(os.path.join(benchmarking_conf_jsons, \"Hybrid_Corina.json\"), \"w+\") as f:\n", + " json.dump(hybrid_corina_conf_json, f, indent=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above cell block saves the `DockStream` `Hybrid` configuration `JSON` in the benchmarking `JSON` folder. Note that `Hybrid` has much fewer parameters that can be tweaked compared to `Glide`. \n", + "\n", + "We are now finished generating the `DockStream` configuration `JSONs`. There is no limit to how many configuration `JSONs` are provided; the `benchmarking script` will continue running `DockStream` until all configuration JSONs are executed. For the purpose of this notebook, only the 2 runs specified above (`Glide with LigPrep` and `Hybrid with Corina`) will be executed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Execute the Benchmarking Script\n", + "\n", + "We are now ready to execute batch `DockStream` runs. Call the `benchmarking script` via command-line and provide the `-input path` argument which is the path to the folder containing all the `DockStream` configuration `JSONs`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# execute this in a command-line environment after replacing the parameters\n", + "!{dockstream_env}/bin/python {benchmarking_script} -input_path {benchmarking_conf_jsons}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As with any `DockStream` run, the docked poses and scores are outputted to `SDF` and `CSV` files as specified in the configuration `JSONs`. As a final note, the `benchmarking script` will output an error message if an invalid path is provided and will also notify the user which `DockStream` run failed with the associated error trace back displayed." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/demo_Glide.ipynb b/notebooks/demo_Glide.ipynb new file mode 100644 index 0000000..7f56406 --- /dev/null +++ b/notebooks/demo_Glide.ipynb @@ -0,0 +1,858 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "How to run this notebook?
\n", + "
    \n", + "
  1. Install the DockStream environment: conda env create -f environment.yml in the DockStream directory
    2. \n", + "
  2. Activate the environment: conda activate DockStreamCommunity
    3. \n", + "
  3. Execute jupyter: jupyter notebook
    4. \n", + "
  4. Copy the link to a browser
    5. \n", + "
  5. Update variables dockstream_path and dockstream_env (the path to the environment DockStream) in the \n", + " first code block below
    6. \n", + "
\n", + "
\n", + "\n", + "# `Glide` backend demo\n", + "This notebook will demonstrate how to **(a)** set up a `Glide` backend run with `DockStream`, including the most important settings and **(b)** how to set up a `REINVENT` run with `Glide` docking enabled as one of the scoring function components.\n", + "\n", + "**Steps:**\n", + "* a: Set up `DockStream` run\n", + " 1. Prepare the receptor / grid with `Maestro`\n", + " 2. Prepare the input: SMILES and configuration file (JSON format)\n", + " 3. Execute the docking and parse the results\n", + "* b: Set up `REINVENT` run with a `DockStream` component\n", + " 1. Prepare the receptor (see *a.1*)\n", + " 2. Prepare the input (see *a.2*)\n", + " 3. Prepare the `REINVENT` configuration (JSON format)\n", + " 4. Execute `REINVENT`\n", + "\n", + "\n", + "If a `Schrodinger` license is available, `DockStream` can make use of `Glide`'s docking capabilities. However, you need to be able to source the _SCHRODINGER_ environment variable in your console. __Note:__ Make sure, you have activated the `DockStream` environment before launching this notebook. By default, this notebook will deposit all files created into `~/Desktop/Glide_demo`.\n", + "\n", + "The following imports / loadings are only necessary when executing this notebook. If you want to use `DockStream` directly from the command-line, it is enough to execute the following with the appropriate configurations:\n", + "\n", + "```\n", + "conda activate DockStream\n", + "python /path/to/DockStream/docker.py -conf docking.json\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "import tempfile\n", + "import seaborn as sns\n", + "\n", + "# update these paths to reflect your system's configuration\n", + "dockstream_path = os.path.expanduser(\"~/Desktop/ProjectData/DockStream\")\n", + "dockstream_env = os.path.expanduser(\"~/miniconda3/envs/DockStream\")\n", + "\n", + "# no changes are necessary beyond this point\n", + "# ---------\n", + "# get the notebook's root path\n", + "try: ipynb_path\n", + "except NameError: ipynb_path = os.getcwd()\n", + "\n", + "# generate the paths to the entry points\n", + "docker = dockstream_path + \"/docker.py\"\n", + "\n", + "# generate a folder to store the results\n", + "output_dir = os.path.expanduser(\"~/Desktop/Glide_demo\")\n", + "try:\n", + " os.mkdir(output_dir)\n", + "except FileExistsError:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# generate the paths to the files shipped with this implementation\n", + "grid_file_path = ipynb_path + \"/../data/Glide/1UYD_grid.zip\"\n", + "smiles_path = ipynb_path + \"/../data/1UYD/ligands_smiles.txt\"\n", + "\n", + "# generate output paths for the configuration file, embedded ligands, the docked ligands and the scores\n", + "docking_path = output_dir + \"/Glide_docking.json\"\n", + "ligands_conformers_path = output_dir + \"/corina_embedded_ligands.sdf\"\n", + "ligands_docked_path = output_dir + \"/Glide_docked_ligands.sdf\"\n", + "ligands_scores_path = output_dir + \"/Glide_scores.csv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Target preparation\n", + "As of yet, `DockStream` does not have a specific target preparator for `Glide`. While this would be possible, feedback from users indicate that they are preferring to set the receptor up with `Maestro` (`Schrodinger`'s GUI), which results in a `zip` file containing all structural and force-field information. `Maestro` has extensive fixing capabilities and a wide variety of additional options. Below are a few screenshots from the \"2019-4\" release, showing how a target grid could be prepared. To reproduce them, download `1UYD` and load it into `Maestro`:\n", + "\n", + "![](img/Glide_01_load_structure.png)\n", + "\n", + "Next, load the protein preparation wizard and fix the most severe issues with your input structure.