{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ascertaining the generalisability of the structure data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "In this notebook, we set out to ascertain the generalisability of our data and analysis to the broader TCR space.\n",
    "We compared sequence properties of our dataset to a background of TCR sequences sampled from [OTS](https://opig.stats.ox.ac.uk/webapps/ots/ots_paired/) and also look at how the selected structures fit in the overall available TCR structures space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmsd(x: np.ndarray) -> float:\n",
    "    '''Compute the RMSD of an array.'''\n",
    "    x_bar = np.mean(x)\n",
    "    n = len(x)\n",
    "    return np.sqrt(np.sum((x - x_bar) ** 2) / n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load OTS data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>cdr1_aa_alpha</th>\n",
       "      <th>cdr2_aa_alpha</th>\n",
       "      <th>cdr3_aa_alpha</th>\n",
       "      <th>cdr1_aa_beta</th>\n",
       "      <th>cdr2_aa_beta</th>\n",
       "      <th>cdr3_aa_beta</th>\n",
       "      <th>v_call_alpha</th>\n",
       "      <th>v_call_beta</th>\n",
       "      <th>j_call_alpha</th>\n",
       "      <th>j_call_beta</th>\n",
       "      <th>Species</th>\n",
       "      <th>sample_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TISGNEY</td>\n",
       "      <td>GLKNN</td>\n",
       "      <td>IVRVVWGGGADGLT</td>\n",
       "      <td>SGHDN</td>\n",
       "      <td>FVKESK</td>\n",
       "      <td>ASSLLGVSTDTQY</td>\n",
       "      <td>TRAV26-1*01</td>\n",
       "      <td>TRBV14*01</td>\n",
       "      <td>TRAJ45*01</td>\n",
       "      <td>TRBJ2-3*01</td>\n",
       "      <td>human</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NSMFDY</td>\n",
       "      <td>ISSIKDK</td>\n",
       "      <td>AASAQGGSYIPT</td>\n",
       "      <td>LGHDT</td>\n",
       "      <td>YNNKEL</td>\n",
       "      <td>ASSRRPTDTQY</td>\n",
       "      <td>TRAV29/DV5*04</td>\n",
       "      <td>TRBV3-1*01</td>\n",
       "      <td>TRAJ6*01</td>\n",
       "      <td>TRBJ2-3*01</td>\n",
       "      <td>human</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SSNFYA</td>\n",
       "      <td>MTLNGDE</td>\n",
       "      <td>ALGRNSGNTPLV</td>\n",
       "      <td>SGHAT</td>\n",
       "      <td>FQNNGV</td>\n",
       "      <td>ASNLAGAYEQY</td>\n",
       "      <td>TRAV24*01</td>\n",
       "      <td>TRBV11-2*01</td>\n",
       "      <td>TRAJ29*01</td>\n",
       "      <td>TRBJ2-7*01</td>\n",
       "      <td>human</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SSVPPY</td>\n",
       "      <td>YTTGATLV</td>\n",
       "      <td>AVSEPGSQGNLI</td>\n",
       "      <td>DFQATT</td>\n",
       "      <td>SNEGSKA</td>\n",
       "      <td>SALGQPLGETQY</td>\n",
       "      <td>TRAV8-4*01</td>\n",
       "      <td>TRBV20-1*02</td>\n",
       "      <td>TRAJ42*01</td>\n",
       "      <td>TRBJ2-5*01</td>\n",
       "      <td>human</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TSGFNG</td>\n",
       "      <td>NVLDGL</td>\n",
       "      <td>AVRDLRGSQGNLI</td>\n",
       "      <td>MGHRA</td>\n",
       "      <td>YSYEKL</td>\n",
       "      <td>ASSQAPQGADTQY</td>\n",
       "      <td>TRAV1-2*01</td>\n",
       "      <td>TRBV4-1*01</td>\n",
       "      <td>TRAJ42*01</td>\n",
       "      <td>TRBJ2-3*01</td>\n",
       "      <td>human</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>SSVSVY</td>\n",
       "      <td>YLSGSTLV</td>\n",
       "      <td>AVSVRGSQGNLI</td>\n",
       "      <td>MNHNY</td>\n",
       "      <td>SVGAGI</td>\n",
       "      <td>ASSYGNREGYT</td>\n",
       "      <td>TRAV8-6*01</td>\n",
       "      <td>TRBV6-6*01</td>\n",
       "      <td>TRAJ42*01</td>\n",
       "      <td>TRBJ1-2*01</td>\n",
       "      <td>human</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>NSAFQY</td>\n",
       "      <td>TYSSGN</td>\n",
       "      <td>AMRRNTNAGKST</td>\n",
       "      <td>DFQATT</td>\n",
       "      <td>SNEGSKA</td>\n",
       "      <td>SAPTDPAGTEAF</td>\n",
       "      <td>TRAV12-3*01</td>\n",
       "      <td>TRBV20-1*01</td>\n",
       "      <td>TRAJ27*01</td>\n",
       "      <td>TRBJ1-1*01</td>\n",
       "      <td>human</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>SVFSS</td>\n",
       "      <td>VVTGGEV</td>\n",
       "      <td>AAIIQGAQKLV</td>\n",
       "      <td>MDHEN</td>\n",
       "      <td>SYDVKM</td>\n",
       "      <td>ASSYQYYEQY</td>\n",
       "      <td>TRAV27*01</td>\n",
       "      <td>TRBV28*01</td>\n",
       "      <td>TRAJ54*01</td>\n",
       "      <td>TRBJ2-7*01</td>\n",
       "      <td>human</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>DSAIYN</td>\n",
       "      <td>IQSSQRE</td>\n",
       "      <td>APYSGGGADGLT</td>\n",
       "      <td>SEHNR</td>\n",
       "      <td>FQNEAQ</td>\n",
       "      <td>ASSSGTGGVEHGYT</td>\n",
       "      <td>TRAV21*02</td>\n",
       "      <td>TRBV7-9*01</td>\n",
       "      <td>TRAJ45*01</td>\n",
       "      <td>TRBJ1-2*01</td>\n",
       "      <td>human</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>NSAFQY</td>\n",
       "      <td>TYSSGN</td>\n",
       "      <td>AMTGAGSYQLT</td>\n",
       "      <td>LGHNA</td>\n",
       "      <td>YSLEER</td>\n",
       "      <td>ASSQELLGAELHYGYT</td>\n",
       "      <td>TRAV12-3*01</td>\n",
       "      <td>TRBV4-3*01</td>\n",
       "      <td>TRAJ28*01</td>\n",
       "      <td>TRBJ1-2*01</td>\n",
       "      <td>human</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cdr1_aa_alpha cdr2_aa_alpha   cdr3_aa_alpha cdr1_aa_beta cdr2_aa_beta  \\\n",
       "0          TISGNEY         GLKNN  IVRVVWGGGADGLT        SGHDN       FVKESK   \n",
       "1           NSMFDY       ISSIKDK    AASAQGGSYIPT        LGHDT       YNNKEL   \n",
       "2           SSNFYA       MTLNGDE    ALGRNSGNTPLV        SGHAT       FQNNGV   \n",
       "3           SSVPPY      YTTGATLV    AVSEPGSQGNLI       DFQATT      SNEGSKA   \n",
       "4           TSGFNG        NVLDGL   AVRDLRGSQGNLI        MGHRA       YSYEKL   \n",
       "...            ...           ...             ...          ...          ...   \n",
       "9995        SSVSVY      YLSGSTLV    AVSVRGSQGNLI        MNHNY       SVGAGI   \n",
       "9996        NSAFQY        TYSSGN    AMRRNTNAGKST       DFQATT      SNEGSKA   \n",
       "9997         SVFSS       VVTGGEV     AAIIQGAQKLV        MDHEN       SYDVKM   \n",
       "9998        DSAIYN       IQSSQRE    APYSGGGADGLT        SEHNR       FQNEAQ   \n",
       "9999        NSAFQY        TYSSGN     AMTGAGSYQLT        LGHNA       YSLEER   \n",
       "\n",
       "          cdr3_aa_beta   v_call_alpha  v_call_beta j_call_alpha j_call_beta  \\\n",
       "0        ASSLLGVSTDTQY    TRAV26-1*01    TRBV14*01    TRAJ45*01  TRBJ2-3*01   \n",
       "1          ASSRRPTDTQY  TRAV29/DV5*04   TRBV3-1*01     TRAJ6*01  TRBJ2-3*01   \n",
       "2          ASNLAGAYEQY      TRAV24*01  TRBV11-2*01    TRAJ29*01  TRBJ2-7*01   \n",
       "3         SALGQPLGETQY     TRAV8-4*01  TRBV20-1*02    TRAJ42*01  TRBJ2-5*01   \n",
       "4        ASSQAPQGADTQY     TRAV1-2*01   TRBV4-1*01    TRAJ42*01  TRBJ2-3*01   \n",
       "...                ...            ...          ...          ...         ...   \n",
       "9995       ASSYGNREGYT     TRAV8-6*01   TRBV6-6*01    TRAJ42*01  TRBJ1-2*01   \n",
       "9996      SAPTDPAGTEAF    TRAV12-3*01  TRBV20-1*01    TRAJ27*01  TRBJ1-1*01   \n",
       "9997        ASSYQYYEQY      TRAV27*01    TRBV28*01    TRAJ54*01  TRBJ2-7*01   \n",
       "9998    ASSSGTGGVEHGYT      TRAV21*02   TRBV7-9*01    TRAJ45*01  TRBJ1-2*01   \n",
       "9999  ASSQELLGAELHYGYT    TRAV12-3*01   TRBV4-3*01    TRAJ28*01  TRBJ1-2*01   \n",
       "\n",
       "     Species  sample_num  \n",
       "0      human           1  \n",
       "1      human           1  \n",
       "2      human           1  \n",
       "3      human           1  \n",
       "4      human           1  \n",
       "...      ...         ...  \n",
       "9995   human          10  \n",
       "9996   human          10  \n",
       "9997   human          10  \n",
       "9998   human          10  \n",
       "9999   human          10  \n",
       "\n",
       "[10000 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ots_sample = pd.read_csv('../data/interim/ots_sample.csv')\n",
    "ots_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>v_call_alpha</th>\n",
       "      <th>v_call_beta</th>\n",
       "      <th>j_call_alpha</th>\n",
       "      <th>j_call_beta</th>\n",
       "      <th>sample_num</th>\n",
       "      <th>alpha_subgroup</th>\n",
       "      <th>beta_subgroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TRAV26-1*01</td>\n",
       "      <td>TRBV14*01</td>\n",
       "      <td>TRAJ45*01</td>\n",
       "      <td>TRBJ2-3*01</td>\n",
       "      <td>1</td>\n",
       "      <td>TRAV26</td>\n",
       "      <td>TRBV14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TRAV29/DV5*04</td>\n",
       "      <td>TRBV3-1*01</td>\n",
       "      <td>TRAJ6*01</td>\n",
       "      <td>TRBJ2-3*01</td>\n",
       "      <td>1</td>\n",
       "      <td>TRAV29</td>\n",
       "      <td>TRBV3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TRAV24*01</td>\n",
       "      <td>TRBV11-2*01</td>\n",
       "      <td>TRAJ29*01</td>\n",
       "      <td>TRBJ2-7*01</td>\n",
       "      <td>1</td>\n",
       "      <td>TRAV24</td>\n",
       "      <td>TRBV11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TRAV8-4*01</td>\n",
       "      <td>TRBV20-1*02</td>\n",
       "      <td>TRAJ42*01</td>\n",
       "      <td>TRBJ2-5*01</td>\n",
       "      <td>1</td>\n",
       "      <td>TRAV8</td>\n",
       "      <td>TRBV20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TRAV1-2*01</td>\n",
       "      <td>TRBV4-1*01</td>\n",
       "      <td>TRAJ42*01</td>\n",
       "      <td>TRBJ2-3*01</td>\n",
       "      <td>1</td>\n",
       "      <td>TRAV1</td>\n",
       "      <td>TRBV4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>TRAV8-6*01</td>\n",
       "      <td>TRBV6-6*01</td>\n",
       "      <td>TRAJ42*01</td>\n",
       "      <td>TRBJ1-2*01</td>\n",
       "      <td>10</td>\n",
       "      <td>TRAV8</td>\n",
       "      <td>TRBV6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>TRAV12-3*01</td>\n",
       "      <td>TRBV20-1*01</td>\n",
       "      <td>TRAJ27*01</td>\n",
       "      <td>TRBJ1-1*01</td>\n",
       "      <td>10</td>\n",
       "      <td>TRAV12</td>\n",
       "      <td>TRBV20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>TRAV27*01</td>\n",
       "      <td>TRBV28*01</td>\n",
       "      <td>TRAJ54*01</td>\n",
       "      <td>TRBJ2-7*01</td>\n",
       "      <td>10</td>\n",
       "      <td>TRAV27</td>\n",
