{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e2c12381",
   "metadata": {},
   "source": [
    "# Identify contact residues on MHC Class I molecules"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1729b13c",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "In this notebook, we aim to determine the IMGT positions of MHC molecules that make contact with the CDR loops of a contacting TCR. These contacts are then plotted based on the identity of the TCR loop to create a finger print of the TCRs on MHC molecules. We also look at the contacts made by TCRs on the presented peptide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e77572dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-13T17:28:16.694911Z",
     "iopub.status.busy": "2024-05-13T17:28:16.694066Z",
     "iopub.status.idle": "2024-05-13T17:28:32.979355Z",
     "shell.execute_reply": "2024-05-13T17:28:32.978332Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from python_pdb.parsers import parse_pdb_to_pandas\n",
    "\n",
    "from tcr_pmhc_interface_analysis.imgt_numbering import assign_cdr_number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7861a522",
   "metadata": {},
   "outputs": [],
   "source": [
    "CUTOFF_DISTANCE = 5 # Å"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15246f0a",
   "metadata": {},
   "source": [
    "## Loading TCR:pMHC-I Structures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e558e68f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-13T17:28:32.985285Z",
     "iopub.status.busy": "2024-05-13T17:28:32.984693Z",
     "iopub.status.idle": "2024-05-13T17:28:32.989070Z",
     "shell.execute_reply": "2024-05-13T17:28:32.988312Z"
    }
   },
   "outputs": [],
   "source": [
    "STCRDAB_PATH = '../data/raw/stcrdab'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cc7f89c7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-13T17:28:32.993200Z",
     "iopub.status.busy": "2024-05-13T17:28:32.992862Z",
     "iopub.status.idle": "2024-05-13T17:28:33.222616Z",
     "shell.execute_reply": "2024-05-13T17:28:33.221754Z"
    }
   },
   "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>pdb</th>\n",
       "      <th>Bchain</th>\n",
       "      <th>Achain</th>\n",
       "      <th>antigen_chain</th>\n",
       "      <th>antigen_name</th>\n",
       "      <th>mhc_chain1</th>\n",
       "      <th>mhc_chain2</th>\n",
       "      <th>docking_angle</th>\n",
       "      <th>beta_subgroup</th>\n",
       "      <th>alpha_subgroup</th>\n",
       "      <th>...</th>\n",
       "      <th>alpha_organism</th>\n",
       "      <th>antigen_organism</th>\n",
       "      <th>mhc_chain1_organism</th>\n",
       "      <th>mhc_chain2_organism</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>engineered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8gom</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "      <td>spike protein s2</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>39.649</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>severe acute respiratory syndrome coronavirus2</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>Wu, D., Mariuzza, R.A.</td>\n",
       "      <td>2.783</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.195</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8gon</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "      <td>spike protein s2</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>38.984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>severe acute respiratory syndrome coronavirus2</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>Wu, D., Mariuzza, R.A.</td>\n",
       "      <td>2.601</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>0.253</td>\n",
       "      <td>0.198</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7q99</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "      <td>asn-leu-ser-ala-leu-gly-ile-phe-ser-thr</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>46.371</td>\n",
       "      <td>TRBV30</td>\n",
       "      <td>TRAV12</td>\n",
       "      <td>...</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>Rizkallah, P.J., Sewell, A.K., Wall, A., Fulle...</td>\n",
       "      <td>2.550</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.218</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7q9a</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "      <td>leu-leu-leu-gly-ile-gly-ile-leu-val-leu</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>48.391</td>\n",
       "      <td>TRBV30</td>\n",
       "      <td>TRAV12</td>\n",
       "      <td>...</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>Rizkallah, P.J., Sewell, A.K., Wall, A., Fulle...</td>\n",
       "      <td>2.100</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>0.243</td>\n",
       "      <td>0.205</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2ak4</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "      <td>ebv peptide lpeplpqgqltay</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>71.108</td>\n",
       "      <td>TRBV6</td>\n",
       "      <td>TRAV19</td>\n",
