{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Conformational analysis of CXCL12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "In this tutorial, we demonstrate how to use the HTMD code for doing conformational analysis.\n",
    "\n",
    "To analyze, one needs MD trajectories first, which can be generated with HTMD. Here, we already provide the trajectories (data) to analyze. You can download the data from [here](http://pub.htmd.org/tutorials/cxcl12-conformational-analysis/filtered.tar.gz). (**Warning: 2.6 GB filesize**)\n",
    "\n",
    "Alternatively, you can download the dataset using `wget`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "assert os.system('wget -rcN -np -nH -q --cut-dirs=2 -R index.html* http://pub.htmd.org/tutorials/cxcl12-conformational-analysis/filtered/') == 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Getting started"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we import the modules we are going to need for the tutorial:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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. \n",
      "https://dx.doi.org/10.1021/acs.jctc.6b00049\n",
      "Documentation: http://software.acellera.com/\n",
      "To update: conda update htmd -c acellera -c psi4\n",
      "\n",
      "You are on the latest HTMD version (unpackaged : /home/joao/maindisk/software/repos/Acellera/htmd/htmd).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "from htmd.ui import *\n",
    "config(viewer='webgl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Introduction to CXCL12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "CXCL12 is a chemokine involved in...\n",
    "\n",
    "*  many types of cancer \n",
    "*  inflammatory diseases \n",
    "*  early development events"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "![system](http://pub.htmd.org/tutorials/cxcl12-conformational-analysis/system-protein2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Sampling major conformational states"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "There are 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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "![conformations](http://pub.htmd.org/tutorials/cxcl12-conformational-analysis/conformations.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Load the trajectories into a simlist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating simlist: 100%|██████████| 289/289 [00:00<00:00, 2795.13it/s]\n"
     ]
    }
   ],
   "source": [
    "fsims = simlist(glob('./filtered/*/'), './filtered/filtered.pdb')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Calculate metrics: protein backbone dihedrals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Projecting trajectories:   0%|          | 1/289 [00:00<01:13,  3.89it/s]2018-03-20 02:12:04,241 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x4/1x4-GERARD_VERYLONG_CXCL12_confAna-0-1-RND4400_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:12:04,259 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x11/1x11-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0220_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:12:04,271 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x3/1x3-GERARD_VERYLONG_CXCL12_confAna-0-1-RND2434_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:12:04,286 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x12/1x12-GERARD_VERYLONG_CXCL12_confAna-0-1-RND5012_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
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      "2018-03-20 02:12:06,341 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x32/1x32-GERARD_VERYLONG_CXCL12_confAna-0-1-RND8404_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
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      "2018-03-20 02:12:06,990 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x38/1x38-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0319_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:12:07,027 - htmd.molecule.readers - WARNING - No time information read from ./filtered/2x3/2x3-GERARD_VERYLONG_CXCL12_confAna-0-1-RND2726_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
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      "2018-03-20 02:12:07,739 - htmd.molecule.readers - WARNING - No time information read from ./filtered/2x24/2x24-GERARD_VERYLONG_CXCL12_confAna-0-1-RND7758_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:12:07,853 - htmd.molecule.readers - WARNING - No time information read from ./filtered/2x25/2x25-GERARD_VERYLONG_CXCL12_confAna-0-1-RND7152_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