       "      <td>TRBV28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>TRAV21*02</td>\n",
       "      <td>TRBV7-9*01</td>\n",
       "      <td>TRAJ45*01</td>\n",
       "      <td>TRBJ1-2*01</td>\n",
       "      <td>10</td>\n",
       "      <td>TRAV21</td>\n",
       "      <td>TRBV7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>TRAV12-3*01</td>\n",
       "      <td>TRBV4-3*01</td>\n",
       "      <td>TRAJ28*01</td>\n",
       "      <td>TRBJ1-2*01</td>\n",
       "      <td>10</td>\n",
       "      <td>TRAV12</td>\n",
       "      <td>TRBV4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       v_call_alpha  v_call_beta j_call_alpha j_call_beta  sample_num  \\\n",
       "0       TRAV26-1*01    TRBV14*01    TRAJ45*01  TRBJ2-3*01           1   \n",
       "1     TRAV29/DV5*04   TRBV3-1*01     TRAJ6*01  TRBJ2-3*01           1   \n",
       "2         TRAV24*01  TRBV11-2*01    TRAJ29*01  TRBJ2-7*01           1   \n",
       "3        TRAV8-4*01  TRBV20-1*02    TRAJ42*01  TRBJ2-5*01           1   \n",
       "4        TRAV1-2*01   TRBV4-1*01    TRAJ42*01  TRBJ2-3*01           1   \n",
       "...             ...          ...          ...         ...         ...   \n",
       "9995     TRAV8-6*01   TRBV6-6*01    TRAJ42*01  TRBJ1-2*01          10   \n",
       "9996    TRAV12-3*01  TRBV20-1*01    TRAJ27*01  TRBJ1-1*01          10   \n",
       "9997      TRAV27*01    TRBV28*01    TRAJ54*01  TRBJ2-7*01          10   \n",
       "9998      TRAV21*02   TRBV7-9*01    TRAJ45*01  TRBJ1-2*01          10   \n",
       "9999    TRAV12-3*01   TRBV4-3*01    TRAJ28*01  TRBJ1-2*01          10   \n",
       "\n",
       "     alpha_subgroup beta_subgroup  \n",
       "0            TRAV26        TRBV14  \n",
       "1            TRAV29         TRBV3  \n",
       "2            TRAV24        TRBV11  \n",
       "3             TRAV8        TRBV20  \n",
       "4             TRAV1         TRBV4  \n",
       "...             ...           ...  \n",
       "9995          TRAV8         TRBV6  \n",
       "9996         TRAV12        TRBV20  \n",
       "9997         TRAV27        TRBV28  \n",
       "9998         TRAV21         TRBV7  \n",
       "9999         TRAV12         TRBV4  \n",
       "\n",
       "[10000 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ots_sample_genes = ots_sample[['v_call_alpha', 'v_call_beta', 'j_call_alpha', 'j_call_beta', 'sample_num']].copy()\n",
    "\n",
    "ots_sample_genes['alpha_subgroup'] = ots_sample_genes['v_call_alpha'].str.extract(r'^(TRAV\\d+)')\n",
    "ots_sample_genes['beta_subgroup'] = ots_sample_genes['v_call_beta'].str.extract(r'^(TRBV\\d+)')\n",
    "\n",
    "ots_sample_genes"
   ]
  },
  {
   "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>sample_num</th>\n",
       "      <th>cdr_type</th>\n",
       "      <th>sequence</th>\n",
       "      <th>cdr</th>\n",
       "      <th>chain_type</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>cdr1_aa_alpha</td>\n",
       "      <td>TISGNEY</td>\n",
       "      <td>1</td>\n",
       "      <td>alpha</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>cdr1_aa_alpha</td>\n",
       "      <td>NSMFDY</td>\n",
       "      <td>1</td>\n",
       "      <td>alpha</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>cdr1_aa_alpha</td>\n",
       "      <td>SSNFYA</td>\n",
       "      <td>1</td>\n",
       "      <td>alpha</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>cdr1_aa_alpha</td>\n",
       "      <td>SSVPPY</td>\n",
       "      <td>1</td>\n",
       "      <td>alpha</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>cdr1_aa_alpha</td>\n",
       "      <td>TSGFNG</td>\n",
       "      <td>1</td>\n",
       "      <td>alpha</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59995</th>\n",
       "      <td>10</td>\n",
       "      <td>cdr3_aa_beta</td>\n",
       "      <td>ASSYGNREGYT</td>\n",
       "      <td>3</td>\n",
       "      <td>beta</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59996</th>\n",
       "      <td>10</td>\n",
       "      <td>cdr3_aa_beta</td>\n",
       "      <td>SAPTDPAGTEAF</td>\n",
       "      <td>3</td>\n",
       "      <td>beta</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59997</th>\n",
       "      <td>10</td>\n",
       "      <td>cdr3_aa_beta</td>\n",
       "      <td>ASSYQYYEQY</td>\n",
       "      <td>3</td>\n",
       "      <td>beta</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59998</th>\n",
       "      <td>10</td>\n",
       "      <td>cdr3_aa_beta</td>\n",
       "      <td>ASSSGTGGVEHGYT</td>\n",
       "      <td>3</td>\n",
       "      <td>beta</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59999</th>\n",
       "      <td>10</td>\n",
       "      <td>cdr3_aa_beta</td>\n",
       "      <td>ASSQELLGAELHYGYT</td>\n",
       "      <td>3</td>\n",
       "      <td>beta</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       sample_num       cdr_type          sequence cdr chain_type  length\n",
       "0               1  cdr1_aa_alpha           TISGNEY   1      alpha       7\n",
       "1               1  cdr1_aa_alpha            NSMFDY   1      alpha       6\n",
       "2               1  cdr1_aa_alpha            SSNFYA   1      alpha       6\n",
       "3               1  cdr1_aa_alpha            SSVPPY   1      alpha       6\n",
       "4               1  cdr1_aa_alpha            TSGFNG   1      alpha       6\n",
       "...           ...            ...               ...  ..        ...     ...\n",
       "59995          10   cdr3_aa_beta       ASSYGNREGYT   3       beta      11\n",
       "59996          10   cdr3_aa_beta      SAPTDPAGTEAF   3       beta      12\n",
       "59997          10   cdr3_aa_beta        ASSYQYYEQY   3       beta      10\n",
       "59998          10   cdr3_aa_beta    ASSSGTGGVEHGYT   3       beta      14\n",
       "59999          10   cdr3_aa_beta  ASSQELLGAELHYGYT   3       beta      16\n",
       "\n",
       "[60000 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ots_sample_cdrs = ots_sample.filter(regex=r'^cdr\\d_aa_[alpha|beta]', axis=1)\n",
    "cdr_cols = ots_sample_cdrs.columns.tolist()\n",
    "\n",
    "ots_sample_cdrs = pd.concat([ots_sample_cdrs, ots_sample['sample_num']], axis='columns')\n",
    "\n",
    "ots_sample_cdrs = ots_sample_cdrs.melt(id_vars='sample_num',\n",
    "                                       value_vars=cdr_cols,\n",
    "                                       var_name='cdr_type',\n",
    "                                       value_name='sequence')\n",
    "ots_sample_cdrs[['cdr', 'chain_type']] = ots_sample_cdrs['cdr_type'].str.extract('cdr(\\d)_aa_(alpha|beta)')\n",
    "\n",
    "ots_sample_cdrs['length'] = ots_sample_cdrs['sequence'].str.len()\n",
    "\n",
    "ots_sample_cdrs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load STCRDab data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>pdb</th>\n",
       "      <th>Bchain</th>\n",
       "      <th>Achain</th>\n",
       "      <th>Dchain</th>\n",
       "      <th>Gchain</th>\n",
       "      <th>TCRtype</th>\n",
       "      <th>model</th>\n",
       "      <th>antigen_chain</th>\n",
       "      <th>antigen_type</th>\n",
       "      <th>antigen_name</th>\n",
       "      <th>...</th>\n",
       "      <th>authors</th>\n",
       "      <th>resolution</th>\n",
       "      <th>method</th>\n",
       "      <th>r_free</th>\n",
       "      <th>r_factor</th>\n",
       "      <th>affinity</th>\n",
       "      <th>affinity_method</th>\n",
       "      <th>affinity_temperature</th>\n",
       "      <th>affinity_pmid</th>\n",
       "      <th>engineered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7zt2</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>abTCR</td>\n",
       "      <td>0</td>\n",
       "      <td>A | A</td>\n",
       "      <td>protein | Hapten</td>\n",
       "      <td>major histocompatibility complex class i-relat...</td>\n",
       "      <td>...</td>\n",
       "      <td>Karuppiah, V., Srikannathasan, V., Robinson, R.A.</td>\n",
       "      <td>2.4</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.215</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7zt3</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>abTCR</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>protein</td>\n",
       "      <td>major histocompatibility complex class i-relat...</td>\n",
       "      <td>...</td>\n",
       "      <td>Karuppiah, V., Srikannathasan, V., Robinson, R.A.</td>\n",
       "      <td>2.4</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.191</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7zt4</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>abTCR</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>protein</td>\n",
       "      <td>major histocompatibility complex class i-relat...</td>\n",
       "      <td>...</td>\n",
       "      <td>Karuppiah, V., Robinson, R.A.</td>\n",
       "      <td>2.02</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>0.268</td>\n",
       "      <td>0.234</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7zt5</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>abTCR</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>protein</td>\n",
       "      <td>major histocompatibility complex class i-relat...</td>\n",
       "      <td>...</td>\n",
       "      <td>Karuppiah, V., Robinson, R.A.</td>\n",
       "      <td>2.09</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>0.266</td>\n",
       "      <td>0.225</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7zt7</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>abTCR</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>protein</td>\n",
       "      <td>major histocompatibility complex class i-relat...</td>\n",
       "      <td>...</td>\n",
       "      <td>Karuppiah, V., Robinson, R.A.</td>\n",
       "      <td>1.84</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.207</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931</th>\n",
       "      <td>3rtq</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>abTCR</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>Hapten</td>\n",
       "      <td>N-[(2S,3S,4R)-3,4-DIHYDROXY-1-{[(1S,2S,3R,4R,5...</td>\n",
       "      <td>...</td>\n",
       "      <td>Yu, E.D., Zajonc, D.M.</td>\n",
       "      <td>2.8</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>0.268</td>\n",
       "      <td>0.227</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>3dxa</td>\n",
       "      <td>O</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>abTCR</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>peptide</td>\n",
       "      <td>ebv decapeptide epitope</td>\n",
       "      <td>...</td>\n",
       "      <td>Archbold, J.K., Macdonald, W.A., Gras, S., Ros...</td>\n",
       "      <td>3.5</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>933</th>\n",
