Hb_pepide_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a747ea1c",
   "metadata": {},
   "source": [
    "# Assessing the 'Hb-likeness' of short peptides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "10132bd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os\n",
    "from collections import Counter\n",

    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from Bio import SeqIO "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5dbe83d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the __file__ variable for this notebook. Drop this line if converting to a module\n",
    "__file__ = os.path.join(os.path.abspath(''), 'Hb_peptide_analysis.ipynb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9fe2eb15",
   "metadata": {},
   "outputs": [],
   "source": [
    "aa_formulas = {'Gly': {'C':2, 'H':5, 'N':1, 'O':2},\n",
    "               'Ala': {'C':3, 'H':7, 'N':1, 'O':2},\n",
    "               'Ser': {'C':3, 'H':7, 'N':1, 'O':3},\n",
    "               'Pro': {'C':5, 'H':9, 'N':1, 'O':2},\n",
    "               'Val': {'C':5, 'H':11, 'N':1, 'O':2},\n",
    "               'Thr': {'C':4, 'H':9, 'N':1, 'O':3},\n",
    "               'Cys': {'C':3, 'H':7, 'N':1, 'O':2, 'S':1},\n",
    "               'Leu': {'C':6, 'H':13, 'N':1, 'O':2},\n",
    "               'Ile': {'C':6, 'H':13, 'N':1, 'O':2},\n",
    "               'Asn': {'C':4, 'H':8, 'N':2, 'O':3},\n",
    "               'Asp': {'C':4, 'H':7, 'N':1, 'O':4},\n",
    "               'Gln': {'C':5, 'H':10, 'N':2, 'O':3},\n",
    "               'Lys': {'C':6, 'H':14, 'N':2, 'O':2},\n",
    "               'Glu': {'C':5, 'H':9, 'N':1, 'O':4},\n",
    "               'Met': {'C':5, 'H':11, 'N':1, 'O':2, 'S':1},\n",
    "               'His': {'C':6, 'H':9, 'N':3, 'O':2},\n",
    "               'Phe': {'C':9, 'H':11, 'N':1, 'O':2},\n",
    "               'Arg': {'C':6, 'H':14, 'N':4, 'O':2},\n",
    "               'Tyr': {'C':9, 'H':11, 'N':1, 'O':3},\n",
    "               'Trp': {'C':11, 'H':12, 'N':2, 'O':2}\n",
    "              }\n",
    "\n",
    "aa_exact_mass = {'Gly': 75.03203,\n",
    "                 'Ala': 89.04768,\n",
    "                 'Ser': 105.04259,\n",
    "                 'Pro': 115.06333,\n",
    "                 'Val': 117.07898,\n",
    "                 'Thr': 119.05824,\n",
    "                 'Cys': 121.01975,\n",
    "                 'Leu': 131.09463,\n",
    "                 'Ile': 131.09463,\n",
    "                 'Asn': 132.05349,\n",
    "                 'Asp': 133.03751,\n",
    "                 'Gln': 146.06914,\n",
    "                 'Lys': 146.10553,\n",
    "                 'Glu': 147.05316,\n",
    "                 'Met': 149.05105,\n",
    "                 'His': 155.06948,\n",
    "                 'Phe': 165.07898,\n",
    "                 'Arg': 174.11168,\n",
    "                 'Tyr': 181.07389,\n",
    "                 'Trp': 204.08988\n",
    "                }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6854aea3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A mapping of mass to peptide sequence, for identification of isomers:\n",
    "\n",
    "import itertools, collections\n",
    "\n",
    "aas = list(aa_exact_mass.keys())\n",
    "\n",
    "peptides_by_mass = collections.defaultdict(list)\n",
    "\n",
    "for N in [1,2,3,4,5,6]:\n",
    "    for seq in itertools.combinations_with_replacement(aas, N):\n",
    "        mass = sum([aa_exact_mass[aa] for aa in seq]) - 18.01056*(N-1)\n",
    "        peptides_by_mass[int(mass*500)/500].append('/'.join(seq))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "40e8ca86",
   "metadata": {},
   "outputs": [],
   "source": [
    "aas = \"ACDEFGHIKLMNPQRSTVWY\"\n",
    "one2three = {\"A\": \"Ala\",\n",
    "             \"C\": \"Cys\",\n",
    "             \"D\": \"Asp\",\n",
    "             \"E\": \"Glu\",\n",
    "             \"F\": \"Phe\",\n",
    "             \"G\": \"Gly\",\n",
