Example_Foundation_Models_in_Bioinformatics_for_Protein_Folding.ipynb

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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/OneFineStarstuff/OneFineStardust/blob/main/Example_Foundation_Models_in_Bioinformatics_for_Protein_Folding.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "code",
      "source": [
        "pip install Bio"
      ],
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from Bio import SeqIO\n",
        "\n",
        "# Replace \"protein_sequences.fasta\" with the path to your FASTA file\n",
        "try:\n",
        "    for record in SeqIO.parse(\"protein_sequences.fasta\", \"fasta\"):\n",
        "        print(record.id)\n",
        "        print(record.seq)\n",
        "except FileNotFoundError:\n",
        "    print(\"The file 'protein_sequences.fasta' was not found. Please make sure it exists in your working directory.\")"
      ],
      "metadata": {
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      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "\n",
        "# Hypothetical endpoint for AlphaFold predictions\n",
        "url = \"https://real-alphafold-api.com/predict\"  # Replace with a real endpoint if available\n",
        "\n",
        "data = {\n",
        "    \"sequence\": \"MVKVYAPASSANMSVGFDVLGAAVTPVDGALLGDVVTVEAAETFSLNNLGQKFVITPVNTTTQ\"\n",
        "}\n",
        "\n",
        "try:\n",
        "    response = requests.post(url, json=data)\n",
        "    predicted_structure = response.json()\n",
        "    print(\"Predicted Protein Structure:\", predicted_structure)\n",
        "except requests.ConnectionError:\n",
        "    print(\"Failed to connect to the AlphaFold API endpoint. Please ensure the URL is correct.\")"
      ],
      "metadata": {
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      "execution_count": null,
      "outputs": []
    }
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