Download_predictions.ipynb

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      "source": [
        "# <b><font color='#009e74'> Visualisation of Variant/Residue Effect Predictions from FunC-ESMs (Functional Characterisation via Evolutionary Scale Models)</font></b>\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/KULL-Centre/_2024_cagiada-jonsson-func/main/images_colab/fig1_main_transparent.png\" height=\"295\">\n",
        "\n",
        "<b> This Colaboratory notebook is designed to facilitate the analysis of predicted variant and residue effects in proteins, focusing on the functional implications. The implementation is based on research by Matteo Cagiada, Nicolas Jonsson, and Kresten Lindorff-Larsen, and draws upon an extensive dataset housed in the Electronic Research Data Archive (ERDA) at the University of Copenhagen.</b>\n",
        "\n",
        "### <b><font color='#56b4e9'> Source and Collaboration </font></b>\n",
        "- **Article**: <b><font color='#009e74'>\"Decoding molecular mechanisms for loss of function variants in the human proteome\"</font></b>\n",
        "- **Authors**: Matteo Cagiada &#10013;, Nicolas Jonsson &#10013;, and Kresten Lindorff-Larsen\n",
        "- **DOI**:\n",
        "- **Project Repository**: [GitHub](https://github.com/KULL-Centre/_2024_cagiada-jonsson-func)\n",
        "\n",
        "**&#10013; Matteo Cagiada and Nicolas Jonsson contributed equally to this project and share first authorship.**\n",
        "\n",
        "### <b><font color='#56b4e9'> Functionalities of the Notebook </font></b>\n",
        "- **Input**: Enter a UniProt IDs to retrieve data.\n",
        "- **Data Retrieval**: Connects to the University of Copenhagen's ERDA portal to fetch protein data using UniProt IDs.\n",
        "- **Data Analysis**: Enables analysis of protein residue/variant effects based on computational predictions and known structural data.\n",
        "- **Data Visualization**: Offers tools for visualizing the implications of mutations on protein function and structure.\n",
        "- **Output**: Generates a dataframe displaying all requested metrics for each UniProt ID, facilitating analysis.\n",
        "\n",
        "### <b><font color='#56b4e9'> Detailed Column Descriptions </font></b>\n",
        "This section offers a comprehensive explanation of each data column within the dataset, focusing on predictive scores, structural metrics, and functional annotations:\n",
        "\n",
        "- **`esm_1b_masked_marginals`**: Predicted score calculated using a masked marginal scoring approach applied to the ESM-1b model (https://doi.org/10.1073/pnas.2016239118).\n",
        "\n",
        "- **`esm_if_masked_marginals`**: Predicted score calculated using a masked marginal scoring approach applied to the ESM-IF model (https://proceedings.mlr.press/v162/hsu22a.html).\n",
        "\n",
        "- **`exposure_plddt`**: pLDDT (predicted local distance difference test) scores for each residue of the AlphaFold model extracted from the AlphaFold DB (https://doi.org/10.1093/nar/gkab1061). This score is in the range of [0, 100] and represents the confidence of the predicted structure.\n",
        "\n",
        "- **`exposure_SS`**: DSSP secondary structure (SS) classes from the DSSP software (https://doi.org/10.1002/bip.360221211). H is 'Alpha helix (4-12)', B is 'Isolated beta-bridge residue', E is 'Strand', G is '3-10 helix', I is 'Pi helix', T is 'Turn', S is 'Bend', - is 'None'.\n",
        "\n",
        "- **`exposure_ASA`**: Accessible surface area (ASA) or solvent-accessible surface area (SASA) values, measuring the surface area of residues exposed to solvent. Calculated using DSSP (https://doi.org/10.1002/bip.360221211).\n",
        "\n",
        "- **`exposure_rASA`**: Relative Accessible Surface Area (rASA) normalized to the maximum possible exposure (https://doi.org/10.1002/prot.340200303).\n",
        "\n",
        "- **`exposure_CN`**: Contact number (CN), quantifying residue exposure as the number of neighboring C-alpha atoms around its C-alpha atom within a radius of 12.0 Angstrom from C-alpha.\n",
        "\n",
        "- **`exposure_HSE_U` and `exposure_HSE_D`**: Half-Sphere Exposure (Up or Down), the exposure of residues on the upper or lower half-sphere relative to a defined plane on the C-beta with a radius of 12.0 Angstrom (https://doi.org/10.1002/prot.20379).\n",
        "\n",
        "- **`exposure_Vol_U_CB` and `exposure_Vol_D_CB`**: Visible Volume measure (Up or Down) within a radius of 12.0 Angstrom from the C-beta atom and a disk radius of 3.8 Angstrom. (https://doi.org/10.1002/prot.24277).\n",
        "\n",
        "- **`exposure_Exp_U_CB` and `exposure_Exp_D_CB`**: Solvent Exposure measure (Up or Down) within a radius of 12.0 Angstrom from the C-beta atom and a disk radius of 3.8 Angstrom. (https://doi.org/10.1002/prot.24277).\n",
        "\n",
        "- **`exposure_WCN_CA` and `exposure_WCN_SC`**: Weighted Contact Number (WCN) calculated on the C-alpha atom or side chain, evaluating the density of neighboring atoms around the C-alpha atom or the side chain atoms of the residue, respectively.\n",
        "\n",
        "- **`spot_disorder`**: Binary indication of a residue being disordered, based on its sequence context and structural features. Data lifted from Alderson et al. 2023 ( https://doi.org/10.1073/pnas.2304302120).\n",
        "\n",
        "- **`dsbdetector_cys_in_disulfide`**: Binary indication of cysteine residues being involved in disulfide bonds based on the analysis of their spatial proximity and angle alignment.\n",
        "\n",
        "- **`func_esms_residue_class`**: Functional classification of residues based on Func-ESMs threshold method, categorizing their role within the protein. 0 is 'tolerant', 1 is 'functionally relevant', 2 is 'structurally critical', and 4 is 'mixed'. As described in Cagiada, Jonsson and Lindorff-Larsen.\n",
        "\n",
        "- **`func_esms_variant_class`**: Functional classification of variants based on Func-ESMs threshold method, categorizing their role within the protein. 0 is 'WT-like', 1 is 'stable-but-inactive', and 2 is 'total-loss'. As described in Cagiada, Jonsson and Lindorff-Larsen.\n",
        "\n",
        "- **`clinvar_clinical_significance`**: Character field summarizing the clinical assessment of a variant in a simplified form parsed from ClinVar. As described in Cagiada, Jonsson and Lindorff-Larsen.\n",
        "\n",
        "This detailed column information enhances understanding and enables more effective utilization of the dataset for comprehensive protein analysis.\n",
        "\n",
        "### <b><font color='#56b4e9'> Contact </font></b>\n",
        "For more information or collaboration inquiries, contact Nicolas Jonsson at <nicolas.jonsson@bio.ku.dk>.\n"
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        "## <b><font color='#009e74'>Reminders and Important Information:</font></b>\n",
        "\n",
        "- **<font color='#56b4e9'>Initial Setup:</font>**\n",
        "  Cells labeled with a <b><font color='#f0e442'>yellow</font></b> background should be executed **ONCE at the beginning** of the session to set up the environment and dependencies.\n",
        "\n",
        "- **<font color='#56b4e9'>Data Retrieval:</font>**\n",
        "  The notebook is configured to allow for the retrieval of data using a single UniProt ID. To perform sequential analysis on other proteins, re-run the **DOWNLOAD RESULTS** cell, directly followed by running the **DATA ANALYSIS** cells to display the data, for each UniProt ID.\n",
