{ "cells": [ { "cell_type": "markdown", "id": "0255a597-f02d-45fa-92b6-45292fa20068", "metadata": {}, "source": [ "## Protein-Ligand Interaction Clustering\n", "\n", "During the protein-ligand simulations, both protein and ligand undergoes for conformational change, and therefore interaction between them is also evolve and changes during the simulations. Sometime, protein and ligand conformation changes such that interaction remains constant. Capturing similarities in protein-ligand interactions while both are undergoing for changes is challenging. Therefore, clustering of protein-ligand interactions will provide collection of complex conformations that are grouped together by the interactions.\n", "\n", "This tutorial provides the steps with examples to perform conformational clustering besed on the protein-ligand interactions. **This method is employed in this [publication](https://pubs.acs.org/doi/full/10.1021/acsinfecdis.4c00531)**. For more details about the theory of the method, please follow the method section of this publication.\n", "\n", "### Instructions\n", "* **Tutorial files**: The tutorial files can be downloaded from [here](https://figshare.com/ndownloader/files/55986245).\n", "* **Extract the files**: `tar -zxvf protein-ligand-interaction.tar.gz`\n", "* **Go to directory**: `cd protein-ligand-interaction`\n", "* **Copy the Jupyter Notebook**: This notebook is available in the GitHub repo. [Download and copy it from the github](https://github.com/rjdkmr/gmx_clusterByFeatures/tree/master/docs/tutorials).\n", "\n", "### Final result\n", "

\n", " \n", " \n", " \n", " \n", "

\n", "\n", "### Required Tools\n", "* GROMACS\n", "* gmx_clusterByFeatures\n", "\n", "### Steps\n", "1. Modification of ligand topology file\n", "2. Calculation of protein-ligand reciprocal-distance-matrix trajectory\n", "3. PCA of the reciprocal-distance-matrix trajectory\n", "4. Calculations of projection of first five PCs on reciprocal-distance-matrix trajectory\n", "5. Pre-Clustering scan for Cluster-Metrics\n", "6. Clustering with pre-determined number of clusters \n", "7. Analysis\n", "8. MM/PBSA Analysis" ] }, { "cell_type": "markdown", "id": "8325352e-b79c-402b-b25f-a9d11ccd8f3b", "metadata": {}, "source": [ "### 1. Modification of ligand topology file\n", "\n", "In general, whole ligand is considered as a single residue. At first, ligand's atoms are grouped as several residues and these are marked in the topology file as shown below in example. Idea is to divide ligand into virtual residues.\n", "\n", "

\n", " \n", " \n", "

\n", " \n", "\n", "Above right side, ligand is colored by the assigned virtual residues.\n", "\n", "**Notes:**\n", "1. The atoms in a residue should be continuous and its ID and Name should be unique.\n", "2. Do not reorder the atoms in topology after simulation is performed because same order is present in trajectory.\n", "3. If required, always reorder atoms during ligand topology generation at the very begining of the simulation setup. In this way, atoms in generated trajectory will have same order as in topology and tpr files.\n", "\n", "After, modifying the topology, rerun `gmx grompp` to regenerate tpr file with ligand having virtual residues. Also, create new index files to include make new groups depending on the requirements." ] }, { "cell_type": "markdown", "id": "2af2ad14-6f3b-4d69-a4b8-9c6608b1309d", "metadata": {}, "source": [ "### 2. Calculation of protein-ligand reciprocal-distance-matrix trajectory\n", "\n", "Now, we calculate reciprocal-distance matrix using `distmat` sub-command. Since, ligand is now made-up of virtual residues, reciprocal-distance-matrix between ligand and protein will contain reciprocal of minimum-distance between ligand's virtual residues and protein's residues. \n", "\n", "**Notes:**\n", "* `-power -1` option is used to calulate 1/(minimum-distance) in place of minimum-distance.\n", "* Selected index group: `1` is protein and `12` is ligand (Other)" ] }, { "cell_type": "code", "execution_count": 1, "id": "d880f2a2-c7f1-456d-8b35-355287a17cb9", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " :-) GROMACS - gmx_clusterByFeatures distmat, 2025.0-dev-20250210-6949615-local (-:\n", "\n", "Executable: gmx_clusterByFeatures distmat\n", "Data prefix: /project/external/gmx_installed\n", "Working dir: /home/raj/workspace/gmx_clusterByFeatrues/tutorials/protein-ligand-interaction\n", "Command line:\n", " 'gmx_clusterByFeatures distmat' -f inputs/trajectory.xtc -s inputs/complex_res_segments.tpr -gx 1 -power -1 -var var_lig_protein.dat -cmap cmap_lig_protein.dat -pca pca.xtc\n", "\n", "\n", " :-) gmx_clusterByFeatures distmat (-:\n", "\n", " Author: Rajendra Kumar\n", "\n", " Copyright (C) 2018-2019 Rajendra Kumar\n", "\n", "\n", "gmx_clusterByFeatures is a free software: you can redistribute it and/or modify\n", "it under the terms of the GNU General Public License as published by\n", "the Free Software Foundation, either version 3 of the License, or\n", "(at your option) any later version.\n", "\n", "gmx_clusterByFeatures is distributed in the hope that it will be useful,\n", "but WITHOUT ANY WARRANTY; without even the implied warranty of\n", "MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n", "GNU General Public License for more details.\n", "\n", "You should have received a copy of the GNU General Public License\n", "along with gmx_clusterByFeatures. If not, see .\n", "\n", "THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n", "\"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n", "LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR\n", "A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\n", "OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\n", "SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED\n", "TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR\n", "PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF\n", "LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING\n", "NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\n", "SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n", " \n", "Reading file inputs/complex_res_segments.tpr, VERSION 5.1.2 (single precision)\n", "Reading file inputs/complex_res_segments.tpr, VERSION 5.1.2 (single precision)\n", "Select first group:\n", "Group 0 ( System) has 8393 elements\n", "Group 1 ( Protein) has 8325 elements\n", "Group 2 ( Protein-H) has 4217 elements\n", "Group 3 ( C-alpha) has 542 elements\n", "Group 4 ( Backbone) has 1626 elements\n", "Group 5 ( MainChain) has 2169 elements\n", "Group 6 ( MainChain+Cb) has 2660 elements\n", "Group 7 ( MainChain+H) has 2668 elements\n", "Group 8 ( SideChain) has 5657 elements\n", "Group 9 ( SideChain-H) has 2048 elements\n", "Group 10 ( Prot-Masses) has 8325 elements\n", "Group 11 ( non-Protein) has 68 elements\n", "Group 12 ( Other) has 68 elements\n", "Group 13 ( L1) has 4 elements\n", "Group 14 ( L2) has 3 elements\n", "Group 15 ( L3) has 8 elements\n", "Group 16 ( L4) has 8 elements\n", "Group 17 ( L5) has 14 elements\n", "Group 18 ( L6) has 2 elements\n", "Group 19 ( L7) has 3 elements\n", "Group 20 ( L8) has 1 elements\n", "Group 21 ( L9) has 10 elements\n", "Group 22 ( L10) has 10 elements\n", "Group 23 ( L11) has 1 elements\n", "Group 24 ( L12) has 4 elements\n", "Select a group: Select second group:\n", "Group 0 ( System) has 8393 elements\n", "Group 1 ( Protein) has 8325 elements\n", "Group 2 ( Protein-H) has 4217 elements\n", "Group 3 ( C-alpha) has 542 elements\n", "Group 4 ( Backbone) has 1626 elements\n", "Group 5 ( MainChain) has 2169 elements\n", "Group 6 ( MainChain+Cb) has 2660 elements\n", "Group 7 ( MainChain+H) has 2668 elements\n", "Group 8 ( SideChain) has 5657 elements\n", "Group 9 ( SideChain-H) has 2048 elements\n", "Group 10 ( Prot-Masses) has 8325 elements\n", "Group 11 ( non-Protein) has 68 elements\n", "Group 12 ( Other) has 68 elements\n", "Group 13 ( L1) has 4 elements\n", "Group 14 ( L2) has 3 elements\n", "Group 15 ( L3) has 8 elements\n", "Group 16 ( L4) has 8 elements\n", "Group 17 ( L5) has 14 elements\n", "Group 18 ( L6) has 2 elements\n", "Group 19 ( L7) has 3 elements\n", "Group 20 ( L8) has 1 elements\n", "Group 21 ( L9) has 10 elements\n", "Group 22 ( L10) has 10 elements\n", "Group 23 ( L11) has 1 elements\n", "Group 24 ( L12) has 4 elements\n", "Select a group: There are 542 residues with 8325 atoms in first group\n", "There are 12 residues with 68 atoms in second group\n", "Reading frame 22000 time 440000.000 \n", "\n", "GROMACS reminds you: \"Chemistry: It tends to be a messy science.\" (Gunnar von Heijne, former chair of the Nobel Committee for chemistry)\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Selected 1: 'Protein'\n", "Selected 12: 'Other'\n", " Number of distance-matrix elements for PCA trajectory: 6505\n", " Number of distance-matrix coordinates in PCA trajectory: 2169\n" ] } ], "source": [ "%%bash \n", "\n", "echo 1 12 | gmx_clusterByFeatures distmat -f inputs/trajectory.xtc -s inputs/complex_res_segments.tpr \\\n", " -gx 1 -power -1 -var var_lig_protein.dat -cmap cmap_lig_protein.dat -pca pca.xtc" ] }, { "cell_type": "markdown", "id": "df03cc08-532c-486d-8c65-2cb0062b7a78", "metadata": {}, "source": [ "### 3. PCA of the reciprocal-distance-matrix trajectory\n", "\n", "Now, we will use `pca.xtc` and `pca_dummy.pdb` generated in the above command as input files to GROMACS tool `gmx covar` to perform PCA. This step will calculate covariance matrix, eigenvectors and eigenvalues. By default, the eigenvectors are written in `eigenvec.trr` while eigenvalues are written in `eigenval.xvg` files.\n", "\n", "`-nofit`, `-nomwa` and `-nopbc` options are used because `xtc` file does not contain cartesian-coordinates and these option has no meanings in the case of the distance-matrix trajectory." ] }, { "cell_type": "code", "execution_count": 2, "id": "d5b6ebb5-7a6b-4d89-b9b2-5c8deaf72e38", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " :-) GROMACS - gmx covar, 2025.2 (-:\n", "\n", "Executable: /opt/gromacs-2025/bin/gmx\n", "Data prefix: /opt/gromacs-2025\n", "Working dir: /home/raj/workspace/gmx_clusterByFeatrues/tutorials/protein-ligand-interaction\n", "Command line:\n", " gmx covar -f pca.xtc -s pca_dummy.pdb -nofit -nomwa -nopbc\n", "\n", "Group 0 ( System) has 2169 elements\n", "Group 1 ( Protein) has 2169 elements\n", "Group 2 ( Protein-H) has 1102 elements\n", "Group 3 ( C-alpha) has 140 elements\n", "Group 4 ( Backbone) has 419 elements\n", "Group 5 ( MainChain) has 558 elements\n", "Group 6 ( MainChain+Cb) has 682 elements\n", "Group 7 ( MainChain+H) has 683 elements\n", "Group 8 ( SideChain) has 1486 elements\n", "Group 9 ( SideChain-H) has 544 elements\n", "Select a group: Calculating the average structure ...\n", "Reading frame 22000 time 440000.000 \n", "Constructing covariance matrix (6507x6507) ...\n", "Reading frame 22000 time 440000.000 \n", "Read 22501 frames\n", "\n", "Trace of the covariance matrix: 140.004 (nm^2)\n", "\n", "Diagonalizing ...\n", "\n", "Sum of the eigenvalues: 140.004 (nm^2)\n", "\n", "Writing eigenvalues to eigenval.xvg\n", "\n", "Writing average structure & eigenvectors 1--6507 to eigenvec.trr\n", "Wrote the log to covar.log\n", "\n", "GROMACS reminds you: \"Can I have everything louder than everything else?