{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cc9ee17f",
   "metadata": {},
   "source": [
    "# pMHC movement between apo and holo conformations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8892397f",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "In this notebook we aim to determine what parts of the pMHC, if any, move when contacted by TCRs. We split the MHC domain into MHC-non-TCR contact and MHC-TCR contact based on previous analysis of the TCR finger print on the MHC molecules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e94bb431",
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6112c618",
   "metadata": {},
   "outputs": [],
   "source": [
    "NOISE_LEVEL = 0.5 # Å"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7de39ad",
   "metadata": {},
   "source": [
    "## Load comparisons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1c9bffbc",
   "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>complex_id</th>\n",
       "      <th>structure_x_name</th>\n",
       "      <th>structure_y_name</th>\n",
       "      <th>chain_type</th>\n",
       "      <th>tcr_contact</th>\n",
       "      <th>rmsd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3qdg_D-E-C-A-B_tcr_pmhc</td>\n",
       "      <td>1jf1_C-A-B_pmhc.pdb</td>\n",
       "      <td>3qdg_D-E-C-A-B_tcr_pmhc.pdb</td>\n",
       "      <td>mhc_chain1</td>\n",
       "      <td>False</td>\n",
       "      <td>0.630013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3qdg_D-E-C-A-B_tcr_pmhc</td>\n",
       "      <td>1jf1_C-A-B_pmhc.pdb</td>\n",
       "      <td>3qdg_D-E-C-A-B_tcr_pmhc.pdb</td>\n",
       "      <td>mhc_chain1</td>\n",
       "      <td>True</td>\n",
       "      <td>0.582077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5c0a_D-E-C-A-B_tcr_pmhc</td>\n",
       "      <td>5c0a_D-E-C-A-B_tcr_pmhc.pdb</td>\n",
       "      <td>5n1y_C-A-B_pmhc.pdb</td>\n",
       "      <td>antigen_chain</td>\n",
       "      <td>False</td>\n",
       "      <td>0.448858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5c0a_D-E-C-A-B_tcr_pmhc</td>\n",
       "      <td>5c0a_D-E-C-A-B_tcr_pmhc.pdb</td>\n",
       "      <td>5n1y_C-A-B_pmhc.pdb</td>\n",
       "      <td>mhc_chain1</td>\n",
       "      <td>False</td>\n",
       "      <td>0.422160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5c0a_D-E-C-A-B_tcr_pmhc</td>\n",
       "      <td>5c0a_D-E-C-A-B_tcr_pmhc.pdb</td>\n",
       "      <td>5n1y_C-A-B_pmhc.pdb</td>\n",
       "      <td>mhc_chain1</td>\n",
       "      <td>True</td>\n",
       "      <td>0.424287</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>3311</th>\n",
       "      <td>7rtr_D-E-C-A-B_tcr_pmhc</td>\n",
       "      <td>7p3d_C-A-B_pmhc.pdb</td>\n",
       "      <td>7rtr_D-E-C-A-B_tcr_pmhc.pdb</td>\n",
       "      <td>mhc_chain1</td>\n",
       "      <td>False</td>\n",
       "      <td>0.497769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3312</th>\n",
       "      <td>7rtr_D-E-C-A-B_tcr_pmhc</td>\n",
       "      <td>7p3d_C-A-B_pmhc.pdb</td>\n",
       "      <td>7rtr_D-E-C-A-B_tcr_pmhc.pdb</td>\n",
       "      <td>mhc_chain1</td>\n",
       "      <td>True</td>\n",
       "      <td>0.458372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3313</th>\n",
       "      <td>7rtr_D-E-C-A-B_tcr_pmhc</td>\n",
       "      <td>7rtd_C-A-B_pmhc.pdb</td>\n",
       "      <td>7rtr_D-E-C-A-B_tcr_pmhc.pdb</td>\n",
       "      <td>antigen_chain</td>\n",
       "      <td>False</td>\n",
       "      <td>0.455078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3314</th>\n",
       "      <td>7rtr_D-E-C-A-B_tcr_pmhc</td>\n",
       "      <td>7rtd_C-A-B_pmhc.pdb</td>\n",
       "      <td>7rtr_D-E-C-A-B_tcr_pmhc.pdb</td>\n",
       "      <td>mhc_chain1</td>\n",
       "      <td>False</td>\n",
       "      <td>0.449558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3315</th>\n",
       "      <td>7rtr_D-E-C-A-B_tcr_pmhc</td>\n",
       "      <td>7rtd_C-A-B_pmhc.pdb</td>\n",
       "      <td>7rtr_D-E-C-A-B_tcr_pmhc.pdb</td>\n",
       "      <td>mhc_chain1</td>\n",
       "      <td>True</td>\n",
       "      <td>0.493225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3316 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   complex_id             structure_x_name  \\\n",
