docs/examples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Brendapyrser: A python parser for the BRENDA database\n",
    "\n",
    "This project provides python classes and functions to parse the text file containing the entire BRENDA enzyme database (https://www.brenda-enzymes.org)\n",
    "\n",
    "Due to BRENDA's license, BRENDA's database cannot be downloaded directly by the parser, instead, the user is asked to download the database as a text file after accepting usage conditions [here](https://www.brenda-enzymes.org/download_brenda_without_registration.php)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from brendapyrser import BRENDA\n",
    "\n",
    "\n",
    "dataFile = \"data/brenda_download.txt\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Parsing BRENDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <table>\n",
       "            <tr>\n",
       "                <td><strong>Number of Enzymes</strong></td><td>7609</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>BRENDA copyright</strong></td><td>Copyrighted by Dietmar Schomburg, Techn. University\n",
       "        Braunschweig, GERMANY. Distributed under the License as stated\n",
       "        at http:/www.brenda-enzymes.org</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Brendapyrser version</strong></td><td>0.0.2</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Author</strong></td><td>Semidán Robaina Estévez, 2020-2022</td>\n",
       "            </tr>\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
       "<brendapyrser.parser.BRENDA at 0x7f994d2c4c10>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's load the database\n",
    "brenda = BRENDA(dataFile)\n",
    "brenda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum and maximum values in database: 0.0 mM, 997.0 mM\n"
     ]
    }
   ],
   "source": [
    "# Plot all Km values in the database\n",
    "BRENDA_KMs = np.array([v for r in brenda.reactions for v in r.KMvalues.get_values()])\n",
    "values = BRENDA_KMs[(BRENDA_KMs < 1000) & (BRENDA_KMs >= 0)]\n",
    "plt.hist(values)\n",
    "plt.title(f\"Median KM value: {np.median(values)}\")\n",
    "plt.xlabel(\"KM (mM)\")\n",
    "plt.show()\n",
    "print(f\"Minimum and maximum values in database: {values.min()} mM, {values.max()} mM\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum and maximum values in database: 5.83e-10 1/s, 997.0 1/s\n"
     ]
    }
   ],
   "source": [
    "# Plot all Km values in the database\n",
    "BRENDA_Kcats = np.array(\n",
    "    [v for r in brenda.reactions for v in r.Kcatvalues.get_values()]\n",
    ")\n",
    "values = BRENDA_Kcats[(BRENDA_Kcats < 1000) & (BRENDA_Kcats >= 0)]\n",
    "plt.hist(values)\n",
    "plt.title(f\"Median Kcat value: {np.median(values)}\")\n",
    "plt.xlabel(\"Kcat (1/s)\")\n",
    "plt.show()\n",
    "print(f\"Minimum and maximum values in database: {values.min()} 1/s, {values.max()} 1/s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum and maximum values in database: 0.0 °C, 125.0 °C\n"
     ]
    }
   ],
   "source": [
    "# Plot all enzyme optimal temperature values in the database\n",
    "BRENDA_TO = np.array(\n",
    "    [\n",
    "        v\n",
    "        for r in brenda.reactions\n",
    "        for v in r.temperature.filter_by_condition(\"optimum\").get_values()\n",
    "    ]\n",
    ")\n",
    "values = BRENDA_TO[(BRENDA_TO >= 0)]\n",
    "plt.hist(values)\n",
    "plt.title(f\"Median Optimum Temperature: {np.median(values)}\")\n",
    "plt.xlabel(\"TO (${}^oC$)\")\n",
    "plt.show()\n",
    "print(f\"Minimum and maximum values in database: {values.min()} °C, {values.max()} °C\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the median optimal temperature for all enzymes in the BRENDA database is 37 °C! That's interesting... perhaps all organisms have agreed to prefer that temperature over other ones... or, more likely, it could be that BRENDA database is biased towards mammals and microorganisms that live within mammals... such as human pathogens.\n",
    "\n",
    "Let's filter results for a particular species, let's try with a hyperthermophylic baterial genus, _Thermotoga_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum and maximum values in database: 20.0 °C, 105.0 °C\n"
     ]
    }
   ],
   "source": [
    "# Plot all enzyme optimal temperature values in the database\n",
    "species = \"Thermotoga\"\n",
    "BRENDA_TO = np.array(\n",
    "    [\n",
    "        v\n",
    "        for r in brenda.reactions.filter_by_organism(species)\n",
    "        for v in r.temperature.filter_by_condition(\"optimum\")\n",
    "        .filter_by_organism(species)\n",
    "        .get_values()\n",
    "    ]\n",
    ")\n",
    "values = BRENDA_TO[(BRENDA_TO >= 0)]\n",
    "plt.hist(values)\n",
    "plt.title(f\"Median Optimum Temperature: {np.median(values)}\")\n",
    "plt.xlabel(\"TO (${}^oC$)\")\n",
    "plt.show()\n",
    "print(f\"Minimum and maximum values in database: {values.min()} °C, {values.max()} °C\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the median optimal temperature among all enzymes in the genus, 80°C, is much higher than in the case of the entire database. That's consistent with the fact that _Thermotoga_ are hyperthermophylic... alright!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Extracting data for _Pyruvate kinase_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <table>\n",
       "            <tr>\n",
       "                <td><strong>Enzyme identifier</strong></td><td>2.7.1.40</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Name</strong></td><td>Pyruvate kinase</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Systematic name</strong></td><td>ATP:pyruvate 2-O-phosphotransferase</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Reaction type</strong></td><td>Phospho group transfer</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Reaction</strong></td><td>ATP + pyruvate <=> ADP + phosphoenolpyruvate</td>\n",