        "\n",
        "- **<font color='#56b4e9'>Reference Data:</font>**\n",
        "  The **DOWNLOAD RESULTS** used is essential for fetching and correlating the correct dataset. All analysis and visualization are based on this primary data source.\n",
        "\n",
        "These guidelines are designed to ensure a smooth and efficient experience while using this notebook for protein variant analysis.\n"
      ],
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      "source": [
        "#@title <b><font color='#f0e442'>PRELIMINARY OPERATIONS:</font></b> Install dependencies\n",
        "#@markdown **Run the cell to install all the necessary packages.**\n",
        "\n",
        "%%bash -s\n",
        "rm -r sample_data\n",
        "\n",
        "# install dependencies present in pip\n",
        "pip install -q numpy scipy matplotlib pandas requests &> /dev/null\n"
      ],
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        "id": "Ox_OK-0oUTmd",
        "cellView": "form"
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        "#@title <b><font color='#f0e422'>PRELIMINARY OPERATIONS:</font></b> Load pipeline functions\n",
        "#@markdown **Run the cell to load the required functions.**\n",
        "\n",
        "import pandas as pd\n",
        "import urllib.request\n",
        "import json\n",
        "import os\n",
        "import re\n",
        "import requests\n",
        "import shutil\n",
        "import numpy as np\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
        "from IPython.display import display, Markdown\n",
        "\n",
        "## dictionaries\n",
        "alphabetAA_L_D={'-':0,'_' :0,'A':1,'C':2,'D':3,'E':4,'F':5,'G':6,'H':7,'I':8,'K':9,'L':10,'M':11,'N':12,'P':13,'Q':14,'R':15,'S':16,'T':17,'V':18,'W':19,'Y':20}\n",
        "alphabetAA_D_L={v: k for k, v in alphabetAA_L_D.items()}\n",
        "\n",
        "AA_to_hydrophobicity_scores={'A':44,'C':50,'D':-37,'E':-12,'F':96,'G':0,'H':-16,'I':100,'K':-30,'L':99,'M':74,'N':-35,'P':-46,'Q':-14,'R':-20,'S':-6,'T':13,'V':78,'W':90,'Y':57}\n",
        "\n",
        "alphabet_class_variant_d_l={0:'WT-like',1:'SBI',2:'Total-loss'}\n",
        "alphabet_class_residue_d_l={0:'WT-like',1:'Functional site',2:'Total-loss',5:'Not assgined'}\n",
        "\n",
        "aa_order_alphabetical = pd.Series([\"A\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"K\", \"L\", \"M\",\n",
        "           \"N\", \"P\", \"Q\", \"R\", \"S\", \"T\", \"V\", \"W\", \"Y\"])\n",
        "\n",
        "alphabetAA_L_D_reordered={0:'R',1:'H',2:'K',3:'D',4:'E',5:'S',6:'T',7:'N',8:'Q',9:'C',10:'G',11:'P',12:'A',13:'V',14:'I',15:'L',16:'M',17:'F',18:'Y',19:'W'}\n",
        "alphabetAA_D_D_reordered={14:0,6:1,8:2,2:3,3:4,15:5,16:6,11:7,13:8,1:9,5:10,12:11,0:12,17:13,7:14,9:15,10:16,4:17,19:18,18:19}\n",
        "\n",
        "## Functions\n",
        "def fetchUniProtData(uniprot, option=None):\n",
        "    \"\"\"\n",
        "    Fetches data from the UniProtKB database using the REST API selectively based on the provided option.\n",
        "    If the gene name contains spaces, they are replaced with hyphens. It also retrieves 'alternativeNames'\n",
        "    and 'submissionNames' from 'proteinDescription' if they exist, extracting all 'value' fields.\n",
        "    \"\"\"\n",
        "    urlTemplateUniProt = f\"https://rest.uniprot.org/uniprotkb/{uniprot}.json\"\n",
        "\n",
        "    try:\n",
        "        with urllib.request.urlopen(urlTemplateUniProt) as link:\n",
        "            dataUniProt = json.loads(link.read().decode())\n",
        "    except Exception as e:\n",
        "        raise Exception(f\"An error occurred accessing the UniProtKB REST API: {e}\")\n",
        "\n",
        "    def get_gene_name(data):\n",
        "        gene_name = next((gene.get('geneName', {}).get('value', 'Unknown')\n",
        "                          for gene in data.get('genes', [])), 'Unknown')\n",
        "        return gene_name.replace(' ', '-')\n",
        "\n",
        "    def get_names_list(data, key):\n",
        "        # Extracts a list of names from the given key in proteinDescription\n",
        "        names_data = data.get('proteinDescription', {}).get(key, [])\n",
        "        names_list = [name.get('fullName', {}).get('value') for name in names_data if 'fullName' in name and 'value' in name['fullName']]\n",
        "\n",
        "        # If there's only one name, return it directly, else return the list of names or 'Unknown'\n",
        "        if len(names_list) == 1:\n",
        "            return names_list[0]\n",
        "        else:\n",
        "            return names_list if names_list else 'Unknown'\n",
        "\n",
        "    fetchedData = {\n",
        "        'primaryAccession': dataUniProt.get('primaryAccession', 'Unknown'),\n",
        "        'uniProtkbId': dataUniProt.get('uniProtkbId', 'Unknown'),\n",
        "        'sequenceVersion': dataUniProt.get('entryAudit', {}).get('sequenceVersion', 'Unknown'),\n",
        "        'geneName': get_gene_name(dataUniProt),\n",
        "        'recommendedName': dataUniProt.get('proteinDescription', {}).get('recommendedName', {}).get('fullName', {}).get('value', 'Unknown'),\n",
        "        'alternativeNames': get_names_list(dataUniProt, 'alternativeNames'),\n",
        "        'submissionNames': get_names_list(dataUniProt, 'submissionNames'),\n",
        "        'organismScientificName': dataUniProt.get('organism', {}).get('scientificName', 'Unknown'),\n",
        "        'organismCommonName': dataUniProt.get('organism', {}).get('commonName', 'Unknown'),\n",
        "        'sequence': dataUniProt.get('sequence', {}).get('value', 'Unknown')\n",
        "    }\n",
        "\n",
        "    # Replace unwanted symbols in 'recommendedName', 'alternativeNames', and 'submissionNames'\n",
        "    for key in ['recommendedName', 'alternativeNames', 'submissionNames']:\n",
        "        if isinstance(fetchedData[key], str):\n",
        "            fetchedData[key] = fetchedData[key].replace(';', '').replace(':', '').replace('[', '').replace(']', '')\n",
        "\n",
        "    optionsMap = {\n",
        "        'primaryAccession': 'primaryAccession',\n",
        "        'uniProtkbId': 'uniProtkbId',\n",
        "        'sequenceVersion': ('entryAudit', 'sequenceVersion'),\n",
        "        'geneName': get_gene_name,\n",
        "        'recommendedName': ('proteinDescription', 'recommendedName', 'fullName', 'value'),\n",
        "        'alternativeNames': lambda data: get_names_list(data, 'alternativeNames'),\n",
        "        'submissionNames': lambda data: get_names_list(data, 'submissionNames'),\n",
        "        'organismScientificName': ('organism', 'scientificName'),\n",
        "        'organismCommonName': ('organism', 'commonName'),\n",
        "        'sequence': ('sequence', 'value')\n",
        "    }\n",
        "\n",
        "    if option is None:\n",
        "        return fetchedData\n",
        "    else:\n",
        "        if option in optionsMap:\n",
        "            if callable(optionsMap[option]):\n",
        "                return optionsMap[option](dataUniProt)\n",
        "            else:\n",
        "                keys = optionsMap[option]\n",
        "                if isinstance(keys, tuple):\n",
        "                    value = dataUniProt\n",
        "                    for key in keys:\n",
        "                        value = value.get(key, 'Unknown')\n",
        "                        if value == 'Unknown':\n",