       "      <td>1d9k</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>abTCR</td>\n",
       "      <td>0</td>\n",
       "      <td>P</td>\n",
       "      <td>peptide</td>\n",
       "      <td>conalbumin peptide</td>\n",
       "      <td>...</td>\n",
       "      <td>Reinherz, E.L., Tan, K., Tang, L., Kern, P., L...</td>\n",
       "      <td>3.2</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>0.293</td>\n",
       "      <td>0.247</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>4gg6</td>\n",
       "      <td>H</td>\n",
       "      <td>G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>abTCR</td>\n",
       "      <td>0</td>\n",
       "      <td>J</td>\n",
       "      <td>peptide</td>\n",
       "      <td>peptide from alpha/beta-gliadin mm1</td>\n",
       "      <td>...</td>\n",
       "      <td>Broughton, S.E., Theodossis, A., Petersen, J.,...</td>\n",
       "      <td>3.2</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.246</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>935</th>\n",
       "      <td>2apf</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Cho, S., Swaminathan, C.P., Yang, J., Kerzic, ...</td>\n",
       "      <td>1.8</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>0.263</td>\n",
       "      <td>0.197</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>936 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      pdb Bchain Achain Dchain Gchain TCRtype  model antigen_chain  \\\n",
       "0    7zt2      E      D    NaN    NaN   abTCR      0         A | A   \n",
       "1    7zt3      E      D    NaN    NaN   abTCR      0             A   \n",
       "2    7zt4      E      D    NaN    NaN   abTCR      0             A   \n",
       "3    7zt5      E      D    NaN    NaN   abTCR      0             A   \n",
       "4    7zt7      E      D    NaN    NaN   abTCR      0             A   \n",
       "..    ...    ...    ...    ...    ...     ...    ...           ...   \n",
       "931  3rtq      D      C    NaN    NaN   abTCR      0             A   \n",
       "932  3dxa      O      N    NaN    NaN   abTCR      0             M   \n",
       "933  1d9k      B      A    NaN    NaN   abTCR      0             P   \n",
       "934  4gg6      H      G    NaN    NaN   abTCR      0             J   \n",
       "935  2apf      A    NaN    NaN    NaN     NaN      0           NaN   \n",
       "\n",
       "         antigen_type                                       antigen_name  ...  \\\n",
       "0    protein | Hapten  major histocompatibility complex class i-relat...  ...   \n",
       "1             protein  major histocompatibility complex class i-relat...  ...   \n",
       "2             protein  major histocompatibility complex class i-relat...  ...   \n",
       "3             protein  major histocompatibility complex class i-relat...  ...   \n",
       "4             protein  major histocompatibility complex class i-relat...  ...   \n",
       "..                ...                                                ...  ...   \n",
       "931            Hapten  N-[(2S,3S,4R)-3,4-DIHYDROXY-1-{[(1S,2S,3R,4R,5...  ...   \n",
       "932           peptide                            ebv decapeptide epitope  ...   \n",
       "933           peptide                                 conalbumin peptide  ...   \n",
       "934           peptide                peptide from alpha/beta-gliadin mm1  ...   \n",
       "935               NaN                                                NaN  ...   \n",
       "\n",
       "                                               authors resolution  \\\n",
       "0    Karuppiah, V., Srikannathasan, V., Robinson, R.A.        2.4   \n",
       "1    Karuppiah, V., Srikannathasan, V., Robinson, R.A.        2.4   \n",
       "2                        Karuppiah, V., Robinson, R.A.       2.02   \n",
       "3                        Karuppiah, V., Robinson, R.A.       2.09   \n",
       "4                        Karuppiah, V., Robinson, R.A.       1.84   \n",
       "..                                                 ...        ...   \n",
       "931                             Yu, E.D., Zajonc, D.M.        2.8   \n",
       "932  Archbold, J.K., Macdonald, W.A., Gras, S., Ros...        3.5   \n",
       "933  Reinherz, E.L., Tan, K., Tang, L., Kern, P., L...        3.2   \n",
       "934  Broughton, S.E., Theodossis, A., Petersen, J.,...        3.2   \n",
       "935  Cho, S., Swaminathan, C.P., Yang, J., Kerzic, ...        1.8   \n",
       "\n",
       "                method r_free  r_factor affinity affinity_method  \\\n",
       "0    X-RAY DIFFRACTION  0.276     0.215      NaN             NaN   \n",
       "1    X-RAY DIFFRACTION  0.236     0.191      NaN             NaN   \n",
       "2    X-RAY DIFFRACTION  0.268     0.234      NaN             NaN   \n",
       "3    X-RAY DIFFRACTION  0.266     0.225      NaN             NaN   \n",
       "4    X-RAY DIFFRACTION  0.255     0.207      NaN             NaN   \n",
       "..                 ...    ...       ...      ...             ...   \n",
       "931  X-RAY DIFFRACTION  0.268     0.227      NaN             NaN   \n",
       "932  X-RAY DIFFRACTION  0.330     0.286      NaN             NaN   \n",
       "933  X-RAY DIFFRACTION  0.293     0.247      NaN             NaN   \n",
       "934  X-RAY DIFFRACTION  0.285     0.246      NaN             NaN   \n",
       "935  X-RAY DIFFRACTION  0.263     0.197      NaN             NaN   \n",
       "\n",
       "    affinity_temperature affinity_pmid engineered  \n",
       "0                    NaN           NaN       True  \n",
       "1                    NaN           NaN       True  \n",
       "2                    NaN           NaN       True  \n",
       "3                    NaN           NaN       True  \n",
       "4                    NaN           NaN       True  \n",
       "..                   ...           ...        ...  \n",
       "931                  NaN           NaN       True  \n",
       "932                  NaN           NaN       True  \n",
       "933                  NaN           NaN       True  \n",
       "934                  NaN           NaN       True  \n",
       "935                  NaN           NaN       True  \n",
       "\n",
       "[936 rows x 39 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stcrdab_summary = pd.read_csv('../data/raw/stcrdab/db_summary.dat', delimiter='\\t')\n",
    "stcrdab_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load *apo*-*holo* data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>file_name</th>\n",
       "      <th>pdb_id</th>\n",
       "      <th>structure_type</th>\n",
       "      <th>state</th>\n",
       "      <th>alpha_chain</th>\n",
       "      <th>beta_chain</th>\n",
       "      <th>antigen_chain</th>\n",
       "      <th>mhc_chain1</th>\n",
       "      <th>mhc_chain2</th>\n",
       "      <th>cdr_sequences_collated</th>\n",
       "      <th>peptide_sequence</th>\n",
       "      <th>mhc_slug</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1ao7_D-E-C-A-B_tcr_pmhc.pdb</td>\n",
       "      <td>1ao7</td>\n",
       "      <td>tcr_pmhc</td>\n",
       "      <td>holo</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>DRGSQS-IYSNGD-AVTTDSWGKLQ-MNHEY-SVGAGI-ASRPGLA...</td>\n",
       "      <td>LLFGYPVYV</td>\n",
       "      <td>hla_a_02_01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1b0g_C-A-B_pmhc.pdb</td>\n",
       "      <td>1b0g</td>\n",
       "      <td>pmhc</td>\n",
       "      <td>apo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ALWGFFPVL</td>\n",
       "      <td>hla_a_02_01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1b0g_F-D-E_pmhc.pdb</td>\n",
       "      <td>1b0g</td>\n",
       "      <td>pmhc</td>\n",
       "      <td>apo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ALWGFFPVL</td>\n",
       "      <td>hla_a_02_01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1bd2_D-E-C-A-B_tcr_pmhc.pdb</td>\n",
       "      <td>1bd2</td>\n",
       "      <td>tcr_pmhc</td>\n",
       "      <td>holo</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>NSMFDY-ISSIKDK-AAMEGAQKLV-MNHEY-SVGAGI-ASSYPGG...</td>\n",
       "      <td>LLFGYPVYV</td>\n",
       "      <td>hla_a_02_01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1bii_P-A-B_pmhc.pdb</td>\n",
       "      <td>1bii</td>\n",
       "      <td>pmhc</td>\n",
       "      <td>apo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RGPGRAFVTI</td>\n",
       "      <td>h2_dd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>7rtd_C-A-B_pmhc.pdb</td>\n",
       "      <td>7rtd</td>\n",
       "      <td>pmhc</td>\n",
       "      <td>apo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>YLQPRTFLL</td>\n",
       "      <td>hla_a_02_01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>7rtr_D-E-C-A-B_tcr_pmhc.pdb</td>\n",
       "      <td>7rtr</td>\n",
       "      <td>tcr_pmhc</td>\n",
       "      <td>holo</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>DRGSQS-IYSNGD-AVNRDDKII-SEHNR-FQNEAQ-ASSPDIEQY</td>\n",
       "      <td>YLQPRTFLL</td>\n",
       "      <td>hla_a_02_01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>8gvb_A-B-P-H-L_tcr_pmhc.pdb</td>\n",
       "      <td>8gvb</td>\n",
       "      <td>tcr_pmhc</td>\n",
       "      <td>holo</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>P</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>YGATPY-YFSGDTLV-AVGFTGGGNKLT-SEHNR-FQNEAQ-ASSD...</td>\n",
       "      <td>RYPLTFGW</td>\n",
       "      <td>hla_a_24_02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>8gvg_A-B-P-H-L_tcr_pmhc.pdb</td>\n",
       "      <td>8gvg</td>\n",
       "      <td>tcr_pmhc</td>\n",
       "      <td>holo</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>P</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>YGATPY-YFSGDTLV-AVGFTGGGNKLT-SEHNR-FQNEAQ-ASSD...</td>\n",
       "      <td>RFPLTFGW</td>\n",
       "      <td>hla_a_24_02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>8gvi_A-B-P-H-L_tcr_pmhc.pdb</td>\n",
       "      <td>8gvi</td>\n",
       "      <td>tcr_pmhc</td>\n",
       "      <td>holo</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>P</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>YGATPY-YFSGDTLV-AVVFTGGGNKLT-SEHNR-FQNEAQ-ASSL...</td>\n",
       "      <td>RYPLTFGW</td>\n",
       "      <td>hla_a_24_02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>391 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       file_name pdb_id structure_type state alpha_chain  \\\n",
       "0    1ao7_D-E-C-A-B_tcr_pmhc.pdb   1ao7       tcr_pmhc  holo           D   \n",
       "1            1b0g_C-A-B_pmhc.pdb   1b0g           pmhc   apo         NaN   \n",
       "2            1b0g_F-D-E_pmhc.pdb   1b0g           pmhc   apo         NaN   \n",
       "3    1bd2_D-E-C-A-B_tcr_pmhc.pdb   1bd2       tcr_pmhc  holo           D   \n",
       "4            1bii_P-A-B_pmhc.pdb   1bii           pmhc   apo         NaN   \n",
