{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# Conformational analysis of CXCL12\n", "by Gerard Martinez\n", "\n", "Download all the required files for the tutorial from this [link](http://pub.htmd.org/confana1036hbl2450olw/filtered.tar.gz)\n", "\n", "(warning: 2.6 Gb)\n", "\n", "You can watch the presentation [here](https://youtu.be/I9VISC29Gc4)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "skip" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n", "\n", "Please cite HTMD: Doerr et al.(2016)JCTC,12,1845. https://dx.doi.org/10.1021/acs.jctc.6b00049\n", "\n", "HTMD Documentation at: https://www.htmd.org/docs/latest/\n", "\n", "You are on the latest HTMD version (1.7.15).\n", "\n" ] } ], "source": [ "%pylab inline\n", "from htmd.ui import *\n", "config(viewer='ngl')\n", "os.chdir('/webdata/confana1036hbl2450olw/') # Skip this command." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true, "slideshow": { "slide_type": "slide" } }, "source": [ "## 1. Introduction\n", "\n", "### CXCL12 is a chemokine involved in...\n", "* many types of cancer \n", "* inflammatory diseases \n", "* early development events\n", "\n", "### We performed...\n", "* ~300 simulations x 200ns each (~60 microseconds in total)\n", "* we filtered out the water from our trajectories \n", "\n", "![](http://pub.htmd.org/confana1036hbl2450olw/system-protein2.png)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true, "slideshow": { "slide_type": "slide" } }, "source": [ "## 2. Sampling major conformational states\n", "\n", "In our lab we have tried many different metrics to assess the overall conformational changes of a protein. From them all, phi and psi angles of the protein backbone (dihedrals) have been the most successful descriptors in \"blindly\" capturing the major protein conformations.\n", "\n", "In this section we will project our trajectories on the backbone dihedrals and, we will reduce the dimensionality by using tICA and then we will build a Markov Model to asses the major protein conformations in equilibrium. \n", "\n", "![](http://pub.htmd.org/confana1036hbl2450olw/conformations.png)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "## Calculate metrics: protein backbone dihedrals" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating simlist: 100% (289/289) [#################################] eta 00:00 -\n" ] } ], "source": [ "fsims = simlist(glob('./filtered/*/'), './filtered/filtered.pdb')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "CXCL12 has a very flexible C-terminus loop as well as a transiently disorderable N-terminal alfa helix. In this study we are not interested in them but in the core of the chemokine. For this reason, we will select residues from 10 to 54. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/joao/miniconda3/lib/python3.5/site-packages/ipykernel/__main__.py:2: DeprecationWarning: The `projection` function/method has been deprecated since version 1.3.2. Use `htmd.projections.metric.Metric.set` instead.\n", " from ipykernel import kernelapp as app\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Projecting trajectories: 100% (289/289) [##########################] eta 00:00 \\\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-04-05 16:06:50,259 - htmd.projections.metric - WARNING - Multiple framesteps [0.0, 0.1] ns were read from the simulations. Taking the statistical mode: 0.1ns. If it looks wrong, you can modify it by manually setting the MetricData.fstep property.\n" ] } ], "source": [ "metr = Metric(fsims)\n", "metr.projection(MetricDihedral(protsel='protein and resid 10 to 54', sincos=True))\n", "data = metr.project()\n", "data.fstep = 0.1" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data.plotTrajSizes()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "## Dimensionality reduction" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "getting output of TICA: 100% (577/577) [###########################] eta 00:01 -" ] } ], "source": [ "tica = TICA(data, 20)\n", "dataTica = tica.project(3)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "## Clustering" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true, "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-04-05 16:07:23,029 - htmd.metricdata - INFO - Mergesmall removed 0 clusters. Original ncluster 200, new ncluster 200.