deeplearning_proteinstructure_3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4n-R2VfKapz8"
      },
      "source": [
        "# DEEP LEARNING FOR PROTEIN STRUCTURES (Part 3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZGJS4u0penv3"
      },
      "source": [
        "## Protein protein interactions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0KLoh58Krofd"
      },
      "source": [
        "### 1. Introduction\n",
        "**Protein-protein interactions (PPI) drive virtually all biological processes in living organisms and are necessary for cellular machinery.**\n",
        "\n",
        "In order to perform various functions in living organisms two or more proteins gather and interact with each other to create a protein complex. The resulted quaternary structure is an association of two or more peptide chains linked to each other by molecular interactions. These protein-protein interactions drives the biological processes in the cell. A protein has high propensity to interact with some proteins which are considered as its potential partners."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Tx8Oi8Brv98"
      },
      "source": [
        "### 2. Binding affinity\n",
        "Binding affinity is a variable defined to numerically measure the propensity and the strength of the binding interaction between two bio-molecule partners.\n",
        "\n",
        "<img src='https://drive.google.com/uc?id=13GHoH4p0BR9Qq4ETZ7kU-AJehH8PJFuV'>\n",
        "\n",
        "From chemical point of view the binding affinity is also expressed as dissociation constant ($K_d$) which is a disposition of a chemical complex in its equilibrium concentration to dissociate into its molecular components. In molecular biology, association constant ($K_a=\\frac{1}{K_d}$) is used to measure the propensity of binding between two molecules. This constant is defined below:\n",
        "\n",
        "$\n",
        " P_1 + P_2 \\rightleftharpoons P_1P_2 \\\\\n",
        " K_a = \\frac{1}{K_d} = \\frac{[P_1P_2]}{[P_1] \\times [P_2]}\n",
        "$\n",
        "\n",
        "where $[P_1]$, $[P_2]$, and $[P_1P_2]$ are the equilibrium concentrations of $P_1$, $P_2$, and protein complex $P_1P_2$, respectively. The larger the $K_a$ value, the greater the binding affinity between $[P_1]$ and $[P_2]$. The smaller the $K_a$ value, the more weakly $[P_1]$ and $[P_2]$ are drawn to and interact with each other. Association constant can be converted to the Gibbs free energy $\\Delta$G using the equation below:\n",
        "\n",
        "\n",
        "$\n",
        "\\Delta G = R \\times T \\times \\ln{(K_d)} = \\Delta H -T\\Delta S\n",
        "$\n",
        "\n",
        "where $R=\\frac{8.314}{4184}\\frac{kcal}{K \\times mol}$ is the ideal gas constant or Boltzmann constant, $T$ is the absolute (thermodynamic) temperature, $H$ is enthalpy, and $S$ is entropy. Henceforth in this tutorial we will use $\\Delta$G as the representation of binding affinity between two proteins.\n",
        "\n",
        "<img src='https://drive.google.com/uc?id=1UO_XdL9g0Bb7ZzCSBLWluCXkdsu9LT5S' height='600'>\n",
        "\n",
        "*This figure is generated in the Analytical Genomics team*\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1GPLTmCbe2Q5"
      },
      "source": [
        "## Mutations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-xPkrYfhHCZz"
      },
      "source": [
        "### 1. Introduction\n",
        "**Disease-causing point mutations on a protein interface affect the protein ability to interact with its partners in the cell. Hence, it becomes crucial to develop systematic and accurate approaches assessing the impact of mutations on the formation and stability of protein complexes.**\n",
        "\n",
        "Mutations happen when DNA fails to accurately replicate itself or the tissues and organs are exposed to specific external agents such as certain chemicals or radiation. In these cases single nucleotide base is substituted, inserted, or deleted in the DNA or RNA molecule. If one nucleotide base is replaced by another in a gene, the resulted mRNA has one nucleotide substitution in its molecule. Consequently, ribosome might choose another amino acid for the triplet in which the mutation happened. If the amino acid does not change for the mutant triplet the mutation is synonymous (or silent) otherwise it is non-synonymous. The non-synonymous mutations might lead to changes in the biological mechanism of a living organism and are therefore subject to the natural selection. For example, they can be deleterious and disturb the cell machinery.\n",
        "\n",
        "The non-synonymous mutations that happen on a protein's interaction surface can affect the interaction process between protein and its potential partners. In this scenario the more physico-chemical properties of mutant amino acid differ from the wild-type's the more the binding affinity is influenced.\n",
        "\n",
        "This type of genetic mutation is called single point mutation. A mutant protein structure might have multiple point mutations in which several amino acids are altered. Although there are various types of mutation, in this tutorial we are interested to study the effects of this type of mutation on the protein-protein interactions and how it influences the binding affinity. An examples of single point mutation is given below. In this mutation both wild-type and mutant amino acids, Tryptophan (Trp or W) and Alanine (Ala or A), have the hydrophobic (non-polar) side-chains. However, the size of side-chain is dramatically changed and if it happens on the interaction surface it can affect the geometrical properties of the binding site.\n",
        "\n",
        "<img src='https://drive.google.com/uc?id=14uctEuqovX6laWf6MI1pWEbLPppoXC2t'>\n",
        "\n",
        "<br>\n",
        "<br>\n",
        "Below is another example on how a point mutation from Alanine to Glutamic\n",
        "Acid affects geometrical and chemical properties of an\n",
        "interaction surface.\n",
        "\n",
        "<img src='https://drive.google.com/uc?id=1kVKrWtMMAM4zgD3jc4jVG5QlhlIeyWyw'>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zkfrebhkvLHj"
      },
      "source": [
        "### 2. How do mutations reshape the protein-protein interaction networks?\n",
        "\n",
        "<img src='https://drive.google.com/uc?id=1jWRaxlEwSEYQAld1CT79waFMUZp5Y10n'>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nS-MmHAjIQTJ"
      },
      "source": [
        "### 3. What are the challenges in measuring effects of mutations?\n",
        "1. Reconstruction of the mutant protein complex after mutagenesis.\n",
        "2. Identification of fine descriptors for the prediction of changes in binding affinity."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y1PbqjJAIYT1"
      },
      "source": [
        "### 4. Mutant protein complex\n",
        "Following single point mutation the side-chain orientation angles ($\\chi_1$,$\\chi_2$,$\\chi_3$, ...) of the mutant amino acid are unknown. Furthermore, the backbone of the protein is subject to slight movements in response to point mutation-induced changes. Therefore, we need to consider the effects of the mutant amino acid on the backbone torsion angles ($\\Phi$ and $\\Psi$). Using [Rosetta-Backrub](https://github.com/Kortemme-Lab/flex_ddG_tutorial) we can explore near-native conformational fluctuations of a protein and to compute an enhanced estimation on side-chain orientation angles of mutant amino acid and on backbone torsion angles.\n",
        "\n",
        "<img src='https://drive.google.com/uc?id=1DznHFE2ptx7uFa8-LQ8-_wf8zekLcU4h'>\n",
        "\n",
        "*Effects of point mutations on backbone.*\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZFF4ZmDaI8pz"
      },
      "source": [
        "**Fetch a protein of your choice in Pymol and apply mutagenesis function to introduce mutations.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z123nO9ijeEs"
      },
      "source": [
        "### 5. Simulation of near-native conformational fluctuations\n",
        "<img src='https://drive.google.com/uc?id=1VfIJFBWLiyEgixPx6evolr5xBgWXVItw'>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gTJVrBdalcVw"
      },
      "source": [
        "This is an example of near-native conformational fluctuations in which only the movement of backbone is shown.\n",
        "\n",
        "<img src='https://drive.google.com/uc?id=1MJwDHpXJUHtNepAkvDfmCZEeR16I1gDw'>\n",
        "\n",
        "*Complex: 5E9D*\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JQohXoLVmLXz"
      },
      "source": [
        "### 6. Case study: Complex 1BRS\n",
        "\n",
        "#### 6.1 Point mutations on 1BRS\n",
        "From database of changes of binding affinity [SKEMPI V2.0](https://life.bsc.es/pid/skempi2)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s791IpNemkjr"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import HTML, display"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 647
        },
        "id": "dy7FYGd7mnly",
        "outputId": "f0756b1a-0ccb-4173-fc0a-de7db3a20473"
      },
      "source": [
        "!gdown --id '1Q_1oy4KQuE1c7FS6b7vUTDSB8PShBy7A'\n",
        "mutations = pd.read_csv('1RBS_mudations.csv', sep=';')\n",
        "mutations[:15]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gdown/__main__.py:140: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1Q_1oy4KQuE1c7FS6b7vUTDSB8PShBy7A\n",
            "To: /content/1RBS_mudations.csv\n",
            "100% 990/990 [00:00<00:00, 3.66MB/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Complex     Mutation   Region Method\n",
              "0   1BRS_A_D        DD35A      COR    ITC\n",
              "1   1BRS_A_D        DD35A      COR    SPR\n",
              "2   1BRS_A_D        DD39A      COR    ITC\n",
              "3   1BRS_A_D        DD39A      COR    SPR\n",
              "4   1BRS_A_D  EA71A,DD35A  SUP,COR    ITC\n",
              "5   1BRS_A_D  EA71A,DD39A  SUP,COR    ITC\n",
              "6   1BRS_A_D        EA71A      SUP    ITC\n",
              "7   1BRS_A_D        EA71C      SUP    ITC\n",
              "8   1BRS_A_D  EA71F,DD35A  SUP,COR    ITC\n",
              "9   1BRS_A_D  EA71F,DD39A  SUP,COR    ITC\n",
              "10  1BRS_A_D        EA71F      SUP    ITC\n",
              "11  1BRS_A_D  EA71Q,DD39A  SUP,COR    ITC\n",
              "12  1BRS_A_D  EA71Q,ED74A  SUP,RIM    ITC\n",
              "13  1BRS_A_D        EA71Q      SUP    ITC\n",
              "14  1BRS_A_D        EA71S      SUP    ITC"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9fa8c07e-1b93-48c9-a19c-8359ea033cf0\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Complex</th>\n",
              "      <th>Mutation</th>\n",
              "      <th>Region</th>\n",
              "      <th>Method</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1BRS_A_D</td>\n",
              "      <td>DD35A</td>\n",
              "      <td>COR</td>\n",
              "      <td>ITC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1BRS_A_D</td>\n",
              "      <td>DD35A</td>\n",
              "      <td>COR</td>\n",
              "      <td>SPR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1BRS_A_D</td>\n",
              "      <td>DD39A</td>\n",
              "      <td>COR</td>\n",
              "      <td>ITC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1BRS_A_D</td>\n",
              "      <td>DD39A</td>\n",
              "      <td>COR</td>\n",
              "      <td>SPR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1BRS_A_D</td>\n",
              "      <td>EA71A,DD35A</td>\n",
