Title: ABConformer: Physics‑inspired Sliding Attention for Antibody-Antigen Interface Prediction
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Antibody–antigen interface prediction, Protein sequence modeling, Conformer, Sliding attention mechanism, Epitope prediction, Paratope prediction, Structural bioinformatics

Abstract: Accurate prediction of antibody-antigen (Ab-Ag) interfaces is critical for vaccine design, immunodiagnostics and therapeutic antibody development. However, achieving reliable predictions from sequences alone remains a challenge. In this paper, we present \textsc{ABConformer}, a model based on the Conformer backbone that captures both local and global features of a biosequence. To accurately capture Ab-Ag interactions, we introduced the physics-inspired sliding attention, enabling residue-level contact recovery without relying on three-dimensional structural data. ABConformer can accurately predict paratopes and epitopes given the antibody and antigen sequence, and predict pan-epitopes on the antigen without antibody information. In comparison experiments, \textsc{ABConformer} achieves state-of-the-art performance on a recent SARS-CoV-2 Ab-Ag dataset, and surpasses widely used sequence-based methods for antibody-agnostic epitope prediction. Ablation studies further quantify the contribution of each component, demonstrating that, compared to conventional cross-attention, sliding attention significantly enhances the precision of epitope prediction. To facilitate reproducibility, we will release the code under an open-source license upon acceptance.