ABConformer: Physics-inspired Sliding Attention for Antibody-Antigen Interface Prediction

• URL: http://arxiv.org/abs/2509.23254v1
• Date: Sat, 27 Sep 2025 11:12:04 GMT
• Title: ABConformer: Physics-inspired Sliding Attention for Antibody-Antigen Interface Prediction
• Authors: Zhang-Yu You, Jiahao Ma, Hongzong Li, Ye-Fan Hu, Jian-Dong Huang,
• Abstract summary: We present ABCONFORMER, a model based on the Conformer backbone that captures both local and global features of a biosequence.<n>ABCONFORMER can accurately predict paratopes and antigens given the antibody and sequence, and predict pan-epitopes on the antigen without antibody information.
• Score: 3.947298454012977
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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, 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.

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