tutorial_PepFun.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial PepFun"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following is a set of exercises to run some functionalities available in PepFun, which cover a set of bioinformatics and cheminformatics protocols for peptide analysis. In addition to this tutorial, the project contains a **test.py** script where many of the package functionalities are tested with basic examples. Here, we will cover some sequence- and structure-based analysis using one or many peptides at the same time. At the end, we will generate some libraries based on certain patterns and restraints.\n",
    "\n",
    "* From publication \"PepFun: open source protocols for peptide-related computational analysis\"\n",
    "* Molecules \n",
    "* Authors: Rodrigo Ochoa, Pilar Cossio\n",
    "* Year: 2021"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Requirements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run PepFun functionalities using Python3, it is recommended to configure a conda environment to have installed all the required dependencies **(which should be done only once)**. One way to create an initial virtual environment from the rdkit channel is:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`conda create -c rdkit -n pepfun-env rdkit biopython matplotlib scipy pip pycairo nb_conda_kernels`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For BioPython, it is recommended using version 1.76. After the installation, you can activate the environment using the command:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`source activate pepfun-env`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After entering the virtual environment, this notebook can be called with jupyter and selecting the kernel provided by this conda instance. Then, we can install the igraph module for python3.6 using pip:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`python3.6 -m pip install python-igraph`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTES:**\n",
    "1. If the script is called as a module from a different folder, the path can be added using the following commands (remove comments):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import sys\n",
    "#sys.path.append('<PATH-TO-PEPFUN>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. The package was build under a Unix environment, but it can be used under any other OS based on the provided paths"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Sequence analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this part, we will calculate a set of properties for a set of peptides reported as binders of the MHC class II allele DRB1\\*0101, used as a benchmark system in the PepFun project. For that purpose, we will upload a list of sequences available in the tutorial:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*First we will import the modules required for the analysis*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modules of Pepfun\n",
    "from pepfun import *\n",
    "\n",
    "# Additional modules to run analysis\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from statistics import mean\n",
    "from statistics import stdev\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*The sequences can be loaded in a list object in python for further analysis. The file in the example contains the peptide sequences of the MHC class II binders with IC50 values below 50 uM*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequences=[x.strip() for x in open(\"auxiliar/sequences_binders_50uM.txt\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is a total of 655 sequences\n",
      "The first peptide sequence is: FWYVNHTGFNVHSLP\n"
     ]
    }
   ],
   "source": [
    "print(\"There is a total of {} sequences\".format(len(sequences)))\n",
    "print(\"The first peptide sequence is: {}\".format(sequences[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.1 Pair-wise alignment examples of peptides**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we will compare two sequences from the dataset using the functions provided in PepFun."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query sequence is: FWYVNHTGFNVHSLP\n",
      "Subject sequence is: KALYDLQRSAMVYSS\n"
     ]
    }
   ],
   "source": [
    "print(\"Query sequence is: {}\".format(sequences[0]))\n",
    "print(\"Subject sequence is: {}\".format(sequences[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we create the object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create object\n",
    "pep=peptide_sequence(sequences[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the new object, we can call the functions and calculate the corresponding scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alignment score by position is: -25\n",
      "The number of mismatches are: 15\n",
      "The similarity using a scoring matrix between peptides is: -0.29\n",
      "The SMILES similarity is: 0.34\n"
     ]
    }
   ],
   "source": [
    "pep.align_position_matrix(sequences[1])\n",
    "print(\"Alignment score by position is: {}\".format(pep.score_matrix))\n",
    "\n",
    "# Run basic alignment position by position\n",
    "pep.align_position_local(sequences[1])\n",
    "print(\"The number of mismatches are: {}\".format(pep.dismatch))\n",
    "\n",
