{ "cells": [ { "attachments": {}, "cell_type": "markdown", "id": "cfcc1fd5-1999-4ae4-8d06-2dab7247c09c", "metadata": {}, "source": [ "## Clustering conformations using distances between atoms\n", "\n", "This tutorial showcase the example where calculated features such as any distances can be used to perform the conformational clustering. The advantages is that it can be used to filter-out parts of the trajectory based on the given features. Since, trajectories could be separated, further analysis can be perofrmed on filtered trajectory.\n", "\n", "### Instructions\n", "* **Tutorial files**: The tutorial files can be downloaded from [here](https://figshare.com/ndownloader/files/55459325).\n", "* **Extract the files**: `tar -zxvf distances.tar.gz`\n", "* **Go to directory**: `cd distances`\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", "### Required Tools\n", "* GROMACS\n", "* gmx_clusterByFeatures\n", "\n", "### Steps\n", "In this tutorial, conformations of G-Quadruplex DNA is clustered according to distances between three atom-pairs. These atom-pairs form hydrogen bonds during the simulations. However, only one atom-pair among them could form hydrogen bond at a time or neither of them ccould form hydrogen bond. The formation of hydrogen bonds are extremely fluctuating between these atom-pairs, and therefore, clustering will filter conformations based on these hydrogen bonds and distances between atom-pairs.\n", "1. Calculation of distances\n", "2. Preparation of feature input file\n", "3. Clustering using three distances\n", "4. Analysis\n", "\n", "### 1. Calculation of distances\n", "\n", "\n", "We will use the GROMACS command `gmx pairdist` ro calculate these three distances between these three atom-pairs as described in following." ] }, { "cell_type": "code", "execution_count": 1, "id": "a96ad629-370b-4d67-b77d-595f266ecbb9", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " :-) GROMACS - gmx pairdist, 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/distances\n", "Command line:\n", " gmx pairdist -s inputs/dna_ion.tpr -f inputs/dna_ion_combine.xtc -ref 'resid 1 and atomname N7' -sel 'resid 17 and atomname H62' -o r1N7-r17H62\n", "\n", "Reading file inputs/dna_ion.tpr, VERSION 2016.5 (single precision)\n", "Reading file inputs/dna_ion.tpr, VERSION 2016.5 (single precision)\n", "Reading frame 40000 time 800000.000 \n", "Analyzed 40639 frames, last time 812760.000\n", "\n", "GROMACS reminds you: \"Load Up Your Rubber Bullets\" (10 CC)\n", "\n", " :-) GROMACS - gmx pairdist, 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/distances\n", "Command line:\n", " gmx pairdist -s inputs/dna_ion.tpr -f inputs/dna_ion_combine.xtc -ref 'resid 1 and atomname H62' -sel 'resid 17 and atomname N3' -o r1H62-r17N3\n", "\n", "Reading file inputs/dna_ion.tpr, VERSION 2016.5 (single precision)\n", "Reading file inputs/dna_ion.tpr, VERSION 2016.5 (single precision)\n", "Reading frame 40000 time 800000.000 \n", "Analyzed 40639 frames, last time 812760.000\n", "\n", "GROMACS reminds you: \"A programmer's spouse says 'While you're at the grocery store, buy some eggs.' The programmer never comes back.\" (Anonymous)\n", "\n", " :-) GROMACS - gmx pairdist, 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/distances\n", "Command line:\n", " gmx pairdist -s inputs/dna_ion.tpr -f inputs/dna_ion_combine.xtc -ref 'resid 1 and atomname H61' -sel 'resid 17 and atomname N1' -o r1H61-r17N1\n", "\n", "Reading file inputs/dna_ion.tpr, VERSION 2016.5 (single precision)\n", "Reading file inputs/dna_ion.tpr, VERSION 2016.5 (single precision)\n", "Reading frame 40000 time 800000.000 \n", "Analyzed 40639 frames, last time 812760.000\n", "\n", "GROMACS reminds you: \"A scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die and a new generation grows up that is familiar with it.