8-Protein_data_acquisition_Protein_Data_Bank_(PDB).ipynb

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        "<a href=\"https://colab.research.google.com/github/shumshersubashgautam/Computer-Aided-Drug-Discovery-Learning/blob/main/8-Protein_data_acquisition_Protein_Data_Bank_(PDB).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "text": [
            "Cloning into 'teachopencadd'...\n",
            "remote: Enumerating objects: 17115, done.\u001b[K\n",
            "remote: Counting objects: 100% (2093/2093), done.\u001b[K\n",
            "remote: Compressing objects: 100% (969/969), done.\u001b[K\n",
            "remote: Total 17115 (delta 1309), reused 1786 (delta 1122), pack-reused 15022\u001b[K\n",
            "Receiving objects: 100% (17115/17115), 849.77 MiB | 33.64 MiB/s, done.\n",
            "Resolving deltas: 100% (11506/11506), done.\n",
            "Updating files: 100% (468/468), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/volkamerlab/teachopencadd.git"
      ]
    },
    {
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      "source": [
        "!pip install rdkit"
      ],
      "metadata": {
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        "outputId": "0b6e5344-72db-42e7-940b-16114d950076"
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting rdkit\n",
            "  Downloading rdkit-2023.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (29.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29.7/29.7 MB\u001b[0m \u001b[31m60.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from rdkit) (1.22.4)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from rdkit) (8.4.0)\n",
            "Installing collected packages: rdkit\n",
            "Successfully installed rdkit-2023.3.1\n"
          ]
        }
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    {
      "cell_type": "code",
      "source": [
        "cd teachopencadd"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ztbhkkOhY9Z",
        "outputId": "ac9a38c7-b330-415c-f753-96c02187d710"
      },
      "execution_count": 3,
      "outputs": [
        {
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          "text": [
            "/content/teachopencadd\n"
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      "cell_type": "code",
      "source": [
        "# !python setup.py install"
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        "# **Theory**\n",
        "# **Protein Data Bank (PDB)**\n",
        "The RCSB Protein Data Bank (PDB) is a comprehensive structural biology information database and a key resource in areas of structural biology, such as structural genomics and drug design (PDB website).\n",
        "\n",
        "Structural data is generated from structural determination methods such as X-ray crystallography (most common method), nuclear magnetic resonance (NMR), and cryo electron microscopy (cryo-EM). For each entry, the database contains (i) the 3D coordinates of the atoms and the bonds connecting these atoms for proteins, ligand, cofactors, water molecules, and ions, as well as (ii) meta information on the structural data such as the PDB ID, the authors, the deposition date, the structural determination method used, and the structural resolution. The structural resolution is a measure of the collected data quality and has the unit Å (Angstrom); the lower the value, the higher the quality of the structure.\n",
        "\n",
        "The PDB website offers a 3D visualization of the protein structures (with ligand interactions if available) and a structure quality metrics, as can be seen for the PDB entry of an example epidermal growth factor receptor (EGFR) with the PDB ID 3UG5.\n",
        "![protein-ligand-complex.png](data:image/png;base64,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)\n",
        "\n",
        "Figure 1: The protein structure (in gray) with an interacting ligand (in green) is shown for an example epidermal growth factor receptor (EGFR) with the PDB ID 3UG5.\n",
        "\n",
        "Query the PDB using the Python packages biotite and pypdb\n",
        "Each structure in the PDB database is linked to many different fields to hold meta information. Check out the complete list of available fields for chemicals/structures and supported operators on the RCSB website. The Python package biotite provides the convenient module databases.rcsb (see docs), which allows us to query one (FieldQuery, see docs) or more (CompositeQuery, see docs) of these fields to retrieve a count (count) or list (search) of PDB IDs that match our criteria.\n",
        "\n",
        "The Python package pypdb offers an interface for the PDB to not only query for PDB IDs but also to download associated metadata and structural files (Bioinformatics (2016), 1, 159-60, documentation). Check out the demo notebook introducing the pypdb API.\n",
        "\n",
        "We will use both packages in this notebook: biotite to quickly filter down the many structures in the PDB given certain criteria and pypdb to download metadata and structural files for specific PDB entries of interest."
