notebooks/02_structural-characterization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PO-4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Section 2: Structural characterization of phosphorylation sites: analysis of the region, secondary structure and accessibility of the phospho-S/T/Y peptides"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* TO-DO Run PfamScan to proteins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [1. Introduction](#introduction)\n",
    "    * [1.1 Description](#project-description)\n",
    "    * [1.2 Packages](#packages)\n",
    "        \n",
    " \n",
    "* [2. Data Loading](#data-loading)\n",
    "    * [2.1 Loading secondary structure predictions](#loading-wt-and-ko-initial-files)\n",
    "    * [2.2 Loading Uniprot reference proteome](#loading-uniprot-reference-proteome)\n",
    "    \n",
    "\n",
    "* [3. Data Preprocessing](#data-preprocessing)\n",
    "    * [3.1 Generating Kmers](#generating-kmers)\n",
    "    * [3.2 Processing peptide files](#processing-peptide-files)\n",
    "\n",
    "\n",
    "* [4. Data Exploration](#data-exploration)\n",
    "    * [4.1 Generating Kmers](#generating-kmers)\n",
    "    * [4.2 Processing peptide files](#processing-peptide-files)\n",
    "\n",
    "\n",
    "* [5. References](#references)\n",
    "\n",
    "* [6. Project contribution](#project-contribution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Description <a class=\"anchor\" id=\"description\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-16T15:57:28.449870Z",
     "start_time": "2020-04-16T15:57:28.446211Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fernando Pozo \n",
      "last updated: Fri Sep 11 2020 11:52:37 CEST \n",
      "\n",
      "pandas 1.0.4\n",
      "numpy 1.18.5\n",
      "matplotlib 3.2.1\n",
      "Git hash: cefe14675e7c07123ef09f36d71551826a082e93\n",
      "/local/fpozoc/projects/dev/ph-proteomics/notebooks\n"
     ]
    }
   ],
   "source": [
    "import glob, os, sys\n",
    "import warnings\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rc\n",
    "\n",
    "sys.path.append('../src/')\n",
    "from utils import *\n",
    "\n",
    "pd.set_option(\"display.max_rows\", 100)\n",
    "pd.set_option(\"display.max_columns\", 100)\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "%load_ext watermark\n",
    "%watermark -a 'Fernando Pozo' -u -n -t -z -g -p pandas,numpy,matplotlib\n",
    "\n",
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ss_pred = pd.concat([parse_ss_features(file) for file in glob.glob('/local/fpozoc/projects/dev/ph-proteomics/data/external/secondary_structure_predictions/*/*.csv')]).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_manual_pred_ids = ['Q8IZP0', 'P15056', 'O75362', 'Q9NQ86', 'Q9BSQ5', 'Q04726', 'Q9Y6Y8']\n",
    "df_ss_pred.loc[df_ss_pred.id.str.contains('|'.join(list_manual_pred_ids)), 'proteinName'] = df_ss_pred['id'].str.split('_').str[1]\n",
    "df_ss_pred.loc[~df_ss_pred.id.str.contains('|'.join(list_manual_pred_ids)), 'proteinName'] = df_ss_pred['id'].str.split('|').str[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>seq</th>\n",
       "      <th>len</th>\n",
       "      <th>rsa</th>\n",
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       "      <th>proteinName</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>sp|Q15717|ELAV1_HUMAN</td>\n",
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       "      <td>MSSLGASFVQIKFDDLQFFENCGGGSFGSVYRAKWISQDKEVAVKK...</td>\n",
       "      <td>800</td>\n",
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       "      <td>sp|Q9NZ09|UBAP1_HUMAN</td>\n",
       "      <td>MASKKLGADFHGTFSYLDDVPFKTGDKFKTPAKVGLPIGFSLPDCL...</td>\n",
       "      <td>502</td>\n",
       "      <td>9998877787877656569576764985754885655885746856...</td>\n",