       "      <td>...</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>NaN</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>Tynan, F.E., Burrows, S.R., Buckle, A.M., Clem...</td>\n",
       "      <td>2.500</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>0.278</td>\n",
       "      <td>0.246</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>297</th>\n",
       "      <td>6q3s</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "      <td>ser-leu-leu-met-trp-ile-thr-gln-val</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>67.059</td>\n",
       "      <td>TRBV6</td>\n",
       "      <td>TRAV21</td>\n",
       "      <td>...</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>Meijers, R., Anjanappa, R., Springer, S., Garc...</td>\n",
       "      <td>2.500</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>0.273</td>\n",
       "      <td>0.229</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>5men</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "      <td>ile-leu-ala-lys-phe-leu-his-trp-leu</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>40.823</td>\n",
       "      <td>TRBV6</td>\n",
       "      <td>TRAV22</td>\n",
       "      <td>...</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>Rizkallah, P.J., Lloyd, A., Crowther, M., Cole...</td>\n",
       "      <td>2.810</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.189</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>1ao7</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "      <td>tax peptide</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>34.827</td>\n",
       "      <td>TRBV6</td>\n",
       "      <td>TRAV12</td>\n",
       "      <td>...</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>human t-lymphotropic virus 1</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>Garboczi, D.N., Ghosh, P., Utz, U., Fan, Q.R.,...</td>\n",
       "      <td>2.600</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.245</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>4jff</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "      <td>melanoma motif</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>42.977</td>\n",
       "      <td>TRBV30</td>\n",
       "      <td>TRAV12</td>\n",
       "      <td>...</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>Rizkallah, P.J., Cole, D.K., Madura, F., Sewel...</td>\n",
       "      <td>2.430</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>0.263</td>\n",
       "      <td>0.210</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>3dxa</td>\n",
       "      <td>O</td>\n",
       "      <td>N</td>\n",
       "      <td>M</td>\n",
       "      <td>ebv decapeptide epitope</td>\n",
       "      <td>K</td>\n",
       "      <td>L</td>\n",
       "      <td>58.225</td>\n",
       "      <td>TRBV7</td>\n",
       "      <td>TRAV26</td>\n",
       "      <td>...</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>NaN</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>homo sapiens</td>\n",
       "      <td>Archbold, J.K., Macdonald, W.A., Gras, S., Ros...</td>\n",
       "      <td>3.500</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.286</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>302 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      pdb Bchain Achain antigen_chain  \\\n",
       "0    8gom      E      D             C   \n",
       "1    8gon      E      D             C   \n",
       "2    7q99      E      D             C   \n",
       "3    7q9a      E      D             C   \n",
       "4    2ak4      E      D             C   \n",
       "..    ...    ...    ...           ...   \n",
       "297  6q3s      E      D             C   \n",
       "298  5men      E      D             C   \n",
       "299  1ao7      E      D             C   \n",
       "300  4jff      E      D             C   \n",
       "301  3dxa      O      N             M   \n",
       "\n",
       "                                antigen_name mhc_chain1 mhc_chain2  \\\n",
       "0                           spike protein s2          A          B   \n",
       "1                           spike protein s2          A          B   \n",
       "2    asn-leu-ser-ala-leu-gly-ile-phe-ser-thr          A          B   \n",
       "3    leu-leu-leu-gly-ile-gly-ile-leu-val-leu          A          B   \n",
       "4                  ebv peptide lpeplpqgqltay          A          B   \n",
       "..                                       ...        ...        ...   \n",
       "297      ser-leu-leu-met-trp-ile-thr-gln-val          A          B   \n",
       "298      ile-leu-ala-lys-phe-leu-his-trp-leu          A          B   \n",
       "299                              tax peptide          A          B   \n",
       "300                           melanoma motif          A          B   \n",
       "301                  ebv decapeptide epitope          K          L   \n",
       "\n",
       "     docking_angle beta_subgroup alpha_subgroup  ... alpha_organism  \\\n",
       "0           39.649           NaN            NaN  ...   homo sapiens   \n",
       "1           38.984           NaN            NaN  ...   homo sapiens   \n",
       "2           46.371        TRBV30         TRAV12  ...   homo sapiens   \n",
