utils.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://search.foldseek.com/search?accession=F1B5A5&source=AlphaFoldDB\n",
      "https://search.foldseek.com/search?accession=B3STG0&source=AlphaFoldDB\n",
      "https://search.foldseek.com/search?accession=A0A499JUY9&source=AlphaFoldDB\n",
      "https://search.foldseek.com/search?accession=A0A2S1HAU7&source=AlphaFoldDB\n",
      "https://search.foldseek.com/search?accession=A0A3G6IML3&source=AlphaFoldDB\n",
      "https://search.foldseek.com/search?accession=A0A220H7Z7&source=AlphaFoldDB\n",
      "https://search.foldseek.com/search?accession=A0A6C0R830&source=AlphaFoldDB\n",
      "https://search.foldseek.com/search?accession=E0D9T2&source=AlphaFoldDB\n",
      "https://search.foldseek.com/search?accession=A0A346KW05&source=AlphaFoldDB\n",
      "https://search.foldseek.com/search?accession=A0A159E4H8&source=AlphaFoldDB\n",
      "https://search.foldseek.com/search?accession=A0A386III0&source=AlphaFoldDB\n",
      "https://search.foldseek.com/search?accession=H2A023&source=AlphaFoldDB\n",
      "https://search.foldseek.com/search?accession=A0A6M3AS98&source=AlphaFoldDB\n",
      "https://search.foldseek.com/search?accession=S5WEZ1&source=AlphaFoldDB\n",
      "https://search.foldseek.com/search?accession=E5FJN9&source=AlphaFoldDB\n",
      "https://search.foldseek.com/search?accession=G8HAR5&source=AlphaFoldDB\n",
      "https://search.foldseek.com/search?accession=A0A7G8KVF0&source=AlphaFoldDB\n",
      "https://search.foldseek.com/search?accession=A0A2P1D389&source=AlphaFoldDB\n",
      "https://search.foldseek.com/search?accession=A0A344VJA7&source=AlphaFoldDB\n",
      "https://search.foldseek.com/search?accession=Q5K401&source=AlphaFoldDB\n",
      "https://search.foldseek.com/search?accession=A0A3Q8HBV0&source=AlphaFoldDB\n",
      "https://search.foldseek.com/search?accession=A0A3G1V387&source=AlphaFoldDB\n",
      "https://search.foldseek.com/search?accession=H6YZH9&source=AlphaFoldDB\n",
      "https://search.foldseek.com/search?accession=A0A2Z4ERT0&source=AlphaFoldDB\n",
      "https://search.foldseek.com/search?accession=A0A858C1S4&source=AlphaFoldDB\n"
     ]
    }
   ],
   "source": [
    "from random import sample\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "proteins = {}\n",
    "PROTEIN_INDEX = pd.read_csv(\"/embeddings/proteins-index.csv\", sep=\"\\t\", header=None)\n",
    "\n",
    "for i in range(25):\n",
    "    protein_sample = PROTEIN_INDEX.sample(1)\n",
    "    protein_sample = protein_sample[0].values[0].split(\",\")\n",
    "    protein_sample = sample(protein_sample[1:], 1)\n",
    "    print(f\"https://search.foldseek.com/search?accession={protein_sample[0].split('-')[1]}&source=AlphaFoldDB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                   0\n",
      "0   proteome-tax_id-882679-0_v3.tar,AF-A0A0A7W7S9-F1\n",
      "1  proteome-tax_id-882756-0_v3.tar,AF-G8Z514-F1,A...\n",
      "2       proteome-tax_id-882753-0_v3.tar,AF-G8Z504-F1\n",
      "3  proteome-tax_id-882762-0_v3.tar,AF-G8Z535-F1,A...\n",
      "4  proteome-tax_id-882772-0_v3.tar,AF-G8Z538-F1,A...\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "PROTEIN_INDEX = pd.read_csv(\"/embeddings/proteins-index.csv\", sep=\"\\t\", header=None)\n",
    "\n",
    "print(PROTEIN_INDEX.head(5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "214684311\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "with open(\"/embeddings/proteins-index.csv\", \"r\") as f:\n",
    "    lines = f.readlines()\n",
    "    # find number of ',' in file\n",
