用新模型精准设计抗体结合区，提升药物研发效率
Towards More Accurate Full-Atom Antibody Co-Design
- 构建端到端框架Igformer，融合局部化学互作与全局构象依赖
- 在抗原表位设计任务中性能超越现有方法，提升结合特异性
- 适合抗体药物设计、计算结构生物学研究者参考
抗体共设计是药物研发的关键前沿，准确预测互补决定区（CDRs）的一维序列和三维结构对靶向特定抗原表位至关重要。尽管等变图神经网络在抗体设计方面取得进展，但现有方法仍难以捕捉驱动抗体-抗原识别与结合特异性的复杂互作。本文提出Igformer，一种新型端到端框架，通过创新建模抗体-抗原结合界面来解决上述问题。该方法通过个性化传播与全局注意力机制整合图间表示，全面捕获局部化学互作与全局构象依赖之间的复杂交互。在表位结合型CDR设计与结构预测任务上，Igformer表现出显著优于现有方法的性能，表明显式建模多尺度残基互作可大幅提升计算抗体设计能力，推动治疗应用发展。
原文摘要 · Abstract (English)
Antibody co-design represents a critical frontier in drug development, where accurate prediction of both 1D sequence and 3D structure of complementarity-determining regions (CDRs) is essential for targeting specific epitopes. Despite recent advances in equivariant graph neural networks for antibody design, current approaches often fall short in capturing the intricate interactions that govern antibody-antigen recognition and binding specificity. In this work, we present Igformer, a novel end-to-end framework that addresses these limitations through innovative modeling of antibody-antigen binding interfaces. Our approach refines the inter-graph representation by integrating personalized propagation with global attention mechanisms, enabling comprehensive capture of the intricate interplay between local chemical interactions and global conformational dependencies that characterize effective antibody-antigen binding. Through extensive validation on epitope-binding CDR design and structure prediction tasks, Igformer demonstrates significant improvements over existing methods, suggesting that explicit modeling of multi-scale residue interactions can substantially advance computational antibody design for therapeutic applications.
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