MutexaGPT: an intuition-to-design translator for physics-based enzyme engineering.

Authors

Qianzhen Shao, Yinjie Zhong, Sebastian Stull, Xinchun Ran, Ning Ding, Kieran Nehil-Puleo, Ruizhe Yao, Han Xu, Zhongyue J Yang

Published in

Nature computational science. Sep 10, 2026. Epub Sep 10, 2026.

Abstract

Physical intuition about how enzyme structure and dynamics shape function has guided successful engineering efforts, yet a systematic approach is still lacking for translating these qualitative and abstract 'thoughts' into quantitative, actionable principles for enzyme design. Here we introduce MutexaGPT, an open-access, multi-agent large language model platform that translates enzyme engineering intuition to physics-based simulations and thus variant designs. Through a web-interface, MutexaGPT takes plain-English, intuition-driven requests as input and leverages large language model agents to elicit missing information, construct physics-based models, configure and execute high-throughput molecular modeling workflows, and convert the results into actionable design proposals, such as smart mutation libraries. We demonstrate the utility of MutexaGPT in two protein engineering tasks: (1) engineering halide methyltransferase toward bulkier substrates and (2) engineering bidomain amylase for enhanced activity at lower temperature. These results establish MutexaGPT as an intuition-to-design translator that integrates human creativity with high-throughput molecular modeling to democratize physics-guided, intuition-driven enzyme engineering.

PMID:
42722895
Bibliographic data and abstract were imported from PubMed on 11 Sep 2026.

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