\" (Max Planck)\n", "\n" ] } ], "source": [ "%%bash\n", "\n", "gmx pairdist -s inputs/dna_ion.tpr -f inputs/dna_ion_combine.xtc -ref \"resid 1 and atomname N7\" -sel \"resid 17 and atomname H62\" -o r1N7-r17H62\n", "gmx pairdist -s inputs/dna_ion.tpr -f inputs/dna_ion_combine.xtc -ref \"resid 1 and atomname H62\" -sel \"resid 17 and atomname N3\" -o r1H62-r17N3\n", "gmx pairdist -s inputs/dna_ion.tpr -f inputs/dna_ion_combine.xtc -ref \"resid 1 and atomname H61\" -sel \"resid 17 and atomname N1\" -o r1H61-r17N1" ] }, { "cell_type": "markdown", "id": "27d38a9e-23bc-48c7-bb74-b7d17ad419f6", "metadata": {}, "source": [ "### 2. Preparation of feature input file\n", "For clustering, these distances are required to put in a single file as the three features. We perform following steps to create a single feature file compatible with the gmx_clusterByFeatures:\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "90dbce02-6498-4c9a-bb73-4fb1e1369970", "metadata": {}, "outputs": [], "source": [ "%%bash\n", "\n", "cat r1N7-r17H62.xvg > distances.xvg\n", "printf \"\\n& \\n\\n\" >> distances.xvg\n", "cat r1H62-r17N3.xvg >> distances.xvg\n", "printf \"\\n& \\n\\n\" >> distances.xvg\n", "cat r1H61-r17N1.xvg >> distances.xvg\n", "printf \"\\n& \\n\\n\" >> distances.xvg" ] }, { "cell_type": "markdown", "id": "2ed5bea3-9b42-4b51-8422-f152dc57022a", "metadata": {}, "source": [ "### 3. Clustering using three distances\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 firt 10 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 3`: There are three distances as the features for clustering.\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 central structure in output clustered trajectory.\n", "* `-ssrchange 2`: Threshold (percentage) of change in SSR/SST ratio in Elbow method to 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 will plot distance vs distance for all three combinations) 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 C-alpha atoms of protein. \n", " \n", "3. **Third index** group - Used for Superposition by least-square fitting. ONLY used in separate clustered trajectories to superimpose conformations on the central structure.\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "a1db275a-12e3-4556-a012-183ddbad7be5", "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/distances\n", "Command line:\n", " 'gmx_clusterByFeatures cluster' -s inputs/dna_ion.tpr -f inputs/dna_ion_combine.xtc -n inputs/dna_ion.ndx -feat distances.xvg -method kmeans -nfeature 3 -cmetric ssr-sst -ncluster 10 -fit2central -sort features -ssrchange 2 -cpdb clustered-trajs/central.pdb -fout clustered-trajs/cluster.xtc -plot features_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", "Reading file inputs/dna_ion.tpr, VERSION 2016.5 (single precision)\n", "Reading file inputs/dna_ion.tpr, VERSION 2016.5 (single precision)\n", "Group 0 ( System) has 774 elements\n", "Group 1 ( DNA) has 719 elements\n", "Group 2 ( K) has 38 elements\n", "Group 3 ( CL) has 17 elements\n", "Group 4 ( Ion) has 55 elements\n", "Group 5 ( K) has 38 elements\n", "Group 6 ( CL) has 17 elements\n", "Group 7 (r_2-4_r_6-7_r_9_r_11-13_r_18-20_&_!O1P_O2P_P_O5'_C5'_C4'_C3'_C2'_C1'_O4'_O3'_H*) has 132 elements\n", "Select a group: Group 0 ( System) has 774 elements\n", "Group 1 ( DNA) has 719 elements\n", "Group 2 ( K) has 38 elements\n", "Group 3 ( CL) has 17 elements\n", "Group 4 ( Ion) has 55 elements\n", "Group 5 ( K) has 38 elements\n", "Group 6 ( CL) has 17 elements\n", "Group 7 (r_2-4_r_6-7_r_9_r_11-13_r_18-20_&_!O1P_O2P_P_O5'_C5'_C4'_C3'_C2'_C1'_O4'_O3'_H*) has 132 elements\n", "Select a group: Group 0 ( System) has 