{
"cells": [
{
"cell_type": "markdown",
"id": "eb08fbc2-abe6-4a38-a54c-456911694d46",
"metadata": {},
"source": [
"## Conformational clustering of ligand with respect to receptor\n",
"\n",
"In this tutorial, we will perform and analyze conformational clustering of ligand with reference to the receptor using coordinate-based PCA. Here, receptor is a\n",
"G-Quadruplex DNA (G4DNA) and ligand is bound on top of the G4DNA. The oreintation of ligand is highly flexible and changes a lot during the simulations. Therefore,\n",
"clustering will enable the grouping of ligand conformations with reference to the G4DNA conformation.\n",
"\n",
"### Final Result\n",
"\n",
"\n",
"\n",
"### Instructions\n",
"* **Tutorial files**: The tutorial files can be downloaded from [here](https://figshare.com/ndownloader/files/55494827).\n",
"* **Extract the files**: `tar -zxvf ligand-pca.tar.gz`\n",
"* **Go to directory**: `cd ligand-pca`\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",
"\n",
"### Required Tools\n",
"* GROMACS\n",
"* gmx_clusterByFeatures\n",
"\n",
"### Steps\n",
"1. PCA of the ligand coordinates with reference to G4DNA\n",
"2. Calculate projections on first five PCs\n",
"3. Clustering using first five PCs as the features \n",
"4. Analysis\n",
"\n",
"\n",
"### 1. PCA of the ligand coordinates with reference to G4DNA\n",
"\n",
"We will use GROMACS tools `gmx covar` to perform the PCA. Here, we will perform PCA of ligand coordinates while G4DNA will be superimposed on reference structure.\n",
"Therefore, it will capture the motions and orientations of ligand with respect to the G4DNA.\n",
"\n",
"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",
"**Note:**\n",
"\n",
"1. **First index** group is Ligand without hydrogen atoms. Covariance matrix, eigenvectors and eigenvalues will be calculated for this group.\n",
"2. **Second index** group is of G4DNA's G-tetrads that are highly rigid during the simulations and serves here the reference for structure-fitting of the whole complex."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d8556dd6-87e8-44cc-8714-22d2fc9849ab",
"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/ligand-pca\n",
"Command line:\n",
" gmx covar -s inputs/input.tpr -f inputs/trajectory.xtc -n inputs/input.ndx\n",
"\n",
"Reading file inputs/input.tpr, VERSION 2016.5 (single precision)\n",
"Reading file inputs/input.tpr, VERSION 2016.5 (single precision)\n",
"Group 0 ( System) has 864 elements\n",
"Group 1 ( DNA) has 783 elements\n",
"Group 2 ( LIG) has 30 elements\n",
"Group 3 ( K) has 37 elements\n",
"Group 4 ( CL) has 14 elements\n",
"Group 5 ( Other) has 30 elements\n",
"Group 6 ( LIG) has 30 elements\n",
"Group 7 ( K) has 37 elements\n",
"Group 8 ( CL) has 14 elements\n",
"Group 9 ( Ion) has 51 elements\n",
"Group 10 ( LIG) has 30 elements\n",
"Group 11 ( K) has 37 elements\n",
"Group 12 ( CL) has 14 elements\n",
"Group 13 (r_4-6_r_8-9_r_13-15_r_17-20_r_23-24_&_!O1P_O2P_P_O5'_C5'_C3'_C2'_C1'_O4'_O3'_H*) has 168 elements\n",
"Group 14 ( LIG_&_!H*) has 21 elements\n",
"Group 15 (r_4_r_8_r_13_r_17_&_!O1P_O2P_P_O5'_C5'_C4'_C3'_C2'_C1'_O4'_O3'_H*_r_4_r_8_r_13_r_17_&_H1_H21_H22) has 56 elements\n",
"Select a group: Group 0 ( System) has 864 elements\n",
"Group 1 ( DNA) has 783 elements\n",
"Group 2 ( LIG) has 30 elements\n",
"Group 3 ( K) has 37 elements\n",
"Group 4 ( CL) has 14 elements\n",
"Group 5 ( Other) has 30 elements\n",
"Group 6 ( LIG) has 30 elements\n",
"Group 7 ( K) has 37 elements\n",
"Group 8 ( CL) has 14 elements\n",
"Group 9 ( Ion) has 51 elements\n",
"Group 10 ( LIG) has 30 elements\n",
"Group 11 ( K) has 37 elements\n",
"Group 12 ( CL) has 14 elements\n",
"Group 13 (r_4-6_r_8-9_r_13-15_r_17-20_r_23-24_&_!O1P_O2P_P_O5'_C5'_C3'_C2'_C1'_O4'_O3'_H*) has 168 elements\n",
"Group 14 ( LIG_&_!H*) has 21 elements\n",
"Group 15 (r_4_r_8_r_13_r_17_&_!O1P_O2P_P_O5'_C5'_C4'_C3'_C2'_C1'_O4'_O3'_H*_r_4_r_8_r_13_r_17_&_H1_H21_H22) has 56 elements\n",
"Select a group: Calculating the average structure ...\n",
"Reading frame 77000 time 770000.000 \n",
"Constructing covariance matrix (63x63) ...\n",
"Reading frame 77000 time 770000.000 \n",
"Read 77637 frames\n",
"\n",
"Trace of the covariance matrix: 4.52557 (nm^2)\n",
"\n",
"Diagonalizing ...\n",
"\n",
"Sum of the eigenvalues: 4.52557 (nm^2)\n",
"\n",
"Writing eigenvalues to eigenval.xvg\n",