    "             \"H\": \"His\",\n",
    "             \"I\": \"Ile\",\n",
    "             \"K\": \"Lys\",\n",
    "             \"L\": \"Leu\",\n",
    "             \"M\": \"Met\",\n",
    "             \"N\": \"Asn\",\n",
    "             \"P\": \"Pro\",\n",
    "             \"Q\": \"Gln\",\n",
    "             \"R\": \"Arg\",\n",
    "             \"S\": \"Ser\",\n",
    "             \"T\": \"Thr\",\n",
    "             \"V\": \"Val\",\n",
    "             \"W\": \"Trp\",\n",
    "             \"Y\": \"Tyr\"}\n",
    "three2one = {v:k for k,v in one2three.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "743702cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "class countPepInSeq(object):\n",
    "    \"\"\"Count the number of occurances of peptides in a protein sequence.\n",
    "    \n",
    "    init with a target protein sequence, and call with pd.Series an ms1-defined 3-letter id as 'name'\n",
    "    \"\"\"\n",
    "    def __init__(self, seq):\n",
    "        self.seq = seq\n",
    "        self._sub_strings = {}\n",
    "    def _calc_sub_strings(self, length):\n",
    "        self._sub_strings[length] = [sorted(self.seq[i:i+length]) for i in range(len(self.seq)-length)]\n",
    "    def __call__(self, pep):\n",
    "        pep = pep.split('/')\n",
    "        length = len(pep)\n",
    "        if not length in self._sub_strings:\n",
    "            self._calc_sub_strings(length)\n",
    "        mass = sum([aa_exact_mass[aa] for aa in pep]) - (len(pep)-1)*18.01056\n",
    "        count = 0\n",
    "        counted = []\n",
    "        for isomer in peptides_by_mass[int(mass*500)/500]:\n",
    "            isomer = sorted(isomer.split('/'))\n",
    "            if isomer in counted:\n",
    "                continue\n",
    "            pepOneLett = [three2one[aa] for aa in isomer]\n",
    "            count += self._sub_strings[length].count(sorted(pepOneLett))\n",
    "            counted.append(isomer)\n",
    "        return count\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "013cfa9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "class countSequencedPepInSeq(countPepInSeq):\n",
    "    \"\"\"Subclass to consider the fact that we have ms2-based sequences for a subset of peptides.\n",
    "    \n",
    "    init with a target protein sequence, and call with pd.Series with an ms2-defined 1-letter sequence \n",
    "    as 'sequence' and an ms1-defined 3-letter id as 'name'\n",
    "    \"\"\"\n",
    "    def __call__(self, pep_series):\n",
    "        count = 0\n",
    "        if pd.isna(pep_series['sequence']):\n",
    "            # No ms2 sequence, so consider all isomers of ms1 ID:\n",
    "            pep = pep_series['name']\n",
    "            pep = pep.split('/')\n",
    "            length = len(pep)\n",
    "            if not length in self._sub_strings:\n",
    "                self._calc_sub_strings(length)\n",
    "            mass = sum([aa_exact_mass[aa] for aa in pep]) - (len(pep)-1)*18.01056\n",
    "            counted = []\n",
    "            for isomer in peptides_by_mass[int(mass*500)/500]:\n",
    "                isomer = sorted(isomer.split('/'))\n",
    "                if isomer in counted:\n",
    "                    continue\n",
    "                pepOneLett = [three2one[aa] for aa in isomer]\n",
    "                count += self._sub_strings[length].count(sorted(pepOneLett))\n",
    "                counted.append(isomer)\n",
    "        else:\n",
    "            pep = pep_series['sequence']\n",
    "            length = len(pep)\n",
    "            if not length in self._sub_strings:\n",
    "                self._calc_sub_strings(length)\n",
    "            count = self._sub_strings[length].count(sorted(pep))\n",
    "        return count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e3b21d55",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load identified peptides:\n",