       "3           48.391        TRBV30         TRAV12  ...   homo sapiens   \n",
       "4           71.108         TRBV6         TRAV19  ...   homo sapiens   \n",
       "..             ...           ...            ...  ...            ...   \n",
       "297         67.059         TRBV6         TRAV21  ...   homo sapiens   \n",
       "298         40.823         TRBV6         TRAV22  ...   homo sapiens   \n",
       "299         34.827         TRBV6         TRAV12  ...   homo sapiens   \n",
       "300         42.977        TRBV30         TRAV12  ...   homo sapiens   \n",
       "301         58.225         TRBV7         TRAV26  ...   homo sapiens   \n",
       "\n",
       "                                    antigen_organism mhc_chain1_organism  \\\n",
       "0    severe acute respiratory syndrome coronavirus2         homo sapiens   \n",
       "1    severe acute respiratory syndrome coronavirus2         homo sapiens   \n",
       "2                                       homo sapiens        homo sapiens   \n",
       "3                                       homo sapiens        homo sapiens   \n",
       "4                                                NaN        homo sapiens   \n",
       "..                                               ...                 ...   \n",
       "297                                     homo sapiens        homo sapiens   \n",
       "298                                     homo sapiens        homo sapiens   \n",
       "299                     human t-lymphotropic virus 1        homo sapiens   \n",
       "300                                     homo sapiens        homo sapiens   \n",
       "301                                              NaN        homo sapiens   \n",
       "\n",
       "    mhc_chain2_organism                                            authors  \\\n",
       "0          homo sapiens                             Wu, D., Mariuzza, R.A.   \n",
       "1          homo sapiens                             Wu, D., Mariuzza, R.A.   \n",
       "2          homo sapiens  Rizkallah, P.J., Sewell, A.K., Wall, A., Fulle...   \n",
       "3          homo sapiens  Rizkallah, P.J., Sewell, A.K., Wall, A., Fulle...   \n",
       "4          homo sapiens  Tynan, F.E., Burrows, S.R., Buckle, A.M., Clem...   \n",
       "..                  ...                                                ...   \n",
       "297        homo sapiens  Meijers, R., Anjanappa, R., Springer, S., Garc...   \n",
       "298        homo sapiens  Rizkallah, P.J., Lloyd, A., Crowther, M., Cole...   \n",
       "299        homo sapiens  Garboczi, D.N., Ghosh, P., Utz, U., Fan, Q.R.,...   \n",
       "300        homo sapiens  Rizkallah, P.J., Cole, D.K., Madura, F., Sewel...   \n",
       "301        homo sapiens  Archbold, J.K., Macdonald, W.A., Gras, S., Ros...   \n",
       "\n",
       "    resolution             method r_free r_factor  engineered  \n",
       "0        2.783  X-RAY DIFFRACTION  0.248    0.195        True  \n",
       "1        2.601  X-RAY DIFFRACTION  0.253    0.198        True  \n",
       "2        2.550  X-RAY DIFFRACTION  0.272    0.218        True  \n",
       "3        2.100  X-RAY DIFFRACTION  0.243    0.205        True  \n",
       "4        2.500  X-RAY DIFFRACTION  0.278    0.246        True  \n",
       "..         ...                ...    ...      ...         ...  \n",
       "297      2.500  X-RAY DIFFRACTION  0.273    0.229        True  \n",
       "298      2.810  X-RAY DIFFRACTION  0.272    0.189        True  \n",
       "299      2.600  X-RAY DIFFRACTION  0.320    0.245        True  \n",
       "300      2.430  X-RAY DIFFRACTION  0.263    0.210        True  \n",
       "301      3.500  X-RAY DIFFRACTION  0.330    0.286        True  \n",
       "\n",
       "[302 rows x 24 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stcrdab_summary = pd.read_csv(os.path.join(STCRDAB_PATH, 'db_summary.dat'), delimiter='\\t')\n",
    "\n",
    "selected_stcrdab = stcrdab_summary.copy()\n",
    "\n",
    "# Resolution better than 3.50 Å\n",
    "selected_stcrdab['resolution'] = pd.to_numeric(selected_stcrdab['resolution'], errors='coerce')\n",
    "selected_stcrdab = selected_stcrdab.query(\"resolution <= 3.50\")\n",
    "\n",
    "# alpha-beta TCRs\n",
    "selected_stcrdab = selected_stcrdab.query(\"TCRtype == 'abTCR'\")\n",
    "\n",
    "# MHC class I\n",
    "selected_stcrdab = selected_stcrdab.query(\"mhc_type == 'MH1'\")\n",
    "\n",
    "# peptide antigen\n",
    "selected_stcrdab = selected_stcrdab.query(\"antigen_type == 'peptide'\")\n",
    "\n",
    "# General clean: drop columns that don't contain anything useful\n",
    "selected_stcrdab = selected_stcrdab.loc[:, selected_stcrdab.nunique() > 1]\n",
    "selected_stcrdab = selected_stcrdab.dropna(axis=1, how='all')\n",
    "\n",
    "# Reset Index\n",
    "selected_stcrdab = selected_stcrdab.reset_index(drop=True)\n",
    "\n",
    "selected_stcrdab"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "febfb4f3",
   "metadata": {},
   "source": [
    "## Determine contacting residues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "239c0629",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-13T17:28:33.227897Z",