\n", + "\n", + "![](img/Glide_02_protein_fix.png)\n", + "\n", + "Once the protein structure is fixed, we need to generate the grid for docking. Start the grid generation assistant and select the reference ligand.\n", + "\n", + "![](img/Glide_03_select_ligand.png)\n", + "![](img/Glide_03_grid_generation.png)\n", + "\n", + "You might want to change some settings, e.g. increasing the space around the reference ligand to be considered. Note, that you should set the write-out folder by clicking on \"Job settings\".\n", + "\n", + "![](img/Glide_03_grid_generation_write_out.png)\n", + "\n", + "Finally, start the run (and be patient, this will take some time). You should see a \"glide-grid_1.zip\" in the output folder specified. One example archive is also shipped in this `DockStream` installation and will be used below.\n", + "\n", + "
\n", + " Warning:
\n", + " If you plan to use your grid file with DockStream, do not specify any rotatable bonds in the receptor \n", + " (e.g. -OH groups). These are incompatible with Glide's output option \"ligandlib_sd\", as they (slightly) \n", + " change the receptor configuration.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Docking\n", + "In this section we consider a case where we have just prepared the receptor and want to dock a bunch of ligands (molecules, compounds) into the binding cleft. Often, we only have the structure of the molecules in the form of `SMILES`, rather than a 3D structure so the first step will be to generate these conformers before proceeding (\"embedding\"). In `DockStream`, you can embed your ligands with a variety of programs including `Corina`, `RDKit`, `OMEGA`, and `LigPrep` and use them freely with any backend. However, here we will use `Corina` for the conformer embedding." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['C#CCCCn1c(Cc2cc(OC)c(OC)c(OC)c2Cl)nc2c(N)ncnc21', 'CCCCn1c(Cc2cc(OC)c(OC)c(OC)c2)nc2c(N)ncnc21', 'CCCCn1c(Cc2cc(OC)ccc2OC)nc2c(N)ncnc21', 'CCCCn1c(Cc2cccc(OC)c2)nc2c(N)ncnc21', 'C#CCCCn1c(Cc2cc(OC)c(OC)c(OC)c2Cl)nc2c(N)nc(F)nc21', 'CCCCn1c(Cc2ccc(OC)cc2)nc2c(N)ncnc21', 'CCCCn1c(Cc2ccc3c(c2)OCO3)nc2c(N)ncnc21', 'CCCCn1c(Cc2cc(OC)ccc2OC)nc2c(N)nc(F)nc21', 'CCCCn1c(Cc2ccc3c(c2)OCO3)nc2c(N)nc(F)nc21', 'C#CCCCn1c(Cc2cc(OC)ccc2OC)nc2c(N)nc(F)nc21', 'CC(C)NCCCn1c(Cc2cc3c(cc2I)OCO3)nc2c(N)nc(F)nc21', 'CC(C)NCCCn1c(Sc2cc3c(cc2Br)OCO3)nc2c(N)ncnc21', 'CC(C)NCCCn1c(Sc2cc3c(cc2I)OCO3)nc2c(N)ncnc21', 'COc1ccc(OC)c(Cc2nc3nc(F)nc(N)c3[nH]2)c1', 'Nc1nccn2c(NCc3ccccc3)c(Cc3cc4c(cc3Br)OCO4)nc12']\n" + ] + } + ], + "source": [ + "# load the smiles (just for illustrative purposes)\n", + "# here, 15 moleucles will be used\n", + "with open(smiles_path, 'r') as f:\n", + " smiles = [smile.strip() for smile in f.readlines()]\n", + "print(smiles)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While the embedding and docking tasks in `DockStream` are both specified in the same configuration file, they are handled independently. This means it is perfectly fine to either load conformers (from an `SDF` file) directly or to use a call of `docker.py` merely to generate conformers without doing the docking afterwards.\n", + "\n", + "`DockStream` uses the notion of (embedding) \"pool\"s, of which multiple can be specified and accessed via identifiers. Note, that while the way conformers are generated is highly backend specific, `DockStream` allows you to use the results interchangably. This allows to (a) re-use embedded molecules for multiple docking runs (e.g. different scoring functions), without the necessity to embed them more than once and (b) to combine embeddings and docking backends freely. Below is also a simple definition of a docking run, note that for `Glide` you might want to do a run from within `Maestro` first and then use the generated `.in` file to set the `glide_keywords` (minimally, you need to specify `PRECISION` and the path to the receptor grid file)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# specify the embedding and docking JSON file as a dictionary and write it out\n", + "ed_dict = {\n", + " \"docking\": {\n", + " \"header\": { # general settings\n", + " \"environment\": {\n", + " }\n", + " },\n", + " \"ligand_preparation\": { # the ligand preparation part, defines how to build the pool\n", + " \"embedding_pools\": [\n", + " {\n", + " \"pool_id\": \"Corina_pool\",\n", + " \"type\": \"Corina\",\n", + " \"parameters\": {\n", + " \"prefix_execution\": \"module load corina\"\n", + " },\n", + " \"input\": {\n", + " \"standardize_smiles\": False,\n", + " \"input_path\": smiles_path,\n", + " \"type\": \"smi\" # expected input is a text file with smiles\n", + " },\n", + " \"output\": { # the conformers can be written to a file, but \"output\" is\n", + " # not required as the ligands are forwarded