              "      <td>SUP,COR</td>\n",
              "      <td>ITC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1BRS_A_D</td>\n",
              "      <td>EA71A,DD39A</td>\n",
              "      <td>SUP,COR</td>\n",
              "      <td>ITC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1BRS_A_D</td>\n",
              "      <td>EA71A</td>\n",
              "      <td>SUP</td>\n",
              "      <td>ITC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1BRS_A_D</td>\n",
              "      <td>EA71C</td>\n",
              "      <td>SUP</td>\n",
              "      <td>ITC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1BRS_A_D</td>\n",
              "      <td>EA71F,DD35A</td>\n",
              "      <td>SUP,COR</td>\n",
              "      <td>ITC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1BRS_A_D</td>\n",
              "      <td>EA71F,DD39A</td>\n",
              "      <td>SUP,COR</td>\n",
              "      <td>ITC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1BRS_A_D</td>\n",
              "      <td>EA71F</td>\n",
              "      <td>SUP</td>\n",
              "      <td>ITC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1BRS_A_D</td>\n",
              "      <td>EA71Q,DD39A</td>\n",
              "      <td>SUP,COR</td>\n",
              "      <td>ITC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1BRS_A_D</td>\n",
              "      <td>EA71Q,ED74A</td>\n",
              "      <td>SUP,RIM</td>\n",
              "      <td>ITC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1BRS_A_D</td>\n",
              "      <td>EA71Q</td>\n",
              "      <td>SUP</td>\n",
              "      <td>ITC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1BRS_A_D</td>\n",
              "      <td>EA71S</td>\n",
              "      <td>SUP</td>\n",
              "      <td>ITC</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9fa8c07e-1b93-48c9-a19c-8359ea033cf0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9fa8c07e-1b93-48c9-a19c-8359ea033cf0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9fa8c07e-1b93-48c9-a19c-8359ea033cf0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-bddef34b-a4aa-4539-8169-8d41cb233a23\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bddef34b-a4aa-4539-8169-8d41cb233a23')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bddef34b-a4aa-4539-8169-8d41cb233a23 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"mutations[:15]\",\n  \"rows\": 15,\n  \"fields\": [\n    {\n      \"column\": \"Complex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"1BRS_A_D\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mutation\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          \"EA71Q\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"SUP,COR\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Method\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"SPR\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2kFb5SzBnFwH"
      },
      "source": [
        "Mutation **EA71Y**: Glutamic Acid (E) which is located on position 71 of chain A is replaced by Tyrosine (Y)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548,
          "resources": {
            "http://localhost:8080/content/EA71Y.mov": {
              "data": "",
              "ok": false,
              "headers": [
                [
                  "content-length",
                  "0"
                ]
              ],
              "status": 404,
              "status_text": ""
            }
          }
        },
        "id": "W0LykYYmnIO0",
        "outputId": "30f1b0bb-c7b3-43db-9a5d-51731645e81b"
      },
      "source": [
        "!gdown --id '1Eo4cwGT0FblmN82AKVO9lC1tOnmuDisL'\n",
        "HTML(\"\"\"\n",
        "    <video width=\"700\" height=\"400\" controls>\n",
        "        <source src=\"/content/EA71Y.mov\">\n",
        "    </video>\n",
        "\"\"\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gdown/__main__.py:140: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1Eo4cwGT0FblmN82AKVO9lC1tOnmuDisL\n",
            "To: /content/EA71Y.mov\n",
            "100% 1.39M/1.39M [00:00<00:00, 158MB/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <video width=\"700\" height=\"400\" controls>\n",
              "        <source src=\"/content/EA71Y.mov\">\n",
              "    </video>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K3hRpUv8pFoI"
      },
      "source": [
        "Mutation **YD29F**: Tyrosine (Y) which is located on position 29 of chain D is replaced by Phenylalanine (F)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548,
          "resources": {
            "http://localhost:8080/YD29F.mov": {
              "data": "",
              "ok": false,
              "headers": [
                [
                  "content-length",
                  "0"
                ]
              ],
              "status": 404,
              "status_text": ""
            }
          }
        },
        "id": "S255Fr3ypW5X",
        "outputId": "7da8ae79-7708-4b22-fadb-f54222c8acb3"
      },
      "source": [
        "!gdown --id 1pm0HceSZLz4dTcVT8PWZIA6qMM0aZg3q\n",
        "HTML(\"\"\"\n",
        "    <video width=\"700\" height=\"400\" controls>\n",
        "        <source src=\"YD29F.mov\">\n",
        "    </video>\n",
        "\"\"\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gdown/__main__.py:140: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1pm0HceSZLz4dTcVT8PWZIA6qMM0aZg3q\n",
            "To: /content/YD29F.mov\n",
            "100% 1.30M/1.30M [00:00<00:00, 86.6MB/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <video width=\"700\" height=\"400\" controls>\n",
              "        <source src=\"YD29F.mov\">\n",
              "    </video>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TV86rwADJrbC"
      },
      "source": [
        "### 7. Effects of the mutations on different interface regions\n",
        "\n",
        "<img src='https://drive.google.com/uc?id=1FAerlQGucjAFikQVLYmNxaR-u40HY4wq'>\n",
        "[E.D. Levy, et al., ‘10]\n",
        "\n",
        "By analyzing the database of [SKEMPI V2.0](https://life.bsc.es/pid/skempi2) we realized that mutations on the Core region of the interaction interface has the most effect on the changes of the binding affinity.\n",
        "<img src='https://drive.google.com/uc?id=14jy0gq-Lct7C69N4D7rsLFwB1Ok6OJRZ'>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SWox-Brjfdp-"
      },
      "source": [
        "### Given what we have learnt until now, what end-to-end deep learning architecture would you propose to measure the binding affinity?\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iTZoquLXjkMu"
      },
      "source": [
        "## Geometric deep learning to measure changes of the binding affinity"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_8sICnRQ7zXJ"
      },
      "source": [
        "### 1. An end-to-end Siamese architecture to predict changes of binding affinity ($\\Delta\\Delta$G)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2q89pVKLJDCe"
      },
      "source": [
        "Here we will implement an end-to-end Siamese convolutional neural network that takes as input the local 3D environment around the mutation site in the wild-type and mutated form and gives $\\Delta\\Delta$G as output. Thanks to the use of locally oriented frames from Part 2 of this tutorial, the architecture is invariant to 3D translations and rotations. The local 3D environments that you are going to work with are extracted from conformations generated with Rosetta-Backrub. Unlike other approaches that calculate $\\Delta$G of mutant and wild-type separately, we focus on local changes and differences between structures, by analyzing the residues of interest on which the mutation happens. We place a sufficiently large cube (24×24×24 $Å^3$) around the wild-type and mutant residues to capture local information on how the mutation affects the interface."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GMDBIOmkJgbq"
      },
      "source": [
        "<img src='https://drive.google.com/uc?id=1Rh74w-X6FH1yUh4DGyAkzJmYGqxxJcdB'>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7yo0rteqJmPx"
      },
      "source": [
        "### 2. Siamese architecture"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dYSUDU69HC7j",
        "outputId": "823e8e6f-eb16-40e7-8835-b6ec99696922"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import logging\n",
        "import seaborn as sns\n",
        "#from scipy import interp\n",
        "from math import exp\n",
        "from random import shuffle, random, seed, sample\n",
        "import matplotlib.pyplot as plt\n",
        "import time\n",
        "import os, sys\n",
        "import glob\n",
        "\n",
        "!pip install pickle5\n",
        "import pickle5\n",
        "\n",
        "#!pip uninstall tensorflow\n",
        "!pip install tensorflow==2.8.0\n",
        "\n",
        "\n",
        "import tensorflow.keras\n",
        "from tensorflow.keras import backend as K\n",
        "from tensorflow.keras import regularizers\n",
        "from tensorflow.keras.models import Model, Sequential,load_model\n",
        "from tensorflow.keras.layers import Input, Conv3D, MaxPooling3D, AveragePooling3D, Dense, Dropout, Activation, Flatten, Concatenate, Dot, BatchNormalization, Add, Lambda\n",
        "from tensorflow.keras.backend import ones, ones_like\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.constraints import max_norm\n",
        "from tensorflow.keras.regularizers import l2\n",
        "\n",
        "from sklearn.model_selection import train_test_split, StratifiedKFold\n",
        "from sklearn.metrics import mean_squared_error, accuracy_score, roc_auc_score, roc_curve, precision_recall_curve, log_loss\n",
        "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
        "\n",
        "from scipy import stats\n",
        "\n",
        "import gc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pickle5 in /usr/local/lib/python3.10/dist-packages (0.0.11)\n",
            "Requirement already satisfied: tensorflow==2.8.0 in /usr/local/lib/python3.10/dist-packages (2.8.0)\n",
            "Requirement already satisfied: absl-py>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.8.0) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.8.0) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=1.12 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.8.0) (24.3.25)\n",
            "Requirement already satisfied: gast>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.8.0) (0.6.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.8.0) (0.2.0)\n",
            "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.8.0) (3.11.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.8.0) (1.1.2)\n",
            "Requirement already satisfied: libclang>=9.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.8.0) (18.1.1)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.8.0) (1.26.4)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.8.0) (3.3.0)\n",
            "Requirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.8.0) (3.20.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.8.0) (71.0.4)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.8.0) (1.16.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.8.0) (2.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.8.0) (4.12.2)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.8.0) (1.16.0)\n",
            "Requirement already satisfied: tensorboard<2.9,>=2.8 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.8.0) (2.8.0)\n",
