models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Models\n",
    "\n",
    "Training and testing models: PSSM, HMM and ensemble of both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies\n",
    "import os\n",
    "import modules.pssm as pssm\n",
    "import modules.hmm as hmm\n",
    "from modules.ensemble import majority_voting\n",
    "import modules.conf_mat as cmat\n",
    "import modules.pdb as pdb\n",
    "import modules.tmalign as tmalign\n",
    "import subprocess\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "from scipy.spatial.distance import pdist\n",
    "\n",
    "# Set matplotlib inline\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constants\n",
    "SEQ_FASTA_PATH = 'data/domain.fasta'  # Input sequence (domain)\n",
    "BLAST_FASTA_PATH = 'data/blast.fasta'  # Get sequence similarity search result\n",
    "MSA_FASTA_PATH = 'data/msa.edited.fasta'  # Multiple sequence alignment (.fasta)\n",
    "HUMAN_CSV_PATH = 'data/human.csv'  # Human proteome .csv dataset\n",
    "HUMAN_FASTA_PATH = 'data/human.fasta'  # Human proteome .fasta dataset\n",
    "FAMILIES_CSV_PATH = 'data/families.csv'  # Families .csv dataset\n",
    "# Hidden Markov Model paths\n",
    "HMMER_MODEL_PATH = 'models/model.hmm'  # Hidden Markov Model path\n",
    "# PSSM paths\n",
    "PSSM_MODEL_PATH = 'models/model.pssm'  # PSSM model path\n",
    "# Results\n",
    "PSSM_RESULTS_PATH = 'data/results/pssm.tsv'  # PSSM model results\n",
    "HMMER_RESULTS_PATH = 'data/results/hmmer.tsv'  # HMMER model results\n",
    "JACK_RESULTS_PATH = 'data/results/jackhmmer.tsv'  # JACKHMMER model results\n",
    "ENSEMBLE_RESULTS_PATH = 'data/results/ensemble.tsv'  # Ensemble model result\n",
    "# SITFTS mapping from pdb to uniprot\n",
    "SIFTS_CSV_PATH = 'data/pdb/pdb_chain_uniprot.tsv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>entry_ac</th>\n",
       "      <th>entry_name</th>\n",
       "      <th>protein_name</th>\n",
       "      <th>len</th>\n",
       "      <th>go</th>\n",
       "      <th>pdb_ids</th>\n",
       "      <th>pfam_ids</th>\n",
       "      <th>string_id</th>\n",
       "      <th>PF00397</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q9Y263</td>\n",
       "      <td>PLAP_HUMAN</td>\n",
       "      <td>Phospholipase A-2-activating protein (PLA2P) (...</td>\n",
       "      <td>795</td>\n",
       "      <td>cell [GO:0005623]; cell junction [GO:0030054];...</td>\n",
       "      <td>2K89;2K8A;2K8B;2K8C;3EBB;</td>\n",
       "      <td>PF09070;PF08324;PF00400;</td>\n",
       "      <td>9606.ENSP00000380460;</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q96RE7</td>\n",
       "      <td>NACC1_HUMAN</td>\n",
       "      <td>Nucleus accumbens-associated protein 1 (NAC-1)...</td>\n",
       "      <td>527</td>\n",
       "      <td>cell junction [GO:0030054]; cytoplasm [GO:0005...</td>\n",
       "      <td>3GA1;4U2N;</td>\n",
       "      <td>PF10523;PF00651;</td>\n",
       "      <td>9606.ENSP00000292431;</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>O43312</td>\n",
       "      <td>MTSS1_HUMAN</td>\n",
       "      <td>Protein MTSS 1 (Metastasis suppressor YGL-1) (...</td>\n",
       "      <td>755</td>\n",
       "      <td>actin cytoskeleton [GO:0015629]; cytoplasm [GO...</td>\n",
       "      <td>2D1K;</td>\n",
       "      <td>PF08397;PF02205;</td>\n",
       "      <td>9606.ENSP00000322804;</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Q9NP80</td>\n",
       "      <td>PLPL8_HUMAN</td>\n",
       "      <td>Calcium-independent phospholipase A2-gamma (EC...</td>\n",
       "      <td>782</td>\n",
       "      <td>endoplasmic reticulum membrane [GO:0005789]; G...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PF01734;</td>\n",
       "      <td>9606.ENSP00000410804;</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Q15319</td>\n",
       "      <td>PO4F3_HUMAN</td>\n",
       "      <td>POU domain, class 4, transcription factor 3 (B...</td>\n",
       "      <td>338</td>\n",
       "      <td>cytoplasm [GO:0005737]; nuclear chromatin [GO:...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PF00046;PF00157;</td>\n",
       "      <td>9606.ENSP00000230732;</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  entry_ac   entry_name                                       protein_name  \\\n",
       "0   Q9Y263   PLAP_HUMAN  Phospholipase A-2-activating protein (PLA2P) (...   \n",
       "1   Q96RE7  NACC1_HUMAN  Nucleus accumbens-associated protein 1 (NAC-1)...   \n",
       "2   O43312  MTSS1_HUMAN  Protein MTSS 1 (Metastasis suppressor YGL-1) (...   \n",
       "3   Q9NP80  PLPL8_HUMAN  Calcium-independent phospholipase A2-gamma (EC...   \n",
       "4   Q15319  PO4F3_HUMAN  POU domain, class 4, transcription factor 3 (B...   \n",
       "\n",
       "   len                                                 go  \\\n",
       "0  795  cell [GO:0005623]; cell junction [GO:0030054];...   \n",
       "1  527  cell junction [GO:0030054]; cytoplasm [GO:0005...   \n",
       "2  755  actin cytoskeleton [GO:0015629]; cytoplasm [GO...   \n",
       "3  782  endoplasmic reticulum membrane [GO:0005789]; G...   \n",
       "4  338  cytoplasm [GO:0005737]; nuclear chromatin [GO:...   \n",
       "\n",
       "                     pdb_ids                  pfam_ids              string_id  \\\n",
       "0  2K89;2K8A;2K8B;2K8C;3EBB;  PF09070;PF08324;PF00400;  9606.ENSP00000380460;   \n",
       "1                 3GA1;4U2N;          PF10523;PF00651;  9606.ENSP00000292431;   \n",
       "2                      2D1K;          PF08397;PF02205;  9606.ENSP00000322804;   \n",
       "3                        NaN                  PF01734;  9606.ENSP00000410804;   \n",
       "4                        NaN          PF00046;PF00157;  9606.ENSP00000230732;   \n",
       "\n",
       "   PF00397  \n",
       "0    False  \n",
       "1    False  \n",
       "2    False  \n",
       "3    False  \n",
       "4    False  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load protein families dataset\n",
    "human_proteome = pd.read_csv(HUMAN_CSV_PATH, sep='\\t')\n",
    "human_proteome.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function for turning matching ranges into positive positions\n",
    "def match_to_pos(matches):\n",
    "    # Define positions container\n",
    "    positions = list()\n",
    "    # Check if matches list is empty\n",
    "    if matches != '':\n",
    "        # Go through each match\n",
    "        for dom in matches.split(','):\n",
    "            # Define start and end of current domain\n",
    "            dom_start, dom_end = tuple(dom.split('..'))\n",
    "            # Add current domain's positions\n",
    "            positions = positions + list(range(int(dom_start), int(dom_end)))\n",
    "    # Return list of matching positions\n",
    "    return set(positions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>entry_ac</th>\n",
       "      <th>match</th>\n",
       "      <th>positive</th>\n",
       "      <th>negative</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q9Y263</td>\n",
       "      <td>False</td>\n",
       "      <td>{}</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q96RE7</td>\n",
       "      <td>False</td>\n",
       "      <td>{}</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>O43312</td>\n",
       "      <td>False</td>\n",
       "      <td>{}</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Q9NP80</td>\n",
       "      <td>False</td>\n",
       "      <td>{}</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Q15319</td>\n",
       "      <td>False</td>\n",
       "      <td>{}</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  entry_ac  match positive                                           negative\n",
       "0   Q9Y263  False       {}  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...\n",
       "1   Q96RE7  False       {}  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...\n",
       "2   O43312  False       {}  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...\n",
       "3   Q9NP80  False       {}  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...\n",
       "4   Q15319  False       {}  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load families positions dataset\n",
    "protein_matching = pd.read_csv('data/protein_matching.tsv', sep='\\t')\n",
    "protein_matching.columns = ['entry_ac', 'source_db', 'protein_name', 'tax_id',\n",
    "                              'tax_name', 'len', 'pfam_id', 'matches']\n",
    "# Take a subset of the original columns\n",
    "protein_matching = protein_matching[['entry_ac', 'matches']]\n",
    "# Join protein matching with all the dataset\n",
    "protein_matching = pd.merge(left=human_proteome[['entry_ac', 'len']],\n",
    "                            right=protein_matching[['entry_ac', 'matches']],\n",
    "                            how='left', on='entry_ac')\n",
    "# Fill na values where there is no domain\n",
    "protein_matching['matches'] = protein_matching['matches'].fillna('')\n",
    "# Define real positive matches\n",
    "protein_matching['positive'] = protein_matching['matches'].apply(match_to_pos)\n",
    "# Define negative matches\n",
    "protein_matching['negative'] = protein_matching.apply(lambda x: set(range(1, x['len'])) - x['positive'], axis=1)\n",
    "# Define a match column: is there the domain in the protein?\n",
    "protein_matching['match'] = protein_matching.apply(lambda x: len(x['positive']) > 0, axis=1)\n",
    "# Subset protein matching dataset (only entry with relative positive and negative matches)\n",
    "protein_matching = protein_matching[['entry_ac', 'match', 'positive', 'negative']]\n",
    "protein_matching.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 38 positive and 20329 negative proteins in human proteome\n"
     ]
    }
   ],
   "source": [
    "# Define set of truly positive protein accessions\n",
    "positive = set(protein_matching[protein_matching['match']]['entry_ac'])\n",
    "negative = set(human_proteome['entry_ac']) - positive\n",
    "\n",
    "print('There are {:d} positive and {:d} negative proteins in human proteome'.format(\n",
    "    len(positive), len(negative)\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PSSM model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>entry_ac</th>\n",
       "      <th>seq_start</th>\n",
       "      <th>seq_end</th>\n",
       "      <th>e_value</th>\n",
       "      <th>positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q8N3X1</td>\n",
       "      <td>598</td>\n",
       "      <td>629</td>\n",
       "      <td>3.67e-10</td>\n",
       "      <td>{598, 599, 600, 601, 602, 603, 604, 605, 606, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q8N3X1</td>\n",
       "      <td>220</td>\n",
       "      <td>248</td>\n",
       "      <td>3.73e-04</td>\n",
       "      <td>{220, 221, 222, 223, 224, 225, 226, 227, 228, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Q13526</td>\n",
