Deciphering Fingerprints of 3D Molecular Surfaces for Accurate Epitope Prediction

Researchers introduced SurfBind, a surface-centric learning framework that directly models 3D molecular surfaces for accurate epitope prediction. SurfBind uses a Transformer-based architecture with patch-level surface modeling and binder-aware cross-attention, achieving state-of-the-art performance on benchmarks SAbDab and DB5.5. The method demonstrates strong generalization across unseen antibodies and conformational states, highlighting the importance of interaction-aware surface modeling for understanding protein-protein interactions.

arXiv:2606.23830v1 Announce Type: new Abstract: 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.