"\n",
"Writing average structure & eigenvectors 1--63 to eigenvec.trr\n",
"Wrote the log to covar.log\n",
"\n",
"GROMACS reminds you: \"Heard a talk introducing a new language called Swift, from a guy named Wozniak, and it had nothing to do with Apple!\" (Adam Cadien)\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Choose a group for the least squares fit\n",
"Selected 13: 'r_4-6_r_8-9_r_13-15_r_17-20_r_23-24_&_!O1P_O2P_P_O5'_C5'_C3'_C2'_C1'_O4'_O3'_H*'\n",
"\n",
"Choose a group for the covariance analysis\n",
"Selected 14: 'LIG_&_!H*'\n"
]
}
],
"source": [
"%%bash\n",
"\n",
"echo 13 14 | gmx covar -s inputs/input.tpr -f inputs/trajectory.xtc -n inputs/input.ndx"
]
},
{
"cell_type": "markdown",
"id": "602ea3d2-22b5-49c7-9af2-ef9a4922380c",
"metadata": {},
"source": [
"### 2. Calculate projections on first five PCs\n",
"\n",
"We will use eigenvectors `eigenvec.trr` as input files to GROMACS tool `gmx anaeig` to calculate projection of first 5 eigenvectors on trajectory. These projections will be written into `proj.xvg` file by default."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "51998f1b-8521-4f30-8132-668c6d26de62",
"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/ligand-pca\n",
"Command line:\n",
" gmx anaeig -s inputs/input.tpr -f inputs/trajectory.xtc -n inputs/input.ndx -proj -first 1 -last 5\n",
"\n",
"trr version: GMX_trn_file (single precision)\n",
"Read non mass weighted average/minimum structure with 21 atoms from eigenvec.trr\n",
"Read 63 eigenvectors (for 21 atoms)\n",
"\n",
"Reading file inputs/input.tpr, VERSION 2016.5 (single precision)\n",
"Reading file inputs/input.tpr, VERSION 2016.5 (single precision)\n",
"Group 0 ( System) has 864 elements\n",
"Group 1 ( DNA) has 783 elements\n",
"Group 2 ( LIG) has 30 elements\n",
"Group 3 ( K) has 37 elements\n",
"Group 4 ( CL) has 14 elements\n",
"Group 5 ( Other) has 30 elements\n",
"Group 6 ( LIG) has 30 elements\n",
"Group 7 ( K) has 37 elements\n",
"Group 8 ( CL) has 14 elements\n",
"Group 9 ( Ion) has 51 elements\n",
"Group 10 ( LIG) has 30 elements\n",
"Group 11 ( K) has 37 elements\n",
"Group 12 ( CL) has 14 elements\n",
"Group 13 (r_4-6_r_8-9_r_13-15_r_17-20_r_23-24_&_!O1P_O2P_P_O5'_C5'_C3'_C2'_C1'_O4'_O3'_H*) has 168 elements\n",
"Group 14 ( LIG_&_!H*) has 21 elements\n",
"Group 15 (r_4_r_8_r_13_r_17_&_!O1P_O2P_P_O5'_C5'_C4'_C3'_C2'_C1'_O4'_O3'_H*_r_4_r_8_r_13_r_17_&_H1_H21_H22) has 56 elements\n",
"Select a group: Group 0 ( System) has 864 elements\n",
"Group 1 ( DNA) has 783 elements\n",
"Group 2 ( LIG) has 30 elements\n",
"Group 3 ( K) has 37 elements\n",
"Group 4 ( CL) has 14 elements\n",
"Group 5 ( Other) has 30 elements\n",
"Group 6 ( LIG) has 30 elements\n",
"Group 7 ( K) has 37 elements\n",
"Group 8 ( CL) has 14 elements\n",
"Group 9 ( Ion) has 51 elements\n",
"Group 10 ( LIG) has 30 elements\n",
"Group 11 ( K) has 37 elements\n",
"Group 12 ( CL) has 14 elements\n",
"Group 13 (r_4-6_r_8-9_r_13-15_r_17-20_r_23-24_&_!O1P_O2P_P_O5'_C5'_C3'_C2'_C1'_O4'_O3'_H*) has 168 elements\n",
"Group 14 ( LIG_&_!H*) has 21 elements\n",
"Group 15 (r_4_r_8_r_13_r_17_&_!O1P_O2P_P_O5'_C5'_C4'_C3'_C2'_C1'_O4'_O3'_H*_r_4_r_8_r_13_r_17_&_H1_H21_H22) has 56 elements\n",
"Select a group: 5 eigenvectors selected for output: 1 2 3 4 5\n",
"Reading frame 77000 time 770000.000 \n",
"\n",
"\n",
"GROMACS reminds you: \"If it's a good idea, go ahead and do it. It's much easier to apologize than it is to get permission.\" (Grace Hopper, developer of COBOL)\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Note: the structure in inputs/input.tpr should be the same\n",
" as the one used for the fit in gmx covar\n",
"\n",
"Select the index group that was used for the least squares fit in gmx covar\n",
"Selected 13: 'r_4-6_r_8-9_r_13-15_r_17-20_r_23-24_&_!O1P_O2P_P_O5'_C5'_C3'_C2'_C1'_O4'_O3'_H*'\n",
"\n",
"Select an index group of 21 elements that corresponds to the eigenvectors\n",
"Selected 14: 'LIG_&_!H*'\n",
"\n"
]
}
],
"source": [
"%%bash\n",
"\n",
"echo 13 14 | gmx anaeig -s inputs/input.tpr -f inputs/trajectory.xtc -n inputs/input.ndx -proj -first 1 -last 5"
]
},
{
"cell_type": "markdown",
"id": "2d8cd138-b25b-4d92-8948-599b5fb92ee7",
"metadata": {},
"source": [
"### 3. Clustering using first five PCs as features\n",
"\n",
