analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "581c753c",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''code written with ChatGPT and underwent human verification'''\n",
    "\n",
    "filename = \"Mb.fa\"\n",
    "residue_positions = [28, 42, 44, 63, 66, 67, 71, 106, 110] # residue number-1\n",
    "output_filename = \"output.csv\"\n",
    "residue_info = {\n",
    "    29: 'L', 43: 'F', 45: 'R', 64: 'V', 67: 'T', 68: 'A',\n",
    "    72: 'L', 107: 'I', 111: 'I'\n",
    "}\n",
    "#residue_numbers = [L29, F43, R45, V64, T67, A68, L72, I107, I111]# Set residue numbers as x-axis labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b2a33812",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output saved to: output.csv\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "\n",
    "def parse_fasta(filename):\n",
    "    entries = []\n",
    "    with open(filename, 'r') as file:\n",
    "        lines = file.readlines()\n",
    "        for i in range(0, len(lines), 2):\n",
    "            entry = {}\n",
    "            header = lines[i].strip().split(',')\n",
    "            entry['id'] = int(header[1].split('=')[1])  # Extracting ID from the fasta file\n",
    "            entry['overall_confidence'] = float(header[4].split('=')[1])\n",
    "            entry['ligand_confidence'] = float(header[5].split('=')[1])\n",
    "            entry['seq_rec'] = float(header[6].split('=')[1])\n",
    "            sequence = lines[i+1].strip()\n",
    "            entry['sequence'] = sequence\n",
    "            residues = [sequence[idx] for idx in residue_positions]\n",
    "            entry['residues'] = residues\n",
    "            entries.append(entry)\n",
    "    return entries\n",
    "\n",
    "def save_to_csv(entries, output_filename):\n",
    "\n",
    "    header = ['id', 'overall_confidence', 'ligand_confidence', 'seq_rec'] + [f\"residue_{pos}\" for pos in residue_positions]\n",
    "\n",
    "    with open(output_filename, mode='w', newline='') as file:\n",
    "        writer = csv.DictWriter(file, fieldnames=header)\n",
    "        writer.writeheader()\n",
    "        for entry in entries:\n",
    "            row = {\n",
    "                'id': entry['id'],\n",
    "                'overall_confidence': entry['overall_confidence'],\n",
    "                'ligand_confidence': entry['ligand_confidence'],\n",
    "                'seq_rec': entry['seq_rec']\n",
    "            }\n",
    "            for idx, pos in enumerate(residue_positions):\n",
    "                row[f\"residue_{pos}\"] = entry['residues'][idx]\n",
    "            writer.writerow(row)\n",
    "\n",
    "\n",
    "entries = parse_fasta(filename)\n",
    "\n",
    "save_to_csv(entries, output_filename)\n",
    "\n",
    "print(\"Output saved to:\", output_filename)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d7ec5133",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Residue at position 28: {'I': 471, 'L': 27, 'A': 2}\n",
      "Residue at position 42: {'F': 500}\n",
      "Residue at position 44: {'A': 500}\n",
      "Residue at position 63: {'A': 500}\n",
      "Residue at position 66: {'A': 112, 'T': 388}\n",
      "Residue at position 67: {'A': 500}\n",
      "Residue at position 71: {'L': 500}\n",
      "Residue at position 106: {'S': 500}\n",
      "Residue at position 110: {'I': 500}\n"
     ]
    }
   ],
   "source": [
    "def count_residue_occurrences(entries):\n",
    "    residue_counts = {pos: {} for pos in residue_positions}\n",
    "\n",
    "    for entry in entries:\n",
    "        for idx, pos in enumerate(residue_positions):\n",
    "            residue = entry['residues'][idx]\n",
    "            residue_counts[pos][residue] = residue_counts[pos].get(residue, 0) + 1\n",
    "\n",
    "    return residue_counts\n",
    "\n",
    "entries = parse_fasta(filename)\n",
    "\n",
