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.