"Now, we will perform clustering using [K-Means algorithm](https://en.wikipedia.org/wiki/K-means_clustering). One of the **drawback** of K-Means clustering is that the number of clusters should be known beforehand. To automate the decision about number of clusters, `gmx_clusterByFeatures` implements several [cluster metrics](https://gmx-clusterbyfeatures.readthedocs.io/en/latest/commands/cluster.html#cmetric-prior). We will use option `-cmetric ssr-sst` to use the [Elbow method](https://en.wikipedia.org/wiki/Elbow_method_(clustering).\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 10`: K-Means clustering will be performed for 1 upto 10 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.\n",
"* `-nfeature 5`: Take 5 feature from `feat proj.xvg` input file. Here it is projection of first 5 eigenvectors on the trajectory.\n",
"* `-sort features`: Sort the output clustered trajectory based on the distance in feature space from its central structure.\n",
"* `-fit2central`: Fit/Superimpose the conformation on cluster's central structure in output clustered trajectory.\n",
"* `-ssrchange 2`: Threshold (percentage) of change in SSR/SST ratio in Elbow method to automatically decide the number of clusters.\n",
"* `-cpdb clustered-trajs/central.pdb`: Dump central conformation of each cluster as a separate pdb file.\n",
"* `-fout clustered-trajs/cluster.xtc`: Dump conformations of each cluster as the separate trajectory file.\n",
"* `-plot pca_cluster.png`: Plot the feature-space (here, it is first 5 PCs from PCA) coloured by the clusters.\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 without hydrogen atoms.\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 G-tetrads of the G4DNA that are highly rigid. \n",
"\n",
"\n",
"**Note: 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.\n",
"\n",
"**Content of output `-g cluster.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.\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "daf68c23-cfcf-4f12-9f6c-440c448fc7c8",
"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/ligand-pca\n",
"Command line:\n",
" 'gmx_clusterByFeatures cluster' -s inputs/input.tpr -f inputs/trajectory.xtc -n inputs/input.ndx -feat proj.xvg -method kmeans -nfeature 5 -cmetric ssr-sst -ncluster 10 -fit2central -sort features -cpdb clustered-trajs/central.pdb -fout clustered-trajs/cluster.xtc -plot pca_cluster.png\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",
"\n",
"Back Off! I just backed up cluster.log to ./#cluster.log.1#\n",
"Reading file inputs/input.tpr, VERSION 2016.5 (single precision)\n",
"Reading file inputs/input.tpr, VERSION 2016.5 (single precision)\n",
"Group 0 ( System) has 864 elements\n",
"Group 1 ( DNA) has 783 elements\n",
"Group 2 ( LIG) has 30 elements\n",
"Group 3 ( K) has 37 elements\n",
"Group 4 ( CL) has 14 elements\n",
"Group 5 ( Other) has 30 elements\n",
"Group 6 ( LIG) has 30 elements\n",
"Group 7 ( K) has 37 elements\n",
"Group 8 ( CL) has 14 elements\n",
"Group 9 ( Ion) has 51 elements\n",
"Group 10 ( LIG) has 30 elements\n",
"Group 11 ( K) has 37 elements\n",
"Group 12 ( CL) has 14 elements\n",
"Group 13 (r_4-6_r_8-9_r_13-15_r_17-20_r_23-24_&_!O1P_O2P_P_O5'_C5'_C3'_C2'_C1'_O4'_O3'_H*) has 168 elements\n",
"Group 14 ( LIG_&_!H*) has 21 elements\n",
"Group 15 (r_4_r_8_r_13_r_17_&_!O1P_O2P_P_O5'_C5'_C4'_C3'_C2'_C1'_O4'_O3'_H*_r_4_r_8_r_13_r_17_&_H1_H21_H22) has 56 elements\n",
"Select a group: Group 0 ( System) has 864 elements\n",
"Group 1 ( DNA) has 783 elements\n",
"Group 2 ( LIG) has 30 elements\n",
"Group 3 ( K) has 37 elements\n",
"Group 4 ( CL) has 14 elements\n",
"Group 5 ( Other) has 30 elements\n",
"Group 6 ( LIG) has 30 elements\n",
"Group 7 ( K) has 37 elements\n",
"Group 8 ( CL) has 14 elements\n",
"Group 9 ( Ion) has 51 elements\n",
"Group 10 ( LIG) has 30 elements\n",
"Group 11 ( K) has 37 elements\n",
"Group 12 ( CL) has 14 elements\n",
"Group 13 (r_4-6_r_8-9_r_13-15_r_17-20_r_23-24_&_!O1P_O2P_P_O5'_C5'_C3'_C2'_C1'_O4'_O3'_H*) has 168 elements\n",
"Group 14 ( LIG_&_!H*) has 21 elements\n",
"Group 15 (r_4_r_8_r_13_r_17_&_!O1P_O2P_P_O5'_C5'_C4'_C3'_C2'_C1'_O4'_O3'_H*_r_4_r_8_r_13_r_17_&_H1_H21_H22) has 56 elements\n",
"Select a group: Group 0 ( System) has 864 elements\n",
"Group 1 ( DNA) has 783 elements\n",
"Group 2 ( LIG) has 30 elements\n",
"Group 3 ( K) has 37 elements\n",
"Group 4 ( CL) has 14 elements\n",