internally\n", + " \"conformer_path\": ligands_conformers_path, \n", + " \"format\": \"sdf\"\n", + " }\n", + " }\n", + " ]\n", + " },\n", + " \"docking_runs\": [\n", + " {\n", + " \"backend\": \"Glide\",\n", + " \"run_id\": \"Glide_run\",\n", + " \"input_pools\": [\"Corina_pool\"],\n", + " \"parameters\": {\n", + " \"prefix_execution\": \"module load schrodinger/2019-4\", # will be executed before a program call\n", + " \"parallelization\": { # if present, the number of cores to be used\n", + " # can be specified\n", + " \"number_cores\": 2\n", + " },\n", + " \"glide_flags\": { # all all command-line flags for Glide here \n", + " \"-HOST\": \"localhost\"\n", + " },\n", + " \"glide_keywords\": { # add all keywords for the \"input.in\" file here\n", + " \"AMIDE_MODE\": \"trans\",\n", + " \"EXPANDED_SAMPLING\": \"True\",\n", + " \"GRIDFILE\": grid_file_path,\n", + " \"NENHANCED_SAMPLING\": \"2\",\n", + " \"POSE_OUTTYPE\": \"ligandlib_sd\",\n", + " \"POSES_PER_LIG\": \"3\",\n", + " \"POSTDOCK_NPOSE\": \"15\",\n", + " \"POSTDOCKSTRAIN\": \"True\",\n", + " \"PRECISION\": \"HTVS\",\n", + " \"REWARD_INTRA_HBONDS\": \"True\"\n", + " }\n", + " },\n", + " \"output\": {\n", + " \"poses\": { \"poses_path\": ligands_docked_path },\n", + " \"scores\": { \"scores_path\": ligands_scores_path }\n", + " }\n", + " }]}}\n", + "\n", + "with open(docking_path, 'w') as f:\n", + " json.dump(ed_dict, f, indent=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tautomer enumeration / Protonation states\n", + "Another option available is to use different tautomers / protonation states for your ligands. Depending on the backend used, but especially for `Glide`, the exact protonation of the ligands matters a lot. We can achieve an enumeration using `TautEnum`, which will produce all states reasonable at a pH of 7.0 (with some leeway). `DockStream`'s internal ligand numbering scheme works as follows: any ligand gets an increasing number (starting with 0), the `ligand number`. Separated by a ':', every enumeration gets another number, e.g. \"1:2\" would be the second ligand's third enumeration. After docking, a third number is added for the pose, e.g. \"2:2:1\" in the final SDF output file would indicate the third ligand's third enumeration, docking pose two. The following block (to be placed in the respective pool specification) would activate the use of `TautEnum` (including protonation states):\n", + "\n", + "```\n", + "...\n", + " \"pool_id\": \"Corina_pool\",\n", + " \"type\": \"Corina\",\n", + " \"input\": {\n", + " ...\n", + " \"use_taut_enum\": {\n", + " \"prefix_execution\": \"module load taut_enum\",\n", + " \"enumerate_protonation\": true\n", + " },\n", + " ...\n", + " },\n", + "...\n", + "```\n", + "\n", + "### Adding constraints and features to the `Glide` run\n", + "`Glide` allows you to set a large number of settings (see the list at the very end of the notebook). Apart from settings on `EXAMPLE_SAMPLING` and `NENHANCED_SAMPLING` (value of 1 to 4), which have proven to be useful, you may also want to set constraints and features. Note, that all parameters have to be given as strings. Here is an example:\n", + "\n", + "```\n", + "...\n", + " \"glide_keywords\": {\n", + " \"AMIDE_MODE\": \"trans\",\n", + " \"EXPANDED_SAMPLING\": \"True\",\n", + " \"GRIDFILE\": \"\",\n", + " \"NENHANCED_SAMPLING\": \"2\",\n", + " \"POSE_OUTTYPE\": \"ligandlib_sd\",\n", + " \"POSES_PER_LIG\": \"3\",\n", + " \"POSTDOCK_NPOSE\": \"15\",\n", + " \"POSTDOCKSTRAIN\": \"True\",\n", + " \"PRECISION\": \"HTVS\",\n", + " \"REWARD_INTRA_HBONDS\": \"True\"\n", + " },\n", + " \"[CONSTRAINT_GROUP:1]\": {\n", + " \"USE_CONS\": \"A:ALA:72:H(hbond):1,\",\n", + " \"NREQUIRED_CONS\": \"ALL\"\n", + " },\n", + " \"[FEATURE:1]\": {\n", + " \"PATTERN1\": \"[N]#C 1 include\",\n", + " \"PATTERN2\": \"[n] 1 include\",\n", + " \"PATTERN3\": \"N(=N=N) 1 include\",\n", + " \"PATTERN4\": \"N(=N)=N 1 include\"\n", + " },\n", + "...\n", + "```\n", + "\n", + "### Token guard\n", + "For the usage of `Glide`, tokens managed by a central licensing server will be consumed for the duration of the run. It might happen that you run out of tokens and the docking will fail for that reason. To ensure protection against that scenario, you can specify a \"token guard\", which will halt the submission of the actual docking subjobs until enough tokens are available. For this to take effect the specification below has to be added to the `parameters` block for the docking run. Note, that you have to specify the token pool you want to protect against (e.g. \"GLIDE_HTVS_DOCKING\" for HTVS runs and \"GLIDE_SP_DOCKING\" for SP runs). You may have any number of token protections activated at a given time. The number next to it specifies the minimum number of tokens for a given pool which need to be available to proceed. Note, that any process will consume 4 tokens, so if you e.g. specify to use parallelization with 8 cores, you will need to request access to at least 32 tokens. The availability will be checked every `wait_interval_seconds` for a maximum duration of `wait_limit_seconds` (note that a value of 0 for the latter means \"unlimited\").\n", + "```\n", + "...\n", + " \"token_guard\": {\n", + " \"prefix_execution\": \"module load schrodinger/2019-4\",\n", + " \"token_pools\": {\n", + " \"GLIDE_HTVS_DOCKING\": 32\n", + " },\n", + " \"wait_interval_seconds\": 30,\n", + " \"wait_limit_seconds\": 1200\n", + " },\n", + "...