774 elements\n", "Group 1 ( DNA) has 719 elements\n", "Group 2 ( K) has 38 elements\n", "Group 3 ( CL) has 17 elements\n", "Group 4 ( Ion) has 55 elements\n", "Group 5 ( K) has 38 elements\n", "Group 6 ( CL) has 17 elements\n", "Group 7 (r_2-4_r_6-7_r_9_r_11-13_r_18-20_&_!O1P_O2P_P_O5'_C5'_C4'_C3'_C2'_C1'_O4'_O3'_H*) has 132 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' -s inputs/dna_ion.tpr -f inputs/dna_ion_combine.xtc -n inputs/dna_ion.ndx -feat distances.xvg -method kmeans -nfeature 3 -cmetric ssr-sst -ncluster 10 -fit2central -sort features -ssrchange 2 -cpdb clustered-trajs/central.pdb -fout clustered-trajs/cluster.xtc -plot features_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 1: 'DNA'\n", "\n", "Choose a group for fitting or superposition:\n", "Selected 7: 'r_2-4_r_6-7_r_9_r_11-13_r_18-20_&_!O1P_O2P_P_O5'_C5'_C4'_C3'_C2'_C1'_O4'_O3'_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\t40639\n", "===========================\n", "\n", "\n", "\n", "###########################################\n", "########## NUMBER OF CLUSTERS : 2 ########\n", "###########################################\n", "\n", "===========================\n", "Cluster-ID\tTotalFrames\n", "1\t\t29163\n", "2\t\t11476\n", "===========================\n", "\n", "\n", "\n", "###########################################\n", "########## NUMBER OF CLUSTERS : 3 ########\n", "###########################################\n", "\n", "===========================\n", "Cluster-ID\tTotalFrames\n", "1\t\t21574\n", "2\t\t11844\n", "3\t\t7221\n", "===========================\n", "\n", "\n", "\n", "###########################################\n", "########## NUMBER OF CLUSTERS : 4 ########\n", "###########################################\n", "\n", "===========================\n", "Cluster-ID\tTotalFrames\n", "1\t\t15783\n", "2\t\t11509\n", "3\t\t8457\n", "4\t\t4890\n", "===========================\n", "\n", "\n", "\n", "###########################################\n", "########## NUMBER OF CLUSTERS : 5 ########\n", "###########################################\n", "\n", "===========================\n", "Cluster-ID\tTotalFrames\n", "1\t\t15640\n", "2\t\t8435\n", "3\t\t6343\n", "4\t\t5325\n", "5\t\t4896\n", "===========================\n", "\n", "\n", "\n", "###########################################\n", "########## NUMBER OF CLUSTERS : 6 ########\n", "###########################################\n", "\n", "===========================\n", "Cluster-ID\tTotalFrames\n", "1\t\t15632\n", "2\t\t6331\n", "3\t\t5360\n", "4\t\t4828\n", "5\t\t4465\n", "6\t\t4023\n", "===========================\n", "\n", "\n", "\n", "###########################################\n", "########## NUMBER OF CLUSTERS : 7 ########\n", "###########################################\n", "\n", "===========================\n", "Cluster-ID\tTotalFrames\n", "1\t\t15632\n", "2\t\t6241\n", "3\t\t4767\n", "4\t\t4766\n", "5\t\t4108\n", "6\t\t2830\n", "7\t\t2295\n", "===========================\n", "\n", "\n", "\n", "###########################################\n", "########## NUMBER OF CLUSTERS : 8 ########\n", "###########################################\n", "\n", "===========================\n", "Cluster-ID\tTotalFrames\n", "1\t\t15631\n", "2\t\t6130\n", "3\t\t4687\n", "4\t\t4073\n", "5\t\t2854\n", "6\t\t2703\n", "7\t\t2428\n", "8\t\t2133\n", "===========================\n", "\n", "\n", "\n", "###########################################\n", "########## NUMBER OF CLUSTERS : 9 ########\n", "###########################################\n", "\n", "===========================\n", "Cluster-ID\tTotalFrames\n", "1\t\t15630\n", "2\t\t6121\n", "3\t\t4680\n", "4\t\t3901\n", "5\t\t2873\n", "6\t\t2293\n", "7\t\t1996\n", "8\t\t1876\n", "9\t\t1269\n", "===========================\n", "\n", "\n", "\n", "###########################################\n", "########## NUMBER