       "0     3qdg_D-E-C-A-B_tcr_pmhc          1jf1_C-A-B_pmhc.pdb   \n",
       "1     3qdg_D-E-C-A-B_tcr_pmhc          1jf1_C-A-B_pmhc.pdb   \n",
       "2     5c0a_D-E-C-A-B_tcr_pmhc  5c0a_D-E-C-A-B_tcr_pmhc.pdb   \n",
       "3     5c0a_D-E-C-A-B_tcr_pmhc  5c0a_D-E-C-A-B_tcr_pmhc.pdb   \n",
       "4     5c0a_D-E-C-A-B_tcr_pmhc  5c0a_D-E-C-A-B_tcr_pmhc.pdb   \n",
       "...                       ...                          ...   \n",
       "3311  7rtr_D-E-C-A-B_tcr_pmhc          7p3d_C-A-B_pmhc.pdb   \n",
       "3312  7rtr_D-E-C-A-B_tcr_pmhc          7p3d_C-A-B_pmhc.pdb   \n",
       "3313  7rtr_D-E-C-A-B_tcr_pmhc          7rtd_C-A-B_pmhc.pdb   \n",
       "3314  7rtr_D-E-C-A-B_tcr_pmhc          7rtd_C-A-B_pmhc.pdb   \n",
       "3315  7rtr_D-E-C-A-B_tcr_pmhc          7rtd_C-A-B_pmhc.pdb   \n",
       "\n",
       "                 structure_y_name     chain_type  tcr_contact      rmsd  \n",
       "0     3qdg_D-E-C-A-B_tcr_pmhc.pdb     mhc_chain1        False  0.630013  \n",
       "1     3qdg_D-E-C-A-B_tcr_pmhc.pdb     mhc_chain1         True  0.582077  \n",
       "2             5n1y_C-A-B_pmhc.pdb  antigen_chain        False  0.448858  \n",
       "3             5n1y_C-A-B_pmhc.pdb     mhc_chain1        False  0.422160  \n",
       "4             5n1y_C-A-B_pmhc.pdb     mhc_chain1         True  0.424287  \n",
       "...                           ...            ...          ...       ...  \n",
       "3311  7rtr_D-E-C-A-B_tcr_pmhc.pdb     mhc_chain1        False  0.497769  \n",
       "3312  7rtr_D-E-C-A-B_tcr_pmhc.pdb     mhc_chain1         True  0.458372  \n",
       "3313  7rtr_D-E-C-A-B_tcr_pmhc.pdb  antigen_chain        False  0.455078  \n",
       "3314  7rtr_D-E-C-A-B_tcr_pmhc.pdb     mhc_chain1        False  0.449558  \n",
       "3315  7rtr_D-E-C-A-B_tcr_pmhc.pdb     mhc_chain1         True  0.493225  \n",
       "\n",
       "[3316 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.read_csv('../data/processed/apo-holo-tcr-pmhc-class-I-comparisons/pmhc_tcr_contact_apo_holo.csv')\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9ef08c8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_holo_holo = pd.read_csv('../data/processed/apo-holo-tcr-pmhc-class-I-comparisons/pmhc_tcr_contact_holo.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8a7931f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_holo_holo = results_holo_holo.query(\"chain_type == 'mhc_chain1' or chain_type == 'antigen_chain'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2bd6dbdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_holo_holo['mhc_slug'] = None\n",
    "results_holo_holo['peptide_sequence'] = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ca10aeb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "mhc_pattern = r'^hla|h2'\n",
    "mhc_complex_ids = results_holo_holo['complex_id'].str.contains(mhc_pattern, regex=True)\n",
    "\n",
    "mhc_slug_peptides = results_holo_holo[mhc_complex_ids]['complex_id'].str.rsplit('_', n=1)\n",
    "mhc_slugs = mhc_slug_peptides.map(lambda composite: composite[0])\n",
    "peptides = mhc_slug_peptides.map(lambda composite: composite[1])\n",
    "\n",
    "results_holo_holo.loc[mhc_complex_ids, 'mhc_slug'] = mhc_slugs\n",
    "results_holo_holo.loc[mhc_complex_ids, 'peptide_sequence'] = peptides\n",
    "\n",
    "results_holo_holo = results_holo_holo[mhc_complex_ids]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "747fb110",
   "metadata": {},
   "source": [
    "## Load summary data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8aac7d79",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apo_holo_summary_df = pd.read_csv('../data/processed/apo-holo-tcr-pmhc-class-I/apo_holo_summary.csv')\n",
    "apo_holo_summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "60f8700d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/scratch/bmcmaste/2178229/ipykernel_2205981/477882467.py:1: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  apo_holo_summary_df['ids'] = apo_holo_summary_df['file_name'].str.replace('.pdb$', '')\n"
     ]
    }
   ],
   "source": [
    "apo_holo_summary_df['ids'] = apo_holo_summary_df['file_name'].str.replace('.pdb$', '')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ee81a47",