"Group 5 ( Other) has 30 elements\n",
"Group 6 ( LIG) has 30 elements\n",
"Group 7 ( K) has 37 elements\n",
"Group 8 ( CL) has 14 elements\n",
"Group 9 ( Ion) has 51 elements\n",
"Group 10 ( LIG) has 30 elements\n",
"Group 11 ( K) has 37 elements\n",
"Group 12 ( CL) has 14 elements\n",
"Group 13 (r_4-6_r_8-9_r_13-15_r_17-20_r_23-24_&_!O1P_O2P_P_O5'_C5'_C3'_C2'_C1'_O4'_O3'_H*) has 168 elements\n",
"Group 14 ( LIG_&_!H*) has 21 elements\n",
"Group 15 (r_4_r_8_r_13_r_17_&_!O1P_O2P_P_O5'_C5'_C4'_C3'_C2'_C1'_O4'_O3'_H*_r_4_r_8_r_13_r_17_&_H1_H21_H22) has 56 elements\n",
"Reading frame 4 time 40.000 "
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"=======================\n",
" Cluster Log output \n",
"=======================\n",
"\n",
"Command:\n",
"=======================\n",
"'gmx_clusterByFeatures cluster' -s inputs/input.tpr -f inputs/trajectory.xtc -n inputs/input.ndx -feat proj.xvg -method kmeans -nfeature 5 -cmetric ssr-sst -ncluster 10 -fit2central -sort features -cpdb clustered-trajs/central.pdb -fout clustered-trajs/cluster.xtc -plot pca_cluster.png\n",
"=======================\n",
"\n",
"Choose a group for the output:\n",
"Selected 0: 'System'\n",
"\n",
"Choose a group for clustering/RMSD calculation:\n",
"Selected 14: 'LIG_&_!H*'\n",
"\n",
"Choose a group for fitting or superposition:\n",
"Selected 13: 'r_4-6_r_8-9_r_13-15_r_17-20_r_23-24_&_!O1P_O2P_P_O5'_C5'_C3'_C2'_C1'_O4'_O3'_H*'\n",
"\n",
"\n",
" Input Trajectory dt = 10 ps\n",
"\n",
"\n",
"\n",
"###########################################\n",
"########## NUMBER OF CLUSTERS : 1 ########\n",
"###########################################\n",
"\n",
"===========================\n",
"Cluster-ID\tTotalFrames\n",
"1\t\t77637\n",
"===========================\n",
"\n",
"\n",
"\n",
"###########################################\n",
"########## NUMBER OF CLUSTERS : 2 ########\n",
"###########################################\n",
"\n",
"===========================\n",
"Cluster-ID\tTotalFrames\n",
"1\t\t45430\n",
"2\t\t32207\n",
"===========================\n",
"\n",
"\n",
"\n",
"###########################################\n",
"########## NUMBER OF CLUSTERS : 3 ########\n",
"###########################################\n",
"\n",
"===========================\n",
"Cluster-ID\tTotalFrames\n",
"1\t\t31277\n",
"2\t\t26396\n",
"3\t\t19964\n",
"===========================\n",
"\n",
"\n",
"\n",
"###########################################\n",
"########## NUMBER OF CLUSTERS : 4 ########\n",
"###########################################\n",
"\n",
"===========================\n",
"Cluster-ID\tTotalFrames\n",
"1\t\t27976\n",
"2\t\t19493\n",
"3\t\t15804\n",
"4\t\t14364\n",
"===========================\n",
"\n",
"\n",
"\n",
"###########################################\n",
"########## NUMBER OF CLUSTERS : 5 ########\n",
"###########################################\n",
"\n",
"===========================\n",
"Cluster-ID\tTotalFrames\n",
"1\t\t19076\n",
"2\t\t15840\n",
"3\t\t15812\n",
"4\t\t14309\n",
"5\t\t12600\n",
"===========================\n",
"\n",
"\n",
"\n",
"###########################################\n",
"########## NUMBER OF CLUSTERS : 6 ########\n",
"###########################################\n",
"\n",
"===========================\n",
"Cluster-ID\tTotalFrames\n",
"1\t\t15849\n",
"2\t\t15625\n",
"3\t\t14289\n",
"4\t\t12636\n",
"5\t\t12436\n",
"6\t\t6802\n",
"===========================\n",
"\n",
"\n",
"\n",
"###########################################\n",
"########## NUMBER OF CLUSTERS : 7 ########\n",
"###########################################\n",
"\n",
"===========================\n",
"Cluster-ID\tTotalFrames\n",
"1\t\t15300\n",
"2\t\t14284\n",
"3\t\t12634\n",
"4\t\t12045\n",
"5\t\t8607\n",
"6\t\t7989\n",
"7\t\t6778\n",
"===========================\n",
"\n",
"\n",
"\n",
"###########################################\n",
"########## NUMBER OF CLUSTERS : 8 ########\n",
"###########################################\n",
"\n",
"===========================\n",
"Cluster-ID\tTotalFrames\n",
"1\t\t14972\n",
"2\t\t14273\n",
"3\t\t11994\n",
"4\t\t11527\n",
"5\t\t8624\n",
"6\t\t7965\n",
"7\t\t6460\n",
"8\t\t1822\n",
"===========================\n",
"\n",
"\n",
"\n",
"###########################################\n",
"########## NUMBER OF CLUSTERS : 9 ########\n",
"###########################################\n",