\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Executing `DockStream`\n", + "The following command executes `DockStream` using the the configuration file we generated earlier. Note, that you might want to use the `-debug` flag when calling `DockStream` to get more comprehesive logging output. Logging and error messages will be saved in a file called `dockstream_run.log` (you will have to delete it manually if you restart a job)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NA\r\n", + "NA\r\n", + "-6.36784\r\n", + "-7.0782\r\n", + "-6.70502\r\n", + "-7.39402\r\n", + "-7.60067\r\n", + "-4.96716\r\n", + "-7.42175\r\n", + "-7.32542\r\n", + "-8.20468\r\n", + "-7.454\r\n", + "-5.03292\r\n", + "-6.9681\r\n", + "-8.0769\r\n" + ] + } + ], + "source": [ + "# execute this in a command-line environment after replacing the parameters\n", + "!{dockstream_env}/bin/python {docker} -conf {docking_path} -print_scores" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note, that the scores are usually outputted to a `CSV` file specified by the `scores` block, but that since we have used parameter `-print_scores` they will also be printed to `stdout` (line-by-line). Also note, that ligands that could not be docked successfully will result in `NA` values (for all backends, but `Glide` is particularly prone to that).\n", + "\n", + "These scores are associated with docking poses (see picture below for a couple of ligands overlaid in the binding pocket).\n", + "\n", + "![](img/docked_ligands_overlay_holo.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ligand_number,enumeration,conformer_number,name,score,smiles,lowest_conformer\r\n", + "2,0,0,2:0:0,-6.36784,[H]c1nc(N([H])[H])c2nc(C([H])([H])c3c([H])c(OC([H])([H])[H])c([H])c([H])c3OC([H])([H])[H])n(C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H])c2n1,True\r\n", + "2,0,1,2:0:1,-6.28967,[H]c1nc(N([H])[H])c2nc(C([H])([H])c3c([H])c(OC([H])([H])[H])c([H])c([H])c3OC([H])([H])[H])n(C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H])c2n1,False\r\n", + "2,0,2,2:0:2,-5.17748,[H]c1nc(N([H])[H])c2nc(C([H])([H])c3c([H])c(OC([H])([H])[H])c([H])c([H])c3OC([H])([H])[H])n(C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H])c2n1,False\r\n", + "3,0,0,3:0:0,-7.0782,[H]c1nc(N([H])[H])c2nc(C([H])([H])c3c([H])c([H])c([H])c(OC([H])([H])[H])c3[H])n(C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H])c2n1,True\r\n", + "3,0,1,3:0:1,-6.5258,[H]c1nc(N([H])[H])c2nc(C([H])([H])c3c([H])c([H])c([H])c(OC([H])([H])[H])c3[H])n(C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H])c2n1,False\r\n", + "3,0,2,3:0:2,-5.75348,[H]c1nc(N([H])[H])c2nc(C([H])([H])c3c([H])c([H])c([H])c(OC([H])([H])[H])c3[H])n(C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H])c2n1,False\r\n", + "4,0,0,4:0:0,-6.70502,[H]C#CC([H])([H])C([H])([H])C([H])([H])n1c(C([H])([H])c2c([H])c(OC([H])([H])[H])c(OC([H])([H])[H])c(OC([H])([H])[H])c2Cl)nc2c(N([H])[H])nc(F)nc21,True\r\n", + "5,0,0,5:0:0,-7.39402,[H]c1nc(N([H])[H])c2nc(C([H])([H])c3c([H])c([H])c(OC([H])([H])[H])c([H])c3[H])n(C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H])c2n1,True\r\n", + "5,0,1,5:0:1,-7.38266,[H]c1nc(N([H])[H])c2nc(C([H])([H])c3c([H])c([H])c(OC([H])([H])[H])c([H])c3[H])n(C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H])c2n1,False\r\n" + ] + } + ], + "source": [ + "# show glimpse into contents of output CSV\n", + "!head -n 10 {ligands_scores_path}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using `DockStream` as a scoring component in `REINVENT`\n", + "The *de novo* design platform `REINVENT` holds a recently added `DockStream` scoring function component (also check out our collection of notebooks in the [ReinventCommunity](https://github.com/MolecularAI/ReinventCommunity) repository). This means, provided that all necessary input files and configurations are available, you may run `REINVENT` and incorporate docking scores into the score of the compounds generated. Together with `FastROCS`, this represents the first step to integrate physico-chemical 3D information.\n", + "\n", + "While the docking scores are a very crude proxy for the actual binding affinity (at best), it does prove useful as a *geometric filter* (removing ligands that obviously do not fit the binding cavity). Furthermore, a severe limitation of knowledge-based predictions e.g. in activity models is the domain applicability. Docking, as a chemical space agnostic component, can enhance the ability of the agent for scaffold-hopping, i.e. to explore novel sub-areas in the chemical space.\n", + "\n", + "### The `REINVENT` configuration JSON\n", + "\n", + "While every docking backend has its own configuration (see section above), calling `DockStream`'s `docker.py` entry point ensures, that they all follow the same external API. Thus the component that needs to be added to `REINVENT`'s JSON configuration (to the `scoring_function`->`parameters` list) looks as follows (irrespective of the backend):\n", + "\n", + "```\n", + "{\n", + " \"component_type\": \"dockstream\",\n", + " \"name\": \"dockstream\",\n", + " \"weight\": 1,\n", + " \"model_path\": \"\",\n", + " \"smiles\": [],\n", + " \"specific_parameters\": {\n", + " \"debug\": false,\n", + " \"transformation\": true,\n", + " \"transformation_type\": \"reverse_sigmoid\",\n", + " \"low\": -20,\n", + " \"high\": -5,\n", + " \"k\": 0.2,\n", + " \"configuration_path\": \"/docking.json\",\n", + " \"docker_script_path\": \"/docker.py\",\n", + " \"environment_path\": \"/miniconda3/envs/DockStream/bin/python\"\n", + " }\n", + "}\n", + "```\n", + "\n", + "You will need to update `configuration_path`, `docker_script_path` and the link to the environment, `environment_path` to match your system's configuration. It might be, that the latter two are already set to meaningful defaults, but your `DockStream` configuration JSON file will be specific for each run. In the example above, we have set `debug` to `false`. Setting that flag to `true`, which will cause `DockStream` to write out a much more comprehensive logging output which is recommended for testing a setup initially.