experiments/3. RBP Computational Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fc5112a9",
   "metadata": {},
   "source": [
    "# Protein embeddings improve phage-host interaction prediction\n",
    "\n",
    "**Mark Edward M. Gonzales<sup>1, 2</sup>, Jennifer C. Ureta<sup>1, 2</sup> & Anish M.S. Shrestha<sup>1, 2</sup>**\n",
    "\n",
    "<sup>1</sup> Bioinformatics Laboratory, Advanced Research Institute for Informatics, Computing and Networking, De La Salle University, Manila, Philippines <br>\n",
    "<sup>2</sup> Department of Software Technology, College of Computer Studies, De La Salle University, Manila, Philippines \n",
    "\n",
    "{mark_gonzales, jennifer.ureta, anish.shrestha}@dlsu.edu.ph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0b95a79",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6cc1abc",
   "metadata": {},
   "source": [
    "## 💡 ProtBert Embeddings\n",
    "This notebook assumes that you have already converted the annotated RBP and hypothetical protein sequences (from running [`1. Sequence Processing.ipynb`](https://github.com/bioinfodlsu/phage-host-prediction/blob/main/experiments/1.%20Sequence%20Preprocessing.ipynb)) into ProtBert embeddings. Refer to [`4. Protein Embedding Generation.ipynb`](https://github.com/bioinfodlsu/phage-host-prediction/blob/main/experiments/4.%20Protein%20Embedding%20Generation.ipynb) for the script to generate these embeddings.\n",
    "\n",
    "Alternatively, you may download the protein embeddings from these Google Drive directories: [Part 1](https://drive.google.com/drive/folders/1deenrDQIr3xcl9QCYH-nPhmpY8x2drQw?usp=sharing) and [Part 2](https://drive.google.com/drive/folders/1jnBFNsC6zJISkc6IAz56257MSXKjY0Ez?usp=sharing). Consolidate the downloaded folders into a single `embeddings` directory and save it inside the `inphared` directory located in the same folder as this notebook. The folder structure should look like this:\n",
    "\n",
    "`experiments` (parent folder of this notebook) <br> \n",
    "↳ `inphared` <br>\n",
    "&nbsp; &nbsp;↳ `embeddings` <br>\n",
    "&nbsp; &nbsp;&nbsp; &nbsp; ↳ `esm` <br>\n",
    "&nbsp; &nbsp;&nbsp; &nbsp; ↳ `esm1b` <br>\n",
    "&nbsp; &nbsp;&nbsp; &nbsp; ↳ ... <br>\n",
    "↳ `3. RBP Computational Prediction.ipynb` (this notebook) <br>\n",
    "\n",
    "However, technically, you only need the embeddings saved in `inphared/embeddings/prottransbert/rbp` and `inphared/embeddings/prottransbert/hypothetical` to run this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94a0e04a",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16afdbf7",
   "metadata": {},
   "source": [
    "## 📁 Output Files\n",
    "If you would like to skip running this notebook, you may download the protein embeddings from these Google Drive directories: [Part 1](https://drive.google.com/drive/folders/1deenrDQIr3xcl9QCYH-nPhmpY8x2drQw?usp=sharing) and [Part 2](https://drive.google.com/drive/folders/1jnBFNsC6zJISkc6IAz56257MSXKjY0Ez?usp=sharing). Consolidate the downloaded folders into a single `embeddings` directory and save it inside the `inphared` directory located in the same folder as this notebook. The folder structure should look like this:\n",
    "\n",
    "`experiments` (parent folder of this notebook) <br> \n",
    "↳ `inphared` <br>\n",
    "&nbsp; &nbsp;↳ `embeddings` <br>\n",
    "&nbsp; &nbsp;&nbsp; &nbsp; ↳ `esm` <br>\n",
    "&nbsp; &nbsp;&nbsp; &nbsp; ↳ `esm1b` <br>\n",
    "&nbsp; &nbsp;&nbsp; &nbsp; ↳ ... <br>\n",
    "↳ `3. RBP Computational Prediction.ipynb` (this notebook) <br>\n",
    "\n",
    "To be specific, this notebook generates the contents of `inphared/embeddings/prottransbert/complete`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb5b0fa4",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78385ec0",
   "metadata": {},
   "source": [
    "# Part I: Preliminaries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c740e39",
   "metadata": {},
   "source": [
    "Import the necessary libraries and modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "56a5eb80",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from ConstantsUtil import ConstantsUtil\n",
    "from RBPPredictionUtil import RBPPredictionUtil\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9bcea770",
   "metadata": {},
   "outputs": [],
   "source": [
    "constants = ConstantsUtil()\n",
    "util = RBPPredictionUtil()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be3ae535",
   "metadata": {},
   "source": [
    "Copy all the RBP ProtBert embeddings:\n",
    "- **FROM `inphared/embeddings/prottransbert/rbp`**: Contains the ProtBert embeddings of the annotated RBPs, i.e., the RBPs selected based on GenBank (for sequences with gene annotations) and Prokka (for sequences without gene annotations) annotations\n",