       "..                           ...    ...            ...   ...         ...   \n",
       "386          7rtd_C-A-B_pmhc.pdb   7rtd           pmhc   apo         NaN   \n",
       "387  7rtr_D-E-C-A-B_tcr_pmhc.pdb   7rtr       tcr_pmhc  holo           D   \n",
       "388  8gvb_A-B-P-H-L_tcr_pmhc.pdb   8gvb       tcr_pmhc  holo           A   \n",
       "389  8gvg_A-B-P-H-L_tcr_pmhc.pdb   8gvg       tcr_pmhc  holo           A   \n",
       "390  8gvi_A-B-P-H-L_tcr_pmhc.pdb   8gvi       tcr_pmhc  holo           A   \n",
       "\n",
       "    beta_chain antigen_chain mhc_chain1 mhc_chain2  \\\n",
       "0            E             C          A          B   \n",
       "1          NaN             C          A          B   \n",
       "2          NaN             F          D          E   \n",
       "3            E             C          A          B   \n",
       "4          NaN             P          A          B   \n",
       "..         ...           ...        ...        ...   \n",
       "386        NaN             C          A          B   \n",
       "387          E             C          A          B   \n",
       "388          B             P          H          L   \n",
       "389          B             P          H          L   \n",
       "390          B             P          H          L   \n",
       "\n",
       "                                cdr_sequences_collated peptide_sequence  \\\n",
       "0    DRGSQS-IYSNGD-AVTTDSWGKLQ-MNHEY-SVGAGI-ASRPGLA...        LLFGYPVYV   \n",
       "1                                                  NaN        ALWGFFPVL   \n",
       "2                                                  NaN        ALWGFFPVL   \n",
       "3    NSMFDY-ISSIKDK-AAMEGAQKLV-MNHEY-SVGAGI-ASSYPGG...        LLFGYPVYV   \n",
       "4                                                  NaN       RGPGRAFVTI   \n",
       "..                                                 ...              ...   \n",
       "386                                                NaN        YLQPRTFLL   \n",
       "387     DRGSQS-IYSNGD-AVNRDDKII-SEHNR-FQNEAQ-ASSPDIEQY        YLQPRTFLL   \n",
       "388  YGATPY-YFSGDTLV-AVGFTGGGNKLT-SEHNR-FQNEAQ-ASSD...         RYPLTFGW   \n",
       "389  YGATPY-YFSGDTLV-AVGFTGGGNKLT-SEHNR-FQNEAQ-ASSD...         RFPLTFGW   \n",
       "390  YGATPY-YFSGDTLV-AVVFTGGGNKLT-SEHNR-FQNEAQ-ASSL...         RYPLTFGW   \n",
       "\n",
       "        mhc_slug  \n",
       "0    hla_a_02_01  \n",
       "1    hla_a_02_01  \n",
       "2    hla_a_02_01  \n",
       "3    hla_a_02_01  \n",
       "4          h2_dd  \n",
       "..           ...  \n",
       "386  hla_a_02_01  \n",
       "387  hla_a_02_01  \n",
       "388  hla_a_24_02  \n",
       "389  hla_a_24_02  \n",
       "390  hla_a_24_02  \n",
       "\n",
       "[391 rows x 12 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apo_holo_summary = pd.read_csv('../data/processed/apo-holo-tcr-pmhc-class-I/apo_holo_summary.csv')\n",
    "apo_holo_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "apo_holo_genes = apo_holo_summary.copy().merge(\n",
    "    stcrdab_summary,\n",
    "    how='left',\n",
    "    left_on=['pdb_id', 'alpha_chain', 'beta_chain'],\n",
    "    right_on=['pdb', 'Achain', 'Bchain'],\n",
    ").drop_duplicates(['cdr_sequences_collated'])[['alpha_subgroup', 'beta_subgroup']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>cdr_type</th>\n",
       "      <th>sequence</th>\n",
       "      <th>cdr</th>\n",
       "      <th>chain_type</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cdr1_alpha</td>\n",
       "      <td>DRGSQS</td>\n",
       "      <td>1</td>\n",
       "      <td>alpha</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cdr1_alpha</td>\n",
       "      <td>NSMFDY</td>\n",
       "      <td>1</td>\n",
       "      <td>alpha</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cdr1_alpha</td>\n",
       "      <td>TQDSSYF</td>\n",
       "      <td>1</td>\n",
       "      <td>alpha</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cdr1_alpha</td>\n",
       "      <td>YSATPY</td>\n",
       "      <td>1</td>\n",
       "      <td>alpha</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cdr1_alpha</td>\n",
       "      <td>TISGTDY</td>\n",
       "      <td>1</td>\n",
       "      <td>alpha</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>cdr3_beta</td>\n",
       "      <td>ASSSANSGELF</td>\n",
       "      <td>3</td>\n",
       "      <td>beta</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>cdr3_beta</td>\n",
       "      <td>ASSYGTGINYGYT</td>\n",
       "      <td>3</td>\n",
       "      <td>beta</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>cdr3_beta</td>\n",
       "      <td>ASSLDPGDTGELF</td>\n",
       "      <td>3</td>\n",
       "      <td>beta</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>cdr3_beta</td>\n",
       "      <td>ASSDRDRVPETQY</td>\n",
       "      <td>3</td>\n",
       "      <td>beta</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>cdr3_beta</td>\n",
       "      <td>ASSLRDRVPETQY</td>\n",
       "      <td>3</td>\n",
       "      <td>beta</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>516 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       cdr_type       sequence cdr chain_type  length\n",
       "0    cdr1_alpha         DRGSQS   1      alpha       6\n",
       "1    cdr1_alpha         NSMFDY   1      alpha       6\n",
       "2    cdr1_alpha        TQDSSYF   1      alpha       7\n",
       "3    cdr1_alpha         YSATPY   1      alpha       6\n",
       "4    cdr1_alpha        TISGTDY   1      alpha       7\n",
       "..          ...            ...  ..        ...     ...\n",
       "511   cdr3_beta    ASSSANSGELF   3       beta      11\n",
       "512   cdr3_beta  ASSYGTGINYGYT   3       beta      13\n",
       "513   cdr3_beta  ASSLDPGDTGELF   3       beta      13\n",
       "514   cdr3_beta  ASSDRDRVPETQY   3       beta      13\n",
       "515   cdr3_beta  ASSLRDRVPETQY   3       beta      13\n",
       "\n",
       "[516 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apo_holo_cdr_sequences = apo_holo_summary['cdr_sequences_collated'].str.split('-').apply(pd.Series)\n",
    "apo_holo_cdr_sequences.columns = ['cdr1_alpha', 'cdr2_alpha', 'cdr3_alpha', 'cdr1_beta', 'cdr2_beta', 'cdr3_beta']\n",
    "\n",
    "apo_holo_cdr_sequences = apo_holo_cdr_sequences.drop_duplicates()\n",
    "apo_holo_cdr_sequences = apo_holo_cdr_sequences.dropna()\n",
    "\n",
    "apo_holo_cdr_sequences\n",
    "apo_holo_cdr_sequences = apo_holo_cdr_sequences.melt(value_vars=apo_holo_cdr_sequences.columns,\n",
    "                                                     var_name='cdr_type',\n",
    "                                                     value_name='sequence')\n",
    "apo_holo_cdr_sequences[['cdr', 'chain_type']] = apo_holo_cdr_sequences['cdr_type'].str.extract('cdr(\\d)_(alpha|beta)')\n",
    "\n",
    "apo_holo_cdr_sequences['length'] = apo_holo_cdr_sequences['sequence'].str.len()\n",
    "\n",
    "apo_holo_cdr_sequences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare CDR Lengths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>sample_num</th>\n",
       "      <th>cdr_type</th>\n",
       "      <th>sequence</th>\n",
       "      <th>cdr</th>\n",
       "      <th>chain_type</th>\n",
       "      <th>length</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>cdr1_aa_alpha</td>\n",
       "      <td>TISGNEY</td>\n",
       "      <td>1</td>\n",
       "      <td>alpha</td>\n",
       "      <td>7</td>\n",
       "      <td>OTS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>cdr1_aa_alpha</td>\n",
       "      <td>NSMFDY</td>\n",
       "      <td>1</td>\n",
       "      <td>alpha</td>\n",
       "      <td>6</td>\n",
       "      <td>OTS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>cdr1_aa_alpha</td>\n",
       "      <td>SSNFYA</td>\n",
       "      <td>1</td>\n",
       "      <td>alpha</td>\n",
       "      <td>6</td>\n",
       "      <td>OTS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>cdr1_aa_alpha</td>\n",
       "      <td>SSVPPY</td>\n",
       "      <td>1</td>\n",
       "      <td>alpha</td>\n",
       "      <td>6</td>\n",
       "      <td>OTS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>cdr1_aa_alpha</td>\n",
       "      <td>TSGFNG</td>\n",
       "      <td>1</td>\n",
       "      <td>alpha</td>\n",
       "      <td>6</td>\n",
       "      <td>OTS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>NaN</td>\n",
       "      <td>cdr3_beta</td>\n",
       "      <td>ASSSANSGELF</td>\n",
       "      <td>3</td>\n",
       "      <td>beta</td>\n",
       "      <td>11</td>\n",
       "      <td>STCRDab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>NaN</td>\n",
       "      <td>cdr3_beta</td>\n",
       "      <td>ASSYGTGINYGYT</td>\n",
       "      <td>3</td>\n",
       "      <td>beta</td>\n",
       "      <td>13</td>\n",
       "      <td>STCRDab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>NaN</td>\n",
       "      <td>cdr3_beta</td>\n",
       "      <td>ASSLDPGDTGELF</td>\n",
       "      <td>3</td>\n",
       "      <td>beta</td>\n",
       "      <td>13</td>\n",
       "      <td>STCRDab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>NaN</td>\n",
       "      <td>cdr3_beta</td>\n",
       "      <td>ASSDRDRVPETQY</td>\n",
       "      <td>3</td>\n",
       "      <td>beta</td>\n",
       "      <td>13</td>\n",
       "      <td>STCRDab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>NaN</td>\n",
       "      <td>cdr3_beta</td>\n",
       "      <td>ASSLRDRVPETQY</td>\n",
       "      <td>3</td>\n",
       "      <td>beta</td>\n",
       "      <td>13</td>\n",
       "      <td>STCRDab</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60516 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sample_num       cdr_type       sequence cdr chain_type  length   source\n",
       "0           1.0  cdr1_aa_alpha        TISGNEY   1      alpha       7      OTS\n",
       "1           1.0  cdr1_aa_alpha         NSMFDY   1      alpha       6      OTS\n",
       "2           1.0  cdr1_aa_alpha         SSNFYA   1      alpha       6      OTS\n",
       "3           1.0  cdr1_aa_alpha         SSVPPY   1      alpha       6      OTS\n",
       "4           1.0  cdr1_aa_alpha         TSGFNG   1      alpha       6      OTS\n",
       "..          ...            ...            ...  ..        ...     ...      ...\n",
       "511         NaN      cdr3_beta    ASSSANSGELF   3       beta      11  STCRDab\n",
       "512         NaN      cdr3_beta  ASSYGTGINYGYT   3       beta      13  STCRDab\n",
       "513         NaN      cdr3_beta  ASSLDPGDTGELF   3       beta      13  STCRDab\n",
       "514         NaN      cdr3_beta  ASSDRDRVPETQY   3       beta      13  STCRDab\n",
       "515         NaN      cdr3_beta  ASSLRDRVPETQY   3       beta      13  STCRDab\n",