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      "2018-03-20 02:12:09,231 - htmd.molecule.readers - WARNING - No time information read from ./filtered/3x5/3x5-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0952_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
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      "2018-03-20 02:12:09,355 - htmd.molecule.readers - WARNING - No time information read from ./filtered/3x11/3x11-GERARD_VERYLONG_CXCL12_confAna-0-1-RND9275_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
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      "2018-03-20 02:12:13,181 - htmd.molecule.readers - WARNING - No time information read from ./filtered/5x2/5x2-GERARD_VERYLONG_CXCL12_confAna-0-1-RND5764_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Projecting trajectories:  64%|██████▎   | 184/289 [00:12<00:07, 14.25it/s]2018-03-20 02:12:16,480 - htmd.molecule.readers - WARNING - No time information read from ./filtered/6x32/6x32-GERARD_VERYLONG_CXCL12_confAna-0-1-RND6786_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
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      "2018-03-20 02:12:19,809 - htmd.molecule.readers - WARNING - No time information read from ./filtered/8x27/8x27-GERARD_VERYLONG_CXCL12_confAna-0-1-RND6514_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
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      "2018-03-20 02:12:21,697 - htmd.molecule.readers - WARNING - No time information read from ./filtered/9x29/9x29-GERARD_VERYLONG_CXCL12_confAna-0-1-RND3439_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:12:21,755 - htmd.molecule.readers - WARNING - No time information read from ./filtered/9x33/9x33-GERARD_VERYLONG_CXCL12_confAna-0-1-RND1977_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
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      "2018-03-20 02:12:23,335 - htmd.molecule.readers - WARNING - No time information read from ./filtered/10x19/10x19-GERARD_VERYLONG_CXCL12_confAna-0-1-RND3196_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories: 100%|██████████| 289/289 [00:19<00:00, 14.52it/s]\n",
      "2018-03-20 02:12:24,038 - htmd.molecule.readers - WARNING - No time information read from ./filtered/10x31/10x31-GERARD_VERYLONG_CXCL12_confAna-0-1-RND4437_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:12:24,959 - htmd.projections.metric - WARNING - Multiple framesteps [0.1, 0.10000000149] ns were read from the simulations. Taking the statistical mode: 0.10000000149011612ns. If it looks wrong, you can modify it by manually setting the MetricData.fstep property.\n"
     ]
    }
   ],
   "source": [
    "metr = Metric(fsims)\n",
    "metr.set(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": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plotTrajSizes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "##  Dimensionality reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d0f2668919e54eedb90106eca7717f1c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/joao/maindisk/SANDBOX/miniconda3/miniconda3/lib/python3.6/site-packages/pyemma/__init__.py:91: UserWarning: You are not using the latest release of PyEMMA. Latest is 2.5.1, you have 2.4.\n",
      "  .format(latest=latest, current=current), category=UserWarning)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d666ce70c8e941608a64b2d45da1973d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tica = TICA(data, 20)\n",
    "dataTica = tica.project(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-03-20 02:13:49,608 - 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,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## MSM analysis and visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f2f3ea06ec2e4c15805dc69881d057ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = Model(dataTica)\n",
    "model.plotTimescales(lags=list(range(1,1000,50)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Build Markov Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-03-20 02:16:18,334 - htmd.model - INFO - 99.7% of the data was used\n",
      "2018-03-20 02:16:18,373 - htmd.model - INFO - Number of trajectories that visited each macrostate:\n",
      "2018-03-20 02:16:18,375 - htmd.model - INFO - [ 39 289  37   7   6  16  15 160]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.01553383  0.0581622   0.03868523  0.05355839  0.0984875   0.1093136\n",
      "  0.25694181  0.36931744]\n"
     ]
    }
   ],
   "source": [
    "model.markovModel(600, 8)\n",
    "eqDist = model.eqDistribution()\n",
