alphafold3_mcp

# AlphaFold3 MCP Server **AI-powered protein structure prediction and variant analysis via Docker** An MCP (Model Context Protocol) server for AlphaFold3 structure prediction with 5 core tools: - Submit structure predictions from sequences or MSA files - Batch process protein variants for engineering workflows - Run end-to-end prepare-and-predict variant pipelines - Monitor long-running prediction jobs - Validate and prepare AlphaFold3 input configurations ## Quick Start with Docker ### Approach 1: Pull Pre-built Image from GitHub The fastest way to get started. A pre-built Docker image is automatically published to GitHub Container Registry on every release. ```bash # Pull the latest image docker pull ghcr.io/macromnex/alphafold3_mcp:latest # Register with Claude Code (runs as current user to avoid permission issues) claude mcp add alphafold3 -- docker run -i --rm --user `id -u`:`id -g` --gpus all --ipc=host -v `pwd`:`pwd` ghcr.io/macromnex/alphafold3_mcp:latest ``` **Note:** Run from your project directory. `` `pwd` `` expands to the current working directory. **Requirements:** - Docker with GPU support (`nvidia-docker` or Docker with NVIDIA runtime) - Claude Code installed That's it! The AlphaFold3 MCP server is now available in Claude Code. --- ### Approach 2: Build Docker Image Locally Build the image yourself and install it into Claude Code. Useful for customization or offline environments. ```bash # Clone the repository git clone https://github.com/MacromNex/alphafold3_mcp.git cd alphafold3_mcp # Build the Docker image docker build -t alphafold3_mcp:latest . # Register with Claude Code (runs as current user to avoid permission issues) claude mcp add alphafold3 -- docker run -i --rm --user `id -u`:`id -g` --gpus all --ipc=host -v `pwd`:`pwd` alphafold3_mcp:latest ``` **Note:** Run from your project directory. `` `pwd` `` expands to the current working directory. **Requirements:** - Docker with GPU support - Claude Code installed - Git (to clone the repository) **About the Docker Flags:** - `-i` — Interactive mode for Claude Code - `--rm` — Automatically remove container after exit - `` --user `id -u`:`id -g` `` — Runs the container as your current user, so output files are owned by you (not root) - `--gpus all` — Grants access to all available GPUs - `--ipc=host` — Uses host IPC namespace for better performance - `-v` — Mounts your project directory so the container can access your data --- ## Verify Installation After adding the MCP server, you can verify it's working: ```bash # List registered MCP servers claude mcp list # You should see 'alphafold3' in the output ``` In Claude Code, you can now use all 5 AlphaFold3 tools: - `submit_structure_prediction` - `submit_batch_variants` - `submit_prepare_and_predict_variants` - `get_job_status` - `get_job_result` --- ## Next Steps - **Detailed documentation**: See [detail.md](detail.md) for comprehensive guides on: - Available MCP tools and parameters - Local Python environment setup (alternative to Docker) - Example workflows and use cases - Configuration file formats - AlphaFold3 license and model weight setup --- ## Usage Examples Once registered, you can use the AlphaFold3 tools directly in Claude Code. Here are some common workflows: ### Example 1: Structure Prediction from Sequence ``` I have a protein sequence in /path/to/protein.fasta. Can you submit an AlphaFold3 structure prediction using submit_structure_prediction and save the results to /path/to/results/? ``` ### Example 2: Batch Variant Analysis ``` I have 50 protein variants in /path/to/variants.fasta and a wild-type data JSON at /path/to/wt_data.json. Can you use submit_prepare_and_predict_variants to run end-to-end structure predictions for all variants and save to /path/to/output/? ``` ### Example 3: Protein-Ligand Complex ``` I want to predict the structure of my protein with a small molecule ligand. The protein is in /path/to/protein.fasta and the ligand SMILES is "CCO". Can you prepare the AlphaFold3 config and submit a structure prediction? ``` --- ## Troubleshooting **Docker not found?** ```bash docker --version # Install Docker if missing ``` **GPU not accessible?** - Ensure NVIDIA Docker runtime is installed - Check with `docker run --gpus all ubuntu nvidia-smi` **Claude Code not found?** ```bash # Install Claude Code npm install -g @anthropic-ai/claude-code ``` **AlphaFold3 license required?** - AlphaFold3 model weights require a license from Google DeepMind - Apply at: https://github.com/google-deepmind/alphafold3 --- ## License CC-BY-NC-SA 4.0 (Google DeepMind)