       "\n",
       "[60516 rows x 7 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ots_sample_cdrs['source'] = 'OTS'\n",
    "apo_holo_cdr_sequences['source'] = 'STCRDab'\n",
    "\n",
    "cdr_lengths = pd.concat([ots_sample_cdrs, apo_holo_cdr_sequences])\n",
    "cdr_lengths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ots_sample_cdrs_proptions = (ots_sample_cdrs.groupby(['chain_type', 'cdr', 'sample_num'])\n",
    "                                            ['length']\n",
    "                                            .value_counts(normalize=True)\n",
    "                                            * 100)\n",
    "ots_sample_cdrs_proptions.name = 'percentage'\n",
    "ots_sample_cdrs_proptions = ots_sample_cdrs_proptions.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ots_sample_cdrs_proptions_agg = (ots_sample_cdrs_proptions.groupby(['chain_type', 'cdr', 'length'])\n",
    "                                                          .agg({'percentage': ['mean', 'sem']})\n",
    "                                                          .fillna(0.0)\n",
    "                                                          .reset_index())\n",
    "ots_sample_cdrs_proptions_agg.columns = ['_'.join(col).rstrip('_').replace('_mean', '')\n",
    "                                         for col in ots_sample_cdrs_proptions_agg.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "apo_holo_cdr_length_proportions = (apo_holo_cdr_sequences.groupby(['chain_type', 'cdr'])\n",
    "                                   ['length']\n",
    "                                   .value_counts(normalize=True)\n",
    "                                   * 100)\n",
    "apo_holo_cdr_length_proportions.name = 'percentage'\n",
    "apo_holo_cdr_length_proportions = apo_holo_cdr_length_proportions.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cdr_length_proportions = ots_sample_cdrs_proptions_agg.merge(apo_holo_cdr_length_proportions,\n",
    "                                                             how='outer',\n",
    "                                                             on=['chain_type', 'cdr', 'length'],\n",
    "                                                             suffixes=['_ots', '_stcrdab']).fillna(0)\n",
    "\n",
    "\n",
    "cdr_length_proportions['difference'] = (cdr_length_proportions['percentage_stcrdab']\n",
    "                                        - cdr_length_proportions['percentage_ots'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "cdr_length_percentages = (cdr_lengths.groupby(['chain_type', 'cdr', 'sample_num', 'source'], dropna=False)\n",
    "                                     ['length']\n",
    "                                     .value_counts(normalize=True)\n",
    "                                     * 100)\n",
    "cdr_length_percentages.name = 'percentage'\n",
    "cdr_length_percentages = cdr_length_percentages.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (chain_type, cdr), group in cdr_length_percentages.groupby(['chain_type', 'cdr']):\n",
    "    length_min = group['length'].min()\n",
    "    length_max = group['length'].max()\n",
    "    expected_length_values = set(range(length_min, length_max + 1))\n",
    "\n",
    "    actual_length_values = set(group['length'].unique())\n",
    "    missing_length_values = expected_length_values - actual_length_values\n",
    "    missing_length_values = sorted(list(missing_length_values))\n",
    "\n",
    "    if len(missing_length_values) > 0:\n",
    "        missing_entries = pd.DataFrame({'chain_type': [chain_type for _ in missing_length_values],\n",
    "                                        'cdr': [cdr for _ in missing_length_values],\n",
    "                                        'source': ['OTS' for _ in missing_length_values],\n",
    "                                        'length': missing_length_values,\n",
    "                                        'percentage': [0.0 for _ in missing_length_values]})\n",
    "\n",
    "        cdr_length_percentages = pd.concat([cdr_length_percentages, missing_entries])\n",
    "\n",
    "cdr_length_percentages = cdr_length_percentages.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1614.12x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.catplot(cdr_length_percentages,\n",
    "            x='length',\n",
    "            y='percentage',\n",
    "            hue='source',\n",
    "            row='chain_type', col='cdr',\n",
    "            kind='bar', sharex=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "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></th>\n",
       "      <th></th>\n",
       "      <th>mode</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chain_type</th>\n",
       "      <th>cdr</th>\n",
       "      <th>source</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">alpha</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>OTS</th>\n",
       "      <td>6</td>\n",
       "      <td>6.003800</td>\n",
       "      <td>0.570279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STCRDab</th>\n",
       "      <td>6</td>\n",
       "      <td>6.127907</td>\n",
       "      <td>0.609658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>OTS</th>\n",
       "      <td>7</td>\n",
       "      <td>6.805900</td>\n",
       "      <td>1.031664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STCRDab</th>\n",
       "      <td>7</td>\n",
       "      <td>6.546512</td>\n",
       "      <td>1.001845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>OTS</th>\n",
       "      <td>12</td>\n",
       "      <td>11.730200</td>\n",
       "      <td>1.692954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STCRDab</th>\n",
       "      <td>11</td>\n",
       "      <td>11.255814</td>\n",
       "      <td>1.688620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">beta</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>OTS</th>\n",
       "      <td>5</td>\n",
       "      <td>5.116500</td>\n",
       "      <td>0.320840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STCRDab</th>\n",
       "      <td>5</td>\n",
       "      <td>5.069767</td>\n",
       "      <td>0.256249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>OTS</th>\n",
       "      <td>6</td>\n",
       "      <td>6.092400</td>\n",
       "      <td>0.363147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STCRDab</th>\n",
       "      <td>6</td>\n",
       "      <td>5.976744</td>\n",
       "      <td>0.215666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>OTS</th>\n",
       "      <td>13</td>\n",
       "      <td>12.568400</td>\n",
       "      <td>1.753918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STCRDab</th>\n",
       "      <td>12</td>\n",
       "      <td>12.046512</td>\n",
       "      <td>1.578584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        mode       mean       std\n",
       "chain_type cdr source                            \n",
       "alpha      1   OTS         6   6.003800  0.570279\n",
       "               STCRDab     6   6.127907  0.609658\n",
       "           2   OTS         7   6.805900  1.031664\n",
       "               STCRDab     7   6.546512  1.001845\n",
       "           3   OTS        12  11.730200  1.692954\n",
       "               STCRDab    11  11.255814  1.688620\n",
       "beta       1   OTS         5   5.116500  0.320840\n",
       "               STCRDab     5   5.069767  0.256249\n",
       "           2   OTS         6   6.092400  0.363147\n",
       "               STCRDab     6   5.976744  0.215666\n",
       "           3   OTS        13  12.568400  1.753918\n",
       "               STCRDab    12  12.046512  1.578584"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdr_lengths.groupby(['chain_type', 'cdr', 'source'])['length'].agg([pd.Series.mode, 'mean', 'std'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=3, nrows=2, tight_layout=True, sharey=True)\n",
    "plt.ylim([-100, 100])\n",
    "fig.suptitle('Relative frequency of structure CDR lengths compared to OTS')\n",
    "\n",
    "for i, chain_type in enumerate(('alpha', 'beta')):\n",
    "    for j, cdr in enumerate(('1', '2', '3')):\n",
    "        proportion_data = cdr_length_proportions.query(\"chain_type == @chain_type and cdr == @cdr\")\n",
    "        colors = proportion_data['difference'].map(lambda diff: 'lightblue' if diff < 0 else 'orange')\n",
    "\n",
    "        axes[i, j].bar(proportion_data['length'], proportion_data['difference'],\n",
    "                       yerr=proportion_data['percentage_sem'],\n",
    "                       color=colors, edgecolor='black')\n",
    "\n",
    "        axes[i, j].set_title(f'CDR{cdr}$\\\\{chain_type}$')\n",
    "        axes[i, j].set_xlabel('Loop Length')\n",
    "        axes[i, j].set_ylabel('Percentage (%)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CDR1alpha RMSD: 5.8\n",
      "CDR2alpha RMSD: 7.14\n",
      "CDR3alpha RMSD: 4.41\n",
      "CDR1beta RMSD: 4.67\n",
      "CDR2beta RMSD: 8.15\n",
      "CDR3beta RMSD: 2.36\n",
      "\n",
      "Overall RMSD: 4.65\n"
     ]
    }
   ],
   "source": [
    "for chain_type in ('alpha', 'beta'):\n",
    "    for cdr in ('1', '2', '3'):\n",
    "        proportion_data = cdr_length_proportions.query(\"chain_type == @chain_type and cdr == @cdr\")\n",
    "        print(f'CDR{cdr}{chain_type} RMSD:', round(rmsd(proportion_data['difference']), 2))\n",
    "\n",
    "print()\n",
    "print('Overall RMSD:', round(rmsd(cdr_length_proportions['difference']), 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall, it seems that the distribution of lengths is similar between the *apo*-*holo* structures taken from the STCRDab and the background TCRs from OTS.\n",
    "The distributions look similar between the data sources, with a slightly larger variety for the OTS TCRs.\n",
    "Also, the mode length for each CDR type is the same between the OTS background and the dataset used in this analysis (6 for CDR1α, 7 for CDR2α, 11  for CDR3α, 5 for CDR1β, 6 for CDR2β, and 13 for CDR3β)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare TCR V Gene Usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>sample_num</th>\n",
       "      <th>source</th>\n",
       "      <th>subgroup</th>\n",
       "      <th>gene</th>\n",
       "      <th>subgroup_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>OTS</td>\n",
       "      <td>alpha_subgroup</td>\n",
       "      <td>TRAV26</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>OTS</td>\n",
       "      <td>alpha_subgroup</td>\n",
       "      <td>TRAV29</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>OTS</td>\n",
       "      <td>alpha_subgroup</td>\n",
       "      <td>TRAV24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>OTS</td>\n",
       "      <td>alpha_subgroup</td>\n",