       "      <td>7</td>\n",
       "      <td>39</td>\n",
       "      <td>1.18e-06</td>\n",
       "      <td>{7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Q9BYW2</td>\n",
       "      <td>2391</td>\n",
       "      <td>2421</td>\n",
       "      <td>2.29e-05</td>\n",
       "      <td>{2391, 2392, 2393, 2394, 2395, 2396, 2397, 239...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>O15428</td>\n",
       "      <td>7</td>\n",
       "      <td>39</td>\n",
       "      <td>2.33e-05</td>\n",
       "      <td>{7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  entry_ac seq_start seq_end   e_value  \\\n",
       "0   Q8N3X1       598     629  3.67e-10   \n",
       "1   Q8N3X1       220     248  3.73e-04   \n",
       "2   Q13526         7      39  1.18e-06   \n",
       "3   Q9BYW2      2391    2421  2.29e-05   \n",
       "4   O15428         7      39  2.33e-05   \n",
       "\n",
       "                                            positive  \n",
       "0  {598, 599, 600, 601, 602, 603, 604, 605, 606, ...  \n",
       "1  {220, 221, 222, 223, 224, 225, 226, 227, 228, ...  \n",
       "2  {7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, ...  \n",
       "3  {2391, 2392, 2393, 2394, 2395, 2396, 2397, 239...  \n",
       "4  {7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, ...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Execute PSSM\n",
    "try:\n",
    "    # Fit the model\n",
    "    pssm.fit(blast_path=BLAST_FASTA_PATH, msa_path=MSA_FASTA_PATH, model_path=PSSM_MODEL_PATH)\n",
    "    # Evaluate the model\n",
    "    pssm_out = pssm.test(model_path=PSSM_MODEL_PATH, test_path=HUMAN_FASTA_PATH,\n",
    "                         num_iterations=4, e_value=1e-3)\n",
    "    # Parse pssm results\n",
    "    pssm_out = pssm.parse(pssm_out)\n",
    "    # Add positive matches\n",
    "    pssm_out['positive'] = pssm_out.apply(lambda x: set(range(int(x.seq_start), int(x.seq_end))), axis=1)\n",
    "    # pssm_out = pssm_out.groupby('entry_ac').agg({\n",
    "    #     'positive': lambda x: set.union(*x)\n",
    "    # }).reset_index()\n",
    "    \n",
    "# Catch error\n",
    "except subprocess.CalledProcessError as e:\n",
    "    print(e)\n",
    "    \n",
    "# Check pssm result\n",
    "pssm_out.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 45 positive  and 20322 negative proteins matched by PSSM\n"
     ]
    }
   ],
   "source": [
    "# Define set of positive protein accessions\n",
    "pssm_positive = set(pssm_out['entry_ac'])\n",
    "pssm_negative = set(human_proteome['entry_ac']) - pssm_positive\n",
    "\n",
    "print('There are {:d} positive  and {:d} negative proteins matched by PSSM'.format(\n",
    "    len(pssm_positive), len(pssm_negative)\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>entry_ac</th>\n",
       "      <th>entry_name</th>\n",
       "      <th>protein_name</th>\n",
       "      <th>len</th>\n",
       "      <th>go</th>\n",
       "      <th>pdb_ids</th>\n",
       "      <th>pfam_ids</th>\n",
       "      <th>string_id</th>\n",
       "      <th>PF00397</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>Q9BTA9</td>\n",
       "      <td>WAC_HUMAN</td>\n",
       "      <td>WW domain-containing adapter protein with coil...</td>\n",
       "      <td>647</td>\n",
       "      <td>nuclear speck [GO:0016607]; nucleoplasm [GO:00...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PF00397;</td>\n",
       "      <td>9606.ENSP00000346986;</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>Q9NZC7</td>\n",
       "      <td>WWOX_HUMAN</td>\n",
       "      <td>WW domain-containing oxidoreductase (EC 1.1.1....</td>\n",
       "      <td>414</td>\n",
       "      <td>cytoplasm [GO:0005737]; cytosol [GO:0005829]; ...</td>\n",
       "      <td>1WMV;</td>\n",
       "      <td>PF00106;PF00397;</td>\n",
       "      <td>9606.ENSP00000457230;</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966</th>\n",
       "      <td>Q9GZV5</td>\n",
       "      <td>WWTR1_HUMAN</td>\n",
       "      <td>WW domain-containing transcription regulator p...</td>\n",
       "      <td>400</td>\n",
       "      <td>cytoplasm [GO:0005737]; cytosol [GO:0005829]; ...</td>\n",
       "      <td>5N5R;5N5T;5N5W;5N75;</td>\n",
       "      <td>PF00397;</td>\n",
       "      <td>9606.ENSP00000419465;</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2246</th>\n",
       "      <td>Q8N3X1</td>\n",
       "      <td>FNBP4_HUMAN</td>\n",
       "      <td>Formin-binding protein 4 (Formin-binding prote...</td>\n",
       "      <td>1017</td>\n",
       "      <td>nuclear speck [GO:0016607]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PF00397;</td>\n",
       "      <td>9606.ENSP00000263773;</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2780</th>\n",
       "      <td>O15428</td>\n",
       "      <td>PINL_HUMAN</td>\n",
       "      <td>Putative PIN1-like protein (Peptidylprolyl cis...</td>\n",
       "      <td>100</td>\n",
       "      <td>cytosol [GO:0005829]; nucleus [GO:0005634]; pe...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PF00397;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     entry_ac   entry_name                                       protein_name  \\\n",
       "428    Q9BTA9    WAC_HUMAN  WW domain-containing adapter protein with coil...   \n",
       "871    Q9NZC7   WWOX_HUMAN  WW domain-containing oxidoreductase (EC 1.1.1....   \n",
       "966    Q9GZV5  WWTR1_HUMAN  WW domain-containing transcription regulator p...   \n",
       "2246   Q8N3X1  FNBP4_HUMAN  Formin-binding protein 4 (Formin-binding prote...   \n",
       "2780   O15428   PINL_HUMAN  Putative PIN1-like protein (Peptidylprolyl cis...   \n",
       "\n",
       "       len                                                 go  \\\n",
       "428    647  nuclear speck [GO:0016607]; nucleoplasm [GO:00...   \n",
       "871    414  cytoplasm [GO:0005737]; cytosol [GO:0005829]; ...   \n",
       "966    400  cytoplasm [GO:0005737]; cytosol [GO:0005829]; ...   \n",
       "2246  1017                         nuclear speck [GO:0016607]   \n",
       "2780   100  cytosol [GO:0005829]; nucleus [GO:0005634]; pe...   \n",
       "\n",
       "                   pdb_ids          pfam_ids              string_id  PF00397  \n",
       "428                    NaN          PF00397;  9606.ENSP00000346986;     True  \n",
       "871                  1WMV;  PF00106;PF00397;  9606.ENSP00000457230;     True  \n",
       "966   5N5R;5N5T;5N5W;5N75;          PF00397;  9606.ENSP00000419465;     True  \n",
       "2246                   NaN          PF00397;  9606.ENSP00000263773;     True  \n",
       "2780                   NaN          PF00397;                    NaN     True  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define dataset of predicted positive rows\n",
    "pssm_results = human_proteome[human_proteome.entry_ac.isin(pssm_positive)]\n",
    "# Save results to disk\n",
    "pssm_results.to_csv(PSSM_RESULTS_PATH, sep='\\t', index=False)\n",
    "# Show some lines\n",
    "pssm_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show confusion matrix\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "_ = cmat.plot(positive=positive, pred_positive=pssm_positive, \n",
    "              negative=negative, pred_negative=pssm_negative,\n",
    "              ax=ax)\n",
    "_ = plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HMMER model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>entry_ac</th>\n",
       "      <th>seq_start</th>\n",
       "      <th>seq_end</th>\n",
       "      <th>e_value</th>\n",
       "      <th>positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q9H0M0</td>\n",
       "      <td>351</td>\n",
       "      <td>381</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>{351, 352, 353, 354, 355, 356, 357, 358, 359, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q9H0M0</td>\n",
       "      <td>383</td>\n",
       "      <td>414</td>\n",
       "      <td>0.00069</td>\n",
       "      <td>{384, 385, 386, 387, 388, 389, 390, 391, 392, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Q9H0M0</td>\n",
       "      <td>458</td>\n",
       "      <td>489</td>\n",
       "      <td>0.00016</td>\n",
       "      <td>{458, 459, 460, 461, 462, 463, 464, 465, 466, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Q8N3X1</td>\n",
       "      <td>220</td>\n",
       "      <td>248</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>{220, 221, 222, 223, 224, 225, 226, 227, 228, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Q8N3X1</td>\n",
       "      <td>598</td>\n",
       "      <td>630</td>\n",
       "      <td>1.2e-10</td>\n",
       "      <td>{598, 599, 600, 601, 602, 603, 604, 605, 606, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>O00308</td>\n",
       "      <td>315</td>\n",
       "      <td>331</td>\n",
       "      <td>0.041</td>\n",
       "      <td>{320, 321, 322, 323, 324, 325, 326, 327, 328, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>O00308</td>\n",
       "      <td>332</td>\n",
       "      <td>363</td>\n",
       "      <td>0.00076</td>\n",
       "      <td>{332, 333, 334, 335, 336, 337, 338, 339, 340, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>O00308</td>\n",
       "      <td>407</td>\n",
       "      <td>437</td>\n",
       "      <td>0.00039</td>\n",
       "      <td>{407, 408, 409, 410, 411, 412, 413, 414, 415, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Q96J02</td>\n",
       "      <td>328</td>\n",
       "      <td>358</td>\n",
       "      <td>0.011</td>\n",
       "      <td>{328, 329, 330, 331, 332, 333, 334, 335, 336, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Q96J02</td>\n",
       "      <td>360</td>\n",
       "      <td>390</td>\n",
       "      <td>0.025</td>\n",
       "      <td>{384, 385, 386, 387, 388, 389, 360, 361, 362, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   entry_ac seq_start seq_end  e_value  \\\n",
       "0    Q9H0M0       351     381   0.0016   \n",
       "1    Q9H0M0       383     414  0.00069   \n",
       "2    Q9H0M0       458     489  0.00016   \n",
       "5    Q8N3X1       220     248   0.0019   \n",
       "6    Q8N3X1       598     630  1.2e-10   \n",
       "8    O00308       315     331    0.041   \n",
       "9    O00308       332     363  0.00076   \n",
       "10   O00308       407     437  0.00039   \n",
       "12   Q96J02       328     358    0.011   \n",
       "13   Q96J02       360     390    0.025   \n",
       "\n",
       "                                             positive  \n",
       "0   {351, 352, 353, 354, 355, 356, 357, 358, 359, ...  \n",
       "1   {384, 385, 386, 387, 388, 389, 390, 391, 392, ...  \n",
       "2   {458, 459, 460, 461, 462, 463, 464, 465, 466, ...  \n",
       "5   {220, 221, 222, 223, 224, 225, 226, 227, 228, ...  \n",
       "6   {598, 599, 600, 601, 602, 603, 604, 605, 606, ...  \n",
       "8   {320, 321, 322, 323, 324, 325, 326, 327, 328, ...  \n",
