{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example MSM for a protein folding process\n", "\n", "by Stefan Doerr" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We demonstrate how to use the HTMD code for analysing a protein folding process in the case of the protein Villin.\n", "\n", "You can download the data and analysis file from the following links:\n", "\n", "* [Datasets](http://pub.htmd.org/tutorials/protein-folding-analysis/datasets.tar.gz). Warning: 3GB filesize.\n", "\n", "Alternatively you can download the dataset using `wget`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "os.system('wget -rcN -np -nH -q --cut-dirs=2 -R index.html* http://pub.htmd.org/tutorials/protein-folding-analysis/datasets/')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting started\n", "\n", "First we import the modules we are going to need for the tutorial" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n", "\n", "Please cite -- HTMD: High-Throughput Molecular Dynamics for Molecular Discovery\n", "J. Chem. Theory Comput., 2016, 12 (4), pp 1845-1852. \n", "http://pubs.acs.org/doi/abs/10.1021/acs.jctc.6b00049\n", "\n", "You are on the latest HTMD version (unpackaged : /shared/sdoerr/Work/htmdacellera/htmd).\n", "\n" ] } ], "source": [ "%pylab inline\n", "from htmd.ui import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating a simulation list\n", "\n", "For the purposes of an analysis, full atom information is required by HTMD. Therefore each simulation trajectory has to be associated with a corresponding PDB file. Additionally, if you plan to run adaptive, each trajectory needs to be associated with an input directory which contains the files used to run the simulations. The simList function allows us to make these associations\n", "\n", " sims = simlist(glob('data/*/'), glob('input/*/structure.pdb'), glob('input/*/'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Filtering trajectories\n", "\n", "Typically waters are removed from the trajectories as they are not used in the analysis and it helps speed up further calculations. These filtered trajectories are written into a new directory. The advantage of filtering is that if your input structures only differ in the number of water molecules, you can produce a single unique pdb file for all simulations once you remove the waters, which will speed up calculations. This step is not necessary for the analysis and you can skip it if you don't mind the slowdown in calculations. In that case replace in the following commands the fsims variable with sims.\n", "\n", " fsims = simfilter(sims, './filtered/', filtersel='not water')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial due to space limitations we provide only the filtered trajectories which are stored in three separate dataset folders. Therefore we will skip the above two commands and construct the simulation list directly from the filtered trajectories." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating simlist: 100% (719/719) [#################################] eta 00:01 \\\n", "Creating simlist: 100% (710/710) [#################################] eta 00:01 -\n", "Creating simlist: 100% (708/708) [#################################] eta 00:01 /\n" ] } ], "source": [ "sets = glob('datasets/*/')\n", "sims = []\n", "for s in sets:\n", " fsims = simlist(glob(s + '/filtered/*/'), 'datasets/1/filtered/filtered.pdb')\n", " sims = simmerge(sims, fsims)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculating metrics\n", "\n", "To build a Markov state model we need to project the atom coordinates onto a lower dimensional space which can be used for clustering the conformations into a set of states. Here we have selected to use the carbon alpha atoms of the protein. This will calculate contacts between all carbon alpha atoms." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Projecting trajectories: 100% (2137/2137) [########################] eta 00:01 \\\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-02-08 17:00:56,745 - 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(sims)\n", "metr.set(MetricSelfDistance('protein and name CA', metric='contacts'))\n", "data = metr.project()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we provide the frame-step in nanoseconds i.e. the time that passes between two consecutive frames in a trajectory. This is automatically read from the trajectories, however not all trajectories contain the correct fstep so it can be useful to manually define it like here." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data.fstep = 0.1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Removing trajectories\n", "\n", "Sometimes the set of trajectories can contain trajectories of incorrect length. These are typically corrupted trajectories and are removed.\n", "\n", "plotTrajSizes plots all trajectory lengths sorted" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data.plotTrajSizes()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "dropTraj has multiple options for removing simulations from the dataset. Here we use it to remove all trajectories whose length is not equal to the mode length. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-02-08 17:01:03,417 - htmd.metricdata - INFO - Dropped 7 trajectories from 2137 resulting in 2130\n" ] }, { "data": { "text/plain": [ "array([ 89, 183, 682, 693, 720, 1597, 1901])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.dropTraj()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TICA\n", "\n", "TICA is a method that can be used to improve the clustering of the conformations. This is done by projecting the data onto a lower-dimensional space which separates well the metastable minima and places clusters on the transition regions. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "getting output of TICA: 100% (2130/2130) [#########################] eta 00:01 |" ] } ], "source": [ "tica = TICA(data, 2, units='ns')\n", "dataTica = tica.project(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bootstrapping\n", "\n", "If we want to bootstrap our calculations we can at this point drop a random 20% of the trajectories and do the rest of the analysis multiple times to see if the results are consistent. Alternatively we can keep on using dataTica in the following commands. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataBoot = dataTica.bootstrap(0.8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Clustering conformations\n", "\n", "Once we have cleaned the dataset we proceed to cluster the conformations.\n", "\n", "Here we use the mini-batch kmeans clustering algorithm to procude 1000 clusters. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "dataBoot.cluster(MiniBatchKMeans(n_clusters=1000))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Building the Markov model\n", "\n", "After clustering it is time to build the Markov model." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model = Model(dataBoot)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before constructing the Markov model we need to choose the lag-time at which it will be built. The lag-time is typically chosen by looking at the implied timescale (ITS) plot and selecting a lag-time at which the top timescales start converging. By constructing Markov models at various lag times HTMD creates a plot which shows the slowest implied timescales of each Markov model at various lag times. If a model is Markovian at a specific lag time, the implied timescales should stay unchanged for any higher lag times. Therefore, given an implied timescales plot, the user can monitor the convergence and choose the lag time at which to construct his Markov model, typically the Markov time which is the shortest lag time at which the timescales converge. Too large lag times can reduce the temporal resolution of the Markov model and can create more statistical uncertainty due to fewer transition counts and thus instability in the implied timescales. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "estimating MaximumLikelihoodMSM: 100% (26/26) [####################] eta 00:07 \\08-02-17 17:06:38 pyemma.msm.estimators.implied_timescales.ImpliedTimescales[2] WARNING Some timescales could not be computed. Timescales array is smaller than expected or contains NaNs\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/shared/sdoerr/Software/anaconda3/lib/python3.5/site-packages/msmtools/analysis/dense/decomposition.py:545: SpectralWarning: Multiple eigenvalues with magnitude one.\n", " warnings.warn('Multiple eigenvalues with magnitude one.', SpectralWarning)\n" ] }, { "data": { "image/png": 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cNuTAYg6yn/wQOFcIsWioZwyFzXGOm6YMfHrqKRk5e+KJA24o9mNMny5DrIY4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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model.plotTimescales()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After seeing the ITS plot we decide on a lag-time of 25ns. Additionally the ITS plot showed us that there is a separation between 3 slow timescales and the rest of the timescales which are fast. Therefore we choose to lump our microstates together into 4 macrostates. " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-02-08 17:07:52,811 - htmd.model - INFO - 100.0% of the data was used\n", "2017-02-08 17:07:53,233 - htmd.model - INFO - Number of trajectories that visited each macrostate:\n", "2017-02-08 17:07:53,234 - htmd.model - INFO - [ 122 569 156 1640]\n" ] } ], "source": [ "model.markovModel(25, 4, units='ns')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also visualize the equilibrium populations of each macrostate using the following command" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "array([ 0.00300214, 0.1017828 , 0.24006395, 0.65515111])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.eqDistribution()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once we have a Markov model we can plot the free energy surface by projecting it on any of our projected coordinates. For example to plot it on the first two TICA coordinates we call it like this." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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fjRh4GUU+NRMAcxdBZesAnAjMM0ODpfyzsZ/Z/P0krWQhJNadJAiJ3UWs6IyS97FtXkZR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\n4taB6Vk0bCNJgpAYyLP4AH/Bc4Hu5LrIFFPNxaI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x768WZUtLarZYzbalCBEkBQqWQtRA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\nLPk/QXTj1AG3OP8AAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model.plotFES(0, 1, temperature=360)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also plot the micro and macrostates on top of the FES by setting `states=True`" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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RZv0WZt4GewZkEySRo5/KsJfOtLK+a1LSGPH3Ayh+uJCcSwZTv72G+m3WGgVp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\n2lYlEub6s8swe9uJKDrqB43F0tGKydvB0E/eQQBmbycT5mie20afh76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Y80YIOSiLgbdl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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model.plotFES(0, 1, temperature=360, states=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing the states\n", "\n", "To see what the states look like we use a Matlab integration of VMD. We load the 3 macrostates and add a protein representation. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model.viewStates(protein=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculating the kinetics\n", "\n", "One of the major advantages of Markov state models is that they can provide quantitative results about the kinetics between states.\n", "\n", "Provide the Kinetics constructor with the system temperature. It automatically then calculates the source and sink states. " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-02-08 17:09:11,915 - htmd.kinetics - INFO - Detecting source state...\n", "2017-02-08 17:09:12,753 - htmd.kinetics - INFO - Guessing the source state as the state with minimum contacts.\n", "2017-02-08 17:09:12,754 - htmd.kinetics - INFO - Source macro = 3\n", "2017-02-08 17:09:12,755 - htmd.kinetics - INFO - Detecting sink state...\n", "2017-02-08 17:09:12,756 - htmd.kinetics - INFO - Sink macro = 2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "3\n", "2\n" ] } ], "source": [ "kin = Kinetics(model, temperature=360)\n", "print(kin.source)\n", "print(kin.sink)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To see the rates between the source and sink states we use the getRates method." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-02-08 17:09:14,394 - htmd.kinetics - INFO - Calculating rates between source: [3] and sink: [2] states.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "mfpton = 1.33E+03 (ns)\n", "mfptoff = 2.37E+02 (ns)\n", "kon = 7.53E+05 (1/M 1/s)\n", "koff = 4.21E+06 (1/s)\n", "koff/kon = 5.60E+00 (M)\n", "kdeq = 2.73E+00 (M)\n", "g0eq = 0.72 (kcal/mol)\n", "\n" ] } ], "source": [ "r = kin.getRates()\n", "print(r)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To plot the free energies and mean first passage times of all state use the `plotRates()` method." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "kin.plotRates()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/shared/sdoerr/Software/anaconda3/lib/python3.5/site-packages/pyemma/plots/networks.py:544: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if state_labels == 'auto':\n", "/shared/sdoerr/Software/anaconda3/lib/python3.5/site-packages/matplotlib/figure.py:402: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", " \"matplotlib is currently using a non-GUI backend, \"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Path flux\t\t%path\t%of total\tpath\n", "7.71026773752569e-05\t99.9%\t99.9%\t\t[3 2]\n", "7.35704668706151e-08\t0.1%\t100.0%\t\t[3 1 2]\n", "1.078895550855035e-12\t0.0%\t100.0%\t\t[3 0 1 2]\n" ] }, { 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aRIxxCunAjaMbH0cahu8nheGfhBAeGq06JQ3MAKznxRjvAx4IIbw2dy2SVBUx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\nhAWZSpIkScNkAO5sboBbjhjjscCZ/S4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BDABAKwIYAIBWBDAAAK0IYAAAWhHAAAC0IoAB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\nrkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "kin.plotFluxPathways()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 0 }