    "    commas = [line.count(\",\") for line in lines]\n",
    "print(sum(commas))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8069"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(next(line for line in lines if \"proteome-tax_id-2480626-0_v3.tar\" in line).split(\",\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9345\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "lengths = []\n",
    "files = [\n",
    "    \"proteome-tax_id-1916222-0_v3.tar\",\n",
    "    \"proteome-tax_id-2292023-0_v3.tar\",\n",
    "    \"proteome-tax_id-37931-1_v3.tar\",\n",
    "    \"proteome-tax_id-1194971-0_v3.tar\",\n",
    "    \"proteome-tax_id-999415-0_v3.tar\"\n",
    "]\n",
    "for line in lines:\n",
    "    if any(file in line.split(\",\")[0] for file in files):\n",
    "        lengths.append(line.count(\",\"))\n",
    "\n",
    "print(sum(lengths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total sample: 95663\n",
      "Proteins with distance < 0.5: 95471\n",
      "Proteins with distance < 0.1: 496\n"
     ]
    }
   ],
   "source": [
    "# plot histogram of values from starterpack/embeddings-computation/AF-A0A6F8FN44-F1-distances.json\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "with open(\"A0A2Z4N0P5_100k_distances.json\", \"r\") as f:\n",
    "    distances = json.load(f)\n",
    "\n",
    "tm_distance = {k: v for k, v in distances.items()}\n",
    "plt.hist([1-v for v in tm_distance.values()], bins=1000)\n",
    "plt.xlabel(\"TM distance\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "# plt.title(\"TM-distance distribution of A0A2Z4N0P5\")\n",
    "# save plt with DPI for print\n",
    "plt.savefig(\"images/A0A2Z4N0P5_100k_distances.png\", dpi=300)\n",
    "plt.show()\n",
    "print(\"Total sample:\", len(tm_distance))\n",
    "print(\"Proteins with distance < 0.5:\", len([v for v in tm_distance.values() if v < 0.5]))\n",
    "print(\"Proteins with distance < 0.1:\", len([v for v in tm_distance.values() if v < 0.1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import json\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "files = os.listdir(\"results/\")\n",
    "files = [file for file in files if file.endswith(\"distances.json\")]\n",
    "files = [file.split(\"-\")[1] for file in files]\n",
    "protein = files[9]\n",
    "\n",
    "with open(f\"results/AF-{protein}-F1-distances.json\", \"r\") as f:\n",
    "    distances = json.load(f)\n",
    "\n",
    "with open(f\"foldseek-files/{protein}\", \"r\") as f:\n",
    "    f = f.read().split()\n",
    "    foldseek_proteins = [w for w in f if w.startswith(\"AF-\")]\n",
    "    foldseek_proteins = [w.split(\"-model\")[0].split(\"-\")[1] for w in foldseek_proteins]\n",
    "\n",
    "plt.hist([v for (k,v) in distances.items() if k not in foldseek_proteins], density=True, histtype='bar', stacked=True, bins=500)\n",
    "plt.hist([v for (k,v) in distances.items() if k in foldseek_proteins], density=True, histtype='bar', stacked=True, bins=500)\n",
    "plt.xlabel(\"TM-Distance\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.legend([\"Random\", \"FoldSeek\"])\n",
    "# plt.title(f\"TM distance distribution for {protein}\")\n",
    "plt.savefig(\"images/A0A3P1W0K9_subset.png\", dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 220.23456790123456 , stdev 86.82894658784035\n",
      "Shortest protein ('A4SGP8', 75) Longest protein ('K4JDQ0', 661)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# average sequence_length\n",
    "sequence_lengths = [result[\"uniprot_entry\"][\"sequence_length\"] for result in results]\n",