    "- **TO `inphared/embeddings/prottransbert/complete`**: Contains the ProtBert embeddings of the annotated RBPs, alongside the ProtBert embeddings of the hypothetical proteins that will be predicted as RBPs after running this notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "60b73ff5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'inphared/embeddings/prottransbert/complete'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shutil.copytree(\n",
    "    f\"{constants.INPHARED}/{constants.PLM['PROTTRANSBERT']}/{constants.RBP}\",\n",
    "    f\"{constants.INPHARED}/{constants.PLM['PROTTRANSBERT']}/{constants.COMPLETE}\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af119af6",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e2461f4",
   "metadata": {},
   "source": [
    "# Part II: Computational Prediction of RBPs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6676cba1",
   "metadata": {},
   "source": [
    "Feed the ProtBert embeddings of the hypothetical proteins to the XGBoost model from this [RBP prediction study](https://www.mdpi.com/1999-4915/14/6/1329) by Boeckaerts <i>et al.</i> (2022) to predict whether they are RBPs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8e6ed377",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "OX241591-hypothetical-embeddings.csv\n",
      "SFU82619-hypothetical-embeddings.csv\n"
     ]
    }
   ],
   "source": [
    "util.predict_rbps(\n",
    "    constants.XGB_RBP_PREDICTION,\n",
    "    f\"{constants.INPHARED}/{constants.PLM['PROTTRANSBERT']}/{constants.HYPOTHETICAL}/{constants.GENBANK}\",\n",
    "    f\"{constants.INPHARED}/{constants.PLM['PROTTRANSBERT']}/{constants.COMPLETE}/{constants.GENBANK}\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3698d54f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "OK490438-hypothetical-embeddings.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK490440-hypothetical-embeddings.csv\n",
      "OK490441-hypothetical-embeddings.csv\n",
      "OK490443-hypothetical-embeddings.csv\n",
      "OK490444-hypothetical-embeddings.csv\n",
      "OK490446-hypothetical-embeddings.csv\n",
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      "OK490451-hypothetical-embeddings.csv\n",
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      "OK638201-hypothetical-embeddings.csv\n",
      "OK638202-hypothetical-embeddings.csv\n",
      "OK638203-hypothetical-embeddings.csv\n",
      "OK905446-hypothetical-embeddings.csv\n",
      "OM234792-hypothetical-embeddings.csv\n",
      "OM373550-hypothetical-embeddings.csv\n",
      "OM373551-hypothetical-embeddings.csv\n",
      "OM373552-hypothetical-embeddings.csv\n",
      "OM373553-hypothetical-embeddings.csv\n",
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      "OM373555-hypothetical-embeddings.csv\n",
      "OM373556-hypothetical-embeddings.csv\n",
      "OM373561-hypothetical-embeddings.csv\n",
      "OM401812-hypothetical-embeddings.csv\n",
      "OM809160-hypothetical-embeddings.csv\n",
      "OM850391-hypothetical-embeddings.csv\n",
      "OM850392-hypothetical-embeddings.csv\n",
      "ON113344-hypothetical-embeddings.csv\n",
      "ON464763-hypothetical-embeddings.csv\n",
      "ON480525-hypothetical-embeddings.csv\n",
      "OP120783-hypothetical-embeddings.csv\n",
      "OU343167-hypothetical-embeddings.csv\n",
      "OU343168-hypothetical-embeddings.csv\n",
      "OU509537-hypothetical-embeddings.csv\n"
     ]
    }
   ],
   "source": [
    "util.predict_rbps(\n",
    "    constants.XGB_RBP_PREDICTION,\n",
    "    f\"{constants.INPHARED}/{constants.PLM['PROTTRANSBERT']}/{constants.HYPOTHETICAL}/{constants.PROKKA}\",\n",
    "    f\"{constants.INPHARED}/{constants.PLM['PROTTRANSBERT']}/{constants.COMPLETE}/{constants.PROKKA}\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1f43c8a",
   "metadata": {},
   "source": [
    "Consolidate the ProtBert embeddings of the RBPs (annotated and computationally predicted) in one directory: `inphared/embeddings/prottransbert/complete/master`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "acd68dbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "complete_genbank = f\"{constants.INPHARED}/{constants.PLM['PROTTRANSBERT']}/{constants.COMPLETE}/{constants.GENBANK}\"\n",
    "complete_prokka = f\"{constants.INPHARED}/{constants.PLM['PROTTRANSBERT']}/{constants.COMPLETE}/{constants.PROKKA}\"\n",
    "complete_master = f\"{constants.INPHARED}/{constants.PLM['PROTTRANSBERT']}/{constants.COMPLETE}/{constants.MASTER}\"\n",
    "\n",
    "if not os.path.exists(complete_master):\n",
    "    os.makedirs(complete_master)\n",
    "\n",
    "for file in os.listdir(complete_genbank):\n",
    "    shutil.copy(f\"{complete_genbank}/{file}\", complete_master)\n",
    "\n",
    "for file in os.listdir(complete_prokka):\n",
    "    if not os.path.exists(f\"{complete_master}/{file}\"):\n",
    "        shutil.copy(f\"{complete_prokka}/{file}\", complete_master)\n",
    "    else:\n",
    "        # Nothing should be printed\n",
    "        print(file)"
   ]
  }
 ],
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