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bioRxiv · 10.64898/2026.03.11.711149

ProteinMCP: An Agentic AI Framework for Autonomous Protein Engineering

Xu, X.·Feng, C.·Zha, C.·He, W.·He, M.·Xiao, B.·Gao, X.
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Abstract

Computational protein design is often constrained by slow, complex, inaccessible, and highly sophiscated and expert-dependent workflows that hinder its transferrability and generalization power for broader applications. We present ProteinMCP, an agentic AI framework designed to accelerate and democratize protein engineering. ProteinMCP automates end-to-end scientific tasks, delivering dramatic gains in efficiency; for instance, a comprehensive protein fitness modeling workflow was completed in just 11 minutes. This performance is achieved by an AI agent that intelligently orchestrates a unified ecosystem of 38 specialized tools, made accessible through a Model-Context-Protocol (MCP). A cornerstone of the framework is an automated pipeline that converts existing software into MCP-compliant servers, ensuring the platform is both powerful and perpetually extensible. We further demonstrate its capabilities through the successful autonomous design and selection of high-affinity de novo binders and therapeutic nanobodies. By removing technical barriers, ProteinMCP has the potential to shorten the design-build-test cycle and make advanced computational protein design accessible to the broader scientific community.

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Xu, X., Feng, C., Zha, C., He, W., He, M., Xiao, B., Gao, X.. 2026-03-14. ProteinMCP: An Agentic AI Framework for Autonomous Protein Engineering. https://doi.org/10.64898/2026.03.11.711149

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