ProteinMCP : An agentic AI framework for autonomous protein engineering
Open accessPublished version of a preprint
Abstract
Computational protein design is often constrained by slow, complex, inaccessible, and highly sophisticated 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 min. 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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- Authors
- Xu, X., Feng, C., Zha, C., He, W., He, M., Xiao, B., & Gao, X.
- Affiliations
- King Abdullah University of Science and Technology (SA)
- Ningxia Medical University (CN)
- Bestzyme Biotech Inc. Piscataway New Jersey USA
- Corresponding author
- Xu, Xiaopeng
- Gao, Xin
- Venue
- Protein Science
- Publisher
- Wiley
- Published
- 2026-03-25
- Volume
- 35
- Issue
- 4
- Pages
- e70547-e70547
- ISSN
- 0961-8368 (ISSN-L)
- 1469-896X
- PMC
- PMC13140703
- OpenAlex
- W7140322094
- Cited by
- 6
- References
- 9
- Citations by year
- 2026: 6
- FWCI
- 12.31
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Protein Science (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- Funders
- King Abdullah University of Science and Technology (REI/1/5234‐01‐01, REI/1/5289‐01‐01, REI/1/5404‐01‐01, REI/1/5414‐01‐01, REI/1/5992‐01‐01, URF/1/4663‐01‐01)
- Natural Science Foundation of Ningxia Province (2024AAC03247, 2024BEH04022)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- agentic AI, computational protein design, AI agents, Model Context Protocol, design-build-test cycle
- MeSH terms
- Intelligent Systems, Artificial Intelligence, Proteins, Software, Protein Engineering
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