\n", + "\n", + "#### How to find an appropriate transformation?\n", + "We use a *reverse sigmoid* score transformation to bring the numeric, continuous value that was outputted by `DockStream` and fed back to `REINVENT` into a 0 to 1 regime. The parameters `low`, `high` and `k` are critical: their exact value naturally depends on the backend used, but also on the scoring function (make sure, \"more negative is better\" - otherwise you are looking for a *sigmoid* transformation) and potentially also the project used. The values reported here can be used as rule-of-thumb for a `Glide` run. Below is a code snippet, that helps to find the appropriate parameters (excerpt of the `ReinventCommunity` notebook `Score_Transformations`)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# load the dependencies and classes used\n", + "%run code/score_transformation.py\n", + "\n", + "# set plotting parameters\n", + "small = 12\n", + "med = 16\n", + "large = 22\n", + "params = {\"axes.titlesize\": large,\n", + " \"legend.fontsize\": med,\n", + " \"figure.figsize\": (16, 10),\n", + " \"axes.labelsize\": med,\n", + " \"axes.titlesize\": med,\n", + " \"xtick.labelsize\": med,\n", + " \"ytick.labelsize\": med,\n", + " \"figure.titlesize\": large}\n", + "plt.rcParams.update(params)\n", + "plt.style.use(\"seaborn-whitegrid\")\n", + "sns.set_style(\"white\")\n", + "%matplotlib inline\n", + "\n", + "# set up Enums and factory\n", + "tt_enum = TransformationTypeEnum()\n", + "csp_enum = ComponentSpecificParametersEnum()\n", + "factory = TransformationFactory()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# reverse sigmoid transformation\n", + "# ---------\n", + "values_list = np.arange(-30, 20, 0.25).tolist()\n", + "specific_parameters = {csp_enum.TRANSFORMATION: True,\n", + " csp_enum.LOW: -20,\n", + " csp_enum.HIGH: -5,\n", + " csp_enum.K: 0.2,\n", + " csp_enum.TRANSFORMATION_TYPE: tt_enum.REVERSE_SIGMOID}\n", + "transform_function = factory.get_transformation_function(specific_parameters)\n", + "transformed_scores = transform_function(predictions=values_list,\n", + " parameters=specific_parameters)\n", + "\n", + "# render the curve\n", + "render_curve(title=\"Reverse Sigmoid Transformation\", x=values_list, y=transformed_scores)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How to specify the `DockStream` configuration file?\n", + "In principle, all options that are supported in a \"normal\" `DockStream` run (see above) are supported for usage with `REINVENT` as well, with a few notable exceptions. First, as we report only one value per ligand (and a \"consensus score\" is not yet supported), you should only use **one** embedding / pool and **one** backend (as in the example above). Second, the prospective ligands are not supplied via a file but from `stdin`, thus we will need to change the `input` part of the pool definition. Also, we might not want to write-out all conformers, so we will remove the `output` block entirely. The updated section then looks as follows:\n", + "\n", + "```\n", + "{\n", + " \"pool_id\": \"Corina_pool\",\n", + " \"type\": \"Corina\",\n", + " \"parameters\": {\n", + " \"prefix_execution\": \"module load corina\"\n", + " },\n", + " \"input\": {\n", + " \"standardize_smiles\": False\n", + " }\n", + "}\n", + "```\n", + "\n", + "Finally, we will update the docking run as well. Typically, we want to see the docked poses per epoch and maybe also the scores and the SMILES in a well-tabulated format. Thus, we might retain the `output` block here, but as every epoch generates each of the files, it would overwrite it by default. If parameter `overwrite` is set to `False`, each consecutive write-out will be appended by a number, e.g. first epoch *poses.sdf* and *scores.csv*, second epoch *0001_poses.sdf* and *0001_scores.csv*, third epoch *0002_poses.sdf* and *0002_scores.csv* and so on.\n", + "\n", + "```\n", + "{\n", + " \"backend\": \"Glide\",\n", + " \"run_id\": \"Glide_run\",\n", + " \"input_pools\": [\"Corina_pool\"],\n", + " \"parameters\": {\n", + " \"prefix_execution\": \"module load schrodinger/2019-4\",\n", + " \"glide_flags\": {\n", + " \"-NJOBS\": 1,\n", + " \"-HOST\": \"localhost\"\n", + " },\n", + " \"glide_keywords\": {\n", + " \"AMIDE_MODE\": \"trans\",\n", + " \"EXPANDED_SAMPLING\": \"True\",\n", + " \"GRIDFILE\": \"/grid.zip\",\n", + " \"NENHANCED_SAMPLING\": \"2\",\n", + " \"POSE_OUTTYPE\": \"ligandlib_sd\",\n", + " \"POSES_PER_LIG\": \"3\",\n", + " \"POSTDOCK_NPOSE\": \"15\",\n", + " \"POSTDOCKSTRAIN\": \"True\",\n", + " \"PRECISION\": \"HTVS\",\n", + " \"REWARD_INTRA_HBONDS\": \"True\"\n", + " }\n", + " },\n", + " \"output\": {\n", + " \"poses\": { \"poses_path\": \"/poses.sdf\", \"overwrite\": False },\n", + " \"scores\": { \"scores_path\": \"/scores.csv\", \"overwrite\": False }\n", + " }\n", + "}\n", + "```\n", + "\n", + "### Block-keywords for `Glide` and list of keywords\n", + "In principle, there is no restriction on which keywords or flags you may use in connection with `Glide` (see the full list of keywords below). Note, however, that blocks (such as constraint definitions) have a slightly different syntax and\n", + "\n", + "```\n", + " \"glide_keywords\": {\n", + " \"PRECISION\": \"HTVS\",\n", + " \"[CONSTRAINT_GROUP:1]\": {\n", + " \"USE_CONS\": \"sulfonamide:1\",\n", + " \"NREQUIRED_CONS\": \"ALL\"\n", + " },\n", + " \"[FEATURE:1]\": {\n", + " \"PATTERN1\": \"[N-]S(=O)=O 1,2,3,4 include\"\n", + " }\n", + " }\n", + "```\n", + "\n", + "will result in\n", + "\n", + "```\n", + "PRECISION: HTVS\n", + "[CONSTRAINT_GROUP:1]\n", + " USE_CONS sulfonamide:1,\n", + " NREQUIRED_CONS ALL\n", + "[FEATURE:1]\n", + " PATTERN1 \"[N-]S(=O)=O 1,2,3,4 include\"\n", + "```\n", + "\n", + "The following is a list of keywords for the `2019-4` realease of `Glide` and might be different for your version. Use `${SCHRODINGER}/glide -k` to see all of them.