       "      <td>CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC...</td>\n",
       "      <td>9999999999999999999999999999999999999999999999...</td>\n",
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       "      <td>0000000000011002332100000341100110000231011101...</td>\n",
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       "      <td>Q9NZ09</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sp|Q8N9B5|JMY_HUMAN</td>\n",
       "      <td>MSFALEETLESDWVAVRPHVFDEREKHKFVFIVAWNEIEGKFAITC...</td>\n",
       "      <td>988</td>\n",
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       "      <td>9999766667896988679889997756999999968788799997...</td>\n",
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       "      <td>0000122222336988435222322266999999973364799997...</td>\n",
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       "      <td>1226</td>\n",
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       "      <td>2970</td>\n",
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       "      <th>3501</th>\n",
       "      <td>sp|Q9H8Y8|GORS2_HUMAN</td>\n",
       "      <td>MGSSQSVEIPGGGTEGYHVLRVQENSPGHRAGLEPFFDFIVSINGS...</td>\n",
       "      <td>452</td>\n",
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       "<p>3502 rows × 10 columns</p>\n",
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      "text/plain": [
       "                         id  \\\n",
       "0     sp|Q15717|ELAV1_HUMAN   \n",
       "1     sp|Q86XN7|PRSR1_HUMAN   \n",
       "2     sp|Q9NYL2|M3K20_HUMAN   \n",
       "3     sp|Q9NZ09|UBAP1_HUMAN   \n",
       "4       sp|Q8N9B5|JMY_HUMAN   \n",
       "...                     ...   \n",
       "3497   sp|P51826|AFF3_HUMAN   \n",
       "3498  sp|Q92619|HMHA1_HUMAN   \n",
       "3499   sp|Q99569|PKP4_HUMAN   \n",
       "3500  sp|Q68DQ2|CRBG3_HUMAN   \n",
       "3501  sp|Q9H8Y8|GORS2_HUMAN   \n",
       "\n",
       "                                                    seq   len  \\\n",
       "0     MSNGYEDHMAEDCRGDIGRTNLIVNYLPQNMTQDELRSLFSSIGEV...   326   \n",
       "1     MDKKSFEMVLDEIRKAVLTEYKLKAIEYVHGYFSSEQVVDLLRYFS...   944   \n",
       "2     MSSLGASFVQIKFDDLQFFENCGGGSFGSVYRAKWISQDKEVAVKK...   800   \n",
       "3     MASKKLGADFHGTFSYLDDVPFKTGDKFKTPAKVGLPIGFSLPDCL...   502   \n",
       "4     MSFALEETLESDWVAVRPHVFDEREKHKFVFIVAWNEIEGKFAITC...   988   \n",
       "...                                                 ...   ...   \n",
       "3497  MDSFDLALLQEWDLESLCVYEPDRNALRRKERERRNQETQQDDGTF...  1226   \n",
       "3498  MFSRKKRELMKTPSISKKNRAGSPSPQPSGELPRKDGADAVFPGPS...  1136   \n",
       "3499  MPAPEQASLVEEGQPQTRQEAASTGPGMEPETTATTILASVKEQEL...  1192   \n",
       "3500  MSSGRRRGSAPWHSFSRFFAPRSPSRDKEEEEEERPGTSPPPAPGR...  2970   \n",
       "3501  MGSSQSVEIPGGGTEGYHVLRVQENSPGHRAGLEPFFDFIVSINGS...   452   \n",
       "\n",
       "                                                    rsa  \\\n",
       "0     9998777766764656455220102301581447404620571261...   \n",
       "1     5776315411540462224742141035243302152004004314...   \n",
       "2     8885555325152841644553466841101203137584400012...   \n",
       "3     9998877787877656569576764985754885655885746856...   \n",
       "4     8966467437553122446347675534110000125364421121...   \n",
       "...                                                 ...   \n",
       "3497  8886755557657576544555645545564566566657777665...   \n",
       "3498  8777777766767767888777777778888677888787778888...   \n",
       "3499  9988988888899888888888888887877764664465466555...   \n",
       "3500  9998888896766666665578788788888899899988888898...   \n",
       "3501  8888765735655440120351477010361515322110221576...   \n",
       "\n",
       "                                                    ss3  \\\n",
