用分子表面特征精准预测抗体结合位点，突破传统方法局限。
Deciphering Fingerprints of 3D Molecular Surfaces for Accurate Epitope Prediction
- 基于表面的Transformer架构，融合几何与理化信息建模
- 在SAbDab和DB5.5数据集上达到当前最优性能
- 适用于未知抗体和构象变化，适合结构生物学研究
分子表面编码了决定抗体-抗原识别的几何与理化模式，是表位预测的核心。然而，现有方法依赖序列或骨架结构，难以捕捉不连续、由表面驱动的表位。本研究提出SurfBind，一种以表面为中心的学习框架，直接在分子表面表示上进行表位预测。SurfBind通过Transformer架构整合几何与理化线索，采用片段级表面建模、结合物感知交叉注意力及分层粗到细预测范式。在SAbDab和DB5.5等挑战性表位识别基准上的实验表明，SurfBind达到当前最优性能，并在未见抗体和构象状态间展现强泛化能力，凸显交互感知表面建模对理解蛋白质-蛋白质相互作用机制的关键价值。
原文摘要 · Abstract (English)
Molecular surfaces encode the geometric and physicochemical patterns that determine antibody-antigen recognition, central to epitope prediction. However, existing methods rely on sequences or backbone structures and struggle to capture discontinuous, surface-driven epitopes. This study presents SurfBind, a surface-centric learning framework for epitope prediction that operates directly on molecular surface representations. SurfBind integrates geometric and physicochemical cues through a Transformer-based architecture with patch-level surface modeling, binder-aware cross-attention, and a hierarchical coarse-to-fine prediction paradigm. Experiments on challenging epitope identification benchmarks, including SAbDab and DB5.5, demonstrate that SurfBind achieves state-of-the-art performance and strong generalization across unseen antibodies and conformational states, highlighting the value of interaction-aware surface modeling for understanding the crucial mechanisms of protein-protein interactions.
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