       "      <td>TRAV8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>OTS</td>\n",
       "      <td>alpha_subgroup</td>\n",
       "      <td>TRAV1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20163</th>\n",
       "      <td>NaN</td>\n",
       "      <td>STCRDab</td>\n",
       "      <td>beta_subgroup</td>\n",
       "      <td>TRBV7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20164</th>\n",
       "      <td>NaN</td>\n",
       "      <td>STCRDab</td>\n",
       "      <td>beta_subgroup</td>\n",
       "      <td>TRBV6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20165</th>\n",
       "      <td>NaN</td>\n",
       "      <td>STCRDab</td>\n",
       "      <td>beta_subgroup</td>\n",
       "      <td>TRBV11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20166</th>\n",
       "      <td>NaN</td>\n",
       "      <td>STCRDab</td>\n",
       "      <td>beta_subgroup</td>\n",
       "      <td>TRBV7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20167</th>\n",
       "      <td>NaN</td>\n",
       "      <td>STCRDab</td>\n",
       "      <td>beta_subgroup</td>\n",
       "      <td>TRBV7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20168 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       sample_num   source        subgroup    gene  subgroup_num\n",
       "0             1.0      OTS  alpha_subgroup  TRAV26            26\n",
       "1             1.0      OTS  alpha_subgroup  TRAV29            29\n",
       "2             1.0      OTS  alpha_subgroup  TRAV24            24\n",
       "3             1.0      OTS  alpha_subgroup   TRAV8             8\n",
       "4             1.0      OTS  alpha_subgroup   TRAV1             1\n",
       "...           ...      ...             ...     ...           ...\n",
       "20163         NaN  STCRDab   beta_subgroup   TRBV7             7\n",
       "20164         NaN  STCRDab   beta_subgroup   TRBV6             6\n",
       "20165         NaN  STCRDab   beta_subgroup  TRBV11            11\n",
       "20166         NaN  STCRDab   beta_subgroup   TRBV7             7\n",
       "20167         NaN  STCRDab   beta_subgroup   TRBV7             7\n",
       "\n",
       "[20168 rows x 5 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ots_sample_genes['source'] = 'OTS'\n",
    "apo_holo_genes['source'] = 'STCRDab'\n",
    "\n",
    "gene_usage = pd.concat([ots_sample_genes, apo_holo_genes])\n",
    "gene_usage = gene_usage.dropna(subset=['alpha_subgroup', 'beta_subgroup'])\n",
    "gene_usage = gene_usage.melt(id_vars=['sample_num', 'source'],\n",
    "                             value_vars=['alpha_subgroup', 'beta_subgroup'],\n",
    "                             var_name='subgroup', value_name='gene')\n",
    "\n",
    "gene_usage['subgroup_num'] = gene_usage['gene'].str.extract(r'TR[A|B]V(\\d+)', expand=False).apply(int)\n",
    "\n",
    "gene_usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_usage_percentages = (gene_usage.groupby(['subgroup', 'source', 'sample_num'], dropna=False)\n",
    "                                    [['gene', 'subgroup_num']]\n",
    "                                    .value_counts(normalize=True)\n",
    "                                    * 100)\n",
    "gene_usage_percentages.name = 'percentage'\n",
    "\n",
    "gene_usage_percentages = gene_usage_percentages.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/scratch/bmcmaste/2178229/ipykernel_2150264/1817500796.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 614.125x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.catplot(gene_usage_percentages.sort_values(['subgroup', 'subgroup_num']),\n",
    "            row='subgroup',\n",
    "            x='gene', y='percentage',\n",
    "            hue='source',\n",
    "            kind='bar',\n",
    "            sharex=False)\n",
    "\n",
    "for ax in grid.axes.flat:\n",
    "    ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",
    "\n",
    "grid.tight_layout(pad=3.0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "ots_sample_gene_usage_percentages = gene_usage_percentages.query(\"source == 'OTS'\")\n",
    "ots_sample_gene_usage_percentages_agg = (ots_sample_gene_usage_percentages.groupby(['gene', 'subgroup', 'subgroup_num'])\n",
    "                                                                          .agg({'percentage': ['mean', 'sem']})\n",
    "                                                                          .reset_index()\n",
    "                                                                          .fillna(0.0))\n",
    "\n",
    "ots_sample_gene_usage_percentages_agg.columns = ['_'.join(col).rstrip('_').replace('_mean', '')\n",
    "                                                 for col in ots_sample_gene_usage_percentages_agg.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "apo_holo_gene_usage_percentages = gene_usage_percentages.query(\"source == 'STCRDab'\")\n",
    "gene_usage_percentages_compare = apo_holo_gene_usage_percentages.merge(ots_sample_gene_usage_percentages_agg,\n",
    "                                                                       how='outer',\n",
    "                                                                       on=['gene', 'subgroup', 'subgroup_num'],\n",
    "                                                                       suffixes=('_stcrdab', '_ots'))\n",
    "\n",
    "gene_usage_percentages_compare = gene_usage_percentages_compare.drop(['source', 'sample_num'], axis='columns')\n",
    "gene_usage_percentages_compare = gene_usage_percentages_compare.fillna(0.0)\n",
    "\n",
    "gene_usage_percentages_compare['difference'] = (gene_usage_percentages_compare['percentage_stcrdab']\n",
    "                                                - gene_usage_percentages_compare['percentage_ots'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, constrained_layout=True, sharey=True)\n",
    "plt.ylim([-100, 100])\n",
    "fig.suptitle('Relative frequency of structure V gene usage compared to OTS')\n",
    "\n",
    "for i, subgroup in enumerate(('alpha_subgroup', 'beta_subgroup')):\n",
    "    plot_data = gene_usage_percentages_compare.query('subgroup == @subgroup').sort_values('subgroup_num')\n",
    "\n",
    "    colors = plot_data['difference'].map(lambda diff: 'lightblue' if diff < 0 else 'orange')\n",
    "\n",
    "    axes[i].bar(plot_data['gene'],\n",
    "               plot_data['difference'],\n",
    "               yerr=plot_data['percentage_sem'],\n",
    "               color=colors,\n",
    "               edgecolor='black')\n",
    "\n",
    "    axes[i].set_xlabel(f\"TR{'A' if subgroup == 'alpha_subgroup' else 'B'}V Gene Usage\")\n",
    "    axes[i].set_ylabel('Percentage (%)')\n",
    "    axes[i].tick_params(axis='x', labelrotation=90)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAV Gene Usage RMSD: 4.33\n",
      "TRBV Gene Usage RMSD: 4.24\n"
     ]
    }
   ],
   "source": [
    "print('TRAV Gene Usage RMSD:',\n",
    "      round(rmsd(gene_usage_percentages_compare.query(\"subgroup == 'alpha_subgroup'\")['difference']), 2))\n",
    "\n",
    "print('TRBV Gene Usage RMSD:',\n",
    "      round(rmsd(gene_usage_percentages_compare.query(\"subgroup == 'beta_subgroup'\")['difference']), 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The gene usage between *apo*-*holo* TCRs from STCRDab and the background TCRs from OTS is largely similar.\n",
    "There are a few exceptions in the STCRDab structures that show an over-representation of certain gene usages.\n",
    "For exampe, TRAV12, TRAV14, TRAV21, and TRBV6 all seem to be in higher abundance in the STCRDab data compared with the OTS background."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison of amino acide types in loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "AMINO_ACIDS = ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y']\n",
    "\n",
    "def count_amino_acids(residues: pd.Series, normalize = True) -> pd.Series:\n",
    "    counts = {res: 0 for res in AMINO_ACIDS}\n",
    "\n",
    "    for res in residues.tolist():\n",
    "        counts[res] += 1\n",
    "\n",
    "    if normalize:\n",
    "        num_residues = len(residues)\n",
    "        counts = {res: count / num_residues for res, count in counts.items()}\n",
    "\n",
    "    counts = pd.Series(counts)\n",
    "    counts.name = 'count' if not normalize else 'proportion'\n",
    "    counts.index.name = 'residue'\n",
    "\n",
    "    return counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>sample_num</th>\n",
       "      <th>chain_type</th>\n",
       "      <th>cdr</th>\n",
       "      <th>residue</th>\n",
       "      <th>proportion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>alpha</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0.067791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>alpha</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>alpha</td>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>0.062615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>alpha</td>\n",
       "      <td>1</td>\n",
       "      <td>E</td>\n",
       "      <td>0.014527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>alpha</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>0.033228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>10</td>\n",
       "      <td>beta</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>0.181695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>10</td>\n",
       "      <td>beta</td>\n",
       "      <td>3</td>\n",
       "      <td>T</td>\n",
       "      <td>0.073013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>10</td>\n",
       "      <td>beta</td>\n",
       "      <td>3</td>\n",
       "      <td>V</td>\n",
       "      <td>0.021305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>10</td>\n",
       "      <td>beta</td>\n",
       "      <td>3</td>\n",
       "      <td>W</td>\n",
       "      <td>0.006623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199</th>\n",
       "      <td>10</td>\n",
       "      <td>beta</td>\n",
       "      <td>3</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.066949</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1200 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      sample_num chain_type cdr residue  proportion\n",
       "0              1      alpha   1       A    0.067791\n",
       "1              1      alpha   1       C    0.000000\n",
       "2              1      alpha   1       D    0.062615\n",
       "3              1      alpha   1       E    0.014527\n",
       "4              1      alpha   1       F    0.033228\n",
       "...          ...        ...  ..     ...         ...\n",
       "1195          10       beta   3       S    0.181695\n",
       "1196          10       beta   3       T    0.073013\n",
       "1197          10       beta   3       V    0.021305\n",
       "1198          10       beta   3       W    0.006623\n",
