diffusion_analysis_sim126_extended.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy\n",
    "import scipy\n",
    "import MDAnalysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import dengue_utility_functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#load full set of skip-10 compact-representation processed sim126 extended (dengue 5-10 microseconds at 323 K) trajectory data:\n",
    "#now updating to use the properly processed trajectory (as was used for Voronoi analysis):\n",
    "gro_file_path = '/sansom/n22/bioc1009/sim126_extended/sim126_insect_dengue_323K_extended.part0026.gro'\n",
    "list_xtc_file_paths = ['/sansom/n22/bioc1009/sim126_extended/translated_test.xtc']\n",
    "universe_object = MDAnalysis.Universe(gro_file_path,list_xtc_file_paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5001"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#confirm that there are about 5000 frames (at 1 ns/ frame; 5 microseconds total):\n",
    "universe_object.trajectory.numframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#to prepare for diffusion analysis, load my github-hosted diffusion module:\n",
    "import sys; sys.path.append('/sansom/sc2/bioc1009/github_projects/diffusion_utility/diffusion_analysis_MD_simulations')\n",
    "import diffusion_analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "window_size_list_frames = [1,3,5,10,25,50,100,200,300,400,500] #window sizes of 1 ns to 500 ns (because skip 10 filtered data has frame : nanosecond 1 : 1 correspondence)\n",
    "dict_particle_selection_strings = {'POPC':'resname POPC','PPCE':'resname PPCE','DPPE':'resname DPPE','CER':'resname CER','DUPC':'resname DUPC','DOPS':'resname DOPS','PPCS':'resname PPCS'} #all lipids in the dengue simulation system\n",
    "dict_MSD_values = diffusion_analysis.mean_square_displacement_by_species(gro_file_path,list_xtc_file_paths,window_size_list_frames,dict_particle_selection_strings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#I want linear and non-linear diffusion fit data for each lipid type\n",
    "dict_MSD_data_combined = {} #populate this dictionary with linear and non-linear fit results for each lipid type\n",
    "for lipid_name in dict_particle_selection_strings.keys():\n",
    "    time_window_array = numpy.array(window_size_list_frames)\n",
    "    MSD_data_array = numpy.array(dict_MSD_values['MSD_value_dict'][lipid_name])\n",
    "    linear_results_tuple = diffusion_analysis.fit_linear_diffusion_data(time_window_array,MSD_data_array)\n",
    "    anomalous_results_tuple = diffusion_analysis.fit_anomalous_diffusion_data(time_window_array,MSD_data_array)\n",
    "    dict_MSD_data_combined[lipid_name] = {'linear_results_tuple':linear_results_tuple,'anomalous_results_tuple':anomalous_results_tuple}\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9c1554ca10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9c1554c9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9c1554ce50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9c1555e4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9c1555eb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now, I need to write a plotting loop I think (and convert values to appropriate units)\n",
    "fig_linear_D_constants = plt.figure()\n",
    "fig_anomalous_D_constants = plt.figure()\n",
    "fig_anomalous_alpha_values = plt.figure()\n",
    "fig_linear_data_fit = plt.figure()\n",
    "fig_anomalous_data_fit = plt.figure()\n",
    "ax_linear_D_constants = fig_linear_D_constants.add_subplot(111)\n",
    "ax_anomalous_D_constants = fig_anomalous_D_constants.add_subplot(111)\n",
    "ax_anomalous_alpha_values = fig_anomalous_alpha_values.add_subplot(111)\n",
    "bar_x_position = 0\n",
    "width = 0.8\n",
    "list_x_species_categories = []\n",
    "list_x_ticks = []\n",
    "data_fit_subplot_number = 1\n",
    "ax_linear_data_fit = fig_linear_data_fit.add_subplot(2,4,data_fit_subplot_number)\n",
    "ax_anomalous_data_fit = fig_anomalous_data_fit.add_subplot(2,4,data_fit_subplot_number)\n",
    "\n",
    "for lipid_name,results_subdictionary in dict_MSD_data_combined.iteritems():\n",