     "iopub.status.busy": "2024-05-13T17:28:33.227521Z",
     "iopub.status.idle": "2024-05-13T17:35:23.646178Z",
     "shell.execute_reply": "2024-05-13T17:35:23.645193Z"
    }
   },
   "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>record_type_tcr</th>\n",
       "      <th>atom_number_tcr</th>\n",
       "      <th>atom_name_tcr</th>\n",
       "      <th>alt_loc_tcr</th>\n",
       "      <th>residue_name_tcr</th>\n",
       "      <th>chain_id_tcr</th>\n",
       "      <th>residue_seq_id_tcr</th>\n",
       "      <th>residue_insert_code_tcr</th>\n",
       "      <th>pos_x_tcr</th>\n",
       "      <th>pos_y_tcr</th>\n",
       "      <th>...</th>\n",
       "      <th>pos_y_peptide</th>\n",
       "      <th>pos_z_peptide</th>\n",
       "      <th>occupancy_peptide</th>\n",
       "      <th>b_factor_peptide</th>\n",
       "      <th>element_peptide</th>\n",
       "      <th>charge_peptide</th>\n",
       "      <th>chain_type_peptide</th>\n",
       "      <th>cdr_peptide</th>\n",
       "      <th>peptide_length</th>\n",
       "      <th>peptide_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69473</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>472</td>\n",
       "      <td>C</td>\n",
       "      <td>None</td>\n",
       "      <td>ASN</td>\n",
       "      <td>E</td>\n",
       "      <td>57</td>\n",
       "      <td>None</td>\n",
       "      <td>12.312</td>\n",
       "      <td>-60.161</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69474</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>472</td>\n",
       "      <td>C</td>\n",
       "      <td>None</td>\n",
       "      <td>ASN</td>\n",
       "      <td>E</td>\n",
       "      <td>57</td>\n",
       "      <td>None</td>\n",
       "      <td>12.312</td>\n",
       "      <td>-60.161</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70937</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>473</td>\n",
       "      <td>O</td>\n",
       "      <td>None</td>\n",
       "      <td>ASN</td>\n",
       "      <td>E</td>\n",
       "      <td>57</td>\n",
       "      <td>None</td>\n",
       "      <td>11.507</td>\n",
       "      <td>-60.966</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70938</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>473</td>\n",
       "      <td>O</td>\n",
       "      <td>None</td>\n",
       "      <td>ASN</td>\n",
       "      <td>E</td>\n",
       "      <td>57</td>\n",
       "      <td>None</td>\n",
       "      <td>11.507</td>\n",
       "      <td>-60.966</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70939</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>473</td>\n",
       "      <td>O</td>\n",
       "      <td>None</td>\n",
       "      <td>ASN</td>\n",
       "      <td>E</td>\n",
       "      <td>57</td>\n",
       "      <td>None</td>\n",
       "      <td>11.507</td>\n",
       "      <td>-60.966</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>31515</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>10078</td>\n",
       "      <td>CD2</td>\n",
       "      <td>None</td>\n",
       "      <td>TYR</td>\n",
       "      <td>N</td>\n",
       "      <td>113</td>\n",
       "      <td>None</td>\n",
       "      <td>-51.375</td>\n",
       "      <td>74.966</td>\n",
       "      <td>...</td>\n",
       "      <td>73.846</td>\n",
       "      <td>-3.678</td>\n",
       "      <td>1.0</td>\n",
       "      <td>63.86</td>\n",
       "      <td>O</td>\n",
       "      <td>None</td>\n",
       "      <td>antigen_chain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31516</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>10078</td>\n",
       "      <td>CD2</td>\n",
       "      <td>None</td>\n",
       "      <td>TYR</td>\n",
       "      <td>N</td>\n",
       "      <td>113</td>\n",
       "      <td>None</td>\n",
       "      <td>-51.375</td>\n",
       "      <td>74.966</td>\n",
       "      <td>...</td>\n",
       "      <td>75.095</td>\n",
       "      <td>-6.148</td>\n",
       "      <td>1.0</td>\n",
       "      <td>63.91</td>\n",
       "      <td>C</td>\n",
       "      <td>None</td>\n",
       "      <td>antigen_chain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31517</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>10078</td>\n",
       "      <td>CD2</td>\n",
       "      <td>None</td>\n",
       "      <td>TYR</td>\n",
       "      <td>N</td>\n",
       "      <td>113</td>\n",
       "      <td>None</td>\n",
       "      <td>-51.375</td>\n",
       "      <td>74.966</td>\n",
       "      <td>...</td>\n",
       "      <td>76.511</td>\n",
       "      <td>-6.671</td>\n",
       "      <td>1.0</td>\n",
       "      <td>63.85</td>\n",
       "      <td>C</td>\n",
       "      <td>None</td>\n",
       "      <td>antigen_chain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31697</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>10080</td>\n",
       "      <td>CE2</td>\n",
       "      <td>None</td>\n",
       "      <td>TYR</td>\n",
       "      <td>N</td>\n",
       "      <td>113</td>\n",
       "      <td>None</td>\n",
       "      <td>-52.313</td>\n",