   "metadata": {},
   "source": [
    "### Annotate results with summary data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9876ee46",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = results.merge(apo_holo_summary_df[['ids', 'cdr_sequences_collated', 'peptide_sequence', 'mhc_slug']],\n",
    "                        left_on='complex_id',\n",
    "                        right_on='ids',\n",
    "                        how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f9e76e8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pd.concat([results, results_holo_holo])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "345f6a2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = results.merge(\n",
    "    apo_holo_summary_df[['file_name', 'pdb_id', 'structure_type', 'state']],\n",
    "    how='left',\n",
    "    left_on='structure_x_name',\n",
    "    right_on='file_name',\n",
    ").merge(\n",
    "    apo_holo_summary_df[['file_name', 'pdb_id', 'structure_type', 'state']],\n",
    "    how='left',\n",
    "    left_on='structure_y_name',\n",
    "    right_on='file_name',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "204f784d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def name_domain(chain_type: str, tcr_contact: bool) -> str | None:\n",
    "    match chain_type:\n",
    "        case 'antigen_chain':\n",
    "            return 'peptide'\n",
    "\n",
    "        case 'mhc_chain1':\n",
    "            if tcr_contact:\n",
    "                return 'mhc_tcr_contact'\n",
    "\n",
    "            return 'mhc'\n",
    "\n",
    "    return None\n",
    "\n",
    "results['domain'] = results.apply(lambda row: name_domain(row.chain_type, row.tcr_contact), axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aa563645",
   "metadata": {},
   "outputs": [],
   "source": [
    "results['comparison'] = results['state_x'] + '-' + results['state_y']\n",
    "results['comparison'] = results['comparison'].map(lambda entry: 'apo-holo' if entry == 'holo-apo' else entry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "94de9656",
   "metadata": {},
   "outputs": [],
   "source": [
    "results['structure_comparison'] = results.apply(\n",
    "    lambda row: '-'.join(sorted([row.structure_x_name, row.structure_y_name])),\n",
    "    axis='columns',\n",
    ")\n",
    "results = results.drop_duplicates(['structure_comparison', 'domain'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "780f1b97",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = results.groupby(['peptide_sequence',\n",
    "                           'mhc_slug',\n",
    "                           'comparison',\n",
    "                           'domain'], dropna=False)['rmsd'].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fe382667",
   "metadata": {},
   "outputs": [],
   "source": [
    "apo_holo_results = results.query(\"comparison == 'apo-holo'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ec49b256",
   "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>peptide_sequence</th>\n",
       "      <th>mhc_slug</th>\n",
       "      <th>comparison</th>\n",
       "      <th>domain</th>\n",
       "      <th>rmsd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AAGIGILTV</td>\n",
       "      <td>hla_a_02_01</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>mhc</td>\n",
       "      <td>0.660376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AAGIGILTV</td>\n",
       "      <td>hla_a_02_01</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>mhc_tcr_contact</td>\n",
       "      <td>0.562211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AAGIGILTV</td>\n",
       "      <td>hla_a_02_01</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>peptide</td>\n",
       "      <td>1.025622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ALGIGILTV</td>\n",
       "      <td>hla_a_02_01</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>mhc</td>\n",
       "      <td>0.620830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ALGIGILTV</td>\n",
       "      <td>hla_a_02_01</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>mhc_tcr_contact</td>\n",