            "Requirement already satisfied: tf-estimator-nightly==2.8.0.dev2021122109 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.8.0) (2.8.0.dev2021122109)\n",
            "Requirement already satisfied: keras<2.9,>=2.8.0rc0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.8.0) (2.8.0)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.8.0) (0.37.1)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.8.0) (1.64.1)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow==2.8.0) (0.44.0)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.9,>=2.8->tensorflow==2.8.0) (2.27.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.9,>=2.8->tensorflow==2.8.0) (0.4.6)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.9,>=2.8->tensorflow==2.8.0) (3.7)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.9,>=2.8->tensorflow==2.8.0) (2.32.3)\n",
            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.9,>=2.8->tensorflow==2.8.0) (0.6.1)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.9,>=2.8->tensorflow==2.8.0) (1.8.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.9,>=2.8->tensorflow==2.8.0) (3.0.4)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow==2.8.0) (5.5.0)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow==2.8.0) (0.4.1)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow==2.8.0) (4.9)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow==2.8.0) (1.3.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow==2.8.0) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow==2.8.0) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow==2.8.0) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow==2.8.0) (2024.8.30)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=0.11.15->tensorboard<2.9,>=2.8->tensorflow==2.8.0) (2.1.5)\n",
            "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow==2.8.0) (0.6.1)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow==2.8.0) (3.2.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "id": "e49hoqH-81wl",
        "outputId": "d98e7a51-187d-40e2-fb06-b105957d51b5"
      },
      "source": [
        "logging.basicConfig(filename='manager.log', filemode='w', format='%(levelname)s: %(message)s', level=logging.DEBUG)\n",
        "mainlog = logging.getLogger('main')\n",
        "logging.Logger"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "logging.Logger"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>logging.Logger</b><br/>def __init__(name, level=NOTSET)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/lib/python3.10/logging/__init__.py</a>Instances of the Logger class represent a single logging channel. A\n",
              "&quot;logging channel&quot; indicates an area of an application. Exactly how an\n",
              "&quot;area&quot; is defined is up to the application developer. Since an\n",
              "application can have any number of areas, logging channels are identified\n",
              "by a unique string. Application areas can be nested (e.g. an area\n",
              "of &quot;input processing&quot; might include sub-areas &quot;read CSV files&quot;, &quot;read\n",
              "XLS files&quot; and &quot;read Gnumeric files&quot;). To cater for this natural nesting,\n",
              "channel names are organized into a namespace hierarchy where levels are\n",
              "separated by periods, much like the Java or Python package namespace. So\n",
              "in the instance given above, channel names might be &quot;input&quot; for the upper\n",
              "level, and &quot;input.csv&quot;, &quot;input.xls&quot; and &quot;input.gnu&quot; for the sub-levels.\n",
              "There is no arbitrary limit to the depth of nesting.</pre>\n",
              "      <script>\n",
              "      if (google.colab.kernel.accessAllowed && google.colab.files && google.colab.files.view) {\n",
              "        for (const element of document.querySelectorAll('.filepath')) {\n",
              "          element.style.display = 'block'\n",
              "          element.onclick = (event) => {\n",
              "            event.preventDefault();\n",
              "            event.stopPropagation();\n",
              "            google.colab.files.view(element.textContent, 1420);\n",
              "          };\n",
              "        }\n",
              "      }\n",
              "      for (const element of document.querySelectorAll('.function-repr-contents')) {\n",
              "        element.onclick = (event) => {\n",
              "          event.preventDefault();\n",
              "          event.stopPropagation();\n",
              "          element.classList.toggle('function-repr-contents-collapsed');\n",
              "        };\n",
              "      }\n",
              "      </script>\n",
              "      </div>"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vDTIXvpE85Yc",
        "outputId": "8b29a722-c139-43b7-ed1b-0e44a1d42f64"
      },
      "source": [
        "print('Your python version: {}'.format(sys.version_info.major))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Your python version: 3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h0Wxhcio89X9"
      },
      "source": [
        "USE_TENSORFLOW_AS_BACKEND = True\n",
        "FORCE_CPU = False\n",
        "\n",
        "if USE_TENSORFLOW_AS_BACKEND:\n",
        "    os.environ['KERAS_BACKEND'] = 'tensorflow'\n",
        "else:\n",
        "    os.environ['KERAS_BACKEND'] = 'theano'\n",
        "if FORCE_CPU:\n",
        "    os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n",
        "    os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tQUxUs169AoL",
        "outputId": "6d93bf94-31ca-4c84-8f6f-357b3ca0ff16"
      },
      "source": [
        "if USE_TENSORFLOW_AS_BACKEND == True:\n",
        "    import tensorflow\n",
        "    print('Your tensorflow version: {}'.format(tensorflow.__version__))\n",
        "    print(\"GPU : \"+tensorflow.test.gpu_device_name())\n",
        "else:\n",
        "    import theano\n",
        "    print('Your theano version: {}'.format(theano.__version__))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Your tensorflow version: 2.8.0\n",
            "GPU : \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7OkzZN_A9iie"
      },
      "source": [
        "#### 2.1 Siamese model architecture based on 3D-CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a3Q_cwfR9bso"
      },
      "source": [
        "def get_siamese_model(input_shape):\n",
        "    \"\"\"\n",
        "    Model architecture\n",
        "    \"\"\"\n",
        "\n",
        "    # Define the tensors for the two input images\n",
        "    left_input = Input(shape=input_shape)\n",
        "    right_input = Input(shape=input_shape)\n",
        "\n",
        "    # Convolutional Neural Network\n",
        "    model = Sequential()\n",
        "\n",
        "    model.add(Conv3D(20, kernel_size=(1, 1, 1), use_bias = True, padding = 'valid', activation='linear', kernel_initializer='he_uniform', kernel_regularizer=l2(2e-4)))\n",
        "    model.add(BatchNormalization())\n",
        "\n",
        "    model.add(Conv3D(20, kernel_size=(3, 3, 3), use_bias = True, padding = 'valid', activation='elu', kernel_initializer='he_uniform', kernel_regularizer=l2(2e-4)))\n",
        "    model.add(BatchNormalization())\n",
        "\n",
        "    model.add(Conv3D(30, kernel_size=(4, 4, 4), use_bias = True, padding = 'valid', activation='elu', kernel_initializer='he_uniform', kernel_regularizer=l2(2e-4)))\n",
        "    model.add(BatchNormalization())\n",
        "\n",
        "    model.add(Conv3D(20, kernel_size=(4, 4, 4), use_bias = True, padding = 'valid', activation='elu', kernel_initializer='he_uniform', kernel_regularizer=l2(2e-4)))\n",
        "    model.add(BatchNormalization())\n",
        "\n",
        "    model.add(AveragePooling3D(pool_size=(4, 4, 4), strides=(4, 4, 4)))\n",
        "    model.add(Flatten())\n",
        "\n",
        "\n",
        "    model.add(Dense(10, use_bias=True, activation='elu', kernel_initializer='he_uniform', kernel_regularizer=l2(1e-3)))\n",
        "\n",
        "    # Generate the encodings (feature vectors) for the two images\n",
        "    encoded_l = model(left_input)\n",
        "    encoded_r = model(right_input)\n",
        "\n",
        "    # Add a customized layer to compute the absolute difference between the encodings\n",
        "    L1_layer = Lambda(lambda tensors:K.abs(tensors[0] - tensors[1]))\n",
        "    L1_distance = L1_layer([encoded_l, encoded_r])\n",
        "\n",
        "\n",
        "    # Add a dense layer with a sigmoid unit to generate the similarity score\n",
        "    prediction = Dense(1, use_bias=True, activation='elu')(L1_distance)\n",
        "\n",
        "    # Connect the inputs with the outputs\n",
        "    siamese_net = Model(inputs=[left_input,right_input],outputs=prediction)\n",
        "\n",
        "    siamese_net.compile(loss='mean_squared_error', optimizer=Adam(learning_rate=0.001))\n",
        "    siamese_net.summary()\n",
        "\n",
        "    # return the model\n",
        "    return siamese_net"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mBxGBGGu9y2q"
      },
      "source": [
        "#### Questions\n",
        "- What activation functions are used in this architecture?\n",
        "- What cost function are we trying to minimize?\n",
        "- How many convolutional layers are used in this architecture?\n",
        "- On which layers do you propose to apply the dropout technique?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CmP65bBr-D68"
      },
      "source": [
        "#### 2.2 The input data: locally oriented cubes\n",
        "\n",
        "Load training samples. Each sample is a tuple of 5 elements:\n",
        "1. The environment of the wild-type residue on the interface.\n",
        "2. The environment of the mutant residue on the interface.\n",
        "3. The value of $\\Delta\\Delta$G.\n",
        "4. The name of the complex and its partner chains.\n",
        "5. The mutation code.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l2YfgzAX-iZB"
      },
      "source": [
        "Functions to serializing and de-serializing pickle object structure."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ADBukuux-hzx"
      },
      "source": [
        "def save_obj(obj, name):\n",
        "    with open(name + '.pkl', 'wb') as f:\n",
        "        pickle5.dump(obj, f, pickle.HIGHEST_PROTOCOL)\n",
        "\n",
        "def load_obj(name):\n",
        "    with open(name + '.pkl', 'rb') as f:\n",
        "        return pickle5.load(f)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ZnYNv7587ut"
      },
      "source": [
        "def load_map(train_path):\n",
        "    X_wt, X_mut, y, com, muta = load_obj(train_path.replace('.pkl',''))\n",
        "    return X_wt, X_mut, y, com, muta"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WMfS4p0i_Hie"
      },
      "source": [
        "Download the dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "80gTa-8-_SD4",
        "outputId": "012a0c31-6a57-4a7a-d732-13eef3251e8e"
      },
      "source": [
        "!gdown --id '1I4nkVeyJRA6BbuL9KCgi_VeN0uvBdQTt'\n",
        "#!gdown --id '1nqX7Y8Pxk5WgQT2tVINUbSN2tuTjyWrR'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gdown/__main__.py:140: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From (original): https://drive.google.com/uc?id=1I4nkVeyJRA6BbuL9KCgi_VeN0uvBdQTt\n",
            "From (redirected): https://drive.google.com/uc?id=1I4nkVeyJRA6BbuL9KCgi_VeN0uvBdQTt&confirm=t&uuid=c8bf62bc-b6fa-43ea-910f-d403d7477c69\n",
            "To: /content/traindata_mut.zip\n",