"\n",
"===========================\n",
"Cluster-ID\tTotalFrames\n",
"1\t\t15253\n",
"2\t\t14261\n",
"3\t\t12611\n",
"4\t\t7429\n",
"5\t\t7126\n",
"6\t\t6915\n",
"7\t\t6636\n",
"8\t\t5418\n",
"9\t\t1988\n",
"===========================\n",
"\n",
"\n",
"\n",
"###########################################\n",
"########## NUMBER OF CLUSTERS : 10 ########\n",
"###########################################\n",
"\n",
"===========================\n",
"Cluster-ID\tTotalFrames\n",
"1\t\t14972\n",
"2\t\t11475\n",
"3\t\t10723\n",
"4\t\t8440\n",
"5\t\t7999\n",
"6\t\t6960\n",
"7\t\t6250\n",
"8\t\t5413\n",
"9\t\t3592\n",
"10\t\t1813\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 36.58 36.578 44774.656 0.378 1.214 \n",
"3 60.11 23.530 58487.125 0.453 0.911 \n",
"4 69.62 9.510 59295.070 0.475 0.870 \n",
"5 78.31 8.693 70071.805 0.504 0.789 \n",
"6 82.85 4.538 74995.758 0.519 0.744 \n",
"7 85.66 2.813 77291.609 0.531 0.783 \n",
"8 87.02 1.364 74378.094 0.546 0.793 \n",
"9 88.38 1.357 73812.672 0.533 0.760 \n",
"10 89.45 1.070 73136.641 0.511 0.823 \n",
"===========================================================================================================\n",
"\n",
"\n",
"#####################################\n",
"Final number of cluster selected: 7\n",
"#####################################\n",
"\n",
"Calculating central structure for cluster-7 ..."
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Reading frame 6 time 237500.000 :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 77000 time 242190.000 \n",
"GROMACS reminds you: \"XML is not a language in the sense of a programming language any more than sketches on a napkin are a language.\" (Charles Simonyi)\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"===========================================\n",
"Cluster-ID\tCentral Frame\tTotal Frames \n",
"1\t\t19604\t\t15300\n",
"2\t\t40230\t\t14284\n",
"3\t\t50481\t\t12634\n",
"4\t\t57523\t\t12045\n",
"5\t\t16039\t\t8607\n",
"6\t\t6092\t\t7989\n",
"7\t\t23750\t\t6778\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 c7 \n",
" 0.000 0.823 0.777 0.729 0.530 0.428 0.567 \n",
" 0.823 0.000 0.764 0.493 0.595 0.877 0.809 \n",
" 0.777 0.764 0.000 0.583 0.793 0.805 0.444 \n",
" 0.729 0.493 0.583 0.000 0.500 0.684 0.564 \n",
" 0.530 0.595 0.793 0.500 0.000 0.366 0.759 \n",
" 0.428 0.877 0.805 0.684 0.366 0.000 0.741 \n",
" 0.567 0.809 0.444 0.564 0.759 0.741 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",
"# create a new folder to contain clustered trajectory and pdb files\n",
"mkdir clustered-trajs\n",
"\n",
"echo 0 14 13 | gmx_clusterByFeatures cluster -s inputs/input.tpr -f inputs/trajectory.xtc -n inputs/input.ndx -feat proj.xvg \\\n",
" -method kmeans -nfeature 5 -cmetric ssr-sst -ncluster 10 -fit2central -sort features -cpdb clustered-trajs/central.pdb \\\n",
" -fout clustered-trajs/cluster.xtc -plot pca_cluster.png"
]
},
{
"cell_type": "markdown",
"id": "dc663b18-7cf2-4efd-8bcd-2c3e65005bb4",
"metadata": {},
"source": [
"### Analysis\n",
"Now, we will perform following analysis on obtained clusters:\n",
"\n",
"1. **Comparison of RMSDs within and between the clusters**: It will highlight the quality of clustering by measuring the difference in the clusters\n",
"2. **Plotting PC vs PC cluster-wise**. In fact, this is already plotted in the above obtained `pca_cluster.png` file. However, we will focus on first three PCs to demonstrate the distribution of conformation in PC space and also location of central structure in this feature-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": 8,
"id": "1db6550a-19c9-4cb1-8cbe-b7d5be3610b4",
"metadata": {},
"outputs": [],
"source": [
"import re\n",
"import sys\n",
"import numpy as np\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "005c2636-c1c4-4418-871a-fbb4473dfeb8",
"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": "5e740a27-e88e-44f7-a0d9-b45654914cad",
"metadata": {},
"source": [
"#### 1a. Calculation of RMSDs within and between the clusters\n",
"\n",
"At first, we need to calculate RMSDs of ligand within and between the clusters using `gmx rms` command as follows.\n",
"\n",
"**Note:** The G4DNA structure is already superimposed when separated cluster-trajectory were written in 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": 14,
"id": "9fdbbb75-2e96-4069-8aca-e04a07fac654",