       "0     CCCCCCCCCCCCCCCCCCCCEEEEECCCCCCCHHHHHHHHCCCCCE...   \n",
       "1     CCHHHHHHHHHHHHHHCCCHHHHHHHHHCCCCCCHHHHHHHHHCCC...   \n",
       "2     CCCCCCCCEECCHHHEEEEEEEEECCCEEEEEEEECCCCEEEEEEE...   \n",
       "3     CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC...   \n",
       "4     CCCCCCCCCCCCEEEECCCCCCCCCCEEEEEEEEEECCCCEEEEEE...   \n",
       "...                                                 ...   \n",
       "3497  CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC...   \n",
       "3498  CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC...   \n",
       "3499  CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC...   \n",
       "3500  CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC...   \n",
       "3501  CCCCCCCCCCCCCCCEEEEEEECCCCHHHHCCCCCCCCEEEEECCE...   \n",
       "\n",
       "                                                  p_ss3  \\\n",
       "0     9999999999999999998699999899999999999998576999...   \n",
       "1     9966899999999997699999999999589967999999996599...   \n",
       "2     9999998678559996999999986998999999999998999999...   \n",
       "3     9999999999999999999999999999999999999999999999...   \n",
       "4     9999766667896988679889997756999999968788799997...   \n",
       "...                                                 ...   \n",
       "3497  9999999999999999999998888887777777887888999999...   \n",
       "3498  9999999999999999999999999999999999999999999999...   \n",
       "3499  9999999999999999999999999999999999999999999999...   \n",
       "3500  9999999999999999999999999999999999999999999999...   \n",
       "3501  9999997799999967999998999999996999889799999999...   \n",
       "\n",
       "                                                    ss8  \\\n",
       "0     CCCCCCCCCCCCCCCCCCCCEEEEESCCTTCCHHHHHHHHTTTSCE...   \n",
       "1     CCHHHHHHHHHHHHHHHCCHHHHHHHHHHTTCCCHHHHHHHHTTSC...   \n",
       "2     CCCCCCCCEECCGGGEEEEEEEEECTTEEEEEEEETTTTEEEEEEE...   \n",
       "3     CCCCCCCCCCCCCCCCCCCCCCCCCCTCCCCCCCCCCCCCCCCCCC...   \n",
       "4     CCCCCCCCCCTCEEEECTTCCCCCCCEEEEEEEEEETTTTEEEEEE...   \n",
       "...                                                 ...   \n",
       "3497  CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC...   \n",
       "3498  CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC...   \n",
       "3499  CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC...   \n",
       "3500  CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC...   \n",
       "3501  CCCCCCCCCCTCCCCEEEEEEECTTCHHHHTTCCCTTCEEEEETTE...   \n",
       "\n",
       "                                                  p_ss8  \\\n",
       "0     0000000000000110231499999610992099999997585519...   \n",
       "1     0066799999999997332999999998445332999999994443...   \n",
       "2     0001211488559996999999983568999999998788999999...   \n",
       "3     0000000000011002332100000341100110000231011101...   \n",
       "4     0000122222336988435222322266999999973364799997...   \n",
       "...                                                 ...   \n",
       "3497  0000001111000000001103211112333333323222111100...   \n",
       "3498  0000000000000000000000000000000000000000000000...   \n",
       "3499  0000000000000000000000000000000000000000000000...   \n",
       "3500  0000000000000000000000000000000000000000000000...   \n",
       "3501  0000113213433246999998099299996901265299999999...   \n",
       "\n",
       "                                                    dis proteinName  \n",
       "0     9999999999999875521000000000000000000000000000...      Q15717  \n",
       "1     7321000000000000000000000000000000000000000000...      Q86XN7  \n",
       "2     9864321000000000000000000000000000000000000000...      Q9NYL2  \n",
