Fig5_MapDiffSelToStructure.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Map differential selection to structures\n",
    "\n",
    "We generated pymol scripts to display positive differential selection values on various PB2 structures.\n",
    "\n",
    "Structure | pdb name | Input txt files | Output pymol scripts\n",
    "--- | --- | --- | ---\n",
    "Polymerase in transcription pre-initiation form | 4wsb | preamble <br/> setview <br/> colorfullstructure <br/> topA549 | map_fullstructure.py <br/> map_topA549.py <br/> map_positive_diff_seq.py \n",
    "Polymerase in apo form | 59d8 | preamble <br/> setview <br/> colorfullstructure <br/> topA549 | map_fullstructure.py <br/> map_topA549.py <br/> map_positive_diff_seq.py \n",
    "PB2 in complex with RNA pol II | 6f5o | preamble <br/> setview <br/> MartinResidues <br/> topA549 | map_MartinResidues.py <br/> map_positive_diff_seq.py\n",
    "PB2 in complex with importin | 4uae | preamble <br/> setview <br/> PumroyResidues <br/> topA549 | map_PumroyResidues.py <br/> map_positive_diff_seq.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import modules, define directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from colour import Color\n",
    "\n",
    "import dms_tools2\n",
    "import dms_tools2.dssp\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"talk\", font_scale=1.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Set plotting parameters/preferences\n",
    "# Set matplotlib rcParams for figures\n",
    "import seaborn as sns\n",
    "sns.set(context='paper', style='ticks', palette='deep', font='Arial', font_scale=1.042, color_codes=True,\n",
    "        rc = {'font.size': 10,\n",
    " 'axes.labelsize': 10,\n",
    " 'axes.titlesize': 10,\n",
    " 'xtick.labelsize': 9,\n",
    " 'ytick.labelsize': 9,\n",
    " 'legend.fontsize': 9,\n",
    " 'axes.linewidth': 1.0,\n",
    " 'grid.linewidth': 0.8,\n",
    " 'lines.linewidth': 1,\n",
    " 'lines.markersize': 4.5,\n",
    " 'patch.linewidth': 0.8,\n",
    " 'xtick.major.width': 1.0,\n",
    " 'ytick.major.width': 1.0,\n",
    " 'xtick.minor.width': 0.8,\n",
    " 'ytick.minor.width': 0.8,\n",
    " 'xtick.major.size': 4.5,\n",
    " 'ytick.major.size': 4.5,\n",
    " 'xtick.minor.size': 3,\n",
    " 'ytick.minor.size': 3}\n",
    "       )\n",
    "palette ={\"None\":\"gray\",\n",
    "          \"A549\":\"#d95f02\",\n",
    "         }\n",
    "# sns.plotting_context()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate `topA549.txt`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gather prior annotations for known human/mammalian adaptive mutations\n",
    "mutsAdaptive = pd.read_table('data/Muts_HumanAvian.txt')\n",
    "knownAdaptive = (set(mutsAdaptive[mutsAdaptive['ExptVerified']=='Yes']['Site']))\n",
    "# Gather top adaptive mutations in A549\n",
    "dmssummarydf = pd.read_csv('results/diffsel/summary_prefs_effects_diffsel.csv')\n",
    "topA549 = (set(dmssummarydf[dmssummarydf['Experimentally adaptive in']=='A549']['site']))\n",
    "# Intersect\n",
    "knownAdaptiveAndTopA549 = (knownAdaptive & topA549)\n",
    "\n",
    "# Set colors for each set of mutations\n",
    "mutsets = [((knownAdaptive, 'knownAdaptive'), 'blue'), \n",
    "           ((topA549, 'topA549'), 'red'), \n",
    "           ((knownAdaptiveAndTopA549, 'knownAdaptiveAndTopA549'), 'magenta')]\n",
    "\n",
    "# Write to file\n",
    "outfile = 'pymol/topA549.txt'\n",
    "f = open(outfile, 'w')\n",
    "f.write('metric = \\'topA549\\'\\n')\n",
    "f.write('cmd.color(\\'white\\', \\'S009\\')\\n\\n')\n",
    "for ((mutlist, mutlistname), col) in mutsets:\n",
    "    f.write('# {0}\\n'.format(mutlistname))\n",
    "    f.write('for r in [{0}]:\\n'.format(', '.join([str(mut) for mut in mutlist])))\n",
    "    f.write('\\tcmd.color(\\'{0}\\', \\'resi {{0}} and S009\\'.format(r))\\n'.format(col))\n",
    "    f.write('\\tcmd.show(\\'spheres\\', \\'resi {{0}} and S009\\'.format(r))\\n\\n')\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate pymol scripts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standard across all structures\n",