    "print(\"Mean:\", np.mean(sequence_lengths), \", stdev\", np.std(sequence_lengths))\n",
    "\n",
    "zipped_proteins = list(zip(candidate_proteins, sequence_lengths))\n",
    "print(\"Shortest protein\", min(zipped_proteins, key=lambda x: x[1]), \"Longest protein\", max(zipped_proteins, key=lambda x: x[1]))\n",
    "\n",
    "plt.hist(sequence_lengths, bins=50)\n",
    "plt.xlabel(\"Sequence length\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Sequence length distribution\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "uniprot_results = []\n",
    "\n",
    "for protein in candidate_proteins:\n",
    "    response = requests.get(f\"https://rest.uniprot.org/uniprotkb/search?query=accession:{protein}\")\n",
    "    uniprot_results.append(response.json())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eukaryota types: {'Viridiplantae', 'Sar', 'Fungi', 'Metazoa'}\n",
      "Bacteria types: {'Chlorobiota', 'Bacillota', 'Cyanobacteriota', 'Actinomycetota', 'Fusobacteriota', 'Pseudomonadota'}\n"
     ]
    }
   ],
   "source": [
    "# plot barchart with occurences\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "organisms = [result[\"results\"][0][\"organism\"][\"lineage\"] for result in uniprot_results if \"organism\" in result[\"results\"][0]]\n",
    "primary_organism = [organism[0] for organism in organisms]\n",
    "\n",
    "counter = Counter(primary_organism)\n",
    "plt.bar(counter.keys(), counter.values())\n",
    "plt.xlabel(\"Organism\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Organism distribution of candidate proteins\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Eukaryota types:\", set([organism[1] for organism in organisms if organism[0] == \"Eukaryota\"]))\n",
    "print(\"Bacteria types:\", set([organism[1] for organism in organisms if organism[0] == \"Bacteria\"]))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "COLOR = plt.colormaps[\"viridis\"]\n",
    "\n",
    "df = pd.read_csv(\"pdbbank-size.csv\")\n",
    "df = df[df[\"Year\"] >= 1994]\n",
    "df = df[df[\"Year\"] <= 2020]\n",
    "\n",
    "# create a dict with year and total number\n",
    "known_structures = {}\n",
    "for year, total in zip(df[\"Year\"], df[\"Total Number of Entries Available\"]):\n",
    "    known_structures[year] = int(total.replace(\",\", \"\")) * 200\n",
    "\n",
    "plt.bar(known_structures.keys(), known_structures.values(), color=COLOR(0.2))\n",
    "plt.bar([2021, 2022], [365000 * 200, 214000000 * 1.5], color=COLOR(0.5))\n",
    "plt.bar([2023], [772000000], color=COLOR(0.8))\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Discovered protein structures\")\n",
    "# y range\n",
    "plt.ylim(0, 800000000)\n",
    "\n",
    "# add text to 2023\n",
    "plt.text(2022, 772000000, \"772 mil\", ha='right', va='top', color='black', fontdict={'fontsize': 10})\n",
    "plt.text(2021, 214000000*1.5, \"214 mil\", ha='right', va='top', color='black')\n",
    "plt.text(2019, known_structures[2020], \"170k\", ha='right', va='bottom', color='black')\n",
    "plt.text(2010, known_structures[2010], \"70k\", ha='center', va='bottom', color='black')\n",
    "plt.text(2000, known_structures[2000], \"13k\", ha='center', va='bottom', color='black')\n",
    "\n",
    "#plt.yscale(\"log\")\n",
    "plt.legend([\"Protein Data Bank\", \"AlphaFold\", \"ESMFold\"])\n",
    "\n",
    "plt.gcf().set_size_inches(6, 4)\n",
    "plt.savefig(f\"images/solved-structures.png\", dpi=300, bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.1.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}