        "                            break\n",
        "                    return value\n",
        "                else:\n",
        "                    return dataUniProt.get(keys, 'Unknown')\n",
        "        else:\n",
        "            raise ValueError(f\"Invalid option provided: {option}\")\n",
        "\n",
        "def make_default_outfolder(uniprot_id, base_prism_dir='./output/'):\n",
        "    \"\"\"\n",
        "    Creates a directory path for output files based on a UniProt ID.\n",
        "\n",
        "    This function constructs a directory path by appending segments of the UniProt ID to the base directory path.\n",
        "    If the resulting directory does not exist, it is created.\n",
        "\n",
        "    Parameters:\n",
        "    uniprot_id (str): The UniProt ID to base the directory path on.\n",
        "    base_prism_dir (str): The base directory path. Defaults to './output/' in the current directory.\n",
        "\n",
        "    Returns:\n",
        "    str: The path of the output directory.\n",
        "    \"\"\"\n",
        "    if uniprot_id != 'None':\n",
        "        outfolder = os.path.join(base_prism_dir, uniprot_id)\n",
        "    else:\n",
        "        outfolder = os.path.join(base_prism_dir, 'NA')\n",
        "\n",
        "    if not os.path.exists(outfolder):\n",
        "        os.makedirs(outfolder)\n",
        "\n",
        "    return outfolder\n",
        "\n",
        "def download_erda_file(sharelink, uniprot_id, gene_name):\n",
        "    \"\"\"\n",
        "    Download a specific ERDA file based on the sharelink, uniprot ID, and gene name.\n",
        "\n",
        "    Parameters:\n",
        "    sharelink (str): The base sharelink URL from ERDA.\n",
        "    uniprot_id (str): The uniprot ID in the form A0A023T787.\n",
        "    gene_name (str): The gene name associated with the uniprot ID.\n",
        "\n",
        "    Returns:\n",
        "    str: The path of the downloaded file or None if the download failed.\n",
        "    \"\"\"\n",
        "    # Partition the uniprot ID\n",
        "    partitioned_uniprot = f\"{uniprot_id[:2]}/{uniprot_id[2:4]}/{uniprot_id[4:6]}\"\n",
        "\n",
        "    # Generate the new directory structure\n",
        "    new_directory = f\"erda/{partitioned_uniprot}\"\n",
        "\n",
        "    # Construct the file name\n",
        "    file_name = f\"prism_func_{uniprot_id}-{gene_name}.txt\"\n",
        "\n",
        "    # Construct the final URL\n",
        "    file_url = f\"https://sid.erda.dk/share_redirect/{sharelink}/{new_directory}/{file_name}\"\n",
        "\n",
        "    # Determine the output folder\n",
        "    output_folder = make_default_outfolder(uniprot_id)\n",
        "    local_filename = os.path.join(output_folder, file_name)\n",
        "\n",
        "    # Download the file\n",
        "    response = requests.get(file_url, stream=True)\n",
        "    if response.status_code != 200:\n",
        "        print(f\"Failed to download file: {file_url}\")\n",
        "        return None\n",
        "\n",
        "    with open(local_filename, 'wb') as f:\n",
        "        for chunk in response.iter_content(chunk_size=8192):\n",
        "            f.write(chunk)\n",
        "\n",
        "    return local_filename\n",
        "\n",
        "def load_data_variant(data: str, wt_seq: str, start_gap: int = 0, score_load: str = '') -> np.ndarray:\n",
        "    \"\"\"\n",
        "    Load mutation data from a txt file, align it with the wild-type sequence, and return a score matrix.\n",
        "\n",
        "    Args:\n",
        "        data (str): Path to the txt file.\n",
        "        wt_seq (str): Wild-type sequence.\n",
        "        start_gap (int, optional): Gap value to start from (default is 0).\n",
        "        score_load (str, optional): Specify the type of score to load (\"esm\", \"rasp\", \"gemme\", or \"func\").\n",
        "\n",
        "    Returns:\n",
        "        np.ndarray: 2D array of scores aligned to the wild-type sequence.\n",
        "    \"\"\"\n",
        "\n",
        "    # Read the data file\n",
        "    df = pd.read_csv(data, comment='#', sep=r'\\s+')\n",
        "\n",
        "    # Extract the reference amino acid (aa_ref)\n",
        "    df['aa_ref'] = df['variant'].str[0]\n",
        "\n",
        "    # Extract the residue number (resi)\n",
        "    df['resi'] = df['variant'].str[1:-1].astype(int)\n",
        "\n",
        "    # Extract the variant amino acid (aa_var)\n",
        "    df['aa_var'] = df['variant'].str[-1]\n",
        "\n",
        "    df = df[df[\"aa_var\"] != \"*\"]\n",
        "\n",
        "    # Load mutation data\n",
        "    mutation_load = np.array(df['variant'])\n",
        "\n",
        "    # Identify the score column based on score_load\n",
        "    score_column_mapping = {\n",
        "        'esm-1b': 'esm_1b_masked_marginals',\n",
        "        'esm-if': 'esm_if_masked_marginals',\n",
        "        'func-var': 'func_esms_variant_class',\n",
        "    }\n",
        "\n",
        "    if score_load not in score_column_mapping:\n",
        "        raise ValueError(f\"Invalid score load value. Expected one of {list(score_column_mapping.keys())}\")\n",
        "\n",
        "    score_column = df[score_column_mapping[score_load]].values\n",
        "\n",
        "    # Initialize the score matrix\n",
        "    scores = np.full((len(wt_seq), 20), np.nan)\n",
        "\n",
        "    # Fill the scores matrix\n",
        "    for mutation, score in zip(mutation_load, score_column):\n",
        "        if mutation[-1] != '=':\n",
        "            pos = int(mutation[1:-1]) - 1 + start_gap\n",
        "            aa_index = alphabetAA_L_D[mutation[-1]] - 1\n",
        "            scores[pos, aa_index] = float(score)\n",
        "\n",
        "    return scores\n",
        "\n",
        "def load_data_residue(data: str, wt_seq: str, start_gap: int = 0) -> np.ndarray:\n",
        "    \"\"\"\n",
        "    Load residue data from a txt file, align it with the wild-type sequence, and return a class array.\n",
        "\n",
        "    Args:\n",
        "        data (str): Path to the txt file.\n",
        "        wt_seq (str): Wild-type sequence.\n",
        "        start_gap (int, optional): Gap value to start from (default is 0).\n",
        "\n",
        "    Returns:\n",
        "        np.ndarray: 1D array of residue classes aligned to the wild-type sequence.\n",
        "    \"\"\"\n",
        "\n",
        "    # Read the data file\n",
        "    df = pd.read_csv(data, comment='#', sep=r'\\s+')\n",
        "\n",
        "    # Extract the reference amino acid (aa_ref)\n",
        "    df['aa_ref'] = df['variant'].str[0]\n",
        "\n",
        "    # Extract the residue number (resi)\n",
        "    df['resi'] = df['variant'].str[1:-1].astype(int)\n",
        "\n",
        "    # Extract the variant amino acid (aa_var)\n",
        "    df['aa_var'] = df['variant'].str[-1]\n",
        "\n",
        "    df = df[df[\"aa_var\"] != \"*\"]\n",
        "\n",
        "    # Remove duplicates, keeping the first occurrence for each residue\n",
        "    df = df.drop_duplicates(subset=['resi'])\n",
        "\n",
        "    # Initialize the residue class array\n",
        "    residue_classes = np.full(len(wt_seq), np.nan)\n",
        "\n",
        "    # Fill the residue class array\n",
        "    for idx, row in df.iterrows():\n",
        "        pos = row['resi'] - 1 + start_gap\n",
