MCP server for protein design: RFdiffusion, ProteinMPNN, ESMFold, AlphaFold2, Boltz, PyRosetta, ESM2, OpenMM
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# Protein Design MCP Server [](https://pypi.org/project/protein-design-mcp/) [](https://hub.docker.com/r/jeonghyeonkim8652/protein-design-mcp) [](https://github.com/jasonkim8652/protein-design-mcp/pkgs/container/protein-design-mcp) [](https://smithery.ai/server/protein-design-mcp) [](LICENSE) An [MCP](https://modelcontextprotocol.io) server that gives LLM agents access to computational protein design tools. Ask your LLM to design binders, generate de novo folds, predict structures, score interfaces, or relax with Rosetta — it calls the right tool automatically. **19 tools total**, spanning generative design, structure prediction, physics-based scoring, and analysis. Built on RFdiffusion, ProteinMPNN, ESMFold, AlphaFold2, **Boltz-2**, **PyRosetta**, ESM2, and OpenMM. | Distribution | Tools out-of-the-box | Extras | |---|---|---| | `pip install "protein-design-mcp[gpu]"` | 13 core tools | `[rosetta]` (license required), `[boltz]` (isolated venv) | | `docker pull jeonghyeonkim8652/protein-design-mcp` | 13 core tools (GPU), 10 (CPU) | PyRosetta / Boltz not bundled (license + torch conflict) | The 6 non-bundled tools (`rosetta_*` x4, `predict_*_boltz` x2) install cleanly via pip extras — see [Optional Tools](#optional-tools-pyrosetta--boltz-2). ## Installation Choose the method that fits your situation. Listed from simplest to most customizable. --- ### 1. Auto-Setup (Recommended) One command. Detects your environment, pulls Docker if available, writes MCP client config. ```bash pip install protein-design-mcp protein-design-mcp-setup ``` What it does: - Checks for Docker and NVIDIA GPU - …
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