       "1199          10       beta   3       Y    0.066949\n",
       "\n",
       "[1200 rows x 5 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ots_sample_cdrs_residues = ots_sample_cdrs[['sample_num', 'chain_type', 'cdr', 'sequence']].copy()\n",
    "ots_sample_cdrs_residues['residue'] = ots_sample_cdrs_residues['sequence'].map(list)\n",
    "ots_sample_cdrs_residues = ots_sample_cdrs_residues.explode('residue')\n",
    "\n",
    "ots_sample_cdrs_residues_counts = (ots_sample_cdrs_residues.groupby(['sample_num', 'chain_type', 'cdr'])\n",
    "                                                            ['residue']\n",
    "                                                            .apply(count_amino_acids))\n",
    "ots_sample_cdrs_residues_counts.name = 'proportion'\n",
    "ots_sample_cdrs_residues_counts = ots_sample_cdrs_residues_counts.to_frame()\n",
    "ots_sample_cdrs_residues_counts = ots_sample_cdrs_residues_counts.reset_index()\n",
    "\n",
    "ots_sample_cdrs_residues_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "ots_sample_cdrs_residues_counts_agg = (ots_sample_cdrs_residues_counts.groupby(['chain_type', 'cdr', 'residue'])\n",
    "                                                                      .agg({'proportion': ['mean', 'sem']})\n",
    "                                                                      .reset_index())\n",
    "\n",
    "ots_sample_cdrs_residues_counts_agg.columns = ['_'.join(col).rstrip('_').replace('_mean', '')\n",
    "                                               for col in ots_sample_cdrs_residues_counts_agg.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>chain_type</th>\n",
       "      <th>cdr</th>\n",
       "      <th>residue</th>\n",
       "      <th>proportion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alpha</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0.055028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alpha</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alpha</td>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>0.087287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>alpha</td>\n",
       "      <td>1</td>\n",
       "      <td>E</td>\n",
       "      <td>0.017078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>alpha</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>0.037951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>beta</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>0.179537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>beta</td>\n",
       "      <td>3</td>\n",
       "      <td>T</td>\n",
       "      <td>0.062741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>beta</td>\n",
       "      <td>3</td>\n",
       "      <td>V</td>\n",
       "      <td>0.023166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>beta</td>\n",
       "      <td>3</td>\n",
       "      <td>W</td>\n",
       "      <td>0.013514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>beta</td>\n",
       "      <td>3</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.076255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    chain_type cdr residue  proportion\n",
       "0        alpha   1       A    0.055028\n",
       "1        alpha   1       C    0.000000\n",
       "2        alpha   1       D    0.087287\n",
       "3        alpha   1       E    0.017078\n",
       "4        alpha   1       F    0.037951\n",
       "..         ...  ..     ...         ...\n",
       "115       beta   3       S    0.179537\n",
       "116       beta   3       T    0.062741\n",
       "117       beta   3       V    0.023166\n",
       "118       beta   3       W    0.013514\n",
       "119       beta   3       Y    0.076255\n",
       "\n",
       "[120 rows x 4 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apo_holo_cdr_sequences_residues = apo_holo_cdr_sequences[['chain_type', 'cdr', 'sequence']].copy()\n",
    "apo_holo_cdr_sequences_residues['residue'] = apo_holo_cdr_sequences_residues['sequence'].map(list)\n",
    "apo_holo_cdr_sequences_residues = apo_holo_cdr_sequences_residues.explode('residue')\n",
    "\n",
    "apo_holo_cdr_sequences_residues_counts = (apo_holo_cdr_sequences_residues.groupby(['chain_type', 'cdr'])\n",
    "                                          ['residue']\n",
    "                                          .apply(count_amino_acids))\n",
    "\n",
    "apo_holo_cdr_sequences_residues_counts.name = 'proportion'\n",
    "apo_holo_cdr_sequences_residues_counts = apo_holo_cdr_sequences_residues_counts.to_frame()\n",
    "apo_holo_cdr_sequences_residues_counts = apo_holo_cdr_sequences_residues_counts.reset_index()\n",
    "\n",
    "apo_holo_cdr_sequences_residues_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "ots_sample_cdrs_residues_counts_agg['source'] = 'ots'\n",
    "apo_holo_cdr_sequences_residues_counts['source'] = 'stcrdab'\n",
    "\n",
    "cdr_residue_counts = pd.concat([ots_sample_cdrs_residues_counts_agg, apo_holo_cdr_sequences_residues_counts])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "angles = np.linspace(0, 2 * np.pi, len(AMINO_ACIDS), endpoint=False).tolist()\n",
    "amino_acid_angles = dict(zip(AMINO_ACIDS, angles))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=3,\n",
    "                         figsize=(15, 12),\n",
    "                         sharey=True,\n",
    "                         subplot_kw={'projection': 'polar'})\n",
    "\n",
    "lines = []\n",
    "for i, chain_type in enumerate(('alpha', 'beta')):\n",
    "    for j, cdr in enumerate(('1', '2', '3')):\n",
    "        for source in 'ots', 'stcrdab':\n",
    "            residue_counts = cdr_residue_counts[(cdr_residue_counts['source'] == source)\n",
    "                                                & (cdr_residue_counts['chain_type'] == chain_type)\n",
    "                                                & (cdr_residue_counts['cdr'] == cdr)].sort_values('residue')\n",
    "\n",
    "            proportions = residue_counts['proportion'].tolist()\n",
    "            label_angles = residue_counts['residue'].map(amino_acid_angles).tolist()\n",
    "\n",
    "            # Join ends\n",
    "            proportions.append(proportions[0])\n",
    "            label_angles.append(label_angles[0])\n",
    "\n",
    "            line, = axes[i, j].plot(label_angles, proportions, marker='o', markersize=3, label=source)\n",
    "            axes[i, j].bar(label_angles, proportions, alpha=0.25, width=0.3)\n",
    "\n",
    "            lines.append(line)\n",
    "\n",
    "        axes[i, j].set_xticks(angles)\n",
    "        axes[i, j].set_xticklabels(AMINO_ACIDS)\n",
    "        axes[i, j].grid(which='major', axis='x', visible=False)\n",
    "\n",
    "        axes[i, j].set_title(f'CDR{cdr}$\\\\{chain_type}$')\n",
    "\n",
    "unique_handles_labels = {}\n",
    "for line in lines:\n",
    "    label = line.get_label()\n",
    "    if label not in unique_handles_labels:\n",
    "        unique_handles_labels[label] = line\n",
    "\n",
    "labels, handles = zip(*unique_handles_labels.items())\n",
    "\n",
    "legend = fig.legend(handles, labels, title='Source', fontsize='large')\n",
    "legend.get_title().set_fontsize('large')\n",
    "\n",
    "fig.suptitle('Comparison of amino acids in CDR loops between selected structures and OTS',\n",
    "             fontsize=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chain_type  cdr\n",
      "alpha       1      0.015719\n",
      "            2      0.013582\n",
      "            3      0.007108\n",
      "beta        1      0.025485\n",
      "            2      0.022489\n",
      "            3      0.007593\n",
      "Name: difference, dtype: float64\n",
      "Overall RMSD: 1.68e-02\n"
     ]
    }
   ],
   "source": [
    "cdr_residue_counts_compare = cdr_residue_counts.pivot(index=['chain_type', 'cdr', 'residue'],\n",
    "                                                      columns='source',\n",
    "                                                      values='proportion').reset_index()\n",
    "\n",
    "cdr_residue_counts_compare['difference'] = cdr_residue_counts_compare['stcrdab'] - cdr_residue_counts_compare['ots']\n",
    "\n",
    "print(cdr_residue_counts_compare.groupby(['chain_type', 'cdr'])['difference'].agg(rmsd))\n",
    "\n",
    "print(f\"Overall RMSD: {rmsd(cdr_residue_counts_compare['difference']):.2e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison of species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>sample_num</th>\n",
       "      <th>species</th>\n",
       "      <th>proportion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>human</td>\n",
       "      <td>0.993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>mouse</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>human</td>\n",
       "      <td>0.983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>mouse</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>human</td>\n",
       "      <td>0.979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>mouse</td>\n",
       "      <td>0.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>human</td>\n",
       "      <td>0.982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>mouse</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>human</td>\n",
       "      <td>0.981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>mouse</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6</td>\n",
       "      <td>human</td>\n",
       "      <td>0.985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6</td>\n",
       "      <td>mouse</td>\n",
       "      <td>0.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7</td>\n",
       "      <td>human</td>\n",
       "      <td>0.984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7</td>\n",
       "      <td>mouse</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8</td>\n",
       "      <td>human</td>\n",
       "      <td>0.975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8</td>\n",
       "      <td>mouse</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9</td>\n",
       "      <td>human</td>\n",
       "      <td>0.991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>9</td>\n",
       "      <td>mouse</td>\n",
       "      <td>0.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10</td>\n",
       "      <td>human</td>\n",
       "      <td>0.986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10</td>\n",
       "      <td>mouse</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sample_num species  proportion\n",
       "0            1   human       0.993\n",
       "1            1   mouse       0.007\n",
       "2            2   human       0.983\n",
       "3            2   mouse       0.017\n",
       "4            3   human       0.979\n",
       "5            3   mouse       0.021\n",
       "6            4   human       0.982\n",
       "7            4   mouse       0.018\n",
