Bit_scores.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#run random_sequences.ipynb first\n",
    "#random sequences\n",
    "\n",
    "rnd = pd.read_pickle('results/random_seq.pkl.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pET = pd.read_pickle('results/pET_all_analysis.pkl.gz')\n",
    "eSOL = pd.read_pickle('results/ecoli_all_analysis.pkl.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pET_insol = pET.loc[pET['Solubility'] == 0]\n",
    "pET_sol = pET.loc[pET['Solubility'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3978, 19), (8238, 19), (3198, 19), (11900, 1))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pET_insol.shape, pET_sol.shape, eSOL.shape, rnd.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "d1 = Counter(pET_sol['Protein'].str.cat())\n",
    "pET_sol_count = pd.DataFrame.from_dict(d1, orient='index')\n",
    "\n",
    "\n",
    "d2 = Counter(pET_insol['Protein'].str.cat())\n",
    "pET_insol_count = pd.DataFrame.from_dict(d2, orient='index')\n",
    "\n",
    "\n",
    "d3 = Counter(eSOL['Sequence'].str.cat())\n",
    "eSOL_count = pd.DataFrame.from_dict(d3, orient='index')\n",
    "\n",
    "d4 = Counter(rnd['Protein'].str.cat())\n",
    "rnd_count = pd.DataFrame.from_dict(d4, orient='index')\n",
    "\n",
    "\n",
    "#normalise\n",
    "pET_sol_count[0] = pET_sol_count[0]/pET_sol_count[0].sum()\n",
    "pET_insol_count[0] = pET_insol_count[0]/pET_insol_count[0].sum()\n",
    "eSOL_count[0] = eSOL_count[0]/eSOL_count[0].sum()\n",
    "rnd_count[0] = rnd_count[0]/rnd_count[0].sum()\n",
    "\n",
    "sol_dic_log = {}\n",
    "insol_dic_log = {}\n",
    "\n",
    "\n",
    "for k, v in enumerate(pET_sol_count.index):\n",
    "    sol_dic_log[v] = np.log2(pET_sol_count.loc[v]/eSOL_count.loc[v])\n",
    "    \n",
    "    \n",
    "for k, v in enumerate(pET_insol_count.index):\n",
    "    insol_dic_log[v] = np.log2(pET_insol_count.loc[v]/eSOL_count.loc[v])\n",
    "\n",
    "insol_dic_log_df = pd.DataFrame.from_dict(insol_dic_log, orient='index').sort_values(0)\n",
    "insol_dic_log_df = insol_dic_log_df.rename(columns={0:'Insoluble'})\n",
    "\n",
    "sol_dic_log_df = pd.DataFrame.from_dict(sol_dic_log, orient='index').sort_values(0)\n",
    "sol_dic_log_df = sol_dic_log_df.rename(columns={0:'Soluble'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = pd.merge(sol_dic_log_df.reset_index(), insol_dic_log_df.reset_index(), on=\"index\").plot.bar()\n",
    "ax1.set_xticklabels(sol_dic_log_df.reset_index()['index'])\n",
    "ax1.set(ylabel='log2(f_(pET)/f_(ecoli))', xlabel='Amino acids')\n",
    "ax1.get_figure().savefig('figs/Aa_comparsion_ecoli_pET_log.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#AA count in all pET and eSOL sequences\n",
    "\n",
    "d0 = Counter(pET['Protein'].str.cat())\n",
    "pET_count = pd.DataFrame.from_dict(d0, orient='index')\n",
    "pET_count[0] = pET_count[0]/pET_count[0].sum()\n",
    "\n",
    "d01 = Counter(eSOL['Sequence'].str.cat())\n",
    "eSOL_count = pd.DataFrame.from_dict(d01, orient='index')\n",
    "eSOL_count[0] = eSOL_count[0]/eSOL_count[0].sum()\n",
    "\n",
    "\n",
    "merged_df_counts = pd.merge(eSOL_count, pET_count, left_index=True,right_index=True)\n",
    "merged_df_counts.rename(columns={'0_x':'eSOL normalised count', '0_y':'pET normalised count'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'eSOL normalised count - pET normalised count')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "merged_df_counts['Delta'] = merged_df_counts['eSOL normalised count'] - merged_df_counts['pET normalised count']\n",
    "\n",
    "ax = merged_df_counts['Delta'].sort_values(ascending=False).plot(kind='bar')\n",
    "ax.set_xticklabels(merged_df_counts.sort_values(by=\"Delta\", ascending=False).index)\n",
    "ax.set_xlabel(\"Residue\")\n",
    "ax.set_ylabel(\"eSOL normalised count - pET normalised count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>eSOL normalised count</th>\n",
       "      <th>pET normalised count</th>\n",