       "      <td>0.554737</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>462</th>\n",
       "      <td>YLQPRTFLL</td>\n",
       "      <td>hla_a_02_01</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>mhc_tcr_contact</td>\n",
       "      <td>0.578509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>YLQPRTFLL</td>\n",
       "      <td>hla_a_02_01</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>peptide</td>\n",
       "      <td>0.584577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>YQFGPDFPIA</td>\n",
       "      <td>hla_a_02_01</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>mhc</td>\n",
       "      <td>0.478941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>YQFGPDFPIA</td>\n",
       "      <td>hla_a_02_01</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>mhc_tcr_contact</td>\n",
       "      <td>0.357204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>YQFGPDFPIA</td>\n",
       "      <td>hla_a_02_01</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>peptide</td>\n",
       "      <td>0.408370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>242 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    peptide_sequence     mhc_slug comparison           domain      rmsd\n",
       "3          AAGIGILTV  hla_a_02_01   apo-holo              mhc  0.660376\n",
       "4          AAGIGILTV  hla_a_02_01   apo-holo  mhc_tcr_contact  0.562211\n",
       "5          AAGIGILTV  hla_a_02_01   apo-holo          peptide  1.025622\n",
       "12         ALGIGILTV  hla_a_02_01   apo-holo              mhc  0.620830\n",
       "13         ALGIGILTV  hla_a_02_01   apo-holo  mhc_tcr_contact  0.554737\n",
       "..               ...          ...        ...              ...       ...\n",
       "462        YLQPRTFLL  hla_a_02_01   apo-holo  mhc_tcr_contact  0.578509\n",
       "463        YLQPRTFLL  hla_a_02_01   apo-holo          peptide  0.584577\n",
       "467       YQFGPDFPIA  hla_a_02_01   apo-holo              mhc  0.478941\n",
       "468       YQFGPDFPIA  hla_a_02_01   apo-holo  mhc_tcr_contact  0.357204\n",
       "469       YQFGPDFPIA  hla_a_02_01   apo-holo          peptide  0.408370\n",
       "\n",
       "[242 rows x 5 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apo_holo_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52811068",
   "metadata": {},
   "source": [
    "## Visualise results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0fe042c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.75, 2.75)"
      ]
     },
     "execution_count": 19,
     "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.boxplot(apo_holo_results, x='domain', y='rmsd')\n",
    "\n",
    "x = np.linspace(-0.75, 2.75)\n",
    "y = np.repeat(NOISE_LEVEL, len(x))\n",
    "\n",
    "plt.plot(x, y, '--r')\n",
    "plt.xlim(-0.75, 2.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45587d19",
   "metadata": {},
   "source": [
    "## Compute Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "154a3ddb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f2641194970>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(apo_holo_results, x='rmsd', col='domain')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "46070795",
   "metadata": {},
   "outputs": [],
   "source": [
    "conditions = {domain: apo_holo_results[apo_holo_results['domain'] == domain]['rmsd'].values for domain in apo_holo_results['domain'].unique()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "832af280",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=13.305204363913258, pvalue=3.320938947108907e-06)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.f_oneway(*conditions.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "864e408e",
   "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>condition_x</th>\n",
       "      <th>condition_y</th>\n",
       "      <th>stat</th>\n",
       "      <th>p_val</th>\n",
       "      <th>significant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mhc</td>\n",
       "      <td>mhc_tcr_contact</td>\n",
       "      <td>2.391609</td>\n",
       "      <td>0.017935</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mhc</td>\n",
       "      <td>peptide</td>\n",
       "      <td>-3.203906</td>\n",