       "8            5   human       0.981\n",
       "9            5   mouse       0.019\n",
       "10           6   human       0.985\n",
       "11           6   mouse       0.015\n",
       "12           7   human       0.984\n",
       "13           7   mouse       0.016\n",
       "14           8   human       0.975\n",
       "15           8   mouse       0.025\n",
       "16           9   human       0.991\n",
       "17           9   mouse       0.009\n",
       "18          10   human       0.986\n",
       "19          10   mouse       0.014"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ots_organism_counts = ots_sample.groupby('sample_num')['Species'].value_counts(normalize=True)\n",
    "ots_organism_counts.name = 'proportion'\n",
    "ots_organism_counts = ots_organism_counts.to_frame().reset_index()\n",
    "ots_organism_counts = ots_organism_counts.rename({'Species': 'species'}, axis='columns')\n",
    "ots_organism_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "apo_holo_annotated = apo_holo_summary.merge(\n",
    "    stcrdab_summary,\n",
    "    how='left',\n",
    "    left_on=['pdb_id', 'alpha_chain', 'beta_chain', 'antigen_chain', 'mhc_chain1', 'mhc_chain2'],\n",
    "    right_on=['pdb', 'Achain', 'Bchain', 'antigen_chain', 'mhc_chain1', 'mhc_chain2'],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "apo_holo_organisms = apo_holo_annotated.drop_duplicates(['cdr_sequences_collated'])[['alpha_organism', 'beta_organism']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>alpha_organism</th>\n",
       "      <th>beta_organism</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [alpha_organism, beta_organism]\n",
       "Index: []"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apo_holo_organisms[apo_holo_organisms['alpha_organism'].notnull()\n",
    "                   | apo_holo_organisms['beta_organism'].notnull()].query(\"alpha_organism != beta_organism\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['homo sapiens', nan, 'mus musculus', 'mus musculus, homo sapiens'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apo_holo_organisms = apo_holo_organisms['beta_organism']\n",
    "apo_holo_organisms.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "apo_holo_organisms = apo_holo_organisms.map({'homo sapiens': 'human',\n",
    "                                             'mus musculus': 'mouse',\n",
    "                                             'mus musculus, homo sapiens': 'human/mouse'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "human          72\n",
       "mouse          10\n",
       "human/mouse     3\n",
       "Name: beta_organism, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apo_holo_organisms.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "human          0.847059\n",
       "mouse          0.117647\n",
       "human/mouse    0.035294\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apo_holo_organisms_counts = apo_holo_organisms.value_counts(normalize=True)\n",
    "apo_holo_organisms_counts.name = 'proportion'\n",
    "apo_holo_organisms_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "apo_holo_organisms_counts = apo_holo_organisms_counts.to_frame().reset_index(names='species')\n",
    "apo_holo_organisms_counts['source'] = 'STCRDab'\n",
    "apo_holo_organisms_counts['sample_num'] = 1\n",
    "\n",
    "ots_organism_counts = ots_organism_counts\n",
    "ots_organism_counts['source'] = 'OTS'\n",
    "\n",
    "organism_counts = pd.concat([apo_holo_organisms_counts, ots_organism_counts]).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='species', ylabel='proportion'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(organism_counts, x='species', y='proportion', hue='source')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selected structures coverage of canonical classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>name</th>\n",
       "      <th>cluster</th>\n",
       "      <th>chain_type</th>\n",
       "      <th>cdr</th>\n",
       "      <th>sequence</th>\n",
       "      <th>cluster_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7zt2_DE</td>\n",
       "      <td>12</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>TSGFNG</td>\n",
       "      <td>pseudo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7zt3_DE</td>\n",
       "      <td>12</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>TSGFNG</td>\n",
       "      <td>pseudo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7zt4_DE</td>\n",
       "      <td>12</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>TSGFNG</td>\n",
       "      <td>pseudo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7zt5_DE</td>\n",
       "      <td>12</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>TSGFNG</td>\n",
       "      <td>pseudo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7zt7_DE</td>\n",
       "      <td>12</td>\n",
       "      <td>alpha_chain</td>\n",
       "      <td>1</td>\n",
       "      <td>TSGFNG</td>\n",
       "      <td>pseudo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4807</th>\n",
       "      <td>6miv_CD</td>\n",
       "      <td>22</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>ASGDEGYTQY</td>\n",
       "      <td>canonical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4808</th>\n",
       "      <td>3rtq_CD</td>\n",
       "      <td>22</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>ASGDEGYTQY</td>\n",
       "      <td>canonical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4809</th>\n",
       "      <td>3dxa_NO</td>\n",
       "      <td>noise</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>ASRYRDDSYNEQF</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4810</th>\n",
       "      <td>1d9k_AB</td>\n",
       "      <td>noise</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>ASGGQGRAEQF</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4811</th>\n",
       "      <td>4gg6_GH</td>\n",
       "      <td>noise</td>\n",
       "      <td>beta_chain</td>\n",
       "      <td>3</td>\n",
       "      <td>ASSVAVSAGTYEQY</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4812 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         name cluster   chain_type  cdr        sequence cluster_type\n",
       "0     7zt2_DE      12  alpha_chain    1          TSGFNG       pseudo\n",
       "1     7zt3_DE      12  alpha_chain    1          TSGFNG       pseudo\n",
       "2     7zt4_DE      12  alpha_chain    1          TSGFNG       pseudo\n",
       "3     7zt5_DE      12  alpha_chain    1          TSGFNG       pseudo\n",
       "4     7zt7_DE      12  alpha_chain    1          TSGFNG       pseudo\n",
       "...       ...     ...          ...  ...             ...          ...\n",
       "4807  6miv_CD      22   beta_chain    3      ASGDEGYTQY    canonical\n",
       "4808  3rtq_CD      22   beta_chain    3      ASGDEGYTQY    canonical\n",
       "4809  3dxa_NO   noise   beta_chain    3   ASRYRDDSYNEQF          NaN\n",
       "4810  1d9k_AB   noise   beta_chain    3     ASGGQGRAEQF          NaN\n",
       "4811  4gg6_GH   noise   beta_chain    3  ASSVAVSAGTYEQY          NaN\n",
       "\n",
       "[4812 rows x 6 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdr_clusters = pd.read_csv('../data/processed/stcrdab_clusters.csv')\n",
    "cdr_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "cdr_clusters[['pdb_id', 'chains']] = cdr_clusters['name'].str.split('_').apply(pd.Series)\n",
    "cdr_clusters[['alpha_chain', 'beta_chain']] = cdr_clusters['chains'].apply(list).apply(pd.Series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "common_columns = ['pdb_id', 'alpha_chain', 'beta_chain']\n",
    "apo_holo_index = pd.MultiIndex.from_frame(apo_holo_summary[common_columns])\n",
    "\n",
    "cdr_clusters['in_apo_holo'] = cdr_clusters[common_columns].apply(tuple, axis=1).isin(apo_holo_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "canonical_cdr_clusters = cdr_clusters.query(\"cluster_type == 'canonical'\").copy().reset_index(drop=True)\n",
    "canonical_cdr_clusters['cluster_num'] = canonical_cdr_clusters['cluster'].map(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped = (canonical_cdr_clusters.groupby(['chain_type', 'cdr', 'cluster', 'in_apo_holo'])\n",
    "                                 .size()\n",
    "                                 .reset_index(name='count'))\n",
    "total_counts = (canonical_cdr_clusters.groupby(['chain_type', 'cdr'])\n",
    "                                      .size()\n",
    "                                      .reset_index(name='total_count'))\n",
    "\n",
    "grouped = grouped.merge(total_counts, on=['chain_type', 'cdr'])\n",
    "grouped['percent'] = grouped['count'] / grouped['total_count'] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped['cluster_num'] = grouped['cluster'].apply(int)\n",
    "grouped = grouped.sort_values('cluster_num')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1593.36x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.displot(grouped,\n",
    "                   row='chain_type', col='cdr',\n",
    "                   x='cluster',\n",
    "                   weights='percent',\n",
    "                   hue='in_apo_holo', multiple='stack',\n",
    "                   facet_kws=dict(sharex=False))\n",
    "\n",
    "grid.set_axis_labels(\"Cluster\", \"Percentage (%)\")\n",
    "grid.set(ylim=(0, 100))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is 86.96% coverage of canonical clusters in the apo-holo dataset.\n"
     ]
    }
   ],
   "source": [
    "apo_holo_coverage = (canonical_cdr_clusters.groupby(['chain_type', 'cdr', 'cluster'])\n",
    "                                           .apply(lambda group: group['in_apo_holo'].any())\n",
    "                                           .value_counts(normalize=True) * 100)[True]\n",
    "\n",
    "print(f'There is {apo_holo_coverage:.2f}% coverage of canonical clusters in the apo-holo dataset.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "Our analysis here shows that the conclusions drawn about TCRs from our structural analysis in this project should generalise well to other TCR:pMHC-I interactions where structure information is not currently available.\n",
    "The comparisons here show that the distribution of CDR lengths, amino acid compisition of CDR loops, and generally the distribution of V gene usage is comparable between the selected STCRDab structures and the background of TCRs sampled from OTS.\n",
    "However, it does seem that there is some over representation bias of certain V genes which may affect results.\n",
    "The analysis also shows that canonical CDR loop clusters are well represented in the selected analysis.\n",
    "We did not look at amino acid composition and other possible comparable properties from these sequences that could be done."
   ]
  }
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