    "    linear_results_tuple = results_subdictionary['linear_results_tuple']\n",
    "    anomalous_results_tuple = results_subdictionary['anomalous_results_tuple']\n",
    "    \n",
    "    ax_linear_D_constants.bar(bar_x_position,linear_results_tuple[0]/(10**7),yerr=linear_results_tuple[1]/(10**7),ecolor='black',alpha=0.5)\n",
    "    list_x_species_categories.append(lipid_name)\n",
    "    list_x_ticks.append(bar_x_position + (width/2.))\n",
    "    ax_linear_D_constants.set_xticks(list_x_ticks)\n",
    "    ax_linear_D_constants.set_xticklabels(list_x_species_categories)\n",
    "    ax_linear_D_constants.set_title('Dengue Lipid Linear\\n Diffusion Constants at 323 K')\n",
    "    ax_linear_D_constants.set_ylabel('D (cm$^2$/s)')\n",
    "    ax_linear_D_constants.axhline(y=0.6*(10**-7),xmin=0.0,xmax=1.0,lw=5,color='black',alpha=0.5) #to indicate D constant for FORS flu virion at 323 K (linear fit)\n",
    "    ax_linear_D_constants.annotate(' Flu 323 K',xy=(6.8,0.6*(10**-7)),size=14,color='black')\n",
    "    \n",
    "    ax_anomalous_D_constants.bar(bar_x_position,anomalous_results_tuple[0]/(10**7),yerr=anomalous_results_tuple[1]/(10**7),ecolor='black',alpha=0.5)\n",
    "    ax_anomalous_D_constants.set_xticks(list_x_ticks)\n",
    "    ax_anomalous_D_constants.set_xticklabels(list_x_species_categories)\n",
    "    ax_anomalous_D_constants.set_title('Dengue Lipid Anomalous\\n Diffusion Constants at 323 K')\n",
    "    ax_anomalous_D_constants.set_ylabel('D$_{\\\\alpha}$ (cm$^2$/s$^{\\\\alpha}$)')\n",
    "    ax_anomalous_D_constants.axhline(y=1.6*(10**-7),xmin=0.0,xmax=1.0,lw=5,color='black',alpha=0.5) #to indicate D constant for FORS flu virion at 323 K (non-linear fit)\n",
    "    ax_anomalous_D_constants.annotate(' Flu 323 K',xy=(6.8,1.6*(10**-7)),size=14,color='black')\n",
    "    \n",
    "    ax_anomalous_alpha_values.bar(bar_x_position,anomalous_results_tuple[2],yerr=anomalous_results_tuple[3],ecolor='black',alpha=0.5)\n",
    "    ax_anomalous_alpha_values.set_xticks(list_x_ticks)\n",
    "    ax_anomalous_alpha_values.set_xticklabels(list_x_species_categories)\n",
    "    ax_anomalous_alpha_values.set_title('Dengue Lipid \\nScaling Exponents at 323 K')\n",
    "    ax_anomalous_alpha_values.set_ylabel('$\\\\alpha$')\n",
    "    bar_x_position += 1\n",
    "    \n",
    "    ax_linear_data_fit.plot(linear_results_tuple[2],linear_results_tuple[3],'-',window_size_list_frames,dict_MSD_values['MSD_value_dict'][lipid_name],'.',ms=14) \n",
    "    ax_linear_data_fit.set_xlabel('Window size (ns)')\n",
    "    ax_linear_data_fit.set_ylabel('MSD ($\\AA^2$)')\n",
    "    ax_linear_data_fit.set_xlim((0,510))\n",
    "    ax_linear_data_fit.set_title(lipid_name + ' linear fit diffusion data')\n",
    "    \n",
    "    ax_anomalous_data_fit.plot(anomalous_results_tuple[-2],anomalous_results_tuple[-1],'-',window_size_list_frames,dict_MSD_values['MSD_value_dict'][lipid_name],'.',ms=14)\n",
    "    ax_anomalous_data_fit.set_xlabel('Window size (ns)')\n",
    "    ax_anomalous_data_fit.set_ylabel('MSD ($\\AA^2$)')\n",
    "    ax_anomalous_data_fit.set_xlim((0,510))\n",
    "    ax_anomalous_data_fit.set_title(lipid_name + ' non-linear fit\\n diffusion data')\n",
    "    \n",
    "    data_fit_subplot_number += 1\n",
    "    ax_linear_data_fit = fig_linear_data_fit.add_subplot(2,4,data_fit_subplot_number)\n",
    "    ax_anomalous_data_fit = fig_anomalous_data_fit.add_subplot(2,4,data_fit_subplot_number)\n",
    "    \n",
    "fig_linear_data_fit.set_size_inches(16,8)\n",
    "fig_linear_data_fit.subplots_adjust(wspace=0.4,hspace=0.3)\n",
    "fig_anomalous_data_fit.set_size_inches(16,8)\n",