    "print(eqDist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We can now visualize representatives for each of the equilibrium species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Getting state Molecules:   0%|          | 0/8 [00:00<?, ?it/s]2018-03-20 02:16:27,708 - htmd.molecule.readers - WARNING - No time information read from ./filtered/9x33/9x33-GERARD_VERYLONG_CXCL12_confAna-0-1-RND1977_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Getting state Molecules:  12%|█▎        | 1/8 [00:01<00:09,  1.32s/it]2018-03-20 02:16:29,036 - htmd.molecule.readers - WARNING - No time information read from ./filtered/6x10/6x10-GERARD_VERYLONG_CXCL12_confAna-0-1-RND7487_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Getting state Molecules:  25%|██▌       | 2/8 [00:02<00:07,  1.24s/it]2018-03-20 02:16:30,182 - htmd.molecule.readers - WARNING - No time information read from ./filtered/4x39/4x39-GERARD_VERYLONG_CXCL12_confAna-0-1-RND5184_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Getting state Molecules:  38%|███▊      | 3/8 [00:03<00:06,  1.32s/it]2018-03-20 02:16:31,640 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x36/1x36-GERARD_VERYLONG_CXCL12_confAna-0-1-RND9406_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:31,644 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x36/1x36-GERARD_VERYLONG_CXCL12_confAna-0-1-RND9406_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:31,647 - htmd.molecule.readers - WARNING - No time information read from ./filtered/4x39/4x39-GERARD_VERYLONG_CXCL12_confAna-0-1-RND5184_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:31,651 - htmd.molecule.readers - WARNING - No time information read from ./filtered/5x10/5x10-GERARD_VERYLONG_CXCL12_confAna-0-1-RND2718_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:31,655 - htmd.molecule.readers - WARNING - No time information read from ./filtered/5x10/5x10-GERARD_VERYLONG_CXCL12_confAna-0-1-RND2718_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:31,658 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x36/1x36-GERARD_VERYLONG_CXCL12_confAna-0-1-RND9406_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:31,662 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x36/1x36-GERARD_VERYLONG_CXCL12_confAna-0-1-RND9406_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:31,666 - htmd.molecule.readers - WARNING - No time information read from ./filtered/5x10/5x10-GERARD_VERYLONG_CXCL12_confAna-0-1-RND2718_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:31,669 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x36/1x36-GERARD_VERYLONG_CXCL12_confAna-0-1-RND9406_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:31,673 - htmd.molecule.readers - WARNING - No time information read from ./filtered/5x10/5x10-GERARD_VERYLONG_CXCL12_confAna-0-1-RND2718_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:31,679 - htmd.molecule.readers - WARNING - No time information read from ./filtered/4x39/4x39-GERARD_VERYLONG_CXCL12_confAna-0-1-RND5184_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:31,686 - htmd.molecule.readers - WARNING - No time information read from ./filtered/4x39/4x39-GERARD_VERYLONG_CXCL12_confAna-0-1-RND5184_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:31,690 - htmd.molecule.readers - WARNING - No time information read from ./filtered/4x39/4x39-GERARD_VERYLONG_CXCL12_confAna-0-1-RND5184_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Getting state Molecules:  50%|█████     | 4/8 [00:06<00:06,  1.54s/it]2018-03-20 02:16:33,860 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x11/1x11-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0220_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:33,864 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x11/1x11-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0220_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:33,868 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x11/1x11-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0220_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:33,871 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x11/1x11-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0220_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:33,875 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x11/1x11-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0220_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:33,879 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x11/1x11-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0220_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:33,882 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x11/1x11-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0220_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:33,886 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x11/1x11-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0220_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:33,890 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x11/1x11-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0220_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:33,893 