       "3     9999999999987765665536664333223477574666554456...      Q9NZ09  \n",
       "4     9865332222110000001111111000000000000000000000...      Q8N9B5  \n",
       "...                                                 ...         ...  \n",
       "3497  9999888888788888777887788887778878776778788898...      P51826  \n",
       "3498  9999999999999999999999999999999999999999999999...      Q92619  \n",
       "3499  9999999999999999999999999999999999999999999999...      Q99569  \n",
       "3500  9999999999999999999999999999999999999999999999...      Q68DQ2  \n",
       "3501  9998753332221000000000000000000000000000000000...      Q9H8Y8  \n",
       "\n",
       "[3502 rows x 10 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ss_pred\n",
    "\n",
    "## solo seleccionar el residuo vs el resto de la proteína"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_exptypes = pd.read_csv('../data/interim/preprocessed_peptides.tsv.gz', compression='gzip', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(df_exptypes, df_ss_pred, how='left', on='proteinName')\n",
    "df[['proteinName', 'geneName', 'proteinSeq']].drop_duplicates().to_csv('../data/processed/proteins_ss.tsv.gz', sep='\\t', compression='gzip', index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# peptide RSPTPER of TTN discarded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = calculate_structure_stats(df)\n",
    "df.to_csv('../data/interim/secondary_structure_analysis.tsv.gz', index=None, compression='gzip', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wt = pd.merge(df_exptypes.loc[df_exptypes.type=='wt'], df_ss_pred, how='left', on='proteinName')\n",
    "df_wt = calculate_structure_stats(df_wt)\n",
    "\n",
    "df_ko = pd.merge(df_exptypes.loc[df_exptypes.type=='ko'], df_ss_pred, how='left', on='proteinName')\n",
    "df_ko = calculate_structure_stats(df_ko)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "rsa = {\n",
    "    'WT ph. res.':\n",
    "    {\n",
    "    'Exposed': (df_wt.ph_rsa.sum()/df_wt.ph_rsa.shape[0])*100,\n",
    "    'Buried': 100-((df_wt.ph_rsa.sum()/df_wt.ph_rsa.shape[0])*100),\n",
    "     },\n",
    "    'KO ph. res.':\n",
    "    {\n",
    "    'Exposed': (df_ko.ph_rsa.sum()/df_ko.ph_rsa.shape[0])*100,\n",
    "    'Buried': 100-((df_ko.ph_rsa.sum()/df_ko.ph_rsa.shape[0])*100),\n",
    "     },\n",
    "    'WT background':\n",
    "    {\n",
    "    'Exposed': (df_wt.nph_rsa.sum()/df_wt.nph_rsa.shape[0])*100,\n",
    "    'Buried': 100-((df_wt.nph_rsa.sum()/df_wt.nph_rsa.shape[0])*100),\n",
    "     },\n",
    "    'KO background':\n",
    "    {\n",
    "    'Exposed': (df_ko.nph_rsa.sum()/df_ko.nph_rsa.shape[0])*100,\n",
    "    'Buried': 100-((df_ko.nph_rsa.sum()/df_ko.nph_rsa.shape[0])*100),\n",
    "     },\n",
    "}\n",
    "df_rsa = pd.DataFrame(rsa).T\n",
    "\n",
    "disorder = {\n",
    "    'WT ph. res.':\n",
    "    {\n",
    "    'Disordered': (df_wt.ph_dis.sum()/df_wt.ph_dis.shape[0])*100,\n",
    "    'Ordered': 100-((df_wt.ph_dis.sum()/df_wt.ph_dis.shape[0])*100),\n",
    "     },\n",
    "    'KO ph. res.':\n",
    "    {\n",
    "    'Disordered': (df_ko.ph_dis.sum()/df_ko.ph_dis.shape[0])*100,\n",
    "    'Ordered': 100-((df_ko.ph_dis.sum()/df_ko.ph_dis.shape[0])*100),\n",
    "     },\n",
    "    'WT background':\n",
    "    {\n",
    "    'Disordered': (df_wt.nph_dis.sum()/df_wt.nph_dis.shape[0])*100,\n",
    "    'Ordered': 100-((df_wt.nph_dis.sum()/df_wt.nph_dis.shape[0])*100),\n",
    "     },\n",
    "    'KO background':\n",
    "    {\n",
    "    'Disordered': (df_ko.nph_dis.sum()/df_ko.nph_dis.shape[0])*100,\n",
    "    'Ordered': 100-((df_ko.nph_dis.sum()/df_ko.nph_dis.shape[0])*100),\n",
    "     },\n",
    "}\n",
    "df_dis = pd.DataFrame(disorder).T\n",
    "\n",
    "ss3 = {\n",
    "    'WT ph. res.':\n",