      ],
      "metadata": {
        "id": "crNMYvD7g_tR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install redo\n",
        "# !pip install requests-cache\n",
        "# !pip install nglview\n",
        "# !pip install pypdb\n",
        "# !pip install biotite"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z13UF2kZhqZK",
        "outputId": "6d824ff5-6c38-45c8-8e3e-1fc1cecf99fb"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0oYMuaHlhyCA",
        "outputId": "7a7b3ae8-1393-4b27-fc3a-79dd887fc99c"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "\u001b[31mERROR: Could not find a version that satisfies the requirement opencadd (from versions: none)\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[31mERROR: No matching distribution found for opencadd\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "65RJFmoAh41I"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IVVB3RXViGL_"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "x1xlEwXmiOJB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import collections\n",
        "import logging\n",
        "import pathlib\n",
        "import time\n",
        "import warnings\n",
        "import datetime\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from bs4 import BeautifulSoup\n",
        "import requests\n",
        "from tqdm.auto import tqdm\n",
        "import redo\n",
        "import requests_cache\n",
        "import nglview\n",
        "import pypdb\n",
        "import biotite.database.rcsb as rcsb\n",
        "from rdkit.Chem import Draw\n",
        "from rdkit.Chem import PandasTools\n",
        "\n",
        "# from opencadd.structure.superposition.api import align, METHODS\n",
        "# from opencadd.structure.core import Structure\n",
        "\n",
        "# # Disable some unneeded warnings\n",
        "# logger = logging.getLogger(\"opencadd\")\n",
        "# logger.setLevel(logging.ERROR)\n",
        "# warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# Cache requests -- this will speed up repeated queries to PDB\n",
        "requests_cache.install_cache(\"rcsb_pdb\", backend=\"memory\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rNAbArAyhD0n",
        "outputId": "7568199f-75bd-4fe7-8aa5-05ec7654831f"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define paths\n",
        "HERE = pathlib.Path(_dh[-1])\n",
        "DATA = HERE / \"data\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ogj1gd9jhn_C",
        "outputId": "65511b41-20ed-4f0f-dfa3-3d83d916b42a"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "uniprot_id = \"P00533\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Pz-AW8ojZzI",
        "outputId": "f0cf1bec-048d-493a-ca35-c4615970662c"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query_by_uniprot_id = rcsb.FieldQuery(\n",
        "    \"rcsb_polymer_entity_container_identifiers.reference_sequence_identifiers.database_accession\",\n",
        "    exact_match=uniprot_id,\n",
        ")\n",
        "today = datetime.datetime.now()\n",
        "print(\n",
        "    f\"Number of structures on {today.year}-{today.month}-{today.day}: {rcsb.count(query_by_uniprot_id)}\"\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rqd2PzEzjbqD",
        "outputId": "fe5e8032-bde7-4c8b-f5ba-1036e52ffac8"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of structures on 2023-6-23: 281\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define lists for years and number of structures available in a given year\n",
        "years = range(1971, datetime.datetime.now().year)\n",
        "n_structures = []\n",
        "\n",
        "for year in years:\n",
        "    # Set latest date for allowed deposition\n",
        "    before_deposition_date = f\"{year}-12-31T23:59:59Z\"\n",
        "    # Set up query for structures deposited latest at given date\n",
        "    query_by_deposition_date = rcsb.FieldQuery(\n",
        "        \"rcsb_accession_info.deposit_date\", less_or_equal=before_deposition_date\n",
        "    )\n",
        "    # Set up combined query\n",
        "    query = rcsb.CompositeQuery(\n",
        "        [query_by_uniprot_id, query_by_deposition_date],\n",
        "        \"and\",\n",
        "    )\n",
        "    # Count matching structures and add to list\n",
        "    n_structures.append(rcsb.count(query))\n",
        "    # wait shortly to not overload the API\n",
        "    time.sleep(0.1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l0gQF0ZrjeXa",
        "outputId": "1afa6a24-49d6-4138-d58b-d2e4de4395ad"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(years, n_structures)\n",
        "plt.title(\"PDB entries for EGFR\")\n",
        "plt.xlabel(\"Year\")\n",
        "plt.ylabel(\"Number of structures available in a given year\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "07zBwC19jg5y",
        "outputId": "0c65fb0e-c983-4236-8fb0-38d873a7dee9"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Find PDB entries fullfilling certain conditions\n",
        "We will search for PDB IDs describing structures in the PDB that fulfill the following criteria:\n",
        "\n",
        "Structures for UniProt ID P00533: This is our target of interest, EGFR!\n",
        "Structures deposited before 2020: This step is for TeachOpenCADD-internal maintanence purposes. We will only consider structures that were deposited before 2020. That way the results of this notebook will stay the same over time, allowing us to check with our continuous integration (CI) that this notebook does not go out of service.\n",
        "Structure resolved by X-ray crystallography: We could include all methods but let's check out the API on how we can select experimental methods.\n",
        "Structure has a resolution less than or equal to 3.0: The lower the resolution value, the higher is the quality of the structure, i.e. the certainty that the assigned 3D coordinates of the atoms are correct. Atomic orientations can be determined below 3 Å. Thus, this threshold is often used for structures relevant for structure-based drug design.\n",
        "Structure has only one chain: We do this to make our lives easier lateron.\n",