\" (Deep Purple)\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "WARNING: Masses and atomic (Van der Waals) radii will be guessed\n", " based on residue and atom names, since they could not be\n", " definitively assigned from the information in your input\n", " files. These guessed numbers might deviate from the mass\n", " and radius of the atom type. Please check the output\n", " files if necessary. Note, that this functionality may\n", " be removed in a future GROMACS version. Please, consider\n", " using another file format for your input.\n", "\n", "\n", "Choose a group for the covariance analysis\n", "Selected 0: 'System'\n" ] } ], "source": [ "%%bash\n", "\n", "echo 0 0 | gmx covar -f pca.xtc -s pca_dummy.pdb -nofit -nomwa -nopbc" ] }, { "cell_type": "markdown", "id": "f054b50e-d32d-4a59-b57b-7db13c131b5e", "metadata": {}, "source": [ "### 4. Projection of first five PCs on reciprocal-distance-matrix trajectory\n", "\n", "We will use eigenvectors written in `eigenvec.trr`, `pca.xtc` and `pca_dummy.pdb` as input files to GROMACS tool `gmx anaeig` to calculate projection of first 5 eigenvectors on distance-matrix trajectory. These projections will be written into `proj.xvg` file." ] }, { "cell_type": "code", "execution_count": 3, "id": "8fe52dc5-345c-4ba8-8041-aa0d023e23bd", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " :-) GROMACS - gmx anaeig, 2025.2 (-:\n", "\n", "Executable: /opt/gromacs-2025/bin/gmx\n", "Data prefix: /opt/gromacs-2025\n", "Working dir: /home/raj/workspace/gmx_clusterByFeatrues/tutorials/protein-ligand-interaction\n", "Command line:\n", " gmx anaeig -f pca.xtc -s pca_dummy.pdb -first 1 -last 5 -proj\n", "\n", "trr version: GMX_trn_file (single precision)\n", "Eigenvectors in eigenvec.trr were determined without fitting\n", "Read non mass weighted average/minimum structure with 2169 atoms from eigenvec.trr\n", "Read 6507 eigenvectors (for 2169 atoms)\n", "\n", "\n", "WARNING: If there are molecules in the input trajectory file\n", " that are broken across periodic boundaries, they\n", " cannot be made whole (or treated as whole) without\n", " you providing a run input file.\n", "\n", "Group 0 ( System) has 2169 elements\n", "Group 1 ( Protein) has 2169 elements\n", "Group 2 ( Protein-H) has 1102 elements\n", "Group 3 ( C-alpha) has 140 elements\n", "Group 4 ( Backbone) has 419 elements\n", "Group 5 ( MainChain) has 558 elements\n", "Group 6 ( MainChain+Cb) has 682 elements\n", "Group 7 ( MainChain+H) has 683 elements\n", "Group 8 ( SideChain) has 1486 elements\n", "Group 9 ( SideChain-H) has 544 elements\n", "Select a group: 5 eigenvectors selected for output: 1 2 3 4 5\n", "Reading frame 22000 time 440000.000 \n", "\n", "\n", "GROMACS reminds you: \"Mathematics is a game played according to certain rules with meaningless marks on paper.\" (David Hilbert)\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Select an index group of 2169 elements that corresponds to the eigenvectors\n", "Selected 0: 'System'\n", "\n" ] } ], "source": [ "%%bash\n", "\n", "echo 0 0 | gmx anaeig -f pca.xtc -s pca_dummy.pdb -first 1 -last 5 -proj" ] }, { "cell_type": "markdown", "id": "6f0a0780-a16f-43ae-a11a-ada036e4c807", "metadata": {}, "source": [ "### 5. Pre-Clustering scan for Cluster-Metrics\n", "\n", "Before performing final clutering, we would first generate cluster-mterics that can be used to make a decision on the number of clusters. One of the **drawback** of [K-Means clustering](https://en.wikipedia.org/wiki/K-means_clustering) is that the number of clusters should be known beforehand. Although, `gmx_clusterByFeatures` implements several [cluster metrics](https://gmx-clusterbyfeatures.readthedocs.io/en/latest/commands/cluster.html#cmetric-prior) and also automatated way to decide number of clusters, here, we first calculate cluster-metrics and final clustering can be performed later.\n", "\n", "Following command will perform the clustering of conformations using first 5 PCs projection. Explanation of options are as follows:\n", "* `-method kmeans`: Use K-Means clustering algorithm\n", "* `-ncluster 15`: K-Means clustering will be performed for 1 upto 15 clusters times each time. Finally, based on `-ssrchange` option, final number of clusters will be automatically selected.\n", "* `-cmetric ssr-sst`: Use Elbow method to decide final number of clusters. It is not for final purpose.\n", "* `-nfeature 5`: Take 5 feature from `feat proj.xvg` input file. Here it is projection of first 5 eigenvectors on the trajectory.\n", "* `-ssrchange 1.0`: Threshold (percentage) of change in SSR/SST ratio in Elbow method to automatically decide the number of clusters.\n", "* `-g clusters_scan.log`: Log file containing clustering information and cluster-mterics.\n", "\n", "**index group order**\n", "\n", "1. **First index** group - Output group of atoms in the central structures and clustered trajectories\n", "\n", "2. **Second index** group - Group of atoms to calculate RMSD between central conformations of clusters as RMSD matrix, which is dumped in the **log file** with `-g` option. Here, it is Ligand.\n", " \n", "3. **Third index** group - Used for Superimposition by least-square fitting. ONLY used in separate clustered trajectories to superimpose conformations on the central structure. Here, it is protein's C-alpha atoms.\n", "\n", "**Content of the output `-g clusters_scan.log` file**\n", "\n", "It contains the command summary, and for each input cluster-numbers, number of frames in each clusters. At the end it dumps the **Cluster Metrics Summary**, which is important for deciding final number of clusters." ] }, { "cell_type": "code", "execution_count": 4, "id": "92117de0-ca8c-4ceb-a27e-fe2da9420a9c", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " :-) GROMACS - gmx_clusterByFeatures cluster, 2025.0-dev-20250210-6949615-local (-:\n", "\n", "Executable: gmx_clusterByFeatures cluster\n", "Data prefix: /project/external/gmx_installed\n", "Working dir: /home/raj/workspace/gmx_clusterByFeatrues/tutorials/protein-ligand-interaction\n", "Command line:\n", " 'gmx_clusterByFeatures cluster' -f inputs/trajectory.xtc -s inputs/complex_res_segments.tpr -n inputs/index.ndx -method kmeans -feat proj.xvg -nfeature 10 -cmetric ssr-sst -ncluster 15 -ssrchange 1.0 -g clusters_scan.log\n", "\n", "\n", " :-) gmx_clusterByFeatures cluster (-:\n", "\n", " Author: Rajendra Kumar\n", "\n", " Copyright (C) 2018-2019 Rajendra Kumar\n", "\n", "\n", "gmx_clusterByFeatures is a free software: you can redistribute it and/or modify\n", "it under the terms of the GNU General Public License as published by\n", "the Free Software Foundation, either version 3 of the License, or\n", "(at your option) any later version.\n", "\n", "gmx_clusterByFeatures is distributed in the hope that it will be useful,\n", "but WITHOUT ANY WARRANTY; without even the implied warranty of\n", "MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n", "GNU General Public License for more details.\n", "\n", "You should have received a copy of the GNU General Public License\n", "along with gmx_clusterByFeatures. If not, see .\n", "\n", "THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n", "\"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n", "LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR\n", "A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\n", "OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\n", "SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED\n", "TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR\n", "PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF\n", "LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING\n", "NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\n", "SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n", " \n", "Reading file inputs/complex_res_segments.tpr, VERSION 5.1.2 (single precision)\n", "Reading file inputs/complex_res_segments.tpr, VERSION 5.1.2 (single precision)\n", "Group 0 ( System) has 8393 elements\n", "Group 1 ( Protein) has 8325 elements\n", "Group 2 ( Protein-H) has 4217 elements\n", "Group 3 ( C-alpha) has 542 elements\n", "Group 4 ( Backbone) has 1626 elements\n", "Group 5 ( MainChain) has 2169 elements\n", "Group 6 ( MainChain+Cb) has 2660 elements\n", "Group 7 ( MainChain+H) has 2668 elements\n", "Group 8 ( SideChain) has 5657 elements\n", "Group 9 ( SideChain-H) has 2048 elements\n", "Group 10 ( Prot-Masses) has 8325 elements\n", "Group 11 ( non-Protein) has 68 elements\n", "Group 12 ( Other) has 68 elements\n", "Group 13 ( L1) has 4 elements\n", "Group 14 ( L2) has 3 elements\n", "Group 15 ( L3) has 8 elements\n", "Group 16 ( L4) has 8 elements\n", "Group 17 ( L5) has 14 elements\n", "Group 18 ( L6) has 2 elements\n", "Group 19 ( L7) has 3 elements\n", "Group 20 ( L8) has 1 elements\n", "Group 21 ( L9) has 10 elements\n", "Group 22 ( L10) has 10 elements\n", "Group 23 ( L11) has 1 elements\n", "Group 24 ( L12) has 4 elements\n", "Group 25 ( Protein_Other) has 8393 elements\n", "Group 26 (C-alpha_Other_&_!H*) has 574 elements\n", "Select a group: Group 0 ( System) has 8393 elements\n", "Group 1 ( Protein) has 8325 elements\n", "Group 2 ( Protein-H) has 4217 elements\n", "Group 3 ( C-alpha) has 542 elements\n", "Group 4 ( Backbone) has 1626 elements\n", "Group 5 ( MainChain) has 2169 elements\n", "Group 6 ( MainChain+Cb) has 2660 elements\n", "Group 7 ( MainChain+H) has 2668 elements\n", "Group 8 ( SideChain) has 5657 elements\n", "Group 9 ( SideChain-H) has 2048 elements\n", "Group 10 ( Prot-Masses) has 8325 elements\n", "Group 11 ( non-Protein) has 68 elements\n", "Group 12 ( Other) has 68 elements\n", "Group 13 ( L1) has 4 elements\n", "Group 14 ( L2) has 3 elements\n", "Group 15 ( L3) has 8 elements\n", "Group 16 ( L4) has 8 elements\n", "Group 17 ( L5) has 14 elements\n", "Group 18 ( L6) has 2 elements\n", "Group 19 ( L7) has 3 elements\n", "Group 20 ( L8) has 1 elements\n", "Group 21 ( L9) has 10 elements\n", "Group 22 ( L10) has 10 elements\n", "Group 23 ( L11) has 1 elements\n", "Group 24 ( L12) has 4 elements\n", "Group 25 ( Protein_Other) has 8393 elements\n", "Group 26 (C-alpha_Other_&_!H*) has 574 elements\n", "Select a group: Group 0 ( System) has 8393 elements\n", "Group 1 ( Protein) has 8325 elements\n", "Group 2 ( Protein-H) has 4217 elements\n", "Group 3 ( C-alpha) has 542 elements\n", "Group 4 ( Backbone) has 1626 elements\n", "Group 5 ( MainChain) has 2169 elements\n", "Group 6 ( MainChain+Cb) has 2660 elements\n", "Group 7 ( MainChain+H) has 2668 elements\n", "Group 8 ( SideChain) has 5657 elements\n", "Group 9 ( SideChain-H) has 2048 elements\n", "Group 10 ( Prot-Masses) has 8325 elements\n", "Group 11 ( non-Protein) has 68 elements\n", "Group 12 ( Other) has 68 elements\n", "Group 13 ( L1) has 4 elements\n", "Group 14 ( L2) has 3 elements\n", "Group 15 ( L3) has 8 elements\n", "Group 16 ( L4) has 8 elements\n", "Group 17 ( L5) has 14 elements\n", "Group 18 ( L6) has 2 elements\n", "Group 19 ( L7) has 3 elements\n", "Group 20 ( L8) has 1 elements\n", "Group 21 ( L9) has 10 elements\n", "Group 22 ( L10) has 10 elements\n", "Group 23 ( L11) has 1 elements\n", "Group 24 ( L12) has 4 elements\n", "Group 25 ( Protein_Other) has 8393 elements\n", "Group 26 (C-alpha_Other_&_!H*) has 574 elements\n", "Reading frame 4 time 80.000 " ] }, { "name": "stdout", "output_type": "stream", "text": [ "=======================\n", " Cluster Log output \n", "=======================\n", "\n", "Command:\n", "=======================\n", "'gmx_clusterByFeatures cluster' -f inputs/trajectory.xtc -s inputs/complex_res_segments.tpr -n inputs/index.ndx -method kmeans -feat proj.xvg -nfeature 10 -cmetric ssr-sst -ncluster 15 -ssrchange 1.0 -g clusters_scan.log\n", "=======================\n", "\n", "Choose a group for the output:\n", "Selected 0: 'System'\n", "\n", "Choose a group for clustering/RMSD calculation:\n", "Selected 12: 'Other'\n", "\n", "Choose a group for fitting or superposition:\n", "Selected 26: 'C-alpha_Other_&_!H*'\n", "\n", "\n", " Input Trajectory dt = 20 ps\n", "\n", "\n", "\n", "###########################################\n", "########## NUMBER OF CLUSTERS : 1 ########\n", "###########################################\n", "\n", "===========================\n", "Cluster-ID\tTotalFrames\n", "1\t\t22501\n", "===========================\n", "\n", "\n", "\n", "###########################################\n", "########## NUMBER OF CLUSTERS : 2 ########\n", "###########################################\n", "\n", "===========================\n", "Cluster-ID\tTotalFrames\n", "1\t\t15000\n", "2\t\t7501\n", "===========================\n", "\n", "\n", "\n", "###########################################\n", "########## NUMBER OF CLUSTERS : 3 ########\n", "###########################################\n", "\n", "===========================\n", "Cluster-ID\tTotalFrames\n", "1\t\t7510\n", "2\t\t7501\n", "3\t\t7490\n", "===========================\n", "\n", "\n", "\n", "###########################################\n", "########## NUMBER OF CLUSTERS : 4 ########\n", "###########################################\n", "\n", "===========================\n", "Cluster-ID\tTotalFrames\n", "1\t\t7501\n", "2\t\t7484\n", "3\t\t5309\n", "4\t\t2207\n", "===========================\n", "\n", "\n", "\n", "###########################################\n", "########## NUMBER OF CLUSTERS : 5 ########\n", "###########################################\n", "\n", "===========================\n", "Cluster-ID\tTotalFrames\n", "1\t\t7501\n", "2\t\t7485\n", "3\t\t3872\n", "4\t\t2205\n", "5\t\t1438\n", "===========================\n", "\n", "\n", "\n", "###########################################\n", "########## NUMBER OF CLUSTERS : 6 ########\n", "###########################################\n", "\n", "===========================\n", "Cluster-ID\tTotalFrames\n", "1\t\t7485\n", "2\t\t5829\n", "3\t\t3872\n", "4\t\t2205\n", "5\t\t1672\n", "6\t\t1438\n", "===========================\n", "\n", "\n", "\n", "###########################################\n", "########## NUMBER OF CLUSTERS : 7 ########\n", "###########################################\n", "\n", "===========================\n", "Cluster-ID\tTotalFrames\n", "1\t\t5829\n", "2\t\t5075\n", "3\t\t3872\n", "4\t\t2415\n", "5\t\t2200\n", "6\t\t1672\n", "7\t\t1438\n", "===========================\n", "\n", "\n", "\n", "###########################################\n", "########## NUMBER OF CLUSTERS : 8 ########\n", "###########################################\n", "\n", "===========================\n", "Cluster-ID\tTotalFrames\n", "1\t\t5830\n", "2\t\t5082\n", "3\t\t2408\n", "4\t\t2386\n", "5\t\t2199\n", "6\t\t1671\n", "7\t\t1491\n", "8\t\t1434\n", "===========================\n", "\n", "\n", "\n", "###########################################\n", "########## NUMBER OF CLUSTERS : 9 ########\n", "###########################################\n", "\n", "===========================\n", "Cluster-ID\tTotalFrames\n", "1\t\t5083\n", "2\t\t4605\n", "3\t\t2407\n", "4\t\t2398\n", "5\t\t2199\n", "6\t\t1552\n", "7\t\t1479\n", "8\t\t1434\n", "9\t\t1344\n", "===========================\n", "\n", "\n", "\n", "###########################################\n", "########## NUMBER OF CLUSTERS : 10 ########\n", "###########################################\n", "\n", "===========================\n", "Cluster-ID\tTotalFrames\n", "1\t\t4900\n", "2\t\t4615\n", "3\t\t2387\n", "4\t\t2195\n", "5\t\t2188\n", "6\t\t1542\n", "7\t\t1489\n", "8\t\t1434\n", "9\t\t1344\n", "10\t\t407\n", "===========================\n", "\n", "\n", "\n", "###########################################\n", "########## NUMBER OF CLUSTERS : 11 ########\n", "###########################################\n", "\n", "===========================\n", "Cluster-ID\tTotalFrames\n", "1\t\t4692\n", "2\t\t4290\n", "3\t\t2387\n", "4\t\t2188\n", "5\t\t2177\n", "6\t\t1489\n", "7\t\t1473\n", "8\t\t1434\n", "9\t\t1336\n", "10\t\t803\n", "11\t\t232\n", "===========================\n", "\n", "\n", "\n", "###########################################\n", "########## NUMBER OF CLUSTERS : 12 ########\n", "###########################################\n", "\n", "===========================\n", "Cluster-ID\tTotalFrames\n", "1\t\t4692\n", "2\t\t4290\n", "3\t\t2189\n", "4\t\t2177\n", "5\t\t1682\n", "6\t\t1473\n", "7\t\t1434\n", "8\t\t1336\n", "9\t\t1118\n", "10\t\t1075\n", "11\t\t803\n", "12\t\t232\n", "===========================\n", "\n", "\n", "\n", "###########################################\n", "########## NUMBER OF CLUSTERS : 13 ########\n", "###########################################\n", "\n", "===========================\n", "Cluster-ID\tTotalFrames\n", "1\t\t3029\n", "2\t\t2944\n", "3\t\t2332\n", "4\t\t2271\n", "5\t\t2189\n", "6\t\t1866\n", "7\t\t1682\n", "8\t\t1434\n", "9\t\t1250\n", "10\t\t1118\n", "11\t\t1075\n", "12\t\t951\n", "13\t\t360\n", "===========================\n", "\n", "\n", "\n", "###########################################\n", "########## NUMBER OF CLUSTERS : 14 ########\n", "###########################################\n", "\n", "===========================\n", "Cluster-ID\tTotalFrames\n", "1\t\t4290\n", "2\t\t2969\n", "3\t\t2357\n", "4\t\t2188\n", "5\t\t2177\n", "6\t\t1432\n", "7\t\t1384\n", "8\t\t1240\n", "9\t\t1092\n", "10\t\t1082\n", "11\t\t935\n", "12\t\t803\n", "13\t\t320\n", "14\t\t232\n", "===========================\n", "\n", "\n", "\n", "###########################################\n", "########## NUMBER OF CLUSTERS : 15 ########\n", "###########################################\n", "\n", "===========================\n", "Cluster-ID\tTotalFrames\n", "1\t\t4339\n", "2\t\t3051\n", "3\t\t2367\n", "4\t\t2248\n", "5\t\t2143\n", "6\t\t1394\n", "7\t\t1250\n", "8\t\t1153\n", "9\t\t952\n", "10\t\t924\n", "11\t\t790\n", "12\t\t787\n", "13\t\t537\n", "14\t\t333\n", "15\t\t233\n", "===========================\n", "\n", "\n", "\n", "===========================================================================================================\n", " Cluster Metrics Summary \n", "-----------------------------------------------------------------------------------------------------------\n", "Clusters SSR/SST D(SSR/SST) (Psuedo)F-stat. Silhouette-score Davies-bouldin-score\n", "2 50.45 50.455 22911.836 0.530 0.729 \n", "3 84.67 34.215 62129.371 0.689 0.503 \n", "4 89.04 4.374 60944.555 0.652 0.591 \n", "5 92.08 3.039 65406.023 0.701 0.477 \n", "6 94.15 2.069 72417.812 0.644 0.538 \n", "7 95.13 0.974 73154.625 0.533 0.738 \n", "8 95.52 0.395 68511.797 0.487 0.908 \n", "9 95.76 0.237 63453.414 0.407 1.049 \n", "10 96.10 0.345 61606.973 0.399 1.024 \n", "11 96.28 0.182 58261.570 0.374 1.043 \n", "12 96.48 0.201 56103.879 0.371 1.070 \n", "13 96.64 0.152 53840.742 0.327 1.200 \n", "14 96.80 0.165 52342.699 0.351 1.065 \n", "15 96.91 0.111 50402.191 0.302 1.157 \n", "===========================================================================================================\n", "\n", "\n", "#####################################\n", "Final number of cluster selected: 6\n", "#####################################\n", "\n", "Calculating central structure for cluster-6 ..." ] }, { "name": "stderr", "output_type": "stream", "text": [ "Reading frame 5 time 188780.000 \n", "GROMACS reminds you: \"No, no, you're not thinking, you're just being logical.\" (Niels Bohr)\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "===========================================\n", "Cluster-ID\tCentral Frame\tTotal Frames \n", "1\t\t18119\t\t7485\n", "2\t\t3515\t\t5829\n", "3\t\t10774\t\t3872\n", "4\t\t12874\t\t2205\n", "5\t\t833\t\t1672\n", "6\t\t9439\t\t1438\n", "===========================================\n", "\n", "\n", "\n", "Extracting coordinates of the central structure...\n", "\n", "\n", "Calculating RMSD between central structures...\n", "\n", "\n", "=====================================\n", " Central structurs - RMSD matrix \n", "=====================================\n", " c1 c2 c3 c4 c5 c6 \n", " 0.000 0.414 0.512 0.479 0.458 0.536 \n", " 0.414 0.000 0.605 0.624 0.214 0.600 \n", " 0.512 0.605 0.000 0.253 0.587 0.221 \n", " 0.479 0.624 0.253 0.000 0.587 0.266 \n", " 0.458 0.214 0.587 0.587 0.000 0.583 \n", " 0.536 0.600 0.221 0.266 0.583 0.000 \n", "=====================================\n" ] } ], "source": [ "%%bash\n", "\n", "echo 0 12 26 | gmx_clusterByFeatures cluster -f inputs/trajectory.xtc -s inputs/complex_res_segments.tpr -n inputs/index.ndx \\\n", " -method kmeans -feat proj.xvg -nfeature 10 -cmetric ssr-sst -ncluster 15 -ssrchange 1.0 \\\n", " -g clusters_scan.log" ] }, { "cell_type": "markdown", "id": "538c1be0-a31e-43d1-98f3-a7de17e3fd16", "metadata": {}, "source": [ "### 6. Clustering with pre-determined number of clusters\n", "\n", "As can be seen above in Cluster-Metrics table obtained in `clusters_scan.log` file, best scores (both Silhouette and Davies-Bouldin) were obtained for 5 clusters. Therefore, now we will do final clustering with 5 clusters and extract trajectories of these clusters.\n", "\n", "Explanation of options are as follows:\n", "* `-method kmeans`: Use K-Means clustering algorithm\n", "* `-ncluster 5`: Five clusters will be generated using K-Means clustering.\n", "* `-cmetric prior`: No cluster-mterics is used, number of clusters is already known.\n", "* `-nfeature 5`: Take 5 feature from `feat proj.xvg` input file. Here it is projection of first 5 eigenvectors on the trajectory.\n", "* `cluster-5`: Cluster log file\n", "* `-cpdb clustered_trajs/central.pdb`: Central structures of each cluster as PDB file\n", "* `-fout clustered_trajs/cluster.xtc`: Trajectory file of each cluster\n", "* `-outframe 1000`: Only first 1000 frame after sorting to be written in cluster trajectory file\n", "* `-sort features`: Sort the conformation in each cluster trajectory file based on the distance between central structure and current conformation in feature sub-space.\n", "\n", "**This command could take a long time to execute!