            "100% 106M/106M [00:05<00:00, 19.1MB/s] \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WjwEX6WN_Yz5"
      },
      "source": [
        "Decompress the two datasets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fST_yx2O_Vov",
        "outputId": "3d807816-99c7-4396-ec88-8281ef82e045"
      },
      "source": [
        "!unzip '/content/traindata_mut.zip'\n",
        "#!unzip '/content/traindata_mut_small.zip'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/traindata_mut.zip\n",
            "  inflating: traindata_mut/1AO7_ABC_DE_AA149G_1.pkl  \n",
            "  inflating: traindata_mut/1AO7_ABC_DE_AA150G_1.pkl  \n",
            "  inflating: traindata_mut/1AO7_ABC_DE_AA150P_1.pkl  \n",
            "  inflating: traindata_mut/1AO7_ABC_DE_AA158G_1.pkl  \n",
            "  inflating: traindata_mut/1AO7_ABC_DE_AA69G_1.pkl  \n",
            "  inflating: traindata_mut/1AO7_ABC_DE_AE96M_1.pkl  \n",
            "  inflating: traindata_mut/1AO7_ABC_DE_DA61A_1.pkl  \n",
            "  inflating: traindata_mut/1BD2_ABC_DE_DD30N_1.pkl  \n",
            "  inflating: traindata_mut/1BD2_ABC_DE_ED93A_1.pkl  \n",
            "  inflating: traindata_mut/1BD2_ABC_DE_EE28A_1.pkl  \n",
            "  inflating: traindata_mut/1BD2_ABC_DE_RA65A_1.pkl  \n",
            "  inflating: traindata_mut/1BD2_ABC_DE_YC5A_1.pkl  \n",
            "  inflating: traindata_mut/1BD2_ABC_DE_YC5F_1.pkl  \n",
            "  inflating: traindata_mut/1BD2_ABC_DE_YC5W_1.pkl  \n",
            "  inflating: traindata_mut/1BJ1_HL_VW_HH101Y_1.pkl  \n",
            "  inflating: traindata_mut/1BJ1_HL_VW_HH107A_1.pkl  \n",
            "  inflating: traindata_mut/1BJ1_HL_VW_NH31H_1.pkl  \n",
            "  inflating: traindata_mut/1BJ1_HL_VW_SH105T_1.pkl  \n",
            "  inflating: traindata_mut/1BJ1_HL_VW_TH28D_1.pkl  \n",
            "  inflating: traindata_mut/1BJ1_HL_VW_TH59A_1.pkl  \n",
            "  inflating: traindata_mut/1BJ1_HL_VW_YH27A_1.pkl  \n",
            "  inflating: traindata_mut/1BRS_A_D_DD35A_1.pkl  \n",
            "  inflating: traindata_mut/1BRS_A_D_DD39A_1.pkl  \n",
            "  inflating: traindata_mut/1BRS_A_D_EA71A_1.pkl  \n",
            "  inflating: traindata_mut/1BRS_A_D_EA71C_1.pkl  \n",
            "  inflating: traindata_mut/1BRS_A_D_EA71F_1.pkl  \n",
            "  inflating: traindata_mut/1BRS_A_D_EA71Q_1.pkl  \n",
            "  inflating: traindata_mut/1BRS_A_D_EA71S_1.pkl  \n",
            "  inflating: traindata_mut/1C4Z_ABC_D_AD56G_1.pkl  \n",
            "  inflating: traindata_mut/1C4Z_ABC_D_AD95G_1.pkl  \n",
            "  inflating: traindata_mut/1C4Z_ABC_D_CA324A_1.pkl  \n",
            "  inflating: traindata_mut/1C4Z_ABC_D_DA145A_1.pkl  \n",
            "  inflating: traindata_mut/1C4Z_ABC_D_DA145K_1.pkl  \n",
            "  inflating: traindata_mut/1C4Z_ABC_D_DA156A_1.pkl  \n",
            "  inflating: traindata_mut/1C4Z_ABC_D_DA156R_1.pkl  \n",
            "  inflating: traindata_mut/1CBW_FGH_I_GI12A_1.pkl  \n",
            "  inflating: traindata_mut/1CBW_FGH_I_GI36A_1.pkl  \n",
            "  inflating: traindata_mut/1CBW_FGH_I_GI37A_1.pkl  \n",
            "  inflating: traindata_mut/1CBW_FGH_I_II19A_1.pkl  \n",
            "  inflating: traindata_mut/1CBW_FGH_I_KI15A_1.pkl  \n",
            "  inflating: traindata_mut/1CBW_FGH_I_KI46A_1.pkl  \n",
            "  inflating: traindata_mut/1CBW_FGH_I_PI13A_1.pkl  \n",
            "  inflating: traindata_mut/1CHO_EFG_I_AI12C_1.pkl  \n",
            "  inflating: traindata_mut/1CHO_EFG_I_AI12D_1.pkl  \n",
            "  inflating: traindata_mut/1CHO_EFG_I_AI12E_1.pkl  \n",
            "  inflating: traindata_mut/1CHO_EFG_I_AI12F_1.pkl  \n",
            "  inflating: traindata_mut/1CHO_EFG_I_AI12G_1.pkl  \n",
            "  inflating: traindata_mut/1CHO_EFG_I_AI12H_1.pkl  \n",
            "  inflating: traindata_mut/1CHO_EFG_I_AI12I_1.pkl  \n",
            "  inflating: traindata_mut/1CSO_E_I_II13L_1.pkl  \n",
            "  inflating: traindata_mut/1CT0_E_I_SI13L_1.pkl  \n",
            "  inflating: traindata_mut/1CT2_E_I_TI13L_1.pkl  \n",
            "  inflating: traindata_mut/1CT4_E_I_VI13L_1.pkl  \n",
            "  inflating: traindata_mut/1CZ8_HL_VW_LL4M_1.pkl  \n",
            "  inflating: traindata_mut/1DQJ_AB_C_DB32A_1.pkl  \n",
            "  inflating: traindata_mut/1DQJ_AB_C_DC101A_1.pkl  \n",
            "  inflating: traindata_mut/1DQJ_AB_C_KC96A_1.pkl  \n",
            "  inflating: traindata_mut/1DQJ_AB_C_KC97A_1.pkl  \n",
            "  inflating: traindata_mut/1DQJ_AB_C_LC75A_1.pkl  \n",
            "  inflating: traindata_mut/1DQJ_AB_C_NA31A_1.pkl  \n",
            "  inflating: traindata_mut/1DQJ_AB_C_NA32A_1.pkl  \n",
            "  inflating: traindata_mut/1EAW_A_B_DA214A_1.pkl  \n",
            "  inflating: traindata_mut/1EAW_A_B_DA46A_1.pkl  \n",
            "  inflating: traindata_mut/1EAW_A_B_DA47A_1.pkl  \n",
            "  inflating: traindata_mut/1EAW_A_B_DA91A_1.pkl  \n",
            "  inflating: traindata_mut/1EAW_A_B_EA163A_1.pkl  \n",
            "  inflating: traindata_mut/1EAW_A_B_FA50A_1.pkl  \n",
            "  inflating: traindata_mut/1EAW_A_B_FA89A_1.pkl  \n",
            "  inflating: traindata_mut/1F47_A_B_DA4A_1.pkl  \n",
            "  inflating: traindata_mut/1F47_A_B_DA7A_1.pkl  \n",
            "  inflating: traindata_mut/1F47_A_B_DA7G_1.pkl  \n",
            "  inflating: traindata_mut/1F47_A_B_DA7S_1.pkl  \n",
            "  inflating: traindata_mut/1F47_A_B_FA11A_1.pkl  \n",
            "  inflating: traindata_mut/1F47_A_B_IA8A_1.pkl  \n",
            "  inflating: traindata_mut/1F47_A_B_KA14A_1.pkl  \n",
            "  inflating: traindata_mut/1F5R_A_I_KA129Q_1.pkl  \n",
            "  inflating: traindata_mut/1GCQ_AB_C_AC42G_1.pkl  \n",
            "  inflating: traindata_mut/1GCQ_AB_C_GC21V_1.pkl  \n",
            "  inflating: traindata_mut/1GCQ_AB_C_PC18A_1.pkl  \n",
            "  inflating: traindata_mut/1GCQ_AB_C_PC19A_1.pkl  \n",
            "  inflating: traindata_mut/1GCQ_AB_C_PC5A_1.pkl  \n",
            "  inflating: traindata_mut/1GCQ_AB_C_PC67A_1.pkl  \n",
            "  inflating: traindata_mut/1GCQ_AB_C_WC47Y_1.pkl  \n",
            "  inflating: traindata_mut/1GRN_A_B_DA38E_1.pkl  \n",
            "  inflating: traindata_mut/1IAR_A_B_EA19A_1.pkl  \n",
            "  inflating: traindata_mut/1IAR_A_B_EA19R_1.pkl  \n",
            "  inflating: traindata_mut/1IAR_A_B_EA9Q_1.pkl  \n",
            "  inflating: traindata_mut/1IAR_A_B_FA82A_1.pkl  \n",
            "  inflating: traindata_mut/1IAR_A_B_FA82D_1.pkl  \n",
            "  inflating: traindata_mut/1IAR_A_B_IA11A_1.pkl  \n",
            "  inflating: traindata_mut/1IAR_A_B_IA5A_1.pkl  \n",
            "  inflating: traindata_mut/1JCK_A_B_FB176A_1.pkl  \n",
            "  inflating: traindata_mut/1JCK_A_B_KB103A_1.pkl  \n",
            "  inflating: traindata_mut/1JCK_A_B_NB60A_1.pkl  \n",
            "  inflating: traindata_mut/1JCK_A_B_TB20A_1.pkl  \n",
            "  inflating: traindata_mut/1JCK_A_B_VB91A_1.pkl  \n",
            "  inflating: traindata_mut/1JCK_A_B_YB26A_1.pkl  \n",
            "  inflating: traindata_mut/1JCK_A_B_YB90A_1.pkl  \n",
            "  inflating: traindata_mut/1JRH_LH_I_DH56A_1.pkl  \n",
            "  inflating: traindata_mut/1JRH_LH_I_DH57A_1.pkl  \n",
            "  inflating: traindata_mut/1JRH_LH_I_DH58A_1.pkl  \n",
            "  inflating: traindata_mut/1JRH_LH_I_DL28A_1.pkl  \n",
            "  inflating: traindata_mut/1JRH_LH_I_EI35Q_1.pkl  \n",
            "  inflating: traindata_mut/1JRH_LH_I_EI45A_1.pkl  \n",
            "  inflating: traindata_mut/1JRH_LH_I_EI45P_1.pkl  \n",
            "  inflating: traindata_mut/1JTG_A_B_DB49A_1.pkl  \n",
            "  inflating: traindata_mut/1JTG_A_B_EA143A_1.pkl  \n",
            "  inflating: traindata_mut/1JTG_A_B_EA79A_1.pkl  \n",
            "  inflating: traindata_mut/1JTG_A_B_EA85A_1.pkl  \n",
            "  inflating: traindata_mut/1JTG_A_B_EB73A_1.pkl  \n",
            "  inflating: traindata_mut/1JTG_A_B_FB142A_1.pkl  \n",
            "  inflating: traindata_mut/1JTG_A_B_FB36A_1.pkl  \n",
            "  inflating: traindata_mut/1KAC_A_B_PA15S_1.pkl  \n",
            "  inflating: traindata_mut/1KAC_A_B_SA87Y_1.pkl  \n",
            "  inflating: traindata_mut/1KIP_AB_C_AB32Y_1.pkl  \n",
            "  inflating: traindata_mut/1KIQ_AB_C_FB101Y_1.pkl  \n",
            "  inflating: traindata_mut/1KIR_AB_C_SA50Y_1.pkl  \n",
            "  inflating: traindata_mut/1LFD_A_B_DA38A_1.pkl  \n",
            "  inflating: traindata_mut/1LFD_A_B_DA38K_1.pkl  \n",
            "  inflating: traindata_mut/1LFD_A_B_DA43A_1.pkl  \n",
            "  inflating: traindata_mut/1LFD_A_B_DA45K_1.pkl  \n",
            "  inflating: traindata_mut/1LFD_A_B_DA81K_1.pkl  \n",
            "  inflating: traindata_mut/1LFD_A_B_DB33A_1.pkl  \n",
            "  inflating: traindata_mut/1LFD_A_B_EA44A_1.pkl  \n",
            "  inflating: traindata_mut/1LP9_ABC_EF_KA66A_1.pkl  \n",
            "  inflating: traindata_mut/1LP9_ABC_EF_TA163A_1.pkl  \n",
            "  inflating: traindata_mut/1LP9_ABC_EF_WA167A_1.pkl  \n",
            "  inflating: traindata_mut/1M9E_A_D_AD76H_1.pkl  \n",
            "  inflating: traindata_mut/1MI5_ABC_DE_AD91G_1.pkl  \n",
            "  inflating: traindata_mut/1MI5_ABC_DE_AE97G_1.pkl  \n",
            "  inflating: traindata_mut/1MI5_ABC_DE_DD29A_1.pkl  \n",
            "  inflating: traindata_mut/1MI5_ABC_DE_EE50A_1.pkl  \n",
            "  inflating: traindata_mut/1MI5_ABC_DE_EE99A_1.pkl  \n",
            "  inflating: traindata_mut/1MI5_ABC_DE_GD92A_1.pkl  \n",
            "  inflating: traindata_mut/1MI5_ABC_DE_GD93A_1.pkl  \n",
            "  inflating: traindata_mut/1MLC_AB_E_KB65D_1.pkl  \n",
            "  inflating: traindata_mut/1MLC_AB_E_NA32G_1.pkl  \n",
            "  inflating: traindata_mut/1MLC_AB_E_NA32Y_1.pkl  \n",
            "  inflating: traindata_mut/1MLC_AB_E_NA92A_1.pkl  \n",
            "  inflating: traindata_mut/1MLC_AB_E_SB57A_1.pkl  \n",
            "  inflating: traindata_mut/1MLC_AB_E_SB57V_1.pkl  \n",
            "  inflating: traindata_mut/1MLC_AB_E_TB28D_1.pkl  \n",
            "  inflating: traindata_mut/1N8O_ABC_E_ME80K_1.pkl  \n",
            "  inflating: traindata_mut/1N8O_ABC_E_ME80L_1.pkl  \n",
            "  inflating: traindata_mut/1N8O_ABC_E_ME80R_1.pkl  \n",
            "  inflating: traindata_mut/1OGA_ABC_DE_AA69G_1.pkl  \n",
            "  inflating: traindata_mut/1OGA_ABC_DE_DA61A_1.pkl  \n",
            "  inflating: traindata_mut/1OGA_ABC_DE_DE28A_1.pkl  \n",
            "  inflating: traindata_mut/1OGA_ABC_DE_DE28S_1.pkl  \n",
            "  inflating: traindata_mut/1OGA_ABC_DE_DE52A_1.pkl  \n",
            "  inflating: traindata_mut/1OGA_ABC_DE_EA58A_1.pkl  \n",
            "  inflating: traindata_mut/1OGA_ABC_DE_EA63A_1.pkl  \n",
            "  inflating: traindata_mut/1P69_A_B_SA15P_1.pkl  \n",
            "  inflating: traindata_mut/1P6A_A_B_YA87S_1.pkl  \n",
            "  inflating: traindata_mut/1PPF_E_I_AI15C_1.pkl  \n",
            "  inflating: traindata_mut/1PPF_E_I_AI15D_1.pkl  \n",
            "  inflating: traindata_mut/1PPF_E_I_AI15E_1.pkl  \n",
            "  inflating: traindata_mut/1PPF_E_I_AI15F_1.pkl  \n",
            "  inflating: traindata_mut/1PPF_E_I_AI15G_1.pkl  \n",
            "  inflating: traindata_mut/1PPF_E_I_AI15H_1.pkl  \n",
            "  inflating: traindata_mut/1PPF_E_I_AI15I_1.pkl  \n",
            "  inflating: traindata_mut/1QSE_ABC_DE_RC7V_1.pkl  \n",
            "  inflating: traindata_mut/1R0R_E_I_AI10C_1.pkl  \n",
            "  inflating: traindata_mut/1R0R_E_I_AI10D_1.pkl  \n",
