analysis/2_fragsys_BS_feats.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EXPLORING GENERAL PROPERTIES OF LIGAND BINDING SITES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we explore the variation across binding sites in terms of size (aa), RSA, evolutionary divergence, and enrichment in missense variation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IMPORTING NECESSARY PACKAGES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fragsys_analysis import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### READING INPUT DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "main_dir = \"./../\"\n",
    "results_dir = os.path.join(main_dir, \"results\")\n",
    "figs_dir = os.path.join(main_dir, \"figs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_bss = pd.read_pickle(os.path.join(results_dir, \"all_bss.pkl\"))\n",
    "all_bs_ress = pd.read_pickle(os.path.join(results_dir, \"all_bs_ress.pkl\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293\n"
     ]
    }
   ],
   "source": [
    "print(len(all_bss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>bs_id</th>\n",
       "      <th>vars</th>\n",
       "      <th>occ</th>\n",
       "      <th>vars_per_occ</th>\n",
       "      <th>MES</th>\n",
       "      <th>p</th>\n",
       "      <th>norm_shenkin_rel</th>\n",
       "      <th>shenkin_ci</th>\n",
       "      <th>MES_ci</th>\n",
       "      <th>number_bs_res</th>\n",
       "      <th>number_ligs</th>\n",
       "      <th>prop_ligs</th>\n",
       "      <th>protein</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BS0</td>\n",
       "      <td>168</td>\n",
       "      <td>420</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.2065</td>\n",
       "      <td>0.0281</td>\n",
       "      <td>54.0885</td>\n",
       "      <td>10.6309</td>\n",
       "      <td>0.1831</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>H0Y4R8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BS0</td>\n",
       "      <td>24</td>\n",
       "      <td>48</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.0623</td>\n",
       "      <td>0.8951</td>\n",
       "      <td>20.8011</td>\n",
       "      <td>9.8766</td>\n",
       "      <td>0.5220</td>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7368</td>\n",
       "      <td>O43809</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BS1</td>\n",
       "      <td>13</td>\n",
       "      <td>26</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.0587</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>28.6694</td>\n",
       "      <td>12.4626</td>\n",
       "      <td>0.6883</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1053</td>\n",
       "      <td>O43809</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  bs_id  vars  occ  vars_per_occ     MES       p  norm_shenkin_rel  \\\n",
       "0   BS0   168  420           0.4  0.2065  0.0281           54.0885   \n",
       "1   BS0    24   48           0.5 -0.0623  0.8951           20.8011   \n",
       "2   BS1    13   26           0.5 -0.0587  1.0000           28.6694   \n",
       "\n",
       "   shenkin_ci  MES_ci  number_bs_res  number_ligs  prop_ligs protein group  \n",
       "0     10.6309  0.1831              6            1     1.0000  H0Y4R8     0  \n",
       "1      9.8766  0.5220             24           14     0.7368  O43809     0  \n",
       "2     12.4626  0.6883             13            2     0.1053  O43809     0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_bss.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LIGAND BINDING SITE SIZE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293\n",
      "\tMIN = 2\tMEAN = 10.57\tMEDIAN = 9\tMAX = 40\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "D = all_bss.number_bs_res\n",
    "print(len(D))\n",
    "\n",
    "bns_size = np.linspace(0, 50, 50)\n",
    "plot_hist(\n",
    "    D, \"Binding site size\", bns_size, \"cornflowerblue\", dpi = 100,\n",
    "    figsize = (5, 5)#, out = os.path.join(figs_dir, \"BSS_size.png\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LIGAND BINDING SITE AVERAGE DIVERGENCE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293\n",
      "\tMIN = 0.0\tMEAN = 40.93\tMEDIAN = 39.8\tMAX = 80.9\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "D = all_bss.norm_shenkin_rel\n",
    "print(len(D))\n",
    "\n",
    "bns_shenk = np.linspace(0, 90, 45)\n",
    "plot_hist(\n",
    "    D, \"Average site NShenkin\", bns_shenk, \"lightgreen\", dpi = 100,\n",
    "    yticks = list(np.linspace(0,15,4)), figsize = (5, 5),# out = os.path.join(figs_dir, \"BSS_shenk.png\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LIGAND BINDING SITE AVERAGE MISSENSE ENRICHMENT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293\n",
      "\tMIN = -0.71\tMEAN = 0.03\tMEDIAN = 0.01\tMAX = 1.04\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "D = all_bss.MES\n",
    "print(len(D))\n",
    "\n",
    "bns_mes = np.linspace(-0.75, 1.00, 50)\n",
    "plot_hist(D, \"Average site MES\", bns_mes, \"khaki\", dpi = 100,\n",
    "         figsize = (5, 5),# out = os.path.join(figs_dir, \"BSS_MES.png\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LIGAND BINDING SITE AVERAGE RELATIVE SOLVENT ACCESSIBILITY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14172, 62)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_bs_ress.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "rsa_profs = get_rsa_profiles(all_bss, all_bs_ress)\n",
    "average_rsas = [statistics.median(v) for v in rsa_profs.values()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293\n",
      "\tMIN = 4.3\tMEAN = 32.07\tMEDIAN = 31.1\tMAX = 81.7\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "D_un = average_rsas\n",
    "print(len(D))\n",
    "\n",
    "bns_RSA = np.linspace(0, 100, 50)\n",
    "plot_hist(D_un, \"Median site RSA %\", bns_RSA, \"tomato\", dpi = 100,\n",
    "         figsize = (5, 5),# out = os.path.join(figs_dir, \"BSS_RSA.png\")\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:myenv2]",
   "language": "python",
   "name": "conda-env-myenv2-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}