"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 7`\n",
"do\n",
" for j in `seq 1 7`\n",
" do\n",
" echo 2 | gmx rms -f clustered-trajs/cluster_c${j}.xtc -s clustered-trajs/central_c${i}.pdb -o clustered-rmsd/c${i}_c${j} -nopbc -fit none\n",
" done\n",
"done"
]
},
{
"cell_type": "markdown",
"id": "168ec192-54de-4f50-abe4-5c5a9db4606c",
"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. "
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "d17fc6dd-2d25-47e5-b273-17b72d2a13d6",
"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=(12,12))\n",
"fig.subplots_adjust(top=0.98, bottom=0.05, hspace=0.25)\n",
"\n",
"for i in range(1,7):\n",
" \n",
" ax = fig.add_subplot(4, 2, i)\n",
" \n",
" for j in range(1,7):\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, 1.8)\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": "0fa00151-10c7-46fc-9476-1d2706f78839",
"metadata": {},
"source": [
"#### 2. 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": 22,
"id": "aec7546d-8af0-4b97-a675-445f44fe7e58",
"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 -o ligand-pca-PCs-vs-PCs.png"
]
},
{
"cell_type": "markdown",
"id": "fd8e7ca4-a624-4e69-8106-490915e858e3",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"id": "5b623bf7-d8d9-4d22-8d3e-e5679527cb43",
"metadata": {},
"source": [
"#### 3. Cluster-ID with time\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": 21,
"id": "cc1c31c1-a78b-4387-a837-3c23cfa22ee9",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Occurance has been calculated manually from the data obtained in cluster.log file\n",
"occur=[ 19.7, 18.4, 16.3, 15.5, 11.1, 10.3, 8.8]\n",
"\n",
"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.tick_params(axis='y', right=True)\n",
"\n",
"ax.set_ylabel('Cluster')\n",
"ax.set_xlabel('Time (ns)')\n",
"ax.set_xlim(-5, 820)\n",
"for i in range(7):\n",
" ax.text(840, i+1, '{0:3.1f}'.format(occur[i]), verticalalignment='center')\n",
"ax.text(960, 4.5, 'Occurence (%)', verticalalignment='center', rotation='vertical', horizontalalignment='center')\n",
"\n",
"plt.savefig('clid.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
}
![](\"https://raw.githubusercontent.com/rjdkmr/gmx_clusterByFeatures/master/docs/images/ligand-pca.png\")
\n",
"\n",
"### Instructions\n",
"* **Tutorial files**: The tutorial files can be downloaded from [here](https://figshare.com/ndownloader/files/55494827).\n",
"* **Extract the files**: `tar -zxvf ligand-pca.tar.gz`\n",
"* **Go to directory**: `cd ligand-pca`\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",
"\n",
"### Required Tools\n",
"* GROMACS\n",
"* gmx_clusterByFeatures\n",
"\n",
"### Steps\n",
"1. PCA of the ligand coordinates with reference to G4DNA\n",
"2. Calculate projections on first five PCs\n",
"3. Clustering using first five PCs as the features \n",
"4. Analysis\n",
"\n",
"\n",
"### 1. PCA of the ligand coordinates with reference to G4DNA\n",
"\n",
"We will use GROMACS tools `gmx covar` to perform the PCA. Here, we will perform PCA of ligand coordinates while G4DNA will be superimposed on reference structure.\n",
"Therefore, it will capture the motions and orientations of ligand with respect to the G4DNA.\n",
"\n",
"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",
"**Note:**\n",
"\n",
"1. **First index** group is Ligand without hydrogen atoms. Covariance matrix, eigenvectors and eigenvalues will be calculated for this group.\n",
"2. **Second index** group is of G4DNA's G-tetrads that are highly rigid during the simulations and serves here the reference for structure-fitting of the whole complex."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d8556dd6-87e8-44cc-8714-22d2fc9849ab",
"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/ligand-pca\n",
"Command line:\n",
" gmx covar -s inputs/input.tpr -f inputs/trajectory.xtc -n inputs/input.ndx\n",
"\n",
"Reading file inputs/input.tpr, VERSION 2016.5 (single precision)\n",
"Reading file inputs/input.tpr, VERSION 2016.5 (single precision)\n",
"Group 0 ( System) has 864 elements\n",
"Group 1 ( DNA) has 783 elements\n",
"Group 2 ( LIG) has 30 elements\n",
"Group 3 ( K) has 37 elements\n",
"Group 4 ( CL) has 14 elements\n",
"Group 5 ( Other) has 30 elements\n",
"Group 6 ( LIG) has 30 elements\n",
"Group 7 ( K) has 37 elements\n",
"Group 8 ( CL) has 14 elements\n",