            "  inflating: traindata_mut/1R0R_E_I_AI10E_1.pkl  \n",
            "  inflating: traindata_mut/1R0R_E_I_AI10F_1.pkl  \n",
            "  inflating: traindata_mut/1R0R_E_I_AI10G_1.pkl  \n",
            "  inflating: traindata_mut/1R0R_E_I_AI10H_1.pkl  \n",
            "  inflating: traindata_mut/1R0R_E_I_AI10I_1.pkl  \n",
            "  inflating: traindata_mut/1REW_AB_C_QC55A_1.pkl  \n",
            "  inflating: traindata_mut/1S1Q_A_B_DA44A_1.pkl  \n",
            "  inflating: traindata_mut/1S1Q_A_B_FA42A_1.pkl  \n",
            "  inflating: traindata_mut/1S1Q_A_B_FA86A_1.pkl  \n",
            "  inflating: traindata_mut/1S1Q_A_B_NA43A_1.pkl  \n",
            "  inflating: traindata_mut/1S1Q_A_B_VA41A_1.pkl  \n",
            "  inflating: traindata_mut/1S1Q_A_B_WA73A_1.pkl  \n",
            "  inflating: traindata_mut/1SBB_A_B_LB20T_1.pkl  \n",
            "  inflating: traindata_mut/1SBB_A_B_VB26Y_1.pkl  \n",
            "  inflating: traindata_mut/1SBB_A_B_YB91V_1.pkl  \n",
            "  inflating: traindata_mut/1SGD_E_I_DI13L_1.pkl  \n",
            "  inflating: traindata_mut/1SGE_E_I_EI13L_1.pkl  \n",
            "  inflating: traindata_mut/1SGN_E_I_NI13L_1.pkl  \n",
            "  inflating: traindata_mut/1SGP_E_I_AI13L_1.pkl  \n",
            "  inflating: traindata_mut/1SGQ_E_I_GI13L_1.pkl  \n",
            "  inflating: traindata_mut/1SGY_E_I_YI13L_1.pkl  \n",
            "  inflating: traindata_mut/1VFB_AB_C_DB100A_1.pkl  \n",
            "  inflating: traindata_mut/1VFB_AB_C_DB54A_1.pkl  \n",
            "  inflating: traindata_mut/1VFB_AB_C_DB58A_1.pkl  \n",
            "  inflating: traindata_mut/1VFB_AB_C_DB58E_1.pkl  \n",
            "  inflating: traindata_mut/1VFB_AB_C_DB58N_1.pkl  \n",
            "  inflating: traindata_mut/1VFB_AB_C_DC119A_1.pkl  \n",
            "  inflating: traindata_mut/1VFB_AB_C_DC18A_1.pkl  \n",
            "  inflating: traindata_mut/1X1W_A_D_AD81E_1.pkl  \n",
            "  inflating: traindata_mut/1X1X_A_D_AD76E_1.pkl  \n",
            "  inflating: traindata_mut/1XGP_AB_C_AB33F_1.pkl  \n",
            "  inflating: traindata_mut/1XGP_AB_C_AB33I_1.pkl  \n",
            "  inflating: traindata_mut/1XGP_AB_C_AB33L_1.pkl  \n",
            "  inflating: traindata_mut/1XGP_AB_C_AB33V_1.pkl  \n",
            "  inflating: traindata_mut/1XGQ_AB_C_VB33A_1.pkl  \n",
            "  inflating: traindata_mut/1XGQ_AB_C_VB33F_1.pkl  \n",
            "  inflating: traindata_mut/1XGQ_AB_C_VB33I_1.pkl  \n",
            "  inflating: traindata_mut/1XGQ_AB_C_VB33L_1.pkl  \n",
            "  inflating: traindata_mut/1XGR_AB_C_IB33A_1.pkl  \n",
            "  inflating: traindata_mut/1XGR_AB_C_IB33F_1.pkl  \n",
            "  inflating: traindata_mut/1XGR_AB_C_IB33L_1.pkl  \n",
            "  inflating: traindata_mut/1XGR_AB_C_IB33V_1.pkl  \n",
            "  inflating: traindata_mut/1XGT_AB_C_LB33A_1.pkl  \n",
            "  inflating: traindata_mut/1XGT_AB_C_LB33F_1.pkl  \n",
            "  inflating: traindata_mut/1XGT_AB_C_LB33I_1.pkl  \n",
            "  inflating: traindata_mut/1XGT_AB_C_LB33V_1.pkl  \n",
            "  inflating: traindata_mut/1XGU_AB_C_FB33A_1.pkl  \n",
            "  inflating: traindata_mut/1XGU_AB_C_FB33I_1.pkl  \n",
            "  inflating: traindata_mut/1XGU_AB_C_FB33L_1.pkl  \n",
            "  inflating: traindata_mut/1XGU_AB_C_FB33V_1.pkl  \n",
            "  inflating: traindata_mut/1YQV_HL_Y_RY45K_1.pkl  \n",
            "  inflating: traindata_mut/2AK4_ABC_DE_AA158G_1.pkl  \n",
            "  inflating: traindata_mut/2AK4_ABC_DE_EA154A_1.pkl  \n",
            "  inflating: traindata_mut/2AK4_ABC_DE_EA161A_1.pkl  \n",
            "  inflating: traindata_mut/2AK4_ABC_DE_EE97A_1.pkl  \n",
            "  inflating: traindata_mut/2AK4_ABC_DE_LA163A_1.pkl  \n",
            "  inflating: traindata_mut/2AK4_ABC_DE_LC10A_1.pkl  \n",
            "  inflating: traindata_mut/2AK4_ABC_DE_LC5A_1.pkl  \n",
            "  inflating: traindata_mut/2B0Z_A_B_IB87F_1.pkl  \n",
            "  inflating: traindata_mut/2B10_A_B_SB87F_1.pkl  \n",
            "  inflating: traindata_mut/2B11_A_B_WB87F_1.pkl  \n",
            "  inflating: traindata_mut/2B42_A_B_HA357A_1.pkl  \n",
            "  inflating: traindata_mut/2B42_A_B_HA357K_1.pkl  \n",
            "  inflating: traindata_mut/2B42_A_B_HA357Q_1.pkl  \n",
            "  inflating: traindata_mut/2BDN_HL_A_DH31E_1.pkl  \n",
            "  inflating: traindata_mut/2BDN_HL_A_LH27K_1.pkl  \n",
            "  inflating: traindata_mut/2BDN_HL_A_LH27R_1.pkl  \n",
            "  inflating: traindata_mut/2BDN_HL_A_NH28D_1.pkl  \n",
            "  inflating: traindata_mut/2BDN_HL_A_NH28Q_1.pkl  \n",
            "  inflating: traindata_mut/2BDN_HL_A_NL31K_1.pkl  \n",
            "  inflating: traindata_mut/2BDN_HL_A_NL31R_1.pkl  \n",
            "  inflating: traindata_mut/2BNR_ABC_DE_AA149G_1.pkl  \n",
            "  inflating: traindata_mut/2BNR_ABC_DE_AA150G_1.pkl  \n",
            "  inflating: traindata_mut/2BNR_ABC_DE_AA69G_1.pkl  \n",
            "  inflating: traindata_mut/2BNR_ABC_DE_DA61A_1.pkl  \n",
            "  inflating: traindata_mut/2BNR_ABC_DE_EA58A_1.pkl  \n",
            "  inflating: traindata_mut/2BNR_ABC_DE_EA63A_1.pkl  \n",
            "  inflating: traindata_mut/2BNR_ABC_DE_HA151A_1.pkl  \n",
            "  inflating: traindata_mut/2C0L_A_B_NA48A_1.pkl  \n",
            "  inflating: traindata_mut/2C0L_A_B_QA239R_1.pkl  \n",
            "  inflating: traindata_mut/2C0L_A_B_SA242Y_1.pkl  \n",
            "  inflating: traindata_mut/2DVW_A_B_EA180A_1.pkl  \n",
            "  inflating: traindata_mut/2DVW_A_B_KA114A_1.pkl  \n",
            "  inflating: traindata_mut/2DVW_A_B_KB62E_1.pkl  \n",
            "  inflating: traindata_mut/2DVW_A_B_RA39A_1.pkl  \n",
            "  inflating: traindata_mut/2DVW_A_B_RB7A_1.pkl  \n",
            "  inflating: traindata_mut/2E7L_EQ_AD_FQ5R_1.pkl  \n",
            "  inflating: traindata_mut/2GOX_A_B_NB38A_1.pkl  \n",
            "  inflating: traindata_mut/2GOX_A_B_RB31A_1.pkl  \n",
            "  inflating: traindata_mut/2HLE_A_B_KA141Q_1.pkl  \n",
            "  inflating: traindata_mut/2HLE_A_B_LA87R_1.pkl  \n",
            "  inflating: traindata_mut/2J12_A_B_SA116A_1.pkl  \n",
            "  inflating: traindata_mut/2J1K_C_T_GC10D_1.pkl  \n",
            "  inflating: traindata_mut/2J1K_C_T_RC155A_1.pkl  \n",
            "  inflating: traindata_mut/2J1K_C_T_RC24A_1.pkl  \n",
            "  inflating: traindata_mut/2J8U_ABC_EF_AA66K_1.pkl  \n",
            "  inflating: traindata_mut/2JCC_ABC_EF_AA167W_1.pkl  \n",
            "  inflating: traindata_mut/2KSO_A_B_HB20E_1.pkl  \n",
            "  inflating: traindata_mut/2KSO_A_B_KA10D_1.pkl  \n",
            "  inflating: traindata_mut/2KSO_A_B_KA49D_1.pkl  \n",
            "  inflating: traindata_mut/2KSO_A_B_KA49E_1.pkl  \n",
            "  inflating: traindata_mut/2KSO_A_B_RA43E_1.pkl  \n",
            "  inflating: traindata_mut/2NU0_E_I_WI13L_1.pkl  \n",
            "  inflating: traindata_mut/2NU1_E_I_HI13L_1.pkl  \n",
            "  inflating: traindata_mut/2NU2_E_I_RI13L_1.pkl  \n",
            "  inflating: traindata_mut/2NU4_E_I_KI13L_1.pkl  \n",
            "  inflating: traindata_mut/2OI9_AQ_BC_FQ5R_1.pkl  \n",
            "  inflating: traindata_mut/2OOB_A_B_AA9E_1.pkl  \n",
            "  inflating: traindata_mut/2OOB_A_B_GA13S_1.pkl  \n",
            "  inflating: traindata_mut/2OOB_A_B_KA7E_1.pkl  \n",
            "  inflating: traindata_mut/2PCC_A_B_AB86G_1.pkl  \n",
            "  inflating: traindata_mut/2PCC_A_B_DA34A_1.pkl  \n",
            "  inflating: traindata_mut/2PCC_A_B_EA290A_1.pkl  \n",
            "  inflating: traindata_mut/2PCC_A_B_KB77A_1.pkl  \n",
            "  inflating: traindata_mut/2PCC_A_B_KB92A_1.pkl  \n",
            "  inflating: traindata_mut/2PCC_A_B_VA197A_1.pkl  \n",
            "  inflating: traindata_mut/2SGP_E_I_PI13L_1.pkl  \n",
            "  inflating: traindata_mut/2SGQ_E_I_QI13L_1.pkl  \n",
            "  inflating: traindata_mut/2UWE_ABC_EF_AA163T_1.pkl  \n",
            "  inflating: traindata_mut/2VLR_ABC_DE_AE95S_1.pkl  \n",
            "  inflating: traindata_mut/3AAA_AB_C_DC43A_1.pkl  \n",
            "  inflating: traindata_mut/3AAA_AB_C_EC77A_1.pkl  \n",
            "  inflating: traindata_mut/3AAA_AB_C_EC77R_1.pkl  \n",
            "  inflating: traindata_mut/3AAA_AB_C_KA248A_1.pkl  \n",
            "  inflating: traindata_mut/3AAA_AB_C_KA248E_1.pkl  \n",
            "  inflating: traindata_mut/3AAA_AB_C_RA252A_1.pkl  \n",
            "  inflating: traindata_mut/3AAA_AB_C_RA252E_1.pkl  \n",
            "  inflating: traindata_mut/3BK3_A_C_AC37R_1.pkl  \n",
            "  inflating: traindata_mut/3BK3_A_C_IC19A_1.pkl  \n",
            "  inflating: traindata_mut/3BK3_A_C_IC19R_1.pkl  \n",
            "  inflating: traindata_mut/3BK3_A_C_IC22A_1.pkl  \n",
            "  inflating: traindata_mut/3BK3_A_C_IC22R_1.pkl  \n",
            "  inflating: traindata_mut/3BK3_A_C_IC28A_1.pkl  \n",
            "  inflating: traindata_mut/3BK3_A_C_IC28R_1.pkl  \n",
            "  inflating: traindata_mut/3BP8_A_C_AC51F_1.pkl  \n",
            "  inflating: traindata_mut/3BP8_A_C_FA115A_1.pkl  \n",
            "  inflating: traindata_mut/3C60_CD_AB_DC56A_1.pkl  \n",
            "  inflating: traindata_mut/3C60_CD_AB_DD13A_1.pkl  \n",
            "  inflating: traindata_mut/3C60_CD_AB_DD86A_1.pkl  \n",
            "  inflating: traindata_mut/3C60_CD_AB_ED2A_1.pkl  \n",
            "  inflating: traindata_mut/3C60_CD_AB_ED69A_1.pkl  \n",
            "  inflating: traindata_mut/3C60_CD_AB_ED76A_1.pkl  \n",
            "  inflating: traindata_mut/3C60_CD_AB_ED79A_1.pkl  \n",
            "  inflating: traindata_mut/3D5R_A_C_AC38N_1.pkl  \n",
            "  inflating: traindata_mut/3D5S_A_C_AC31R_1.pkl  \n",
            "  inflating: traindata_mut/3EG5_A_B_HA104F_1.pkl  \n",
            "  inflating: traindata_mut/3EQS_A_B_EB5A_1.pkl  \n",
            "  inflating: traindata_mut/3EQS_A_B_FB3A_1.pkl  \n",
            "  inflating: traindata_mut/3EQS_A_B_LB10A_1.pkl  \n",
            "  inflating: traindata_mut/3EQS_A_B_LB9A_1.pkl  \n",
            "  inflating: traindata_mut/3EQS_A_B_NB8A_1.pkl  \n",
            "  inflating: traindata_mut/3EQS_A_B_SB11A_1.pkl  \n",
            "  inflating: traindata_mut/3EQS_A_B_SB2A_1.pkl  \n",
            "  inflating: traindata_mut/3EQY_A_C_EC5A_1.pkl  \n",
            "  inflating: traindata_mut/3EQY_A_C_FC3A_1.pkl  \n",
            "  inflating: traindata_mut/3EQY_A_C_LC10A_1.pkl  \n",
            "  inflating: traindata_mut/3EQY_A_C_LC9A_1.pkl  \n",
            "  inflating: traindata_mut/3EQY_A_C_NC8A_1.pkl  \n",
            "  inflating: traindata_mut/3EQY_A_C_PC12A_1.pkl  \n",
            "  inflating: traindata_mut/3EQY_A_C_SC11A_1.pkl  \n",
            "  inflating: traindata_mut/3H9S_ABC_DE_AA150P_1.pkl  \n",
            "  inflating: traindata_mut/3HFM_HL_Y_DH32A_1.pkl  \n",
            "  inflating: traindata_mut/3HFM_HL_Y_DH32N_1.pkl  \n",
            "  inflating: traindata_mut/3HFM_HL_Y_KY96A_1.pkl  \n",
            "  inflating: traindata_mut/3HFM_HL_Y_KY97A_1.pkl  \n",
            "  inflating: traindata_mut/3HFM_HL_Y_KY97M_1.pkl  \n",
            "  inflating: traindata_mut/3HFM_HL_Y_NL31A_1.pkl  \n",
            "  inflating: traindata_mut/3HFM_HL_Y_NL31D_1.pkl  \n",
            "  inflating: traindata_mut/3HH2_AB_C_FC47A_1.pkl  \n",
            "  inflating: traindata_mut/3HH2_AB_C_FC47E_1.pkl  \n",
            "  inflating: traindata_mut/3HH2_AB_C_FC52A_1.pkl  \n",
            "  inflating: traindata_mut/3HH2_AB_C_FC52E_1.pkl  \n",
            "  inflating: traindata_mut/3HH2_AB_C_IC51A_1.pkl  \n",