    "postamble = 'pymol/postamble.txt'\n",
    "fullstructure = 'pymol/colorfullstructure.txt'\n",
    "topA549 = 'pymol/topA549.txt'\n",
    "pumroy = 'pymol/PumroyResidues.txt'\n",
    "martin = 'pymol/MartinResidues.txt'\n",
    "specialmetricfiles = {'fullstructure': fullstructure,\n",
    "                     'topA549': topA549, \n",
    "                     'pumroy': pumroy,\n",
    "                     'martin': martin}\n",
    "# For non-specialmetrics\n",
    "diffsel_file = 'results/diffsel/summary_A549vCCL141-meansitediffsel.csv'\n",
    "metricdf = pd.read_csv(diffsel_file).sort_values('site')\n",
    "colspectrum = 'white_red'\n",
    "\n",
    "def write_pymol_script(structure, metric, metricdf, colspectrum):\n",
    "    outfile = 'pymol/{0}_map_{1}.py'.format(structure, metric)\n",
    "    preamble = 'pymol/preamble_{0}.txt'.format(structure)\n",
    "    setview = 'pymol/setviewamble_{0}.txt'.format(structure)\n",
    "\n",
    "    f = open(outfile, 'w')\n",
    "\n",
    "    with open(preamble, 'r') as addtext:\n",
    "        for line in addtext.readlines():\n",
    "            f.write(line)\n",
    "            if metric=='fullstructure' and line=='### To get PB2 only\\n': \n",
    "                #don't print lines that will remove non-PB2 subunits\n",
    "                break\n",
    "        f.write('\\n\\n')\n",
    "    \n",
    "    # Write commands to color structure\n",
    "    if metric in specialmetricfiles:\n",
    "        with open(specialmetricfiles[metric], 'r') as addtext:\n",
    "            for line in addtext.readlines():\n",
    "                f.write(line)\n",
    "            f.write('\\n\\n')\n",
    "    else:\n",
    "        f.write(\"metric = \\'{0}\\'\\n\".format(metric))\n",
    "        for row in metricdf.itertuples(index=True, name='Pandas'):\n",
    "            site, met = (getattr(row, \"site\"), getattr(row, metric))\n",
    "            f.write(\"cmd.alter(\\'resi {0}\\', \\'b = {1}\\')\\n\".format(site, met))\n",
    "        f.write('\\n')\n",
    "        f.write(\"cmd.spectrum(\\'b\\', \\'{0}\\', \\'S009\\')\\n\".format(colspectrum))\n",
    "        f.write('\\n')\n",
    "\n",
    "    with open(setview, 'r') as addtext:\n",
    "        for line in addtext.readlines():\n",
    "            f.write(line)\n",
    "    f.write('\\n')\n",
    "    with open(postamble, 'r') as addtext:\n",
    "        for line in addtext.readlines():\n",
    "            f.write(line)\n",
    "\n",
    "    f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "structures = ['4wsb', '5d98']\n",
    "metrics = ['fullstructure', 'topA549', 'positive_diffsel']\n",
    "\n",
    "for structure in structures:\n",
    "    for metric in metrics:\n",
    "        write_pymol_script(structure, metric, metricdf, colspectrum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "structures = ['6f5o']\n",
    "metrics = ['martin', 'positive_diffsel', 'topA549']\n",
    "\n",
    "for structure in structures:\n",
    "    for metric in metrics:\n",
    "        write_pymol_script(structure, metric, metricdf, colspectrum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "structures = ['4uad']\n",
    "metrics = ['pumroy', 'positive_diffsel', 'topA549']\n",
    "\n",
    "for structure in structures:\n",
    "    for metric in metrics:\n",
    "        write_pymol_script(structure, metric, metricdf, colspectrum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Are differentially selected residues on the surface of PB2/polymerase? \n",
    "\n",
    "Calculate RSA for top A549 differentially selected residues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pymol/4wsb_superS009.py  pymol/5d98_superS009.py\n"
     ]
    }
   ],
   "source": [
    "# Manually generate new pdb files for S009-PB2 superimposed on 4wsb and 5d98 structures, using following scripts.\n",