       "      <th>Delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.094465</td>\n",
       "      <td>0.076149</td>\n",
       "      <td>0.018316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>0.076750</td>\n",
       "      <td>0.068125</td>\n",
       "      <td>0.008625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <td>0.101891</td>\n",
       "      <td>0.095169</td>\n",
       "      <td>0.006722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>0.061944</td>\n",
       "      <td>0.056307</td>\n",
       "      <td>0.005637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>0.043463</td>\n",
       "      <td>0.038340</td>\n",
       "      <td>0.005123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>0.028535</td>\n",
       "      <td>0.025247</td>\n",
       "      <td>0.003288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>0.053178</td>\n",
       "      <td>0.050834</td>\n",
       "      <td>0.002345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P</th>\n",
       "      <td>0.044812</td>\n",
       "      <td>0.042873</td>\n",
       "      <td>0.001939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>0.013210</td>\n",
       "      <td>0.011847</td>\n",
       "      <td>0.001363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.014077</td>\n",
       "      <td>0.012990</td>\n",
       "      <td>0.001087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>0.037929</td>\n",
       "      <td>0.038030</td>\n",
       "      <td>-0.000100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V</th>\n",
       "      <td>0.064703</td>\n",
       "      <td>0.065815</td>\n",
       "      <td>-0.001112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>0.053962</td>\n",
       "      <td>0.055299</td>\n",
       "      <td>-0.001337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.054498</td>\n",
       "      <td>0.056602</td>\n",
       "      <td>-0.002104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>0.056034</td>\n",
       "      <td>0.058957</td>\n",
       "      <td>-0.002923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>0.033638</td>\n",
       "      <td>0.037671</td>\n",
       "      <td>-0.004033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>0.026324</td>\n",
       "      <td>0.032015</td>\n",
       "      <td>-0.005691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>0.040355</td>\n",
       "      <td>0.051231</td>\n",
       "      <td>-0.010877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.057447</td>\n",
       "      <td>0.070238</td>\n",
       "      <td>-0.012791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K</th>\n",
       "      <td>0.042784</td>\n",
       "      <td>0.056263</td>\n",
       "      <td>-0.013479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   eSOL normalised count  pET normalised count     Delta\n",
       "A               0.094465              0.076149  0.018316\n",
       "G               0.076750              0.068125  0.008625\n",
       "L               0.101891              0.095169  0.006722\n",
       "R               0.061944              0.056307  0.005637\n",
       "Q               0.043463              0.038340  0.005123\n",
       "M               0.028535              0.025247  0.003288\n",
       "T               0.053178              0.050834  0.002345\n",
       "P               0.044812              0.042873  0.001939\n",
       "W               0.013210              0.011847  0.001363\n",
       "C               0.014077              0.012990  0.001087\n",
       "N               0.037929              0.038030 -0.000100\n",
       "V               0.064703              0.065815 -0.001112\n",
       "I               0.053962              0.055299 -0.001337\n",
       "D               0.054498              0.056602 -0.002104\n",
       "S               0.056034              0.058957 -0.002923\n",
       "F               0.033638              0.037671 -0.004033\n",
