用能量引导的流匹配优化抗体结构，显著提升结合区精度。
Efficient Antibody Structure Refinement Using Energy-Guided SE(3) Flow Matching
- 基于SE(3)流匹配，融合物理先验知识生成结构
- 在抗体结合区（CDR）上达到新最佳性能
- 计算开销小，适合实际抗体工程应用
抗体是免疫系统产生的蛋白质，可识别并结合特定抗原，其三维结构对理解结合机制和设计治疗手段至关重要。抗体与抗原结合的特异性主要取决于抗体内的互补决定区（CDR）。尽管抗体结构预测取得进展，但预测的CDR质量仍不理想。本文提出一种名为FlowAB的新方法，基于能量引导的流匹配，采用强大的深度生成模型SE(3)流匹配，并将重要的物理先验知识融入流模型以指导生成过程。大量实验表明，FlowAB能显著改善抗体CDR结构，在结合适当先验模型时达到抗体结构预测任务的新最优表现，且计算开销仅略有增加。这一优势使FlowAB成为抗体工程中的实用工具。
原文摘要 · Abstract (English)
Antibodies are proteins produced by the immune system that recognize and bind to specific antigens, and their 3D structures are crucial for understanding their binding mechanism and designing therapeutic interventions. The specificity of antibody-antigen binding predominantly depends on the complementarity-determining regions (CDR) within antibodies. Despite recent advancements in antibody structure prediction, the quality of predicted CDRs remains suboptimal. In this paper, we develop a novel antibody structure refinement method termed FlowAB based on energy-guided flow matching. FlowAB adopts the powerful deep generative method SE(3) flow matching and simultaneously incorporates important physical prior knowledge into the flow model to guide the generation process. The extensive experiments demonstrate that FlowAB can significantly improve the antibody CDR structures. It achieves new state-of-the-art performance on the antibody structure prediction task when used in conjunction with an appropriate prior model while incurring only marginal computational overhead. This advantage makes FlowAB a practical tool in antibody engineering.
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