"Group 9 ( Ion) has 51 elements\n",
"Group 10 ( LIG) has 30 elements\n",
"Group 11 ( K) has 37 elements\n",
"Group 12 ( CL) has 14 elements\n",
"Group 13 (r_4-6_r_8-9_r_13-15_r_17-20_r_23-24_&_!O1P_O2P_P_O5'_C5'_C3'_C2'_C1'_O4'_O3'_H*) has 168 elements\n",
"Group 14 ( LIG_&_!H*) has 21 elements\n",
"Group 15 (r_4_r_8_r_13_r_17_&_!O1P_O2P_P_O5'_C5'_C4'_C3'_C2'_C1'_O4'_O3'_H*_r_4_r_8_r_13_r_17_&_H1_H21_H22) has 56 elements\n",
"Select a group: Group 0 ( System) has 864 elements\n",
"Group 1 ( DNA) has 783 elements\n",
"Group 2 ( LIG) has 30 elements\n",
"Group 3 ( K) has 37 elements\n",
"Group 4 ( CL) has 14 elements\n",
"Group 5 ( Other) has 30 elements\n",
"Group 6 ( LIG) has 30 elements\n",
"Group 7 ( K) has 37 elements\n",
"Group 8 ( CL) has 14 elements\n",
"Group 9 ( Ion) has 51 elements\n",
"Group 10 ( LIG) has 30 elements\n",
"Group 11 ( K) has 37 elements\n",
"Group 12 ( CL) has 14 elements\n",
"Group 13 (r_4-6_r_8-9_r_13-15_r_17-20_r_23-24_&_!O1P_O2P_P_O5'_C5'_C3'_C2'_C1'_O4'_O3'_H*) has 168 elements\n",
"Group 14 ( LIG_&_!H*) has 21 elements\n",
"Group 15 (r_4_r_8_r_13_r_17_&_!O1P_O2P_P_O5'_C5'_C4'_C3'_C2'_C1'_O4'_O3'_H*_r_4_r_8_r_13_r_17_&_H1_H21_H22) has 56 elements\n",
"Select a group: Calculating the average structure ...\n",
"Reading frame 77000 time 770000.000 \n",
"Constructing covariance matrix (63x63) ...\n",
"Reading frame 77000 time 770000.000 \n",
"Read 77637 frames\n",
"\n",
"Trace of the covariance matrix: 4.52557 (nm^2)\n",
"\n",
"Diagonalizing ...\n",
"\n",
"Sum of the eigenvalues: 4.52557 (nm^2)\n",
"\n",
"Writing eigenvalues to eigenval.xvg\n",
"\n",
"Writing average structure & eigenvectors 1--63 to eigenvec.trr\n",
"Wrote the log to covar.log\n",
"\n",
"GROMACS reminds you: \"Heard a talk introducing a new language called Swift, from a guy named Wozniak, and it had nothing to do with Apple!\" (Adam Cadien)\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Choose a group for the least squares fit\n",
"Selected 13: 'r_4-6_r_8-9_r_13-15_r_17-20_r_23-24_&_!O1P_O2P_P_O5'_C5'_C3'_C2'_C1'_O4'_O3'_H*'\n",
"\n",
"Choose a group for the covariance analysis\n",
"Selected 14: 'LIG_&_!H*'\n"
]
}
],
"source": [
"%%bash\n",
"\n",
"echo 13 14 | gmx covar -s inputs/input.tpr -f inputs/trajectory.xtc -n inputs/input.ndx"
]
},
{
"cell_type": "markdown",
"id": "602ea3d2-22b5-49c7-9af2-ef9a4922380c",
"metadata": {},
"source": [
"### 2. Calculate projections on first five PCs\n",
"\n",
"We will use eigenvectors `eigenvec.trr` as input files to GROMACS tool `gmx anaeig` to calculate projection of first 5 eigenvectors on trajectory. These projections will be written into `proj.xvg` file by default."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "51998f1b-8521-4f30-8132-668c6d26de62",
"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/ligand-pca\n",
"Command line:\n",
" gmx anaeig -s inputs/input.tpr -f inputs/trajectory.xtc -n inputs/input.ndx -proj -first 1 -last 5\n",
"\n",
"trr version: GMX_trn_file (single precision)\n",
"Read non mass weighted average/minimum structure with 21 atoms from eigenvec.trr\n",
"Read 63 eigenvectors (for 21 atoms)\n",
"\n",
"Reading file inputs/input.tpr, VERSION 2016.5 (single precision)\n",
"Reading file inputs/input.tpr, VERSION 2016.5 (single precision)\n",
"Group 0 ( System) has 864 elements\n",
"Group 1 ( DNA) has 783 elements\n",
"Group 2 ( LIG) has 30 elements\n",
"Group 3 ( K) has 37 elements\n",
"Group 4 ( CL) has 14 elements\n",
"Group 5 ( Other) has 30 elements\n",
"Group 6 ( LIG) has 30 elements\n",
"Group 7 ( K) has 37 elements\n",
"Group 8 ( CL) has 14 elements\n",
"Group 9 ( Ion) has 51 elements\n",
"Group 10 ( LIG) has 30 elements\n",
"Group 11 ( K) has 37 elements\n",
"Group 12 ( CL) has 14 elements\n",
"Group 13 (r_4-6_r_8-9_r_13-15_r_17-20_r_23-24_&_!O1P_O2P_P_O5'_C5'_C3'_C2'_C1'_O4'_O3'_H*) has 168 elements\n",
"Group 14 ( LIG_&_!H*) has 21 elements\n",
"Group 15 (r_4_r_8_r_13_r_17_&_!O1P_O2P_P_O5'_C5'_C4'_C3'_C2'_C1'_O4'_O3'_H*_r_4_r_8_r_13_r_17_&_H1_H21_H22) has 56 elements\n",
"Select a group: Group 0 ( System) has 864 elements\n",
"Group 1 ( DNA) has 783 elements\n",
"Group 2 ( LIG) has 30 elements\n",
"Group 3 ( K) has 37 elements\n",
"Group 4 ( CL) has 14 elements\n",
"Group 5 ( Other) has 30 elements\n",
"Group 6 ( LIG) has 30 elements\n",
"Group 7 ( K) has 37 elements\n",