       "9   {332, 333, 334, 335, 336, 337, 338, 339, 340, ...  \n",
       "10  {407, 408, 409, 410, 411, 412, 413, 414, 415, ...  \n",
       "12  {328, 329, 330, 331, 332, 333, 334, 335, 336, ...  \n",
       "13  {384, 385, 386, 387, 388, 389, 360, 361, 362, ...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Execute HMM\n",
    "\n",
    "# Define e-value threshold\n",
    "e_value_threshold = 0.7\n",
    "\n",
    "# Run hmm\n",
    "try:\n",
    "    \n",
    "    # Fit the HMM model\n",
    "    hmm.fit(msa_path=MSA_FASTA_PATH, model_path=HMMER_MODEL_PATH)\n",
    "    \n",
    "    # Evaluate the model\n",
    "    hmmer_out = hmm.test(test_path=HUMAN_FASTA_PATH, model_path=HMMER_MODEL_PATH, algorithm=hmm.HMMSEARCH)\n",
    "    hmmer_out = hmm.parse(hmmer_out)\n",
    "    \n",
    "    # Make a threshold on e-value\n",
    "    e_value = hmmer_out['e_value'].astype(float)\n",
    "    hmmer_out = hmmer_out[e_value < e_value_threshold]\n",
    "    \n",
    "    # Add positive matches\n",
    "    hmmer_out['positive'] = hmmer_out.apply(lambda x: set(range(int(x.seq_start), int(x.seq_end))), axis=1)\n",
    "    # hmmer_out = hmmer_out.groupby('entry_ac').agg({\n",
    "    #     'positive': lambda x: set.union(*x)\n",
    "    # }).reset_index()\n",
    "        \n",
    "# Catch error\n",
    "except subprocess.CalledProcessError as e:\n",
    "    print(e)\n",
    "    \n",
    "# Show dataset\n",
    "hmmer_out.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 28 positive  and 20339 negative proteins matched by HMM\n"
     ]
    }
   ],
   "source": [
    "# Define set of positive protein accessions\n",
    "hmmer_positive = set(hmmer_out['entry_ac'])\n",
    "hmmer_negative = set(human_proteome['entry_ac']) - hmmer_positive\n",
    "\n",
    "print('There are {:d} positive  and {:d} negative proteins matched by HMM'.format(\n",
    "    len(hmmer_positive), len(hmmer_negative)\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>entry_ac</th>\n",
       "      <th>entry_name</th>\n",
       "      <th>protein_name</th>\n",
       "      <th>len</th>\n",
       "      <th>go</th>\n",
       "      <th>pdb_ids</th>\n",
       "      <th>pfam_ids</th>\n",
       "      <th>string_id</th>\n",
       "      <th>PF00397</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>Q9BTA9</td>\n",
       "      <td>WAC_HUMAN</td>\n",
       "      <td>WW domain-containing adapter protein with coil...</td>\n",
       "      <td>647</td>\n",
       "      <td>nuclear speck [GO:0016607]; nucleoplasm [GO:00...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PF00397;</td>\n",
       "      <td>9606.ENSP00000346986;</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2246</th>\n",
       "      <td>Q8N3X1</td>\n",
       "      <td>FNBP4_HUMAN</td>\n",
       "      <td>Formin-binding protein 4 (Formin-binding prote...</td>\n",
       "      <td>1017</td>\n",
       "      <td>nuclear speck [GO:0016607]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PF00397;</td>\n",
       "      <td>9606.ENSP00000263773;</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2780</th>\n",
       "      <td>O15428</td>\n",
       "      <td>PINL_HUMAN</td>\n",
       "      <td>Putative PIN1-like protein (Peptidylprolyl cis...</td>\n",
       "      <td>100</td>\n",
       "      <td>cytosol [GO:0005829]; nucleus [GO:0005634]; pe...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PF00397;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3679</th>\n",
       "      <td>Q6AWC2</td>\n",
       "      <td>WWC2_HUMAN</td>\n",
       "      <td>Protein WWC2 (BH-3-only member B) (WW domain-c...</td>\n",
       "      <td>1192</td>\n",
       "      <td>cytosol [GO:0005829]; kinase binding [GO:00199...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PF00397;</td>\n",
       "      <td>9606.ENSP00000384222;</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4895</th>\n",
       "      <td>Q9BYW2</td>\n",
       "      <td>SETD2_HUMAN</td>\n",
       "      <td>Histone-lysine N-methyltransferase SETD2 (EC 2...</td>\n",
       "      <td>2564</td>\n",
       "      <td>chromosome [GO:0005694]; nucleoplasm [GO:00056...</td>\n",
       "      <td>2A7O;2MDC;2MDI;2MDJ;4FMU;4H12;5JJY;5JLB;5JLE;5...</td>\n",
       "      <td>PF17907;PF00856;PF08236;PF00397;</td>\n",
       "      <td>9606.ENSP00000386759;</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     entry_ac   entry_name                                       protein_name  \\\n",
       "428    Q9BTA9    WAC_HUMAN  WW domain-containing adapter protein with coil...   \n",
       "2246   Q8N3X1  FNBP4_HUMAN  Formin-binding protein 4 (Formin-binding prote...   \n",
       "2780   O15428   PINL_HUMAN  Putative PIN1-like protein (Peptidylprolyl cis...   \n",
       "3679   Q6AWC2   WWC2_HUMAN  Protein WWC2 (BH-3-only member B) (WW domain-c...   \n",
       "4895   Q9BYW2  SETD2_HUMAN  Histone-lysine N-methyltransferase SETD2 (EC 2...   \n",
       "\n",
       "       len                                                 go  \\\n",
       "428    647  nuclear speck [GO:0016607]; nucleoplasm [GO:00...   \n",
       "2246  1017                         nuclear speck [GO:0016607]   \n",
       "2780   100  cytosol [GO:0005829]; nucleus [GO:0005634]; pe...   \n",
       "3679  1192  cytosol [GO:0005829]; kinase binding [GO:00199...   \n",
       "4895  2564  chromosome [GO:0005694]; nucleoplasm [GO:00056...   \n",
       "\n",
       "                                                pdb_ids  \\\n",
       "428                                                 NaN   \n",
       "2246                                                NaN   \n",
       "2780                                                NaN   \n",
       "3679                                                NaN   \n",
       "4895  2A7O;2MDC;2MDI;2MDJ;4FMU;4H12;5JJY;5JLB;5JLE;5...   \n",
       "\n",
       "                              pfam_ids              string_id  PF00397  \n",
       "428                           PF00397;  9606.ENSP00000346986;     True  \n",
       "2246                          PF00397;  9606.ENSP00000263773;     True  \n",
       "2780                          PF00397;                    NaN     True  \n",
       "3679                          PF00397;  9606.ENSP00000384222;     True  \n",
       "4895  PF17907;PF00856;PF08236;PF00397;  9606.ENSP00000386759;     True  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define dataset of predicted positive rows\n",
    "hmmer_results = human_proteome[human_proteome.entry_ac.isin(hmmer_positive)]\n",
    "# Save results to disk\n",
    "hmmer_results.to_csv(HMMER_RESULTS_PATH, sep='\\t', index=False)\n",
    "# Show some lines\n",
    "hmmer_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show confusion matrix\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "_ = cmat.plot(positive=positive, pred_positive=hmmer_positive, \n",
    "              negative=negative, pred_negative=hmmer_negative,\n",
    "              ax=ax)\n",
    "_ = plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## JACKHMMER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>entry_ac</th>\n",
       "      <th>seq_start</th>\n",
       "      <th>seq_end</th>\n",
       "      <th>e_value</th>\n",
       "      <th>positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q96PU5</td>\n",
       "      <td>195</td>\n",
       "      <td>224</td>\n",
       "      <td>1.1e-14</td>\n",
       "      <td>{195, 196, 197, 198, 199, 200, 201, 202, 203, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q96PU5</td>\n",
       "      <td>387</td>\n",
       "      <td>416</td>\n",
       "      <td>6.1e-15</td>\n",
       "      <td>{387, 388, 389, 390, 391, 392, 393, 394, 395, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Q96PU5</td>\n",
       "      <td>499</td>\n",
       "      <td>528</td>\n",
       "      <td>3.3e-16</td>\n",
       "      <td>{512, 513, 514, 515, 516, 517, 518, 519, 520, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Q96PU5</td>\n",
       "      <td>550</td>\n",
       "      <td>579</td>\n",
       "      <td>3.2e-14</td>\n",
       "      <td>{550, 551, 552, 553, 554, 555, 556, 557, 558, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Q9H0M0</td>\n",
       "      <td>351</td>\n",
       "      <td>380</td>\n",
       "      <td>7.9e-16</td>\n",
       "      <td>{351, 352, 353, 354, 355, 356, 357, 358, 359, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Q9H0M0</td>\n",
       "      <td>383</td>\n",
       "      <td>412</td>\n",
       "      <td>1.6e-14</td>\n",
       "      <td>{384, 385, 386, 387, 388, 389, 390, 391, 392, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Q9H0M0</td>\n",
       "      <td>458</td>\n",
       "      <td>487</td>\n",
       "      <td>5.7e-16</td>\n",
       "      <td>{458, 459, 460, 461, 462, 463, 464, 465, 466, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Q9H0M0</td>\n",
       "      <td>498</td>\n",
       "      <td>527</td>\n",
       "      <td>4.6e-13</td>\n",
       "      <td>{512, 513, 514, 515, 516, 517, 518, 519, 520, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>O00308</td>\n",
       "      <td>302</td>\n",
       "      <td>331</td>\n",
       "      <td>3.5e-15</td>\n",
       "      <td>{302, 303, 304, 305, 306, 307, 308, 309, 310, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>O00308</td>\n",
       "      <td>332</td>\n",
       "      <td>361</td>\n",
       "      <td>8.4e-15</td>\n",
       "      <td>{332, 333, 334, 335, 336, 337, 338, 339, 340, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  entry_ac seq_start seq_end  e_value  \\\n",
       "0   Q96PU5       195     224  1.1e-14   \n",
       "1   Q96PU5       387     416  6.1e-15   \n",
       "2   Q96PU5       499     528  3.3e-16   \n",
       "3   Q96PU5       550     579  3.2e-14   \n",
       "4   Q9H0M0       351     380  7.9e-16   \n",
       "5   Q9H0M0       383     412  1.6e-14   \n",
       "6   Q9H0M0       458     487  5.7e-16   \n",
       "7   Q9H0M0       498     527  4.6e-13   \n",
       "8   O00308       302     331  3.5e-15   \n",
       "9   O00308       332     361  8.4e-15   \n",
       "\n",
       "                                            positive  \n",
       "0  {195, 196, 197, 198, 199, 200, 201, 202, 203, ...  \n",
       "1  {387, 388, 389, 390, 391, 392, 393, 394, 395, ...  \n",
       "2  {512, 513, 514, 515, 516, 517, 518, 519, 520, ...  \n",
       "3  {550, 551, 552, 553, 554, 555, 556, 557, 558, ...  \n",
       "4  {351, 352, 353, 354, 355, 356, 357, 358, 359, ...  \n",
       "5  {384, 385, 386, 387, 388, 389, 390, 391, 392, ...  \n",
       "6  {458, 459, 460, 461, 462, 463, 464, 465, 466, ...  \n",
       "7  {512, 513, 514, 515, 516, 517, 518, 519, 520, ...  \n",
       "8  {302, 303, 304, 305, 306, 307, 308, 309, 310, ...  \n",
       "9  {332, 333, 334, 335, 336, 337, 338, 339, 340, ...  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Execute HMM\n",
    "\n",
    "# Define e-value threshold\n",