            "  inflating: traindata_mut/3HH2_AB_C_IC51E_1.pkl  \n",
            "  inflating: traindata_mut/3HH2_AB_C_VC15E_1.pkl  \n",
            "  inflating: traindata_mut/3L5X_A_HL_YL94A_1.pkl  \n",
            "  inflating: traindata_mut/3LNZ_A_B_AB8N_1.pkl  \n",
            "  inflating: traindata_mut/3M62_A_B_DA46A_1.pkl  \n",
            "  inflating: traindata_mut/3M62_A_B_EA32A_1.pkl  \n",
            "  inflating: traindata_mut/3M62_A_B_EA55A_1.pkl  \n",
            "  inflating: traindata_mut/3M62_A_B_FB7A_1.pkl  \n",
            "  inflating: traindata_mut/3M62_A_B_GA102A_1.pkl  \n",
            "  inflating: traindata_mut/3M62_A_B_IA110A_1.pkl  \n",
            "  inflating: traindata_mut/3M62_A_B_IB43A_1.pkl  \n",
            "  inflating: traindata_mut/3M63_A_B_DA46A_1.pkl  \n",
            "  inflating: traindata_mut/3M63_A_B_EA33A_1.pkl  \n",
            "  inflating: traindata_mut/3M63_A_B_EA55A_1.pkl  \n",
            "  inflating: traindata_mut/3M63_A_B_FA113A_1.pkl  \n",
            "  inflating: traindata_mut/3M63_A_B_GA102A_1.pkl  \n",
            "  inflating: traindata_mut/3M63_A_B_IA110A_1.pkl  \n",
            "  inflating: traindata_mut/3M63_A_B_LA50A_1.pkl  \n",
            "  inflating: traindata_mut/3MZG_A_B_HA125A_1.pkl  \n",
            "  inflating: traindata_mut/3MZG_A_B_HA14A_1.pkl  \n",
            "  inflating: traindata_mut/3MZG_A_B_HA160A_1.pkl  \n",
            "  inflating: traindata_mut/3MZG_A_B_HA167A_1.pkl  \n",
            "  inflating: traindata_mut/3MZG_A_B_HA17A_1.pkl  \n",
            "  inflating: traindata_mut/3MZG_A_B_HA182A_1.pkl  \n",
            "  inflating: traindata_mut/3MZG_A_B_HA33A_1.pkl  \n",
            "  inflating: traindata_mut/3MZW_A_B_NB18A_1.pkl  \n",
            "  inflating: traindata_mut/3MZW_A_B_NB18G_1.pkl  \n",
            "  inflating: traindata_mut/3MZW_A_B_NB18T_1.pkl  \n",
            "  inflating: traindata_mut/3NCC_A_B_AB188H_1.pkl  \n",
            "  inflating: traindata_mut/3Q3J_A_B_YB100F_1.pkl  \n",
            "  inflating: traindata_mut/3Q8D_A_E_FA227A_1.pkl  \n",
            "  inflating: traindata_mut/3Q8D_A_E_FA227D_1.pkl  \n",
            "  inflating: traindata_mut/3Q8D_A_E_RA12A_1.pkl  \n",
            "  inflating: traindata_mut/3Q8D_A_E_RA131A_1.pkl  \n",
            "  inflating: traindata_mut/3Q8D_A_E_RA202A_1.pkl  \n",
            "  inflating: traindata_mut/3Q8D_A_E_RA202E_1.pkl  \n",
            "  inflating: traindata_mut/3Q8D_A_E_RA209A_1.pkl  \n",
            "  inflating: traindata_mut/3QDG_ABC_DE_AE52P_1.pkl  \n",
            "  inflating: traindata_mut/3QDG_ABC_DE_DD26W_1.pkl  \n",
            "  inflating: traindata_mut/3QDG_ABC_DE_DD26Y_1.pkl  \n",
            "  inflating: traindata_mut/3QDG_ABC_DE_FE97W_1.pkl  \n",
            "  inflating: traindata_mut/3QDG_ABC_DE_FE97Y_1.pkl  \n",
            "  inflating: traindata_mut/3QDG_ABC_DE_GD28I_1.pkl  \n",
            "  inflating: traindata_mut/3QDG_ABC_DE_GD28L_1.pkl  \n",
            "  inflating: traindata_mut/3QDJ_ABC_DE_AC2L_1.pkl  \n",
            "  inflating: traindata_mut/3QDJ_ABC_DE_AE52P_1.pkl  \n",
            "  inflating: traindata_mut/3QDJ_ABC_DE_DD26W_1.pkl  \n",
            "  inflating: traindata_mut/3QDJ_ABC_DE_DD26Y_1.pkl  \n",
            "  inflating: traindata_mut/3QDJ_ABC_DE_FE97W_1.pkl  \n",
            "  inflating: traindata_mut/3QDJ_ABC_DE_FE97Y_1.pkl  \n",
            "  inflating: traindata_mut/3QDJ_ABC_DE_GD28I_1.pkl  \n",
            "  inflating: traindata_mut/3QIB_ABP_CD_AA61G_1.pkl  \n",
            "  inflating: traindata_mut/3QIB_ABP_CD_AA64G_1.pkl  \n",
            "  inflating: traindata_mut/3QIB_ABP_CD_AA68G_1.pkl  \n",
            "  inflating: traindata_mut/3QIB_ABP_CD_AB71G_1.pkl  \n",
            "  inflating: traindata_mut/3QIB_ABP_CD_DB74A_1.pkl  \n",
            "  inflating: traindata_mut/3QIB_ABP_CD_EA75A_1.pkl  \n",
            "  inflating: traindata_mut/3QIB_ABP_CD_EB57A_1.pkl  \n",
            "  inflating: traindata_mut/3S9D_A_B_AA116G_1.pkl  \n",
            "  inflating: traindata_mut/3S9D_A_B_AA116M_1.pkl  \n",
            "  inflating: traindata_mut/3S9D_A_B_AA12W_1.pkl  \n",
            "  inflating: traindata_mut/3S9D_A_B_AB67M_1.pkl  \n",
            "  inflating: traindata_mut/3S9D_A_B_DA25A_1.pkl  \n",
            "  inflating: traindata_mut/3S9D_A_B_DA28A_1.pkl  \n",
            "  inflating: traindata_mut/3S9D_A_B_DB121R_1.pkl  \n",
            "  inflating: traindata_mut/3SF4_A_D_ED19R_1.pkl  \n",
            "  inflating: traindata_mut/3SF4_A_D_KD27D_1.pkl  \n",
            "  inflating: traindata_mut/3SF4_A_D_NA264R_1.pkl  \n",
            "  inflating: traindata_mut/3SF4_A_D_RA202E_1.pkl  \n",
            "  inflating: traindata_mut/3SF4_A_D_WD8A_1.pkl  \n",
            "  inflating: traindata_mut/3SGB_E_I_AI9C_1.pkl  \n",
            "  inflating: traindata_mut/3SGB_E_I_AI9D_1.pkl  \n",
            "  inflating: traindata_mut/3SGB_E_I_AI9E_1.pkl  \n",
            "  inflating: traindata_mut/3SGB_E_I_AI9F_1.pkl  \n",
            "  inflating: traindata_mut/3SGB_E_I_AI9G_1.pkl  \n",
            "  inflating: traindata_mut/3SGB_E_I_AI9H_1.pkl  \n",
            "  inflating: traindata_mut/3SGB_E_I_AI9I_1.pkl  \n",
            "  inflating: traindata_mut/3TGK_E_I_NE170D_1.pkl  \n",
            "  inflating: traindata_mut/3UII_A_P_KA58A_1.pkl  \n",
            "  inflating: traindata_mut/3W2D_A_HL_KA67N_1.pkl  \n",
            "  inflating: traindata_mut/3W2D_A_HL_KA67R_1.pkl  \n",
            "  inflating: traindata_mut/3W2D_A_HL_YA45F_1.pkl  \n",
            "  inflating: traindata_mut/3W2D_A_HL_YA45L_1.pkl  \n",
            "  inflating: traindata_mut/3WWN_A_B_KA225E_1.pkl  \n",
            "  inflating: traindata_mut/3WWN_A_B_RA221E_1.pkl  \n",
            "  inflating: traindata_mut/3WWN_A_B_RA224E_1.pkl  \n",
            "  inflating: traindata_mut/4B0M_A_BM_AB127G_1.pkl  \n",
            "  inflating: traindata_mut/4B0M_A_BM_LM40A_1.pkl  \n",
            "  inflating: traindata_mut/4B0M_A_BM_MA8A_1.pkl  \n",
            "  inflating: traindata_mut/4B0M_A_BM_PM98A_1.pkl  \n",
            "  inflating: traindata_mut/4B0M_A_BM_PM98S_1.pkl  \n",
            "  inflating: traindata_mut/4B0M_A_BM_PM99A_1.pkl  \n",
            "  inflating: traindata_mut/4B0M_A_BM_PM99S_1.pkl  \n",
            "  inflating: traindata_mut/4CVW_A_C_DA720R_1.pkl  \n",
            "  inflating: traindata_mut/4CVW_A_C_DA720W_1.pkl  \n",
            "  inflating: traindata_mut/4CVW_A_C_LC34G_1.pkl  \n",
            "  inflating: traindata_mut/4CVW_A_C_LC34W_1.pkl  \n",
            "  inflating: traindata_mut/4CVW_A_C_RC27A_1.pkl  \n",
            "  inflating: traindata_mut/4CVW_A_C_RC31A_1.pkl  \n",
            "  inflating: traindata_mut/4CVW_A_C_RC31W_1.pkl  \n",
            "  inflating: traindata_mut/4EKD_A_B_CA147R_1.pkl  \n",
            "  inflating: traindata_mut/4FTV_ABC_DE_DD93A_1.pkl  \n",
            "  inflating: traindata_mut/4FTV_ABC_DE_KD66A_1.pkl  \n",
            "  inflating: traindata_mut/4FTV_ABC_DE_QE101E_1.pkl  \n",
            "  inflating: traindata_mut/4FTV_ABC_DE_QE101R_1.pkl  \n",
            "  inflating: traindata_mut/4FTV_ABC_DE_RA65A_1.pkl  \n",
            "  inflating: traindata_mut/4FTV_ABC_DE_TA163A_1.pkl  \n",
            "  inflating: traindata_mut/4HFK_A_BD_EB46A_1.pkl  \n",
            "  inflating: traindata_mut/4HFK_A_BD_EB56A_1.pkl  \n",
            "  inflating: traindata_mut/4HFK_A_BD_LB45A_1.pkl  \n",
            "  inflating: traindata_mut/4HFK_A_BD_LB50A_1.pkl  \n",
            "  inflating: traindata_mut/4HFK_A_BD_NB49A_1.pkl  \n",
            "  inflating: traindata_mut/4HFK_A_BD_RB22A_1.pkl  \n",
            "  inflating: traindata_mut/4HFK_A_BD_SB48A_1.pkl  \n",
            "  inflating: traindata_mut/4I77_HL_Z_DH54A_1.pkl  \n",
            "  inflating: traindata_mut/4I77_HL_Z_NL95A_1.pkl  \n",
            "  inflating: traindata_mut/4I77_HL_Z_WH52A_1.pkl  \n",
            "  inflating: traindata_mut/4I77_HL_Z_WH52F_1.pkl  \n",
            "  inflating: traindata_mut/4I77_HL_Z_YH100A_1.pkl  \n",
            "  inflating: traindata_mut/4I77_HL_Z_YH100F_1.pkl  \n",
            "  inflating: traindata_mut/4I77_HL_Z_YH101A_1.pkl  \n",
            "  inflating: traindata_mut/4JFF_ABC_DE_EC1A_1.pkl  \n",
            "  inflating: traindata_mut/4JFF_ABC_DE_GC4A_1.pkl  \n",
            "  inflating: traindata_mut/4JFF_ABC_DE_GC6A_1.pkl  \n",
            "  inflating: traindata_mut/4JFF_ABC_DE_IC7A_1.pkl  \n",
            "  inflating: traindata_mut/4JFF_ABC_DE_LC8A_1.pkl  \n",
            "  inflating: traindata_mut/4JFF_ABC_DE_TC9A_1.pkl  \n",
            "  inflating: traindata_mut/4JGH_ABC_D_WD44A_1.pkl  \n",
            "  inflating: traindata_mut/4KRL_A_B_DB112A_1.pkl  \n",
            "  inflating: traindata_mut/4KRL_A_B_RB30A_1.pkl  \n",
            "  inflating: traindata_mut/4KRO_A_B_DB92A_1.pkl  \n",
            "  inflating: traindata_mut/4KRO_A_B_RB15A_1.pkl  \n",
            "  inflating: traindata_mut/4KRP_A_B_EB105A_1.pkl  \n",
            "  inflating: traindata_mut/4L3E_ABC_DE_EA166A_1.pkl  \n",
            "  inflating: traindata_mut/4L3E_ABC_DE_ND52A_1.pkl  \n",
            "  inflating: traindata_mut/4L3E_ABC_DE_QA155A_1.pkl  \n",
            "  inflating: traindata_mut/4L3E_ABC_DE_RA65A_1.pkl  \n",
            "  inflating: traindata_mut/4L3E_ABC_DE_YD26A_1.pkl  \n",
            "  inflating: traindata_mut/4L3E_ABC_DE_YD50A_1.pkl  \n",
            "  inflating: traindata_mut/4L3E_ABC_DE_YE48A_1.pkl  \n",
            "  inflating: traindata_mut/4LRX_AB_CD_HA56N_1.pkl  \n",
            "  inflating: traindata_mut/4N8V_G_ABC_KG38A_1.pkl  \n",
            "  inflating: traindata_mut/4N8V_G_ABC_PG10A_1.pkl  \n",
            "  inflating: traindata_mut/4N8V_G_ABC_YG124A_1.pkl  \n",
            "  inflating: traindata_mut/4N8V_G_ABC_YG39F_1.pkl  \n",
            "  inflating: traindata_mut/4OZG_ABJ_GH_DH98A_1.pkl  \n",
            "  inflating: traindata_mut/4OZG_ABJ_GH_FH94A_1.pkl  \n",
            "  inflating: traindata_mut/4OZG_ABJ_GH_FH96A_1.pkl  \n",
            "  inflating: traindata_mut/4OZG_ABJ_GH_LG50A_1.pkl  \n",
            "  inflating: traindata_mut/4OZG_ABJ_GH_NG29A_1.pkl  \n",
            "  inflating: traindata_mut/4OZG_ABJ_GH_NH50A_1.pkl  \n",
            "  inflating: traindata_mut/4OZG_ABJ_GH_QH48A_1.pkl  \n",
            "  inflating: traindata_mut/4P23_CD_AB_DC56A_1.pkl  \n",
            "  inflating: traindata_mut/4P23_CD_AB_DD13A_1.pkl  \n",
            "  inflating: traindata_mut/4P23_CD_AB_DD85A_1.pkl  \n",
            "  inflating: traindata_mut/4P23_CD_AB_ED2A_1.pkl  \n",
            "  inflating: traindata_mut/4P23_CD_AB_ED68A_1.pkl  \n",
            "  inflating: traindata_mut/4P23_CD_AB_ED75A_1.pkl  \n",
            "  inflating: traindata_mut/4P23_CD_AB_ED78A_1.pkl  \n",
            "  inflating: traindata_mut/4P5T_CD_AB_DC56A_1.pkl  \n",
            "  inflating: traindata_mut/4P5T_CD_AB_DD13A_1.pkl  \n",
            "  inflating: traindata_mut/4P5T_CD_AB_DD84A_1.pkl  \n",
            "  inflating: traindata_mut/4P5T_CD_AB_ED2A_1.pkl  \n",
            "  inflating: traindata_mut/4P5T_CD_AB_ED67A_1.pkl  \n",
            "  inflating: traindata_mut/4P5T_CD_AB_ED74A_1.pkl  \n",
            "  inflating: traindata_mut/4P5T_CD_AB_ED77A_1.pkl  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0MITfdiW_mxG"
      },
      "source": [
        "Load the samples of wild-type and mutant environments."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZTynVsYS_qpu"
      },
      "source": [
        "samples = glob.glob(os.path.join('traindata_mut', '*.pkl'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Function to reduce number of channels."