        "        if pos < len(residue_classes):\n",
        "            residue_classes[pos] = row['func_esms_residue_class']\n",
        "\n",
        "    return residue_classes\n",
        "\n",
        "def remove_WT_score(score: np.ndarray, WT_seq: str) -> np.ndarray:\n",
        "    \"\"\"\n",
        "    Remove wild-type (WT) scores from a 2D score array.\n",
        "\n",
        "    Args:\n",
        "        score (np.ndarray): 2D array of scores.\n",
        "        WT_seq (str): Wild-type sequence.\n",
        "\n",
        "    Returns:\n",
        "        np.ndarray: Updated 2D array with WT scores set to NaN.\n",
        "    \"\"\"\n",
        "    for i in range(len(WT_seq)):\n",
        "        score[i, alphabetAA_L_D[WT_seq[i]] - 1] = np.nan\n",
        "    return score\n",
        "\n",
        "def heatmap(ext_score: np.ndarray, label_cm: str, WT_mask: np.ndarray, lim_down: float, lim_up: float,\n",
        "            color: str, x: tuple, uniprot_id: str, title_figure: str = '',\n",
        "            WT_cmap: str = 'gnuplot_r', nan_cmap: str = 'Dark2_r', figs: list = [30, 7],\n",
        "            xtick_spacing: int = 10, font_size: int = 14, colorbar_padding: float = 0.15):\n",
        "    \"\"\"\n",
        "    Create and display a heatmap of scores with masked and WT values.\n",
        "\n",
        "    Args:\n",
        "        ext_score (numpy.ndarray): The input scores array.\n",
        "        label_cm (str): Label for the colorbar.\n",
        "        WT_mask (numpy.ndarray): Mask for WT values.\n",
        "        lim_down (float): Lower limit for color scale.\n",
        "        lim_up (float): Upper limit for color scale.\n",
        "        color (str): Colormap for the heatmap.\n",
        "        x (tuple): Tuple representing x-axis limits (start, end).\n",
        "        uniprot_id (str): UniProt ID for naming and saving files.\n",
        "        title_figure (str, optional): Title of the figure. Default is ''.\n",
        "        WT_cmap (str, optional): Color for WT values. Defaults to 'coolwarm'.\n",
        "        nan_cmap (str, optional): Colormap for NaN values. Defaults to 'Dark2_r'.\n",
        "        figs (list, optional): Figure size. Defaults to [30, 7].\n",
        "        xtick_spacing (int, optional): Spacing between x-axis ticks. Defaults to 10.\n",
        "        font_size (int, optional): Font size for labels. Defaults to 14.\n",
        "        colorbar_padding (float, optional): Padding for the colorbar. Defaults to 0.125.\n",
        "\n",
        "    Returns:\n",
        "        None\n",
        "    \"\"\"\n",
        "    where_save = make_default_outfolder(uniprot_id)\n",
        "\n",
        "    # Set Matplotlib font and label sizes\n",
        "    mpl.rcParams['font.size'] = font_size\n",
        "    mpl.rcParams['xtick.labelsize'] = font_size\n",
        "    mpl.rcParams['ytick.labelsize'] = font_size\n",
        "    mpl.rcParams['axes.labelsize'] = font_size\n",
        "\n",
        "    # Create a copy of ext_score as score\n",
        "    score = np.copy(ext_score)\n",
        "\n",
        "    # Reorder columns in score using alphabetAA_D_D_reordered\n",
        "    for i in range(score.shape[1]):\n",
        "        score[:, alphabetAA_D_D_reordered[i]] = ext_score[:, i]\n",
        "\n",
        "    # Create a copy of ext_score as score_nan filled with infinity\n",
        "    score_nan = np.copy(ext_score)\n",
        "    score_nan[:] = np.inf\n",
        "\n",
        "    # Fill score_nan with 1 where ext_score contains NaN and the corresponding WT_mask condition is met\n",
        "    for i in range(score.shape[0]):\n",
        "        for j in range(score.shape[1]):\n",
        "            if np.isnan(ext_score[i, j]) and WT_mask[i] != alphabetAA_D_L[j + 1]:\n",
        "                score_nan[i, alphabetAA_D_D_reordered[j]] = 1\n",
        "\n",
        "    # Modify the colormap for WT scores (dark gray)\n",
        "    cmap = plt.get_cmap(nan_cmap)\n",
        "    cmap.set_bad(color='gray')\n",
        "\n",
        "    # Create a figure and axes\n",
        "    fig, ax = plt.subplots(figsize=(figs[0], figs[1]))\n",
        "\n",
        "    # Create ylabels\n",
        "    ylabels = [alphabetAA_L_D_reordered[i] for i in range(0, 20)]\n",
        "    ax.set_yticks([i for i in range(0, 20)])\n",
        "    ax.set_yticklabels(ylabels, fontsize=font_size)\n",
        "\n",
        "    # Create and display the heatmap with the modified colormap for WT scores\n",
        "    pos = ax.imshow(np.transpose(score), aspect='auto', cmap=f'{colour_map}_r', vmin=lim_down, vmax=lim_up, interpolation='nearest')\n",
        "\n",
        "    # Create a divider for the colorbar axes\n",
        "    divider = make_axes_locatable(ax)\n",
        "\n",
        "    # Append a new axes for the colorbar with specified padding\n",
        "    cax = divider.append_axes(\"right\", size=\"1.5%\", pad=colorbar_padding)\n",
        "\n",
        "    # Add a colorbar with the specified label\n",
        "    cbar = plt.colorbar(pos, cax=cax, label=label_cm)\n",
        "    cbar.ax.tick_params(labelsize=font_size)  # Set colorbar label font size\n",
        "\n",
        "    # Configure plot appearance\n",
        "    ax.set_xlabel(\"residue\", fontsize=font_size)\n",
        "    ax.set_title(title_figure, fontsize=font_size)\n",
        "\n",
        "    start, end = ax.get_xlim()\n",
        "    ax.xaxis.set_ticks(np.arange(start + 0.5, end, xtick_spacing))\n",
        "    ax.tick_params(axis='x', rotation=90)\n",
        "    ax.tick_params(axis='y', rotation=90)\n",
        "\n",
        "    # Create a temporary array filled with infinity and fill it based on WT_mask and alphabetAA_D_D_reordered\n",
        "    tmp = np.empty((len(WT_mask), 20), dtype=float)\n",
        "    tmp[:] = np.inf\n",
        "\n",
        "    for i in range(len(WT_mask)):\n",
        "        tmp[i, alphabetAA_D_D_reordered[alphabetAA_L_D[WT_mask[i]] - 1]] = 1\n",
        "\n",
        "    # Overlay WT and NaN masks on the heatmap\n",
        "    ax.imshow(np.transpose(score_nan), cmap=nan_cmap, aspect='auto', interpolation='nearest')\n",
        "    ax.imshow(np.transpose(tmp), cmap=WT_cmap, aspect='auto', interpolation='nearest')\n",
        "\n",
        "    # Save the figure\n",
        "    #plt.savefig(os.path.join(where_save, uniprot_id + '_' + 'heatmap.png'), dpi=300, bbox_inches='tight')\n",
        "\n",
        "    # Display the figure\n",
        "    plt.show()\n",
        "\n",
        "def functional_var_heatmap(ext_score: np.ndarray, WT_mask: np.ndarray, x: tuple, uniprot_id: str, title_figure: str = '',\n",
        "                           WT_cmap: str = 'gnuplot_r', nan_cmap: str = 'Dark2_r', figs: list = [30, 7],\n",
        "                           xtick_spacing: int = 10, font_size: int = 14) -> None:\n",
        "    \"\"\"\n",
        "    Create and save a heatmap visualizing the extent score with specific color mappings for WT and NaN values.\n",
        "\n",
        "    Args:\n",
        "        ext_score (numpy.ndarray): The input scores array.\n",
        "        WT_mask (numpy.ndarray): Mask for WT (Wild Type) values.\n",
        "        x (tuple): Tuple representing x-axis limits (start, end).\n",
        "        uniprot_id (str): UniProt ID for naming and saving files.\n",
        "        WT_cmap (str, optional): Colormap for WT values. Defaults to 'gnuplot_r'.\n",