    "# S009-PB2 is saved as chain X.\n",
    "# For each structure, I made two files: one with only PB2, and one with PB2 as part of polymerase complex.\n",
    "!ls pymol/*superS009.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pymol/4wsb_superS009.pdb\t   pymol/5d98_superS009.pdb\n",
      "pymol/4wsb_superS009_S009only.pdb  pymol/5d98_superS009_S009only.pdb\n"
     ]
    }
   ],
   "source": [
    "# Convert the following files to dssp: https://swift.cmbi.umcn.nl/gv/dssp/\n",
    "!ls pymol/*superS009*pdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pymol/4wsb_superS009.dssp\t    pymol/5d98_superS009.dssp\n",
      "pymol/4wsb_superS009_S009only.dssp  pymol/5d98_superS009_S009only.dssp\n"
     ]
    }
   ],
   "source": [
    "# Converted to dssp\n",
    "!ls pymol/*superS009*dssp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   site amino_acid  ASA       RSA SS SS_class structure   context\n",
      "0     1          M  104  0.464286  -     loop      4wsb  full pol\n",
      "1     2          E  155  0.695067  H    helix      4wsb  full pol\n",
      "2     3          R   61  0.222628  H    helix      4wsb  full pol\n",
      "3     4          I    3  0.015228  H    helix      4wsb  full pol\n",
      "4     5          K   77  0.326271  H    helix      4wsb  full pol\n",
      "      site Experimentally adaptive in\n",
      "921    163                       A549\n",
      "1385   627                       A549\n",
      "1459   701                       A549\n",
      "1700   183                       A549\n",
      "2049   532                       A549\n"
     ]
    }
   ],
   "source": [
    "dssplist = []\n",
    "structures = ['4wsb', '5d98']\n",
    "contexts = [('full pol', ''), ('S009 only', '_S009only')]\n",
    "for structure in structures:\n",
    "    for context, contextlab in contexts:\n",
    "        dsspdf = dms_tools2.dssp.processDSSP('pymol/{0}_superS009{1}.dssp'.format(structure, contextlab), chain='X')\n",
    "        dsspdf['structure'] = structure\n",
    "        dsspdf['context'] = context\n",
    "        dssplist.append(dsspdf)\n",
    "dssps = pd.concat(dssplist)\n",
    "print(dssps.head())\n",
    "# Gather annotations of mutations (known, novel)\n",
    "annot = pd.read_csv('results/diffsel/summary_prefs_effects_diffsel.csv')[['site', 'Experimentally adaptive in']]\n",
    "annot = annot[annot['Experimentally adaptive in']=='A549'].drop_duplicates()\n",
    "print(annot.head())\n",
    "\n",
    "dssps = pd.merge(left=dssps, right=annot, on='site', how='left')\n",
    "dssps.fillna('None', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>site</th>\n",
       "      <th>amino_acid</th>\n",
       "      <th>ASA</th>\n",
       "      <th>RSA</th>\n",
       "      <th>SS</th>\n",
       "      <th>SS_class</th>\n",
       "      <th>structure</th>\n",
       "      <th>context</th>\n",
       "      <th>Experimentally adaptive in</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>104</td>\n",
       "      <td>0.464286</td>\n",
       "      <td>-</td>\n",
       "      <td>loop</td>\n",
       "      <td>4wsb</td>\n",
       "      <td>full pol</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>162</td>\n",
       "      <td>0.723214</td>\n",
       "      <td>-</td>\n",
       "      <td>loop</td>\n",
       "      <td>4wsb</td>\n",
       "      <td>S009 only</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>95</td>\n",
       "      <td>0.424107</td>\n",
       "      <td>-</td>\n",
       "      <td>loop</td>\n",
       "      <td>5d98</td>\n",
       "      <td>full pol</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2221</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>157</td>\n",
       "      <td>0.700893</td>\n",
       "      <td>-</td>\n",
       "      <td>loop</td>\n",
       "      <td>5d98</td>\n",
       "      <td>S009 only</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      site amino_acid  ASA       RSA SS SS_class structure    context  \\\n",
