{ "cells": [ { "cell_type": "code", "execution_count": 5, "id": "d2208d17-47b6-4ff1-b6b6-ba09a9d490c7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "* Running on local URL: http://127.0.0.1:7864\n", "\n", "To create a public link, set `share=True` in `launch()`.\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import gradio as gr\n", "import requests\n", "from Bio.PDB import PDBParser\n", "from gradio_molecule3d import Molecule3D\n", "import numpy as np\n", "\n", "# Function to fetch a PDB file from RCSB PDB\n", "def fetch_pdb(pdb_id):\n", " pdb_url = f'https://files.rcsb.org/download/{pdb_id}.pdb'\n", " pdb_path = f'{pdb_id}.pdb'\n", " response = requests.get(pdb_url)\n", " if response.status_code == 200:\n", " with open(pdb_path, 'wb') as f:\n", " f.write(response.content)\n", " return pdb_path\n", " else:\n", " return None\n", "\n", "# Function to process the PDB file and return random predictions\n", "def process_pdb(pdb_id, segment):\n", " pdb_path = fetch_pdb(pdb_id)\n", " if not pdb_path:\n", " return \"Failed to fetch PDB file\", None, None\n", "\n", " parser = PDBParser(QUIET=True)\n", " structure = parser.get_structure('protein', pdb_path)\n", " \n", " try:\n", " chain = structure[0][segment]\n", " except KeyError:\n", " return \"Invalid Chain ID\", None, None\n", "\n", " sequence = [residue.get_resname() for residue in chain if residue.id[0] == ' ']\n", " random_scores = np.random.rand(len(sequence))\n", "\n", " result_str = \"\\n\".join(\n", " f\"{seq} {res.id[1]} {score:.2f}\" \n", " for seq, res, score in zip(sequence, chain, random_scores)\n", " )\n", "\n", " # Save the predictions to a file\n", " prediction_file = f\"{pdb_id}_predictions.txt\"\n", " with open(prediction_file, \"w\") as f:\n", " f.write(result_str)\n", " \n", " return result_str, pdb_path, prediction_file\n", "\n", "#reps = [{\"model\": 0, \"style\": \"cartoon\", \"color\": \"spectrum\"}]\n", "\n", "reps = [\n", " {\n", " \"model\": 0,\n", " \"style\": \"cartoon\",\n", " \"color\": \"whiteCarbon\",\n", " \"residue_range\": \"\",\n", " \"around\": 0,\n", " \"byres\": False,\n", " },\n", " {\n", " \"model\": 0,\n", " \"chain\": \"A\",\n", " \"resname\": \"HIS\",\n", " \"style\": \"stick\",\n", " \"color\": \"red\"\n", " }\n", " ]\n", "\n", "\n", "# Gradio UI\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\"# Protein Binding Site Prediction (Random Scores)\")\n", "\n", " with gr.Row():\n", " pdb_input = gr.Textbox(value=\"2IWI\", label=\"PDB ID\", placeholder=\"Enter PDB ID here...\")\n", " segment_input = gr.Textbox(value=\"A\", label=\"Chain ID\", placeholder=\"Enter Chain ID here...\")\n", " visualize_btn = gr.Button(\"Visualize Structure\")\n", " prediction_btn = gr.Button(\"Predict Random Binding Site Scores\")\n", "\n", " molecule_output = Molecule3D(label=\"Protein Structure\", reps=reps)\n", " predictions_output = gr.Textbox(label=\"Binding Site Predictions\")\n", " download_output = gr.File(label=\"Download Predictions\")\n", "\n", " visualize_btn.click(fetch_pdb, inputs=[pdb_input], outputs=molecule_output)\n", " prediction_btn.click(process_pdb, inputs=[pdb_input, segment_input], outputs=[predictions_output, molecule_output, download_output])\n", "\n", " gr.Markdown(\"## Examples\")\n", " gr.Examples(\n", " examples=[\n", " [\"2IWI\", \"A\"],\n", " [\"7RPZ\", \"B\"],\n", " [\"3TJN\", \"C\"]\n", " ],\n", " inputs=[pdb_input, segment_input],\n", " outputs=[predictions_output, molecule_output, download_output]\n", " )\n", "\n", "demo.launch()" ] }, { "cell_type": "code", "execution_count": null, "id": "bd50ff2e-ed03-498e-8af2-73c0fb8ea07e", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 4, "id": "a1088e14-f09c-48ff-8632-cc4685306d7c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "* Running on local URL: http://127.0.0.1:7863\n", "\n", "To create a public link, set `share=True` in `launch()`.