    "e_value_threshold = 0.7\n",
    "\n",
    "# Run hmm\n",
    "try:\n",
    "    \n",
    "    # Evaluate the model\n",
    "    jack_out = hmm.test(test_path=HUMAN_FASTA_PATH, seq_path=SEQ_FASTA_PATH, algorithm=hmm.JACKHMMER)\n",
    "    jack_out = hmm.parse(jack_out)\n",
    "    \n",
    "    # Make a threshold on e-value\n",
    "    e_value = jack_out['e_value'].astype(float)\n",
    "    jack_out = jack_out[e_value < e_value_threshold]\n",
    "    \n",
    "    # Add positive matches\n",
    "    jack_out['positive'] = jack_out.apply(lambda x: set(range(int(x.seq_start), int(x.seq_end))), axis=1)\n",
    "    # jack_out = jack_out.groupby('entry_ac').agg({\n",
    "    #     'positive': lambda x: set.union(*x)\n",
    "    # }).reset_index()\n",
    "        \n",
    "# Catch error\n",
    "except subprocess.CalledProcessError as e:\n",
    "    print(e)\n",
    "    \n",
    "# Show results\n",
    "jack_out.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 49 positive  and 20318 negative proteins matched by HMM\n"
     ]
    }
   ],
   "source": [
    "# Define set of positive protein accessions\n",
    "jack_positive = set(jack_out['entry_ac'])\n",
    "jack_negative = set(human_proteome['entry_ac']) - jack_positive\n",
    "\n",
    "print('There are {:d} positive  and {:d} negative proteins matched by HMM'.format(\n",
    "    len(jack_positive), len(jack_negative)\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>entry_ac</th>\n",
       "      <th>entry_name</th>\n",
       "      <th>protein_name</th>\n",
       "      <th>len</th>\n",
       "      <th>go</th>\n",
       "      <th>pdb_ids</th>\n",
       "      <th>pfam_ids</th>\n",
       "      <th>string_id</th>\n",
       "      <th>PF00397</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>Q9BTA9</td>\n",
       "      <td>WAC_HUMAN</td>\n",
       "      <td>WW domain-containing adapter protein with coil...</td>\n",
       "      <td>647</td>\n",
       "      <td>nuclear speck [GO:0016607]; nucleoplasm [GO:00...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PF00397;</td>\n",
       "      <td>9606.ENSP00000346986;</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>Q9NZC7</td>\n",
       "      <td>WWOX_HUMAN</td>\n",
       "      <td>WW domain-containing oxidoreductase (EC 1.1.1....</td>\n",
       "      <td>414</td>\n",
       "      <td>cytoplasm [GO:0005737]; cytosol [GO:0005829]; ...</td>\n",
       "      <td>1WMV;</td>\n",
       "      <td>PF00106;PF00397;</td>\n",
       "      <td>9606.ENSP00000457230;</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966</th>\n",
       "      <td>Q9GZV5</td>\n",
       "      <td>WWTR1_HUMAN</td>\n",
       "      <td>WW domain-containing transcription regulator p...</td>\n",
       "      <td>400</td>\n",
       "      <td>cytoplasm [GO:0005737]; cytosol [GO:0005829]; ...</td>\n",
       "      <td>5N5R;5N5T;5N5W;5N75;</td>\n",
       "      <td>PF00397;</td>\n",
       "      <td>9606.ENSP00000419465;</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2246</th>\n",
       "      <td>Q8N3X1</td>\n",
       "      <td>FNBP4_HUMAN</td>\n",
       "      <td>Formin-binding protein 4 (Formin-binding prote...</td>\n",
       "      <td>1017</td>\n",
       "      <td>nuclear speck [GO:0016607]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PF00397;</td>\n",
       "      <td>9606.ENSP00000263773;</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2520</th>\n",
       "      <td>Q5VWI1</td>\n",
       "      <td>TCRGL_HUMAN</td>\n",
       "      <td>Transcription elongation regulator 1-like protein</td>\n",
       "      <td>586</td>\n",
       "      <td>nucleus [GO:0005634]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PF01846;</td>\n",
       "      <td>9606.ENSP00000357631;</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     entry_ac   entry_name                                       protein_name  \\\n",
       "428    Q9BTA9    WAC_HUMAN  WW domain-containing adapter protein with coil...   \n",
       "871    Q9NZC7   WWOX_HUMAN  WW domain-containing oxidoreductase (EC 1.1.1....   \n",
       "966    Q9GZV5  WWTR1_HUMAN  WW domain-containing transcription regulator p...   \n",
       "2246   Q8N3X1  FNBP4_HUMAN  Formin-binding protein 4 (Formin-binding prote...   \n",
       "2520   Q5VWI1  TCRGL_HUMAN  Transcription elongation regulator 1-like protein   \n",
       "\n",
       "       len                                                 go  \\\n",
       "428    647  nuclear speck [GO:0016607]; nucleoplasm [GO:00...   \n",
       "871    414  cytoplasm [GO:0005737]; cytosol [GO:0005829]; ...   \n",
       "966    400  cytoplasm [GO:0005737]; cytosol [GO:0005829]; ...   \n",
       "2246  1017                         nuclear speck [GO:0016607]   \n",
       "2520   586                               nucleus [GO:0005634]   \n",
       "\n",
       "                   pdb_ids          pfam_ids              string_id  PF00397  \n",
       "428                    NaN          PF00397;  9606.ENSP00000346986;     True  \n",
       "871                  1WMV;  PF00106;PF00397;  9606.ENSP00000457230;     True  \n",
       "966   5N5R;5N5T;5N5W;5N75;          PF00397;  9606.ENSP00000419465;     True  \n",
       "2246                   NaN          PF00397;  9606.ENSP00000263773;     True  \n",
       "2520                   NaN          PF01846;  9606.ENSP00000357631;    False  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define dataset of predicted positive rows\n",
    "jack_results = human_proteome[human_proteome.entry_ac.isin(jack_positive)]\n",
    "# Save results to disk\n",
    "jack_results.to_csv(JACK_RESULTS_PATH, sep='\\t', index=False)\n",
    "# Show some lines\n",
    "jack_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show confusion matrix\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "_ = cmat.plot(positive=positive, pred_positive=jack_positive, \n",
    "              negative=negative, pred_negative=jack_negative,\n",
    "              ax=ax)\n",
    "_ = plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensemble model\n",
    "\n",
    "Here the ensemble of PSSM and HMM prediction are kept as ensemble model prediction. Then, ensemble model prediction output is evaluated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 51 positive and 20316 negative proteins matched by ensemble method\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/damiclem/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/generic.py:1455: FutureWarning: using a dict with renaming is deprecated and will be removed\n",
      "in a future version.\n",
      "\n",
      "For column-specific groupby renaming, use named aggregation\n",
      "\n",
      "    >>> df.groupby(...).agg(name=('column', aggfunc))\n",
      "\n",
      "  return super().aggregate(arg, *args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# Compute ensemble\n",
    "ensemble_out = majority_voting([pssm_out, hmmer_out, jack_out], threshold=2)\n",
    "# ensemble_out = ensemble_out.groupby('entry_ac').agg({\n",
    "#     'positive': lambda x: set.union(*x)\n",
    "# }).reset_index()\n",
    "\n",
    "# Define set of positive protein accessions\n",
    "ensemble_positive = set(ensemble_out['entry_ac'])\n",
    "ensemble_negative = set(human_proteome['entry_ac']) - ensemble_positive\n",
    "\n",
    "print('There are {:d} positive and {:d} negative proteins matched by ensemble method'.format(\n",
    "    len(ensemble_positive), len(ensemble_negative)\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>entry_ac</th>\n",
       "      <th>entry_name</th>\n",
       "      <th>protein_name</th>\n",
       "      <th>len</th>\n",
       "      <th>go</th>\n",
       "      <th>pdb_ids</th>\n",
       "      <th>pfam_ids</th>\n",
       "      <th>string_id</th>\n",
       "      <th>PF00397</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>Q9BTA9</td>\n",
       "      <td>WAC_HUMAN</td>\n",
       "      <td>WW domain-containing adapter protein with coil...</td>\n",
       "      <td>647</td>\n",
       "      <td>nuclear speck [GO:0016607]; nucleoplasm [GO:00...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PF00397;</td>\n",
       "      <td>9606.ENSP00000346986;</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>Q9NZC7</td>\n",
       "      <td>WWOX_HUMAN</td>\n",
       "      <td>WW domain-containing oxidoreductase (EC 1.1.1....</td>\n",
       "      <td>414</td>\n",
       "      <td>cytoplasm [GO:0005737]; cytosol [GO:0005829]; ...</td>\n",
       "      <td>1WMV;</td>\n",
       "      <td>PF00106;PF00397;</td>\n",
       "      <td>9606.ENSP00000457230;</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966</th>\n",
       "      <td>Q9GZV5</td>\n",
       "      <td>WWTR1_HUMAN</td>\n",
       "      <td>WW domain-containing transcription regulator p...</td>\n",
       "      <td>400</td>\n",
       "      <td>cytoplasm [GO:0005737]; cytosol [GO:0005829]; ...</td>\n",
       "      <td>5N5R;5N5T;5N5W;5N75;</td>\n",
       "      <td>PF00397;</td>\n",
       "      <td>9606.ENSP00000419465;</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2246</th>\n",
       "      <td>Q8N3X1</td>\n",
       "      <td>FNBP4_HUMAN</td>\n",
       "      <td>Formin-binding protein 4 (Formin-binding prote...</td>\n",
       "      <td>1017</td>\n",
       "      <td>nuclear speck [GO:0016607]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PF00397;</td>\n",
       "      <td>9606.ENSP00000263773;</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2520</th>\n",
       "      <td>Q5VWI1</td>\n",
       "      <td>TCRGL_HUMAN</td>\n",
       "      <td>Transcription elongation regulator 1-like protein</td>\n",
       "      <td>586</td>\n",
       "      <td>nucleus [GO:0005634]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PF01846;</td>\n",
       "      <td>9606.ENSP00000357631;</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     entry_ac   entry_name                                       protein_name  \\\n",
       "428    Q9BTA9    WAC_HUMAN  WW domain-containing adapter protein with coil...   \n",
       "871    Q9NZC7   WWOX_HUMAN  WW domain-containing oxidoreductase (EC 1.1.1....   \n",
       "966    Q9GZV5  WWTR1_HUMAN  WW domain-containing transcription regulator p...   \n",
       "2246   Q8N3X1  FNBP4_HUMAN  Formin-binding protein 4 (Formin-binding prote...   \n",
       "2520   Q5VWI1  TCRGL_HUMAN  Transcription elongation regulator 1-like protein   \n",
       "\n",
       "       len                                                 go  \\\n",
       "428    647  nuclear speck [GO:0016607]; nucleoplasm [GO:00...   \n",
       "871    414  cytoplasm [GO:0005737]; cytosol [GO:0005829]; ...   \n",
       "966    400  cytoplasm [GO:0005737]; cytosol [GO:0005829]; ...   \n",
       "2246  1017                         nuclear speck [GO:0016607]   \n",
       "2520   586                               nucleus [GO:0005634]   \n",
       "\n",