       "0        1          M  104  0.464286  -     loop      4wsb   full pol   \n",
       "733      1          M  162  0.723214  -     loop      4wsb  S009 only   \n",
       "1466     1          M   95  0.424107  -     loop      5d98   full pol   \n",
       "2221     1          M  157  0.700893  -     loop      5d98  S009 only   \n",
       "\n",
       "     Experimentally adaptive in  \n",
       "0                          None  \n",
       "733                        None  \n",
       "1466                       None  \n",
       "2221                       None  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dssps[dssps['site']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>site</th>\n",
       "      <th>amino_acid</th>\n",
       "      <th>Experimentally adaptive in</th>\n",
       "      <th>RSA_4wsbfullpol</th>\n",
       "      <th>RSA_4wsbS009only</th>\n",
       "      <th>RSA_5d98fullpol</th>\n",
       "      <th>RSA_5d98S009only</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>None</td>\n",
       "      <td>0.464286</td>\n",
       "      <td>0.723214</td>\n",
       "      <td>0.424107</td>\n",
       "      <td>0.700893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>E</td>\n",
       "      <td>None</td>\n",
       "      <td>0.695067</td>\n",
       "      <td>0.695067</td>\n",
       "      <td>0.663677</td>\n",
       "      <td>0.663677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>R</td>\n",
       "      <td>None</td>\n",
       "      <td>0.222628</td>\n",
       "      <td>0.773723</td>\n",
       "      <td>0.445255</td>\n",
       "      <td>0.791971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>0.015228</td>\n",
       "      <td>0.512690</td>\n",
       "      <td>0.035533</td>\n",
       "      <td>0.614213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>K</td>\n",
       "      <td>None</td>\n",
       "      <td>0.326271</td>\n",
       "      <td>0.457627</td>\n",
       "      <td>0.309322</td>\n",
       "      <td>0.347458</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   site amino_acid Experimentally adaptive in  RSA_4wsbfullpol  \\\n",
       "0     1          M                       None         0.464286   \n",
       "1     2          E                       None         0.695067   \n",
       "2     3          R                       None         0.222628   \n",
       "3     4          I                       None         0.015228   \n",
       "4     5          K                       None         0.326271   \n",
       "\n",
       "   RSA_4wsbS009only  RSA_5d98fullpol  RSA_5d98S009only  \n",
       "0          0.723214         0.424107          0.700893  \n",
       "1          0.695067         0.663677          0.663677  \n",
       "2          0.773723         0.445255          0.791971  \n",
       "3          0.512690         0.035533          0.614213  \n",
       "4          0.457627         0.309322          0.347458  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dssp_4wsb = pd.merge(left=dssps[(dssps['structure']=='4wsb') & (dssps['context']=='full pol')][['site', 'amino_acid', 'Experimentally adaptive in', 'RSA']],\n",
    "                    right=dssps[(dssps['structure']=='4wsb') & (dssps['context']=='S009 only')][['site', 'amino_acid', 'Experimentally adaptive in', 'RSA']],\n",
    "                    on=['site', 'amino_acid', 'Experimentally adaptive in'],\n",
    "                    suffixes=['_4wsbfullpol','_4wsbS009only']\n",
    "                   )\n",
    "\n",
    "dssp_5d98 = pd.merge(left=dssps[(dssps['structure']=='5d98') & (dssps['context']=='full pol')][['site', 'amino_acid', 'Experimentally adaptive in', 'RSA']],\n",
    "                    right=dssps[(dssps['structure']=='5d98') & (dssps['context']=='S009 only')][['site', 'amino_acid', 'Experimentally adaptive in', 'RSA']],\n",
    "                    on=['site', 'amino_acid', 'Experimentally adaptive in'],\n",
    "                    suffixes=['_5d98fullpol','_5d98S009only']\n",