    "fig_anomalous_data_fit.subplots_adjust(wspace=0.4,hspace=0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The lipid diffusion constants from the final five $\\mu$s of the dengue simulation at 323 K are shown above for both linear and non-linear (anomalous) fits to the MSD vs. time data. All of the lipids exhibited subdiffusive ($\\alpha$ < 1) behaviour, which is a special case of anomalous diffusion frequently observed in lipid bilayers. The dengue lipid diffusion constants are only slightly greater than the average lipid diffusion constants for the (+FORS) flu virion simulated at the same temperature. The close apposition of dengue E/M proteins to the lipid bilayer and the presence of 720 transmembrane domains in the viral envelope may explain why the lipids diffuse slowly despite the lack of cholesterol. The influenza A lipid envelope is approximately 52 % cholesterol, but has far fewer protein TMDs and a much larger outer diameter. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9c122f8e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#produce suitable version of Figure 2A (Figure 4 now) for short dengue paper:\n",
    "\n",
    "\n",
    "\n",
    "fig_2A = plt.figure()\n",
    "ax = fig_2A.add_subplot('211')\n",
    "ax_anomalous_alpha_values = fig_2A.add_subplot('212')\n",
    "bar_x_position = 0.0\n",
    "width = 0.8\n",
    "list_x_species_categories = []\n",
    "list_x_ticks = []\n",
    "\n",
    "for lipid_name,results_subdictionary in dict_MSD_data_combined.iteritems():\n",
    "    anomalous_results_tuple = results_subdictionary['anomalous_results_tuple']\n",
    "    list_x_species_categories.append(lipid_name)\n",
    "    list_x_ticks.append(bar_x_position + (width/2.) + 0.15)\n",
    "    ax.bar(bar_x_position,anomalous_results_tuple[0],yerr=anomalous_results_tuple[1],ecolor='black',alpha=0.5)\n",
    "    ax.set_xticks(list_x_ticks)\n",
    "    ax.set_yticks([0,2,4,6,8,10])\n",
    "    ax.set_xticklabels(list_x_species_categories,rotation=90)\n",
    "    #ax.set_title('Dengue Lipid Anomalous\\n Diffusion Constants at 323 K')\n",
    "    ax.set_ylabel('D$_{\\\\alpha}$ ($10^{-7}$ cm$^2$/s$^{\\\\alpha}$)')\n",
    "    ax.axhline(y=1.6,ls='--',xmin=0.0,xmax=1.0,lw=3,color='black',alpha=0.5) #to indicate D constant for FORS flu virion at 323 K (non-linear fit)\n",
    "    #ax.annotate(' Flu 323 K',xy=(6.8,1.6*(10**-7)),size=14,color='black')\n",
    "    ax.tick_params(axis='both',which='both',bottom='off',top='off',left='off',right='off')\n",
    "    \n",
    "    ax_anomalous_alpha_values.bar(bar_x_position,anomalous_results_tuple[2],yerr=anomalous_results_tuple[3],ecolor='black',alpha=0.5)\n",
    "    ax_anomalous_alpha_values.set_xticks(list_x_ticks)\n",
    "    ax_anomalous_alpha_values.set_xticklabels(list_x_species_categories,rotation=90)\n",
    "    #ax_anomalous_alpha_values.set_title('Dengue Lipid \\nScaling Exponents at 323 K')\n",
    "    ax_anomalous_alpha_values.set_ylabel('\\n\\n\\n$\\\\alpha$')\n",
    "    ax_anomalous_alpha_values.tick_params(axis='both',which='both',bottom='off',top='off',left='off',right='off')\n",
    "    ax_anomalous_alpha_values.axhline(y=0.85,ls='--',xmin=0.0,xmax=1.0,lw=3,color='black',alpha=0.5)\n",
    "    \n",
    "    bar_x_position += 1\n",
    "\n",
    "fig_2A.set_size_inches(3.3,3.3)\n",
    "fig_2A.subplots_adjust(left=0.15,right=0.95,bottom=0.15,top=0.9,wspace=0.1,hspace=0.5)\n",
    "fig_2A.savefig('fig_4.png',dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#produce some png files suitable for SysL presentation on October 8/ 2014:\n",
    "output_path = '/sansom/sc2/bioc1009/postdoc_work/literature/JClub/SysL_October_8_2014/'\n",
    "for fig in [fig_linear_D_constants,fig_anomalous_D_constants,fig_anomalous_alpha_values]:\n",
    "    fig.set_size_inches(3.8,3.4)\n",
    "ax_linear_D_constants.set_title('Linear 323 K')\n",
    "fig_linear_D_constants.savefig(output_path + 'linear_D.png', dpi=300)\n",
    "ax_anomalous_D_constants.set_title('Anomalous 323 K')\n",
    "fig_anomalous_D_constants.savefig(output_path + 'anomalous_D.png',dpi=300)\n",
    "ax_anomalous_alpha_values.set_title('$\\\\alpha$ 323 K')\n",
    "ax_anomalous_alpha_values.set_ylabel('$\\\\alpha$',size=18)\n",
    "ax_anomalous_alpha_values.set_xticklabels(list_x_species_categories)\n",
    "ax_anomalous_alpha_values.set_xticks(list_x_ticks)\n",
    "ax_anomalous_alpha_values.set_xlabel('')\n",
    "fig_anomalous_alpha_values.savefig(output_path + 'alpha_values.png',dpi=300)\n"
   ]
  }
 ],
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    "name": "ipython",
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