        "        nan_cmap (str, optional): Colormap for NaN values. Defaults to 'Dark2_r'.\n",
        "        figs (list, optional): Figure size as a list of two values [width, height]. Defaults to [30, 7].\n",
        "        xtick_spacing (int, optional): Spacing between x-axis ticks. Defaults to 10.\n",
        "        font_size (int, optional): Font size for labels and ticks. Defaults to 14.\n",
        "\n",
        "    Returns:\n",
        "        None: The function saves the heatmap as an image file and also displays it.\n",
        "    \"\"\"\n",
        "    where_save = make_default_outfolder(uniprot_id)\n",
        "\n",
        "    # Set Matplotlib font and label sizes\n",
        "    mpl.rcParams['font.size'] = font_size\n",
        "    mpl.rcParams['xtick.labelsize'] = font_size\n",
        "    mpl.rcParams['ytick.labelsize'] = font_size\n",
        "    mpl.rcParams['axes.labelsize'] = font_size\n",
        "\n",
        "    # Prepare the score array by reordering columns\n",
        "    score = np.copy(ext_score)\n",
        "\n",
        "    # Reorder columns in score using alphabetAA_D_D_reordered\n",
        "    for i in range(score.shape[1]):\n",
        "        score[:, alphabetAA_D_D_reordered[i]] = ext_score[:, i]\n",
        "\n",
        "    # Create a copy of ext_score as score_nan filled with infinity\n",
        "    score_nan = np.copy(ext_score)\n",
        "    score_nan[:] = np.inf\n",
        "\n",
        "    # Fill score_nan with 1 where ext_score contains NaN and the corresponding WT_mask condition is met\n",
        "    for i in range(score.shape[0]):\n",
        "        for j in range(score.shape[1]):\n",
        "            if np.isnan(ext_score[i, j]) and WT_mask[i] != alphabetAA_D_L[j + 1]:\n",
        "                score_nan[i, alphabetAA_D_D_reordered[j]] = 1\n",
        "\n",
        "    # Modify the colormap for WT scores (dark gray)\n",
        "    cmap = plt.get_cmap(nan_cmap)\n",
        "    cmap.set_bad(color='gray')\n",
        "\n",
        "    # Create a figure and axes\n",
        "    fig, ax = plt.subplots(figsize=(figs[0], figs[1]))\n",
        "\n",
        "    # Set y-axis labels\n",
        "    ylabels = [alphabetAA_L_D_reordered[i] for i in range(0, 20)]\n",
        "    ax.set_yticks([i for i in range(0, 20)])\n",
        "    ax.set_yticklabels(ylabels, fontsize=font_size)\n",
        "\n",
        "    # Determine the number of unique colors required\n",
        "    num_colors = len(np.unique(score)) - 1 if np.isnan(np.unique(score)).any() else len(np.unique(score))\n",
        "\n",
        "    # Display the heatmap with appropriate color map\n",
        "    if num_colors==3:\n",
        "      pos=ax.imshow(np.transpose(score), aspect = 'auto', cmap=mpl.colors.ListedColormap([\"#009e74\", \"#56b4e9\",\"#d55c00\"]),interpolation='nearest')\n",
        "    elif num_colors==4:\n",
        "      pos=ax.imshow(np.transpose(score), aspect = 'auto', cmap=mpl.colors.ListedColormap([\"#009e74\", \"#56b4e9\",\"#d55c00\",\"#f0e442\"]),interpolation='nearest')\n",
        "\n",
        "    # Set colormap for bad (NaN) values\n",
        "    current_cmap = plt.get_cmap()\n",
        "    current_cmap.set_bad(color='gray')\n",
        "\n",
        "    # Configure plot appearance\n",
        "    ax.set_xlabel(\"residue\", fontsize=font_size)\n",
        "    ax.set_title(title_figure, fontsize=font_size)\n",
        "\n",
        "    start, end = ax.get_xlim()\n",
        "    ax.xaxis.set_ticks(np.arange(start + 0.5, end, xtick_spacing))\n",
        "    ax.tick_params(axis='x', rotation=90)\n",
        "    ax.tick_params(axis='y', rotation=90)\n",
        "\n",
        "    # Create a temporary array filled with infinity and fill it based on WT_mask and alphabetAA_D_D_reordered\n",
        "    tmp = np.empty((len(WT_mask), 20), dtype=float)\n",
        "    tmp[:] = np.inf\n",
        "\n",
        "    for i in range(len(WT_mask)):\n",
        "        tmp[i, alphabetAA_D_D_reordered[alphabetAA_L_D[WT_mask[i]] - 1]] = 1\n",
        "\n",
        "    # Overlay WT and NaN masks on the heatmap\n",
        "    ax.imshow(np.transpose(score_nan), cmap=nan_cmap, aspect='auto', interpolation='nearest')\n",
        "    ax.imshow(np.transpose(tmp), cmap=WT_cmap, aspect='auto', interpolation='nearest')\n",
        "\n",
        "    # Save the figure\n",
        "    #plt.savefig(os.path.join(where_save, uniprot_id + '_variant_map.png'), dpi=400, bbox_inches='tight')\n",
        "\n",
        "    # Display the figure\n",
        "    plt.show()\n",
        "\n",
        "def functional_res_heatmap(ext_score: np.ndarray, x: tuple, uniprot_id: str, title_figure: str = '',\n",
        "                           figs: list = [20, 1], xtick_spacing: int = 10, font_size: int = 14) -> None:\n",
        "    \"\"\"\n",
        "    Create and save a heatmap of reordered residue classes.\n",
        "\n",
        "    Args:\n",
        "        ext_score (np.ndarray): Array of residue classes.\n",
        "        x (tuple): Tuple representing x-axis limits (start, end).\n",
        "        uniprot_id (str): UniProt ID for naming and saving files.\n",
        "        title_figure (str, optional): Title of the figure. Default is ''.\n",
        "        figs (list, optional): Figure size as a list of two values [width, height]. Default is [20, 1].\n",
        "        xtick_spacing (int, optional): Spacing between x-axis ticks. Default is 10.\n",
        "        font_size (int, optional): Font size for labels and ticks. Defaults to 14.\n",
        "\n",
        "    Returns:\n",
        "        None: The function saves the figure as an image file and also displays it.\n",
        "    \"\"\"\n",
        "    where_save = make_default_outfolder(uniprot_id)\n",
        "\n",
        "    # Set Matplotlib font and label sizes\n",
        "    mpl.rcParams['font.size'] = font_size\n",
        "    mpl.rcParams['xtick.labelsize'] = font_size\n",
        "    mpl.rcParams['ytick.labelsize'] = font_size\n",
        "    mpl.rcParams['axes.labelsize'] = font_size\n",
        "\n",
        "    # Create a figure and axis\n",
        "    fig, ax = plt.subplots(1, 1, figsize=(figs[0], figs[1]))\n",
        "    fig.tight_layout()  # Adjust layout\n",
        "\n",
        "    # Determine the colormap based on the unique values in ext_score\n",
        "    if 4. in ext_score:\n",
        "      cmap = mpl.colors.ListedColormap([\"#009e74\", \"#56b4e9\",\"#d55c00\",\"#5A5A5A\",\"#5A5A5A\"])\n",
        "    else:\n",
        "      cmap = mpl.colors.ListedColormap([\"#009e74\", \"#56b4e9\", \"#d55c00\"])\n",
        "\n",
        "    # Display the heatmap\n",
        "    ax.imshow(ext_score.reshape(1, -1), aspect='auto', cmap=cmap)\n",
        "\n",
        "    # Set y-axis labels\n",
        "    ax.set_ylabel('FunC-ESMs', fontsize=font_size)\n",
        "\n",
        "    # Remove y-axis ticks\n",
        "    ax.set_yticks([])\n",
        "\n",
        "    # Set x-axis limits\n",
        "    plt.xlim(x[0] - 0.5, x[1] + 0.5)\n",
        "\n",
        "    # Set x-axis ticks and labels\n",
        "    start, end = ax.get_xlim()\n",
        "    ax.xaxis.set_ticks(np.arange(start + 0.5, end, xtick_spacing))\n",
        "    ax.tick_params(axis='x', rotation=90)\n",
        "\n",
        "    # Set x-axis label and title\n",
        "    ax.set_xlabel('residue', fontsize=font_size)\n",
        "    ax.set_title(title_figure, fontsize=font_size)\n",
        "\n",
        "    # Save the figure\n",