       "Y               0.026324              0.032015 -0.005691\n",
       "H               0.040355              0.051231 -0.010877\n",
       "E               0.057447              0.070238 -0.012791\n",
       "K               0.042784              0.056263 -0.013479"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_counts.sort_values(by=\"Delta\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SpearmanrResult(correlation=0.4917293233082706, pvalue=0.027658227513046127)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Correlation between weights and frequency of AA in pET\n",
    "\n",
    "from scipy.stats import spearmanr\n",
    "\n",
    "flexibilities_smith = {'A': 0.717, 'C': 0.668, 'E': 0.963, 'D': 0.921,\\\n",
    "                         'G': 0.843, 'F': 0.599, 'I': 0.632, 'H': 0.754, \\\n",
    "                         'K': 0.912, 'M': 0.685, 'L': 0.681, 'N': 0.851,\\\n",
    "                         'Q': 0.849, 'P': 0.85, 'S': 0.84, 'R': 0.814, 'T': 0.758, \\\n",
    "                         'W': 0.626, 'V': 0.619, 'Y': 0.615}\n",
    "\n",
    "freq_ = []\n",
    "for i, v in flexibilities_smith.items():\n",
    "    freq_.append(pET_count.loc[i, 0])\n",
    "\n",
    "    \n",
    "weights_from_optimisation_on_pET = [0.8356471476582918,\n",
    " 0.5208088354857734,\n",
    " 0.9876987431418378,\n",
    " 0.9079044671339564,\n",
    " 0.7997168496420723,\n",
    " 0.5849790194237692,\n",
    " 0.6784124413866582,\n",
    " 0.8947913996466419,\n",
    " 0.9267104557513497,\n",
    " 0.6296623675420369,\n",
    " 0.6554221515081433,\n",
    " 0.8597433107431216,\n",
    " 0.789434648348208,\n",
    " 0.8235328714705341,\n",
    " 0.7440908318492778,\n",
    " 0.7712466317693457,\n",
    " 0.8096922697856334,\n",
    " 0.6374678690957594,\n",
    " 0.7357837119163659,\n",
    " 0.6112801822947587]\n",
    "\n",
    "spearmanr(weights_from_optimisation_on_pET, freq_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SpearmanrResult(correlation=0.26766917293233083, pvalue=0.2538915426582106)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Correlation between eSOl weights and frequency\n",
    "freq_esol = []\n",
    "for i, v in flexibilities_smith.items():\n",
    "    freq_esol.append(eSOL_count.loc[i, 0])\n",
    "freq_esol\n",
    "\n",
    "weights_from_optimisation_on_eSOL = [0.7753128 , 0.75267699, 0.96855732, 0.99198437, 0.79871142,\n",
    "       0.58665948, 0.65250384, 0.95981116, 0.88626844, 0.69924406,\n",
    "       0.68868975, 0.79789734, 0.77431079, 0.83904596, 0.7736663 ,\n",
    "       0.6864538 , 0.79759916, 0.51460814, 0.73007091, 0.53931017]\n",
    "\n",
    "spearmanr(weights_from_optimisation_on_eSOL, freq_esol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "## optimised weights have no statistically significant correlation with amino acid frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 4))\n",
    "ax = sns.scatterplot(freq_, [v for k, v in flexibilities_smith.items()],\\\n",
    "                     hue=weights_from_optimisation_on_pET, label=\"Optimised weights from pET\")\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "ax.set_xlabel('Normalised frequency in pET')\n",
    "ax.set_ylabel('Flexibility (Smith et al.)')\n",
    "for i, v in enumerate([k for k, v in flexibilities_smith.items()]):\n",
    "    ax.annotate(' ' +v, [freq_[i], [v for k, v in flexibilities_smith.items()][i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 4))\n",
    "ax = sns.scatterplot(freq_esol, [v for k, v in flexibilities_smith.items()],\\\n",
    "                     hue=weights_from_optimisation_on_eSOL, label=\"Optimised weights from eSOL\")\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "ax.set_xlabel('Normalised frequency in eSOL')\n",
    "ax.set_ylabel('Flexibility (Smith et al.)')\n",
    "for i, v in enumerate([k for k, v in flexibilities_smith.items()]):\n",
    "    ax.annotate(' ' +v, [freq_esol[i], [v for k, v in flexibilities_smith.items()][i]])"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}