OF CLUSTERS : 10 ########\n", "###########################################\n", "\n", "===========================\n", "Cluster-ID\tTotalFrames\n", "1\t\t15633\n", "2\t\t5792\n", "3\t\t4203\n", "4\t\t3343\n", "5\t\t2877\n", "6\t\t2116\n", "7\t\t1950\n", "8\t\t1857\n", "9\t\t1613\n", "10\t\t1255\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 65.54 65.541 77290.039 0.611 0.628 \n", "3 79.25 13.707 77587.961 0.565 0.781 \n", "4 90.08 10.832 122991.211 0.661 0.550 \n", "5 93.78 3.699 153133.891 0.713 0.430 \n", "6 95.60 1.826 176765.344 0.686 0.520 \n", "7 96.87 1.263 209391.516 0.687 0.534 \n", "8 97.35 0.485 213448.906 0.675 0.611 \n", "9 97.68 0.330 214039.922 0.674 0.621 \n", "10 97.98 0.299 219102.672 0.670 0.680 \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 787380.000 :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", "Reading frame 40000 time 712940.000 \n", "GROMACS reminds you: \"You Fill Your Space So Sweet\" (F. Apple)\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "===========================================\n", "Cluster-ID\tCentral Frame\tTotal Frames \n", "1\t\t27707\t\t15640\n", "2\t\t19707\t\t8435\n", "3\t\t2166\t\t6343\n", "4\t\t24630\t\t5325\n", "5\t\t39369\t\t4896\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 0.522 0.192 0.266 0.401 \n", " 0.522 0.000 0.562 0.453 0.408 \n", " 0.192 0.562 0.000 0.271 0.418 \n", " 0.266 0.453 0.271 0.000 0.385 \n", " 0.401 0.408 0.418 0.385 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 1 7 | gmx_clusterByFeatures cluster -s inputs/dna_ion.tpr -f inputs/dna_ion_combine.xtc -n inputs/dna_ion.ndx -feat distances.xvg \\\n", " -method kmeans -nfeature 3 -cmetric ssr-sst -ncluster 10 -fit2central \\\n", " -sort features -ssrchange 2 -cpdb clustered-trajs/central.pdb \\\n", " -fout clustered-trajs/cluster.xtc -plot features_cluster.png \\" ] }, { "cell_type": "markdown", "id": "ca8edee8-8245-47f7-8ebf-d4012622dc7e", "metadata": {}, "source": [ "#### Number of clusters\n", "As can be seen above in the Cluster Metrics Summary, both **Silhouette** and **Davies-Bouldin** score is best when 5 clusters are generated. Using Elbow method also, same number of clusters have been automatically decided. Therefore, for this purpose, total number of clusters should be 5 according to the cluster-metrics.\n", "\n", "\n", "### 4. Analysis\n", "\n", "We will perform following analyses on how the above clustering is able to separate conformations based on these three distances.\n", "1. **Mapping of cluster with the distances as a function of time**\n", "2. **Distribution of distance cluster-wise**\n", "\n", "**Setup for analysis:** At first, we defined few Python functions to read the file in Python." ] }, { "cell_type": "code", "execution_count": 4, "id": "3ce6b283-34df-4d8b-a5f6-be10b1bb0c19", "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", "\n", "def read_clid(filename):\n", " ''' Read the clid.xvg file\n", "\n", " It returns the dictionary listing all the frame indices belonging to that given cluster.\n", "\n", " Returns\n", " -------\n", " Dictionary of clid to index\n", " '''\n", "\n", " fin = open(filename, 'r')\n", "\n", " data = dict()\n", " index = 0\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", "\n", " clid = int(temp[1])\n", "\n", " if clid not in data:\n", " data[clid] = [ index ]\n", " else:\n", " data[clid] = data[clid] + [ index ]\n", "\n", " index = index + 1\n", "\n", " return data\n", "\n", "\n", "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": "a25c42a9-849d-476a-9908-7ff44d3521ce", "metadata": {}, "source": [ "#### 1. Mapping of cluster with the distances as a function of time\n", "Here, we will highlight the distances according to the clusters as a function of time.