        "    #plt.savefig(os.path.join(where_save, uniprot_id + '_residue_map.png'), dpi=400, bbox_inches='tight')\n",
        "\n",
        "    # Display the figure\n",
        "    plt.show()\n"
      ],
      "metadata": {
        "id": "1KIPblRGqoTJ",
        "cellView": "form"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title <b><font color='#56b4e9'>DOWNLOAD RESULTS:</font></b> Download results by UniProt ID\n",
        "#@markdown **This cell downloads the predicted results based on the provided UniProt ID and displays relevant information about the protein and its variants.**\n",
        "#@markdown\n",
        "#@markdown The following details are retrieved and displayed:\n",
        "#@markdown\n",
        "#@markdown - **<font color='#009E74'>UniProt ID</font>**: The unique identifier for the protein in the UniProt database.\n",
        "#@markdown - **<font color='#009E74'>Isoform</font>**: The specific isoform of the protein, if available.\n",
        "#@markdown - **<font color='#009E74'>Protein Name</font>**: The name of the protein.\n",
        "#@markdown - **<font color='#009E74'>Recommended Name</font>**: The recommended name for the protein as per UniProt.\n",
        "#@markdown - **<font color='#009E74'>Alternative Names</font>**: Other names used for the protein.\n",
        "#@markdown - **<font color='#009E74'>Submission Names</font>**: Names used in submissions.\n",
        "#@markdown - **<font color='#009E74'>Organism Scientific Name</font>**: The scientific name of the organism from which the protein is derived.\n",
        "#@markdown - **<font color='#009E74'>Organism Common Name</font>**: The common name of the organism from which the protein is derived.\n",
        "#@markdown - **<font color='#009E74'>Protein Sequence</font>**: The amino acid sequence of the protein.\n",
        "#@markdown\n",
        "#@markdown **Columns in the Data:**\n",
        "#@markdown\n",
        "#@markdown The data includes various columns with scores and classifications related to protein variants and residues. The columns provide insights into the protein's structure, function, and stability, and are described in detail at the top of the notebook.\n",
        "\n",
        "#@markdown ---\n",
        "#@markdown ### <b><font color='#d55c00'>Provide the UniProt ID below:</font></b>\n",
        "#@markdown Enter the UniProt ID to download and display the corresponding results.\n",
        "\n",
        "UniProt = 'Q9NV35'  #@param {type:\"string\"}\n",
        "\n",
        "# Ensure the UniProt ID is uppercase\n",
        "UniProt = UniProt.upper()\n",
        "\n",
        "# Fetch information about the UniProt ID\n",
        "protein_info = fetchUniProtData(UniProt)\n",
        "\n",
        "# Define other required parameters\n",
        "sharelink = 'DUWFpyjZp0'  # You should provide the actual sharelink\n",
        "gene_name = protein_info['geneName']\n",
        "\n",
        "# Handling 'YAMLException: special characters are not allowed' if they are in geneName\n",
        "# Regular expression for special characters\n",
        "import re\n",
        "yamlChars = re.compile(r'[!#%&()*+,-./:;<=>?@\\[\\\\\\]^_`{|}~]')\n",
        "\n",
        "# Sanitize geneName for use in the file name by removing special characters\n",
        "sanitized_gene_name = yamlChars.sub('', gene_name)\n",
        "sanitized_gene_name = sanitized_gene_name.replace(' ', '_')\n",
        "\n",
        "# Download the relevant data file\n",
        "data_file_path = None\n",
        "try:\n",
        "    data_file_path = download_erda_file(sharelink, UniProt, sanitized_gene_name)\n",
        "except Exception as e:\n",
        "    print(str(e))\n",
        "\n",
        "# Check if the file was downloaded successfully\n",
        "if data_file_path and os.path.exists(data_file_path):\n",
        "    print(f\"The prism file has been downloaded to: {data_file_path}\")\n",
        "\n",
        "    # Display the fetched information\n",
        "    print(f\"UniProt ID: {protein_info['primaryAccession']}\")\n",
        "    print(f\"Isoform: {protein_info.get('isoform', 'Unknown')}\")\n",
        "    print(f\"Protein Name: {protein_info['uniProtkbId']}\")\n",
        "    print(f\"Recommended Name: {protein_info['recommendedName']}\")\n",
        "    print(f\"Alternative Names: {protein_info['alternativeNames']}\")\n",
        "    print(f\"Submission Names: {protein_info['submissionNames']}\")\n",
        "    print(f\"Organism Scientific Name: {protein_info['organismScientificName']}\")\n",
        "    print(f\"Organism Common Name: {protein_info['organismCommonName']}\")\n",
        "    print(f\"Protein Sequence: {protein_info['sequence']}\")\n",
        "\n",
        "    # Read the CSV file, skipping lines that start with #\n",
        "    func_df = pd.read_csv(data_file_path, comment='#', sep=r'\\s+')\n",
        "    #display(func_df.head())\n",
        "\n",
        "    #@title <b><font color='#56b4e9'>SAVE AND DOWNLOAD RESULTS:</font></b> Save and download results in the chosen format\n",
        "    #@markdown **This cell saves the results in the chosen format and provides an option to download the results as a zip file.**\n",
        "    #@markdown\n",
        "    #@markdown Select the output formats for the result files:\n",
        "    save_as_csv = True  #@param {type:\"boolean\"}\n",
        "    save_as_excel = True  #@param {type:\"boolean\"}\n",
        "\n",
        "    # Create a directory for the job outputs if it doesn't exist\n",
        "    output_dir = f\"./output/{UniProt}\"\n",
        "    if not os.path.exists(output_dir):\n",
        "        os.makedirs(output_dir)\n",
        "\n",
        "    # Save results in the chosen formats\n",
        "    if save_as_csv:\n",
        "        func_df.to_csv(f\"{output_dir}/func_{UniProt}-{sanitized_gene_name}.csv\", index=False)\n",
        "        print(f\"Results saved as CSV to {output_dir}/func_{UniProt}-{sanitized_gene_name}.csv\")\n",
        "\n",
        "    if save_as_excel:\n",
        "        func_df.to_excel(f\"{output_dir}/func_{UniProt}-{sanitized_gene_name}.xlsx\", index=False)\n",
        "        print(f\"Results saved as Excel to {output_dir}/func_{UniProt}-{sanitized_gene_name}.xlsx\")\n",
        "\n",
        "else:\n",
        "    print(\"No valid data file was downloaded. Please check the UniProt ID and try again.\")\n",
        "    # Remove the newly created folder if no file is downloaded\n",
        "    output_folder = make_default_outfolder(UniProt)\n",
        "    if os.path.exists(output_folder):\n",
        "        shutil.rmtree(output_folder)\n",
        "        print(f\"Temporary folder {output_folder} has been removed.\")"
      ],
      "metadata": {
        "cellView": "form",
        "id": "XyEt_qJCY6GF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "061da5ef-6621-47e0-98e8-8d251d1a8317"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The prism file has been downloaded to: ./output/Q9NV35/prism_func_Q9NV35-NUDT15.txt\n",
            "UniProt ID: Q9NV35\n",
            "Isoform: Unknown\n",
            "Protein Name: NUD15_HUMAN\n",
            "Recommended Name: Nucleotide triphosphate diphosphatase NUDT15\n",
            "Alternative Names: ['MutT homolog 2', 'Nucleoside diphosphate-linked moiety X motif 15', 'Nucleoside diphosphate-linked to another moiety X hydrolase 15']\n",
            "Submission Names: Unknown\n",
            "Organism Scientific Name: Homo sapiens\n",
            "Organism Common Name: Human\n",