    "    {\n",
    "    'Loop': (df_wt.ph_ss3_C.sum()/(df_wt.ph_ss3_C.sum()+df_wt.ph_ss3_E.sum()+df_wt.ph_ss3_H.sum()))*100,\n",
    "    'Helix': (df_wt.ph_ss3_H.sum()/(df_wt.ph_ss3_C.sum()+df_wt.ph_ss3_E.sum()+df_wt.ph_ss3_H.sum()))*100,\n",
    "    'Beta-Sheet': (df_wt.ph_ss3_E.sum()/(df_wt.ph_ss3_C.sum()+df_wt.ph_ss3_E.sum()+df_wt.ph_ss3_H.sum()))*100,\n",
    "     },\n",
    "    'KO ph. res.':\n",
    "    {\n",
    "    'Loop': (df_ko.ph_ss3_C.sum()/(df_ko.ph_ss3_C.sum()+df_ko.ph_ss3_E.sum()+df_ko.ph_ss3_H.sum()))*100,\n",
    "    'Helix': (df_ko.ph_ss3_H.sum()/(df_ko.ph_ss3_C.sum()+df_ko.ph_ss3_E.sum()+df_ko.ph_ss3_H.sum()))*100,\n",
    "    'Beta-Sheet': (df_ko.ph_ss3_E.sum()/(df_ko.ph_ss3_C.sum()+df_ko.ph_ss3_E.sum()+df_ko.ph_ss3_H.sum()))*100,\n",
    "     },\n",
    "    'WT background':\n",
    "    {\n",
    "    'Loop': (df_wt.nph_ss3_C.sum()/(df_wt.nph_ss3_C.sum()+df_wt.nph_ss3_E.sum()+df_wt.nph_ss3_H.sum()))*100,\n",
    "    'Helix': (df_wt.nph_ss3_H.sum()/(df_wt.nph_ss3_C.sum()+df_wt.nph_ss3_E.sum()+df_wt.nph_ss3_H.sum()))*100,\n",
    "    'Beta-Sheet': (df_wt.nph_ss3_E.sum()/(df_wt.nph_ss3_C.sum()+df_wt.nph_ss3_E.sum()+df_wt.nph_ss3_H.sum()))*100,\n",
    "     },\n",
    "    'KO background':\n",
    "    {\n",
    "    'Loop': (df_ko.nph_ss3_C.sum()/(df_ko.nph_ss3_C.sum()+df_ko.nph_ss3_E.sum()+df_ko.nph_ss3_H.sum()))*100,\n",
    "    'Helix': (df_ko.nph_ss3_H.sum()/(df_ko.nph_ss3_C.sum()+df_ko.nph_ss3_E.sum()+df_ko.nph_ss3_H.sum()))*100,\n",
    "    'Beta-Sheet': (df_ko.nph_ss3_E.sum()/(df_ko.nph_ss3_C.sum()+df_ko.nph_ss3_E.sum()+df_ko.nph_ss3_H.sum()))*100,\n",
    "     }, \n",
    "}\n",
    "df_ss3 = pd.DataFrame(ss3).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Exposed</th>\n",
       "      <th>Buried</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>WT ph. res.</th>\n",
       "      <td>66.5</td>\n",
       "      <td>33.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KO ph. res.</th>\n",
       "      <td>66.2</td>\n",
       "      <td>33.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WT background</th>\n",
       "      <td>45.8</td>\n",
       "      <td>54.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KO background</th>\n",
       "      <td>47.4</td>\n",
       "      <td>52.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Exposed  Buried\n",
       "WT ph. res.       66.5    33.5\n",
       "KO ph. res.       66.2    33.8\n",
       "WT background     45.8    54.2\n",
       "KO background     47.4    52.6"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rsa.round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Disordered</th>\n",
       "      <th>Ordered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>WT ph. res.</th>\n",
       "      <td>73.7</td>\n",
       "      <td>26.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KO ph. res.</th>\n",
       "      <td>75.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WT background</th>\n",
       "      <td>39.2</td>\n",
       "      <td>60.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KO background</th>\n",
       "      <td>41.2</td>\n",
       "      <td>58.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Disordered  Ordered\n",
       "WT ph. res.          73.7     26.3\n",
       "KO ph. res.          75.0     25.0\n",
       "WT background        39.2     60.8\n",
       "KO background        41.2     58.8"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dis.round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loop</th>\n",
       "      <th>Helix</th>\n",
       "      <th>Beta-Sheet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>WT ph. res.</th>\n",
       "      <td>96.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KO ph. res.</th>\n",