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x11/1x11-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0220_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:33,897 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x11/1x11-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0220_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:33,901 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x11/1x11-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0220_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:33,904 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x11/1x11-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0220_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:33,907 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x11/1x11-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0220_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:33,911 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x11/1x11-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0220_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Getting state Molecules:  75%|███████▌  | 6/8 [00:09<00:03,  1.64s/it]2018-03-20 02:16:37,521 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x11/1x11-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0220_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:37,525 - htmd.molecule.readers - WARNING - No time information read from ./filtered/8x14/8x14-GERARD_VERYLONG_CXCL12_confAna-0-1-RND5878_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:37,533 - htmd.molecule.readers - WARNING - No time information read from ./filtered/8x14/8x14-GERARD_VERYLONG_CXCL12_confAna-0-1-RND5878_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:37,539 - htmd.molecule.readers - WARNING - No time information read from ./filtered/8x14/8x14-GERARD_VERYLONG_CXCL12_confAna-0-1-RND5878_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:37,545 - htmd.molecule.readers - WARNING - No time information read from ./filtered/8x14/8x14-GERARD_VERYLONG_CXCL12_confAna-0-1-RND5878_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Getting state Molecules:  88%|████████▊ | 7/8 [00:11<00:01,  1.66s/it]2018-03-20 02:16:39,314 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x32/1x32-GERARD_VERYLONG_CXCL12_confAna-0-1-RND8404_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:39,320 - htmd.molecule.readers - WARNING - No time information read from ./filtered/7x15/7x15-GERARD_VERYLONG_CXCL12_confAna-0-1-RND9516_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:16:39,331 - htmd.molecule.readers - WARNING - No time information read from ./filtered/8x14/8x14-GERARD_VERYLONG_CXCL12_confAna-0-1-RND5878_9.filtered.xtc. Defaulting to 0.1ns framestep.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Getting state Molecules: 100%|██████████| 8/8 [00:13<00:00,  1.70s/it]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "42a0ad9bf6bd45c89d48404b778a9d45",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "341b8712e61f44869543fe24120c2feb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.numsamples=1\n",
    "model.viewStates(protein=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Statistics: What are the major differences between the states X and Y?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>atomIndexes</th>\n",
       "      <th>description</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>[253, 255, 262, 264]</td>\n",
       "      <td>Sine of angle of (SER 16 N A A) (SER 16 CA A A...</td>\n",
       "      <td>dihedral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>[253, 255, 262, 264]</td>\n",
       "      <td>Cosine of angle of (SER 16 N A A) (SER 16 CA A...</td>\n",
       "      <td>dihedral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>[262, 264, 266, 279]</td>\n",
       "      <td>Sine of angle of (SER 16 C A A) (HSD 17 N A A)...</td>\n",
       "      <td>dihedral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>[371, 373, 391, 393]</td>\n",
       "      <td>Cosine of angle of (LYS 24 N A A) (LYS 24 CA A...</td>\n",
       "      <td>dihedral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>[723, 725, 727, 737]</td>\n",
       "      <td>Sine of angle of (ASN 44 C A A) (ASN 45 N A A)...</td>\n",
       "      <td>dihedral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              atomIndexes                                        description  \\\n",
       "22   [253, 255, 262, 264]  Sine of angle of (SER 16 N A A) (SER 16 CA A A...   \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",
       "55   [371, 373, 391, 393]  Cosine of angle of (LYS 24 N A A) (LYS 24 CA A...   \n",
       "140  [723, 725, 727, 737]  Sine of angle of (ASN 44 C A A) (ASN 45 N A A)...   \n",
       "\n",