\n", + "\n", + "```\n", + "AMIDE_MODE = option('penal', 'fixed', 'free', 'trans', 'generalized', default='generalized') # amide bond rotation behavior: \"fixed\", \"free\", \"penal\", \"trans\", \"gen[eralized]\"\n", + "AMIDE_TRANS_ALL = boolean(default=False) # include \"nonstandard\" amides in trans enforcement\n", + "AMIDE_TRANSTOL = float(default=20.0) # trans amide tolerance (in degrees)\n", + "ASL_RES_INTERACTION = string(default=None) # If present, use it; else, use \"radius_res_interaction.\"\n", + "ASLSTRINGS = string_list(default=list()) # ASL strings for receptor scaling\n", + "CALC_INPUT_RMS = boolean(default=False) # report RMS deviation against input geometry of each ligand\n", + "CANONICALIZE = boolean(default=True) # docking initiated from a canonical conformation per input ligand (false by default for HTVS precision)\n", + "COMPRESS_POSES = boolean(default=True) # generate compressed maestro pose and _raw files\n", + "CORE_ATOMS = int_list(default=None) # index into reference ligand for each atom in core\n", + "CORE_DEFINITION = option('all', 'allheavy', 'smarts', 'mcssmarts', 'atomlist', default='allheavy') # is core \"all\" atoms in molecule, \"allheavy\" (default), \"smarts\" pattern, \"mcssmarts\" (generate SMARTS from maximum common substructure), or \"atomlist\"\n", + "CORE_FILTER = boolean(default=False) # skip ligands that do not contain the core\n", + "CORE_POS_MAX_RMSD = float(default=0.1) # maximum RMSD of core atom positions\n", + "CORE_RESTRAIN = boolean(default=False) # restrain core atoms\n", + "CORE_RESTRAIN_V = float(default=5.0) # strength of core restraining potential\n", + "CORE_SMARTS = string(default=None) # SMARTS pattern to match for core RMSD calculation/restraint\n", + "CORE_SNAP = boolean(default=None) # When using core constraints (\"CORE_RESTRAIN yes\"): if \"yes\", use \"snapping\" core constraints algorithm. If \"no\", use filtering algorithm. If not set, choose automatically based on CORE_POS_MAX_RMSD (\"yes\" if < 0.75; \"no\" otherwise)\n", + "CORECONS_FALLBACK = boolean(default=False) # if a ligand fails to dock with CORE_SNAP retry it without\n", + "CSV_PROPS_FILE = string(default='') # file containing names of m2io properties to be added to csvfile\n", + "CV_CUTOFF = float(default=0.0) # Coulomb-van der Waals energy cutoff used for final filtering\n", + "DIELMOD = option('rdiel', 'cdiel', default='rdiel') # type of dielectric to use: distance-dependent (rdiel) or constant (cdiel)\n", + "DOCKING_METHOD = option('confgen', 'rigid', 'inplace', 'mininplace', default='confgen') # docking method: confgen=flexible docking; rigid=rigid docking; mininplace=refine (do not dock); inplace=score in place (do not dock)\n", + "DOINTRA = boolean(default=False) # relax bad intramolecular contacts\n", + "DOINTRA_SCALE = float(default=1.0) # scaling factor for intramolecular pose relaxation\n", + "EPIK_PENALTIES = boolean(default=True) # include ligand Epik state penalties in the Glide DockingScore scoring function\n", + "EXPANDED_SAMPLING = boolean(default=False) # bypass elimination of poses in rough scoring stage (useful for fragment docking)\n", + "FITDEN = boolean(default=False) # activate docking with ligand density data from PrimeX\n", + "FLEXASL = string(default=None) # ASL defining flexible receptor atoms\n", + "FORCEFIELD = string(default='OPLS3') # force field\n", + "FORCEPLANAR = boolean(default=False) # trigger MMFFLD planarity options\n", + "GLIDE_CONFGEN_BADDIST2 = float(default=6.0, min=0.0) # distance cutoff, squared, for bad contacts in confgen\n", + "GLIDE_CONFGEN_EFCUT = float(default=12.0, min=0.0) # energy cutoff during ligand conformer generation\n", + "GLIDE_CONS_FEAT_FILE = string(default=None) # feature file name for constraints jobs\n", + "GLIDE_CONS_FINALONLY = boolean(default=False) # only check for constraint satisfaction after docking is complete\n", + "GLIDE_CONS_RMETCOORD = float_list(default=list()) # sphere radii of Glide metal_coordination constraints\n", + "GLIDE_CONS_RNOEMAX = float_list(default=list()) # maximum distances for Glide NOE constraints\n", + "GLIDE_CONS_RNOEMIN = float_list(default=list()) # minimum distances for Glide NOE constraints\n", + "GLIDE_CONS_RPOS = float_list(default=list()) # sphere radii of Glide positional constraints\n", + "GLIDE_CONS_XMETCOORD = float_list(default=list()) # X-coordinates of Glide metal-coordination constraints\n", + "GLIDE_CONS_XNOE = float_list(default=list()) # X-coordinates of targets for Glide NOE constraints\n", + "GLIDE_CONS_XPOS = float_list(default=list()) # X-coordinates of Glide positional constraints\n", + "GLIDE_CONS_YMETCOORD = float_list(default=list()) # Y-coordinates of Glide metal-coordination constraints\n", + "GLIDE_CONS_YNOE = float_list(default=list()) # Y-coordinates of targets for Glide NOE constraints\n", + "GLIDE_CONS_YPOS = float_list(default=list()) # Y-coordinates of Glide positional constraints\n", + "GLIDE_CONS_ZMETCOORD = float_list(default=list()) # Z-coordinates of Glide metal-coordination constraints\n", + "GLIDE_CONS_ZNOE = float_list(default=list()) # Z-coordinates of targets for Glide NOE constraints\n", + "GLIDE_CONS_ZPOS = float_list(default=list()) # Z-coordinates of Glide positional constraints\n", + "GLIDE_DIELCO = float(default=2.0, min=0.0, max=9999.9) # dielectric constant\n", + "GLIDE_ELEMENTS = boolean(default=False) # run in \"Glide Elements\" mode\n", + "GLIDE_EXVOL_PENAL_NUM = float_list(default=list()) # maximum penalties in kcal/mol for each Glide excluded volume violation\n", + "GLIDE_EXVOL_PENAL_STRENGTH = option('low', 'small', 'medium', 'high', 'large', default='large') # penalty specification for (all) Glide excluded volumes.