    "                   )\n",
    "\n",
    "dsspwide = pd.merge(left=dssp_4wsb, right=dssp_5d98,on=['site', 'amino_acid', 'Experimentally adaptive in'])\n",
    "dsspwide.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "dssp_bystructure = pd.merge(left=dssps[dssps['context']=='full pol'][['structure', 'site', 'amino_acid', 'Experimentally adaptive in', 'RSA']],\n",
    "                    right=dssps[dssps['context']=='S009 only'][['structure', 'site', 'amino_acid', 'Experimentally adaptive in', 'RSA']],\n",
    "                    on=['structure', 'site', 'amino_acid', 'Experimentally adaptive in'],\n",
    "                    suffixes=['_fullpol','_S009only']\n",
    "                   )\n",
    "\n",
    "dssp_bystructure.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "sns.set_style(\"ticks\")\n",
    "df = dsspwide\n",
    "hue = 'Experimentally adaptive in'\n",
    "x, xlab = 'RSA_4wsbfullpol', '4wsb full pol'\n",
    "y, ylab = 'RSA_5d98fullpol', '5d98 full pol'\n",
    "hue_order = ['None', 'A549']\n",
    "hue_kws = dict(alpha=[0.2,1], facecolors=[None,None], s=[20,20], linewidths=[1,1], marker=[\"o\",\"o\"])\n",
    "lowlim, highlim = -0.05, 1.05\n",
    "g = sns.FacetGrid(df, height=3, aspect=1,\n",
    "                  hue=hue, hue_order=hue_order, hue_kws=hue_kws,\n",
    "                  palette=palette, \n",
    "                  despine=False,\n",
    "                 )\n",
    "g = (g.map(plt.scatter, x, y)\n",
    "     .set_axis_labels(xlab, ylab)\n",
    "     .set(xlim=(lowlim, highlim), ylim=(lowlim, highlim), \n",
    "         xticks=[0, 0.2, 0.4, 0.6, 0.8, 1], yticks=[0, 0.2, 0.4, 0.6, 0.8, 1])\n",
    "    )\n",
    "plt.plot([0.2, 0.2], [lowlim, highlim], linewidth=1, color='b', alpha=0.5)\n",
    "plt.plot([lowlim, highlim], [0.2, 0.2], linewidth=1, color='b', alpha=0.5)\n",
    "plt.plot([lowlim, highlim], [lowlim, highlim], linewidth=1, color='b', alpha=0.5)\n",
    "\n",
    "resi_xy = zip(df[df[hue]=='A549'][x], df[df[hue]=='A549'][y])\n",
    "resi_sites = df[df[hue]=='A549']['site']\n",
    "residues = zip(resi_sites, resi_xy)\n",
    "for (resi_site, (resi_x, resi_y)) in residues:\n",
    "    plt.text(resi_x+0.02, resi_y, resi_site, horizontalalignment='left', verticalalignment='bottom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scatterplotRSA(df, hue, x, xlab, y, ylab):\n",
    "    hue_order = ['None', 'A549']\n",
    "    hue_kws = dict(alpha=[0.2,1], facecolors=[None,None], s=[20,20], linewidths=[1,1], marker=[\"o\",\"o\"])\n",
    "    lowlim, highlim = -0.05, 1.05\n",
    "    g = sns.FacetGrid(df, height=3, aspect=1,\n",
    "                      hue=hue, hue_order=hue_order, hue_kws=hue_kws,\n",
    "                      palette=palette, \n",
    "                      despine=False,\n",
    "                     )\n",
    "    g = (g.map(plt.scatter, x, y)\n",
    "         .set_axis_labels(xlab, ylab)\n",
    "         .set(xlim=(lowlim, highlim), ylim=(lowlim, highlim), \n",
    "             xticks=[0, 0.2, 0.4, 0.6, 0.8, 1], yticks=[0, 0.2, 0.4, 0.6, 0.8, 1])\n",
    "        )\n",
    "    plt.plot([0.2, 0.2], [lowlim, highlim], linewidth=1, color='b', alpha=0.5)\n",
    "    plt.plot([lowlim, highlim], [0.2, 0.2], linewidth=1, color='b', alpha=0.5)\n",
    "    plt.plot([lowlim, highlim], [lowlim, highlim], linewidth=1, color='b', alpha=0.5)\n",
    "\n",
    "    resi_xy = zip(df[df[hue]=='A549'][x], df[df[hue]=='A549'][y])\n",
    "    resi_sites = df[df[hue]=='A549']['site']\n",
    "    residues = zip(resi_sites, resi_xy)\n",
    "    for (resi_site, (resi_x, resi_y)) in residues:\n",
    "        plt.text(resi_x+0.02, resi_y, resi_site, horizontalalignment='left', verticalalignment='bottom')\n",
    "    plt.savefig('pymol/{0}_{1}.pdf'.format(x, y), dpi=300, bbox_inches='tight')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"ticks\")\n",
    "df = dsspwide\n",
    "hue = 'Experimentally adaptive in'\n",
    "x, xlab = 'RSA_4wsbfullpol', '4wsb full pol'\n",