        "Structure has a ligand with molecular weight greater than 100.0 Da: PDB-annotated ligands can be ligands, but also solvents and ions. In order to filter only ligand-bound structures, we keep only structures with annotated ligand of a at least 100.0 Da (many solvents and ions weight less). Note: this is a simple, but not comprehensive exclusion of solvents and ions.\n",
        "We make use of the biotite package again to query for PDB IDs based on the following combined queries:"
      ],
      "metadata": {
        "id": "F8qVJGJRjnw0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "uniprot_id = \"P00533\"\n",
        "before_deposition_date = \"2020-01-01T00:00:00Z\"\n",
        "experimental_method = \"X-RAY DIFFRACTION\"\n",
        "max_resolution = 3.0\n",
        "n_chains = 1\n",
        "min_ligand_molecular_weight = 100.0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LHxLm5eWjkmJ",
        "outputId": "5363dd50-0adc-4c94-c8c0-5402ca79382d"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query_by_uniprot_id = rcsb.FieldQuery(\n",
        "    \"rcsb_polymer_entity_container_identifiers.reference_sequence_identifiers.database_accession\",\n",
        "    exact_match=uniprot_id,\n",
        ")\n",
        "query_by_deposition_date = rcsb.FieldQuery(\n",
        "    \"rcsb_accession_info.deposit_date\", less=before_deposition_date\n",
        ")\n",
        "query_by_experimental_method = rcsb.FieldQuery(\"exptl.method\", exact_match=experimental_method)\n",
        "query_by_resolution = rcsb.FieldQuery(\n",
        "    \"rcsb_entry_info.resolution_combined\", less_or_equal=max_resolution\n",
        ")\n",
        "query_by_polymer_count = rcsb.FieldQuery(\n",
        "    \"rcsb_entry_info.deposited_polymer_entity_instance_count\", equals=n_chains\n",
        ")\n",
        "query_by_ligand_mw = rcsb.FieldQuery(\n",
        "    \"chem_comp.formula_weight\", molecular_definition=True, greater=min_ligand_molecular_weight\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_uW3SxEkjsWc",
        "outputId": "e12104ae-ba0f-4c15-ef69-66e09fac9ace"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Number of structures with UniProt ID {uniprot_id}: {rcsb.count(query_by_uniprot_id)}\")\n",
        "time.sleep(0.1)  # wait shortly to not overload the API\n",
        "print(\n",
        "    f\"Number of structures deposited before {before_deposition_date}: {rcsb.count(query_by_deposition_date)}\"\n",
        ")\n",
        "time.sleep(0.1)\n",
        "print(\n",
        "    f\"Number of structures resolved by {experimental_method}: {rcsb.count(query_by_experimental_method)}\"\n",
        ")\n",
        "time.sleep(0.1)\n",
        "print(\n",
        "    f\"Number of structures with resolution less than or equal to {max_resolution}: {rcsb.count(query_by_resolution)}\"\n",
        ")\n",
        "time.sleep(0.1)\n",
        "print(f\"Number of structures with only {n_chains} chain: {rcsb.count(query_by_polymer_count)}\")\n",
        "time.sleep(0.1)\n",
        "print(\n",
        "    f\"Number of structures with ligand of more than or equal to {min_ligand_molecular_weight} Da: {rcsb.count(query_by_ligand_mw)}\"\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LeU3iv48juv5",
        "outputId": "b4e57753-d835-4dbd-d33d-72e1052b824b"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of structures with UniProt ID P00533: 281\n",
            "Number of structures deposited before 2020-01-01T00:00:00Z: 166265\n",
            "Number of structures resolved by X-RAY DIFFRACTION: 176296\n",
            "Number of structures with resolution less than or equal to 3.0: 168815\n",
            "Number of structures with only 1 chain: 75027\n",
            "Number of structures with ligand of more than or equal to 100.0 Da: 206461\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query = rcsb.CompositeQuery(\n",
        "    [\n",
        "        query_by_uniprot_id,\n",
        "        query_by_deposition_date,\n",
        "        query_by_experimental_method,\n",
        "        query_by_resolution,\n",
        "        query_by_polymer_count,\n",
        "        query_by_ligand_mw,\n",
        "    ],\n",
        "    \"and\",\n",
        ")\n",
        "pdb_ids = rcsb.search(query)\n",
        "print(f\"Number of matches: {len(pdb_ids)}\")\n",
        "print(\"Selected PDB IDs:\")\n",
        "print(*pdb_ids)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ccRA6u71jw8E",
        "outputId": "0f2b121b-f5d5-4d42-b422-866cc5c6ec30"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of matches: 110\n",
            "Selected PDB IDs:\n",
            "1M14 1M17 1XKK 2EB2 2EB3 2GS2 2ITN 2ITP 2ITQ 2ITT 2ITU 2ITV 2ITW 2ITX 2ITZ 2J5F 2RGP 3BEL 3GOP 3POZ 3UG1 3UG2 3VJN 3VJO 3W2O 3W2P 3W2Q 3W2R 3W2S 3W32 3W33 4G5J 4HJO 4I1Z 4I22 4I23 4JQ7 4JQ8 4JR3 4JRV 4LI5 4LQM 4RJ4 4RJ6 4RJ7 4RJ8 4WKQ 4WRG 4ZAU 5C8K 5C8M 5C8N 5CAL 5CAN 5CAO 5CAP 5CAQ 5CAS 5CAU 5CAV 5EDP 5EDQ 5EDR 5EM5 5EM6 5EM7 5EM8 5FED 5FEE 5GMP 5GNK 5GTZ 5HCX 5HCY 5HCZ 5HG5 5HG7 5HG8 5HG9 5HIB 5HIC 5J9Y 5J9Z 5U8L 5UG8 5UG9 5UGA 5UGB 5UGC 5X26 5X27 5X28 5XDK 5XDL 5XGM 5ZTO 5ZWJ 6D8E 6JRJ 6JRK 6JRX 6JWL 6JX0 6JX4 6JXT 6JZ0 6S89 6S8A 6S9C 6S9D\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Select PDB entries with the hightest resolution\n",
        "So far we have used certain search criteria to find PDB entries of interest.\n",
        "\n",
        "At the moment, we cannot access the structures' resolution directly via biotite; with biotite we can only check if the resolution fullfils a certain condition. Instead we download the full metadata for our selected PDB IDs. To this end, we use from the pypdb package the method describe_pdb. Each structure's metadata is returned as a dictionary.\n",
        "\n",
        "Note: we only fetch meta information on PDB structures here, we do not fetch the structures (3D coordinates), yet.\n",
        "\n",
        "The redo.retriable line is a decorator. This wraps the function and provides extra functionality. In this case, it will retry failed queries automatically (10 times maximum)."