\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import gradio as gr\n", "from gradio_molecule3d import Molecule3D\n", "\n", "\n", "example = Molecule3D().example_value()\n", "\n", "\n", "reps = [\n", " {\n", " \"model\": 0,\n", " \"style\": \"cartoon\",\n", " \"color\": \"whiteCarbon\",\n", " \"residue_range\": \"\",\n", " \"around\": 0,\n", " \"byres\": False,\n", " },\n", " {\n", " \"model\": 0,\n", " \"chain\": \"A\",\n", " \"resname\": \"HIS\",\n", " \"style\": \"stick\",\n", " \"color\": \"red\"\n", " }\n", " ]\n", "\n", "\n", "\n", "def predict(x):\n", " print(\"predict function\", x)\n", " print(x.name)\n", " return x\n", "\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\"# Molecule3D\")\n", " inp = Molecule3D(label=\"Molecule3D\", reps=reps)\n", " out = Molecule3D(label=\"Output\", reps=reps)\n", "\n", " btn = gr.Button(\"Predict\")\n", " gr.Markdown(\"\"\" \n", " You can configure the default rendering of the molecule by adding a list of representations\n", " 
\n",
    "        reps =    [\n",
    "        {\n",
    "          \"model\": 0,\n",
    "          \"style\": \"cartoon\",\n",
    "          \"color\": \"whiteCarbon\",\n",
    "          \"residue_range\": \"\",\n",
    "          \"around\": 0,\n",
    "          \"byres\": False,\n",
    "        },\n",
    "        {\n",
    "          \"model\": 0,\n",
    "          \"chain\": \"A\",\n",
    "          \"resname\": \"HIS\",\n",
    "          \"style\": \"stick\",\n",
    "          \"color\": \"red\"\n",
    "        }\n",
    "      ]\n",
    "
\n", " \"\"\")\n", " btn.click(predict, inputs=inp, outputs=out)\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()" ] }, { "cell_type": "code", "execution_count": null, "id": "d27cc368-26a0-42c2-a68a-8833de7bb4a0", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 8, "id": "cdf7fd26-0464-40d9-9107-71c29dbcaef8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "* Running on local URL: http://127.0.0.1:7867\n", "\n", "To create a public link, set `share=True` in `launch()`.\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/tm/ym2tckv54b96ws82y3b7cqhh0000gn/T/ipykernel_11794/4072855226.py:39: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n", " colors = [cm.get_cmap('coolwarm')(score)[:3] for score in normalized_scores]\n", "Traceback (most recent call last):\n", " File \"/Users/thorben_froehlking/anaconda3/envs/LLM/lib/python3.12/site-packages/gradio/queueing.py\", line 622, in process_events\n", " response = await route_utils.call_process_api(\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/Users/thorben_froehlking/anaconda3/envs/LLM/lib/python3.12/site-packages/gradio/route_utils.py\", line 323, in call_process_api\n", " output = await app.get_blocks().process_api(\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/Users/thorben_froehlking/anaconda3/envs/LLM/lib/python3.12/site-packages/gradio/blocks.py\", line 2024, in process_api\n", " data = await self.postprocess_data(block_fn, result[\"prediction\"], state)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/Users/thorben_froehlking/anaconda3/envs/LLM/lib/python3.12/site-packages/gradio/blocks.py\", line 1830, in postprocess_data\n", " prediction_value = block.postprocess(prediction_value)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/Users/thorben_froehlking/anaconda3/envs/LLM/lib/python3.12/site-packages/gradio_molecule3d/molecule3d.py\", line 210, in postprocess\n", " orig_name=Path(file).name,\n", " ^^^^^^^^^^\n", " File \"/Users/thorben_froehlking/anaconda3/envs/LLM/lib/python3.12/pathlib.py\", line 1162, in __init__\n", " super().