    "# Run local alignment with the matrix\n",
    "similarity=pep.similarity_pair(sequences[0],sequences[1])\n",
    "print(\"The similarity using a scoring matrix between peptides is: {0:.2f}\".format(similarity))\n",
    "    \n",
    "# Compare the SMILES of the two sequences\n",
    "pep.similar_smiles(sequences[1])\n",
    "print(\"The SMILES similarity is: {0:.2f}\".format(pep.smiles_similarity))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous example, we have compared scores for the alignment of the two sequences using several methodologies. The alignment score by position is the sum of the match or mismatch scores based on a selected matrix from the AAindex database. The number of mismatches are the amino acids that are not identical at the same position in the sequence. The similarity metric is a normalized score of the alignment, where the maximum value is 1. The SMILES similarity is a value (Tanimoto metric) between 0 an 1, the closes to 1, the more similar the sequence. All these measures and the references are included in the main manuscript (Ochoa & Cossio, Molecules, 2021)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.2 Calculation of properties with the whole library**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this part, we will calculate four basic sequence properties: net charge, molecular weight, Crippen logP and Eisenberg's hydrophobicity (see paper for References). For that purpose, we will iterate over the sequences as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of peptides with properties calculated is: 655\n"
     ]
    }
   ],
   "source": [
    "# List with the properties\n",
    "charges=[]\n",
    "weights=[]\n",
    "logps=[]\n",
    "hydro=[]\n",
    "\n",
    "# Loop over the sequences\n",
    "for seq in sequences:\n",
    "    # Create the object and run the calculations\n",
    "    pep=peptide_sequence(seq)\n",
    "    pep.compute_peptide_charges()\n",
    "    pep.calculate_properties_from_mol()\n",
    "    pep.calculate_properties_from_sequence()\n",
    "    \n",
    "    # Append the properties to the lists\n",
    "    charges.append(pep.netCharge)\n",
    "    weights.append(pep.mol_weight)\n",
    "    logps.append(pep.mol_logp)\n",
    "    hydro.append(pep.avg_hydro)\n",
    "\n",
    "print(\"The number of peptides with properties calculated is: {}\".format(len(charges)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we can plot the properties as histograms using matplot functionalities. We will convert the lists to numpy arrays first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "chargesData=np.array(charges).astype(np.float)\n",
    "weightsData=np.array(weights).astype(np.float)\n",
    "logpsData=np.array(logps).astype(np.float)\n",
    "hydroData=np.array(hydro).astype(np.float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the histogram for the charges:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "bins = np.linspace(-6,8,20)\n",
    "plt.hist(chargesData, bins, alpha=0.5, color='b', linewidth=0)\n",
    "plt.axvline(x=mean(charges), color='b', linestyle='--')\n",
    "plt.title('Net charge (at pH=7)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hydrophobicity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "bins = np.linspace(-10,11,20)\n",
    "plt.hist(hydroData, bins, alpha=0.5, color='r', linewidth=0)\n",
    "plt.axvline(x=mean(hydro), color='r', linestyle='--')\n",
    "plt.title('Hydrophobicity Eisenberg scale')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Molecular weight:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "bins = np.linspace(1200,2100,20)\n",
    "plt.hist(weightsData, bins, alpha=0.5, color='g', linewidth=0)\n",
    "plt.axvline(x=mean(weights), color='g', linestyle='--')\n",
    "plt.title('Molecular weight (g/mol)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and Crippen logP:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "bins = np.linspace(-14,4,20)\n",
    "plt.hist(logpsData, bins, alpha=0.5, color='purple', linewidth=0)\n",
    "plt.axvline(x=mean(logps), color='purple', linestyle='--')\n",
    "plt.title('LogP (Crippen)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Structure analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will calculate a number of interactions for a protease-peptide complex presented in the Results section of the paper. First, we will upload the structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The peptide sequence based on the PDB file is: IEPDTDAP\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pdb_file=\"auxiliar/protease_structure.pdb\"\n",
    "dssp_route=\"auxiliar/mkdssp\"\n",
    "chain=\"B\"\n",
    "pepStr=peptide_structure(pdb_file,chain)\n",
    "print(\"The peptide sequence based on the PDB file is: {}\\n\".format(pepStr.sequence))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the structural conformation of the peptide, we can calculate the secondary structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The predicted secondary structure is: -EESS---\n"
     ]
    }
   ],
   "source": [
    "pepStr.get_secondary_structure(dssp_route)\n",
    "print(\"The predicted secondary structure is: {}\".format(pepStr.total_dssp))\n",
    "\n",
    "#The secondary structure convention in DSSP is:\n",
    "#    B - beta bridge\n",
    "#    H - alpha helix\n",