       "      <td>73.948</td>\n",
       "      <td>...</td>\n",
       "      <td>73.846</td>\n",
       "      <td>-3.678</td>\n",
       "      <td>1.0</td>\n",
       "      <td>63.86</td>\n",
       "      <td>O</td>\n",
       "      <td>None</td>\n",
       "      <td>antigen_chain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31698</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>10080</td>\n",
       "      <td>CE2</td>\n",
       "      <td>None</td>\n",
       "      <td>TYR</td>\n",
       "      <td>N</td>\n",
       "      <td>113</td>\n",
       "      <td>None</td>\n",
       "      <td>-52.313</td>\n",
       "      <td>73.948</td>\n",
       "      <td>...</td>\n",
       "      <td>75.095</td>\n",
       "      <td>-6.148</td>\n",
       "      <td>1.0</td>\n",
       "      <td>63.91</td>\n",
       "      <td>C</td>\n",
       "      <td>None</td>\n",
       "      <td>antigen_chain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156312 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      record_type_tcr  atom_number_tcr atom_name_tcr alt_loc_tcr  \\\n",
       "69473            ATOM              472             C        None   \n",
       "69474            ATOM              472             C        None   \n",
       "70937            ATOM              473             O        None   \n",
       "70938            ATOM              473             O        None   \n",
       "70939            ATOM              473             O        None   \n",
       "...               ...              ...           ...         ...   \n",
       "31515            ATOM            10078           CD2        None   \n",
       "31516            ATOM            10078           CD2        None   \n",
       "31517            ATOM            10078           CD2        None   \n",
       "31697            ATOM            10080           CE2        None   \n",
       "31698            ATOM            10080           CE2        None   \n",
       "\n",
       "      residue_name_tcr chain_id_tcr  residue_seq_id_tcr  \\\n",
       "69473              ASN            E                  57   \n",
       "69474              ASN            E                  57   \n",
       "70937              ASN            E                  57   \n",
       "70938              ASN            E                  57   \n",
       "70939              ASN            E                  57   \n",
       "...                ...          ...                 ...   \n",
       "31515              TYR            N                 113   \n",
       "31516              TYR            N                 113   \n",
       "31517              TYR            N                 113   \n",
       "31697              TYR            N                 113   \n",
       "31698              TYR            N                 113   \n",
       "\n",
       "      residue_insert_code_tcr  pos_x_tcr  pos_y_tcr  ...  pos_y_peptide  \\\n",
       "69473                    None     12.312    -60.161  ...            NaN   \n",
       "69474                    None     12.312    -60.161  ...            NaN   \n",
       "70937                    None     11.507    -60.966  ...            NaN   \n",
       "70938                    None     11.507    -60.966  ...            NaN   \n",
       "70939                    None     11.507    -60.966  ...            NaN   \n",
       "...                       ...        ...        ...  ...            ...   \n",
       "31515                    None    -51.375     74.966  ...         73.846   \n",
       "31516                    None    -51.375     74.966  ...         75.095   \n",
       "31517                    None    -51.375     74.966  ...         76.511   \n",
       "31697                    None    -52.313     73.948  ...         73.846   \n",
       "31698                    None    -52.313     73.948  ...         75.095   \n",
       "\n",
       "       pos_z_peptide  occupancy_peptide b_factor_peptide element_peptide  \\\n",
       "69473            NaN                NaN              NaN             NaN   \n",
       "69474            NaN                NaN              NaN             NaN   \n",
       "70937            NaN                NaN              NaN             NaN   \n",
       "70938            NaN                NaN              NaN             NaN   \n",
       "70939            NaN                NaN              NaN             NaN   \n",
       "...              ...                ...              ...             ...   \n",
       "31515         -3.678                1.0            63.86               O   \n",
       "31516         -6.148                1.0            63.91               C   \n",
       "31517         -6.671                1.0            63.85               C   \n",
       "31697         -3.678                1.0            63.86               O   \n",
       "31698         -6.148                1.0            63.91               C   \n",
       "\n",
       "      charge_peptide  chain_type_peptide cdr_peptide  peptide_length  \\\n",
       "69473            NaN                 NaN         NaN             NaN   \n",