      ],
      "metadata": {
        "id": "aRfhSe6fj3vg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "@redo.retriable(attempts=10, sleeptime=2)\n",
        "def describe_one_pdb_id(pdb_id):\n",
        "    \"\"\"Fetch meta information from PDB.\"\"\"\n",
        "    described = pypdb.describe_pdb(pdb_id)\n",
        "    if described is None:\n",
        "        print(f\"! Error while fetching {pdb_id}, retrying ...\")\n",
        "        raise ValueError(f\"Could not fetch PDB id {pdb_id}\")\n",
        "    return described"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RNJfEFhzjzvC",
        "outputId": "083c7e61-5953-4e86-99e4-e4ce97840f89"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pdbs_data = [describe_one_pdb_id(pdb_id) for pdb_id in tqdm(pdb_ids)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
          "referenced_widgets": [
            "d34ade4cc1854c55b3bfd80ab13227b7",
            "1dd1cf015ad84023bde4b507577118d3",
            "50aa2cb1f40a40f495563b694be1dbf0",
            "85a8f0c74b7f4af1a3cd6b731ee8a446",
            "d59086a6c6d84fe5a6809122b0a22ce7",
            "32657b2a7ab34e9db6d89ef8044b3550",
            "c4db289a284b484abd2a2d5715674d53",
            "a91731882f694dff9cd5f127f4eea35e",
            "bfb2384c4f5a4aa3b78e9b84a08ee4aa",
            "07dad52d6d8c45078975784357e5ed7b",
            "e552e5e2ca62406f987366c35062448f"
          ]
        },
        "id": "7aP_wwnwj6FU",
        "outputId": "98661cff-304e-4b61-bf83-ce1758fa7616"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/110 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d34ade4cc1854c55b3bfd80ab13227b7"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n\".join(pdbs_data[0].keys()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qqp4TtD_j76p",
        "outputId": "0fdeb894-f62e-4915-d76e-a94d183a3f84"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "audit_author\n",
            "cell\n",
            "citation\n",
            "diffrn\n",
            "diffrn_detector\n",
            "diffrn_radiation\n",
            "diffrn_source\n",
            "entry\n",
            "exptl\n",
            "exptl_crystal\n",
            "exptl_crystal_grow\n",
            "pdbx_audit_revision_details\n",
            "pdbx_audit_revision_group\n",
            "pdbx_audit_revision_history\n",
            "pdbx_database_related\n",
            "pdbx_database_status\n",
            "rcsb_accession_info\n",
            "rcsb_entry_container_identifiers\n",
            "rcsb_entry_info\n",
            "rcsb_primary_citation\n",
            "refine\n",
            "refine_analyze\n",
            "refine_hist\n",
            "refine_ls_restr\n",
            "reflns\n",
            "reflns_shell\n",
            "software\n",
            "struct\n",
            "struct_keywords\n",
            "symmetry\n",
            "rcsb_id\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pdbs_data[0][\"entry\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xs1eNFedj-OL",
        "outputId": "36796c4a-a3d6-4245-e5e2-32978b725fd5"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': '1M14'}"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pdbs_data[0][\"rcsb_entry_info\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KXI1PlVkkAdz",
        "outputId": "5c4d90c4-26de-4e0f-fe5b-c849eada3387"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'assembly_count': 1,\n",
              " 'branched_entity_count': 0,\n",
              " 'cis_peptide_count': 0,\n",
              " 'deposited_atom_count': 2469,\n",
              " 'deposited_hydrogen_atom_count': 0,\n",
              " 'deposited_model_count': 1,\n",
              " 'deposited_modeled_polymer_monomer_count': 307,\n",
              " 'deposited_nonpolymer_entity_instance_count': 0,\n",
              " 'deposited_polymer_entity_instance_count': 1,\n",
              " 'deposited_polymer_monomer_count': 333,\n",
              " 'deposited_solvent_atom_count': 17,\n",
              " 'deposited_unmodeled_polymer_monomer_count': 26,\n",
              " 'diffrn_radiation_wavelength_maximum': 1.008,\n",
              " 'diffrn_radiation_wavelength_minimum': 1.008,\n",
              " 'disulfide_bond_count': 0,\n",
              " 'entity_count': 2,\n",
              " 'experimental_method': 'X-ray',\n",
              " 'experimental_method_count': 1,\n",
              " 'inter_mol_covalent_bond_count': 0,\n",
              " 'inter_mol_metalic_bond_count': 0,\n",
              " 'molecular_weight': 37.88,\n",
              " 'na_polymer_entity_types': 'Other',\n",
              " 'nonpolymer_entity_count': 0,\n",
              " 'polymer_composition': 'homomeric protein',\n",
              " 'polymer_entity_count': 1,\n",
              " 'polymer_entity_count_dna': 0,\n",
              " 'polymer_entity_count_rna': 0,\n",
              " 'polymer_entity_count_nucleic_acid': 0,\n",