       "      <td>0.001638</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mhc_tcr_contact</td>\n",
       "      <td>peptide</td>\n",
       "      <td>-4.201672</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       condition_x      condition_y      stat     p_val  significant\n",
       "0              mhc  mhc_tcr_contact  2.391609  0.017935        False\n",
       "1              mhc          peptide -3.203906  0.001638         True\n",
       "2  mhc_tcr_contact          peptide -4.201672  0.000044         True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combos = list(itertools.combinations(conditions.items(), 2))\n",
    "\n",
    "significane_level = 0.05 / len(combos)\n",
    "\n",
    "ad_hoc_results = {'condition_x': [], 'condition_y': [], 'stat': [], 'p_val': []}\n",
    "for (condition_x_name, condition_x), (condition_y_name, condition_y) in combos:\n",
    "    ad_hoc_results['condition_x'].append(condition_x_name)\n",
    "    ad_hoc_results['condition_y'].append(condition_y_name)\n",
    "\n",
    "    stat, p_val = scipy.stats.ttest_ind(condition_x, condition_y)\n",
    "\n",
    "    ad_hoc_results['stat'].append(stat)\n",
    "    ad_hoc_results['p_val'].append(p_val)\n",
    "\n",
    "ad_hoc_results = pd.DataFrame(ad_hoc_results)\n",
    "\n",
    "ad_hoc_results['significant'] = ad_hoc_results['p_val'] < significane_level\n",
    "\n",
    "ad_hoc_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2781d50",
   "metadata": {},
   "source": [
    "Based on these results it seems that the peptide undergoes conformational change but not the MHC molecule- either TCR-contacting or not."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2c2985b",
   "metadata": {},
   "source": [
    "## Comparing the differences in binding between movement types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "78d467d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f263ee23370>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1511.11x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(results,\n",
    "            x='comparison', y='rmsd',\n",
    "            col='domain',\n",
    "            kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fb7a1d0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f263ee23fa0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1614.38x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(results,\n",
    "            hue='comparison', x='rmsd',\n",
    "            col='domain',\n",
    "            kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "97de8d03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KruskalResult(statistic=114.03107622089692, pvalue=5.640784471619738e-21)\n"
     ]
    }
   ],
   "source": [
    "treatment_options = ['comparison', 'domain']\n",
    "treatments = [(group, df['rmsd'].to_numpy()) for group, df in results.groupby(treatment_options)]\n",
    "treatments\n",
    "print(scipy.stats.kruskal(*[values for _, values in treatments]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3fc99044",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.005555555555555556\n"
     ]
    },
    {
     "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>comparison_x</th>\n",
       "      <th>domain_x</th>\n",
       "      <th>comparison_y</th>\n",
       "      <th>domain_y</th>\n",
       "      <th>statistic</th>\n",
       "      <th>p_val</th>\n",
       "      <th>significant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>mhc</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>mhc</td>\n",
       "      <td>-5.543219</td>\n",
       "      <td>2.97e-08</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>mhc</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>mhc</td>\n",
       "      <td>-3.284693</td>\n",
       "      <td>1.02e-03</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>mhc_tcr_contact</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>mhc_tcr_contact</td>\n",
       "      <td>-6.324955</td>\n",
       "      <td>2.53e-10</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>mhc_tcr_contact</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>mhc_tcr_contact</td>\n",
       "      <td>-4.029224</td>\n",
       "      <td>5.60e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>peptide</td>\n",