\n" ] }, { "cell_type": "code", "execution_count": 17, "id": "76957575-9ab1-41f4-968b-fc91c14af4c6", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dist_files=[\"r1N7-r17H62\", \"r1H62-r17N3\", \"r1H61-r17N1\" ]\n", "labels = [\"A1.N7-A17.H62\", \"A1.H62-A17.N3\", \"A1.H61-A17.N1\" ]\n", "clid_index = read_clid(\"clid.xvg\")\n", "clid = list(sorted(clid_index.keys()))\n", "\n", "fig = plt.figure()\n", "fig.subplots_adjust(hspace=0)\n", "axes=[]\n", "\n", "for i in range(len(dist_files)):\n", " data = read_xvg(dist_files[i]+'.xvg')\n", " data[0] = data[0]/1000\n", " \n", " if i == 0:\n", " ax = fig.add_subplot(3,1,i+1)\n", " else:\n", " ax = fig.add_subplot(3,1,i+1, sharex=axes[0])\n", " ax.tick_params(axis='x', bottom=True, top=True, direction='inout', length=10)\n", " ax.set_ylabel(labels[i])\n", " axes.append(ax)\n", " \n", " plot_data = []\n", " time = []\n", " for ix in clid:\n", " plot_data.append(data[1][clid_index[ix]])\n", " time.append(data[0][clid_index[ix]])\n", " ax.scatter(time, plot_data, label='cluster-{0}'.format(ix), s=0.2)\n", " plot_data = []\n", " time = []\n", " \n", " if i==0:\n", " handles, legend_labels = ax.get_legend_handles_labels()\n", "\n", "\n", "\n", "axes[2].set_xlabel('Time (ns)')\n", "fig.text(0.03, 0.4,'Distance (nm)', rotation='vertical')\n", "\n", "fig.legend(handles, legend_labels, ncol=5, loc='upper center',scatterpoints=4,markerscale=8, handletextpad=0.1, \n", " columnspacing=1.4, handlelength=2, borderaxespad=1.2)\n", "\n", "\n", "plt.savefig('distance_cluster.png', dpi=300)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "7332f505-a4e9-4c95-a466-bd7d2698eaa9", "metadata": {}, "source": [ "#### 2. Distribution of distance cluster-wise\n", "\n", "We will calculate and plot distribution of all three distances with respect to each cluster. It will demonstrate that which distance is filtered out in which cluster more easily." ] }, { "cell_type": "code", "execution_count": 23, "id": "bb65ebf4-86ce-4c5a-a267-5d73d025fdfa", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "fig.subplots_adjust(hspace=0)\n", "axes=[]\n", "\n", "for i in range(len(dist_files)):\n", " clid = list(sorted(clid_index.keys()))\n", " data = read_xvg(dist_files[i]+'.xvg')\n", "\n", " if i == 0:\n", " ax = fig.add_subplot(3,1,i+1)\n", " else:\n", " ax = fig.add_subplot(3,1,i+1, sharex=axes[0])\n", " ax.tick_params(axis='x', bottom=True, top=True, direction='inout', length=7)\n", " ax.set_ylabel(labels[i])\n", " axes.append(ax)\n", "\n", "\n", " plot_data = []\n", " plot_data.append(data[1])\n", " for ix in clid:\n", " plot_data.append(data[1][clid_index[ix]])\n", "\n", " clid = [0] + clid\n", " ax.violinplot(plot_data, positions=clid, showmedians = True, showextrema=False, points=500)\n", "\n", " xticks = []\n", " for c in clid:\n", " if c == 0:\n", " xticks.append('Full')\n", " else:\n", " xticks.append('Cluster-{0}'.format(c))\n", " ax.set_xticks([ cid for cid in clid])\n", " ax.set_xticklabels(xticks)\n", "\n", "fig.text(0.03, 0.4,'Distance (nm)', rotation='vertical')\n", "plt.savefig(\"distance_distribution.png\", dpi=300)" ] }, { "cell_type": "markdown", "id": "2e99ed16-3143-484f-8f93-36669da60e7e", "metadata": {}, "source": [ "**Above distance distributions clearly show:**\n", "* **Cluster-1** : Hydrogen bond between A1.N7 and A17.H62\n", "* **Cluster-2** : All three distances between 0.5 to 1.5 nm\n", "* **Cluster-3** : Hydrogen bond between A1.H61 and A17.N1\n", "* **Cluster-4** : Hydrogen bond between A1.H62 and A17.N3\n", "* **Cluster-5** : All distances above 1.5 nm" ] } ], "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 }