用序列生成抗体突变，提升结合亲和力，无需实验迭代。
AffinityFlow: Guided Flows for Antibody Affinity Maturation
- 交替优化：先用结构预测引导序列生成，再反向折叠生成突变序列。
- 在无标签数据下仍达到顶尖亲和力提升效果，关键在双向自教学。
- 适合抗体药物研发人员，尤其关注低成本快速优化的团队。
抗体广泛用于治疗，但其开发需耗时耗资的亲和力成熟过程，通常通过迭代突变增强结合亲和力。本文探索仅基于抗体与抗原序列的亲和力成熟方案。受AlphaFlow启发，该方法将AlphaFold嵌入流匹配框架，实现结构条件生成。提出交替优化框架：（1）固定序列，利用基于结构的亲和力预测器引导结构生成以提高结合亲和力；（2）通过逆向折叠生成序列突变，并由基于序列的亲和力预测器进行后选优化。核心挑战是缺乏标注数据训练预测器。为此，设计共教学模块，将噪声生物物理能量信息融入预测器精炼过程：序列预测器选择共识样本教结构预测器，反之亦然。所提方法AffinityFlow在亲和力成熟实验中表现优于现有方法，计划在录用后开源代码。
原文摘要 · Abstract (English)
Antibodies are widely used as therapeutics, but their development requires costly affinity maturation, involving iterative mutations to enhance binding affinity.This paper explores a sequence-only scenario for affinity maturation, using solely antibody and antigen sequences. Recently AlphaFlow wraps AlphaFold within flow matching to generate diverse protein structures, enabling a sequence-conditioned generative model of structure. Building on this, we propose an alternating optimization framework that (1) fixes the sequence to guide structure generation toward high binding affinity using a structure-based affinity predictor, then (2) applies inverse folding to create sequence mutations, refined by a sequence-based affinity predictor for post selection. A key challenge is the lack of labeled data for training both predictors. To address this, we develop a co-teaching module that incorporates valuable information from noisy biophysical energies into predictor refinement. The sequence-based predictor selects consensus samples to teach the structure-based predictor, and vice versa. Our method, AffinityFlow, achieves state-of-the-art performance in affinity maturation experiments. We plan to open-source our code after acceptance.
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