analyses/01_working_with_proteins.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial 1\n",
    "\n",
    "## Working with proteins and structures\n",
    "\n",
    "In this notebook we will see the basics of protein and structure manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some imports\n",
    "import manas_cafa.bio.protein\n",
    "from manas_cafa.bio.protein import Protein"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proteins will be handled following the Uniprot's XML format. When loading a protein, the package will attempt to load the protein's XML from the `uniprot_entries_path` folder. If the file is not found there, it will be downloaded from the Uniprot api, whose url is specified at `uniprot_entries_url`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/data/uniprot/entries/'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "manas_cafa.bio.protein.uniprot_entries_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://rest.uniprot.org/uniprotkb/'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "manas_cafa.bio.protein.uniprot_entries_url"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a protein object\n",
    "\n",
    "To create a protein instance only needs its protein Uniprot code. To load its XML data, you just need to call the `load_uniprot` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "protein = Protein('P01112')\n",
    "protein.load_uniprot()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this package, we will work mainly with protein sequences, GeneOntology(GO) terms and protein structures. If the XML data was loaded, you can ask for the protein sequence:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHQYREQIKRVKDSDDVPMVLVGNKCDLAARTVESRQAQDLARSYGIPYIETSAKTRQGVEDAFYTLVREIRQHKLRKLNPPDESGPGCMSCKCVLS'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protein.sequence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For its GO terms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'type': 'go',\n",
       "  'id': 'GO:0005737',\n",
       "  'properties': {'term': 'C:cytoplasm',\n",
       "   'evidence': 'ECO:0000304',\n",
       "   'project': 'ProtInc'}},\n",
       " {'type': 'go',\n",
       "  'id': 'GO:0005829',\n",
       "  'properties': {'term': 'C:cytosol',\n",
       "   'evidence': 'ECO:0000314',\n",
       "   'project': 'HPA'}},\n",
       " {'type': 'go',\n",
       "  'id': 'GO:0005789',\n",
       "  'properties': {'term': 'C:endoplasmic reticulum membrane',\n",
       "   'evidence': 'ECO:0000304',\n",
       "   'project': 'Reactome'}},\n",
       " {'type': 'go',\n",
       "  'id': 'GO:0098978',\n",
       "  'properties': {'term': 'C:glutamatergic synapse',\n",
       "   'evidence': 'ECO:0000314',\n",
       "   'project': 'SynGO'}},\n",
       " {'type': 'go',\n",
       "  'id': 'GO:0005794',\n",
       "  'properties': {'term': 'C:Golgi apparatus',\n",
       "   'evidence': 'ECO:0000314',\n",
       "   'project': 'UniProtKB'}}]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protein.go_terms()[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And for the list of its structures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'type': 'pdb',\n",
       "  'id': '121P',\n",
       "  'properties': {'method': 'X-ray',\n",
       "   'resolution': '1.54 A',\n",
       "   'chains': 'A=1-166'}},\n",
       " {'type': 'pdb',\n",
       "  'id': '1AA9',\n",
       "  'properties': {'method': 'NMR', 'chains': 'A=1-171'}},\n",
       " {'type': 'pdb',\n",
       "  'id': '1AGP',\n",
       "  'properties': {'method': 'X-ray',\n",
       "   'resolution': '2.30 A',\n",
       "   'chains': 'A=1-166'}},\n",
       " {'type': 'pdb',\n",
       "  'id': '1BKD',\n",
       "  'properties': {'method': 'X-ray',\n",
       "   'resolution': '2.80 A',\n",
       "   'chains': 'R=1-166'}},\n",
       " {'type': 'pdb',\n",
       "  'id': '1CLU',\n",
       "  'properties': {'method': 'X-ray',\n",
       "   'resolution': '1.70 A',\n",
       "   'chains': 'A=1-166'}}]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protein.structure_terms()[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing GO parent and children terms of protein assigned terms\n",
    "\n",
    "Given an obo file ─for this example we are using the GO full graph given by the CAFA5 competition organizators─ we can construct a graph (using `obonet` and `networkx` libraries) to ask for parent and childen terms of the terms assigned to the protein of interest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = Protein.build_graph('../cafa-5-protein-function-prediction/Train/go-basic.obo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['GO:2000348',\n",
       " 'GO:0010990',\n",
       " 'GO:0003202',\n",
       " 'GO:0048340',\n",
       " 'GO:1990574',\n",
       " 'GO:0110032',\n",
       " 'GO:2000342',\n",
       " 'GO:0061742',\n",
       " 'GO:0003412',\n",
       " 'GO:0035766']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protein.go_terms_parents(graph=g, max_distance=1)[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['GO:1900274',\n",
       " 'GO:0042325',\n",
       " 'GO:0009892',\n",
       " 'GO:0043170',\n",
       " 'GO:0072657',\n",
       " 'GO:0045859',\n",
       " 'GO:0032502',\n",
       " 'GO:0120032',\n",
       " 'GO:0048856',\n",
       " 'GO:0031529']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protein.go_terms_children(graph=g, max_distance=2)[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with protein structures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's import the correspondent module\n",
    "from manas_cafa.bio.structure import Structure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We just have to do as we did for `Protein` objects, in this case loading the PDB, the reference database for protein structures. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "structure = Structure(protein.structure_terms()[1]['id'])\n",
    "structure.load_pdb()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can ask, for example, about the structure chains. The structure attribute is just as a biopython structure object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structure.get_chain_ids()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can ask for the protein contact map, in this case the inner contacts for the chain A with 12 armstrong as a distance threshold:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., ..., 0., 1., 1.],\n",
       "       [1., 1., 1., ..., 0., 1., 1.],\n",
       "       [1., 1., 1., ..., 0., 1., 1.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structure.contact_map('A', 'A', 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the contact map in a more comprehensive way to see which residues are in contact amongst them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7feb62484e50>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.heatmap(structure.contact_map('A', 'A', 12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's change the threshold to a more restrictive one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7feba016e970>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(structure.contact_map('A', 'A', 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}