       "69474            NaN                 NaN         NaN             NaN   \n",
       "70937            NaN                 NaN         NaN             NaN   \n",
       "70938            NaN                 NaN         NaN             NaN   \n",
       "70939            NaN                 NaN         NaN             NaN   \n",
       "...              ...                 ...         ...             ...   \n",
       "31515           None       antigen_chain         NaN            10.0   \n",
       "31516           None       antigen_chain         NaN            10.0   \n",
       "31517           None       antigen_chain         NaN            10.0   \n",
       "31697           None       antigen_chain         NaN            10.0   \n",
       "31698           None       antigen_chain         NaN            10.0   \n",
       "\n",
       "      peptide_position  \n",
       "69473              NaN  \n",
       "69474              NaN  \n",
       "70937              NaN  \n",
       "70938              NaN  \n",
       "70939              NaN  \n",
       "...                ...  \n",
       "31515              4.0  \n",
       "31516              4.0  \n",
       "31517              4.0  \n",
       "31697              4.0  \n",
       "31698              4.0  \n",
       "\n",
       "[156312 rows x 56 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contacts = []\n",
    "\n",
    "for _, row in selected_stcrdab.iterrows():\n",
    "    path = os.path.join(STCRDAB_PATH, 'imgt', f'{row.pdb}.pdb')\n",
    "\n",
    "    with open(path, 'r') as fh:\n",
    "        df = parse_pdb_to_pandas(fh.read())\n",
    "\n",
    "    chain_annotations = {}\n",
    "    chain_annotations[row.Achain] = 'alpha_chain'\n",
    "    chain_annotations[row.Bchain] = 'beta_chain'\n",
    "    chain_annotations[row.antigen_chain] = 'antigen_chain'\n",
    "    chain_annotations[row.mhc_chain1] = 'mhc_chain'\n",
    "\n",
    "    df['chain_type'] = df['chain_id'].map(lambda id_: chain_annotations[id_] if id_ in chain_annotations else None)\n",
    "    df['cdr'] = df['residue_seq_id'].map(assign_cdr_number)\n",
    "\n",
    "    mhc_df = df.query(\"chain_type == 'mhc_chain' and residue_seq_id < 1090\")\n",
    "    peptide_df = df.query(\"chain_type == 'antigen_chain'\").copy()\n",
    "    tcr_cdrs_df = df.query(\"(chain_type == 'alpha_chain' or chain_type == 'beta_chain') and cdr.notnull()\")\n",
    "\n",
    "    peptide_df_per_res = peptide_df.groupby(['residue_seq_id', 'residue_insert_code', 'residue_name'], dropna=False)\n",
    "    group_mapping = {group: idx for idx, group in enumerate(peptide_df_per_res.groups.keys(), 1)}\n",
    "\n",
    "    peptide_df['peptide_length'] = max(group_mapping.values())\n",
    "    peptide_df['peptide_position'] = peptide_df[['residue_seq_id',\n",
    "                                                 'residue_insert_code',\n",
    "                                                 'residue_name']].apply(tuple, axis='columns').map(group_mapping)\n",
    "\n",
    "    tcr_mhc_interface = tcr_cdrs_df.merge(mhc_df, how='cross', suffixes=('_tcr', '_mhc'))\n",
    "    tcr_mhc_interface['distance'] = np.sqrt(np.square(tcr_mhc_interface['pos_x_tcr']\n",
    "                                                       - tcr_mhc_interface['pos_x_mhc'])\n",
    "                                             + np.square(tcr_mhc_interface['pos_y_tcr']\n",
    "                                                         - tcr_mhc_interface['pos_y_mhc'])\n",
    "                                             + np.square(tcr_mhc_interface['pos_z_tcr']\n",
    "                                                         - tcr_mhc_interface['pos_z_mhc']))\n",
    "\n",
    "    tcr_peptide_interface = tcr_cdrs_df.merge(peptide_df, how='cross', suffixes=('_tcr', '_peptide'))\n",
    "    tcr_peptide_interface['distance'] = np.sqrt(np.square(tcr_peptide_interface['pos_x_tcr']\n",
    "                                                          - tcr_peptide_interface['pos_x_peptide'])\n",
    "                                                + np.square(tcr_peptide_interface['pos_y_tcr']\n",
    "                                                            - tcr_peptide_interface['pos_y_peptide'])\n",
    "                                                + np.square(tcr_peptide_interface['pos_z_tcr']\n",
    "                                                            - tcr_peptide_interface['pos_z_peptide']))\n",
    "\n",
    "    contacts_tcr_mhc = tcr_mhc_interface.query('distance < @CUTOFF_DISTANCE').copy()\n",
    "    contacts_tcr_mhc['path'] = path\n",
    "    contacts_tcr_mhc['interface_type'] = 'tcr_mhc'\n",
    "    contacts.append(contacts_tcr_mhc)\n",
    "\n",
    "    contacts_tcr_peptide = tcr_peptide_interface.query('distance < @CUTOFF_DISTANCE').copy()\n",
    "    contacts_tcr_peptide['path'] = path\n",
    "    contacts_tcr_peptide['interface_type'] = 'tcr_peptide'\n",
    "    contacts.append(contacts_tcr_peptide)\n",
    "\n",
    "contacts = pd.concat(contacts)\n",
    "contacts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "79fe8e08",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-13T17:35:23.674710Z",