       "         type  \n",
       "22   dihedral  \n",
       "23   dihedral  \n",
       "28   dihedral  \n",
       "55   dihedral  \n",
       "140  dihedral  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "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",
    "data.map.ix[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "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": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Mapping back: Which trajectory originated the state X?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 40,  87, 115]),)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(model.macro_ofmicro == 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[ 175, 1223],\n",
      "       [ 201,  119],\n",
      "       [ 155,  404],\n",
      "       [   0, 1344],\n",
      "       [ 215,  354],\n",
      "       [  16,   15],\n",
      "       [ 189,  197],\n",
      "       [ 175, 1192],\n",
      "       [ 177, 1406],\n",
      "       [ 262,   33]])]\n"
     ]
    }
   ],
   "source": [
    "_,rel = model.sampleStates(10, 10, statetype='micro')\n",
    "print(rel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "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": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Studying a defined reaction coordinate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "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 figure). To try to capture this phenomenon in our simulations, we will project our trajectories along the 2nd and 3rd beta-sheet distance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "![openclose_struc](http://pub.htmd.org/tutorials/cxcl12-conformational-analysis/openclose_struc.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "_Figure extracted from Bernini et al., 2014, 1844(3):561-6. DOI:10.1016/j.bbapap.2013.12.012._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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. This means: create an interatom matrix and with 1's if the distance is below cutoff and 0's otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Projecting trajectories:   0%|          | 1/289 [00:00<02:09,  2.22it/s]2018-03-20 02:20:30,897 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x3/1x3-GERARD_VERYLONG_CXCL12_confAna-0-1-RND2434_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:20:30,899 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x11/1x11-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0220_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:20:30,889 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x4/1x4-GERARD_VERYLONG_CXCL12_confAna-0-1-RND4400_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:20:30,961 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x12/1x12-GERARD_VERYLONG_CXCL12_confAna-0-1-RND5012_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  10%|█         | 30/289 [00:01<00:10, 24.47it/s]2018-03-20 02:20:31,494 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x16/1x16-GERARD_VERYLONG_CXCL12_confAna-0-1-RND7668_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:20:31,634 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x32/1x32-GERARD_VERYLONG_CXCL12_confAna-0-1-RND8404_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  13%|█▎        | 38/289 [00:01<00:12, 20.11it/s]2018-03-20 02:20:32,148 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x36/1x36-GERARD_VERYLONG_CXCL12_confAna-0-1-RND9406_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:20:32,207 - htmd.molecule.readers - WARNING - No time information read from ./filtered/1x38/1x38-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0319_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  15%|█▌        | 44/289 [00:02<00:13, 18.70it/s]2018-03-20 02:20:32,354 - htmd.molecule.readers - WARNING - No time information read from ./filtered/2x3/2x3-GERARD_VERYLONG_CXCL12_confAna-0-1-RND2726_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  18%|█▊        | 52/289 [00:02<00:11, 20.22it/s]2018-03-20 02:20:32,907 - htmd.molecule.readers - WARNING - No time information read from ./filtered/2x19/2x19-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0663_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:20:32,990 - htmd.molecule.readers - WARNING - No time information read from ./filtered/2x24/2x24-GERARD_VERYLONG_CXCL12_confAna-0-1-RND7758_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:20:33,001 - htmd.molecule.readers - WARNING - No time information read from ./filtered/2x25/2x25-GERARD_VERYLONG_CXCL12_confAna-0-1-RND7152_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  21%|██        | 60/289 [00:03<00:12, 19.04it/s]2018-03-20 02:20:33,485 - htmd.molecule.readers - WARNING - No time information read from ./filtered/2x35/2x35-GERARD_VERYLONG_CXCL12_confAna-0-1-RND3807_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  23%|██▎       | 66/289 [00:03<00:12, 18.45it/s]2018-03-20 02:20:33,595 - htmd.molecule.readers - WARNING - No time information read from ./filtered/3x2/3x2-GERARD_VERYLONG_CXCL12_confAna-0-1-RND3083_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  26%|██▌       | 74/289 [00:03<00:11, 19.30it/s]2018-03-20 02:20:34,020 - htmd.molecule.readers - WARNING - No time information read from ./filtered/3x5/3x5-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0952_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:20:34,117 - htmd.molecule.readers - WARNING - No time information read from ./filtered/3x8/3x8-GERARD_VERYLONG_CXCL12_confAna-0-1-RND1271_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:20:34,166 - htmd.molecule.readers - WARNING - No time information read from ./filtered/3x11/3x11-GERARD_VERYLONG_CXCL12_confAna-0-1-RND9275_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  27%|██▋       | 77/289 [00:04<00:11, 18.23it/s]2018-03-20 02:20:34,236 - htmd.molecule.readers - WARNING - No time information read from ./filtered/3x14/3x14-GERARD_VERYLONG_CXCL12_confAna-0-1-RND5196_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  30%|███       | 88/289 [00:04<00:11, 18.25it/s]2018-03-20 02:20:34,848 - htmd.molecule.readers - WARNING - No time information read from ./filtered/3x25/3x25-GERARD_VERYLONG_CXCL12_confAna-0-1-RND7475_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  37%|███▋      | 106/289 [00:05<00:09, 18.63it/s]2018-03-20 02:20:35,954 - htmd.molecule.readers - WARNING - No time information read from ./filtered/4x9/4x9-GERARD_VERYLONG_CXCL12_confAna-0-1-RND8576_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  38%|███▊      | 110/289 [00:06<00:09, 18.32it/s]2018-03-20 02:20:35,968 - htmd.molecule.readers - WARNING - No time information read from ./filtered/4x13/4x13-GERARD_VERYLONG_CXCL12_confAna-0-1-RND1867_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  40%|████      | 117/289 [00:06<00:09, 18.54it/s]2018-03-20 02:20:36,359 - htmd.molecule.readers - WARNING - No time information read from ./filtered/4x22/4x22-GERARD_VERYLONG_CXCL12_confAna-0-1-RND4765_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  42%|████▏     | 121/289 [00:06<00:09, 18.44it/s]2018-03-20 02:20:36,653 - htmd.molecule.readers - WARNING - No time information read from ./filtered/4x31/4x31-GERARD_VERYLONG_CXCL12_confAna-0-1-RND3619_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:20:36,694 - htmd.molecule.readers - WARNING - No time information read from ./filtered/4x29/4x29-GERARD_VERYLONG_CXCL12_confAna-0-1-RND5193_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  46%|████▌     | 132/289 [00:07<00:08, 18.56it/s]2018-03-20 02:20:37,182 - htmd.molecule.readers - WARNING - No time information read from ./filtered/4x39/4x39-GERARD_VERYLONG_CXCL12_confAna-0-1-RND5184_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:20:37,184 - htmd.molecule.readers - WARNING - No time information read from ./filtered/4x40/4x40-GERARD_VERYLONG_CXCL12_confAna-0-1-RND3949_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:20:37,248 - htmd.molecule.readers - WARNING - No time information read from ./filtered/5x2/5x2-GERARD_VERYLONG_CXCL12_confAna-0-1-RND5764_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:20:37,260 - htmd.molecule.readers - WARNING - No time information read from ./filtered/5x4/5x4-GERARD_VERYLONG_CXCL12_confAna-0-1-RND2596_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  48%|████▊     | 140/289 [00:07<00:08, 18.60it/s]2018-03-20 02:20:37,598 - htmd.molecule.readers - WARNING - No time information read from ./filtered/5x10/5x10-GERARD_VERYLONG_CXCL12_confAna-0-1-RND2718_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  56%|█████▌    | 162/289 [00:08<00:06, 18.56it/s]2018-03-20 02:20:38,719 - htmd.molecule.readers - WARNING - No time information read from ./filtered/6x3/6x3-GERARD_VERYLONG_CXCL12_confAna-0-1-RND8829_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  57%|█████▋    | 165/289 [00:08<00:06, 18.66it/s]2018-03-20 02:20:38,810 - htmd.molecule.readers - WARNING - No time information read from ./filtered/6x2/6x2-GERARD_VERYLONG_CXCL12_confAna-0-1-RND8150_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:20:39,107 - htmd.molecule.readers - WARNING - No time information read from ./filtered/6x10/6x10-GERARD_VERYLONG_CXCL12_confAna-0-1-RND7487_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  58%|█████▊    | 168/289 [00:09<00:06, 18.28it/s]2018-03-20 02:20:39,170 - htmd.molecule.readers - WARNING - No time information read from ./filtered/6x13/6x13-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0906_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  62%|██████▏   | 180/289 [00:09<00:05, 18.34it/s]2018-03-20 02:20:39,846 - htmd.molecule.readers - WARNING - No time information read from ./filtered/6x32/6x32-GERARD_VERYLONG_CXCL12_confAna-0-1-RND6786_9.filtered.xtc. Defaulting to 0.1ns framestep.