       "                   pdb_ids          pfam_ids              string_id  PF00397  \n",
       "428                    NaN          PF00397;  9606.ENSP00000346986;     True  \n",
       "871                  1WMV;  PF00106;PF00397;  9606.ENSP00000457230;     True  \n",
       "966   5N5R;5N5T;5N5W;5N75;          PF00397;  9606.ENSP00000419465;     True  \n",
       "2246                   NaN          PF00397;  9606.ENSP00000263773;     True  \n",
       "2520                   NaN          PF01846;  9606.ENSP00000357631;    False  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define dataset of predicted positive rows\n",
    "ensemble_results = human_proteome[human_proteome.entry_ac.isin(ensemble_positive)]\n",
    "# Save results to disk\n",
    "ensemble_results.to_csv(ENSEMBLE_RESULTS_PATH, sep='\\t', index=False)\n",
    "# Show some lines\n",
    "ensemble_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show confusion matrix\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "_ = cmat.plot(positive=positive, pred_positive=ensemble_positive, \n",
    "              negative=negative, pred_negative=ensemble_negative,\n",
    "              ax=ax)\n",
    "_ = plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model comparison\n",
    "\n",
    "Here various metrics are compared for all the different models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a dictionary model -> predicted positive set\n",
    "models = {\n",
    "    'pssm': {\n",
    "        'positive': pssm_positive, \n",
    "        'negative': pssm_negative\n",
    "    },\n",
    "    'hmmer': {\n",
    "        'positive': hmmer_positive,\n",
    "        'negative': hmmer_negative\n",
    "    },\n",
    "    'jackhmmer': {\n",
    "        'positive': jack_positive,\n",
    "        'negative': jack_negative\n",
    "    },\n",
    "    'ensemble': {\n",
    "        'positive': ensemble_positive, \n",
    "        'negative': ensemble_negative\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute statistics for each model\n",
    "for model, attr in models.items():\n",
    "    # Compute scores for current model (precision, recall, accuracy, weighted accuracy)\n",
    "    _ , prc, rec, acc, w_acc = cmat.compute(positive=positive, pred_positive=attr['positive'],\n",
    "                                            negative=negative, pred_negative=attr['negative'])\n",
    "    # Add scores to each model\n",
    "    models[model]['prc'] = prc\n",
    "    models[model]['rec'] = rec\n",
    "    models[model]['acc'] = acc\n",
    "    models[model]['w_acc'] = w_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show statistics comparison\n",
    "\n",
    "# Initialize plot: one for each statistic\n",
    "fig, axs = plt.subplots(2, 2, figsize=(20, 20), sharey=True)\n",
    "# Flatten axis\n",
    "axs = axs.flat\n",
    "# Define titles for each statistic\n",
    "_ = axs[0].set_title('Precision', fontsize=16)\n",
    "_ = axs[1].set_title('Recall', fontsize=16)\n",
    "_ = axs[2].set_title('Accuracy', fontsize=16)\n",
    "_ = axs[3].set_title('Weighted accuracy', fontsize=16)\n",
    "\n",
    "# Create a barplot for each statistic\n",
    "# Fill values for each model\n",
    "for i, model in enumerate(models.keys()):\n",
    "    _ = axs[0].bar(i, models[model]['prc'])  # Precision\n",
    "    _ = axs[1].bar(i, models[model]['rec'])  # Recall\n",
    "    _ = axs[2].bar(i, models[model]['acc'])  # Accuracy\n",
    "    _ = axs[3].bar(i, models[model]['w_acc'])  # Weighted accuracy\n",
    "    \n",
    "# Set properties for each plot\n",
    "for i in range(4):\n",
    "    # Set labels\n",
    "    _ = axs[i].set_xticks(range(len(models.keys())))\n",
    "    _ = axs[i].set_xticklabels(list(models.keys()), rotation=30)\n",
    "    # Set limits\n",
    "    _ = axs[i].set_ylim(0.0, 1.0)\n",
    "    # Set axes style\n",
    "    _ = axs[i].tick_params(labelsize=16)\n",
    "    \n",
    "# Show plot\n",
    "_ = plt.savefig('images/classification.png')\n",
    "_ = plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sequence matching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define different models output\n",
    "models_out = {\n",
    "    'pssm': pssm_out,\n",
    "    'hmmer': hmmer_out,\n",
    "    'jackhmmer': jack_out,\n",
    "    'ensemble': ensemble_out\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For each model output, define matching sequences\n",
    "\n",
    "# Define model protein matching container\n",
    "models_matching = dict()\n",
    "\n",
    "# loop through each model\n",
    "for model in models_out.keys():\n",
    "    \n",
    "    # Define model output\n",
    "    model_out = models_out[model]\n",
    "    \n",
    "    # Aggregate domains\n",
    "    model_out = model_out.groupby('entry_ac').agg({\n",
    "        'positive': lambda x: set.union(*x)\n",
    "    }).reset_index()\n",
    "\n",
    "    # Create dataframe for pssm output comparison\n",
    "    model_matching = pd.merge(left=model_out, right=protein_matching, \n",
    "                              how='left', on='entry_ac', suffixes=('_pred', ''))\n",
    "    # Fill na values with empty sets\n",
    "    model_matching = model_matching.fillna('').applymap(lambda x: x if x != '' else set())\n",
    "    # Handle missing values in positive predictions\n",
    "    model_matching['positive_pred'] = model_matching['positive_pred'].fillna('').apply(lambda x: x if x != '' else set())\n",
    "    # Add negative predictions\n",
    "    model_matching['negative_pred'] = model_matching.apply(lambda x: (x['positive'] | x['negative']) - x['positive_pred'], axis=1)\n",
    "    # Reorder columns\n",
    "    model_matching = model_matching[['entry_ac', 'positive', 'negative', 'positive_pred', 'negative_pred']]\n",
    "    \n",
    "    # Add statistics columns\n",
    "    # Fill statistics for every row\n",
    "    for i, row in model_matching.iterrows():\n",
    "        # Compute all the statistics\n",
    "        _, prc, rec, acc, w_acc = cmat.compute(positive=row['positive'], pred_positive=row['positive_pred'],\n",
    "                                               negative=row['negative'], pred_negative=row['negative_pred'])\n",
    "        # Set values\n",
    "        # model_matching.loc[i, 'f1s'] = 2 * (prc * rec) / (prc + rec)\n",
    "        model_matching.loc[i, 'prc'] = prc\n",
    "        model_matching.loc[i, 'rec'] = rec\n",
    "        model_matching.loc[i, 'acc'] = acc\n",
    "        model_matching.loc[i, 'w_acc'] = w_acc\n",
    "    \n",
    "    # Store model matching\n",
    "    models_matching[model] = model_matching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show statistics graphically for each model\n",
    "\n",
    "# Define columns\n",
    "cols = [('prc', 'Precision'), ('rec', 'Recall'), ('acc', 'Accuracy'), ('w_acc', 'Weighted Accuracy')]\n",
    "\n",
    "# Define colors\n",
    "colors = [*mcolors.TABLEAU_COLORS.values()]\n",
    "\n",
    "# Initialize plots (one for each statistic)\n",
    "fig, axs = plt.subplots(2, 2, figsize=(20, 20), sharey=True)\n",
    "axs = axs.flat\n",
    "\n",
    "# Set plots attributes\n",
    "for i, col in enumerate(cols):\n",
    "    # Get stat and title\n",
    "    stat, title = col\n",
    "    # Add title\n",
    "    _ = axs[i].set_title(title, fontsize=16)\n",
    "    # Create a box for each model\n",
    "    boxplot = axs[i].boxplot([matching[stat].dropna().tolist() for model, matching in models_matching.items()],\n",
    "                             medianprops={\n",
    "                                 'color': 'black',\n",
    "                                 'linestyle': '-',\n",
    "                                 'linewidth': 3\n",
    "                             },\n",
    "                             meanprops={\n",
    "                                 'marker': 'D',\n",
    "                                 'markeredgecolor': 'black',\n",
    "                                 'markerfacecolor': 'yellow'\n",
    "                             },\n",
    "                             meanline=False, showmeans=True, patch_artist=True)\n",
    "    # Set box labels\n",
    "    _ = axs[i].set_xticklabels(models_matching.keys(), rotation=30)\n",
    "    _ = axs[i].tick_params(labelsize=16)\n",
    "    # Set boxplot colours\n",
    "    for i, box in enumerate(boxplot['boxes']):\n",
    "        # Change box color\n",
    "        box.set_facecolor(colors[i])\n",
    "        \n",
    "# Save plot\n",
    "_ = plt.savefig('images/matching.png')\n",
    "# Show plot\n",
    "_ = plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Structure matching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>entry_ac</th>\n",
       "      <th>pdb_id</th>\n",
       "      <th>positive</th>\n",
       "      <th>negative</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0A075B6N1</td>\n",
       "      <td>5hhm</td>\n",
       "      <td>{21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 3...</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A0A075B6N1</td>\n",
       "      <td>5hho</td>\n",
       "      <td>{21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 3...</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A0A075B6N1</td>\n",
       "      <td>5nqk</td>\n",
       "      <td>{22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 3...</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A0A075B6T6</td>\n",
       "      <td>1ao7</td>\n",
       "      <td>{23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 3...</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A0A075B6T6</td>\n",
       "      <td>4zdh</td>\n",
       "      <td>{24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 3...</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     entry_ac pdb_id                                           positive  \\\n",
       "0  A0A075B6N1   5hhm  {21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 3...   \n",
       "1  A0A075B6N1   5hho  {21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 3...   \n",
       "2  A0A075B6N1   5nqk  {22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 3...   \n",
       "3  A0A075B6T6   1ao7  {23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 3...   \n",
       "4  A0A075B6T6   4zdh  {24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 3...   \n",
       "\n",
       "                                            negative  \n",
       "0  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...  \n",
       "1  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...  \n",
       "2  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...  \n",
       "3  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...  \n",
       "4  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load SITFS dataset mapping from PDB sequences to UniProt sequences\n",
    "sifts = pd.read_csv(SIFTS_CSV_PATH, sep='\\t', header=1)\n",
    "# Rename columns\n",