     "iopub.status.busy": "2024-05-13T17:35:23.674262Z",
     "iopub.status.idle": "2024-05-13T17:35:23.791940Z",
     "shell.execute_reply": "2024-05-13T17:35:23.790941Z"
    }
   },
   "outputs": [],
   "source": [
    "contacts['pdb_id'] = contacts['path'].map(lambda path: path.split('/')[-1].split('.')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "335afa90",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-13T17:35:23.796449Z",
     "iopub.status.busy": "2024-05-13T17:35:23.796110Z",
     "iopub.status.idle": "2024-05-13T17:35:23.896895Z",
     "shell.execute_reply": "2024-05-13T17:35:23.896057Z"
    }
   },
   "outputs": [],
   "source": [
    "def create_resi(seq_id: float, insert_code: str | float) -> str | None:\n",
    "    if pd.isnull(seq_id):\n",
    "        return None\n",
    "\n",
    "    return str(int(seq_id)) + (insert_code if not pd.isnull(insert_code) else '')\n",
    "\n",
    "contacts['resi_mhc'] = contacts.apply(lambda row: create_resi(row.residue_seq_id_mhc, row.residue_insert_code_mhc),\n",
    "                                      axis='columns')\n",
    "contacts['resi_tcr'] = contacts.apply(lambda row: create_resi(row.residue_seq_id_tcr, row.residue_insert_code_tcr),\n",
    "                                      axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "063f2492",
   "metadata": {},
   "outputs": [],
   "source": [
    "contacts.loc[contacts['peptide_position'].notnull(),\n",
    "             'peptide_position'] = contacts.loc[contacts['peptide_position'].notnull(),\n",
    "                                                'peptide_position'].apply(int).apply(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "291ebd44",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-13T17:35:23.901435Z",
     "iopub.status.busy": "2024-05-13T17:35:23.901111Z",
     "iopub.status.idle": "2024-05-13T17:35:24.116778Z",
     "shell.execute_reply": "2024-05-13T17:35:24.115765Z"
    }
   },
   "outputs": [],
   "source": [
    "contacts['cdr_name'] = contacts.apply(\n",
    "    lambda row: f\"CDR-{'A' if row.chain_type_tcr == 'alpha_chain' else 'B'}{int(row.cdr_tcr)}\",\n",
    "    axis='columns',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d983b908",
   "metadata": {},
   "outputs": [],
   "source": [
    "contacts['cdr_name'] = pd.Categorical(contacts['cdr_name'],\n",
    "                                      ['CDR-A1', 'CDR-A2', 'CDR-A3', 'CDR-B1', 'CDR-B2', 'CDR-B3'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6706c080",
   "metadata": {},
   "source": [
    "## Visualising contact maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eaec77ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "palette = sns.color_palette(['#' + colour.lower()\n",
    "                             for colour in ['DC91BE','C0C0C2', '6490C7', '963E87','2D2E62','56BBD3']], 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36022ec6",
   "metadata": {},
   "source": [
    "### TCR Contacts on MHC Molecules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ffcc9cd7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-13T17:35:24.121858Z",
     "iopub.status.busy": "2024-05-13T17:35:24.121136Z",
     "iopub.status.idle": "2024-05-13T17:35:28.559368Z",
     "shell.execute_reply": "2024-05-13T17:35:28.557503Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "ax = sns.histplot(contacts.query(\"interface_type == 'tcr_mhc'\").sort_values('residue_seq_id_mhc'),\n",
    "                  x='resi_mhc',\n",
    "                  hue='cdr_name',\n",
    "                  multiple='stack',\n",
    "                  stat='percent',\n",
    "                  palette=palette)\n",
    "\n",
    "ax.legend_.set_title(title='CDR Type')\n",
    "ax.set_xlabel('IMGT Residue Number')\n",
    "ax.set_ylabel('Percent of total MHC contacts per residue')\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3a289a62",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-13T17:35:28.566631Z",
     "iopub.status.busy": "2024-05-13T17:35:28.566113Z",
     "iopub.status.idle": "2024-05-13T17:35:28.971460Z",
     "shell.execute_reply": "2024-05-13T17:35:28.970497Z"
    }
   },
   "outputs": [],
   "source": [
    "mhc_labels = contacts[\n",
    "    ['residue_seq_id_mhc', 'residue_insert_code_mhc', 'resi_mhc']\n",
    "].fillna('').sort_values(['residue_seq_id_mhc', 'residue_insert_code_mhc'])['resi_mhc'].unique().tolist()\n",
    "\n",
    "tcr_labels = contacts[\n",
    "    ['residue_seq_id_tcr', 'residue_insert_code_tcr', 'resi_tcr']\n",
    "    ].fillna('').sort_values(['residue_seq_id_tcr', 'residue_insert_code_tcr'])['resi_tcr'].unique().tolist()\n",
    "\n",
    "contacts['resi_mhc'] = pd.Categorical(contacts['resi_mhc'], mhc_labels)\n",
    "contacts['resi_tcr'] = pd.Categorical(contacts['resi_tcr'], tcr_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "062b101d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-13T17:35:28.976396Z",
     "iopub.status.busy": "2024-05-13T17:35:28.975914Z",
     "iopub.status.idle": "2024-05-13T17:35:34.152255Z",
     "shell.execute_reply": "2024-05-13T17:35:34.151433Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "ax = sns.histplot(contacts.query(\"interface_type == 'tcr_mhc'\"),\n",