"Group 8 ( CL) has 14 elements\n",
"Group 9 ( Ion) has 51 elements\n",
"Group 10 ( LIG) has 30 elements\n",
"Group 11 ( K) has 37 elements\n",
"Group 12 ( CL) has 14 elements\n",
"Group 13 (r_4-6_r_8-9_r_13-15_r_17-20_r_23-24_&_!O1P_O2P_P_O5'_C5'_C3'_C2'_C1'_O4'_O3'_H*) has 168 elements\n",
"Group 14 ( LIG_&_!H*) has 21 elements\n",
"Group 15 (r_4_r_8_r_13_r_17_&_!O1P_O2P_P_O5'_C5'_C4'_C3'_C2'_C1'_O4'_O3'_H*_r_4_r_8_r_13_r_17_&_H1_H21_H22) has 56 elements\n",
"Select a group: 5 eigenvectors selected for output: 1 2 3 4 5\n",
"Reading frame 77000 time 770000.000 \n",
"\n",
"\n",
"GROMACS reminds you: \"If it's a good idea, go ahead and do it. It's much easier to apologize than it is to get permission.\" (Grace Hopper, developer of COBOL)\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Note: the structure in inputs/input.tpr should be the same\n",
" as the one used for the fit in gmx covar\n",
"\n",
"Select the index group that was used for the least squares fit in gmx covar\n",
"Selected 13: 'r_4-6_r_8-9_r_13-15_r_17-20_r_23-24_&_!O1P_O2P_P_O5'_C5'_C3'_C2'_C1'_O4'_O3'_H*'\n",
"\n",
"Select an index group of 21 elements that corresponds to the eigenvectors\n",
"Selected 14: 'LIG_&_!H*'\n",
"\n"
]
}
],
"source": [
"%%bash\n",
"\n",
"echo 13 14 | gmx anaeig -s inputs/input.tpr -f inputs/trajectory.xtc -n inputs/input.ndx -proj -first 1 -last 5"
]
},
{
"cell_type": "markdown",
"id": "2d8cd138-b25b-4d92-8948-599b5fb92ee7",
"metadata": {},
"source": [
"### 3. Clustering using first five PCs as features\n",
"\n",
"Now, we will perform clustering using [K-Means algorithm](https://en.wikipedia.org/wiki/K-means_clustering). One of the **drawback** of K-Means clustering is that the number of clusters should be known beforehand. To automate the decision about number of clusters, `gmx_clusterByFeatures` implements several [cluster metrics](https://gmx-clusterbyfeatures.readthedocs.io/en/latest/commands/cluster.html#cmetric-prior). We will use option `-cmetric ssr-sst` to use the [Elbow method](https://en.wikipedia.org/wiki/Elbow_method_(clustering).\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 10`: K-Means clustering will be performed for 1 upto 10 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.\n",
"* `-nfeature 5`: Take 5 feature from `feat proj.xvg` input file. Here it is projection of first 5 eigenvectors on the trajectory.\n",
"* `-sort features`: Sort the output clustered trajectory based on the distance in feature space from its central structure.\n",
"* `-fit2central`: Fit/Superimpose the conformation on cluster's central structure in output clustered trajectory.\n",
"* `-ssrchange 2`: Threshold (percentage) of change in SSR/SST ratio in Elbow method to automatically decide the number of clusters.\n",
"* `-cpdb clustered-trajs/central.pdb`: Dump central conformation of each cluster as a separate pdb file.\n",
"* `-fout clustered-trajs/cluster.xtc`: Dump conformations of each cluster as the separate trajectory file.\n",
"* `-plot pca_cluster.png`: Plot the feature-space (here, it is first 5 PCs from PCA) coloured by the clusters.\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 without hydrogen atoms.\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 G-tetrads of the G4DNA that are highly rigid. \n",
"\n",
"\n",
"**Note: 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.\n",
"\n",
"**Content of output `-g cluster.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.\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "daf68c23-cfcf-4f12-9f6c-440c448fc7c8",
"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/ligand-pca\n",
"Command line:\n",
" 'gmx_clusterByFeatures cluster' -s inputs/input.tpr -f inputs/trajectory.xtc -n inputs/input.ndx -feat proj.xvg -method kmeans -nfeature 5 -cmetric ssr-sst -ncluster 10 -fit2central -sort features -cpdb clustered-trajs/central.pdb -fout clustered-trajs/cluster.xtc -plot pca_cluster.png\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 ![](\"ligand-pca-PCs-vs-PCs.png\")
"
]
},
{
"cell_type": "markdown",
"id": "5b623bf7-d8d9-4d22-8d3e-e5679527cb43",
"metadata": {},
"source": [
"#### 3. Cluster-ID with time\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": 21,
"id": "cc1c31c1-a78b-4387-a837-3c23cfa22ee9",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"