**\n", "\n", "This command could take a long time to execute because it is writing output trajectory file for each cluster sorted by distance in feature-space. Therefore, it needs **to read input trajectory back-and-forth** many time to extract the conformations in sorted manner. **XTC** format is fast for **back-and-forth** reading, and it still could take long time to dump the output trajectories." ] }, { "cell_type": "code", "execution_count": 5, "id": "8210e95e-fb50-426a-8b8e-ae42063c0a2d", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " :-) GROMACS - gmx_clusterByFeatures cluster, 2025.0-dev-20250210-6949615-local (-:\n", "\n", "Executable: gmx_clusterByFeatures cluster\n", "Data prefix: /project/external/gmx_installed\n", "Working dir: /home/raj/workspace/gmx_clusterByFeatrues/tutorials/protein-ligand-interaction\n", "Command line:\n", " 'gmx_clusterByFeatures cluster' -f inputs/trajectory.xtc -s inputs/complex_res_segments.tpr -n inputs/index.ndx -feat proj.xvg -nfeature 5 -method kmeans -cmetric prior -ncluster 5 -g cluster-5 -cpdb clustered_trajs/central.pdb -fout clustered_trajs/cluster.xtc -outframe 1000 -plot pca-cluster-5.png -sort features\n", "\n", "\n", " :-) gmx_clusterByFeatures cluster (-:\n", "\n", " Author: Rajendra Kumar\n", "\n", " Copyright (C) 2018-2019 Rajendra Kumar\n", "\n", "\n", "gmx_clusterByFeatures is a free software: you can redistribute it and/or modify\n", "it under the terms of the GNU General Public License as published by\n", "the Free Software Foundation, either version 3 of the License, or\n", "(at your option) any later version.\n", "\n", "gmx_clusterByFeatures is distributed in the hope that it will be useful,\n", "but WITHOUT ANY WARRANTY; without even the implied warranty of\n", "MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n", "GNU General Public License for more details.\n", "\n", "You should have received a copy of the GNU General Public License\n", "along with gmx_clusterByFeatures. If not, see .\n", "\n", "THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n", "\"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n", "LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR\n", "A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\n", "OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\n", "SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED\n", "TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR\n", "PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF\n", "LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING\n", "NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\n", "SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n", " \n", "Reading file inputs/complex_res_segments.tpr, VERSION 5.1.2 (single precision)\n", "Reading file inputs/complex_res_segments.tpr, VERSION 5.1.2 (single precision)\n", "Group 0 ( System) has 8393 elements\n", "Group 1 ( Protein) has 8325 elements\n", "Group 2 ( Protein-H) has 4217 elements\n", "Group 3 ( C-alpha) has 542 elements\n", "Group 4 ( Backbone) has 1626 elements\n", "Group 5 ( MainChain) has 2169 elements\n", "Group 6 ( MainChain+Cb) has 2660 elements\n", "Group 7 ( MainChain+H) has 2668 elements\n", "Group 8 ( SideChain) has 5657 elements\n", "Group 9 ( SideChain-H) has 2048 elements\n", "Group 10 ( Prot-Masses) has 8325 elements\n", "Group 11 ( non-Protein) has 68 elements\n", "Group 12 ( Other) has 68 elements\n", "Group 13 ( L1) has 4 elements\n", "Group 14 ( L2) has 3 elements\n", "Group 15 ( L3) has 8 elements\n", "Group 16 ( L4) has 8 elements\n", "Group 17 ( L5) has 14 elements\n", "Group 18 ( L6) has 2 elements\n", "Group 19 ( L7) has 3 elements\n", "Group 20 ( L8) has 1 elements\n", "Group 21 ( L9) has 10 elements\n", "Group 22 ( L10) has 10 elements\n", "Group 23 ( L11) has 1 elements\n", "Group 24 ( L12) has 4 elements\n", "Group 25 ( Protein_Other) has 8393 elements\n", "Group 26 (C-alpha_Other_&_!H*) has 574 elements\n", "Select a group: Group 0 ( System) has 8393 elements\n", "Group 1 ( Protein) has 8325 elements\n", "Group 2 ( Protein-H) has 4217 elements\n", "Group 3 ( C-alpha) has 542 elements\n", "Group 4 ( Backbone) has 1626 elements\n", "Group 5 ( MainChain) has 2169 elements\n", "Group 6 ( MainChain+Cb) has 2660 elements\n", "Group 7 ( MainChain+H) has 2668 elements\n", "Group 8 ( SideChain) has 5657 elements\n", "Group 9 ( SideChain-H) has 2048 elements\n", "Group 10 ( Prot-Masses) has 8325 elements\n", "Group 11 ( non-Protein) has 68 elements\n", "Group 12 ( Other) has 68 elements\n", "Group 13 ( L1) has 4 elements\n", "Group 14 ( L2) has 3 elements\n", "Group 15 ( L3) has 8 elements\n", "Group 16 ( L4) has 8 elements\n", "Group 17 ( L5) has 14 elements\n", "Group 18 ( L6) has 2 elements\n", "Group 19 ( L7) has 3 elements\n", "Group 20 ( L8) has 1 elements\n", "Group 21 ( L9) has 10 elements\n", "Group 22 ( L10) has 10 elements\n", "Group 23 ( L11) has 1 elements\n", "Group 24 ( L12) has 4 elements\n", "Group 25 ( Protein_Other) has 8393 elements\n", "Group 26 (C-alpha_Other_&_!H*) has 574 elements\n", "Select a group: Group 0 ( System) has 8393 elements\n", "Group 1 ( Protein) has 8325 elements\n", "Group 2 ( Protein-H) has 4217 elements\n", "Group 3 ( C-alpha) has 542 elements\n", "Group 4 ( Backbone) has 1626 elements\n", "Group 5 ( MainChain) has 2169 elements\n", "Group 6 ( MainChain+Cb) has 2660 elements\n", "Group 7 ( MainChain+H) has 2668 elements\n", "Group 8 ( SideChain) has 5657 elements\n", "Group 9 ( SideChain-H) has 2048 elements\n", "Group 10 ( Prot-Masses) has 8325 elements\n", "Group 11 ( non-Protein) has 68 elements\n", "Group 12 ( Other) has 68 elements\n", "Group 13 ( L1) has 4 elements\n", "Group 14 ( L2) has 3 elements\n", "Group 15 ( L3) has 8 elements\n", "Group 16 ( L4) has 8 elements\n", "Group 17 ( L5) has 14 elements\n", "Group 18 ( L6) has 2 elements\n", "Group 19 ( L7) has 3 elements\n", "Group 20 ( L8) has 1 elements\n", "Group 21 ( L9) has 10 elements\n", "Group 22 ( L10) has 10 elements\n", "Group 23 ( L11) has 1 elements\n", "Group 24 ( L12) has 4 elements\n", "Group 25 ( Protein_Other) has 8393 elements\n", "Group 26 (C-alpha_Other_&_!H*) has 574 elements\n", "Reading frame 4 time 80.000 " ] }, { "name": "stdout", "output_type": "stream", "text": [ "=======================\n", " Cluster Log output \n", "=======================\n", "\n", "Command:\n", "=======================\n", "'gmx_clusterByFeatures cluster' -f inputs/trajectory.xtc -s inputs/complex_res_segments.tpr -n inputs/index.ndx -feat proj.xvg -nfeature 5 -method kmeans -cmetric prior -ncluster 5 -g cluster-5 -cpdb clustered_trajs/central.pdb -fout clustered_trajs/cluster.xtc -outframe 1000 -plot pca-cluster-5.png -sort features\n", "=======================\n", "\n", "Choose a group for the output:\n", "Selected 0: 'System'\n", "\n", "Choose a group for clustering/RMSD calculation:\n", "Selected 12: 'Other'\n", "\n", "Choose a group for fitting or superposition:\n", "Selected 26: 'C-alpha_Other_&_!H*'\n", "\n", "\n", " Input Trajectory dt = 20 ps\n", "\n", "\n", "\n", "###########################################\n", "########## NUMBER OF CLUSTERS : 5 ########\n", "###########################################\n", "\n", "===========================\n", "Cluster-ID\tTotalFrames\n", "1\t\t7501\n", "2\t\t7485\n", "3\t\t3872\n", "4\t\t2205\n", "5\t\t1438\n", "===========================\n", "\n", "\n", "\n", "#####################################\n", "Final number of cluster selected: 5\n", "#####################################\n", "\n", "Calculating central structure for cluster-5 ..." ] }, { "name": "stderr", "output_type": "stream", "text": [ "Reading frame 4 time 188780.000 :127: MatplotlibDeprecationWarning: The non_interactive_bk attribute was deprecated in Matplotlib 3.9 and will be removed in 3.11. Use ``matplotlib.backends.backend_registry.list_builtin(matplotlib.backends.BackendFilter.NON_INTERACTIVE)`` instead.\n", "\n", "Back Off! I just backed up clid.xvg to ./#clid.xvg.1#\n", "Reading frame 4000 time 188780.000 \n", "GROMACS reminds you: \"Those people who think they know everything are a great annoyance to those of us who do.\" (Isaac Asimov)\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "===========================================\n", "Cluster-ID\tCentral Frame\tTotal Frames \n", "1\t\t2786\t\t7501\n", "2\t\t18119\t\t7485\n", "3\t\t10774\t\t3872\n", "4\t\t12874\t\t2205\n", "5\t\t9439\t\t1438\n", "===========================================\n", "\n", "\n", "\n", "Extracting coordinates of the central structure...\n", "\n", "\n", "Calculating RMSD between central structures...\n", "\n", "\n", "=====================================\n", " Central structurs - RMSD matrix \n", "=====================================\n", " c1 c2 c3 c4 c5 \n", " 0.000 0.418 0.603 0.619 0.599 \n", " 0.418 0.000 0.512 0.479 0.536 \n", " 0.603 0.512 0.000 0.253 0.221 \n", " 0.619 0.479 0.253 0.000 0.266 \n", " 0.599 0.536 0.221 0.266 0.000 \n", "=====================================\n", "\n", "\n", "Writing central structure to pdb-files...\n", "\n", "\n", "Writing trajectory for each cluster...\n" ] } ], "source": [ "%%bash\n", "\n", "mkdir clustered_trajs\n", "\n", "echo 0 12 26 | gmx_clusterByFeatures cluster -f inputs/trajectory.xtc -s inputs/complex_res_segments.tpr -n inputs/index.ndx \\\n", " -feat proj.xvg -nfeature 5 -method kmeans -cmetric prior -ncluster 5 -g cluster-5 \\\n", " -cpdb clustered_trajs/central.pdb -fout clustered_trajs/cluster.xtc -outframe 1000 \\\n", " -plot pca-cluster-5.png -sort features" ] }, { "cell_type": "markdown", "id": "d92e10d9-0306-4a77-8b2c-fc7fe37b3ee9", "metadata": {}, "source": [ "### 7. Analysis\n", "Now, we will perform following analysis on obtained clusters:\n", "\n", "1. **Comparison of RMSDs within and between the clusters**: It will show the qualitative measure of separation of conformations of clusters in term of RMSD.\n", "2. **Plotting PC vs PC cluster-wise**. In fact, this is already plotted in the above obtained `pca-cluster-5.png` file. However, we will focus on first three PCs to demonstrate the distribution of conformation in PC space.\n", "3. **Cluster-ID with time**: We will plot cluster-id as a function of time to analyze, how conformation is changing between clusters as a function of time.\n", "\n", "At first, we will load Python modules and define some functions as follows:" ] }, { "cell_type": "code", "execution_count": 2, "id": "fc372630-c7fc-4915-9738-e9fa275a52f9", "metadata": {}, "outputs": [], "source": [ "import re\n", "import sys\n", "import numpy as np\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import csv" ] }, { "cell_type": "code", "execution_count": 3, "id": "e6ab9a52-4e2b-4b42-9ece-14cf8ad8da80", "metadata": {}, "outputs": [], "source": [ "def read_xvg(filename):\n", " ''' Read any XVG file and return the data as 2D array where data is row-wise with respect to time.\n", " '''\n", " fin = open(filename, 'r')\n", "\n", " data = []\n", " for line in fin:\n", " line = line.rstrip().lstrip()\n", " if not line:\n", " continue\n", "\n", " if re.search('^#|^@', line) is not None:\n", " continue\n", "\n", " temp = re.split('\\s+', line)\n", " data.append(list(map(float, temp)))\n", "\n", " data = np.asarray(data)\n", "\n", " return data.T" ] }, { "cell_type": "markdown", "id": "30b93e96-2ad8-4224-b863-d7e4e4f3ed0d", "metadata": {}, "source": [ "#### 1a. Calculation of RMSDs within and between the clusters\n", "\n", "At first, we need to calculate RMSDs of complex within and between the clusters using `gmx rms` command as follows.