            "Protein Sequence: MTASAQPRGRRPGVGVGVVVTSCKHPRCVLLGKRKGSVGAGSFQLPGGHLEFGETWEECAQRETWEEAALHLKNVHFASVVNSFIEKENYHYVTILMKGEVDVTHDSEPKNVEPEKNESWEWVPWEELPPLDQLFWGLRCLKEQGYDPFKEDLNHLVGYKGNHL\n",
            "Results saved as CSV to ./output/Q9NV35/func_Q9NV35-NUDT15.csv\n",
            "Results saved as Excel to ./output/Q9NV35/func_Q9NV35-NUDT15.xlsx\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title <b><font color='#56b4e9'>DATA ANALYSIS:</font></b> Display functional classification results at both the variant and residue levels\n",
        "#@markdown **Plot and save the results of the functional classification for variants and residues of the query sequence.**\n",
        "#@markdown\n",
        "#@markdown **Color keys:**\n",
        "#@markdown - <b><font color='#009E74'>GREEN</font></b>: **Tolerant** (variant) or **WT-like** (residue)\n",
        "#@markdown - <b><font color='#56b4e9'>BLUE</font></b>: **Stable-but-inactive** (variant) or **Functionally relevant** (residue)\n",
        "#@markdown - <b><font color='#D55C00'>RED</font></b>: **Structurally critical** (variant) or **Total-loss** (residue)\n",
        "#@markdown - <b><font color='grey'>GREY</font></b>: Class not assigned (residue)\n",
        "#@markdown - <b><font color='#F0E442'>YELLOW</font></b>: WT amino acid\n",
        "#@markdown\n",
        "#@markdown **N.B.: Residue numeration in the plots starts from 0.**\n",
        "\n",
        "plot_width=30 #@param\n",
        "\n",
        "# Load the downloaded data\n",
        "wt_seq = protein_info['sequence']\n",
        "variant_data = load_data_variant(data_file_path, wt_seq, score_load='func-var')\n",
        "residue_data = load_data_residue(data_file_path, wt_seq)\n",
        "\n",
        "# Process the data\n",
        "processed_variant_data = remove_WT_score(variant_data, wt_seq)\n",
        "\n",
        "# Visualize the results using heatmaps\n",
        "functional_var_heatmap(\n",
        "    ext_score=processed_variant_data,\n",
        "    WT_mask=wt_seq,\n",
        "    x=[0, len(wt_seq) - 1],\n",
        "    uniprot_id=UniProt,\n",
        "    title_figure=f'{UniProt} Func-ESMs Variant-level Classification',\n",
        "    figs=[plot_width, 6]\n",
        ")\n",
        "\n",
        "functional_res_heatmap(\n",
        "    ext_score=residue_data,\n",
        "    x=[0, len(wt_seq) - 1],\n",
        "    uniprot_id=UniProt,\n",
        "    title_figure=f'{UniProt} Func-ESMs Residue-level Classification',\n",
        "    figs=[plot_width, 2]\n",
        ")\n",
        "\n",
        "# Save the figures\n",
        "variant_output_path = os.path.join(make_default_outfolder(UniProt), f'{UniProt}_variant_map.png')\n",
        "residue_output_path = os.path.join(make_default_outfolder(UniProt), f'{UniProt}_residue_map.png')\n",
        "\n",
        "plt.savefig(variant_output_path, dpi=400)\n",
        "plt.savefig(residue_output_path, dpi=400)\n",
        "\n",
        "print(f\"Variant classification heatmap saved to: {variant_output_path}\")\n",
        "print(f\"Residue classification heatmap saved to: {residue_output_path}\")"
      ],
      "metadata": {
        "id": "gUlMzkbGVLiN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694
        },
        "cellView": "form",
        "outputId": "3aaf6dbd-355c-41dd-9ed0-436bc0a532c0"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3000x600 with 1 Axes>"
            ],
            "image/png": 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rSgJJxHeJbosWLVppux06dIiDDjoonnjiiZgwYUKVvrF48eIYM2ZMFBcXx7HHHhsREdtuu2188skn0apVqyrLmThxYvTo0SMuueSSKsmEy0yePDleeeWV+PGPf7zSmCK+S5b5YQLi4sWLo2fPnnH99dfHmWeeWe1xf//4xz/ilFNOiRtvvDGaNPnu/w927949fvOb38RNN91Uuf8PGTIkJk2aFP/+97/r7bGR/+///b+4++67Y8CAAfGXv/wlmjVrFhHfzSJ01FFHxdVXXx1HH3107LLLLrH55pvHXnvtFRMmTIhPPvmkyqPSvvjii3jssceiS5culY8VXLRoUfTt2zeWLl0ar776auy0006V5V944YXo1q1bnHnmmdUS6mprypQpEfHduqoP06ZNq7btPvnkk+jSpUucd955lUlBEd+ttzXWWCPeeuutagme339U34gRI6KioiImTpwYO+yww3LL1aR///4xa9asuP7662PHHXes1TYvLy+Pvn37xv/+7//GY489FoccckiVz78/jqy//vrxn//8p9rMg1OnTo2uXbvGH/7wh3j66acj4rt+/tVXX8VDDz0UvXr1qvXsZM8991zceOONse2228ZLL71Umdw6ZMiQ6Nq1a9xwww1x1FFHxT777FOl3mOPPRZjx46NPn36VL7361//OkaPHh0PPvhg9O3bt1btAwAAQGMzUxQAAACsxLLZnjp06LDSssvKzJkzJyKi8pFYDz74YLVHIl100UWV//7qq69qXF4mk4nhw4fH0qVLqyTwLE+/fv2iqKiocnarZT799NN49NFHo127dtV+qF/m1VdfjaFDh8Zll10Wd911V6y11lrxwAMPRM+ePauVHTBgQDz77LMxb968+O9//xt///vf47jjjovXXnstDjjggPj6669XGuv3PfvsszF06NBqr2XrPtXmm28e5513XpX3liWFfX8mlldffTVef/312HfffaslREV896i6bCxZsqTG7zN06NAqSWaZTCbuvffe2GuvveKFF16IE044IX784x9HixYtokePHpWz8SzPGmusEb/61a/iySefjI8//jgivksEadasWZUZg2rSvHnzau8VFxfHOuusU6vvuLzHNT766KMxb968OPzwwyuToFq2bFktISoiKh8b+Mwzz9TYxoknnljrhKiImmdkKyoqipNPPjmWLFlS46PY1l577Rg+fHhlQlTE//Wj7+8jDeGmm26KtddeO26++ebKhKiI77brpZdeGhERd999d+X7xx13XCxZsqTKexER99xzTyxcuLAyCS3iu+0wa9asOO+886okREVE7L333nH44YfHY489lvxozGV9M9u+sTw1bbuNN944jjzyyPjggw+qzSbWrFmzKutsmWWzr31fTft6TeXq6qGHHoo5c+bEscceW+M4+/11VVxcXC0hKiKic+fOsf/++8dzzz1XqwTFFbnjjjsi4rskqO/P9rf++utXzjJV02P09t133yoJURFR+SjOhu4TAAAAUJ/MFAUAAAANYFlC0jrrrBPXXHNN/OY3v4k99tgjjjrqqNhoo43ixRdfjDfeeCO22WabeO+996okZPzQ/vvvH4ccckg8/vjjMXny5Nhvv/2WW3ajjTaKww47LB566KF4+umn4+CDD46I7x6ttWjRoujXr99yZyA67bTT4rTTTotvvvkmPvjgg7jmmmvi0EMPjeHDh1d5jFdEVHts04477hh33nlnZVu33XZbnHPOOStfUf+/kpKSWiV9ZWvHHXestm6XJSZ8PxHt1VdfjYiIgw46aKXLvO6666olsfXv3z8222yzyr+Li4tr/ZipzTbbLF544YV466234plnnonXX389pkyZEs8++2w8++yzceedd8bjjz++3EeQHX/88XHDDTfEHXfcEWeffXaMHTs2fvrTn8aGG25YY1LZtttuGz/5yU/i7rvvjo8++ih69eoV3bp1q3Fdrcjhhx8eG264YTzwwANRWlpamXSxvBnJJk2aFNddd1288sor8dlnn8XixYsrP1tjjTVqbGO33XardTwREV9//XVcddVV8eCDD8aMGTPiv//9b5XPlyWOfd9WW21VLRGsqKgo2rZtu9xkxZrUZr/4vgULFsTbb78d7dq1i+HDh1f7fFlCzHvvvVf5Xu/eveOMM86I0aNHV+lfY8aMiaKiojj66KMr33v55Zcj4rvHQtY0y9HcuXNj6dKl8a9//Su6dOlS26/ZYD788MMoKSmJCRMmxJw5c6K8vLzK5x9//HHl4wv79u0b//M//xPbb799HHPMMbH//vvH3nvvHS1atKhSp2/fvnH//fdH165d45hjjokDDjgg9tlnn8oZ/OpbNuNIRMRbb70VV1xxRbzwwgsxd+7caklQn332WZUZwbK1LBG3W7du1T7bf//9K2P4oV122aXaezWNmwAAALCqkxQFAAAAK7HRRhvFe++9F7Nnz46tt956hWVnz54dEVFlBpCBAwdGu3bt4oorroiHHnoolixZErvuums8++yzMXz48HjvvfeqPQLqhy6//PJ46qmn4n/+53/ilVdeWWHZgQMHxkMPPRQjRoyoTIoaOXJkRPzfbB8r0rx58/jJT34So0aNik8//TTOP//8OOSQQ2L77bdfad2TTjopRo8eHVOmTMkqKaqh/DBJIuK7hJeIqDIDU2lpaUREjTO3/NB1111Xbdaabt26LTf5pbZ23HHH2HHHHSv/njRpUhx77LExceLE+NOf/hRnn312jfV22GGH2HnnnWPkyJGx6aabxldffbXC7VxUVBQTJkyIIUOGxH333Re/+93vIiJiww03jNNOOy0uuOCCWj26b9lsVNdcc0389a9/jd/+9rcxd+7cePzxx2PTTTeNHj16VJb929/+Fn369Il11lknDj744Nhss81irbXWikwmE6NGjaq2Ppdp27btSuNYZuHChdGtW7d48803Y6eddorjjjsuWrduHUVFRTFr1qy44447qiXaRNS8j0R8t55WNEvXD2W7X3z55ZdRUVERc+bMiaFDhy53ud9P7FpvvfXipz/9adx3330xbdq02G677WLGjBnx4osvRs+ePauMI1988UVERNx1110rjPuHiWO1tWxcnDNnzkrHxZWZPn167LbbblFWVhb7779//OxnP4sWLVpEkyZNYtKkSTF58uQq2+7cc8+N1q1bxy233BJXX311XHXVVVFUVBSHHXZYXHvttZWzTv3yl7+MBx98MK655pr485//HDfffHNkMpnYf//94+qrr67S3+pDNuPIiy++WPnowYMOOig6deoU66yzTmQymXjwwQfjH//4R437azbKysqiSZMmseGGG1b7rG3btpHJZGqcKay24yYAAACs6jw+DwAAAFZizz33jIjvHvG2Iu+99158/PHHsf7668dGG21U5bNDDz00Jk6cGF9//XUsWLAgJk+eHHvvvXe888470aRJk9h5551XuOwddtghfvWrX8Wrr74af/vb31ZYtmfPnrHxxhvHQw89FF988UW89tpr8c4778R+++0XW265ZS2+8f856KCDYunSpfH888/XqvyyGVhSEy2WZ9kMRt+fXWiZZYkIdbHeeutFxP899nBFZs2aFRUVFVVeNc3EUlfdunWLYcOGRUTEhAkTVlh24MCB8cEHH8T5558f7dq1i0MPPXSF5Vu3bh033nhjzJkzJ6ZNmxY33XRTtGrVKgYPHhxXXHFFrWNcNhvU7bffHhHfzRK2ePHiGDBgQJVZp4YMGRJrrrlmvPHGG/G3v/0trrzyyhg6dGjl+8uTyWRqHctDDz0Ub775ZgwcODDefPPNuOWWW+KSSy6JIUOGLPeRkfUp2/1iWeLJLrvsUq3e918/fOTfsscijh49OiK+myXq++//cPmPPPLICpe/opnnVmSvvfaKiJWPi7Vx7bXXxpdffhmjRo2Kp59+Oq677rq4+OKLY8iQIbHNNttUK5/JZOL444+P1157LT799NN44IEH4he/+EU89NBD8dOf/rRK4s7hhx8ekydPji+//DIef/zx+M1vfhOTJk2KQw45pN5nPcpmHLn00kujvLw8nnnmmXj44Yfj6quvruwTPzx+pGrRokUsXbo0Pv3002qfzZ8/PyoqKpabFAgAAACFQFIUAAAArES/fv2iSZMmcdttt9X44/Iyl156aUREHHvssbV6DNmUKVNi1qxZccghh1Q+emxFhg0bFsXFxXHBBRfUmBy0TNOmTaNfv35RXl4eY8aMWe7jzGpj2ePGmjVrVqvyy2axquusST+0/vrrR0TNyQbLHhFVF8se0/bUU0/VeVn16YePdVueY445JtZcc82YM2dO/PrXv67VTE8R3yWXbLvttnHqqafG008/HRERDz/8cK3j22677aJr167xxhtvxD//+c8YOXJkZDKZGDBgQJVyM2bMiG233TY6depU5f1PPvkkPvzww1q3tyIzZsyIiO+SYH6otkl9K7NsvdbHbDnrrrtubLvttvHuu+9mlZzTs2fPaN26dfz1r3+NpUuXxl133RXrrrtute+9++67R0TESy+9VOdYa9K/f/9o2rRp3HrrrSscFyNipTMeLW/bVVRUxJQpU1ZYt3Xr1tGrV6+45557onv37jFt2rSYPn16tXLrrrtuHHLIIXHrrbdG//79Y968eSuddS9b2YwjM2bMiFatWsXee+9d5f0FCxbEm2++Wa18yr630047RcR3s8790LL36nu2LAAAAFiVSIoCAACAldhqq63inHPOic8//zx+9rOfxSeffFLl86VLl8awYcNizJgxsd5668VZZ51V5fOaHk/08ccfx29+85soKiqqnA1oZTp27BinnHJKfPDBBzFq1KgVll32+LRbb701xo4dGy1btoyjjjqqxrKvv/56je+/9dZb8ec//zmaNWtW5VFo7733XixYsKBa+ffeey/OP//8iPguSac+7brrrhERceedd8bSpUsr33/ppZdW+niw2i5/1113jeeeey5uu+2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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3000x200 