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Projecting trajectories:  65%|██████▌   | 188/289 [00:10<00:05, 18.67it/s]2018-03-20 02:20:40,215 - htmd.molecule.readers - WARNING - No time information read from ./filtered/6x36/6x36-GERARD_VERYLONG_CXCL12_confAna-0-1-RND0862_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:20:40,306 - htmd.molecule.readers - WARNING - No time information read from ./filtered/6x39/6x39-GERARD_VERYLONG_CXCL12_confAna-0-1-RND6616_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  69%|██████▊   | 198/289 [00:10<00:04, 18.63it/s]2018-03-20 02:20:40,793 - htmd.molecule.readers - WARNING - No time information read from ./filtered/7x15/7x15-GERARD_VERYLONG_CXCL12_confAna-0-1-RND9516_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  70%|██████▉   | 201/289 [00:10<00:04, 18.35it/s]2018-03-20 02:20:40,937 - htmd.molecule.readers - WARNING - No time information read from ./filtered/7x21/7x21-GERARD_VERYLONG_CXCL12_confAna-0-1-RND1683_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:20:40,957 - htmd.molecule.readers - WARNING - No time information read from ./filtered/7x19/7x19-GERARD_VERYLONG_CXCL12_confAna-0-1-RND5246_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  76%|███████▋  | 221/289 [00:11<00:03, 18.65it/s]2018-03-20 02:20:41,968 - htmd.molecule.readers - WARNING - No time information read from ./filtered/8x2/8x2-GERARD_VERYLONG_CXCL12_confAna-0-1-RND9548_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  78%|███████▊  | 224/289 [00:12<00:03, 18.34it/s]2018-03-20 02:20:42,198 - htmd.molecule.readers - WARNING - No time information read from ./filtered/8x12/8x12-GERARD_VERYLONG_CXCL12_confAna-0-1-RND8911_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:20:42,235 - htmd.molecule.readers - WARNING - No time information read from ./filtered/8x14/8x14-GERARD_VERYLONG_CXCL12_confAna-0-1-RND5878_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  81%|████████  | 234/289 [00:12<00:02, 18.52it/s]2018-03-20 02:20:42,721 - htmd.molecule.readers - WARNING - No time information read from ./filtered/8x25/8x25-GERARD_VERYLONG_CXCL12_confAna-0-1-RND1273_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:20:42,778 - htmd.molecule.readers - WARNING - No time information read from ./filtered/8x27/8x27-GERARD_VERYLONG_CXCL12_confAna-0-1-RND6514_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  84%|████████▍ | 244/289 [00:13<00:02, 18.63it/s]2018-03-20 02:20:43,192 - htmd.molecule.readers - WARNING - No time information read from ./filtered/8x36/8x36-GERARD_VERYLONG_CXCL12_confAna-0-1-RND7283_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:20:43,375 - htmd.molecule.readers - WARNING - No time information read from ./filtered/9x6/9x6-GERARD_VERYLONG_CXCL12_confAna-0-1-RND9895_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  89%|████████▊ | 256/289 [00:13<00:01, 18.62it/s]2018-03-20 02:20:43,992 - htmd.molecule.readers - WARNING - No time information read from ./filtered/9x24/9x24-GERARD_VERYLONG_CXCL12_confAna-0-1-RND4916_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  91%|█████████ | 262/289 [00:14<00:01, 18.53it/s]2018-03-20 02:20:44,223 - htmd.molecule.readers - WARNING - No time information read from ./filtered/9x29/9x29-GERARD_VERYLONG_CXCL12_confAna-0-1-RND3439_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  92%|█████████▏| 267/289 [00:14<00:01, 18.66it/s]2018-03-20 02:20:44,421 - htmd.molecule.readers - WARNING - No time information read from ./filtered/9x33/9x33-GERARD_VERYLONG_CXCL12_confAna-0-1-RND1977_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  93%|█████████▎| 270/289 [00:14<00:01, 18.44it/s]2018-03-20 02:20:44,665 - htmd.molecule.readers - WARNING - No time information read from ./filtered/10x2/10x2-GERARD_VERYLONG_CXCL12_confAna-0-1-RND3128_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories:  99%|█████████▊| 285/289 [00:15<00:00, 18.58it/s]2018-03-20 02:20:45,314 - htmd.molecule.readers - WARNING - No time information read from ./filtered/10x14/10x14-GERARD_VERYLONG_CXCL12_confAna-0-1-RND4610_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "Projecting trajectories: 100%|██████████| 289/289 [00:15<00:00, 18.71it/s]\n",
      "2018-03-20 02:20:45,655 - htmd.molecule.readers - WARNING - No time information read from ./filtered/10x19/10x19-GERARD_VERYLONG_CXCL12_confAna-0-1-RND3196_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:20:46,216 - htmd.molecule.readers - WARNING - No time information read from ./filtered/10x31/10x31-GERARD_VERYLONG_CXCL12_confAna-0-1-RND4437_9.filtered.xtc. Defaulting to 0.1ns framestep.\n",