\n", "\n", "**Note:** The complex structure is already superimposed when separated cluster-trajectory were written in the previous step, therefore, we are not performing fitting in RMSD calculations below,\n", "\n", "Note: Remove `%%capture --no-stdout` and `%%capture --no-stderr` to populate all the output generated from `gmx rms` commands. " ] }, { "cell_type": "code", "execution_count": 8, "id": "11754f41-039c-4e32-9e54-a763bf11bbfa", "metadata": {}, "outputs": [], "source": [ "%%capture --no-stdout\n", "%%capture --no-stderr\n", "%%script bash\n", "\n", "# make directory for rmsd files\n", "mkdir clustered-rmsd\n", "\n", "for i in `seq 1 5`\n", "do\n", " for j in `seq 1 5`\n", " do\n", " echo 26 | gmx rms -f clustered_trajs/cluster_c${j}.xtc -s clustered_trajs/central_c${i}.pdb -n inputs/index.ndx -o clustered-rmsd/c${i}_c${j} -nopbc -fit none\n", " done\n", "done" ] }, { "cell_type": "markdown", "id": "0e5aef1a-3364-4efc-b4f5-390f3a882124", "metadata": {}, "source": [ "#### 1b. Comparison of ligand RMSDs within and between the clusters\n", "\n", "We will use Python to plot all the obtained RMSDs above. Conformations in cluster trajectory is sorted by distance in feature-space and therefore, RMSD will be randomly fluctuate. RMSDs within the same cluster is expected to be lowest." ] }, { "cell_type": "code", "execution_count": 9, "id": "f2a19d17-f8a6-4796-8709-469daca4ce3d", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mpl.rcParams['font.size']=12\n", "fig = plt.figure(figsize=(12,16))\n", "fig.subplots_adjust(top=0.98, bottom=0.05, hspace=0.25)\n", "\n", "for i in range(1,6):\n", " \n", " ax = fig.add_subplot(6, 2, i)\n", " \n", " for j in range(1,6):\n", " filename = 'clustered-rmsd/c{0}_c{1}.xvg'.format(i, j)\n", " label = \"{0} vs {1}\".format(i, j)\n", " data = read_xvg(filename) # read file\n", " if i==j:\n", " ax.scatter(data[0]/1000, data[1], label=label, lw=0, s=2)\n", " else:\n", " ax.scatter(data[0]/1000, data[1], label=label, lw=0, s=0.5)\n", " \n", " ax.set_ylim(0, 0.5)\n", " ax.set_xlabel('Time (ns)')\n", " ax.set_ylabel('RMSD (nm)')\n", " plt.legend(loc='upper center', ncol=4, markerscale=10, borderaxespad=0.1, columnspacing=1, handlelength=1, handletextpad=0.4)\n", " \n", "\n", "plt.savefig('rmsd-comparison.png', dpi=300)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "73b303c2-3e0f-4a88-9cf0-e27b991c51bb", "metadata": {}, "source": [ "#### 3. Plotting PC vs PC cluster-wise\n", "\n", "It will be done in two steps:\n", "\n", "1. An input file will be prepared containing information about feature searial and their labels.\n", "2. `gmx_clusterByFeatures featuresplot` will be used to generate the plot." ] }, { "cell_type": "code", "execution_count": 1, "id": "c3d5f4d0-8965-4a8a-957f-3c697a9c98a1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1,2,PC-1,PC-2\n", "2,3,PC-2,PC-3\n", "1,3,PC-1,PC-3\n", "1,4,PC-1,PC-4\n" ] } ], "source": [ "%%bash\n", "\n", "# First step - preparation of input file\n", "echo \"1,2,PC-1,PC-2\" > features-label.txt\n", "echo \"2,3,PC-2,PC-3\" >> features-label.txt\n", "echo \"1,3,PC-1,PC-3\" >> features-label.txt\n", "echo \"1,4,PC-1,PC-4\" >> features-label.txt\n", "cat features-label.txt\n", "\n", "# Second step - plotting\n", "gmx_clusterByFeatures featuresplot -i features-label.txt -feat proj.xvg -clid clid.xvg -lcols 6 -o protein-ligand-interaction-PCs-vs-PCs.png\n", "gmx_clusterByFeatures featuresplot -i features-label.txt -feat proj.xvg -clog cluster-5.log -hist -bins 20 -lcols 6 -o protein-ligand-interaction-PCs-vs-PCs-hist.png" ] }, { "cell_type": "markdown", "id": "b4d78ac8-b159-451d-801c-ee284ce1d53c", "metadata": {}, "source": [ "##### Conformations are highlighted in the feature sub-space: \n", " " ] }, { "cell_type": "markdown", "id": "4b5cc262-bc23-487b-91e9-597cbaa2074c", "metadata": {}, "source": [ "##### Histogram of conformations with central structures in the feature sub-space:\n", " " ] }, { "cell_type": "markdown", "id": "25edd6a6-d409-457a-9620-a87f09853b3e", "metadata": {}, "source": [ "#### 3. Cluster-ID with time\n", "\n", "We will use `clid.xvg` obtained from the `cluster` subcommand to plot both cluster-id and also highlight the occurance of the given cluster. " ] }, { "cell_type": "code", "execution_count": 11, "id": "5f0548ab-d28e-4dc1-8817-56a538a2dc09", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mpl.rcParams['font.size']=12\n", "\n", "data=read_xvg('clid.xvg')\n", "fig = plt.figure(figsize=(8,6))\n", "fig.subplots_adjust(right=0.85)\n", "\n", "ax = fig.add_subplot(111)\n", "ax.scatter(data[0]/1000, data[1], marker='x', color='k')\n", "ax.set_ylabel('Cluster')\n", "ax.set_xlabel('Time (ns)')\n", "\n", "plt.savefig('clid.png', dpi=300)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "ce001fda-2beb-42b0-b411-9317eb8f61bb", "metadata": {}, "source": [ "### 8. MM/PBSA Analysis\n", "\n", "**There are advantages of performing MM/PBSA after the clustering:**\n", "1. Speed-up in MM/PBSA calculations - In this particular case, if we have used whole trajectory of 450 ns with a time-difference of 100ps, a total of **4500 frames** will be used for the calcualtions. However, if we restrict MM/PBSA calculations to central structure and near-by conformations in feature-space of each cluster, we could use only few frames for MM/PBSA calculations. For example, if we use only 10 frames per cluster, in total **only 50 frames** will be considered for the calculations. Therefore, we will gain **90 times speed-up** without losing the accuracy in the final average binding energy (shown below).\n", "2. By calculating residues contribution to Binding Energy, we could easily discriminate residues that are forming interactions between diffrent clusters with their interaction strength as energy value.\n", "\n", "\n", "#### 1. MM/PBSA calculations\n", "\n", "Now, we will perform MM/PBSA calculations using [g_mmpbsa](https://g-mmpbsa.readthedocs.io/en/latest/). This will highlight binding energy difference between clusters and also difference in interacting residues that are contributing to the binding.\n", "\n", "At first, MM/PBSA calculation is performed as follows. For each cluster, only 10 frames will be used with a virtual time-diffrence of 100 ps for the MM/PBSA calculations. This setup starts calculation with central structure and slowly moves away from it in feature-space and picks 9 more structure from the same cluster.\n", "\n", "**Options for MM/PBSA calculations**\n", "* `-dt 100` - consider frame at 100ps time-difference\n", "* `-e 1000` - consider frames upto 1000ps\n", "* `-unit1 1` - First atoms-group for binding energy - here it is protein\n", "* `-unit2 12` - Second atoms-group for binding energy - here it is ligand\n", "* `-pdie 1` - Dielectric constant during MM Electrostatic energy calculation\n", "* `-ddc` - Enable distance-depenedent dielectric constant during MM Electrostatic calculations. It means that farther the atoms from each other, larger the decrease in electrostatic interaction. It particulalry reduces the interaction energy of charged ligand with charged protein-residues that are far apart in the complex, thereby highlighting contribution of charged residues that are only nearby to the ligand. Therefore, it is particulalry useful for protein and charges ligand where residues contribution to binding need to be studied. It does not affect MM van der Waals interaction. **This option should not be used when comparing with experimental binding energy or for final binding energy as the results are not yet validated.**\n", "* `-mme` Enable MM calculations\n", "* `-pbsa` Enable PBSA calcualtions\n", "* `-decomp` Enable binding energy decomposition over residues to calculate residues contribution to binding energy\n", "* `-silent` Do not display output from APBS\n", "\n", "**This command could take a long time to execute!**\n", "\n", "**Also following command will utilize all the CPU cores to speed-up the calculations**\n", "\n", "**Note:** Remove `%%capture --no-stdout` and `%%capture --no-stderr` to populate all the output generated from `gmx rms` commands. " ] }, { "cell_type": "code", "execution_count": 1, "id": "3a78d412-0c82-4408-93cd-bcd3170698fe", "metadata": {}, "outputs": [], "source": [ "%%capture --no-stderr\n", "%%capture --no-stdout\n", "%%script bash\n", "\n", "mkdir mmpbsa\n", "\n", "for i in `seq 1 5`\n", "do\n", " g_mmpbsa run -s inputs/complex_res_segments.tpr -f clustered_trajs/cluster_c${i}.xtc -n inputs/index.ndx \\\n", " -i inputs/pbsa.mdp -mm mmpbsa/energy_MM_c${i}.xvg -pol mmpbsa/polar_c${i}.xvg \\\n", " -apol mmpbsa/apolar_c${i}.xvg -mmcon mmpbsa/residues_MM_c${i}.dat \\\n", " -pcon mmpbsa/residues_polar_c${i}.dat -apcon mmpbsa/residues_apolar_c${i}.dat \\\n", " -o mmpbsa/binding_energy_c${i}.xvg -os mmpbsa/energy_summary_c${i}.csv \\\n", " -ores mmpbsa/residues_energy_summary_c${i}.csv -opdb mmpbsa/energy_c${i}.pdb \\\n", " -dt 100 -e 1000 -unit1 1 -unit2 12 -pdie 1 -ddc -mme -pbsa -decomp -silent\n", "done" ] }, { "cell_type": "markdown", "id": "6e2aba9d-f8d4-4203-9e21-23fd5c38861b", "metadata": {}, "source": [ "#### 2. Extract and plot binding-energy cluster-wise\n", "\n", "We will extract the binding energy from the output CSV file and plot the binding energy for all the clusters.\n", "We will also calculate **weighted average binding energy** (using cluster population) representing final binding energy from full trajectory.\n", "\n", "For comparison, we also plot binding energy obtained from **full trajectory with a time-difference of 500ps (900 frames)**. The output files of these calculations are provided in `mmpbsa-full` folder." ] }, { "cell_type": "code", "execution_count": 51, "id": "fc3ae7d6-e15e-4e8f-aa92-b18ee85c190d", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Population fraction of each cluster, manually calculated from cluster log file\n", "population_weights = [0.333362962, 0.332651882, 0.172081241, 0.097995645, 0.063908271]\n", "\n", "# First we extract both average binding energy and its standard deviation for each cluster from respective CSV file\n", "average, stddev = [], []\n", "for i in range(1, 6):\n", " with open(f'mmpbsa/energy_summary_c{i}.csv') as csvfile:\n", " reader = csv.DictReader(csvfile, quotechar='\"')\n", " for row in reader:\n", " if row['Energy'] == 'Total':\n", " average.append(float(row['Average']))\n", " stddev.append(float(row['Standard-Deviation']))\n", "\n", "\n", "# Now calculate weighted avearge and combined standard-deviation\n", "weighted_average = sum([av*wt for av, wt in zip(average, population_weights)])\n", "combined_stddev = 0\n", "for sd, wt in zip(stddev, population_weights): # here we combine SD one-by-one (https://www.statstodo.com/CombineMeansSDs.php)\n", " if combined_stddev == 0:\n", " combined_stddev = sd\n", " else:\n", " combined_stddev = (combined_stddev**2 + sd**2)**0.5\n", "\n", "# Plotting the results\n", "fig = plt.figure(figsize=(8,6))\n", "ax = fig.add_subplot(111)\n", "ax.bar(np.arange(2, 7), average, yerr = stddev, error_kw={'elinewidth':5})\n", "ax.bar([0, 1], [-132.386, weighted_average], yerr = [45, combined_stddev], error_kw={'elinewidth':5}) # plot energy from full trajectory and weighted average energy from clusters\n", "ax.set_ylabel('Energy (kJ/mol)')\n", "ax.set_xticks(np.arange(0, 7))\n", "ax.set_xticklabels(['Full\\nTrajectory', 'Weighted\\nAverage\\nClusters'] + [f'Cluster-{cid}' for cid in list(np.arange(1, 6))])\n", "ax.tick_params(bottom=False, labelbottom=False, top=True, labeltop=True)\n", "plt.savefig('mmpbsa-binding-energy.png', dpi=300)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "4bd54213-2307-4912-b894-a5dc60468984", "metadata": {}, "source": [ "#### 3. Extract and plot protein residues contribution to the