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Variant classification heatmap saved to: ./output/Q9NV35/Q9NV35_variant_map.png\n",
            "Residue classification heatmap saved to: ./output/Q9NV35/Q9NV35_residue_map.png\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title <b><font color='#56b4e9'>DATA ANALYSIS:</font></b> Visualize the ESM-1b variant conservation score\n",
        "#@markdown **Color keys:**\n",
        "#@markdown - <b> LIGHT SHADED VARIANTS</b>: non-detrimental / Dark shade variants: detrimental\n",
        "#@markdown - <b><font color='grey'>GREY</font></b>: Missing positions\n",
        "#@markdown - <b><font color='#F0E442'>YELLOW</font></b>: WT amino acid\n",
        "#@markdown\n",
        "#@markdown Changing the `min_colormap` value (suggested from -5 to -30) will make the heatmap closer to binary (~5) or more detailed (<<-5).\n",
        "#@markdown\n",
        "#@markdown **N.B.:** Residue numeration in the figure starts from 0 (remember to add +1 for original residue numeration).\n",
        "\n",
        "plot_width = 30  #@param\n",
        "min_colormap = -15  #@param\n",
        "colour_map = 'Greens'  #@param [\"Blues\", \"Greens\", \"Purples\"]\n",
        "\n",
        "# Load the downloaded data\n",
        "wt_seq = protein_info['sequence']\n",
        "conservation_data = load_data_variant(data_file_path, wt_seq, score_load='esm-1b')\n",
        "\n",
        "# Process the data\n",
        "processed_conservation_data = remove_WT_score(conservation_data, wt_seq)\n",
        "\n",
        "# Visualize the results using heatmaps\n",
        "heatmap(\n",
        "    ext_score=processed_conservation_data,\n",
        "    label_cm='ESM-1B score',\n",
        "    WT_mask=wt_seq,\n",
        "    lim_down=min_colormap,\n",
        "    lim_up=0.0,\n",
        "    color=colour_map,\n",
        "    x=[0, len(wt_seq) - 1],\n",
        "    uniprot_id=UniProt,\n",
        "    title_figure=f'{UniProt} ESM-1b',\n",
        "    figs=[plot_width,6]\n",
        ")\n",
        "\n",
        "# Save the figure\n",
        "conservation_output_path = os.path.join(make_default_outfolder(UniProt), f'{UniProt}_esm-1b_map.png')\n",
        "plt.savefig(conservation_output_path, dpi=400)\n",
        "\n",
        "print(f\"ESM-1b variant conservation score heatmap saved to: {conservation_output_path}\")"
      ],
      "metadata": {
        "id": "wg23Bt-ruJlo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "cellView": "form",
        "outputId": "4cd85cc9-7934-49dd-aa6e-ad8fe1340d2e"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3000x600 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ESM-1b variant conservation score heatmap saved to: ./output/Q9NV35/Q9NV35_esm-1b_map.png\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title <b><font color='#56b4e9'>DATA ANALYSIS:</font></b> Visualize the ESM-IF variant stability change score\n",
        "#@markdown **Color keys:**\n",
        "#@markdown - <b> LIGHT SHADED VARIANTS</b>: non-detrimental / Dark shade variants: detrimental\n",
        "#@markdown - <b><font color='grey'>GREY</font></b>: Missing positions\n",
        "#@markdown - <b><font color='#F0E442'>YELLOW</font></b>: WT amino acid\n",
        "#@markdown\n",
        "#@markdown Changing the `min_colormap` value (suggested from -5 to -30) will make the heatmap closer to binary (~5) or more detailed (<<-5).\n",
        "#@markdown\n",
        "#@markdown **N.B.:** Residue numeration in the figure starts from 0 (remember to add +1 for original residue numeration).\n",
        "\n",
        "plot_width = 30  #@param\n",
        "min_colormap = -15  #@param\n",
        "colour_map = 'Blues'  #@param [\"Blues\", \"Greens\", \"Purples\"]\n",
        "\n",
        "# Load the downloaded data\n",
        "wt_seq = protein_info['sequence']\n",
        "stability_data = load_data_variant(data_file_path, wt_seq, score_load='esm-if')\n",
        "\n",
        "# Process the data\n",
        "processed_stability_data = remove_WT_score(stability_data, wt_seq)\n",
        "\n",
        "# Visualize the results using heatmaps\n",
        "heatmap(\n",
        "    ext_score=processed_stability_data,\n",
        "    label_cm='ESM-IF score',\n",
        "    WT_mask=wt_seq,\n",
        "    lim_down=min_colormap,\n",
        "    lim_up=0,\n",
        "    color=colour_map,\n",
        "    x=[0, len(wt_seq) - 1],\n",
        "    uniprot_id=UniProt,\n",
        "    title_figure=f'{UniProt} ESM-IF',\n",
        "    figs=[plot_width, 6]\n",
        ")\n",
        "\n",
        "# Save the figure\n",
        "stability_output_path = os.path.join(make_default_outfolder(UniProt), f'{UniProt}_esm-if_map.png')\n",
        "plt.savefig(stability_output_path, dpi=400)\n",
        "\n",
        "print(f\"ESM-IF variant stability score heatmap saved to: {stability_output_path}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "cellView": "form",
        "id": "XviSXBxD_AwA",
        "outputId": "f96244d0-d511-4d8f-9d50-1a86b6bf8e58"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3000x600 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ESM-IF variant stability score heatmap saved to: ./output/Q9NV35/Q9NV35_esm-if_map.png\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title <b><font color='#56b4e9'>SAVE AND DOWNLOAD RESULTS:</font></b> Save and download results as a ZIP file\n",
        "#@markdown **This cell saves the results in the chosen format and provides an option to download the results as a zip file.**\n",
        "#@markdown\n",
        "#@markdown Mark the next box if you want a zip file with results (if not, files will be stored in the colab output folder)\n",
        "download_results = True  #@param {type:\"boolean\"}\n",
        "\n",
        "# Optionally, create a zip file and provide download link\n",
        "if download_results:\n",
        "    import shutil\n",
        "    from google.colab import files\n",
        "\n",
        "    # Zip the entire output directory\n",
        "    shutil.make_archive('output', 'zip', './output')\n",
        "\n",
        "    # Download the zip file\n",
        "    files.download('output.zip')"
      ],
      "metadata": {
        "id": "Ya086b6hR-oP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "f196d42a-2ef6-4aa2-f57e-3247260c4648",
        "cellView": "form"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_fde550c0-336d-4bdb-9a8d-881d52b64c29\", \"output.zip\", 449215)"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}