       "      <td>apo-holo</td>\n",
       "      <td>peptide</td>\n",
       "      <td>-5.153956</td>\n",
       "      <td>2.55e-07</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>apo-apo</td>\n",
       "      <td>peptide</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>peptide</td>\n",
       "      <td>-3.624112</td>\n",
       "      <td>2.90e-04</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>apo-holo</td>\n",
       "      <td>mhc</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>mhc</td>\n",
       "      <td>0.602876</td>\n",
       "      <td>5.47e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>apo-holo</td>\n",
       "      <td>mhc_tcr_contact</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>mhc_tcr_contact</td>\n",
       "      <td>1.496293</td>\n",
       "      <td>1.35e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>apo-holo</td>\n",
       "      <td>peptide</td>\n",
       "      <td>holo-holo</td>\n",
       "      <td>peptide</td>\n",
       "      <td>-0.102800</td>\n",
       "      <td>9.18e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  comparison_x         domain_x comparison_y         domain_y  statistic  \\\n",
       "0      apo-apo              mhc     apo-holo              mhc  -5.543219   \n",
       "1      apo-apo              mhc    holo-holo              mhc  -3.284693   \n",
       "2      apo-apo  mhc_tcr_contact     apo-holo  mhc_tcr_contact  -6.324955   \n",
       "3      apo-apo  mhc_tcr_contact    holo-holo  mhc_tcr_contact  -4.029224   \n",
       "4      apo-apo          peptide     apo-holo          peptide  -5.153956   \n",
       "5      apo-apo          peptide    holo-holo          peptide  -3.624112   \n",
       "6     apo-holo              mhc    holo-holo              mhc   0.602876   \n",
       "7     apo-holo  mhc_tcr_contact    holo-holo  mhc_tcr_contact   1.496293   \n",
       "8     apo-holo          peptide    holo-holo          peptide  -0.102800   \n",
       "\n",
       "      p_val  significant  \n",
       "0  2.97e-08         True  \n",
       "1  1.02e-03         True  \n",
       "2  2.53e-10         True  \n",
       "3  5.60e-05         True  \n",
       "4  2.55e-07         True  \n",
       "5  2.90e-04         True  \n",
       "6  5.47e-01        False  \n",
       "7  1.35e-01        False  \n",
       "8  9.18e-01        False  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combos = []\n",
    "for pairing in list(itertools.combinations(treatments, 2)):\n",
    "    domain_x = pairing[0][0][1]\n",
    "    domain_y = pairing[1][0][1]\n",
    "\n",
    "    if domain_x == domain_y:\n",
    "        combos.append(pairing)\n",
    "\n",
    "significance_level = 0.05 / len(combos)\n",
    "print(significance_level)\n",
    "statistics = []\n",
    "p_vals = []\n",
    "\n",
    "for ((comparison_x, domain_x), sample_x), ((comparison_y, domain_y), sample_y) in combos:\n",
    "    stat, p_val = scipy.stats.ranksums(sample_x, sample_y)\n",
    "\n",
    "    statistics.append(stat)\n",
    "    p_vals.append(p_val)\n",
    "\n",
    "momvement_type_statistics = pd.DataFrame({\n",
    "    'comparison_x': [name for ((name, _,), _), _ in combos],\n",
    "    'domain_x': [name for ((_, name), _), _ in combos],\n",
    "    'comparison_y': [name for _, ((name, _), _) in combos],\n",
    "    'domain_y': [name for _, ((_, name), _) in combos],\n",
    "    'statistic': statistics,\n",
    "    'p_val': p_vals,\n",
    "    'significant': [p_val < significance_level for p_val in p_vals],\n",
    "})\n",
    "\n",
    "momvement_type_statistics['p_val'] = momvement_type_statistics['p_val'].map(lambda num: f'{num:.2e}')\n",
    "\n",
    "momvement_type_statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1a72f9b",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "This analysis reveals that the peptide undergoes the most change from *apo* to *holo* states and the MHC molecule has little change between these states. It does not matter whether it is the TCR contacting portion of the MHC or not. The statistical analysis comparing the differences between comparion types gives inconclusive results."
   ]
  }
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