       "            </tr>\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
       "<brendapyrser.parser.Reaction at 0x7fead617e820>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can retrieve an enzyme entry by its EC number like this\n",
    "r = brenda.reactions.get_by_id(\"2.7.1.40\")\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here are all the KM values for phosphoenolpyruvate associated with this enzyme class\n",
    "compound = \"phosphoenolpyruvate\"\n",
    "kms = r.KMvalues.filter_by_compound(compound).get_values()\n",
    "plt.hist(kms)\n",
    "plt.xlabel(\"KM (mM)\")\n",
    "plt.title(f\"{r.name} ({compound})\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here are all the KM values for phosphoenolpyruvate associated with this enzyme class\n",
    "compound = \"phosphoenolpyruvate\"\n",
    "KMs = r.KMvalues.filter_by_compound(compound).get_values()\n",
    "plt.hist(KMs)\n",
    "plt.xlabel(\"KM (mM)\")\n",
    "plt.title(f\"{r.name} ({compound})\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.051500000000000004, 0.18]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And further filtered by organism\n",
    "r.KMvalues.filter_by_organism(\"Bos taurus\").filter_by_compound(\n",
    "    \"phosphoenolpyruvate\"\n",
    ").get_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here are all the Kcat values for phosphoenolpyruvate associated with this enzyme class\n",
    "compound = \"phosphoenolpyruvate\"\n",
    "kcats = r.Kcatvalues.filter_by_compound(compound).get_values()\n",
    "plt.hist(kcats)\n",
    "plt.xlabel(\"Kcat ($s^{-1}$)\")\n",
    "plt.title(f\"{r.name} ({compound})\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 Finding all KM values for a given substrate and organism\n",
    "Next, we will retrieve KM values associated to a particular substrate for all enzymes in a given species. Will t he KM values distribute around a narrow or wider concentration range? Since substrate concentration in cytoplasma is the same for all enzymes it makes sense that all cytoplasmi enzymes utilizing that substrate have similar KM values. Let's test this idea with _Escherichia coli_ and some common substrates participating in the central carbon metabolism."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "species, compound = \"Escherichia coli\", \"NADH\"\n",
    "KMs = np.array(\n",
    "    [\n",
    "        v\n",
    "        for r in brenda.reactions.filter_by_organism(species)\n",
    "        for v in r.KMvalues.filter_by_compound(compound)\n",
    "        .filter_by_organism(species)\n",
    "        .get_values()\n",
    "    ]\n",
    ")\n",
    "\n",
    "if len(KMs) > 0:\n",
    "    plt.hist(KMs)\n",
    "    plt.xlabel(\"KM (mM)\")\n",
    "    plt.title(f\"{species} KMs ({compound}), median = {np.median((KMs))}\")\n",
    "    plt.show()\n",
    "else:\n",
    "    print(\"No KM values for compound\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's interesting! typical NADH concentrations are low in _Escherichia coli_, e.g., from [BioNumbers](http://book.bionumbers.org/what-are-the-concentrations-of-free-metabolites-in-cells/) we get a value of 0.083 mM. The median KM value for NADH among all enzymes binding it is lower as we see in the plot above! Hence, it looks like most enzymes are (nearly) saturated for NADH and thus fluxes are sort of independent of NADH concentration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 Filtering reactions by specific compound\n",
    "\n",
    "We can also filter reactions in BRENDA by a specific compound: substrate, product or either of the two. Let's filter reactions containing _phosphoenolpyruvate_ as a substrate, product or both to exemplify this feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <table>\n",
       "            <tr>\n",
       "                <td><strong>Enzyme identifier</strong></td><td>2.5.1.19</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Name</strong></td><td>3-phosphoshikimate 1-carboxyvinyltransferase</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Systematic name</strong></td><td>phosphoenolpyruvate:3-phosphoshikimate 5-O-(1-carboxyvinyl)-transferase</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Reaction type</strong></td><td>Enolpyruvate group transfer (#3,52,55# induced-fit mechanism, formation</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Reaction</strong></td><td>phosphoenolpyruvate + 3-phosphoshikimate <=> phosphate +5-O-</td>\n",
       "            </tr>\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
       "<brendapyrser.parser.Reaction at 0x7fe7502c1280>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "substrate_rxns = brenda.reactions.filter_by_substrate(\"phosphoenolpyruvate\")\n",
    "substrate_rxns[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <table>\n",
       "            <tr>\n",
       "                <td><strong>Enzyme identifier</strong></td><td>2.5.1.7</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Name</strong></td><td>Udp-n-acetylglucosamine 1-carboxyvinyltransferase</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Systematic name</strong></td><td>phosphoenolpyruvate:UDP-N-acetyl-D-glucosamine</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Reaction type</strong></td><td>Carboxyvinyl group transfer</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Reaction</strong></td><td>phosphoenolpyruvate + UDP-N-acetyl-alpha-D-glucosamine <=> phosphate +UDP-N-acetyl-3-O-</td>\n",
       "            </tr>\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
       "<brendapyrser.parser.Reaction at 0x7fe7502c1d00>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compound_rxns = brenda.reactions.filter_by_compound(\"phosphoenolpyruvate\")\n",
    "compound_rxns[7]"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "367a058ebb24ea2c2bb1633bf810ec6a1a05f59e065f27f721ea93103e797079"
  },
  "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}