    "#    E - beta strand\n",
    "#    S - bend\n",
    "#    T - turn\n",
    "#    G - 3/10 helix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we can calculate the number of contacts after establishing a contact threshold (in angstroms):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of contacts is: 118\n"
     ]
    }
   ],
   "source": [
    "contact_threshold=4.0\n",
    "pepStr.get_heavy_atom_contacts(contact_threshold)\n",
    "print(\"The total number of contacts is: {}\".format(pepStr.total_contacts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we can calculate the hydrogen bonds based on DSSP:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are the hydrogen bonds detected:\n",
      "I1 interacts with residue G200 from chain A\n",
      "I1 interacts with residue S198 from chain A\n",
      "E2 interacts with residue Y199 from chain A\n",
      "E2 interacts with residue Y199 from chain A\n",
      "E2 interacts with residue N84 from chain A\n",
      "P3 interacts with residue S198 from chain A\n",
      "D4 interacts with residue V197 from chain A\n",
      "D4 interacts with residue D182 from chain A\n",
      "D4 interacts with residue F179 from chain A\n",
      "D4 interacts with residue K180 from chain A\n",
      "T5 interacts with residue G181 from chain A\n",
      "T5 interacts with residue F179 from chain A\n",
      "T5 interacts with residue R28 from chain A\n",
      "D6 interacts with residue K27 from chain A\n",
      "D6 interacts with residue K180 from chain A\n",
      "A7 interacts with residue F179 from chain A\n",
      "The total number of hydrogen bonds are: 16\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pepStr.get_hydrogen_bonds(dssp_route)\n",
    "print(\"The total number of hydrogen bonds are: {}\".format(pepStr.number_hydrogen_bonds))\n",
    "    \n",
    "#The hydrogen bonds can be plotted in a linear or cyclic conformation using the function\n",
    "pepStr.plot_hydrogen_bonds(\"linear\")\n",
    "from IPython.display import Image\n",
    "Image(filename='plot_hbs_IEPDTDAP.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can check amino acid by amino acid in the peptide what is the accesible surface area (ASA) value, as well as the specific contacts created by each residue:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following are the details per amino acid in the peptide:\n",
      "AA\tPos\tASA\tContacts\n",
      "I\t1\t0.30\t16\n",
      "E\t2\t0.42\t13\n",
      "P\t3\t0.14\t16\n",
      "D\t4\t0.02\t42\n",
      "T\t5\t0.02\t15\n",
      "D\t6\t0.18\t16\n",
      "A\t7\t0.45\t0\n",
      "P\t8\t1.00\t0\n"
     ]
    }
   ],
   "source": [
    "print(\"The following are the details per amino acid in the peptide:\")\n",
    "print(\"AA\\tPos\\tASA\\tContacts\")\n",
    "for position in pepStr.positions:\n",
    "    print(\"{:s}\\t{:d}\\t{:.2f}\\t{:d}\".format(pepStr.positions[position]['aa'],position,\n",
    "          pepStr.positions[position]['asa'],pepStr.positions[position]['contacts']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Generation of libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this part, we will generate two independent libraries. For the first case, we will create a library of 10-mer peptides with a uniform distribution of the natural amino acids in all the positions of the peptide:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_peptides=combinatorial_library(10,\"natural\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check what is the distribution of **Alanine** at each position of the peptide. If the distribution is uniform, the percentage should be around 5%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ALA% for position 1 is: 4.67\n",
      "ALA% for position 2 is: 4.67\n",
      "ALA% for position 3 is: 4.17\n",
      "ALA% for position 4 is: 4.17\n",
      "ALA% for position 5 is: 5.17\n",
      "ALA% for position 6 is: 4.83\n",
      "ALA% for position 7 is: 4.33\n",
      "ALA% for position 8 is: 5.50\n",
      "ALA% for position 9 is: 6.00\n",
      "ALA% for position 10 is: 5.00\n"
     ]
    }
   ],
   "source": [
    "counter=frequencies_library(list_peptides)\n",
    "\n",
    "for pos in counter:\n",
    "    count_alanine=counter[pos]['A']\n",
    "    total=0\n",
    "    for aa in counter[pos]:\n",
    "        total+=counter[pos][aa]\n",
    "    perc_alanine=float(count_alanine)/float(total)*100\n",
    "    print(\"ALA% for position {:d} is: {:.2f}\".format(pos,perc_alanine))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the second example, we will generate a library of 5-mer peptides with pattern \"XERTX\". The pattern means that the ERT motif will be conserved, and the X positions will be changed by one of the 20 amino acids (without repetition). Based on the combinations, a total of 400 peptides should be generated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total amount of peptides in the library is 400\n"
     ]
    }
   ],
   "source": [
    "list_peptides=generate_peptide_pattern(\"XERTX\")\n",
    "print(\"The total amount of peptides in the library is {}\".format(len(list_peptides)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For any questions, and developer feedback, please contact us to the email: rodrigo.ochoa@udea.edu.co"
   ]
  }
 ],
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