              " 'polymer_entity_count_nucleic_acid_hybrid': 0,\n",
              " 'polymer_entity_count_protein': 1,\n",
              " 'polymer_entity_taxonomy_count': 1,\n",
              " 'polymer_molecular_weight_maximum': 37.88,\n",
              " 'polymer_molecular_weight_minimum': 37.88,\n",
              " 'polymer_monomer_count_maximum': 333,\n",
              " 'polymer_monomer_count_minimum': 333,\n",
              " 'resolution_combined': [2.6],\n",
              " 'selected_polymer_entity_types': 'Protein (only)',\n",
              " 'software_programs_combined': ['AMORE',\n",
              "  'CCP4',\n",
              "  'HKL-2000',\n",
              "  'TRUNCATE',\n",
              "  'X-PLOR'],\n",
              " 'solvent_entity_count': 1,\n",
              " 'structure_determination_methodology': 'experimental',\n",
              " 'structure_determination_methodology_priority': 10,\n",
              " 'diffrn_resolution_high': {'provenance_source': 'Depositor assigned',\n",
              "  'value': 2.6}}"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "resolution = pd.DataFrame(\n",
        "    [\n",
        "        [pdb_data[\"entry\"][\"id\"], pdb_data[\"rcsb_entry_info\"][\"resolution_combined\"][0]]\n",
        "        for pdb_data in pdbs_data\n",
        "    ],\n",
        "    columns=[\"pdb_id\", \"resolution\"],\n",
        ").sort_values(by=\"resolution\", ignore_index=True)\n",
        "resolution"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "wHheM3lKkCKQ",
        "outputId": "83e52971-f6e3-427b-c7d6-1d48c1f9724d"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    pdb_id  resolution\n",
              "0     5UG9       1.330\n",
              "1     5HG8       1.420\n",
              "2     5UG8       1.460\n",
              "3     3POZ       1.500\n",
              "4     5HG5       1.520\n",
              "..     ...         ...\n",
              "105   2ITX       2.980\n",
              "106   5GTZ       2.999\n",
              "107   2J5F       3.000\n",
              "108   4I1Z       3.000\n",
              "109   5C8K       3.000\n",
              "\n",
              "[110 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ebd2c015-c9b2-4f3e-a870-3e7a15ba5a76\">\n",
              "    <div 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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pdb_id</th>\n",
              "      <th>resolution</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5UG9</td>\n",
              "      <td>1.330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5HG8</td>\n",
              "      <td>1.420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5UG8</td>\n",
              "      <td>1.460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3POZ</td>\n",
              "      <td>1.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5HG5</td>\n",
              "      <td>1.520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>2ITX</td>\n",
              "      <td>2.980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>5GTZ</td>\n",
              "      <td>2.999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>2J5F</td>\n",
              "      <td>3.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>4I1Z</td>\n",
              "      <td>3.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>5C8K</td>\n",
              "      <td>3.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>110 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ebd2c015-c9b2-4f3e-a870-3e7a15ba5a76')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
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              "      height: 32px;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
              "    [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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ebd2c015-c9b2-4f3e-a870-3e7a15ba5a76 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-ebd2c015-c9b2-4f3e-a870-3e7a15ba5a76');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "top_num = 6  # Number of top structures"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ePuC7D0wkEah",
        "outputId": "82559020-53e8-4afe-8400-5c7c169d92fe"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "selected_pdb_ids = resolution[:top_num][\"pdb_id\"].to_list()\n",
        "print(f\"Selected PDB IDs: {selected_pdb_ids}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "81IrLBqAkGim",
        "outputId": "53f98280-a484-49d8-8ecc-768003a71e7e"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected PDB IDs: ['5UG9', '5HG8', '5UG8', '3POZ', '5HG5', '5UGC']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "selected_pdb_ids = [\"5UG9\", \"5HG8\", \"5UG8\", \"5UGC\"]\n",
        "print(f\"Selected PDB IDs (frozen set): {selected_pdb_ids}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5auvs6RokIVv",