    "residue_counts = count_residue_occurrences(entries)\n",
    "\n",
    "# Printing the occurrence counts for each residue position\n",
    "for pos, counts in residue_counts.items():\n",
    "    print(f\"Residue at position {pos}: {counts}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1499d78d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data = {'A': [0.004, 0.0, 1.0, 1.0, 0.224, 1.0, 0.0, 0.0, 0.0], 'I': [0.942, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], 'F': [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'T': [0.0, 0.0, 0.0, 0.0, 0.776, 0.0, 0.0, 0.0, 0.0], 'L': [0.054, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0], 'S': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0]}\n"
     ]
    }
   ],
   "source": [
    "def calculate_frequencies(entries):\n",
    "    amino_acids = set()\n",
    "    amino_acid_counts = {}\n",
    "\n",
    "    # Collect all unique amino acids\n",
    "    for entry in entries:\n",
    "        for residue in entry['residues']:\n",
    "            amino_acids.add(residue)\n",
    "\n",
    "    # Initialize count dictionary\n",
    "    counts = {pos: {aa: 0 for aa in amino_acids} for pos in residue_positions}\n",
    "\n",
    "    # Count occurrences of each amino acid at each position\n",
    "    for entry in entries:\n",
    "        for idx, residue in enumerate(entry['residues']):\n",
    "            counts[residue_positions[idx]][residue] += 1\n",
    "\n",
    "    # Initialize frequency dictionary\n",
    "    frequencies = {aa: [0] * len(residue_positions) for aa in amino_acids}\n",
    "\n",
    "    # Calculate frequencies\n",
    "    for idx in range(len(residue_positions)):\n",
    "        total_count = sum(counts[residue_positions[idx]].values())\n",
    "        for aa in amino_acids:\n",
    "            frequencies[aa][idx] = counts[residue_positions[idx]][aa] / total_count if total_count > 0 else 0\n",
    "\n",
    "    return frequencies\n",
    "\n",
    "entries = parse_fasta(filename)\n",
    "frequencies = calculate_frequencies(entries)\n",
    "\n",
    "# Format the frequencies into the required variable format\n",
    "data = {aa: freqs for aa, freqs in frequencies.items()}\n",
    "\n",
    "# Print the data\n",
    "print(\"data =\", data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8743b354",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import logomaker\n",
    "import pandas as pd\n",
    "\n",
    "def plot_logo(data):\n",
    "    # Convert the data dictionary into a pandas DataFrame\n",
    "    df = pd.DataFrame(data)\n",
    "    \n",
    "    residue_labels = [f\"{residue_info[pos]}{pos}\" for pos in residue_info.keys()]\n",
    "    \n",
    "    # Define color scheme for amino acid letters\n",
    "    color_scheme = {    \n",
    "        'A': 'green', 'R': 'red', 'N': 'blue', 'D': 'purple', 'C': 'cyan',\n",
    "    'Q': 'orange', 'E': 'magenta', 'G': 'yellow', 'H': 'lime', 'I': 'brown',\n",
    "    'L': 'pink', 'K': 'teal', 'M': 'olive', 'F': 'navy', 'P': 'gold',\n",
    "    'S': 'indigo', 'T': 'violet', 'W': 'turquoise', 'Y': 'salmon', 'V': 'steelblue'\n",
    "}\n",
    "    \n",
    "    # Create the logo plot\n",
    "    logo = logomaker.Logo(df, color_scheme=color_scheme)\n",
    "\n",
    "    # Customize the appearance of the logo plot\n",
    "    logo.style_xticks(fmt='%d', anchor=0, spacing=20)\n",
    "    logo.ax.set_xticks(range(len(residue_labels)))\n",
    "    logo.ax.set_xticklabels(residue_labels)\n",
    "    logo.ax.set_title('Sequence Logo Plot')\n",
    "    \n",
    "    # Draw the plot\n",
    "    logo.draw()\n",
    "\n",
    "# Assuming 'data' variable is already defined\n",
    "plot_logo(data)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "591ae3ab",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b99ad9db",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}