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. 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.

翻译：抗体作为治疗剂被广泛应用，但其开发需要进行成本高昂的亲和力成熟过程，这涉及通过迭代突变来增强结合亲和力。本文探讨了一种仅基于序列的亲和力成熟方案，仅使用抗体和抗原序列。近期，AlphaFlow将AlphaFold封装在流匹配框架内，以生成多样化的蛋白质结构，从而实现了结构上的序列条件生成模型。在此基础上，我们提出了一种交替优化框架：首先固定序列，利用基于结构的亲和力预测器引导结构生成朝向高结合亲和力；随后应用逆折叠技术产生序列突变，并通过基于序列的亲和力预测器进行后选择精炼。为解决此问题，我们开发了一个协同教学模块，将来自噪声生物物理能量的有价值信息整合到预测器精炼过程中。基于序列的预测器选择共识样本来指导基于结构的预测器，反之亦然。我们的方法——亲和力流，在亲和力成熟实验中实现了最先进的性能。我们计划在论文被接受后开源代码。