        "outputId": "2cc20f8b-4965-4ad6-e1a4-56065d360272"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected PDB IDs (frozen set): ['5UG9', '5HG8', '5UG8', '5UGC']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_ligands(pdb_id):\n",
        "    \"\"\"\n",
        "    RCSB has not provided a new endpoint for ligand information yet. As a\n",
        "    workaround we are obtaining extra information from ligand-expo.rcsb.org,\n",
        "    using HTML parsing. Check Talktorial T011 for more info on this technique!\n",
        "    \"\"\"\n",
        "    pdb_info = _fetch_pdb_nonpolymer_info(pdb_id)\n",
        "    ligand_expo_ids = [\n",
        "        nonpolymer_entities[\"pdbx_entity_nonpoly\"][\"comp_id\"]\n",
        "        for nonpolymer_entities in pdb_info[\"data\"][\"entry\"][\"nonpolymer_entities\"]\n",
        "    ]\n",
        "\n",
        "    ligands = {}\n",
        "    for ligand_expo_id in ligand_expo_ids:\n",
        "        ligand_expo_info = _fetch_ligand_expo_info(ligand_expo_id)\n",
        "        ligands[ligand_expo_id] = ligand_expo_info\n",
        "\n",
        "    return ligands\n",
        "\n",
        "\n",
        "def _fetch_pdb_nonpolymer_info(pdb_id):\n",
        "    \"\"\"\n",
        "    Fetch nonpolymer data from rcsb.org.\n",
        "    Thanks @BJWiley233 and Rachel Green for this GraphQL solution.\n",
        "    \"\"\"\n",
        "    query = (\n",
        "        \"\"\"{\n",
        "          entry(entry_id: \"%s\") {\n",
        "            nonpolymer_entities {\n",
        "              pdbx_entity_nonpoly {\n",
        "                comp_id\n",
        "                name\n",
        "                rcsb_prd_id\n",
        "              }\n",
        "            }\n",
        "          }\n",
        "        }\"\"\"\n",
        "        % pdb_id\n",
        "    )\n",
        "\n",
        "    query_url = f\"https://data.rcsb.org/graphql?query={query}\"\n",
        "    response = requests.get(query_url)\n",
        "    response.raise_for_status()\n",
        "    info = response.json()\n",
        "    return info\n",
        "\n",
        "\n",
        "def _fetch_ligand_expo_info(ligand_expo_id):\n",
        "    \"\"\"\n",
        "    Fetch ligand data from ligand-expo.rcsb.org.\n",
        "    \"\"\"\n",
        "    r = requests.get(f\"http://ligand-expo.rcsb.org/reports/{ligand_expo_id[0]}/{ligand_expo_id}/\")\n",
        "    r.raise_for_status()\n",
        "    html = BeautifulSoup(r.text)\n",
        "    info = {}\n",
        "    for table in html.find_all(\"table\"):\n",
        "        for row in table.find_all(\"tr\"):\n",
        "            cells = row.find_all(\"td\")\n",
        "            if len(cells) != 2:\n",
        "                continue\n",
        "            key, value = cells\n",
        "            if key.string and key.string.strip():\n",
        "                info[key.string.strip()] = \"\".join(value.find_all(string=True))\n",
        "\n",
        "    # Postprocess some known values\n",
        "    info[\"Molecular weight\"] = float(info[\"Molecular weight\"].split()[0])\n",
        "    info[\"Formal charge\"] = int(info[\"Formal charge\"])\n",
        "    info[\"Atom count\"] = int(info[\"Atom count\"])\n",
        "    info[\"Chiral atom count\"] = int(info[\"Chiral atom count\"])\n",
        "    info[\"Bond count\"] = int(info[\"Bond count\"])\n",
        "    info[\"Aromatic bond count\"] = int(info[\"Aromatic bond count\"])\n",
        "    return info"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lyyGX8lEkKfO",
        "outputId": "71985489-6af5-4a39-9079-c3160ab4f9bd"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "columns = [\n",
        "    \"@structureId\",\n",
        "    \"@chemicalID\",\n",
        "    \"@type\",\n",
        "    \"@molecularWeight\",\n",
        "    \"chemicalName\",\n",
        "    \"formula\",\n",
        "    \"InChI\",\n",
        "    \"InChIKey\",\n",
        "    \"smiles\",\n",
        "]\n",
        "rows = []\n",
        "for pdb_id in selected_pdb_ids:\n",
        "    ligands = get_ligands(pdb_id)\n",
        "    # If several ligands contained, take largest (first in results)\n",
        "    ligand_id, properties = max(ligands.items(), key=lambda kv: kv[1][\"Molecular weight\"])\n",
        "    rows.append(\n",
        "        [\n",
        "            pdb_id,\n",
        "            ligand_id,\n",
        "            properties[\"Component type\"],\n",
        "            properties[\"Molecular weight\"],\n",
        "            properties[\"Name\"],\n",
        "            properties[\"Formula\"],\n",
        "            properties[\"InChI descriptor\"],\n",
        "            properties[\"InChIKey descriptor\"],\n",
        "            properties[\"Stereo SMILES (OpenEye)\"],\n",
        "        ]\n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jMnXkIOlkNf0",