    "                  x='resi_mhc', y='resi_tcr',\n",
    "                  hue='cdr_name',\n",
    "                  palette=sns.color_palette(palette, 6))\n",
    "\n",
    "ax.legend_.set_title(title='CDR Type')\n",
    "ax.set_xlabel('MHC IMGT Residue Number')\n",
    "ax.set_ylabel('TCR IMGT Residue Number')\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbc5354c",
   "metadata": {},
   "source": [
    "### TCR Contacts on Peptides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "186eb61e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.histplot(contacts.query(\"interface_type == 'tcr_peptide'\").sort_values('residue_seq_id_peptide'),\n",
    "                  x='peptide_position',\n",
    "                  hue='cdr_name',\n",
    "                  multiple='stack',\n",
    "                  stat='percent',\n",
    "                  palette=sns.color_palette(palette, 6))\n",
    "\n",
    "ax.set_title('TCR Contacts on the Peptide')\n",
    "ax.set_xlabel('Peptide Position')\n",
    "ax.set_ylabel('Percent of total peptide contacts per residue')\n",
    "ax.legend_.set_title(title='CDR Type')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "43ba4d00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='peptide_length', ylabel='Count'>"
      ]
     },
     "execution_count": 16,
     "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.histplot(contacts.query(\"interface_type == 'tcr_peptide'\").drop_duplicates('path'), x='peptide_length')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1103673",
   "metadata": {},
   "source": [
    "The mode of peptide lengths in 9-mers!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a5be5a04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.histplot(contacts.query(\"interface_type == 'tcr_peptide' and peptide_length == 9.0\").sort_values('residue_seq_id_peptide'),\n",
    "                  x='peptide_position',\n",
    "                  hue='cdr_name',\n",
    "                  multiple='stack',\n",
    "                  stat='percent',\n",
    "                  palette=sns.color_palette(palette, 6))\n",
    "\n",
    "ax.set_title('TCR Contacts on the nonamer-peptides')\n",
    "ax.set_xlabel('Peptide Position')\n",
    "ax.set_ylabel('Percent of total peptide contacts per residue')\n",
    "ax.legend_.set_title(title='CDR Type')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5781377b",
   "metadata": {},
   "source": [
    "For the nonamers, which is the dominant contacting loop at each peptide position?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "062e79aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "peptide_position\n",
       "1    CDR-A3\n",
       "2    CDR-A3\n",
       "3    CDR-A3\n",
       "4    CDR-A3\n",
       "5    CDR-A3\n",
       "6    CDR-B3\n",
       "7    CDR-B3\n",
       "8    CDR-B3\n",
       "9    CDR-B3\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def select_dominant(group: pd.DataFrame) -> str:\n",
    "    return group.sort_values('count', ascending=False).iloc[0]['cdr_name']\n",
    "\n",
    "nonanmer_peptide_tcr_contact_counts = (contacts.query(\"interface_type == 'tcr_peptide' and peptide_length == 9.0\")\n",
    "                                               .value_counts(['peptide_position', 'cdr_name']))\n",
    "nonanmer_peptide_tcr_contact_counts.name = 'count'\n",
    "nonanmer_peptide_tcr_contact_counts = nonanmer_peptide_tcr_contact_counts.reset_index()\n",
    "\n",
    "nonanmer_peptide_tcr_contact_counts.groupby('peptide_position').apply(select_dominant)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10dc5785",
   "metadata": {},
   "source": [
    "Do the non-nonamers have the same trend?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3d5b75f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.histplot(contacts.query(\"interface_type == 'tcr_peptide' and peptide_length != 9.0\").sort_values('residue_seq_id_peptide'),\n",
    "                  x='peptide_position',\n",
    "                  hue='cdr_name',\n",
    "                  multiple='stack',\n",
    "                  stat='percent',\n",
    "                  palette=sns.color_palette(palette, 6))\n",
    "\n",
    "ax.set_title('TCR Contacts on the non-nonamer-peptides')\n",
    "ax.set_xlabel('Peptide Position')\n",
    "ax.set_ylabel('Percent of total peptide contacts per residue')\n",
    "ax.legend_.set_title(title='CDR Type')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbc13da8",
   "metadata": {},
   "source": [
    "Yes, it seems that they do."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29be32bb",
   "metadata": {},
   "source": [
    "## Exporting residue list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d9ccde91",
   "metadata": {},
   "outputs": [],
   "source": [
    "contact_positions = contacts.value_counts(['cdr_name', 'resi_mhc'])\n",
    "contact_positions.name = 'count'\n",
    "contact_positions = contact_positions.to_frame().reset_index().sort_values('resi_mhc')\n",
    "\n",
    "contact_positions.to_csv('../data/processed/mhc_contacts.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}