       "      <td>96.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WT background</th>\n",
       "      <td>65.7</td>\n",
       "      <td>25.0</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KO background</th>\n",
       "      <td>67.0</td>\n",
       "      <td>24.2</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Loop  Helix  Beta-Sheet\n",
       "WT ph. res.    96.9    2.3         0.9\n",
       "KO ph. res.    96.8    2.5         0.8\n",
       "WT background  65.7   25.0         9.4\n",
       "KO background  67.0   24.2         8.8"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ss3.round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": [
    "font = {'family' : 'Arial',\n",
    "        'weight' : 'normal',\n",
    "        'size'   : 14}\n",
    "\n",
    "rc('font', **font)\n",
    "\n",
    "df = df_ss3\n",
    "color1 = '#7da96a'\n",
    "color2 = '#adc3a2'\n",
    "color3 = '#e2e2e2'\n",
    "\n",
    "bars1 = df['Loop'].values\n",
    "bars2 = df['Helix'].values\n",
    "bars3 = df['Beta-Sheet'].values\n",
    "bars = np.add(bars1, bars2).tolist()\n",
    "\n",
    "names = df.T.columns\n",
    "r = [0,1,2,3]\n",
    "barWidth = 1\n",
    "\n",
    "plt.figure(figsize=(5,5)) \n",
    "plt.bar(r, bars1, color=color1, edgecolor='white', width=barWidth)\n",
    "plt.bar(r, bars2, bottom=bars1, color=color2, edgecolor='white', width=barWidth)\n",
    "plt.bar(r, bars3, bottom=bars, color=color3, edgecolor='white', width=barWidth)\n",
    "\n",
    "plt.xticks(r, names, fontweight='bold', rotation=45)\n",
    "plt.ylabel(\"[%]\")\n",
    "plt.legend(df.columns)\n",
    "plt.title('Predicted Secondary Structure')\n",
    "plt.savefig('../reports/figures/figure2A.png', bbox_inches='tight', dpi=360)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": [
    "font = {'family' : 'Arial',\n",
    "        'weight' : 'normal',\n",
    "        'size'   : 14}\n",
    "\n",
    "rc('font', **font)\n",
    "\n",
    "df = df_dis\n",
    "color1 = '#e57c84'\n",
    "color2 = '#c6c6c6'\n",
    "\n",
    "bars1 = df['Disordered'].values\n",
    "bars2 = df['Ordered'].values\n",
    "\n",
    "names = df.T.columns\n",
    "r = [0,1,2,3]\n",
    "barWidth = 1\n",
    "\n",
    "plt.figure(figsize=(5,5)) \n",
    "plt.bar(r, bars1, color=color1, edgecolor='white', width=barWidth)\n",
    "plt.bar(r, bars2, bottom=bars1, color=color2, edgecolor='white', width=barWidth)\n",
    "plt.xticks(r, names, fontweight='bold', rotation=45)\n",
    "plt.ylabel(\"[%]\")\n",
    "plt.legend(df.columns)\n",
    "plt.title('Predicted Disorder')\n",
    "plt.savefig('../reports/figures/figure2B.png', bbox_inches='tight', dpi=360)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": [
    "font = {'family' : 'Arial',\n",
    "        'weight' : 'normal',\n",
    "        'size'   : 14}\n",
    "\n",
    "rc('font', **font)\n",
    "\n",
    "df = df_rsa\n",
    "color1 = '#6087a1'\n",
    "color2 = '#b1bab3'\n",
    "\n",
    "bars1 = df['Exposed'].values\n",
    "bars2 = df['Buried'].values\n",
    "\n",
    "names = df.T.columns\n",
    "r = [0,1,2,3]\n",
    "barWidth = 1\n",
    "\n",
    "plt.figure(figsize=(5,5)) \n",
    "plt.bar(r, bars1, color=color1, edgecolor='white', width=barWidth)\n",
    "plt.bar(r, bars2, bottom=bars1, color=color2, edgecolor='white', width=barWidth)\n",
    "plt.xticks(r, names, fontweight='bold', rotation=45)\n",
    "plt.ylabel(\"[%]\")\n",
    "plt.legend(df.columns)\n",
    "plt.title('Predicted Relative Solvent Exposure')\n",
    "plt.savefig('../reports/figures/figure2C.png', bbox_inches='tight', dpi=360)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_stacked_data(df_ss3, title='A', cmp='Paired')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_stacked_data(df_dis, title='B', cmp='Set2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_stacked_data(df_rsa, title='C', cmp='Set1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# old format\n",