      ],
      "metadata": {
        "id": "aezkxSqpy_xy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "channels = {'ALA':['C','N','O','CA','CB'],\n",
        "            'ARG':['C','N','O','CA','CB','CG','CD','NE','CZ','NH1','NH2'],\n",
        "            'ASN':['C','N','O','CA','CB','CG','ND2','OD1'],\n",
        "            'ASP':['C','N','O','CA','CB','CG','OD1','OD2'],\n",
        "            'CYS':['C','N','O','CA','CB','SG'],\n",
        "            'GLN':['C','N','O','CA','CB','CG','CD','NE2','OE1'],\n",
        "            'GLU':['C','N','O','CA','CB','CG','CD','OE1','OE2'],\n",
        "            'GLY':['C','N','O','CA'],\n",
        "            'HIS':['C','N','O','CA','CB','CG','CD2','ND1','CE1','NE2'],\n",
        "            'ILE':['C','N','O','CA','CB','CG1','CG2','CD1'],\n",
        "            'LEU':['C','N','O','CA','CB','CG','CD1','CD2'],\n",
        "            'LYS':['C','N','O','CA','CB','CG','CD','CE','NZ'],\n",
        "            'MET':['C','N','O','CA','CB','CG','SD','CE'],\n",
        "            'PHE':['C','N','O','CA','CB','CG','CD1','CD2','CE1','CE2','CZ'],\n",
        "            'PRO':['C','N','O','CA','CB','CG','CD'],\n",
        "            'SER':['C','N','O','CA','CB','OG'],\n",
        "            'THR':['C','N','O','CA','CB','CG2','OG1'],\n",
        "            'TRP':['C','N','O','CA','CB','CG','CD1','CD2','CE2','CE3','NE1','CZ2','CZ3','CH2'],\n",
        "            'TYR':['C','N','O','CA','CB','CG','CD1','CD2','CE1','CE2','CZ','OH'],\n",
        "            'VAL':['C','N','O','CA','CB','CG1','CG2']}\n",
        "v_dim = 24\n",
        "n_channels = 4\n",
        "\n",
        "all_channels = []\n",
        "for aa, a_vector in channels.items():\n",
        "    all_channels += a_vector\n",
        "\n",
        "C_index, O_index, N_index, S_index = [], [], [], []\n",
        "for i,a in enumerate(all_channels):\n",
        "    if a[0] == \"C\":\n",
        "        C_index.append(i)\n",
        "    if a[0] == \"O\":\n",
        "        O_index.append(i)\n",
        "    if a[0] == \"N\":\n",
        "        N_index.append(i)\n",
        "    if a[0] == \"S\":\n",
        "        S_index.append(i)\n",
        "\n",
        "def reduce_channels(X):\n",
        "    try:\n",
        "        X_new = np.zeros(X.shape[:-1] + tuple([n_channels]))\n",
        "\n",
        "        X_new[:,:,:,0] = X[:,:,:,C_index].sum(axis=3)\n",
        "        X_new[:,:,:,1] = X[:,:,:,N_index].sum(axis=3)\n",
        "        X_new[:,:,:,2] = X[:,:,:,O_index].sum(axis=3)\n",
        "        X_new[:,:,:,3] = X[:,:,:,S_index].sum(axis=3)\n",
        "    except:\n",
        "        pass\n",
        "    return X_new"
      ],
      "metadata": {
        "id": "q6JEOhAkx9EF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KwXWEjiN_4ff",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d6f77967-6ec7-4ea4-fa7b-7c085fdf6d8b"
      },
      "source": [
        "X, y = [],[]\n",
        "X_train, X_test, y_train, y_test = [],[],[],[]\n",
        "for sample in samples[:100]:\n",
        "  print('Loading ' + sample)\n",
        "  sample_X_wt, sample_X_mut, sample_y, sample_com, sample_muta = load_obj(sample.replace('.pkl',''))\n",
        "  #sample_X_wt = reduce_channels(sample_X_wt[0])\n",
        "  #sample_X_mut = reduce_channels(sample_X_mut[0])\n",
        "  X.append([sample_X_wt[0], sample_X_mut[0]])\n",
        "  y.append(sample_y)\n",
        "X = np.array(X)\n",
        "y = np.array(y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading traindata_mut/1OGA_ABC_DE_DE52A_1.pkl\n",
            "Loading traindata_mut/3WWN_A_B_RA221E_1.pkl\n",
            "Loading traindata_mut/3HH2_AB_C_FC52A_1.pkl\n",
            "Loading traindata_mut/1AO7_ABC_DE_DA61A_1.pkl\n",
            "Loading traindata_mut/2BNR_ABC_DE_DA61A_1.pkl\n",
            "Loading traindata_mut/1GRN_A_B_DA38E_1.pkl\n",
            "Loading traindata_mut/1MLC_AB_E_NA92A_1.pkl\n",
            "Loading traindata_mut/1YQV_HL_Y_RY45K_1.pkl\n",
            "Loading traindata_mut/2J1K_C_T_RC155A_1.pkl\n",
            "Loading traindata_mut/1XGT_AB_C_LB33I_1.pkl\n",
            "Loading traindata_mut/2PCC_A_B_DA34A_1.pkl\n",
            "Loading traindata_mut/3AAA_AB_C_KA248A_1.pkl\n",
            "Loading traindata_mut/2AK4_ABC_DE_EA154A_1.pkl\n",
            "Loading traindata_mut/4CVW_A_C_LC34W_1.pkl\n",
            "Loading traindata_mut/1SGP_E_I_AI13L_1.pkl\n",
            "Loading traindata_mut/1F47_A_B_DA7G_1.pkl\n",
            "Loading traindata_mut/3AAA_AB_C_EC77A_1.pkl\n",
            "Loading traindata_mut/1AO7_ABC_DE_AA149G_1.pkl\n",
            "Loading traindata_mut/3HH2_AB_C_VC15E_1.pkl\n",
            "Loading traindata_mut/1AO7_ABC_DE_AA69G_1.pkl\n",
            "Loading traindata_mut/1BRS_A_D_EA71C_1.pkl\n",
            "Loading traindata_mut/2BDN_HL_A_NH28D_1.pkl\n",
            "Loading traindata_mut/1OGA_ABC_DE_EA63A_1.pkl\n",
            "Loading traindata_mut/1C4Z_ABC_D_DA145K_1.pkl\n",
            "Loading traindata_mut/3SGB_E_I_AI9E_1.pkl\n",
            "Loading traindata_mut/4N8V_G_ABC_YG39F_1.pkl\n",
            "Loading traindata_mut/4P5T_CD_AB_ED2A_1.pkl\n",
            "Loading traindata_mut/1S1Q_A_B_WA73A_1.pkl\n",
            "Loading traindata_mut/1MLC_AB_E_KB65D_1.pkl\n",
            "Loading traindata_mut/1BRS_A_D_EA71Q_1.pkl\n",
            "Loading traindata_mut/3SGB_E_I_AI9F_1.pkl\n",
            "Loading traindata_mut/2DVW_A_B_RB7A_1.pkl\n",
            "Loading traindata_mut/1JCK_A_B_VB91A_1.pkl\n",
            "Loading traindata_mut/3QDJ_ABC_DE_FE97W_1.pkl\n",
            "Loading traindata_mut/2SGP_E_I_PI13L_1.pkl\n",
            "Loading traindata_mut/3QIB_ABP_CD_EA75A_1.pkl\n",
            "Loading traindata_mut/3EQS_A_B_LB10A_1.pkl\n",
            "Loading traindata_mut/1GCQ_AB_C_GC21V_1.pkl\n",
            "Loading traindata_mut/2KSO_A_B_HB20E_1.pkl\n",
            "Loading traindata_mut/1BD2_ABC_DE_YC5F_1.pkl\n",
            "Loading traindata_mut/3EQY_A_C_NC8A_1.pkl\n",
            "Loading traindata_mut/3AAA_AB_C_DC43A_1.pkl\n",
            "Loading traindata_mut/1PPF_E_I_AI15C_1.pkl\n",
            "Loading traindata_mut/3BK3_A_C_IC19A_1.pkl\n",
            "Loading traindata_mut/1EAW_A_B_DA214A_1.pkl\n",
            "Loading traindata_mut/1BD2_ABC_DE_RA65A_1.pkl\n",
            "Loading traindata_mut/1XGR_AB_C_IB33V_1.pkl\n",
            "Loading traindata_mut/1PPF_E_I_AI15D_1.pkl\n",
            "Loading traindata_mut/3AAA_AB_C_EC77R_1.pkl\n",
            "Loading traindata_mut/2BNR_ABC_DE_EA58A_1.pkl\n",
            "Loading traindata_mut/1CHO_EFG_I_AI12G_1.pkl\n",
            "Loading traindata_mut/3C60_CD_AB_ED69A_1.pkl\n",
            "Loading traindata_mut/1BD2_ABC_DE_ED93A_1.pkl\n",
            "Loading traindata_mut/4JFF_ABC_DE_LC8A_1.pkl\n",
            "Loading traindata_mut/2OOB_A_B_AA9E_1.pkl\n",
            "Loading traindata_mut/4I77_HL_Z_WH52F_1.pkl\n",
            "Loading traindata_mut/4JFF_ABC_DE_GC6A_1.pkl\n",
            "Loading traindata_mut/2AK4_ABC_DE_LC5A_1.pkl\n",
            "Loading traindata_mut/1X1X_A_D_AD76E_1.pkl\n",
            "Loading traindata_mut/3C60_CD_AB_DC56A_1.pkl\n",
            "Loading traindata_mut/1JRH_LH_I_DL28A_1.pkl\n",
            "Loading traindata_mut/1BD2_ABC_DE_EE28A_1.pkl\n",
            "Loading traindata_mut/1DQJ_AB_C_DC101A_1.pkl\n",
            "Loading traindata_mut/1JRH_LH_I_EI45A_1.pkl\n",
            "Loading traindata_mut/4JFF_ABC_DE_EC1A_1.pkl\n",
            "Loading traindata_mut/1MI5_ABC_DE_EE99A_1.pkl\n",
            "Loading traindata_mut/3EQS_A_B_SB11A_1.pkl\n",
            "Loading traindata_mut/4FTV_ABC_DE_DD93A_1.pkl\n",
            "Loading traindata_mut/1F47_A_B_FA11A_1.pkl\n",
            "Loading traindata_mut/1JCK_A_B_TB20A_1.pkl\n",
            "Loading traindata_mut/4JFF_ABC_DE_GC4A_1.pkl\n",
            "Loading traindata_mut/1VFB_AB_C_DB54A_1.pkl\n",
            "Loading traindata_mut/1KIR_AB_C_SA50Y_1.pkl\n",
            "Loading traindata_mut/1SBB_A_B_LB20T_1.pkl\n",
            "Loading traindata_mut/3SF4_A_D_KD27D_1.pkl\n",
            "Loading traindata_mut/1EAW_A_B_DA91A_1.pkl\n",
            "Loading traindata_mut/3HFM_HL_Y_KY97M_1.pkl\n",
            "Loading traindata_mut/1XGT_AB_C_LB33A_1.pkl\n",
            "Loading traindata_mut/4HFK_A_BD_EB56A_1.pkl\n",
            "Loading traindata_mut/2HLE_A_B_LA87R_1.pkl\n",
            "Loading traindata_mut/1DQJ_AB_C_KC97A_1.pkl\n",
            "Loading traindata_mut/3EQS_A_B_LB9A_1.pkl\n",
            "Loading traindata_mut/1MI5_ABC_DE_GD93A_1.pkl\n",
            "Loading traindata_mut/1F47_A_B_KA14A_1.pkl\n",
            "Loading traindata_mut/4N8V_G_ABC_PG10A_1.pkl\n",
            "Loading traindata_mut/3EG5_A_B_HA104F_1.pkl\n",
            "Loading traindata_mut/1PPF_E_I_AI15I_1.pkl\n",
            "Loading traindata_mut/1GCQ_AB_C_WC47Y_1.pkl\n",
            "Loading traindata_mut/2NU1_E_I_HI13L_1.pkl\n",
            "Loading traindata_mut/4I77_HL_Z_YH101A_1.pkl\n",
            "Loading traindata_mut/3SGB_E_I_AI9C_1.pkl\n",
            "Loading traindata_mut/4OZG_ABJ_GH_NH50A_1.pkl\n",
            "Loading traindata_mut/1CBW_FGH_I_KI15A_1.pkl\n",
            "Loading traindata_mut/1LP9_ABC_EF_KA66A_1.pkl\n",
            "Loading traindata_mut/1AO7_ABC_DE_AE96M_1.pkl\n",
            "Loading traindata_mut/1XGQ_AB_C_VB33L_1.pkl\n",
            "Loading traindata_mut/3BK3_A_C_IC22A_1.pkl\n",
            "Loading traindata_mut/3EQY_A_C_FC3A_1.pkl\n",
            "Loading traindata_mut/3C60_CD_AB_DD86A_1.pkl\n",
            "Loading traindata_mut/2OOB_A_B_KA7E_1.pkl\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-eZ7gK40AjwT"
      },
      "source": [
        "#### 2.3 Split data into train and test sets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-h_S52pbAkB5",
        "outputId": "23b21779-3731-419f-8dc4-c1142f7a38ae"
      },
      "source": [
        "seed(int(np.round(np.random.random()*10)))\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=4)\n",
        "%xdel X\n",
        "%xdel y\n",
        "gc.collect()\n",
        "print(X_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(30, 2, 24, 24, 24, 170)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xlrUMY1OArrm"
      },
      "source": [
        "#### 2.4 Training Process"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YfZzLtDmAq1-"
      },
      "source": [
        "num_epochs = 20\n",
        "batch_size = 10\n",
        "v_dim = 24\n",
        "n_features = 167+3"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DovbF7LXA3zt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "50acaf88-2fad-454e-e26f-316fcf032570"
      },
      "source": [
        "input_shape=(v_dim,v_dim,v_dim,n_features)\n",
        "model = get_siamese_model(input_shape)\n",
        "\n",
        "# --------------------------------------\n",
        "# Training\n",
        "# --------------------------------------\n",
        "history = model.fit([X_train[:,0], X_train[:,1]], y_train, # Train the model using the training set...\n",
        "                    batch_size=batch_size, epochs=num_epochs,\n",
        "                    verbose=1, validation_split=0.3) # ...holding out 30% of the data for validation"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"functional_3\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_3\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m       Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to          \u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_3             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m,     │              \u001b[38;5;34m0\u001b[0m │ -                      │\n",
              "│ (\u001b[38;5;33mInputLayer\u001b[0m)              │ \u001b[38;5;34m170\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ input_layer_4             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m,     │              \u001b[38;5;34m0\u001b[0m │ -                      │\n",