    "sifts.columns = ['pdb_id', 'chain', 'entry_ac', 'res_beg', 'res_end', 'pdb_beg', 'pdb_end', 'sp_beg', 'sp_end']\n",
    "# Subset columns:  (keep only pdb id plus the ones mapping to uniprot)\n",
    "sifts = sifts[['pdb_id', 'chain', 'entry_ac', 'sp_beg', 'sp_end']]\n",
    "# Rename last columns\n",
    "sifts.columns = [*sifts.columns[:-2]] + ['seq_start', 'seq_end']\n",
    "# Group sifts on sequence start and end: various pdb ids map to same areas, but just ine is needed\n",
    "sifts = sifts.groupby(by=['entry_ac', 'seq_start', 'seq_end']).agg({\n",
    "    'pdb_id': lambda pdb_ids: pdb_ids.values[0]\n",
    "}).reset_index()\n",
    "# Join with human proteome in order to retrieve length of the whole sequence\n",
    "sifts = sifts.merge(human_proteome[['entry_ac', 'len']], on='entry_ac', how='inner')\n",
    "# Redefine sequence interval as set of positions\n",
    "sifts['positive'] = sifts.apply(lambda x: set(range(x['seq_start'], x['seq_end'])), axis=1)\n",
    "sifts['negative'] = sifts.apply(lambda x: set(range(1, x['len'])) - x['positive'], axis=1)\n",
    "# Remove useless columns\n",
    "sifts = sifts[['entry_ac', 'pdb_id', 'positive', 'negative']]\n",
    "sifts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Remove redundant PDBs, i.e. the ones which contains other ones\n",
    "# \n",
    "# # Define rows which will be dropped\n",
    "# drop_rows = set()\n",
    "# \n",
    "# # Group for every accession\n",
    "# for entry_ac, group in sifts.groupby(by='entry_ac'):\n",
    "#     # Make a copy of the actual dataset slice\n",
    "#     group = group.copy()\n",
    "#     # Sort group by PDB dimension\n",
    "#     group['pdb_len'] = group['positive'].apply(len)\n",
    "#     group.sort_values('pdb_len', ascending=False, inplace=True)\n",
    "#     # Loop through each component of the group\n",
    "#     # Note: indices are actual sifts dataset indices\n",
    "#     for i, _  in group.iterrows():\n",
    "#         # # Define a set of positions covered without i-th rows\n",
    "#         # positive = set()\n",
    "#         # Loop through other group's rows\n",
    "#         for j, _ in group.iterrows():\n",
    "#             # Skip itself\n",
    "#             if i == j: continue\n",
    "#             # If j-th row has been removed: skip it\n",
    "#             if j in drop_rows: continue\n",
    "#             # If i-th row is fully contained in j-th, drop i\n",
    "#             if not len(group.loc[i, 'positive'] - group.loc[j, 'positive']):\n",
    "#                 drop_rows.add(i)  # Drop row\n",
    "#             # # Save j-th row positive\n",
    "#             # positive = positive | group.loc[j, 'positive']\n",
    "#         # # Check if i-th row can be dropped without losing information\n",
    "#         # if not len(positive - group.loc[i, 'positive']):\n",
    "#         #     drop_rows.add(i)  # Drop row\n",
    "# # Drop rows\n",
    "# sifts = sifts.drop(list(drop_rows))\n",
    "# # Show result\n",
    "# sifts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>entry_ac</th>\n",
       "      <th>seq_start</th>\n",
       "      <th>seq_end</th>\n",
       "      <th>e_value</th>\n",
       "      <th>positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q96PU5</td>\n",
       "      <td>195</td>\n",
       "      <td>224</td>\n",
       "      <td>1.1e-14</td>\n",
       "      <td>{195, 196, 197, 198, 199, 200, 201, 202, 203, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q96PU5</td>\n",
       "      <td>387</td>\n",
       "      <td>416</td>\n",
       "      <td>6.1e-15</td>\n",
       "      <td>{387, 388, 389, 390, 391, 392, 393, 394, 395, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Q96PU5</td>\n",
       "      <td>499</td>\n",
       "      <td>528</td>\n",
       "      <td>3.3e-16</td>\n",
       "      <td>{512, 513, 514, 515, 516, 517, 518, 519, 520, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Q96PU5</td>\n",
       "      <td>550</td>\n",
       "      <td>579</td>\n",
       "      <td>3.2e-14</td>\n",
       "      <td>{550, 551, 552, 553, 554, 555, 556, 557, 558, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Q9H0M0</td>\n",
       "      <td>351</td>\n",
       "      <td>380</td>\n",
       "      <td>7.9e-16</td>\n",
       "      <td>{351, 352, 353, 354, 355, 356, 357, 358, 359, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Q9H0M0</td>\n",
       "      <td>383</td>\n",
       "      <td>412</td>\n",
       "      <td>1.6e-14</td>\n",
       "      <td>{384, 385, 386, 387, 388, 389, 390, 391, 392, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Q9H0M0</td>\n",
       "      <td>458</td>\n",
       "      <td>487</td>\n",
       "      <td>5.7e-16</td>\n",
       "      <td>{458, 459, 460, 461, 462, 463, 464, 465, 466, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Q9H0M0</td>\n",
       "      <td>498</td>\n",
       "      <td>527</td>\n",
       "      <td>4.6e-13</td>\n",
       "      <td>{512, 513, 514, 515, 516, 517, 518, 519, 520, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>O00308</td>\n",
       "      <td>302</td>\n",
       "      <td>331</td>\n",
       "      <td>3.5e-15</td>\n",
       "      <td>{302, 303, 304, 305, 306, 307, 308, 309, 310, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>O00308</td>\n",
       "      <td>332</td>\n",
       "      <td>361</td>\n",
       "      <td>8.4e-15</td>\n",
       "      <td>{332, 333, 334, 335, 336, 337, 338, 339, 340, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  entry_ac seq_start seq_end  e_value  \\\n",
       "0   Q96PU5       195     224  1.1e-14   \n",
       "1   Q96PU5       387     416  6.1e-15   \n",
       "2   Q96PU5       499     528  3.3e-16   \n",
       "3   Q96PU5       550     579  3.2e-14   \n",
       "4   Q9H0M0       351     380  7.9e-16   \n",
       "5   Q9H0M0       383     412  1.6e-14   \n",
       "6   Q9H0M0       458     487  5.7e-16   \n",
       "7   Q9H0M0       498     527  4.6e-13   \n",
       "8   O00308       302     331  3.5e-15   \n",
       "9   O00308       332     361  8.4e-15   \n",
       "\n",
       "                                            positive  \n",
       "0  {195, 196, 197, 198, 199, 200, 201, 202, 203, ...  \n",
       "1  {387, 388, 389, 390, 391, 392, 393, 394, 395, ...  \n",
       "2  {512, 513, 514, 515, 516, 517, 518, 519, 520, ...  \n",
       "3  {550, 551, 552, 553, 554, 555, 556, 557, 558, ...  \n",
       "4  {351, 352, 353, 354, 355, 356, 357, 358, 359, ...  \n",
       "5  {384, 385, 386, 387, 388, 389, 390, 391, 392, ...  \n",
       "6  {458, 459, 460, 461, 462, 463, 464, 465, 466, ...  \n",
       "7  {512, 513, 514, 515, 516, 517, 518, 519, 520, ...  \n",
       "8  {302, 303, 304, 305, 306, 307, 308, 309, 310, ...  \n",
       "9  {332, 333, 334, 335, 336, 337, 338, 339, 340, ...  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Execute HMM\n",
    "\n",
    "# Run hmm\n",
    "try:\n",
    "    \n",
    "    # Evaluate the model\n",
    "    model_out = hmm.test(test_path=HUMAN_FASTA_PATH, seq_path=SEQ_FASTA_PATH, algorithm=hmm.JACKHMMER)\n",
    "    model_out = hmm.parse(model_out)\n",
    "    \n",
    "    # Make a threshold on e-value\n",
    "    e_value = model_out['e_value'].astype(float)\n",
    "    model_out = model_out[e_value < 0.7]\n",
    "    \n",
    "    # Add positive matches\n",
    "    model_out['positive'] = model_out.apply(lambda x: set(range(int(x.seq_start), int(x.seq_end))), axis=1)\n",
    "    \n",
    "    # Get only few column\n",
    "    model_out = model_out[['entry_ac', 'positive']]\n",
    "        \n",
    "# Catch error\n",
    "except subprocess.CalledProcessError as e:\n",
    "    print(e)\n",
    "    \n",
    "# Show results\n",
    "jack_out.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>entry_ac</th>\n",
       "      <th>pdb_id</th>\n",
       "      <th>positive</th>\n",
       "      <th>negative</th>\n",
       "      <th>positive_pred</th>\n",
       "      <th>negative_pred</th>\n",
       "      <th>prc</th>\n",
       "      <th>rec</th>\n",
       "      <th>f1s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q96PU5</td>\n",
       "      <td>2nsq</td>\n",
       "      <td>{195, 196, 197, 198, 199, 200, 201, 202, 203, ...</td>\n",
       "      <td>{154, 155, 156, 157, 158, 159, 160, 161, 162, ...</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{154, 155, 156, 157, 158, 159, 160, 161, 162, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q96PU5</td>\n",
       "      <td>2lty</td>\n",
       "      <td>{195, 196, 197, 198, 199, 200, 201, 202, 203, ...</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{385, 386, 387, 388, 389, 390, 391, 392, 393, ...</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Q96PU5</td>\n",
       "      <td>2lb2</td>\n",
       "      <td>{195, 196, 197, 198, 199, 200, 201, 202, 203, ...</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{386, 387, 388, 389, 390, 391, 392, 393, 394, ...</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Q96PU5</td>\n",
       "      <td>2laj</td>\n",
       "      <td>{195, 196, 197, 198, 199, 200, 201, 202, 203, ...</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{512, 513, 514, 515, 516, 517, 518, 519, 520, ...</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Q96PU5</td>\n",
       "      <td>2mpt</td>\n",
       "      <td>{195, 196, 197, 198, 199, 200, 201, 202, 203, ...</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{512, 513, 514, 515, 516, 517, 518, 519, 520, ...</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  entry_ac pdb_id                                           positive  \\\n",
       "0   Q96PU5   2nsq  {195, 196, 197, 198, 199, 200, 201, 202, 203, ...   \n",
       "1   Q96PU5   2lty  {195, 196, 197, 198, 199, 200, 201, 202, 203, ...   \n",
       "2   Q96PU5   2lb2  {195, 196, 197, 198, 199, 200, 201, 202, 203, ...   \n",
       "3   Q96PU5   2laj  {195, 196, 197, 198, 199, 200, 201, 202, 203, ...   \n",
       "4   Q96PU5   2mpt  {195, 196, 197, 198, 199, 200, 201, 202, 203, ...   \n",
       "\n",
       "                                            negative  \\\n",
       "0  {154, 155, 156, 157, 158, 159, 160, 161, 162, ...   \n",
       "1  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "2  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "3  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "4  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "\n",
       "                                       positive_pred  \\\n",
       "0  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "1  {385, 386, 387, 388, 389, 390, 391, 392, 393, ...   \n",
       "2  {386, 387, 388, 389, 390, 391, 392, 393, 394, ...   \n",
       "3  {512, 513, 514, 515, 516, 517, 518, 519, 520, ...   \n",
       "4  {512, 513, 514, 515, 516, 517, 518, 519, 520, ...   \n",
       "\n",
       "                                       negative_pred  prc  rec  f1s  \n",
       "0  {154, 155, 156, 157, 158, 159, 160, 161, 162, ...  NaN  0.0  NaN  \n",