    "y, ylab = 'RSA_5d98fullpol', '5d98 full pol'\n",
    "scatterplotRSA(df, hue, x, xlab, y, ylab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, xlab = 'RSA_4wsbfullpol', '4wsb full pol'\n",
    "y, ylab = 'RSA_4wsbS009only', '4wsb PB2 only'\n",
    "scatterplotRSA(df, hue, x, xlab, y, ylab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, xlab = 'RSA_5d98fullpol', '5d98 full pol'\n",
    "y, ylab = 'RSA_5d98S009only', '5d98 PB2 only'\n",
    "scatterplotRSA(df, hue, x, xlab, y, ylab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "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>site</th>\n",
       "      <th>amino_acid</th>\n",
       "      <th>Experimentally adaptive in</th>\n",
       "      <th>RSA_4wsbfullpol</th>\n",
       "      <th>RSA_4wsbS009only</th>\n",
       "      <th>RSA_5d98fullpol</th>\n",
       "      <th>RSA_5d98S009only</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>D</td>\n",
       "      <td>A549</td>\n",
       "      <td>0.590674</td>\n",
       "      <td>0.590674</td>\n",
       "      <td>0.450777</td>\n",
       "      <td>0.450777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>69</td>\n",
       "      <td>E</td>\n",
       "      <td>A549</td>\n",
       "      <td>0.399103</td>\n",
       "      <td>0.399103</td>\n",
       "      <td>0.300448</td>\n",
       "      <td>0.300448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>82</td>\n",
       "      <td>N</td>\n",
       "      <td>A549</td>\n",
       "      <td>0.374359</td>\n",
       "      <td>0.476923</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>156</td>\n",
       "      <td>A</td>\n",
       "      <td>A549</td>\n",
       "      <td>0.534884</td>\n",
       "      <td>0.534884</td>\n",
       "      <td>0.627907</td>\n",
       "      <td>0.627907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>158</td>\n",
       "      <td>E</td>\n",
       "      <td>A549</td>\n",
       "      <td>0.201794</td>\n",
       "      <td>0.201794</td>\n",
       "      <td>0.103139</td>\n",
       "      <td>0.103139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>163</td>\n",
       "      <td>I</td>\n",
       "      <td>A549</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060914</td>\n",
       "      <td>0.040609</td>\n",
       "      <td>0.086294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>169</td>\n",
       "      <td>P</td>\n",
       "      <td>A549</td>\n",
       "      <td>0.327044</td>\n",
       "      <td>0.471698</td>\n",
       "      <td>0.201258</td>\n",
       "      <td>0.201258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>176</td>\n",
       "      <td>I</td>\n",
       "      <td>A549</td>\n",
       "      <td>0.659898</td>\n",
       "      <td>0.746193</td>\n",
       "      <td>0.279188</td>\n",
       "      <td>0.416244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>182</td>\n",
       "      <td>Q</td>\n",
       "      <td>A549</td>\n",
       "      <td>0.004444</td>\n",
       "      <td>0.004444</td>\n",
       "      <td>0.106667</td>\n",
       "      <td>0.106667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>183</td>\n",
       "      <td>L</td>\n",
       "      <td>A549</td>\n",
       "      <td>0.258706</td>\n",
       "      <td>0.258706</td>\n",
       "      <td>0.064677</td>\n",
       "      <td>0.064677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>190</td>\n",
       "      <td>K</td>\n",
       "      <td>A549</td>\n",
       "      <td>0.588983</td>\n",
       "      <td>0.588983</td>\n",
       "      <td>0.033898</td>\n",
       "      <td>0.033898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>292</td>\n",
       "      <td>I</td>\n",
       "      <td>A549</td>\n",
       "      <td>0.441624</td>\n",
       "      <td>0.441624</td>\n",
       "      <td>0.187817</td>\n",
       "      <td>0.187817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>355</td>\n",
       "      <td>R</td>\n",
