发表机构
Institute of AI Industry Research (AIR), Tsinghua University; Department of Computer Science and Technology, Tsinghua University; Pharmolix Inc.（清华大学人工智能产业研究院（AIR）; 清华大学计算机科学与技术系; Pharmolix公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出结构感知强化学习框架StructEvo，利用增量结构融合编码器和分层动作网络解决蛋白质定向进化中结构信息整合难题，在两个基准上分别提升9.2%和16.3%，并验证了GFP中的上位效应。
AI 中文摘要
蛋白质优化一直是生命科学领域的长期目标。现有的机器学习辅助定向进化（MLDE）方法主要依赖仅序列特征，忽视了蛋白质结构中编码的关键空间约束和共进化相互作用。然而，由于可靠的突变体结构稀缺，直接整合结构信息仍然具有挑战性。为解决这些问题，我们提出了StructEvo，一种新颖的结构感知强化学习框架，用于蛋白质定向进化。StructEvo采用增量结构融合编码器，通过特征差异近似突变体结构特征，从而实现空间知识的动态整合。庞大的突变空间随后通过结构对齐的分层动作网络被分解为可管理的子空间，同时几何约束进一步稳定增量特征学习。我们的方法在两个具有挑战性的优化基准上分别比先前最先进的方法提高了9.2%和16.3%，并进一步在绿色荧光蛋白（GFP）中识别出实验验证的上位效应模式，突显了结构指导对有效蛋白质定向进化的重要性。
英文摘要
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.