    "combined_data = pd.read_excel(os.path.join(os.path.dirname(__file__), \"comnined-analysis-v3.xlsx\"), sheet_name='Sheet3')\n",
    "\n",
    "ms2peptides = pd.read_csv(os.path.join(os.path.dirname(__file__), \"MS2_peptides.csv\"))\n",
    "ms2peptides = ms2peptides.dropna(subset=['name', 'HB'])\n",
    "ms2peptides = ms2peptides.drop(columns=['Unnamed: 3']).rename(columns={'Unnamed: 5':'sequence'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "084e4af0",
   "metadata": {},
   "outputs": [],
   "source": [
    "ms2peptides.loc[11,'name'] = \"Val/Ser\"\n",
    "ms2peptides.loc[16,'name'] = \"Thr/Pro\"\n",
    "ms2peptides.loc[24,'name'] = \"Ala/His\"\n",
    "ms2peptides.loc[32,'name'] = \"Asp/Val\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f7f23d7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_data = combined_data.merge(ms2peptides[['name', 'sequence', 'HB']], on='name', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ea90e9d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The blood-stage Pf spectral library:\n",
    "pf_seen = pd.read_excel(os.path.join(os.path.dirname(__file__), \"Peptides_proteins.xlsx\"), sheet_name='Pf_peptide_counts')\n",
    "hs_seen = pd.read_excel(os.path.join(os.path.dirname(__file__), \"Peptides_proteins.xlsx\"), sheet_name='Hs_peptide_counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f2ff6860",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalised peptide counts for each protein in the library:\n",
    "for protein in SeqIO.parse(os.path.join(os.path.dirname(__file__), \"PfHs_PlasmoDB-45_Pfalciparum3D7_plus_UP00005640.fasta\"), 'fasta'):\n",
    "    if protein.id.startswith('PF3D7_'):\n",
    "        acc = protein.id\n",
    "        count = combined_data.loc[(combined_data['P-VALUE inhibitor/control']<0.05)&(combined_data['P-VALUE kd/control']<0.05)].apply(countSequencedPepInSeq(protein.seq), axis=1)\n",
    "        pf_seen.loc[pf_seen.BGSInferenceId==acc, 'NormalisedPeptideCounts'] = count.sum()/len(protein.seq)\n",
    "    else:\n",
    "        if protein.id.startswith('tr|'):\n",
    "            acc = protein.id.split('|')[-1].split('_')[0]\n",
    "        elif protein.id.startswith('sp|'):\n",
    "            acc = protein.id.split('|')[1]\n",
    "        count = combined_data.loc[(combined_data['P-VALUE inhibitor/control']<0.05)&(combined_data['P-VALUE kd/control']<0.05)].apply(countSequencedPepInSeq(protein.seq), axis=1)\n",
    "        if hs_seen.BGSInferenceId.str.contains(acc).sum() > 1:\n",
    "            print(f\"Multiple matches to UniprotID {acc} from {protein.id}\")\n",
    "        hs_seen.loc[hs_seen.BGSInferenceId.str.contains(acc), 'NormalisedPeptideCounts'] = count.sum()/len(protein.seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1482010b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(pd.concat([hs_seen.NormalisedPeptideCounts, pf_seen.NormalisedPeptideCounts]), bins=100, density=True, histtype='step', label='Hs')\n",
    "for prot, pos in [('HBA',(0.55633,1.5)), ('HBB',(0.517,1.5))]:\n",
    "    plt.annotate(prot, xy=(pos[0], 0), xycoords='data', xytext=pos, textcoords='data', \n",
    "                 arrowprops={'width':2,'headwidth':2,'headlength':0.1,'ec':'red','fc':'red','alpha':0.6,'connectionstyle':'arc3'}, horizontalalignment='center')\n",
    "plt.xticks(size=14)\n",
    "plt.yticks(size=14)\n",
    "plt.xlabel(\"Normalised peptide matches\", fontdict={'size':16})\n",
    "plt.ylabel(\"Density\", fontdict={'size':16})\n",
    "plt.savefig(os.path.join(os.path.dirname(__file__), \"Hb_NormalisedPeptideCounts_MS2update.png\"))\n",
    "plt.savefig(os.path.join(os.path.dirname(__file__), \"Hb_NormalisedPeptideCounts_MS2update.svg\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3617a37",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e61b05c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ideomDB",
   "language": "python",
   "name": "ideomdb"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}