\n", + "GLIDE_NTOTALCONS = integer(default=0, min=0, max=10) # number of receptor atoms having constraints\n", + "GLIDE_NUMEXVOL = integer(default=0, min=0) # number of receptor excluded-volume regions\n", + "GLIDE_NUMMETCOORDCONS = integer(default=0, min=0) # number of receptor metal-coordination constraints\n", + "GLIDE_NUMMETCOORDSITES = int_list(default=list()) # number of available coordination sites per metal-coordination constraint\n", + "GLIDE_NUMNOECONS = integer(default=0, min=0) # number of receptor NOE constraints\n", + "GLIDE_NUMPOSITCONS = integer(default=0, min=0) # number of receptor positional constraints\n", + "GLIDE_NUMUSEXVOL = integer(default=0, min=0) # number of excluded-volume regions to use\n", + "GLIDE_OUTPUT_USEHTOR = boolean(default=True) # use rotation of polar hydrogens as pose-distinguishing criterion\n", + "GLIDE_RECEP_ASLSCALE = boolean(default=False) # for Glide gridgen jobs, take per-atom charge and radius scaling factors from ASL specification\n", + "GLIDE_RECEP_MAESCALE = boolean(default=False) # for Glide gridgen jobs, take per-atom charge and radius scaling factors from properties written by Maestro to the receptor file\n", + "GLIDE_REFLIG_FORMAT = option('maestro', 'sd', 'mol2', default='maestro') # Glide reference ligand file format\n", + "GLIDE_REXVOL = float_list(default=list()) # sphere radii of Glide excluded volumes\n", + "GLIDE_REXVOLIN = float_list(default=list()) # inner sphere (max penalty) radii of Glide excluded volumes\n", + "GLIDE_TORCONS_ALLBONDS = bool_list(default=list()) # constrain all independent dihedrals (one per rotatable bond) contained in SMARTS pattern (if false, specified dihedrals only)\n", + "GLIDE_TORCONS_IATOMS = int_list(default=list()) # first of four atoms (index into SMARTS pattern) forming dihedral to be constrained\n", + "GLIDE_TORCONS_JATOMS = int_list(default=list()) # second of four atoms (index into SMARTS pattern) forming dihedral to be constrained\n", + "GLIDE_TORCONS_KATOMS = int_list(default=list()) # third of four atoms (index into SMARTS pattern) forming dihedral to be constrained\n", + "GLIDE_TORCONS_LATOMS = int_list(default=list()) # fourth of four atoms (index into SMARTS pattern) forming dihedral to be constrained\n", + "GLIDE_TORCONS_PATTERN_INDEX = int_list(default=list()) # index into TORCONS_PATTERNS string array indicating which SMARTS pattern a given quartet of atom indices refers to\n", + "GLIDE_TORCONS_PATTERNS = string_list(default=list()) # SMARTS patterns for matching docked ligands to torsional constraints\n", + "GLIDE_TORCONS_SETVAL = bool_list(default=list()) # apply user-supplied value for constrained torsions (if false, use input value in each docked ligand)\n", + "GLIDE_TORCONS_VALUES = float_list(default=list()) # Values to set constrained torsions to. (Ignored if corresponding element of TORCONS_SETVAL is false.)\n", + "GLIDE_TORCONSFILE = string(default=None) # m2io-format file containing SMARTS pattern and bond (and optional dihedral angle) specifications for torsional constraints\n", + "GLIDE_XEXVOL = float_list(default=list()) # X-coordinates of centers of Glide excluded volumes\n", + "GLIDE_XP_NMAXCORE = integer(default=4, min=0) # maximum number of anchors to use in XP refinement\n", + "GLIDE_XP_RMSCUT = float(default=2.5) # RMS cutoff for \"fast XP\" min-and-score\n", + "GLIDE_YEXVOL = float_list(default=list()) # Y-coordinates of centers of Glide excluded volumes\n", + "GLIDE_ZEXVOL = float_list(default=list()) # Z-coordinates of centers of Glide excluded volumes\n", + "GLIDECONS = boolean(default=False) # use constraints\n", + "GLIDECONSATOMS = int_list(default=list()) # receptor constraint atom list (H-bond and metal)\n", + "GLIDECONSFEATATOMS = string_list(default=list()) # array of comma-separated lists of atom indices, giving positions in the SMARTS of constraint-satisfying ligand atoms\n", + "GLIDECONSFEATHASINCLUDE = bool_list(default=list()) # indicates whether the indexed feature has a valid value for the \"GLIDECONSFEATINCLUDE\" keyword\n", + "GLIDECONSFEATINCLUDE = bool_list(default=list()) # include the SMARTS pattern as a match for the indexed feature? (false means matches for that SMARTS *don't* satisfy the constraint\n", + "GLIDECONSFEATINDEX = int_list(default=list()) # indicates which feature the given SMARTS pattern, atom list, etc., belong to\n", + "GLIDECONSFEATPATTERNS = string_list(default=None) # SMARTS patterns that constitute constraint-satisfying ligand features\n", + "GLIDECONSGROUPNREQUIRED = int_list(default=list()) # number of constraints in each group required to be satisfied\n", + "GLIDECONSNAMES = string_list(default=list()) # constraint label list\n", + "GLIDECONSUSEMET = boolean(default=False) # use element-based metal radii for Glide constraints\n", + "GLIDECONSUSESYMATOMS = bool_list(default=list()) # include symmetry-related receptor atoms for current H-bond constraint (default = all true)\n", + "GLIDERECEPTORSCALECHARGES = float_list(default=list()) # per-atom scale factors for receptor charges\n", + "GLIDERECEPTORSCALERADII = float_list(default=list()) # per-atom scale factors for receptor radii\n", + "GLIDESCORUSEMET = boolean(default=False) # use element-based metal radii in Glide scoring\n", + "GLIDEUSEALLEXVOL = boolean(default=False) # use all excluded volumes in file (as opposed to selected ones)\n", + "GLIDEUSECONSFEAT = boolean(default=False) # use constraints feature (SMARTS) file\n", + "GLIDEUSECONSFEATINDEX = int_list(default=list()) # indicates which ligand feature satisfies the given constraint\n", + "GLIDEUSECONSGROUPINDEX = int_list(default=list()) # indicates which constraint group the given constraint belongs to\n", + "GLIDEUSECONSLABELS = string_list(default=list()) # array of constraint labels to be used in docking job\n", + "GLIDEUSEXVOL = boolean(default=False) # use excluded volumes\n", + "GLIDEUSEXVOLNAMES = string_list(default=list()) # excluded-volume labels to use in docking job\n", + "GLIDEXVOLNAMES = string_list(default=list()) # excluded-volume label list\n", + "GRID_CENTER = float_list(default=list(0.0, 0.0, 0.0)) # coordinates of the grid center\n", + "GRID_CENTER_ASL = string(default=None) # ASL expression defining the grid center\n", + "GRIDFILE = string(default=None) # path to grid (.grd or .zip) file\n", + "GSCORE = option('SP3.5', 'SP4.0', 'SP4.5', 'SP5.0', default='SP5.0') # GlideScore version (\"SP5.0\" etc.)\n", + "GSCORE_CUTOFF = float(default=100.0) # GlideScore cutoff\n", + "HAVEGLIDECONSFEAT = boolean(default=False) # use pre-existing feature file for Glide constraints\n", + "HBOND_ACCEP_HALO = boolean(default=False) # include halogens as possible H-bond acceptors in scoring\n", + "HBOND_CONSTRAINTS = string_list(default=list()) # HBOND_CONSTRAINTS \"hbond1 47\", \"hbond2 55\"\n", + "HBOND_CUTOFF = float(default=0.0) # H-bond cutoff used for final filtering\n", + "HBOND_DONOR_AROMH = boolean(default=False) # include aromatic H as a possible H-bond donor in scoring\n", + "HBOND_DONOR_AROMH_CHARGE = float(default=0.0) # count aromatic H as a donor if its partial charge exceeds this value\n", + "HBOND_DONOR_HALO = boolean(default=False) # include halogens as possible H-bond donors in scoring\n", + "INCLUDE_INPUT_CONF = boolean(default=False) # include input conformation in confgen output\n", + "INCLUDE_INPUT_RINGS = boolean(default=False) # include input ring structures in confgen\n", + "INNERBOX = int_list(default=list(10, 10, 10)) # size of the box bounding the possible placements for the ligand centroid\n", + "JOBNAME = string(default='impact') # job name used for job control and as a filename prefix\n", + "KEEP_SUBJOB_POSES = boolean(default=True) # keep _subjob.poses.zip at the end of a distributed docking job\n", + "KEEPRAW = boolean(default=False) # do not delete the unsorted/unfiltered (\"raw\") pose file, _raw.mae[gz]\n", + "KEEPSKIPPED = boolean(default=False) # save skipped ligands to _skipped.mae[gz]\n", + "LIG_CCUT = float(default=0.15, min=0.0) # charge cutoff to determine whether to use vdW scaling of ligand atoms\n", + "LIG_MAECHARGES = boolean(default=False) # use charges from ligand Maestro file instead of those from the force field\n", + "LIG_VSCALE = float(default=0.8, min=0.0) # ligand vdW scaling (see also LIG_CCUT)\n", + "LIGAND_END = integer(default=0, min=0) # end ligand\n", + "LIGAND_INDEX = integer(default=None, min=1) # index of the ligand entry to use for computing default GRID_CENTER and OUTERBOX\n", + "LIGAND_MOLECULE = integer(default=None, min=1) # index of the ligand molecule to be removed\n", + "LIGAND_START = integer(default=1, min=1) # start ligand\n", + "LIGANDFILE = string(default='') # Glide docking ligands file name\n", + "LIGANDFILES = string_list(default=list()) # array of filenames to dock. Can be used instead of LIGANDFILE\n", + "LIGFORMAT = option('maestro', 'sd', 'mol2', default='maestro') # Glide docking ligands file format\n", + "MACROCYCLE = boolean(default=False) # generate macrocycle ring templates on the fly using Prime\n", + "MACROCYCLE_OPTIONS = string(default='') # options string to pass to Prime's macrocycle conformer generator\n", + "MAX_ITERATIONS = integer(default=100, min=0) # maximum number of iterations during docking minimization\n", + "MAXATOMS = integer(default=500, min=1, max=500) # maximum number of ligand atoms; larger ligands will be skipped\n", + "MAXKEEP = integer(default=5000, min=1) # maximum number of poses to keep after the rough scoring stage\n", + "MAXREF = integer(default=400, min=1) # maximum number of poses to refine\n", + "MAXROTBONDS = integer(default=100, min=0, max=100) # maximum number of rotatable bonds. Ligands exceeding this limit will be skipped\n", + "METAL_CONSTRAINTS = string_list(default=list()) # METAL_CONSTRAINTS \"metal1 64\", ...\n", + "METAL_CUTOFF = float(default=10.0) # metal bond cutoff used for final filtering\n", + "METCOORD_CONSTRAINTS = string_list(default=list()) # METCOORD_CONSTRAINTS \"label \", ...\n", + "METCOORD_SITES = string_list(default=list()) # METCOORD_SITES \" \", \" \", ...; Note that the list of \" \" tuples in a single METCOORD_SITES specification covers *all* of the specified METCOORD_CONSTRAINTS. Thus the number of such tuples is equal to the *sum* of all the specifications.\n", + "NENHANCED_SAMPLING = integer(default=1, min=1, max=4) # expand size of the Glide funnel by N times to process poses from N confgen runs with minor perturbations to the input ligand coordinates\n", + "NMAXRMSSYM = integer(default=100, min=0) # max number of poses to compare taking symmetry into account\n", + "NOE_CONSTRAINTS = string_list(default=list()) # NOE_CONSTRAINTS \"