Structure-aware Reinforcement Learning for Protein Directed Evolution

Abstract:Protein optimization remains a longstanding goal in life sciences. Existing machine learning-assisted directed evolution (MLDE) methods primarily rely on sequence-only features, overlooking the critical spatial constraints and co-evolutionary interactions encoded in protein structures. However, directly integrating structural information remains challenging due to the scarcity of reliable mutant structures. To address these issues, we propose StructEvo, a novel structure-aware reinforcement learning framework for protein directed evolution. StructEvo employs a delta-structure fusion encoder to approximate mutant structure features via feature differences, enabling dynamic incorporation of spatial knowledge. The vast mutation space is then decomposed into manageable subspaces through a structure-aligned hierarchical action network, while a geometric constraint further stabilizes delta feature learning. Our approach outperforms prior state-of-the-art methods by 9.2% and 16.3% on two challenging optimization benchmarks, and further identifies an experimentally validated epistasis pattern in GFP, highlighting the importance of structural guidance for effective protein directed evolution.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.39048 [cs.LG]

(or arXiv:2609.39048v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.39048 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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