        "outputId": "1267e9a9-e33e-4878-9023-9e5370733039"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# NBVAL_CHECK_OUTPUT\n",
        "# Change the format to DataFrame\n",
        "ligands = pd.DataFrame(rows, columns=columns)\n",
        "ligands"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "hmXjOOWgkPhZ",
        "outputId": "dd436998-74e0-44c8-91a2-cc356cb8cdc6"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  @structureId @chemicalID        @type  @molecularWeight  \\\n",
              "0         5UG9         8AM  NON-POLYMER           445.494   \n",
              "1         5HG8         634  NON-POLYMER           377.400   \n",
              "2         5UG8         8BP  NON-POLYMER           415.468   \n",
              "3         5UGC         8BS  NON-POLYMER           417.441   \n",
              "\n",
              "                                        chemicalName          formula  \\\n",
              "0  N-[(3R,4R)-4-fluoro-1-{6-[(3-methoxy-1-methyl-...  C20 H28 F N9 O2   \n",
              "1  N-[3-({2-[(1-methyl-1H-pyrazol-4-yl)amino]-7H-...    C19 H19 N7 O2   \n",
              "2  N-[(3R,4R)-4-fluoro-1-{6-[(1-methyl-1H-pyrazol...   C19 H26 F N9 O   \n",
              "3  N-[(3R,4R)-4-fluoro-1-{6-[(3-methoxy-1-methyl-...  C18 H24 F N9 O2   \n",
              "\n",
              "                                               InChI  \\\n",
              "0  InChI=1S/C20H28FN9O2/c1-6-15(31)23-13-9-29(7-1...   \n",
              "1  InChI=1S/C19H19N7O2/c1-3-16(27)22-12-5-4-6-14(...   \n",
              "2  InChI=1S/C19H26FN9O/c1-5-15(30)24-14-9-28(8-13...   \n",
              "3  InChI=1S/C18H24FN9O2/c1-5-13(29)21-11-8-28(6-1...   \n",
              "\n",
              "                      InChIKey  \\\n",
              "0  MJLFLAORJNTNDV-CHWSQXEVSA-N   \n",
              "1  YWNHZBNRKJYHTR-UHFFFAOYSA-N   \n",
              "2  CGULPICMFDDQRH-ZIAGYGMSSA-N   \n",
              "3  XWNKXCUQRQRAFF-GHMZBOCLSA-N   \n",
              "\n",
              "                                              smiles  \n",
              "0  CCC(=O)N[C@@H]1CN(C[C@H]1F)c2nc(c3c(n2)n(cn3)C...  \n",
              "1   CCC(=O)Nc1cccc(c1)Oc2c3cc[nH]c3nc(n2)Nc4cnn(c4)C  \n",
              "2  CCC(=O)N[C@@H]1CN(C[C@H]1F)c2nc(c3c(n2)n(cn3)C...  \n",
              "3  CCC(=O)N[C@@H]1CN(C[C@H]1F)c2nc(c3c(n2)n(cn3)C...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1b156034-a189-4ffd-bb23-aa3abff1b432\">\n",
              "    <div 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>@structureId</th>\n",
              "      <th>@chemicalID</th>\n",
              "      <th>@type</th>\n",
              "      <th>@molecularWeight</th>\n",
              "      <th>chemicalName</th>\n",
              "      <th>formula</th>\n",
              "      <th>InChI</th>\n",
              "      <th>InChIKey</th>\n",
              "      <th>smiles</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5UG9</td>\n",
              "      <td>8AM</td>\n",
              "      <td>NON-POLYMER</td>\n",
              "      <td>445.494</td>\n",
              "      <td>N-[(3R,4R)-4-fluoro-1-{6-[(3-methoxy-1-methyl-...</td>\n",
              "      <td>C20 H28 F N9 O2</td>\n",
              "      <td>InChI=1S/C20H28FN9O2/c1-6-15(31)23-13-9-29(7-1...</td>\n",
              "      <td>MJLFLAORJNTNDV-CHWSQXEVSA-N</td>\n",
              "      <td>CCC(=O)N[C@@H]1CN(C[C@H]1F)c2nc(c3c(n2)n(cn3)C...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5HG8</td>\n",
              "      <td>634</td>\n",
              "      <td>NON-POLYMER</td>\n",
              "      <td>377.400</td>\n",
              "      <td>N-[3-({2-[(1-methyl-1H-pyrazol-4-yl)amino]-7H-...</td>\n",
              "      <td>C19 H19 N7 O2</td>\n",
              "      <td>InChI=1S/C19H19N7O2/c1-3-16(27)22-12-5-4-6-14(...</td>\n",
              "      <td>YWNHZBNRKJYHTR-UHFFFAOYSA-N</td>\n",
              "      <td>CCC(=O)Nc1cccc(c1)Oc2c3cc[nH]c3nc(n2)Nc4cnn(c4)C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5UG8</td>\n",
              "      <td>8BP</td>\n",
              "      <td>NON-POLYMER</td>\n",
              "      <td>415.468</td>\n",
              "      <td>N-[(3R,4R)-4-fluoro-1-{6-[(1-methyl-1H-pyrazol...</td>\n",
              "      <td>C19 H26 F N9 O</td>\n",
              "      <td>InChI=1S/C19H26FN9O/c1-5-15(30)24-14-9-28(8-13...</td>\n",
              "      <td>CGULPICMFDDQRH-ZIAGYGMSSA-N</td>\n",
              "      <td>CCC(=O)N[C@@H]1CN(C[C@H]1F)c2nc(c3c(n2)n(cn3)C...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5UGC</td>\n",
              "      <td>8BS</td>\n",
              "      <td>NON-POLYMER</td>\n",
              "      <td>417.441</td>\n",
              "      <td>N-[(3R,4R)-4-fluoro-1-{6-[(3-methoxy-1-methyl-...</td>\n",
              "      <td>C18 H24 F N9 O2</td>\n",
              "      <td>InChI=1S/C18H24FN9O2/c1-5-13(29)21-11-8-28(6-1...</td>\n",
              "      <td>XWNKXCUQRQRAFF-GHMZBOCLSA-N</td>\n",
