{"ID":2864686,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.23254","arxiv_id":"2509.23254","title":"ABConformer: Physics-inspired Sliding Attention for Antibody-Antigen Interface Prediction","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.","short_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 captur...","url_abs":"https://arxiv.org/abs/2509.23254","url_pdf":"https://arxiv.org/pdf/2509.23254v1","authors":"[\"Zhang-Yu You\",\"Jiahao Ma\",\"Hongzong Li\",\"Ye-Fan Hu\",\"Jian-Dong Huang\"]","published":"2025-09-27T11:12:04Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"q-bio.BM\"]","methods":"[]","has_code":false}