       "      <td>A549</td>\n",
       "      <td>0.587591</td>\n",
       "      <td>0.587591</td>\n",
       "      <td>0.419708</td>\n",
       "      <td>0.419708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>521</td>\n",
       "      <td>T</td>\n",
       "      <td>A549</td>\n",
       "      <td>0.418605</td>\n",
       "      <td>0.418605</td>\n",
       "      <td>0.604651</td>\n",
       "      <td>0.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>522</td>\n",
       "      <td>Q</td>\n",
       "      <td>A549</td>\n",
       "      <td>0.168889</td>\n",
       "      <td>0.168889</td>\n",
       "      <td>0.577778</td>\n",
       "      <td>0.577778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>532</td>\n",
       "      <td>S</td>\n",
       "      <td>A549</td>\n",
       "      <td>0.541935</td>\n",
       "      <td>0.541935</td>\n",
       "      <td>0.458065</td>\n",
       "      <td>0.458065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>627</td>\n",
       "      <td>E</td>\n",
       "      <td>A549</td>\n",
       "      <td>0.403587</td>\n",
       "      <td>0.403587</td>\n",
       "      <td>0.573991</td>\n",
       "      <td>0.573991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>669</td>\n",
       "      <td>G</td>\n",
       "      <td>A549</td>\n",
       "      <td>0.288462</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.336538</td>\n",
       "      <td>0.336538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>676</td>\n",
       "      <td>T</td>\n",
       "      <td>A549</td>\n",
       "      <td>0.261628</td>\n",
       "      <td>0.261628</td>\n",
       "      <td>0.069767</td>\n",
       "      <td>0.069767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>684</td>\n",
       "      <td>A</td>\n",
       "      <td>A549</td>\n",
       "      <td>0.891473</td>\n",
       "      <td>0.891473</td>\n",
       "      <td>0.449612</td>\n",
       "      <td>0.449612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>698</td>\n",
       "      <td>G</td>\n",
       "      <td>A549</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0.211538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>701</td>\n",
       "      <td>D</td>\n",
       "      <td>A549</td>\n",
       "      <td>0.409326</td>\n",
       "      <td>0.409326</td>\n",
       "      <td>0.253886</td>\n",
       "      <td>0.419689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     site amino_acid Experimentally adaptive in  RSA_4wsbfullpol  \\\n",
       "8       9          D                       A549         0.590674   \n",
       "68     69          E                       A549         0.399103   \n",
       "81     82          N                       A549         0.374359   \n",
       "155   156          A                       A549         0.534884   \n",
       "157   158          E                       A549         0.201794   \n",
       "162   163          I                       A549         0.000000   \n",
       "168   169          P                       A549         0.327044   \n",
       "175   176          I                       A549         0.659898   \n",
       "181   182          Q                       A549         0.004444   \n",
       "182   183          L                       A549         0.258706   \n",
       "189   190          K                       A549         0.588983   \n",
       "291   292          I                       A549         0.441624   \n",
       "354   355          R                       A549         0.587591   \n",
       "512   521          T                       A549         0.418605   \n",
       "513   522          Q                       A549         0.168889   \n",
       "523   532          S                       A549         0.541935   \n",
       "618   627          E                       A549         0.403587   \n",
       "660   669          G                       A549         0.288462   \n",