              "      <td>CCC(=O)N[C@@H]1CN(C[C@H]1F)c2nc(c3c(n2)n(cn3)C...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1b156034-a189-4ffd-bb23-aa3abff1b432')\"\n",
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              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\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",
              "    [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",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-1b156034-a189-4ffd-bb23-aa3abff1b432');\n",
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              "            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",
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          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ligands.to_csv(DATA / \"/content/teachopencadd/teachopencadd/talktorials/T008_query_pdb/data/PDB_top_ligands.csv\", header=True, index=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iY-T3xPKkRiG",
        "outputId": "e925602f-9d21-4377-b305-d5257ae12d11"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "PandasTools.AddMoleculeColumnToFrame(ligands, \"smiles\")\n",
        "Draw.MolsToGridImage(\n",
        "    mols=list(ligands.ROMol),\n",
        "    legends=list(ligands[\"@chemicalID\"] + \", \" + ligands[\"@structureId\"]),\n",
        "    molsPerRow=top_num,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        },
        "id": "fk46cXIAkUQg",
        "outputId": "42126363-76b6-4065-9f81-57dad919ebc3"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# NBVAL_CHECK_OUTPUT\n",
        "pairs = collections.OrderedDict(zip(ligands[\"@structureId\"], ligands[\"@chemicalID\"]))\n",
        "pairs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0_HTxDlmkfJs",
        "outputId": "a2f77787-e1bf-4818-9fe8-20f4aafc7b38"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderedDict([('5UG9', '8AM'),\n",
              "             ('5HG8', '634'),\n",
              "             ('5UG8', '8BP'),\n",
              "             ('5UGC', '8BS')])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Align PDB structures and extract ligands\n",
        "Since we want to build ligand-based ensemble pharmacophores in the next talktorial, it is necessary to align all structures to each other in 3D.\n",
        "\n",
        "We will use the Python package opencadd (repository), which includes a 3D superposition subpackage to guide the structural alignment of the proteins. The approach is based on superposition guided by sequence alignment of provided matched residues. There are other methods in the package, but this simple one will be enough for the task at hand.\n",
        "\n",
        "Get the PDB structure files\n",
        "We now fetch the PDB structure files, i.e. 3D coordinates of the protein, ligand (and if available other atomic or molecular entities such as cofactors, water molecules, and ions) from the PDB using opencadd.structure.superposition.\n",
        "\n",
        "Available file formats are pdb and cif, which store the 3D coordinations of atoms of the protein (and ligand, cofactors, water molecules, and ions) as well as information on bonds between atoms. Here, we work with pdb files."
      ],
      "metadata": {
        "id": "ooMByWlFkjxk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# # Download PDB structures\n",
        "# structures = [Structure.from_pdbid(pdb_id) for pdb_id in pairs]\n",
        "# structures"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "HlJC8XJfkhe_",
        "outputId": "837ab113-67b2-4d1a-a64d-e57f6e4af859"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-51-1d49b7408b49>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Download PDB structures\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mstructures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mStructure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pdbid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpdb_id\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpdb_id\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpairs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mstructures\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-51-1d49b7408b49>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Download PDB structures\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mstructures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mStructure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pdbid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpdb_id\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpdb_id\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpairs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mstructures\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'Structure' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XW5lTunAkmGC"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}