       "1  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...  NaN  0.0  NaN  \n",
       "2  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...  NaN  0.0  NaN  \n",
       "3  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...  NaN  0.0  NaN  \n",
       "4  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...  NaN  0.0  NaN  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join model output with sifts\n",
    "# Note that inner join has been used: we want to get only those PDBs which best match the sequences\n",
    "structure_matching = pd.merge(left=model_out, right=sifts, how='inner', on='entry_ac', suffixes=('', '_pred'))\n",
    "structure_matching.columns=['entry_ac', 'positive', 'pdb_id', 'positive_pred', 'negative_pred']\n",
    "# Add negative column\n",
    "structure_matching['negative'] = structure_matching.apply(lambda x: (x['positive_pred'] | x['negative_pred']) - x['positive_pred'], axis=1)\n",
    "\n",
    "# Loop through each row\n",
    "for i, row in structure_matching.iterrows():\n",
    "    # Compute statistics\n",
    "    cm, prc, rec, acc, w_acc = cmat.compute(positive=row['positive'], pred_positive=row['positive_pred'],\n",
    "                                            negative=row['negative'], pred_negative=row['negative_pred'])\n",
    "    \n",
    "    # Get true positive (tp), false positive (fp), true negative (tn), false negative(fn)\n",
    "    tp, fp, tn, fn = cm[1,0], cm[1,1], cm[0,1], cm[0,0]\n",
    "    # Save statistics\n",
    "    structure_matching.loc[i, 'prc'] = prc\n",
    "    structure_matching.loc[i, 'rec'] = rec\n",
    "    # structure_matching.loc[i, 'f1s'] = 2 * ((tp / (tp + fp)) * (tp / (tp + fn))) / ((tp / (tp + fp)) + (tp / (tp + fn)))\n",
    "\n",
    "# Compute F1-score\n",
    "structure_matching['f1s'] = 2 * (structure_matching['prc'] * structure_matching['rec'])\n",
    "structure_matching['f1s'] = structure_matching['f1s'] / (structure_matching['prc'] + structure_matching['rec'])\n",
    "\n",
    "# Reorder columns\n",
    "structure_matching = structure_matching[['entry_ac', 'pdb_id', 'positive', 'negative', 'positive_pred', 'negative_pred', 'prc', 'rec', 'f1s']]\n",
    "# Show result\n",
    "structure_matching.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 77 PDBs matching predicted domain\n",
      "There are 23 proteins which have at least one PDB matching predicted domain\n",
      "Matching pdb ids are:\n",
      "2lty,2lb2,2laj,2mpt,6j1x,6j1y,2op7,6j1z,5tj7,6rss,2dmv,2kyk,5cq2,4rof,5dzd,2ysf,2kpz,2m3o,4n7h,4n7f,2kxq,2djy,5ydx,1k5r,4rex,1jmq,2lax,5ydy,2law,2ltv,3l4h,1wmv,2laz,2lb1,2ltx,2ysd,2zaj,2yse,2mw9,2mwd,2mwf,2dk7,2l5f,1ywi,1zr7,5o9z,2jxw,2dk1,2m8i,1f8a,5b3z,2kbu,2lb3,1i6c,3tcz,2n1o,4qib,2ysg,2ysh,2mdi,2mdc,1eg3,2e45,2ho2,2ysc,2jx8,3le4\n"
     ]
    }
   ],
   "source": [
    "# Get all matching PDBs\n",
    "pdbs_matching = structure_matching[(structure_matching.rec.isna() != True) & \n",
    "                                   (structure_matching.rec >= 0.8)]\n",
    "pdbs_matching = pdbs_matching.copy()\n",
    "\n",
    "print('There are {:d} PDBs matching predicted domain'.format(\n",
    "    pdbs_matching.shape[0])\n",
    ")\n",
    "\n",
    "print('There are {:d} proteins which have at least one PDB matching predicted domain'.format(\n",
    "    pdbs_matching.entry_ac.unique().shape[0]\n",
    "))\n",
    "\n",
    "# Show retrieved PDB ids\n",
    "print('Matching pdb ids are:')\n",
    "print(','.join(pdbs_matching.pdb_id.unique().tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>entry_ac</th>\n",
       "      <th>pdb_id</th>\n",
       "      <th>positive</th>\n",
       "      <th>negative</th>\n",
       "      <th>positive_pred</th>\n",
       "      <th>negative_pred</th>\n",
       "      <th>prc</th>\n",
       "      <th>rec</th>\n",
       "      <th>f1s</th>\n",
       "      <th>pdb_file</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Q96PU5</td>\n",
       "      <td>2lty</td>\n",
       "      <td>{387, 388, 389, 390, 391, 392, 393, 394, 395, ...</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{385, 386, 387, 388, 389, 390, 391, 392, 393, ...</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>data/pdb/2lty.pdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Q96PU5</td>\n",
       "      <td>2lb2</td>\n",
       "      <td>{387, 388, 389, 390, 391, 392, 393, 394, 395, ...</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{386, 387, 388, 389, 390, 391, 392, 393, 394, ...</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>data/pdb/2lb2.pdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Q96PU5</td>\n",
       "      <td>2laj</td>\n",
       "      <td>{512, 513, 514, 515, 516, 517, 518, 519, 520, ...</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{512, 513, 514, 515, 516, 517, 518, 519, 520, ...</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>data/pdb/2laj.pdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Q96PU5</td>\n",
       "      <td>2mpt</td>\n",
       "      <td>{512, 513, 514, 515, 516, 517, 518, 519, 520, ...</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{512, 513, 514, 515, 516, 517, 518, 519, 520, ...</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>data/pdb/2mpt.pdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Q9H0M0</td>\n",
       "      <td>6j1x</td>\n",
       "      <td>{384, 385, 386, 387, 388, 389, 390, 391, 392, ...</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{379, 380, 381, 382, 383, 384, 385, 386, 387, ...</td>\n",
       "      <td>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>data/pdb/6j1x.pdb</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   entry_ac pdb_id                                           positive  \\\n",
       "10   Q96PU5   2lty  {387, 388, 389, 390, 391, 392, 393, 394, 395, ...   \n",
       "11   Q96PU5   2lb2  {387, 388, 389, 390, 391, 392, 393, 394, 395, ...   \n",
       "21   Q96PU5   2laj  {512, 513, 514, 515, 516, 517, 518, 519, 520, ...   \n",
       "22   Q96PU5   2mpt  {512, 513, 514, 515, 516, 517, 518, 519, 520, ...   \n",
       "41   Q9H0M0   6j1x  {384, 385, 386, 387, 388, 389, 390, 391, 392, ...   \n",
       "\n",
       "                                             negative  \\\n",
       "10  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "11  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "21  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "22  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "41  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "\n",
       "                                        positive_pred  \\\n",
       "10  {385, 386, 387, 388, 389, 390, 391, 392, 393, ...   \n",
       "11  {386, 387, 388, 389, 390, 391, 392, 393, 394, ...   \n",
       "21  {512, 513, 514, 515, 516, 517, 518, 519, 520, ...   \n",
       "22  {512, 513, 514, 515, 516, 517, 518, 519, 520, ...   \n",
       "41  {379, 380, 381, 382, 383, 384, 385, 386, 387, ...   \n",
       "\n",
       "                                        negative_pred  prc  rec  f1s  \\\n",
       "10  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...  1.0  1.0  1.0   \n",
       "11  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...  1.0  1.0  1.0   \n",
       "21  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...  1.0  1.0  1.0   \n",
       "22  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...  1.0  1.0  1.0   \n",
       "41  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...  1.0  1.0  1.0   \n",
       "\n",
       "             pdb_file  \n",
       "10  data/pdb/2lty.pdb  \n",
       "11  data/pdb/2lb2.pdb  \n",
       "21  data/pdb/2laj.pdb  \n",
       "22  data/pdb/2mpt.pdb  \n",
       "41  data/pdb/6j1x.pdb  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For each structure matching pdb, retrieve the corrisponding pdb file\n",
    "\n",
    "# Define output path\n",
    "out_path = 'data/pdb'\n",
    "out_format = 'pdb'\n",
    "\n",
    "# Initialize file paths container\n",
    "pdbs_matching['pdb_file'] = None\n",
    "# Loop through each row\n",
    "for i, row in pdbs_matching.iterrows():\n",
    "    # Get current PDB id\n",
    "    pdb_id = row['pdb_id']\n",
    "    # Define path to PDB file\n",
    "    pdb_path = ''.join([out_path, '/',  pdb_id, '.', out_format])\n",
    "    # Check if there is the actual PDB file\n",
    "    if not os.path.exists(pdb_path):\n",
    "        # Download PDB for current PDB id\n",
    "        status, pdb_path, _ = pdb.download(pdb_id, out_path=pdb_path, format=out_format, compressed=True)\n",
    "        # Check response status\n",
    "        if not status: continue\n",
    "    # Store downloaded file reference\n",
    "    pdbs_matching.loc[i, 'pdb_file'] =  pdb_path\n",
    "    \n",
    "# Show dataset\n",
    "pdbs_matching.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Structural alignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a matrix containing RMSD scores for pairwise structural alignments between different PDBs\n",
    "\n",
    "# Get only passing PDB ids\n",
    "pdb_files = pdbs_matching['pdb_file']\n",
    "pdb_files = pdb_files.unique().tolist()\n",
    "\n",
    "# Compute pairwise alignment among all selected pdb ids\n",
    "rmsd, tm_score = tmalign.multi_align(pdb_files, script_path=tmalign.SCRIPT_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RMSD clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2lty</th>\n",
       "      <th>2lb2</th>\n",
       "      <th>2laj</th>\n",
       "      <th>2mpt</th>\n",
       "      <th>6j1x</th>\n",
       "      <th>6j1y</th>\n",
       "      <th>2op7</th>\n",
       "      <th>6j1z</th>\n",
       "      <th>5tj7</th>\n",
       "      <th>6rss</th>\n",
       "      <th>...</th>\n",
       "      <th>2ysg</th>\n",
       "      <th>2ysh</th>\n",
       "      <th>2mdi</th>\n",
       "      <th>2mdc</th>\n",
       "      <th>1eg3</th>\n",
       "      <th>2e45</th>\n",
       "      <th>2ho2</th>\n",
       "      <th>2ysc</th>\n",
       "      <th>2jx8</th>\n",
       "      <th>3le4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2lty</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1.54</td>\n",
       "      <td>1.57</td>\n",
       "      <td>1.59</td>\n",
       "      <td>1.54</td>\n",
       "      <td>1.89</td>\n",
       "      <td>2.96</td>\n",
       "      <td>1.56</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.86</td>\n",
       "      <td>...</td>\n",
       "      <td>1.80</td>\n",
       "      <td>2.37</td>\n",
       "      <td>2.21</td>\n",
       "      <td>2.29</td>\n",
       "      <td>2.36</td>\n",
       "      <td>1.99</td>\n",
       "      <td>1.88</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.08</td>\n",
       "      <td>1.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2lb2</th>\n",
       "      <td>1.54</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.67</td>\n",
       "      <td>1.94</td>\n",
       "      <td>2.40</td>\n",
       "      <td>3.81</td>\n",