    "# df = pd.merge(df_exptypes, df_ss_pred, how='left', on='proteinName')\n",
    "# df = df[~df['ss3'].isnull()]\n",
    "# df['kmer_rsa'] = [str(rsa)[ploc-6:ploc+7] for ploc, rsa  in zip(df['proteinSeq-loc'], df['rsa'])]\n",
    "# df['kmer_rsa_n'] = [round(sum(list(map(int, rsa)))/117, 2) for rsa in df['kmer_rsa'].str.split('').str[1:-1]]\n",
    "# df['no_kmer_rsa'] = [str(rsa)[:ploc-6] + str(rsa)[ploc+7:] for ploc, rsa in zip(df['proteinSeq-loc'], df['rsa'])]\n",
    "# df['no_kmer_rsa_n'] = [round(sum(list(map(int, rsa)))/len(rsa), 2) for rsa in df['no_kmer_rsa'].str.split('').str[1:-1]]\n",
    "\n",
    "# df['kmer_ss3'] = [str(ss3)[ploc-6:ploc+7] for ploc, ss3 in zip(df['proteinSeq-loc'], df['ss3'])]\n",
    "# df['no_kmer_ss3'] = [str(ss3)[:ploc-6] + str(ss3)[ploc+7:] for ploc, ss3 in zip(df['proteinSeq-loc'], df['ss3'])]\n",
    "# df['kmer_p_ss3'] = [str(p_ss3)[ploc-6:ploc+7] for ploc, p_ss3  in zip(df['proteinSeq-loc'], df['p_ss3'])]\n",
    "# df['ptm_ss3'] = [str(ss3)[ploc:ploc+1] + '(' + str(p_ss3)[ploc:ploc+1] + ')' for ploc, ss3, p_ss3  in zip(df['proteinSeq-loc'], df['ss3'], df['p_ss3'])]\n",
    "# df['kmer_p_ss3_n'] = [round(sum(list(map(int, pss3)))/117, 2) for pss3 in df['kmer_p_ss3'].str.split('').str[1:-1]]\n",
    "# df['kmer_ss3_len'] = df['kmer_ss3'].str.len()\n",
    "# df['no_kmer_ss3_len'] = df['no_kmer_ss3'].str.len()\n",
    "# df['kmer_ss3_C'] = df['kmer_ss3'].str.count('C')\n",
    "# df['no_kmer_ss3_C'] = df['no_kmer_ss3'].str.count('C')\n",
    "# df['kmer_ss3_E'] = df['kmer_ss3'].str.count('E')\n",
    "# df['no_kmer_ss3_E'] = df['no_kmer_ss3'].str.count('E')\n",
    "# df['kmer_ss3_H'] = df['kmer_ss3'].str.count('H')\n",
    "# df['no_kmer_ss3_H'] = df['no_kmer_ss3'].str.count('H')\n",
    "\n",
    "# df['kmer_ss8'] = [str(ss8)[ploc-6:ploc+7] for ploc, ss8  in zip(df['proteinSeq-loc'], df['ss8'])]\n",
    "# df['kmer_p_ss8'] = [str(p_ss8)[ploc-6:ploc+7] for ploc, p_ss8  in zip(df['proteinSeq-loc'], df['p_ss8'])] \n",
    "# df['ptm_ss8'] = [str(ss8)[ploc:ploc+1] + '(' + str(p_ss8)[ploc:ploc+1] + ')' for ploc, ss8, p_ss8  in zip(df['proteinSeq-loc'], df['ss8'], df['p_ss8'])]\n",
    "# df['kmer_p_ss8_n'] = [round(sum(list(map(int, pss8)))/117, 2) for pss8 in df['kmer_p_ss8'].str.split('').str[1:-1]]\n",
    "\n",
    "# df['kmer_dis'] = [str(dis)[ploc-6:ploc+7] for ploc, dis  in zip(df['proteinSeq-loc'], df['dis'])]\n",
    "# df['ptm_dis'] = [str(dis)[ploc:ploc+1] for ploc, dis in zip(df['proteinSeq-loc'], df['dis'])]\n",
    "# df['no_kmer_dis'] = [str(dis)[:ploc-6] + str(dis)[ploc+7:] for ploc, dis in zip(df['proteinSeq-loc'], df['dis'])]\n",
    "# df['kmer_dis_n'] = [round(sum(list(map(int, dis)))/117, 2) for dis in df['kmer_dis'].str.split('').str[1:-1]]\n",
    "# df['no_kmer_dis_n'] = [round(sum(list(map(int, dis)))/len(dis), 2) for dis in df['no_kmer_dis'].str.split('').str[1:-1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Author information**:\n",
    "Fernando Pozo ([@fpozoc](https://gitlab.com/fpozoc))\n",
    "\n",
    "You can find the data driven project jupyter notebook template [here](https://gitlab.com/fpozoc/data-driven-project-template/-/blob/master/notebooks/1.0-nb_template.ipynb)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
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     "varRefreshCmd": "print(var_dic_list())"
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     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
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   "types_to_exclude": [
    "module",
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 "nbformat": 4,
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