\n" ] } ], "source": [ "dataTica.cluster(MiniBatchKMeans(n_clusters=200), mergesmall=5)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "## MSM analysis and visualization" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "estimating MaximumLikelihoodMSM: 100% (20/20) [####################] eta 00:00 \\" ] }, { "data": { "image/png": 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nFNGh/CEePvQwjxx+hIcPPcyjhx9lymsJz9rM2g5haPhrMmtOLcUBShQOAgdm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\nYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model = Model(dataTica)\n", "model.plotTimescales(lags=list(range(1,1000,50)))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-04-05 16:09:18,533 - htmd.model - INFO - 99.7% of the data was used\n", "2017-04-05 16:09:18,620 - htmd.model - INFO - Number of trajectories that visited each macrostate:\n", "2017-04-05 16:09:18,622 - htmd.model - INFO - [ 64 45 2 31 289 16 15 10]\n", "2017-04-05 16:09:18,624 - htmd.model - INFO - Take care! Macro 2 has been visited only in 2 trajectories:\n", "2017-04-05 16:09:18,625 - htmd.model - INFO - \n", "simid = 232\n", "parent = None\n", "input = []\n", "trajectory = ['./filtered/9x9/9x9-GERARD_VERYLONG_CXCL12_confAna-0-1-RND2283_9.filtered.xtc']\n", "molfile = ./filtered/filtered.pdb\n", "numframes = [2000]\n", "\n", "2017-04-05 16:09:18,634 - htmd.model - INFO - \n", "simid = 265\n", "parent = None\n", "input = []\n", "trajectory = ['./filtered/10x11/10x11-GERARD_VERYLONG_CXCL12_confAna-0-1-RND4691_9.filtered.xtc']\n", "molfile = ./filtered/filtered.pdb\n", "numframes = [2000]\n", "\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[ 0.0053318 0.0216567 0.02094427 0.0179421 0.14541308 0.25231467\n", " 0.20907099 0.3273264 ]\n" ] } ], "source": [ "model.markovModel(600, 8)\n", "eqDist = model.eqDistribution()\n", "print(eqDist)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Getting state Molecules: 100% (8/8) [##############################] eta 00:01 |\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "The installed widget Javascript is the wrong version.\n", "The installed widget Javascript is the wrong version.\n" ] } ], "source": [ "#we can now visualize representatives for each of the equilibrium species \n", "model.numsamples=1\n", "model.viewStates(protein=True)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "## Statistics\n", "### What are the major differences between the states X and Y?" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " atomIndexes description \\\n", "23 [253, 255, 262, 264] Cosine of angle of (SER 16 N A A) (SER 16 CA A... \n", "28 [262, 264, 266, 279] Sine of angle of (SER 16 C A A) (HSD 17 N A A)... \n", "86 [517, 521, 529, 531] Sine of angle of (PRO 32 N A A) (PRO 32 CA A A... \n", "87 [517, 521, 529, 531] Cosine of angle of (PRO 32 N A A) (PRO 32 CA A... \n", "92 [529, 531, 533, 543] Sine of angle of (PRO 32 C A A) (ASN 33 N A A)... \n", "134 [711, 713, 723, 725] Sine of angle of (ASN 44 N A A) (ASN 44 CA A A... \n", "135 [711, 713, 723, 725] Cosine of angle of (ASN 44 N A A) (ASN 44 CA A... \n", "139 [725, 727, 737, 739] Cosine of angle of (ASN 45 N A A) (ASN 45 CA A... \n", "140 [723, 725, 727, 737] Sine of angle of (ASN 44 C A A) (ASN 45 N A A)... \n", "141 [723, 725, 727, 737] Cosine of angle of (ASN 44 C A A) (ASN 45 N A ... \n", "144 [737, 739, 741, 751] Sine of angle of (ASN 45 C A A) (ASN 46 N A A)... \n", "\n", " type \n", "23 dihedral \n", "28 dihedral \n", "86 dihedral \n", "87 dihedral \n", "92 dihedral \n", "134 dihedral \n", "135 dihedral \n", "139 dihedral \n", "140 dihedral \n", "141 dihedral \n", "144 dihedral \n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYElqlAE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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "means = getStateStatistic(model, data, range(model.macronum))\n", "plt.figure()\n", "plt.bar(range(len(means[0])), means[7] - means[6])\n", "idx = np.where(np.abs(means[7] - means[6]) > 0.6)[0]\n", "print(data.map.ix[idx])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "The installed widget Javascript is the wrong version.\n" ] } ], "source": [ "# we can visualize which residues are different between states\n", "filtered = Molecule('./filtered/filtered.pdb')\n", "filtered.view(sel='protein',style='NewCartoon',hold=True)\n", "filtered.view(sel='resid 16 17 24 25 32 33 44 45',style='Licorice')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "## Mapping back\n", "### Which trajectory originated the state X?" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "(array([ 4, 8, 50]),)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.where(model.macro_ofmicro == 6)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[array([[ 132, 986],\n", " [ 247, 670],\n", " [ 35, 1354],\n", " [ 132, 1767],\n", " [ 132, 1412],\n", " [ 132, 1943],\n", " [ 133, 1895],\n", " [ 35, 1322],\n", " [ 249, 953],\n", " [ 247, 1568]])]\n" ] } ], "source": [ "_,rel = model.sampleStates(10, 10, statetype='micro')\n", "print(rel)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "simid = 232\n", "parent = None\n", "input = []\n", "trajectory = ['./filtered/9x9/9x9-GERARD_VERYLONG_CXCL12_confAna-0-1-RND2283_9.filtered.xtc']\n", "molfile = ./filtered/filtered.pdb\n", "numframes = [2000]\n", "\n" ] } ], "source": [ "print(model.data.simlist[232])" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true, "slideshow": { "slide_type": "slide" } }, "source": [ "## 3. Studying a defined reaction coordinate\n", "\n", "Revising the literature related to CXCL12, we find a paper published by Andrea Bernini et al. (2014) where they describe the opening of a pocket in CXCL12 located between the 2nd and 3rd beta sheet (see pictures attached). To try to capture this phenomenon in our simulations, we will project our trajectories along the 2nd and 3rd beta-sheet distance. \n", "\n", "![](http://pub.htmd.org/confana1036hbl2450olw/openclose_struc.jpg)\n", "![](http://pub.htmd.org/confana1036hbl2450olw/openclose_asa.png)\n", "\n", "*Figures extracted from \"Searching for protein binding sites from Molecular Dynamics simulations and paramagnetic fragment-based NMR studies\", Andrea bernini et al., 2014 Mar;1844(3):561-6. doi: 10.1016/j.bbapap.2013.12.012. Epub 2013 Dec 27*" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/joao/miniconda3/lib/python3.5/site-packages/ipykernel/__main__.py:5: DeprecationWarning: The `projection` function/method has been deprecated since version 1.3.2. Use `htmd.projections.metric.Metric.set` instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Projecting trajectories: 100% (289/289) [##########################] eta 00:01 /\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-04-05 16:10:15,167 - htmd.projections.metric - WARNING - Multiple framesteps [0.0, 0.1] ns were read from the simulations. Taking the statistical mode: 0.1ns. If it looks wrong, you can modify it by manually setting the MetricData.fstep property.\n" ] } ], "source": [ "# The first selection corresponds to beta-sheet 2 carbons alpha, the second one to beta-sheet 3 CA.\n", "# We specify metric='contacts' to create contact maps instead of proper distances,\n", "# this means: create an interatom matrix and put 1 if the distance is below cutoff; 0 otherwise. \n", "metr = Metric(fsims)\n", "metr.projection(MetricDistance('resid 38 to 42 and noh', 'resid 22 to 28 and noh', metric='contacts'))\n", "data3 = metr.project()\n", "data3.fstep = 0.1" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "getting output of TICA: 100% (577/577) [###########################] eta 00:01 /" ] } ], "source": [ "# tICA projection (dimensionality reduction along the slow process)\n", "tica3 = TICA(data3, 20)\n", "dataTica3 = tica3.project(3)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-04-05 16:16:49,252 - htmd.metricdata - INFO - Mergesmall removed 0 clusters. Original ncluster 200, new ncluster 200.\n" ] } ], "source": [ "# Clustering\n", "dataTica3.cluster(MiniBatchKMeans(n_clusters=200), mergesmall=5)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "estimating MaximumLikelihoodMSM: 100% (20/20) [####################] eta 00:01 -" ] }, { "data": { "image/png": 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OFgcNKxqwt9hxtDjKZquzIbSFedhbUsIxG+67T23ffffdwHcfh2x2Sbip4tk4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\nEakXkU8ZY/zAF4G1wF7gSWPMnlP+EH8vvup+CMYdXfQH0N/fT21DKYh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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot timescales\n", "model3 = Model(dataTica3)\n", "model3.plotTimescales(lags=list(range(1,1000,50)))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-04-05 16:17:04,181 - htmd.model - INFO - 100.0% of the data was used\n", "2017-04-05 16:17:04,208 - htmd.model - INFO - Number of trajectories that visited each macrostate:\n", "2017-04-05 16:17:04,210 - htmd.model - INFO - [ 4 3 19 289]\n", "2017-04-05 16:17:04,212 - htmd.model - INFO - Take care! Macro 1 has been visited only in 3 trajectories:\n", "2017-04-05 16:17:04,214 - htmd.model - INFO - \n", "simid = 59\n", "parent = None\n", "input = []\n", "trajectory = ['./filtered/3x9/3x9-GERARD_VERYLONG_CXCL12_confAna-0-1-RND1251_9.filtered.xtc']\n", "molfile = ./filtered/filtered.pdb\n", "numframes = [2000]\n", "\n", "2017-04-05 16:17:04,216 - htmd.model - INFO - \n", "simid = 277\n", "parent = None\n", "input = []\n", "trajectory = ['./filtered/10x23/10x23-GERARD_VERYLONG_CXCL12_confAna-0-1-RND9861_9.filtered.xtc']\n", "molfile = ./filtered/filtered.pdb\n", "numframes = [2000]\n", "\n", "2017-04-05 16:17:04,219 - htmd.model - INFO - \n", "simid = 280\n", "parent = None\n", "input = []\n", "trajectory = ['./filtered/10x27/10x27-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0101_9.filtered.xtc']\n", "molfile = ./filtered/filtered.pdb\n", "numframes = [2000]\n", "\n" ] }, { "data": { "image/png": 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SWoaCJKllKEiSWoaCJKllKEiSWoaCJKllKEiSWoaCJKl18NAFSAeSEy/8+NAlDOrLF581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\n/w/T8V43jqDrcwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[ 0.0113379 0.02560474 0.05651182 0.90654554]\n" ] } ], "source": [ "# Make Markov Model. we want to pick a lagtime where the timescales are converged (timescale is flat).