binding\n", "\n", "Here, we extracts residues contribution towards binding that have at least -4 kJ/Mol of energy. This analysis highights the differences \n", "in interaction residue-wise between the clusters." ] }, { "cell_type": "code", "execution_count": 31, "id": "af78e32c-d180-4cc3-aaab-dfa0685c23db", "metadata": {}, "outputs": [ { "data": { "image/png": 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DAgLM3LlzPdM/+OAD06RJE6viecXTTz9tYmNj83yO+/v72oWggXeQsmXLmq1bt+YZP2LECFO9enXz3Xff+WUDv3z5clO7dm0zZsyYXEee/f2N4e8N/N/t3bvXjB071tSoUcMvl/ekSZNMw4YNzY4dO8yUKVNM69atPR/8u3btMnFxceb666+3OKV3jBgxwkRHR5sPP/zQHD161DP+6NGj5sMPPzQxMTFm5MiRFib0jtKlS5sNGzYUOP3nn3/2yzMPrrzySvPEE08YY4yZOXOmiYyMNI888ohn+lNPPWVatGhhVTyvue6660yfPn3M8ePHTVZWlrn33ntNnTp1PNN/+OEHU6VKFQsTel92drYZM2aMiY6ONl988YUJCgry68+z8PBws3v3bs9wTk6OCQwMNMnJycYYY9avX2/KlCljUTrvcmLttWvXznXEffr06SY9Pd0zvHbtWr/dxkNDQ82+fftyDf/yyy+e4V27dvnlF7OrV6829erVM/fff7/Jysoyxvj/fvqFoIF3kMsvv9y89dZb+U4bPny4iYiI8MuGzhhj0tLSzM0332xatWrlaeb8/Y2hoAb+jJycHLNkyRIfJvKde+65xwQFBZkGDRqY0NBQExAQYIKDg01AQIBp2bKlOXDggNURvSIzM9MMHTrUU2toaGiu+u+++26TmZlpdcxi16NHD9OxY0eTkpKSZ1pKSorp3Lmz6dmzpwXJvOuLL74woaGhJjg42ISFhZnvvvvO1KtXz1x++eXmyiuvNKVKlTLvv/++1TGL3c6dO03t2rVNYGCgCQoKMhERESYxMdEzfdasWbm+yPBny5cvN7GxsSYgIMCvP8+aNWtmXn/9dc/wV199ZcLDw01OTo4xxpht27b5ZVNjjDNrv+uuu8wbb7xR4PRnnnnGJCQk+DCR70RGRuY6W7ZNmzbm999/9wz/8ssvfnsW4bFjx8xtt91mLrnkErNx40a//2LyQnAXegd55pln9P3332vx4sX5Th82bJheffVV5eTk+DiZ78yaNUuPPfaYxo8frxEjRmj9+vV57sbvL2JjY/XTTz/p4osvtjqKJX755RctXLhQu3btUk5OjqpWraq2bduqU6dOfv9YkvT0dM/d2CWpSpUqatGihcqVK2dxMu9ISkpSQkKCtm3b5rn7vsvlUkpKijZv3qxGjRpp0aJFql69utVRi93u3bu1bt06tWzZUjVq1NDBgwc1bdo0ZWRkqHv37mrfvr3VEb0iIyNDK1asUGZmpq688kq/vRt3YRw/flw7d+5UgwYN/PaxgR988IFuvfVWXXfddQoNDdW8efM0YsQIPfPMM5Kk1157TXPmzNHKlSstTlr8nFx7QXbv3q3Q0FBVrVrV6ijFrkOHDhowYECBz3r/8MMP9eyzz+qnn37ycTLfmTt3ru6991798ccf2rRpk9/up18IGng4zo4dO9SvXz/99NNPnp17APaWk5OjL7/8Uj/88EOuLy5at26t+Ph4Rz16B86QnZ2tP//8U6VKlXLEFxiff/65/ve//ykzM1NdunTRkCFDPNMOHz4sSX77hbWTa3ea7du3KygoSLGxsflOf/fddxUYGKgbb7zRx8l86/fff9fatWvVqVMnlS5d2uo4JQ4NPBwpJydHx44dU7ly5fz+aKwTZWdna9++fapRo4YCAgKUmZmpTz/9VDk5OWrfvr0iIyOtjuhVv//+uyIiIvI8/9ztdmvVqlVq166dRcmA4uPU9XzRokV69tlntXr1as/jEcuWLauePXtqwoQJiomJsTghcOEOHz6sjRs36tJLL1WFChX0559/asaMGcrMzNQNN9zAs8HhaByScJCePXvq7bff1l9//WV1FMsFBASofPnyft28O3V5b9iwQdWrV1edOnXUvHlz/f7772rZsqVuv/12DRkyRA0bNtSaNWusjukVBw4cUKtWrVSjRg1FRERowIABOn78uGd6amqq355S7VTTp09Xp06ddOONN2rZsmW5pv3555+qVauWRcm8x8nr+dtvv61bbrlFLVq00OjRo1WpUiU99NBDmjhxopKSktSiRQvt2LHD6pjABVm9erVq166tjh07qk6dOlq7dq1atWqlGTNm6O2331aLFi20bt06q2P61MGDB7Vv3z6rY3hNYmKixo4d6/kc++6779StWzd16NBBs2bNsjhdCWTlBfjwLZfLZQIDA0358uXN0KFDzU8//WR1JJ8oU6aMuf322/M8TszfOXV5x8fHm+uvv95s2rTJjBo1yjRq1MjccMMNJisry7jdbnPrrbeaTp06WR3TK2677TZz5ZVXmjVr1pjExETTsmVL06JFC8+jplJSUozL5bI4ZfHLysoyDz74oKldu7a5/PLL8zwj218fkfnCCy+Y8PBwM3z4cHPrrbeakJAQ8/TTT3um+2vdTl3PjTGmQYMGuR4ptWbNGlO9enXPDc1uuukmc91111kVz6umTZtmOnbsaG644YY8j5v6448/TGxsrEXJvM9ptXfq1MkMHjzYpKenm0mTJpnq1aubwYMHe6bfcccdplevXhYm9J709HTTr18/ExMTY2677TaTmZlphg0bZlwulwkICDDt2rUzaWlpVscsVm+//bYJDAw0l112mSlTpoyZNWuWiYiIMIMHDzZ33HGHCQ4ONh9++KHVMUsUGngHcblcZsuWLea5554zTZs2NQEBAeaSSy4xL730kmfHxx+5XC7TuHFj43K5TIMGDczkyZPPend2f+HU5X3RRRd5HpeYkZFhSpUqZX788UfP9M2bN5uLL77YqnheFRUVlavWkydPmmuvvdY0a9bMHD582G8burFjx5rIyEgzadIk8/jjj5vy5cubO++80zPdXxu6Ro0amXfeecczvHLlSlO5cmXz5JNPGmP8t4F36npuzOnnY//9kWLGnH6iyv79+40xxvz4448mIiLCgmTe5dQvq4xxZu1//xzPysoyAQEBubb5devWmWrVqlkVz6tGjBhhGjRoYF588UUTFxdnrr32WtOkSROzfPly891335kmTZqYxx57zOqYxapZs2bmhRdeMMYYs3TpUhMWFmamTp3qmT5lyhTTtm1bq+KVSDTwDvLPx4r9+OOP5s477zTly5c3YWFh5pZbbsnzza4/OFP3+vXrzYgRI0yFChVMcHCw6d27t1m8eLHnyIW/ceryjoiIMNu3bzfGnP7gL1WqlFm7dq1n+i+//GIuuugiq+J5VenSpT21n+F2u02vXr08j2Xxtx09Y4ypU6eOWbBggWf4t99+M3Xr1jUDBw40OTk5frmDa0z+zdzmzZs9z4P317qdup4bY0zDhg1zHYlau3atCQ4ONqdOnTLGGLNjxw5TunRpq+J5jVO/rDLGmbWXLl0613tbmTJlzM6dOz3De/fuNaGhoRYk877o6GizbNkyY4wx+/fvNy6Xy3z22Wee6YsWLTL169e3Kp5XlC5d2uzatcszHBQUZDZs2OAZ3rZtm98eeDlfXAPvYK1atdJrr72mAwcOaPr06UpKSlLnzp2tjuU1l156qV566SUdOHBAs2fPVlpamnr06KGYmBiNGTPG6nhe55Tl3aJFCz377LPav3+/nnnmGcXGxurll1/2TH/ppZfUpEkTCxN6T61atbRx48Zc4wIDA/Xhhx+qVq1a6tGjh0XJvGv//v25lmnt2rX1zTffaNWqVerfv7+ys7MtTOc9FStWVFJSUq5xjRs31rJlyzRr1iw9+OCDFiXzLqeu55I0fPhwDR48WA8//LDGjh2rnj17qn///ipVqpQk6ccff1S9evUsTln8du/erTZt2niGW7durWXLlun111/Xo48+amEy73Ni7dHR0dq1a5dneO7cubkeGXfgwAG/ffLCoUOHVKdOHUlSVFSUwsLCVL9+fc/0xo0b53nft7ugoCBlZWV5hkNCQnLdnDQ4ONhx93M6J6u/QYDv/POIbH7+eVTDHwQEBBRY9+7du80TTzxhoqOjfZzK+5y6vFevXm0qVKhgAgICTOXKlc2WLVvMFVdcYapUqWKioqJMWFiYWbp0qdUxveKhhx4y8fHx+U5zu93mmmuu8bsjNcYYExsbm+8y3b9/v6lXr57p1KmTX9Z9yy23mFGjRuU7bfPmzaZSpUp+WbdT1/Mzpk+fbtq0aWNatGhhHnvsMfPXX395pm3fvt388ssvFqbzjujoaPPdd9/lGb9lyxYTGRlp+vfv77fL3Im1jxs3zrz33nsFTn/sscdM7969fZjId6KionKdNXjLLbfk2pfbvHmz351F2LJlS/PJJ594htPS0nKdHZuYmGjq1atnRbQSi8fIOUj79u01f/58RUREWB3FpwICApSSkqLKlSsX+BpjjN/dkd6py1uSjh8/rl9//VX169dXmTJldPLkSb3zzjv666+/1Llz51zfZvuTU6dOKSMjQ+XKlct3enZ2tn7//XfVqFHDx8m8a/DgwTLGaMaMGXmm7d+/X3Fxcdq1a5ffHYnfuHGj1q5dq0GDBuU7fcuWLfroo480duxYHyfzLqeu507Wt29fVa5cWc8//3yeaVu2bFH79u11+PBhv9vGJWfXXpCMjAyVKlVKISEhVkcpdt26dVOvXr1011135Tt99uzZeuONN7RixQofJ/Oe+fPn6+KLLy7w0Z8TJ07UiRMn9O9//9vHyUouGnj4vfHjx+vBBx9UeHi41VEAn1uxYoVatmzplzs6Z+zdu1fbtm1Tly5d8p1+4MABLVmyRAMGDPBxMviKE9ZzJ3Pql1WSs2v/O6ds46mpqQoICCjw4Mvnn3+usLAwxcXF+TQXShYaeIebOHGihg4d6sijtE7k1OXdtGlTLV68WNHR0VZH8bly5cpp/fr1fvk8cOTl1HXdSev5okWLNH/+fFWoUEGDBg1Sw4YNPdOOHDmiPn36eJ6lDPgLJ23jcO7+amFxEzuHe/rpp5Wammp1DJ/r3r27Dhw4YHUMn3Pq8t6zZ4/cbrfVMSzh1O9oy5Url+smSE7h1HXdKev5u+++q2uvvVYpKSlatWqVLrvsMr3zzjue6VlZWfr2228tTOg7TZs29bubeRWWE2t3yjaeHycub6furxZWoNUBYC2nviF+9913jryjpVOXN5yHdR3+aPLkyXruued0zz33SJI++ugjDRo0SCdPntQdd9xhcTrfcuqXVZKza3ciJy5vPsPPjgYegN+76qqrFBYWZnUMS7z22muKjIy0OgZ8xKnrulPW8+3bt+d6TN7111+vihUr6pprrpHb7dZ1111nYTrAe5yyjQOFQQPvcFu3blVUVJRneP/+/apWrZqFiXyjRo0aCgoKsjqGzzlleR87dkw//PCD3G63WrVqpcWLF1sdyTJ9+/a1OoIlbr311gLvUu5PWNdPc8p6Xq5cOR08eFCxsbGecXFxcVqwYIF69Oih33//3cJ0vuXUL6skZ9bulG08P05c3k7ZXz1f3MQOkqSUlBRNmDBBb775pl+eWm6M0W+//Sa326169eopMNDZ31358/LeuHGjunXrppSUFBljVK5cOX300Ufq1KmT1dG8rn379vk+DrF8+fKqX7++hg8f7ribm0mn70I/YcIEvfzyy1ZHKVZOXdedvJ736tVLl156qcaPH59n2jfffKMePXror7/+ctQjxeB/nLyNIzd/3l+9ENzEzkGOHj2qfv36qVKlSoqKitKLL76onJwcjRkzRrVq1dIPP/ygmTNnWh2z2O3Zs0fNmjVTgwYN1LRpU9WpU0fr1q2zOpbXOXV5P/LII4qJidH333+vn376SVdffbVGjBhhdSyfaNasmS699NI8PxEREVq8eLEaNmyo9evXWx3TK7Zu3app06bp9ddf19GjRyVJf/75p0aPHq1atWr55V25nbquO3k9Hz16tEJDQ/OdFhcXp4ULF+q2227zcSrvGzZsmI4fP+4Zfvvtt3MNHz16VAkJCVZE8zon1u7kbdyJy9up+6sXxMAx7r77blO9enVz//33m8aNG5uAgADTrVs30759e/PNN99YHc9rbrzxRlOvXj3zzjvvmI8//thceeWVpmXLllbH8jqnLu9KlSqZNWvWeIb//PNPExAQYI4dO2ZhqpJh2LBhplu3blbHKHYLFiwwwcHBxuVyGZfLZWrXrm2WLVtmKlasaOLi4syCBQusjugVrOv589f13MkCAgLMwYMHPcNly5Y1O3fu9AynpKSYgIAAK6J5nZNrL4g/b+NOXN5O3V+9EDTwDhITE2MSExONMcbs3LnTuFwuM2rUKGtD+UDVqlVzvQEkJSWZgIAAk5GRYWEq73Pq8na5XLk+/IwxpkyZMmbXrl0WJSo5NmzYYKpUqWJ1jGJ35ZVXmpEjR5pjx46ZKVOmGJfLZerVq2e+/fZbq6N5Fet6/vx1PS9IQkKCSU5OtjqGV/1zXS9TpozfNzVnOLn2gvjzNu7E5e3U/dULwSn0DpKcnKxGjRpJkmrVqqXQ0FANHjzY4lTel5KSogYNGniGq1evrrCwMB08eNDCVN7n1OXtcrl07NgxpaenKz09XWlpaXnGpaenWx3TEmFhYTp58qTVMYrdL7/8ouHDh6tMmTIaOXKkAgIC9Pzzz6tdu3ZWR/Mq1vX8+et6XhCnPhYVzuW0bdzfOXV/9UI4+05eDpOTk5PrzuulSpVS6dKlLUzkGy6XSwEBub+rCggI8PtnTDp1eRtjVK9evTzjmjdv7vl/l8vlyJs8LVmyJM+88Qfp6emKiIiQJAUGBiosLMwv6/wn1vX8+et6DuA0tnH/4tT91QtBA+8gxhgNHDhQISEhkqSTJ09q6NCheTaSefPmWRHPa87s5P79jqbHjx9X8+bNczX2qampVsTzGqcu76+//trqCJb57LPP8h2flpamNWvWaMaMGZo9e7ZvQ/nI1q1blZKSIun0uv/rr7/qxIkTuV5zySWXWBHNa5y6rjt5Pc+PUx6LOmbMGIWHh0uSsrKyNGHCBJUvX16SlJGRYWU0r3Na7U7fxp22vJ26v3oheIycgwwcODDfx3L806xZs3yQxnfmzJlTqNcNGDDAy0l8y6nL28n+eabJGWXLllWDBg30wAMP6IYbbvBxKu8LCAiQy+XK96yaM+OdeCTaXzl1PXeyuLi4Qn2e+eOXWk6s3cnbuBOXN/urRUcDD8BR1q1bpzFjxmjhwoVWR/G5I0eOaMGCBX73mKm9e/cW6nU1atTwcpKSxanrur+u55L03//+V/fcc4/CwsIknb7+/YorrvAcuTp27JgefvhhTZ8+3cqYgFf58zYOFAY3sXOQPn366PDhw1bHKBFOnjypOXPmaPr06dqxY4fVcbzCycs7MTFRDz74oB577DHt2rVLkrRt2zb16tVLl19+uU6dOmVxQmvs27dPgwYNsjpGsatRo0ahfvwR63pe/rqeS9Kjjz6qY8eOeYZ79Oih/fv3e4YzMjL02muvWREN8Bl/3sadyMn7q+eLa+Ad5MCBA2rcuLHeeOMN9ezZ0+o4PvPggw8qKytLL7zwgqTT1xO1bt1aW7ZsUXh4uB566CElJiaqdevWFictXk5d3nPmzNGgQYNUoUIFpaam6s0339TUqVM1bNgw9enTRxs2bFCTJk2sjoli5NSjkqzrzvPPkyadchLlfffdV6jXTZ061ctJfM/JtTuRE5e3U/dXLwQNvIOsWLFCkydP1k033aRbbrlFzz//vMqWLWt1LK/7/PPP9fTTT3uG33nnHe3du1c7duxQTEyMbr/9dv3nP//RokWLLExZ/Jy6vJ977jk9/fTTeuSRR/TBBx/o5ptv1nPPPaeff/5ZtWvXtjoevODRRx/VwIEDPQ18jx49tH79etWqVUvS/x2V9LcGnnUdTvHzzz/nGl6+fLlatGjh2eYlFeoaWjtycu1O5MTl7dT91QvBNfAOtG3bNg0aNEgHDhzQyJEjFRiY+3uckSNHWpTMO8qVK6d169apTp06kqRbbrlFZcuW1euvvy5JWr9+vRISEpScnGxlTK9x2vIuW7asNm7cqNjYWOXk5CgkJERLly7V1VdfbXU0y23YsEGXXXaZ393MLSAgQCkpKapcubKk0+vAhg0bPA38wYMHFRUV5Xd1s67nz1/Xc8m56/o//bNuJ3Fy7Wf48zb+T05a3k7bX70QHIF3oAYNGuiOO+7Q0KFD9dxzz+XaQFwul99tIP985vsPP/ygJ5980jMcERGhI0eOWBHNJ5y2vE+cOOF59EhAQIBCQ0MVHR1tcSrfePHFF886/e/XysL+nLquO309f/PNN1WmTBlJ0qlTpzR79mxVrFhRknJdHw/YldO3cady2v7qhaCBd5iDBw9q8ODBWr58uWbMmOF3j07LT4MGDbRgwQLdd9992rJli/bt26f27dt7pu/du1eRkZEWJvQeJy5vSfryyy89z0zNycnRV199pc2bN+d6zTXXXGNFNK967rnnzvmamJgYHySBrzhxXXfyeh4TE6M33njDM1ylShW9/fbbeV4D2JmTt3Gncur+6vmigXeQuXPnasSIEWrevLk2btzoiCM10umb2N1yyy1atGiRtmzZooSEBMXGxnqmL168WK1atbIwoXc4dXlLyvPGf9ddd+Ua9tdngu/evdvqCJZx6lFJJ67rTl7P9+zZY3UEwOucvI07kZP3V88X18A7SOnSpfXss89qxIgRVkfxuaVLl2rRokWqUqWK7rnnHoWHh3umjR8/XuXKldPo0aMtTFj8nLy8nerkyZNaunSpevToIen0zd0yMzM90wMDA/XUU08pNDTUqoheUbNmzULd1IedQv/g1PW8MJKSkjR27FjNnDnT6ijFauPGjbmG27Rpow8++EDVq1fPNf6SSy7xZSyfcGLtTt7Gnbi82V8tOhp4B+nXr59effVV7uz4/6Wlpemdd97Rm2++qQ0bNvjdUSqnLu/bb79dL7zwguPqlqTXXntNCxcu1IIFCySdvvlN48aNPXev3bZtmx588MFCP6YGJZtT13XW84L56829AgIC5HK58n1s3pnx/ni2ieTM2p28jTtxeTt1f/VC0MA7SKlSpXTgwAHP3WudatmyZZo5c6bmzZunGjVqqE+fPurTp4+aN29udbRi5dTl7dS6Jaldu3YaPXq0rrvuOkl57177v//9T9OmTdOqVausjFnsfvzxR6Wmpqpbt26ecW+99ZbGjh2rEydOqFevXnrppZc8z4X3F05d1526nheGvzbwe/fuLdTratSo4eUkvufE2p28jTtxeTv1s+xCcA28gzj5u5rff/9ds2fP1syZM3XixAndeOONcrvd+vjjj9WoUSOr43mFU5e3U+uWpO3bt6tevXqe4dDQUAUEBHiGW7VqpeHDh1sRzavGjh2r9u3bexr4TZs26Y477tDAgQPVsGFDTZo0SVFRURo3bpy1QYuZU9d1p67nTla5cmU98MAD+uSTT+R2u9WpUye9+OKLnvtc+DMn1u7kbdyJy9upn2UXIuDcL4E/Kcx1ov4mISFBjRo10tatW/XSSy8pOTlZL730ktWxfMKJy1tybt1paWm5Hrvyxx9/qGbNmp7hnJycXNcR+osNGzaoY8eOnuG5c+fqiiuu0BtvvKH77rtPL774oj744AMLE3qPE9d1p67nTjZmzBjNnj1b3bt3180336zExETdfffdVsfyCSfW7uRt3InLW3LmZ9mF4Ai8w9SrV++cG0lqaqqP0vjGkiVLNHLkSN19992qW7eu1XF8yonLW3Ju3dWrV9fmzZtVv379fKdv3Lgxz41w/MGRI0dyPQry22+/VdeuXT3Dl19+uZKSkqyI5nVOXNedup5LUu/evc86/ejRo74J4mPz5s3TjBkzdPPNN0uSbr31VrVt21bZ2dkqVaqUxem8y4m1O3kbd+Lylpz5WXYhaOAdZvz48Z5nBjvF999/r5kzZ6ply5Zq0KCB+vfvr5tuusnqWD7hxOUtObfuhIQEjRkzRt27d89zd96//vpL48ePV/fu3S1K5z2RkZHavXu3oqOjlZWVpXXr1mn8+PGe6ceOHVNQUJCFCb3Hieu6U9dzSSpXrtxZd3LLly+v2267zYeJfCMpKUlXXXWVZ7hVq1YKDAxUcnKy3z9yyom1O3kbd+Lylpz5WXYhuImdgwQEBCglJcWxN4nIyMjQ3LlzNXPmTK1evVrZ2dmaOnWqbr/9dr+886VTl7dT65akgwcPqlmzZgoODtaIESM832hv27ZNL7/8sk6dOqWff/4519Fqf3DXXXdp06ZNevbZZ/XJJ59ozpw5Sk5OVnBwsCTpnXfe0fPPP681a9ZYnLR4OXVdd+p67mSlSpVSSkqKKlWq5BlXtmxZbdy4UbGxsRYm8z4n1u7kbdyJy9upn2UXggbeQbjL4//59ddfNWPGDL399ts6evSoOnfurM8++8zqWMXKqcvbqXWfsXv3bt19991KTEz03BjG5XKpc+fOmj59uucuvv7kjz/+UO/evbVixQqVKVNGc+bM8dy9WJI6duyoK6+8UhMmTLAwZfFz8rruxPVcOvcp9NLp+fDxxx/7II3vBAQEqFu3brmeJLFgwQJ16NBBpUuX9oybN2+eFfG8yqm1O3Ubd+LydvJn2fmigXcQvuHKKzs7WwsWLNDMmTP9roF36vJ2at3/lJqaqt9++02SVKdOHVWoUMHiRN6XlpamMmXK5LlOMDU1VWXKlPEckfcXrOvOW88HDRpUqNfNmjXLy0l8y6l1S86uXWIbL4g/LW8+y4qOBh4AAAAAABvgMXIAAAAAANgADTwAAAAAADZAAw8AAAAAgA3QwEOZmZkaN26cMjMzrY7iU9RN3U5A3c6qW3Ju7dRN3U5A3c6qW3Ju7U6tuzC4iR2Unp6u8uXLKy0tTeXKlbM6js9QN3U7AXU7q27JubVTN3U7AXU7q27JubU7te7C4Ag8AAAAAAA2QAMPAAAAAIANBFodoKTJyclRcnKyypYtK5fLZXUcn0hPT8/1X6egbup2Aup2Vt2Sc2unbup2Aup2Vt2Sc2t3Yt3GGB07dkxRUVEKCCj4ODvXwP/D77//rujoaKtjAAAAAAAcJikpSdWrVy9wOkfg/6Fs2bKSTs84p9wwwe12a8mSJYqPj1dQUJDVcXyGuqnbCajbWXVLzq2duqnbCajbWXVLzq3diXWnp6crOjra048WhAb+H86cNl+uXDlHNfDh4eEqV66cYzYQibqp2xmo21l1S86tnbqp2wmo21l1S86t3al1SzrnZdzcxA4AAAAAABuggQcAAAAAwAZo4AEAAAAAsAEaeAAAAAAAbIAGHgAAAAAAG6CBBwAAAADABmjgAQAAAACwARp4AAAAAABsgAYeAAAAAAAboIEHAAAAAMAGaOABAAAAALABGngAAAAAAGyABh4AAAAAABuggQcAAAAAwAZo4AEAAAAAsAEaeAAAAAAAbIAGHgAAAAAAG6CBBwAAAADABgKtDgAAKHmm3NQj3/H3v7/Qx0kAAABwBkfgAQAAAACwARp4AAAAAABsgAYeAAAAAAAboIEHAAAAAMAGaOABAAAAALABGngAAAAAAGyABh4AAAAAABuggQcAAAAAwAZo4AEAAAAAsAEaeAAAAAAAbIAGHgAAAAAAG6CBBwAAAADABmjgAQAAAACwARp4AAAAAABsgAYeAAAAAAAboIEHAAAAAMAGaOABAAAAALABGngAAAAAAGwg0OoAAIDi0XRO03zH/yfiPz5OAgAAAG/gCDwAAAAAADZAAw8AAAAAgA3QwAMAAAAAYAM08AAAAAAA2AANPAAAAAAANkADDwAAAACADdDAAwAAAABgAzTwAAAAAADYAA08AAAAAAA2QAMPAAAAAIAN0MADAAAAAGADNPAAAAAAANiArRr47777Tj179lRUVJRcLpc++eSTXNONMRo3bpyioqIUFhamuLg4bdmyxZqwAAAAAAAUI1s18CdOnNCll16ql19+Od/p//3vfzV16lS9/PLLWrNmjapUqaLOnTvr2LFjPk4KAAAAAEDxCrQ6QFF069ZN3bp1y3eaMUbPP/+8Hn/8cfXu3VuSNGfOHEVGRurdd9/VXXfd5cuoAFDiTRu6zOoIAAAAKAJbHYE/m927dyslJUXx8fGecSEhIbr66qu1cuVKC5MBAAAAAHDhbHUE/mxSUlIkSZGRkbnGR0ZGau/evQX+XmZmpjIzMz3D6enpkiS32y232+2FpCXPmTqdUu8Z1E3dTlJcddtl/jl5eTu1duqmbiegbmfVLTm3difWXdhaXcYY4+UsXuFyuTR//nz16tVLkrRy5Uq1bdtWycnJqlq1qud1Q4YMUVJSkr744ot8/864ceM0fvz4POPfffddhYeHeyU7AHjDE0efyHf8fyL+k+/43z8vW+DfOnlkar7j6/QdUvRgAAAAOKuMjAz17dtXaWlpKleuXIGv85sj8FWqVJF0+kj83xv4Q4cO5Tkq/3ePPvqo7rvvPs9wenq6oqOjFR8ff9YZ50/cbrcSExPVuXNnBQUFWR3HZ6ibuv3NE+/m38BLyrfu1z//vsj/RkJCQpF/xwpOWN4FcWrt1E3dTkDdzqpbcm7tTqz7zJng5+I3DXxsbKyqVKmixMRENW/eXJKUlZWlb7/9Vs8++2yBvxcSEqKQkJA844OCghyzspzhxJol6nYa6r7wv2MnTl3eknNrp25noW5ncWrdknNrd1Ldha3TVg388ePH9dtvv3mGd+/erfXr16tChQqKiYnRvffeq6efflp169ZV3bp19fTTTys8PFx9+/a1MDUAAAAAABfOVg38Tz/9pPbt23uGz5z6PmDAAM2ePVsPPfSQ/vrrLw0bNkxHjhzRFVdcoSVLlqhs2YKv8wQAAAAAwA5s1cDHxcXpbPfcc7lcGjdunMaNG+e7UAAAAAAA+IDfPAceAAAAAAB/RgMPAAAAAIAN0MADAAAAAGADNPAAAAAAANgADTwAAAAAADZAAw8AAAAAgA3QwAMAAAAAYAM08AAAAAAA2AANPAAAAAAANkADDwAAAACADdDAAwAAAABgAzTwAAAAAADYAA08AAAAAAA2QAMPAAAAAIAN0MADAAAAAGADNPAAAAAAANgADTwAAAAAADZAAw8AAAAAgA3QwAMAAAAAYAM08AAAAAAA2AANPAAAAAAANkADDwAAAACADdDAAwAAAABgAzTwAAAAAADYAA08AAAAAAA2QAMPAAAAAIAN0MADAAAAAGADNPAAAAAAANgADTwAAAAAADYQaHUAAIB31Xv4Ef328CN5J8RN830YAAAAnDeOwAMAAAAAYAM08AAAAAAA2AANPAAAAAAANkADDwAAAACADdDAAwAAAABgAzTwAAAAAADYAA08AAAAAAA2QAMPAAAAAIAN0MADAAAAAGADNPAAAAAAANgADTwAAAAAADZAAw8AAAAAgA3QwAMAAAAAYAM08AAAAAAA2AANPAAAAAAANkADDwAAAACADdDAAwAAAABgAzTwAAAAAADYAA08AAAAAAA2QAMPAAAAAIAN0MADAAAAAGADNPAAAAAAANgADTwAAAAAADZAAw8AAAAAgA3QwAMAAAAAYAM08AAAAAAA2AANPAAAAAAANkADDwAAAACADdDAAwAAAABgAzTwAAAAAADYAA08AAAAAAA2QAMPAAAAAIAN0MADAAAAAGADgVYHAIrNuPJnmZbmuxwAAAAA4AUcgQcAAAAAwAZo4AEAAAAAsAEaeAAAAAAAbIAGHgAAAAAAG+AmdgAAoOThxqQAAORBAw8AsLWmc5rmO/4/Ef/xcRIAAADv4hR6AAAAAABsgAYeAAAAAAAboIEHAAAAAMAGaOABAAAAALABGngAAAAAAGyABh4AAAAAABuggQcAAAAAwAZo4AEAAAAAsAEaeAAAAAAAbIAGHgAAAAAAG6CBBwAAAADABmjgAQAAAACwARp4AAAAAABsgAYeAAAAAAAboIEHAAAAAMAGaOABAAAAALABGngAAAAAAGwg0OoAAAAUyrjy+Y+PjfFtDgAAAIv45RH46dOnKzY2VqGhoWrRooW+//57qyMBAAAAAHBB/K6Bf//993Xvvffq8ccf188//6yrrrpK3bp10759+6yOBgAAAADAefO7U+inTp2qO+64Q4MHD5YkPf/88/ryyy/1yiuv6JlnnrE4HQAAAIqi6Zym+Y7/T8R/fJwEAKznVw18VlaW1q5dq0ceeSTX+Pj4eK1cuTLf38nMzFRmZqZnOD09XZLkdrvldru9F7YEOVOn3esNOsu0/Grzl7qLirqdVXdxs3L+nW0bL4gTl7e/rOu8pxeOU+s+w2l1O3V5O7Vuybm1O7HuwtbqMsYYL2fxmeTkZFWrVk0rVqxQmzZtPOOffvppzZkzR7/++mue3xk3bpzGjx+fZ/y7776r8PBwr+a9UKNW5f/9y57QvvmOb3qWGz198MypfMcvi5uW7/iTR6bmO75O3yEF/hvFpaC6X2idfw2S9MTRJ/Id7w91F7S8pYKXeXHVLXm/9oLqloq+rhe1bsley/zT5m95M47PnM82np96Dz9S4DR/2MZ5T8/LH+o+23t6Qdt4Qeu6v7ynF1fdkr2WuVM/y4pat+Qf27i/v6dbub/qi7qLQ0ZGhvr27au0tDSVK1euwNf51RH4M1wuV65hY0yecWc8+uijuu+++zzD6enpio6OVnx8/FlnXEkwatUSqyPkkZCQ4PV/o6C6z/ZvP/Fu/jt7xcXKuq3m7dqdWrdU9Nrzy+R2u5WYmKjOnTsrKOh8jmH73vls4//kdru19ywNfFGVxOXtC7yne8/5LO+CtvGC1vU7X7oq3/Ev3lpwA18S39OLWvf5KKnL3Nuo23uou+TwRd3F4cyZ4OfiVw18xYoVVapUKaWkpOQaf+jQIUVGRub7OyEhIQoJCckzPigoyDY7uyWJlfPMqf+2L9z//kKrI5Q4JXGZny2TP7ynsY37nlPneUld3kXNdT51lMTafZGpJNbtC9TtLNRdshU2p1818MHBwWrRooUSExN13XXXecYnJibq2muvtTAZULJsf3aiEhIS8rxRLBu6zKJEwP/ZM7G71REAWGFcmtUJAKDE86sGXpLuu+8+9e/fXy1btlTr1q31+uuva9++fRo6dKjV0WChTQM25Rnndrv12zOXWJAGAAAAAIrO7xr4m266SYcPH9ZTTz2lAwcOqEmTJlq8eLFq1KhhdTTAO852xKKAR+8AAAAAsB+/a+AladiwYRo2bJjVMQAAALyuoMuiAAD+J8DqAAAAAAAA4Nz88gi84xV0SjWnUwOA/fCeDgBnPdOEm/DCSWjgYTvcoRoAYHcFfpaN82kMwC8Mf7VDvuOn3DTVx0kA7+MUegAAAAAAbIAj8AAAAPAJzqIDgAtDAw8AACxDQwcAQOHRwAMAAMBvFHQ9tMQ10QDsj2vgAQAAAACwARp4AAAAAABsgFPoAQAASopxaVYnAPzG/e8vtDoCUOxo4AGb4EZPQPHhmcEAYLGCvqya09S3OUqITQM25Tve7Xbrt2cu8XEalGScQg8AAAAAgA3QwAMAAAAAYAM08AAAAAAA2AANPAAAAAAANkADDwAAAACADZzXXeiTkpK0Z88eZWRkqFKlSmrcuLFCQkKKOxsAAMAF46kDAAB/UegGfu/evXr11Vf13nvvKSkpScYYz7Tg4GBdddVVuvPOO9WnTx8FBHBgHwAAAACA4lSoBn7UqFGaNWuW4uPj9dRTT6lVq1aqVq2awsLClJqaqs2bN+v777/Xk08+qfHjx2vWrFm6/PLLvZ0dAIBidf/7C62OAAAAUKBCNfDBwcHauXOnKlWqlGda5cqV1aFDB3Xo0EFjx47V4sWLtXfvXhp4AAAAAACKUaEa+EmTJhX6DyYkJJx3GADA/9kzsXv+E8b5NAZQ4mwasCnPOLfbrd+eucSCNAAA+A4XqwMAAAAAYAOFOgLfvHlzuVyuQv3BdevWXVAgAAAAAACQV6Ea+F69enk5BgAAAAB/U+DlYADOS6Ea+LFjx3o7BwAvKOg60cWLF1uQBgAAAMCFKPRz4P9p7dq1+uWXX+RyudSoUSM1b968OHMBAAAA/m9cWr6j834Fz80aAZxHA3/o0CHdfPPN+uabbxQRESFjjNLS0tS+fXvNnTs330fNAQAAAACAC1PkBv6ee+5Renq6tmzZooYNG0qStm7dqgEDBmjkyJF67733ij0kAADAuWx/dqISEhIUFBRkdRT4AMsbgBMVuYH/4osvtHTpUk/zLkmNGjXStGnTFB8fX6zhAAAAAH/AzdxQ3Ia/2iHf8VNumurjJPClIjfwOTk5+X7TGRQUpJycnGIJhcLhgwAAAAAAnKPIDXyHDh00atQovffee4qKipIk7d+/X6NHj1bHjh2LPSBQktz//kKrIwAAAORR0NFYiSOygD8pcgP/8ssv69prr1XNmjUVHR0tl8ulffv2qWnTpvrf//7njYwoJvk9UkzijqYAAAAAYAdFbuCjo6O1bt06JSYmatu2bTLGqFGjRurUqZM38gEAAKAYcTYZANjXeT8HvnPnzurcuXNxZgEAAABwFtx9H3C282rgV69erW+++UaHDh3Kc+O6qVO5xgYAAAAAgOJW5Ab+6aef1hNPPKH69esrMjJSLpfLM+3v/w8AAAAAAIpPkRv4F154QTNnztTAgQO9EAcAABQGNyYFAMB5Aor8CwEBatu2rTeyAAAAAACAAhS5gR89erSmTZvmjSwAAAAAAKAART6F/oEHHlD37t1Vu3ZtNWrUKM8dMOfNm1ds4QD41vBXO1gdAQAAAEABitzA33PPPfr666/Vvn17XXzxxdy4DgAAAAAAHyhyA//WW2/p448/Vvfu3b2RBwAAAAAA5KPI18BXqFBBtWvX9kYWAAAAAABQgCIfgR83bpzGjh2rWbNmKTw83BuZAAC4YNufnaiEhIQ892oBzrj//YVWRwB8gnUd8B9FbuBffPFF7dy5U5GRkapZs2aeHaN169YVWzjAKtzMDQAAAOdrz8QCLjce59MY8ENFbuB79erlhRgAADhbgTt7AAAA/1+hG/jt27erXr16Gjt2rDfzAAAAAACAfBT6JnbNmzdXw4YN9fDDD2vVqlXezAQAAAAAAP6h0EfgDx8+rMTERH366ae67rrrZIxRjx49dO211yo+Pl6hoaHezIkShpuhAAAAAIBvFfoIfGhoqHr27Kk333xTBw4c0Pz581WpUiU98sgjuvjii3Xttddq5syZOnTokDfzAgAAAADgSEV+DrwkuVwutWnTRhMnTtTWrVu1fv16tWvXTrNnz1Z0dLSmTZtW3DkBAAAAAHC0It+FPj9169bV/fffr/vvv1+HDx9WampqcfxZwOt4TjQAAAAAuyh0A//ZZ5+d+48FBqpq1apq3LjxBYUCAAAAihv38AFgd4Vu4Ivy/PcqVaro/fff11VXXXU+mQAAAAAAwD8U+hr4nJycc/5kZ2crOTlZvXv31qhRo7yZGwAAAAAARynyNfBHjx5VREREvtN27typOnXq6IEHHlCDBg0uNBt8qKBrwRtalAcAAAAAkFuR70KfkJCgv/76K8/4X3/9VXFxcZKkmjVr6uDBgxccDgAAAAAAnFbkI/AXXXSRrrvuOi1cuFCBgad//ZdfflGHDh104403el5Xvnz54ksJAAAAwO9tGrApzzi3263FixdbkAYoeYp8BP7jjz/WiRMn1LdvXxljtHnzZsXFxemWW27RCy+84I2MAAAAAAA4XpEb+NDQUC1cuFA7duzQDTfcoI4dO+q2227T1KlTvZEPAAAAAACokKfQp6en5xp2uVx6//331alTJ/Xp00dPPvmk5zXlypUr/pQAAAAAADhcoRr4iIgIuVyuPOONMXr11Vf12muvyRgjl8ul7OzsYg8JAAAAAE5U0NOi4EyFauC//vprb+cAAAAAAABnUagG/uqrr/Z2DgAAAAAAcBaFuondvn37ivRH9+/ff15hAAAAAABA/grVwF9++eUaMmSIVq9eXeBr0tLS9MYbb6hJkyaaN29esQUEAAAAAACFPIX+l19+0dNPP62uXbsqKChILVu2VFRUlEJDQ3XkyBFt3bpVW7ZsUcuWLTVp0iR169bN27kBAAAAwF7GpVmdADZXqCPwFSpU0OTJk5WcnKxXXnlF9erV059//qkdO3ZIkvr166e1a9dqxYoVNO8AAAAAAHhBoY7AnxEaGqrevXurd+/e3soDAAAAAADyUagj8AAAAAAAwFo08AAAAAAA2AANPAAAAAAANkADDwAAAACADRS5gT9x4oQ3cgAAAAAAgLMocgMfGRmp22+/XcuXL/dGHgAAAAAAkI8iPUZOkt577z3Nnj1bHTt2VI0aNXT77bfrtttuU1RUlDfyAQAAAAAK6f73F1odAV5U5Aa+Z8+e6tmzpw4fPqy33npLs2fP1pNPPqkuXbro9ttv1zXXXKPAwCL/WQAA4GXDX+2Q7/gpN031cRIAAHA+zvsmdhdffLFGjx6tDRs2aOrUqVq6dKmuv/56RUVFacyYMcrIyCjOnAAAAAAAONp5HypPSUnRW2+9pVmzZmnfvn26/vrrdccddyg5OVkTJ07UDz/8oCVLlhRnVgAAAAAAHKvIDfy8efM0a9Ysffnll2rUqJGGDx+uW2+9VREREZ7XNGvWTM2bNy/OnAAAAAAAOFqRG/hBgwbp5ptv1ooVK3T55Zfn+5patWrp8ccfv+BwAAAAAADgtCI38AcOHFB4ePhZXxMWFqaxY8eedygAAAAAAJBbkRv4U6dOKT09Pc94l8ulkJAQBQcHF0swAAAAAADwf4rcwEdERMjlchU4vXr16ho4cKDGjh2rgIDzvsk9AAAAAAD4myI38LNnz9bjjz+ugQMHqlWrVjLGaM2aNZozZ46eeOIJ/fHHH5o8ebJCQkL02GOPeSMzAAAAAACOU+QGfs6cOZoyZYpuvPFGz7hrrrlGTZs21WuvvaavvvpKMTExmjBhAg08AAAAAADFpMjnuK9atSrfR8Q1b95cq1atkiT961//0r59+y48HQAAAAAAkHQeDXz16tU1Y8aMPONnzJih6OhoSdLhw4d10UUXXXi6v5kwYYLatGmj8PDwXM+c/7t9+/apZ8+eKl26tCpWrKiRI0cqKyurWHMAAAAAAGCFIp9CP3nyZN1www36/PPPdfnll8vlcmnNmjXatm2bPvroI0nSmjVrdNNNNxVr0KysLN1www1q3bp1vl8gZGdnq3v37qpUqZKWL1+uw4cPa8CAATLG6KWXXirWLAAAAAAA+FqRG/hrrrlG27dv16uvvqpff/1Vxhh169ZNn3zyiWrWrClJuvvuu4s7p8aPHy/p9E308rNkyRJt3bpVSUlJioqKkiRNmTJFAwcO1IQJE1SuXLlizwQAAAAAgK8UqYF3u92Kj4/Xa6+9pmeeecZbmc7LqlWr1KRJE0/zLkldunRRZmam1q5dq/bt2+f7e5mZmcrMzPQMn3nGvdvtltvt9m7oEuJMnU6p9wzqdlbdxc3K+RdUwPj8Mjl1eTu1bunsNRd1fthp/jl1mVM3dTuBU+uWnF17cbHLvCtsziI18EFBQdq8efNZnwNvlZSUFEVGRuYad9FFFyk4OFgpKSkF/t4zzzzjObr/d0uWLFF4eHix5yzJEhMTrY5gCeou6Yp8olCBtj87Mf8Jnxf9by1evPjCwlyAawsYf7ZM9lnexcupddcrYHxR11sr1/Pz5dRlTt3OQt3OY5/ai77f9p+I/xQw5ZELi/L/2eWzLCMjo1CvK/Icvu222zRjxgxNnFjAjnARjBs3Lt/m+e/WrFmjli1bFurv5ffFgjHmrF84PProo7rvvvs8w+np6YqOjlZ8fLxjTrt3u91KTExU586dFRRU0LE9/0Pd9qh71Kolxfr38qv79c+/L/LfSUhIKK5IRfdz/qPzy2S35V1cnFq3dLr2vQ/nv9NT0Hr74rtvFOn1JZFTlzl1U7cTOLVuyX61n89+W0H7LwV9lhXH3y+JzpwJfi5FbuCzsrL05ptvKjExUS1btlTp0qVzTZ86dWqh/9aIESN08803n/U1Z66rP5cqVaroxx9/zDXuyJEjcrvdeY7M/11ISIhCQkLyjA8KCrLFRlKcnFizRN1OU1x1l8R5d7ZMLG9IRV9v7TjvnLrMqdtZqNt5/Ll2b9dll/lW2JxFbuA3b96syy67TJK0ffv2XNOKemp9xYoVVbFixaJGyFfr1q01YcIEHThwQFWrVpV0+jT4kJAQtWjRolj+DQDOc//7C62OAAAAAEg6jwb+66+/9kaOc9q3b59SU1O1b98+ZWdna/369ZKkOnXqqEyZMoqPj1ejRo3Uv39/TZo0SampqXrggQc0ZMgQx5wKDwAAAADwX+d9d6jffvtNO3fuVLt27RQWFnbOa80v1JgxYzRnzhzPcPPmzSWd/kIhLi5OpUqV0qJFizRs2DC1bdtWYWFh6tu3ryZPnuy1TAAAAAAA+EqRG/jDhw/rxhtv1Ndffy2Xy6UdO3aoVq1aGjx4sCIiIjRlyhRv5NTs2bMLfAb8GTExMVq4kNNdAQAAAAD+J6CovzB69GgFBQVp3759uR6zdtNNN+mLL74o1nAAAAAAAOC0Ih+BX7Jkib788ktVr1491/i6detq7969xRYMAAAAAAD8nyI38CdOnMh15P2MP//8M9/HsQEAAN/a/uxEJSQk2ObROQAAoHCKfAp9u3bt9NZbb3mGXS6XcnJyNGnSJLVv375YwwEAAAAAgNOKfAR+0qRJiouL008//aSsrCw99NBD2rJli1JTU7VixQpvZAQAAAAAwPGKfAS+UaNG2rhxo1q1aqXOnTvrxIkT6t27t37++WfVrl3bGxkBAAAAAHC883oOfJUqVTR+/PjizgIAAAAAAApwXg380aNHtXr1ah06dEg5OTm5pt12223FEgwAAAAAAPyfIjfwCxYsUL9+/XTixAmVLVtWLpfLM83lctHAAwAAAADgBUW+Bv7+++/X7bffrmPHjuno0aM6cuSI5yc1NdUbGQEAAAAAcLwiH4Hfv3+/Ro4cme+z4AEAAAAAzrRnYvf8J4zzaQy/VuQj8F26dNFPP/3kjSwAAAAAAKAART4C3717dz344IPaunWrmjZtqqCgoFzTr7nmmmILBwAAAAAATityAz9kyBBJ0lNPPZVnmsvlUnZ29oWnAgAAAAD4h3FpVifwG0Vu4P/52DgAAAAAAOB9Rb4GHgAAAAAA+F6hG/iEhASlpf3fqQ8TJkzQ0aNHPcOHDx9Wo0aNijUcAAAAAAA4rdAN/JdffqnMzEzP8LPPPpvrue+nTp3Sr7/+WrzpAAAAAACApCI08MaYsw4DAAAAAADv4Rp4AAAAAABsoNANvMvlksvlyjMOAAAAAAB4X6EfI2eM0cCBAxUSEiJJOnnypIYOHarSpUtLUq7r4wEAAAAAOF/bn52ohIQEBQUF5Rq/bOgyixKVDIVu4AcMGJBr+NZbb83zmttuu+3CEwEAAJ+6//2FVkcAAACFUOgGftasWd7MAQAAAAAAzoKb2AEAAAAAYAOFPgIPAAAAAICVhr/aId/xU26a6uMk1uAIPAAAAAAANkADDwAAAACADdDAAwAAAABgAzTwAAAAAADYAA08AAAAAAA2QAMPAAAAAIAN0MADAAAAAGADNPAAAAAAANgADTwAAAAAADYQaHUAADibPRO7FzxxnM9iAAAAoAS7//2FVkfwCRp4AI43/NUOVkcAAAAAzolT6AEAAAAAsAEaeAAAAAAAbIAGHgAAAAAAG6CBBwAAAADABmjgAQAAAACwAe5CD8C+xqXlP35OU9/mAAAAAHyAI/AAAAAAANgADTwAAAAAADZAAw8AAAAAgA3QwAMAAAAAYAM08AAAAAAA2AANPAAAAAAANkADDwAAAACADdDAAwAAAABgAzTwAAAAAADYAA08AAAAAAA2QAMPAAAAAIAN0MADAAAAAGADNPAAAAAAANgADTwAAAAAADZAAw8AAAAAgA0EWh0AAIrbpgGb8oxzu91avHixBWkAAACA4sEReAAAAAAAbIAGHgAAAAAAG6CBBwAAAADABmjgAQAAAACwARp4AAAAAABsgAYeAAAAAAAboIEHAAAAAMAGaOABAAAAALABGngAAAAAAGyABh4AAAAAABuggQcAAAAAwAZo4AEAAAAAsAEaeAAAAAAAbIAGHgAAAAAAG6CBBwAAAADABmjgAQAAAACwARp4AAAAAABsgAYeAAAAAAAboIEHAAAAAMAGaOABAAAAALABGngAAAAAAGyABh4AAAAAABuggQcAAAAAwAZo4AEAAAAAsAEaeAAAAAAAbIAGHgAAAAAAG6CBBwAAAADABmjgAQAAAACwAVs08Hv27NEdd9yh2NhYhYWFqXbt2ho7dqyysrJyvW7fvn3q2bOnSpcurYoVK2rkyJF5XgMAAAAAgB0FWh2gMLZt26acnBy99tprqlOnjjZv3qwhQ4boxIkTmjx5siQpOztb3bt3V6VKlbR8+XIdPnxYAwYMkDFGL730ksUVAAAAAABwYWzRwHft2lVdu3b1DNeqVUu//vqrXnnlFU8Dv2TJEm3dulVJSUmKioqSJE2ZMkUDBw7UhAkTVK5cOUuyAwAAAABQHGzRwOcnLS1NFSpU8AyvWrVKTZo08TTvktSlSxdlZmZq7dq1at++fb5/JzMzU5mZmZ7h9PR0SZLb7Zbb7fZS+pLlTJ1OqfcM6qZuuwoqYHx+tflT3UXh1Lol59ZO3dTtBNTtrLol59buxLoLW6vLGGO8nKXY7dy5U5dddpmmTJmiwYMHS5LuvPNO7dmzR0uWLMn12pCQEM2ePVu33HJLvn9r3LhxGj9+fJ7x7777rsLDw4s/PABcoGt/vi3f8Z82f8vHSQAAAFAcMjIy1LdvX6WlpZ317HFLj8AX1Dz/3Zo1a9SyZUvPcHJysrp27aobbrjB07yf4XK58vy+MSbf8Wc8+uijuu+++zzD6enpio6OVnx8vGNOu3e73UpMTFTnzp0VFFTQsT3/Q93UbVs/5z86ISEhzzi/qrsInFq35NzaqZu6nYC6nVW35NzanVj3mTPBz8XSBn7EiBG6+eabz/qamjVrev4/OTlZ7du3V+vWrfX666/nel2VKlX0448/5hp35MgRud1uRUZGFvj3Q0JCFBISkmd8UFCQY1aWM5xYs0TdTuPPdZ+tLn+u+2ycWrfk3Nqp21mo21mcWrfk3NqdVHdh67S0ga9YsaIqVqxYqNfu379f7du3V4sWLTRr1iwFBOR+Al7r1q01YcIEHThwQFWrVpV0+sZ2ISEhatGiRbFnBwAAAADAl2xxE7vk5GTFxcUpJiZGkydP1h9//OGZVqVKFUlSfHy8GjVqpP79+2vSpElKTU3VAw88oCFDhjjmVHgAAAAAgP+yRQO/ZMkS/fbbb/rtt99UvXr1XNPO3IOvVKlSWrRokYYNG6a2bdsqLCxMffv29TxmDgAAAAAAO7NFAz9w4EANHDjwnK+LiYnRwoULvR8IAAAAAAAfCzj3SwAAAAAAgNVo4AEAAAAAsAEaeAAAAAAAbIAGHgAAAAAAG6CBBwAAAADABmjgAQAAAACwARp4AAAAAABsgAYeAAAAAAAboIEHAAAAAMAGaOABAAAAALABGngAAAAAAGyABh4AAAAAABuggQcAAAAAwAZo4AEAAAAAsAEaeAAAAAAAbIAGHgAAAAAAG6CBBwAAAADABmjgAQAAAACwARp4AAAAAABsgAYeAAAAAAAboIEHAAAAAMAGaOABAAAAALABGngAAAAAAGyABh4AAAAAABuggQcAAAAAwAZo4AEAAAAAsAEaeAAAAAAAbIAGHgAAAAAAG6CBBwAAAADABmjgAQAAAACwARp4AAAAAABsgAYeAAAAAAAboIEHAAAAAMAGaOABAAAAALABGngAAAAAAGyABh4AAAAAABuggQcAAAAAwAZo4AEAAAAAsAEaeAAAAAAAbIAGHgAAAAAAG6CBBwAAAADABmjgAQAAAACwARp4AAAAAABsgAYeAAAAAAAboIEHAAAAAMAGaOABAAAAALABGngAAAAAAGyABh4AAAAAABuggQcAAAAAwAZo4AEAAAAAsAEaeAAAAAAAbIAGHgAAAAAAG6CBBwAAAADABmjgAQAAAACwARp4AAAAAABsgAYeAAAAAAAboIEHAAAAAMAGaOABAAAAALABGngAAAAAAGyABh4AAAAAABuggQcAAAAAwAZo4AEAAAAAsAEaeAAAAAAAbIAGHgAAAAAAG6CBBwAAAADABmjgAQAAAACwARp4AAAAAABsgAYeAAAAAAAboIEHAAAAAMAGaOABAAAAALCBQKsDAACAC5ednS232y23263AwECdPHlS2dnZVsfyGeqmbiegbmfVLTm3dn+sOygoSKVKlbrgv0MDDwCAjRljlJKSoqNHj3qGq1SpoqSkJLlcLmvD+RB1U7cTULez6pacW7u/1h0REaEqVapcUE008AAA2NiZ5r1y5coKDw+XMUbHjx9XmTJlFBDgnCvlcnJyqJu6/R51O6tuybm1+1vdxhhlZGTo0KFDkqSqVaue99+igQcAwKays7M9zfvFF18s6fROT1ZWlkJDQ/1ip6ewqJu6nYC6nVW35Nza/bHusLAwSdKhQ4dUuXLl8z6d3j/mBgAADuR2uyVJ4eHhFicBAADncubz+szn9/mggQcAwOb86fpAAAD8VXF8XtPAAwAAAABgAzTwAACgxPnmm2/kcrk8d9efPXu2IiIiLM0EAIDVuIkdAAB+qNZjn/vs39ozsft5/+7KlSt11VVXqXPnzvriiy+KMVUxGlfex/9e2nn9WkpKiiZMmKBFixZp//79qly5spo1a6Z7771XHTt21Ouvv653331X69at07Fjx3TkyBFLvxRpOqepT/+9TQM2Ffl3zjZPmzdvrrFjx2rJkiVKSkpSxYoV1atXL/373/9W+fI+Xmf+v18aNPTpv9dw2y/n9XvnWlfvuusuLV26VMnJySpTpoxat26tJ554Qi1btizmCs5t2tBlPv33hr/a4bx+71zz9AxjjBISEvTFF19o/vz56tWrVzElL5opN/Xw2b91//sLz+v3zjVP4+Li9O233+b6nZtuuklz584tjtgFooEHAACWmTlzpu655x69+eab2rdvn2JiYqyOZEt79uzRVVddpYiICP33v//VJZdcIrfbrS+//FLDhw/Xtm3blJGRoa5du6pr16569NFHrY5c4u3Zs0dt27YtcJ5+9NFHSk5O1uTJk9WoUSPt3btXQ4cOVXJysj766COr45dY55qv27ZtU4sWLdSvXz/FxMQoNTVVY8eOVe/evbV7926/uSN5cSrMPD3j+eef574phVDYeTpkyBA99dRTnt87c6d5b6KBBwAAljhx4oQ++OADrVmzRikpKZo9e7bGjBljdSxbGj58uFwul1avXq3SpUt7xjdu3Fi33367JOnee++VdPryBJzbsGHDzjpPIyIi9PHHH3vG165dWxMmTNCtt96qU6dOKTCQ3ez8nGu+StKdd97pGV+zZk39+9//VvPmzbVnzx7VrVvX55lLusLMU0nasGGDpk6dqjVr1lzQc8idoLDzNDw8XFWqVPFpNr7CAgAAlnj//fdVv3591a9fX7feeqtmzZolY4zVsWznyJEjnqNCf9/RPIN7BxRdamqqvvjiiyLP07S0NJUrV47mvQDnM19PnDih2bNnq0aNGoqOjvZBSnsp7DzNyMjQLbfcopdfftnnDafdFGU9feedd1SxYkU1btxYDzzwgI4dO+b1fLy7AIDdnOf1sUBJM2PGDN16662SpK5du+r48eP66quv1KlTJ4uT2cuuXbtkjFGDBg2sjuI3fvvttyLP08OHD+vf//637rrrLi8ms7eizNfp06froYce0okTJ9SgQQPNnz9fwcHBPkhpL4Wdp6NHj1abNm107bXX+iiZfRV2nvbr10+xsbGqUqWKNm/erEcffVQbNmxQYmKiV/PRwAMAAJ/79ddftXr1as2bN0+SFBgYqJtuukkzZ86kgS+iM2ctcF1r8SnqPE1PT1f37t3VqFEjjR071pvRbK0o87Vfv37q3LmzDhw4oEmTJmnQoEFauXKlwsPDvR3TVgozTz/77DMtW7ZMP//8s69i2Vph19MhQ4Z4/r9JkyaqW7euWrZsqXXr1umyyy7zWj5OoQcAAD43Y8YMnTp1StWqVVNgYKACAwP1yiuvaN68eTpy5IjV8Wyldu3acrlc+uWX87sjOPKqW7duoefpsWPH1LVrV5UpU0bz589XUFCQDxLaU1Hma/ny5VW3bl21a9dOH374oXbs2KH58+f7IKW9FGaeLlu2TDt37lRERITn/VaS+vTpo7i4OB8ltY+irKd/d9lllykoKEg7duzwUrLTaOABAIBPnTp1Sm+99ZamTJmi9evXe342bNigGjVq6J133rE6oq1cdNFFio+P17Rp03TixIk8048ePer7UDZXoUIFdenS5ZzzND09XfHx8QoODtZnn32m0NBQHye1l8LO1/wYY5SZmenFdPZUmHn6yCOPaOPGjbnebyXpueee06xZs3ycuOQ73/V0y5YtcrvdXr9BIA08AADwqYULF+rIkSO644471KRJk1w/119/vWbMmGF1RNuZNm2asrOz1apVK3388cfasWOHfvnlF7344otq3bq1pNPPNF6/fr1+++03SdKmTZu0fv16paamWhm9xJo+ffpZ5+mxY8cUHx+vEydOaMaMGUpPT1dKSopSUlKUnZ1tdfwS61zzddeuXXrmmWe0du1a7du3T6tWrdJNN92k0NBQJSQkWB2/RDrXPK1SpUqe91pJiomJUWxsrMXpS6ZzzdOdO3fqqaee0k8//aQ9e/Zo8eLFuuGGG9S8eXO1bdvWq9ls08Bfc801iomJUWhoqKpWrar+/fsrOTk512v27dunnj17qnTp0qpYsaJGjhyprKwsixIDAID8zJgxQ506dVL58uXzTOvTp4/Wr1+vdevWWZDMvmJjY7Vu3Tq1b99e999/v5o0aaLOnTvrq6++0iuvvCJJevXVV9W8eXPPdZvt2rVT8+bN9dlnn1kZvcQ61zxdu3atfvzxR23atEl16tRR1apVPT9JSUlWxy+xzjVfQ0ND9f333yshIUF16tTRjTfeqNKlS+vLL79U5cqVrY5fIhVm+0fRnGueBgcH66uvvlKXLl1Uv359jRw5UvHx8Vq6dKlKlSrl1WwuY5PntTz33HNq3bq1qlatqv379+uBBx6QJK1cuVKSlJ2drWbNmqlSpUqaMmWKDh8+rAEDBqh379566aWXCv3vpKenq3z58p7HgDiB2+3W4sWLlZCQ4Kjrtqibup2Auv277pMnT2r37t2KjY31nLqbk5Oj9PR0lStXTgEBtvme/oJRN3U7AXU7q27JubX7a935fW6fUdg+1DZ3oR89erTn/2vUqKFHHnlEvXr1ktvtVlBQkJYsWaKtW7cqKSlJUVFRkqQpU6Zo4MCBmjBhgmOacQAAAACAf7Ll1xmpqal655131KZNG8+RlVWrVqlJkyae5l2SunTposzMTK1du9aqqAAAAAAAFAvbHIGXpIcfflgvv/yyMjIydOWVV2rhwoWeaSkpKYqMjMz1+osuukjBwcFKSUkp8G9mZmbmuqNlenq6pNOnX7rd7mKuoGQ6U6dT6j2DuqnbCajbv+t2u90yxignJ0c5OTmS/u/5tWfGOwV1U7cTULez6pacW7u/1p2TkyNjjNxud55r5Qu7z2LpNfDjxo3T+PHjz/qaNWvWqGXLlpKkP//8U6mpqdq7d6/Gjx+v8uXLa+HChXK5XLrzzju1d+9effnll7l+Pzg4WG+99ZZuvvnmImV49913FR4efp6VAQDgfYGBgapSpYqio6MVHBxsdRwAAHAWWVlZSkpKUkpKik6dOpVrWkZGhvr27XvOa+AtbeD//PNP/fnnn2d9Tc2aNfN9pubvv/+u6OhorVy5Uq1bt9aYMWP06aefasOGDZ7XHDlyRBUqVNCyZcvUvn37fP9+fkfgo6Oj9eeffzrmunm3263ExER17tzZr2/29E/UTd1OQN3+XXdmZqb27dunGjVqKCwsTNLpoxXHjh1T2bJl5XK5LE7oO9RN3U5A3c6qW3Ju7f5a919//aW9e/cqJiZGISEhuaalp6erYsWKJfsmdhUrVlTFihXP63fPfO9wpvlu3bq1JkyYoAMHDqhq1aqSpCVLligkJEQtWrQo8O+EhITkmXmSFBQU5Nc7fflxYs0SdTsNdTuLv9cdEBAgl8ulkydPqnTp0pLkOdXQ5XL51Z17z4W6qdsJqNtZdUvOrd1f6z558qRcLpfCwsLynEJf2P0VW1wDv3r1aq1evVr/+te/dNFFF2nXrl0aM2aMateurdatW0uS4uPj1ahRI/Xv31+TJk1SamqqHnjgAQ0ZMsQxR9IBAM5SqlQpRURE6NChQ5Kk8PBwGWOUlZWlkydP+tVOz7nk5ORQN3X7Pep2Vt2Sc2v3t7qNMcrIyNChQ4cUERFxQc+Kt0UDHxYWpnnz5mns2LE6ceKEqlatqq5du2ru3Lmeo+elSpXSokWLNGzYMLVt21ZhYWHq27evJk+ebHF6AAC8p0qVKpLkaeKNMfrrr78UFhbmV6cdngt1U7cTULez6pacW7u/1h0REeH53D5ftmjgmzZtqmXLlp3zdTExMbnuTA8AgL9zuVyqWrWqKleu7HmCynfffad27dr59eUD/0Td1O0E1O2suiXn1u6PdQcFBV3QkfczbNHAAwCAsytVqpTn59SpUwoNDfWbnZ7CoG7qdgLqdlbdknNrd2rdhWH/CwoAAAAAAHAAGngAAAAAAGyABh4AAAAAABvgGvh/OPN8+fT0dIuT+I7b7VZGRobS09MddY0JdVO3E1C3s+qWnFs7dVO3E1C3s+qWnFu7E+s+03+e6UcLQgP/D8eOHZMkRUdHW5wEAAAAAOAkx44dU/ny5Quc7jLnavEdJicnR8nJySpbtqxfPXMQAAAAAFAyGWN07NgxRUVFKSCg4CvdaeABAAAAALABbmIHAAAAAIAN0MADAAAAAGADNPAAAAAAANgADTwAAAAAADZAAw8AAAAAgA3QwAMAAAAAYAM08AAAAAAA2MD/A2pWu8iF0ABYAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Population fraction of each cluster, manually calculated from cluster log file\n", "population_weights = [0.333362962, 0.332651882, 0.172081241, 0.097995645, 0.063908271]\n", "\n", "# Read CSV files and extract the energy\n", "residues, average_clusters, stddev_clusters = [], [], []\n", "for i in range(1, 6):\n", " average, stddev = [], []\n", " with open(f'mmpbsa/residues_energy_summary_c{i}.csv') as csvfile:\n", " reader = csv.DictReader(csvfile)\n", " for row in reader:\n", " average.append(float(row['total']))\n", " stddev.append(float(row['total-stddev']))\n", " if i == 1:\n", " residues.append(row['Residue'])\n", " average_clusters.append(average)\n", " stddev_clusters.append(stddev)\n", "\n", "# Calculate weighted-average and add data as first column\n", "weighted_average = np.average(average_clusters, axis=0, weights=population_weights)\n", "average_clusters = [weighted_average, *average_clusters]\n", "average_clusters = np.array(average_clusters)\n", "\n", "# First, only consider protein residues, and \n", "# filter out residues that have equal/less than -4 kJ/mol energy in at least one cluster\n", "average_clusters_only_protein = average_clusters[1:,:-12] # there are 12 ligand residues, so discarded last 12\n", "min_values_by_clusters = np.amin(average_clusters_only_protein, axis=0) # Extract minimum energy values among all clusters\n", "indices_residues_energy_threshold = np.nonzero(min_values_by_clusters < -4) # Determine the indices of residues that are under the thershold value\n", "filtered_residues = np.asarray(residues)[indices_residues_energy_threshold] # Extract the residues that are under the thershold value\n", "\n", "## Now plot the energies (adapted from here: https://matplotlib.org/stable/gallery/lines_bars_and_markers/barchart.html#sphx-glr-gallery-lines-bars-and-markers-barchart-py)\n", "x = np.arange(len(filtered_residues)) # the label locations\n", "width = 0.1 # the width of the bars\n", "multiplier = 0\n", "\n", "fig = plt.figure(figsize=(12,6))\n", "ax = fig.add_subplot(111)\n", "ax.grid()\n", "\n", "for values in average_clusters[:,indices_residues_energy_threshold[0]]:\n", " offset = width * multiplier\n", " if multiplier == 0:\n", " label = 'All'\n", " else:\n", " label = f'C{multiplier}'\n", " rects = ax.bar(x + offset, values, width, label=label, zorder=10)\n", " multiplier += 1\n", "\n", "\n", "ax.set_ylabel('Energy (kJ/mol)')\n", "ax.tick_params(bottom=False, labelbottom=False, top=True, labeltop=True)\n", "ax.set_xticks(x + 0.25, filtered_residues, rotation=90)\n", "ax.legend(loc='lower right', ncols=6)\n", "plt.savefig('mmpbsa-residues-binding-energy.png', dpi=300)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "9f3020cd-69f3-4465-aa91-ce0e2070f360", "metadata": {}, "source": [ "#### 4. Extract and plot ligand residues contribution to the binding\n", "\n", "We have divided ligand as collection of virtual-residues. It means, their contribution towards binding energy is also calculated automatically during MM/PBSA calculation. Here, we extracts ligand residues contribution towards binding. This analysis highights the differences in interaction residue-wise between the clusters. This also highlights which part of the ligand is favorable or unfavorable for the binding. " ] }, { "cell_type": "code", "execution_count": 34, "id": "796b5aaa-2fbd-4a5f-bba2-82dbc50f91bb", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "average_clusters_only_ligand = average_clusters[:,-12:]\n", "filtered_residues = np.asarray(residues)[-12:]\n", "\n", "x = np.arange(len(filtered_residues)) # the label locations\n", "width = 0.1 # the width of the bars\n", "multiplier = 0\n", "\n", "fig = plt.figure(figsize=(12,6))\n", "ax = fig.add_subplot(111)\n", "ax.grid()\n", "\n", "for values in average_clusters_only_ligand:\n", " offset = width * multiplier\n", " if multiplier == 0:\n", " label = 'All'\n", " else:\n", " label = f'C{multiplier}'\n", " rects = ax.bar(x + offset, values, width, label=label)\n", " multiplier += 1\n", "\n", "ax.set_ylabel('Energy (kJ/mol)')\n", "ax.tick_params(bottom=False, labelbottom=False, top=True, labeltop=True)\n", "ax.set_xticks(x + 0.25, filtered_residues, rotation=90)\n", "ax.legend(loc='lower right', ncols=6)\n", "plt.savefig('mmpbsa-ligand-residues-binding-energy.png', dpi=300)\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.18" } }, "nbformat": 4, "nbformat_minor": 5 }