__init__(*args)\n", " File \"/Users/thorben_froehlking/anaconda3/envs/LLM/lib/python3.12/pathlib.py\", line 373, in __init__\n", " raise TypeError(\n", "TypeError: argument should be a str or an os.PathLike object where __fspath__ returns a str, not 'dict'\n" ] } ], "source": [ "import gradio as gr\n", "import requests\n", "from Bio.PDB import PDBParser\n", "from gradio_molecule3d import Molecule3D\n", "import numpy as np\n", "from matplotlib import cm\n", "\n", "# Function to fetch a PDB file from RCSB PDB\n", "def fetch_pdb(pdb_id):\n", " pdb_url = f'https://files.rcsb.org/download/{pdb_id}.pdb'\n", " pdb_path = f'{pdb_id}.pdb'\n", " response = requests.get(pdb_url)\n", " if response.status_code == 200:\n", " with open(pdb_path, 'wb') as f:\n", " f.write(response.content)\n", " return pdb_path\n", " else:\n", " return None\n", "\n", "# Function to process the PDB file and return random predictions\n", "def process_pdb(pdb_id, segment):\n", " pdb_path = fetch_pdb(pdb_id)\n", " if not pdb_path:\n", " return \"Failed to fetch PDB file\", None, None, None\n", "\n", " parser = PDBParser(QUIET=True)\n", " structure = parser.get_structure('protein', pdb_path)\n", "\n", " try:\n", " chain = structure[0][segment]\n", " except KeyError:\n", " return \"Invalid Chain ID\", None, None, None\n", "\n", " sequence = [residue.get_resname() for residue in chain if residue.id[0] == ' ']\n", " random_scores = np.random.rand(len(sequence))\n", "\n", " # Normalize scores for coloring (0 = blue, 1 = red)\n", " normalized_scores = (random_scores - np.min(random_scores)) / (np.max(random_scores) - np.min(random_scores))\n", " colors = [cm.get_cmap('coolwarm')(score)[:3] for score in normalized_scores]\n", " hex_colors = [f'#{int(r*255):02x}{int(g*255):02x}{int(b*255):02x}' for r, g, b in colors]\n", "\n", " # Result string and representation\n", " result_str = \"\\n\".join(\n", " f\"{seq} {res.id[1]} {score:.2f}\" \n", " for seq, res, score in zip(sequence, chain, random_scores)\n", " )\n", "\n", " # Representation for the protein structure\n", " reps = [\n", " {\n", " \"model\": 0,\n", " \"style\": \"cartoon\",\n", " \"color\": \"whiteCarbon\"\n", " }\n", " ] + [\n", " {\n", " \"model\": 0,\n", " \"style\": \"cartoon\",\n", " \"residue_index\": i,\n", " \"color\": color\n", " }\n", " for i, color in enumerate(hex_colors)\n", " ]\n", "\n", " # Save the predictions to a file\n", " prediction_file = f\"{pdb_id}_predictions.txt\"\n", " with open(prediction_file, \"w\") as f:\n", " f.write(result_str)\n", " \n", " return result_str, reps, prediction_file\n", "\n", "# Gradio UI\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\"# Protein Binding Site Prediction (Random Scores)\")\n", "\n", " with gr.Row():\n", " pdb_input = gr.Textbox(value=\"2IWI\", label=\"PDB ID\", placeholder=\"Enter PDB ID here...\")\n", " segment_input = gr.Textbox(value=\"A\", label=\"Chain ID\", placeholder=\"Enter Chain ID here...\")\n", " visualize_btn = gr.Button(\"Visualize Structure\")\n", " prediction_btn = gr.Button(\"Predict Random Binding Site Scores\")\n", "\n", " molecule_output = Molecule3D(label=\"Protein Structure\", reps=reps)\n", " predictions_output = gr.Textbox(label=\"Binding Site Predictions\")\n", " download_output = gr.File(label=\"Download Predictions\")\n", "\n", " prediction_btn.click(\n", " fn=process_pdb,\n", " inputs=[pdb_input, segment_input],\n", " outputs=[predictions_output, molecule_output, download_output]\n", " )\n", "\n", " gr.Markdown(\"## Examples\")\n", " gr.Examples(\n", " examples=[\n", " [\"2IWI\", \"A\"],\n", " [\"7RPZ\", \"B\"],\n", " [\"3TJN\", \"C\"]\n", " ],\n", " inputs=[pdb_input, segment_input],\n", " outputs=[predictions_output, molecule_output, download_output]\n", " )\n", "\n", "demo.launch()" ] }, { "cell_type": "code", "execution_count": null, "id": "ee215c16-a1fb-450f-bb93-37aaee6fb3f1", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python (LLM)", "language": "python", "name": "llm" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.7" } }, "nbformat": 4, "nbformat_minor": 5 }