      "2018-03-20 02:20:46,590 - htmd.projections.metric - WARNING - Multiple framesteps [0.1, 0.10000000149] ns were read from the simulations. Taking the statistical mode: 0.10000000149011612ns. If it looks wrong, you can modify it by manually setting the MetricData.fstep property.\n"
     ]
    }
   ],
   "source": [
    "metr = Metric(fsims)\n",
    "metr.set(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": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## tICA projection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Dimensionality reduction along the slow process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4bc1bf41f49a4252bccf8f34099f66eb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e969d6ad68f643d4b02ea21dd3d7d132",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tica3 = TICA(data3, 20)\n",
    "dataTica3 = tica3.project(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-03-20 02:29:10,507 - htmd.metricdata - INFO - Mergesmall removed 0 clusters. Original ncluster 200, new ncluster 200.\n"
     ]
    }
   ],
   "source": [
    "dataTica3.cluster(MiniBatchKMeans(n_clusters=200), mergesmall=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Plot timescales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0e979a7d796e47f8839c36f97ade3d88",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model3 = Model(dataTica3)\n",
    "model3.plotTimescales(lags=list(range(1,1000,50)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Build a Markov Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "We want to pick a lagtime where the timescales are converged (timescale is flat). 600 is the lagtime we want to use (600 frames is equivalent to 60ns). 4 is the number of macrostates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-03-20 02:29:38,610 - htmd.model - INFO - 100.0% of the data was used\n",
      "2018-03-20 02:29:38,638 - htmd.model - INFO - Number of trajectories that visited each macrostate:\n",
      "2018-03-20 02:29:38,640 - htmd.model - INFO - [  5   9  20 289]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.02611681  0.06178354  0.10258197  0.80951768]\n"
     ]
    }
   ],
   "source": [
    "model3.markovModel(600, 4)\n",
    "eqDist = model3.eqDistribution(plot=True)\n",
    "print(eqDist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Visualize states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Getting state Molecules: 100%|██████████| 4/4 [00:07<00:00,  1.83s/it]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "54b7f972d428417e92aad4d0d8c966a0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c52307d18a1d43859c0702390aa9dd23",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model3.viewStates(protein=True, numsamples=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Analyse Open Conformation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": null,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 5, 17]),)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(model3.macro_ofmicro ==1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[ 159,  622],\n",
      "       [ 203,  622],\n",
      "       [  79, 1356],\n",
      "       [ 123, 1653],\n",
      "       [ 151, 1222]])]\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": 24,
   "metadata": {
    "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": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Calculate RMSD of the site of interest for a selected trajectory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating simlist: 100%|██████████| 1/1 [00:00<00:00, 523.83it/s]\n"
     ]
    }
   ],
   "source": [
    "simus = simlist(glob('./filtered/10x23/'), './filtered/filtered.pdb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Projecting trajectories: 100%|██████████| 1/1 [00:00<00:00,  1.46it/s]\n",
      "2018-03-20 02:30:34,130 - htmd.projections.metric - INFO - Frame step 0.10000000149011612ns 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.set(MetricRmsd(refmol, 'resid 38 to 42 or resid 22 to 28 and noh', trajalnstr='protein'))\n",
    "rmsd = metr.project()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Do you see the pocket opening at 50ns?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(rmsd.dat[0])\n",
    "plt.xlabel('Simulation length (frames; 0.1ns)', fontsize=10)\n",
    "plt.ylabel('RMSD (Angstroms)', fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Visualize the trajectory from your browser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc15edb2af9946a295d544528aa21c20",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": {},
   "source": [
    "This concludes the conformational analysis tutorial"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}