              "│ (\u001b[38;5;33mInputLayer\u001b[0m)              │ \u001b[38;5;34m170\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ sequential_1 (\u001b[38;5;33mSequential\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │        \u001b[38;5;34m104,260\u001b[0m │ input_layer_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],   │\n",
              "│                           │                        │                │ input_layer_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ lambda_1 (\u001b[38;5;33mLambda\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │              \u001b[38;5;34m0\u001b[0m │ sequential_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],    │\n",
              "│                           │                        │                │ sequential_1[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m11\u001b[0m │ lambda_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)              </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">        Param # </span>┃<span style=\"font-weight: bold\"> Connected to           </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_3             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>,     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                      │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)              │ <span style=\"color: #00af00; text-decoration-color: #00af00\">170</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ input_layer_4             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>,     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                      │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)              │ <span style=\"color: #00af00; text-decoration-color: #00af00\">170</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ sequential_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">104,260</span> │ input_layer_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],   │\n",
              "│                           │                        │                │ input_layer_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ lambda_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Lambda</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],    │\n",
              "│                           │                        │                │ sequential_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span> │ lambda_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m104,271\u001b[0m (407.31 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">104,271</span> (407.31 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m104,091\u001b[0m (406.61 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">104,091</span> (406.61 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m180\u001b[0m (720.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">180</span> (720.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 3s/step - loss: 5.9144 - val_loss: 2.1713\n",
            "Epoch 2/20\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 297ms/step - loss: 4.8941 - val_loss: 2.1641\n",
            "Epoch 3/20\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 204ms/step - loss: 3.3272 - val_loss: 2.1639\n",
            "Epoch 4/20\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 294ms/step - loss: 0.9167 - val_loss: 2.1674\n",
            "Epoch 5/20\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 196ms/step - loss: 1.7649 - val_loss: 2.1693\n",
            "Epoch 6/20\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 219ms/step - loss: 0.6164 - val_loss: 2.1704\n",
            "Epoch 7/20\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 217ms/step - loss: 0.5652 - val_loss: 2.1688\n",
            "Epoch 8/20\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 299ms/step - loss: 1.0198 - val_loss: 2.1674\n",
            "Epoch 9/20\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 420ms/step - loss: 1.4895 - val_loss: 2.1648\n",
            "Epoch 10/20\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 199ms/step - loss: 1.0079 - val_loss: 2.1666\n",
            "Epoch 11/20\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 197ms/step - loss: 1.0054 - val_loss: 2.1684\n",
            "Epoch 12/20\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 191ms/step - loss: 0.7026 - val_loss: 2.1685\n",
            "Epoch 13/20\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 193ms/step - loss: 1.0240 - val_loss: 2.1680\n",
            "Epoch 14/20\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 212ms/step - loss: 0.6963 - val_loss: 2.1683\n",
            "Epoch 15/20\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 218ms/step - loss: 0.4720 - val_loss: 2.1677\n",
            "Epoch 16/20\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 343ms/step - loss: 0.4849 - val_loss: 2.1651\n",
            "Epoch 17/20\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 232ms/step - loss: 2.3841 - val_loss: 2.1625\n",
            "Epoch 18/20\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 303ms/step - loss: 1.4538 - val_loss: 2.1632\n",
            "Epoch 19/20\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 282ms/step - loss: 0.9856 - val_loss: 2.1629\n",
            "Epoch 20/20\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 212ms/step - loss: 0.9858 - val_loss: 2.1604\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "78bPecgkBsBY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 917
        },
        "outputId": "49a3ef85-44fe-493b-d166-2f205fb308d9"
      },
      "source": [
        "# --------------------------------------\n",
        "# Loss functions evolution\n",
        "# --------------------------------------\n",
        "plt.figure(figsize=(10,10))\n",
        "plt.rcParams.update({'font.size': 22})\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model loss by epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'valid'], loc='right')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tqRPGfO3Buus"
      },
      "source": [
        "Question: Is this evaluation of the performance reliable?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TgUjunFsByIC"
      },
      "source": [
        "#### 2.5 Let's take a look at the performance of the model on test samples."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g7EHKQjSCIOY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5040fa72-da1a-4962-ce4d-1cc977954d35"
      },
      "source": [
        "%xdel X_train\n",
        "gc.collect()\n",
        "\n",
        "#train_preds = model.predict([X_train[:,0], X_train[:,1]])\n",
        "test_preds = model.predict([X_test[:,0], X_test[:,1]])\n",
        "\n",
        "#train_preds = np.array([x[0] for x in train_preds])\n",
        "test_preds = np.array([x[0] for x in test_preds])\n",
        "\n",
        "#print(train_preds)\n",
        "print(test_preds)\n",
        "\n",
        "#corr_pearson_train = stats.pearsonr(y_train, train_preds)\n",
        "#corr_spearmanr_train = stats.spearmanr(y_train, train_preds)\n",
        "\n",
        "corr_pearson_test = stats.pearsonr(y_test, test_preds)\n",
        "corr_spearman_test = stats.spearmanr(y_test, test_preds)\n",
        "\n",
        "#print('corr_pearson_train', corr_pearson_train)\n",
        "#print('corr_spearmanr_train',corr_spearmanr_train)\n",
        "print('corr_pearson_test', corr_pearson_test)\n",
        "print('corr_spearman_test', corr_spearman_test)\n",
        "\n",
        "plt.rcParams.update({'font.size': 24})\n",
        "plt.figure(figsize=(12,12))\n",
        "#plt.scatter(y_train, train_preds, color = 'red', marker = 'o', label = 'Train', alpha=0.5)\n",
        "plt.scatter(y_test, test_preds, color = 'blue', marker = 's', label = 'Test', alpha=0.5)\n",
        "plt.xlabel('Experimental ddg', fontsize=24)\n",
        "plt.ylabel('Predicted ddg', fontsize=24)\n",
        "plt.legend(fontsize=24)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step\n",
            "[ 0.06592977  0.12669732  0.01107451  0.34849766  0.0473529  -0.135094\n",
            " -0.01784213 -0.02624139  0.00591075 -0.12147682  0.0310522  -0.03301936\n",
            "  0.06638463  0.07721885  0.06713911 -0.03948407  0.08543336  0.04010062\n",
            "  0.0930258  -0.05500339  0.04494818  0.03826061  0.10597586  0.06451645\n",
            " -0.01855968 -0.12290119  0.15029949  0.01276718  0.05074295  0.06990551]\n",
            "corr_pearson_test PearsonRResult(statistic=-0.15738680790020929, pvalue=0.406194806117472)\n",
            "corr_spearman_test SignificanceResult(statistic=-0.3210233592880979, pvalue=0.0836800072526214)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1xfbAWO8df-H"
      },
      "source": [
        "#### 2.5 Not enough time and data for training? Download the pre-trained Siamese model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SmYxxm8IKNve",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "158fb1e5-2551-44d9-bdbe-e7b9faf68139"
      },
      "source": [
        "!gdown --id '1A7IDHWMo9ph67Kf6jQpGlxkNwDKY5y8s'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gdown/__main__.py:140: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1A7IDHWMo9ph67Kf6jQpGlxkNwDKY5y8s\n",
            "To: /content/model_s.zip\n",
            "100% 1.22M/1.22M [00:00<00:00, 10.1MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZVMSVFLhdmi4"
      },
      "source": [
        "Decompress the model and load it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vXWXNQr-yH6C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8855c693-de22-4bd8-a8e4-d819319eefc8"
      },
      "source": [
        "!unzip '/content/model_s.zip'\n",
        "model_s = load_model('/content/model_s')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/model_s.zip\n",
            "   creating: model_s/\n",
            "  inflating: model_s/saved_model.pb  \n",
            "   creating: model_s/assets/\n",
            "   creating: model_s/variables/\n",
            "  inflating: model_s/variables/variables.data-00000-of-00001  \n",
            "  inflating: model_s/variables/variables.index  \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named \"keras_metadata.pb\" in the SavedModel directory.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "sqwvcJLEC57H",
        "outputId": "42bc2097-1bab-4872-a23f-69ef3c2dab2d"
      },
      "source": [
        "test_preds = model_s.predict([X_test[:,0], X_test[:,1]])\n",
        "test_preds = np.array([x[0] for x in test_preds])\n",
        "print(test_preds)\n",
        "\n",
        "corr_pearson_test = stats.pearsonr(y_test, test_preds)\n",
        "corr_spearman_test = stats.spearmanr(y_test, test_preds)\n",
        "\n",
        "print('corr_pearson_test', corr_pearson_test)\n",
        "print('corr_spearman_test', corr_spearman_test)\n",
        "\n",
        "plt.rcParams.update({'font.size': 24})\n",
        "plt.figure(figsize=(12,12))\n",
        "plt.scatter(y_test, test_preds, color = 'blue', marker = 's', label = 'Test', alpha=0.5)\n",
        "plt.xlabel('Experimental ddg', fontsize=24)\n",
        "plt.ylabel('Predicted ddg', fontsize=24)\n",
        "plt.legend(fontsize=24)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 2.0023823   0.22643822  0.7541527   0.11190522 -0.06585819  0.3825363\n",
            "  0.8881961   0.43179816  0.5801184   0.15823603  1.2304866   0.49883536\n",
            " -0.25230312  0.9317641   1.0410495   0.66903687  0.8143809   2.0103385\n",
            "  0.600096    1.3416843   0.40906692  0.54962707  0.5701124  -0.28204936\n",
            "  1.0687672   0.86105394  0.31278956  0.85893476  0.55897343  0.03490567]\n",
            "corr_pearson_test PearsonRResult(statistic=0.15232811950438296, pvalue=0.4216343204491357)\n",
            "corr_spearman_test SignificanceResult(statistic=0.1635150166852058, pvalue=0.3879257345165761)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0JEHCrDsXIBN"
      },
      "source": [
        "Designed by Yasser MOHSENI BEHBAHANI, LCQB, Sorbonne Université, 2021\n",
        "\n",
        "<img src='https://drive.google.com/uc?id=1shvaqXgnRa2aCji30gJ02hwI_Ux6NEp6'>\n"
      ]
    }
  ]
}