\n", "# 600 is the lagtime we want to use (600 frames is equivalent to 60ns). 4 is the number of macrostates.\n", "model3.markovModel(600, 4)\n", "eqDist = model3.eqDistribution()\n", "print(eqDist)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Getting state Molecules: 100% (4/4) [##############################] eta 00:00 -\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "The installed widget Javascript is the wrong version.\n", "The installed widget Javascript is the wrong version.\n" ] } ], "source": [ "# Visualize states\n", "model3.viewStates(protein=True, numsamples=1)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "![](http://pub.htmd.org/confana1036hbl2450olw/conformation_open.png)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Did you see any macrostate where the pocket is open? what is the equilibrium population probability? Let's try to find the trajectory that produced the state..." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "(array([4]),)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Map back the trajectory/ies that originated the macro. Substitute 1 for the macro that showed the pocket opening.\n", "# This function is giving you the microclusters that are inside a given macrocluster\n", "np.where(model3.macro_ofmicro ==1)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[array([[ 225, 939],\n", " [ 184, 1082],\n", " [ 233, 1927],\n", " [ 206, 1849],\n", " [ 227, 983]])]\n" ] } ], "source": [ "# substitute 48 for the micro number from the previous step\n", "# This function gives you trajectory-frame pairs that visited a given micro\n", "_, rel = model3.sampleStates(48, 5, statetype='micro')\n", "print(rel)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "simid = 277\n", "parent = None\n", "input = []\n", "trajectory = ['./filtered/10x23/10x23-GERARD_VERYLONG_CXCL12_confAna-0-1-RND9861_9.filtered.xtc']\n", "molfile = ./filtered/filtered.pdb\n", "numframes = [2000]\n", "\n" ] } ], "source": [ "print(model3.data.simlist[277])" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating simlist: 100% (1/1) [#####################################] eta --:-- |\n" ] } ], "source": [ "# Calculate RMSD of the site of interest for a selected trajectory\n", "simus = simlist(glob('./filtered/10x23/'), './filtered/filtered.pdb')" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/joao/miniconda3/lib/python3.5/site-packages/ipykernel/__main__.py:3: DeprecationWarning: The `projection` function/method has been deprecated since version 1.3.2. Use `htmd.projections.metric.Metric.set` instead.\n", " app.launch_new_instance()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Projecting trajectories: 100% (1/1) [##############################] eta --:-- -\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-04-05 16:17:25,838 - htmd.projections.metric - INFO - Frame step 0.1ns was read from the trajectories. If it looks wrong, redefine it by manually setting the MetricData.fstep property.\n" ] } ], "source": [ "refmol = Molecule('./filtered/filtered.pdb')\n", "metr = Metric(simus)\n", "metr.projection(MetricRmsd(refmol, 'resid 38 to 42 or resid 22 to 28 and noh', trajalnstr='protein'))\n", "rmsd = metr.project()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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s7i1TZz070VXMF2/e5astif7MVNVQdrvjz4SWFbM9Yguf3A7g3vCbJgSFnUAw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\n3gCwEcDBzKxkmrJmbhvB5YX6X6jJ4q2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AC9AHMwbcrmXQ/LvGNfaKvuyF+vmdBWAGgN+G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\ncgQRfEEQhBzh/wPFtQCNwd1HzwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Do you see the pocket opening at 50ns?\n", "plt.plot(rmsd.dat[0])\n", "plt.xlabel('Simulation length (frames; 0.1ns)', fontsize=10)\n", "plt.ylabel('RMSD (Angstroms)', fontsize=10)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "deletable": true, "editable": true, "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "The installed widget Javascript is the wrong version.\n" ] } ], "source": [ "# You can also visualize the trajectory from your browser\n", "refmol.read('./filtered/10x23/10x23-GERARD_VERYLONG_CXCL12_confAna-0-1-RND9861_9.filtered.xtc')\n", "refmol.align('protein')\n", "refmol.view()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "![](http://pub.htmd.org/confana1036hbl2450olw/view_trajectory.png)" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python [default]", "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": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 0 }