AbFlow : 通过交互增强的流匹配进行端到端的抗原导向抗体设计
AbFlow : End-to-end Paratope-Centric Antibody Design by Interaction Enhanced Flow Matching
AI总结：
AbFlow通过交互增强的流匹配框架实现了端到端的抗原导向抗体设计，优化了抗体结构和结合亲和力。
摘要：
抗原-抗体结合是免疫反应中的关键过程。尽管最近的进展推动了抗体设计的发展，但当前的方法缺乏一个用于端到端建模完整原子抗体结构的生成框架，并且难以充分利用抗原特异性几何信息以优化局部结合界面和全局结构。为克服这些限制，我们引入了AbFlow，一种利用最优传输设计完整原子抗体的流匹配框架。AbFlow结合了一个扩展的速度场网络，其中包含一个等变的表面多通道编码器，该编码器利用表面层面的抗原相互作用数据来优化抗体结构，特别是CDR-H3区域。在抗原导向抗体设计、多CDRs和完整原子抗体设计、结合亲和力优化以及复杂结构预测的大量实验中，AbFlow产生了更优的抗原-抗体复合物，特别是在接触界面处，并显著提高了生成抗体的结合亲和力。
英文摘要：
Antigen-antibody binding is a critical process in the immune response. Although recent progress has advanced antibody design, current methods lack a generative framework for end-to-end modeling of full-atom antibody structures and struggle to fully exploit antigen-specific geometric information for optimizing local binding interfaces and global structures. To overcome these limitations, we introduce AbFlow, a paratope-restricted one-step flow-matching framework for designing full-atom antibodies end-to-end. AbFlow incorporates an extended velocity field network featuring an equivariant Surface Multi-channel Encoder, which uses surface-level antigen interaction data to refine the antibody structure, particularly the CDR-H3 region. Extensive experiments in paratope-centric antibody design, multi-CDRs and full-atom antibody design, binding affinity optimization, and complex structure prediction show that AbFlow produces superior antigen-antibody complexes, especially at the contact interface, and markedly improves the binding affinity of generated antibodies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