       "667   676          T                       A549         0.261628   \n",
       "675   684          A                       A549         0.891473   \n",
       "689   698          G                       A549         0.384615   \n",
       "692   701          D                       A549         0.409326   \n",
       "\n",
       "     RSA_4wsbS009only  RSA_5d98fullpol  RSA_5d98S009only  \n",
       "8            0.590674         0.450777          0.450777  \n",
       "68           0.399103         0.300448          0.300448  \n",
       "81           0.476923         0.400000          0.400000  \n",
       "155          0.534884         0.627907          0.627907  \n",
       "157          0.201794         0.103139          0.103139  \n",
       "162          0.060914         0.040609          0.086294  \n",
       "168          0.471698         0.201258          0.201258  \n",
       "175          0.746193         0.279188          0.416244  \n",
       "181          0.004444         0.106667          0.106667  \n",
       "182          0.258706         0.064677          0.064677  \n",
       "189          0.588983         0.033898          0.033898  \n",
       "291          0.441624         0.187817          0.187817  \n",
       "354          0.587591         0.419708          0.419708  \n",
       "512          0.418605         0.604651          0.604651  \n",
       "513          0.168889         0.577778          0.577778  \n",
       "523          0.541935         0.458065          0.458065  \n",
       "618          0.403587         0.573991          0.573991  \n",
       "660          0.625000         0.336538          0.336538  \n",
       "667          0.261628         0.069767          0.069767  \n",
       "675          0.891473         0.449612          0.449612  \n",
       "689          0.384615         0.009615          0.211538  \n",
       "692          0.409326         0.253886          0.419689  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsspwide[dsspwide['Experimentally adaptive in']=='A549']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Copy files to paper figures directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "paperdir = './paper'\n",
    "figuresdir = os.path.join(paperdir, 'figures/')\n",
    "myfiguresdir = os.path.join(figuresdir, 'Fig5/')\n",
    "if not os.path.isdir(myfiguresdir):\n",
    "    os.mkdir(myfiguresdir)\n",
    "\n",
    "filespy = !ls pymol/*.py\n",
    "filespdb = !ls pymol/*.pdb\n",
    "filespdf = !ls pymol/*.pdf\n",
    "for f in filespy + filespdb + filespdf:\n",
    "    shutil.copy(f, myfiguresdir)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pymol/4uad_map_positive_diffsel.py',\n",
       " 'pymol/4uad_map_pumroy.py',\n",
       " 'pymol/4uad_map_topA549.py',\n",
       " 'pymol/4wsb_map_fullstructure.py',\n",
       " 'pymol/4wsb_map_positive_diffsel.py',\n",
       " 'pymol/4wsb_map_topA549.py',\n",
       " 'pymol/4wsb_superS009.py',\n",
       " 'pymol/5d98_map_fullstructure.py',\n",
       " 'pymol/5d98_map_positive_diffsel.py',\n",
       " 'pymol/5d98_map_topA549.py',\n",
       " 'pymol/5d98_superS009.py',\n",
       " 'pymol/6f5o_map_martin.py',\n",
       " 'pymol/6f5o_map_positive_diffsel.py',\n",
       " 'pymol/6f5o_map_topA549.py',\n",
       " 'pymol/4wsb_superS009.pdb',\n",
       " 'pymol/4wsb_superS009_S009only.pdb',\n",
       " 'pymol/5d98_superS009.pdb',\n",
       " 'pymol/5d98_superS009_S009only.pdb',\n",
       " 'pymol/S009PB2_4uae.pdb',\n",
       " 'pymol/S009PB2_4wsb.pdb',\n",
       " 'pymol/S009PB2_5d98.pdb',\n",
       " 'pymol/S009PB2_6f5o.pdb',\n",
       " 'pymol/RSA_4wsbfullpol_RSA_4wsbS009only.pdf',\n",
       " 'pymol/RSA_4wsbfullpol_RSA_5d98fullpol.pdf',\n",
       " 'pymol/RSA_5d98fullpol_RSA_5d98S009only.pdf']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filespy + filespdb + filespdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.6.5"
  }
 },
 "nbformat": 4,
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