       "      <td>2.73</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.82</td>\n",
       "      <td>2.04</td>\n",
       "      <td>...</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.42</td>\n",
       "      <td>2.32</td>\n",
       "      <td>3.47</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2.27</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.84</td>\n",
       "      <td>2.41</td>\n",
       "      <td>2.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2laj</th>\n",
       "      <td>1.57</td>\n",
       "      <td>1.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.42</td>\n",
       "      <td>2.45</td>\n",
       "      <td>3.58</td>\n",
       "      <td>2.78</td>\n",
       "      <td>1.91</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.79</td>\n",
       "      <td>2.37</td>\n",
       "      <td>2.07</td>\n",
       "      <td>3.03</td>\n",
       "      <td>1.97</td>\n",
       "      <td>2.16</td>\n",
       "      <td>1.64</td>\n",
       "      <td>1.70</td>\n",
       "      <td>2.78</td>\n",
       "      <td>2.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2mpt</th>\n",
       "      <td>1.59</td>\n",
       "      <td>1.94</td>\n",
       "      <td>1.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.03</td>\n",
       "      <td>2.11</td>\n",
       "      <td>2.64</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.33</td>\n",
       "      <td>...</td>\n",
       "      <td>1.36</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.15</td>\n",
       "      <td>2.68</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.91</td>\n",
       "      <td>1.88</td>\n",
       "      <td>1.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6j1x</th>\n",
       "      <td>1.54</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.45</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.61</td>\n",
       "      <td>2.99</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.58</td>\n",
       "      <td>4.92</td>\n",
       "      <td>...</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.80</td>\n",
       "      <td>2.95</td>\n",
       "      <td>2.54</td>\n",
       "      <td>5.73</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1.42</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.18</td>\n",
       "      <td>2.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      2lty  2lb2  2laj  2mpt  6j1x  6j1y  2op7  6j1z  5tj7  6rss  ...  2ysg  \\\n",
       "2lty  0.00  1.54  1.57  1.59  1.54  1.89  2.96  1.56  1.95  1.86  ...  1.80   \n",
       "2lb2  1.54  0.00  1.67  1.94  2.40  3.81  2.73  2.43  1.82  2.04  ...  2.10   \n",
       "2laj  1.57  1.67  0.00  1.42  2.45  3.58  2.78  1.91  2.36  2.01  ...  1.79   \n",
       "2mpt  1.59  1.94  1.42  0.00  1.03  2.11  2.64  0.99  1.69  1.33  ...  1.36   \n",
       "6j1x  1.54  2.40  2.45  1.03  0.00  1.61  2.99  1.25  1.58  4.92  ...  1.31   \n",
       "\n",
       "      2ysh  2mdi  2mdc  1eg3  2e45  2ho2  2ysc  2jx8  3le4  \n",
       "2lty  2.37  2.21  2.29  2.36  1.99  1.88  1.73  2.08  1.99  \n",
       "2lb2  2.42  2.32  3.47  1.86  2.27  1.75  1.84  2.41  2.77  \n",
       "2laj  2.37  2.07  3.03  1.97  2.16  1.64  1.70  2.78  2.05  \n",
       "2mpt  2.31  1.15  2.68  1.60  1.52  1.48  1.91  1.88  1.66  \n",
       "6j1x  1.80  2.95  2.54  5.73  1.60  1.42  1.90  2.18  2.85  \n",
       "\n",
       "[5 rows x 67 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get RMSD scores head\n",
    "rmsd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show dendrogram for RMSD\n",
    "\n",
    "# Retrieve matrix dimensions\n",
    "nrows, ncols = rmsd.shape\n",
    "# Compute linkage (exclude diagonal)\n",
    "l = linkage([rmsd.iloc[i, j] for i in range(nrows) for j in range(i+1, ncols)], 'complete')\n",
    "\n",
    "# Show plot\n",
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "_ = dendrogram(l, labels=rmsd.columns, ax=ax)\n",
    "_ = ax.tick_params(axis='both', which='major', labelsize=16)\n",
    "_ = plt.savefig('images/dendrogram_rmsd.png')\n",
    "_ = plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TM score clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>2lty</th>\n",
       "      <th>2lb2</th>\n",
       "      <th>2laj</th>\n",
       "      <th>2mpt</th>\n",
       "      <th>6j1x</th>\n",
       "      <th>6j1y</th>\n",
       "      <th>2op7</th>\n",
       "      <th>6j1z</th>\n",
       "      <th>5tj7</th>\n",
       "      <th>6rss</th>\n",
       "      <th>...</th>\n",
       "      <th>2ysg</th>\n",
       "      <th>2ysh</th>\n",
       "      <th>2mdi</th>\n",
       "      <th>2mdc</th>\n",
       "      <th>1eg3</th>\n",
       "      <th>2e45</th>\n",
       "      <th>2ho2</th>\n",
       "      <th>2ysc</th>\n",
       "      <th>2jx8</th>\n",
       "      <th>3le4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2lty</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.42919</td>\n",
       "      <td>0.36217</td>\n",
       "      <td>0.38580</td>\n",
       "      <td>0.87379</td>\n",
       "      <td>0.90351</td>\n",
       "      <td>0.63941</td>\n",
       "      <td>0.86301</td>\n",
       "      <td>0.88816</td>\n",
       "      <td>0.66050</td>\n",
       "      <td>...</td>\n",
       "      <td>0.41587</td>\n",
       "      <td>0.48625</td>\n",
       "      <td>0.51782</td>\n",
       "      <td>0.56182</td>\n",
       "      <td>0.80700</td>\n",
       "      <td>0.46992</td>\n",
       "      <td>0.41389</td>\n",
       "      <td>0.35642</td>\n",
       "      <td>0.47344</td>\n",
       "      <td>0.48664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2lb2</th>\n",
       "      <td>0.42919</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.41698</td>\n",
       "      <td>0.52160</td>\n",
       "      <td>0.88134</td>\n",
       "      <td>0.89844</td>\n",
       "      <td>0.58231</td>\n",
       "      <td>0.86856</td>\n",
       "      <td>0.89049</td>\n",
       "      <td>0.69165</td>\n",
       "      <td>...</td>\n",
       "      <td>0.55164</td>\n",
       "      <td>0.54275</td>\n",
       "      <td>0.54858</td>\n",
       "      <td>0.64083</td>\n",
       "      <td>0.81049</td>\n",
       "      <td>0.55823</td>\n",
       "      <td>0.48330</td>\n",
       "      <td>0.49184</td>\n",
       "      <td>0.54585</td>\n",
       "      <td>0.57137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2laj</th>\n",
       "      <td>0.36217</td>\n",
       "      <td>0.41698</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.39405</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.88458</td>\n",
       "      <td>0.65643</td>\n",
       "      <td>0.85885</td>\n",
       "      <td>0.88343</td>\n",
       "      <td>0.66398</td>\n",
       "      <td>...</td>\n",
       "      <td>0.44909</td>\n",
       "      <td>0.52244</td>\n",
       "      <td>0.54522</td>\n",
       "      <td>0.59702</td>\n",
       "      <td>0.79674</td>\n",
       "      <td>0.47759</td>\n",
       "      <td>0.40456</td>\n",
       "      <td>0.45945</td>\n",
       "      <td>0.49815</td>\n",
       "      <td>0.51635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2mpt</th>\n",
       "      <td>0.38580</td>\n",
       "      <td>0.52160</td>\n",
       "      <td>0.39405</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.87683</td>\n",
       "      <td>0.90862</td>\n",
       "      <td>0.66538</td>\n",
       "      <td>0.86167</td>\n",
       "      <td>0.88866</td>\n",
       "      <td>0.62999</td>\n",
       "      <td>...</td>\n",
       "      <td>0.35565</td>\n",
       "      <td>0.44092</td>\n",
       "      <td>0.47888</td>\n",
       "      <td>0.57265</td>\n",
       "      <td>0.80653</td>\n",
       "      <td>0.43330</td>\n",
       "      <td>0.37444</td>\n",
       "      <td>0.43681</td>\n",
       "      <td>0.45884</td>\n",
       "      <td>0.48908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6j1x</th>\n",
       "      <td>0.87379</td>\n",
       "      <td>0.88134</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.87683</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.14037</td>\n",
       "      <td>0.91455</td>\n",
       "      <td>0.11490</td>\n",
       "      <td>0.09330</td>\n",
       "      <td>0.84014</td>\n",
       "      <td>...</td>\n",
       "      <td>0.87279</td>\n",
       "      <td>0.87383</td>\n",
       "      <td>0.88388</td>\n",
       "      <td>0.89815</td>\n",
       "      <td>0.73935</td>\n",
       "      <td>0.87629</td>\n",
       "      <td>0.87847</td>\n",
       "      <td>0.88029</td>\n",
       "      <td>0.87676</td>\n",
       "      <td>0.86134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         2lty     2lb2     2laj     2mpt     6j1x     6j1y     2op7     6j1z  \\\n",
       "2lty  0.00000  0.42919  0.36217  0.38580  0.87379  0.90351  0.63941  0.86301   \n",
       "2lb2  0.42919  0.00000  0.41698  0.52160  0.88134  0.89844  0.58231  0.86856   \n",
       "2laj  0.36217  0.41698  0.00000  0.39405  0.86810  0.88458  0.65643  0.85885   \n",
       "2mpt  0.38580  0.52160  0.39405  0.00000  0.87683  0.90862  0.66538  0.86167   \n",
       "6j1x  0.87379  0.88134  0.86810  0.87683  0.00000  0.14037  0.91455  0.11490   \n",
       "\n",
       "         5tj7     6rss  ...     2ysg     2ysh     2mdi     2mdc     1eg3  \\\n",
       "2lty  0.88816  0.66050  ...  0.41587  0.48625  0.51782  0.56182  0.80700   \n",
       "2lb2  0.89049  0.69165  ...  0.55164  0.54275  0.54858  0.64083  0.81049   \n",
       "2laj  0.88343  0.66398  ...  0.44909  0.52244  0.54522  0.59702  0.79674   \n",
       "2mpt  0.88866  0.62999  ...  0.35565  0.44092  0.47888  0.57265  0.80653   \n",
       "6j1x  0.09330  0.84014  ...  0.87279  0.87383  0.88388  0.89815  0.73935   \n",
       "\n",
       "         2e45     2ho2     2ysc     2jx8     3le4  \n",
       "2lty  0.46992  0.41389  0.35642  0.47344  0.48664  \n",
       "2lb2  0.55823  0.48330  0.49184  0.54585  0.57137  \n",
       "2laj  0.47759  0.40456  0.45945  0.49815  0.51635  \n",
       "2mpt  0.43330  0.37444  0.43681  0.45884  0.48908  \n",
       "6j1x  0.87629  0.87847  0.88029  0.87676  0.86134  \n",
       "\n",
       "[5 rows x 67 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get TM scores head\n",
    "tm_score.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show dendrogram for TM score\n",
    "\n",
    "# Retrieve matrix dimensions\n",
    "nrows, ncols = tm_score.shape\n",
    "# Compute linkage (exclude diagonal)\n",
    "l = linkage([tm_score.iloc[i, j] for i in range(nrows) for j in range(i+1, ncols)], 'complete')\n",
    "\n",
    "# Show plot\n",
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "_ = dendrogram(l, labels=tm_score.columns, color_threshold=0.5, ax=ax)\n",
    "_ = ax.tick_params(axis='both', which='major', labelsize=16)\n",
    "_ = plt.savefig('images/dendrogram_tmscore.png')\n",
    "_ = plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}