BConformeR: A Conformer Based on Mutual Sampling for Unified Prediction of Continuous and Discontinuous Antibody Binding Sites

• URL: http://arxiv.org/abs/2508.12029v2
• Date: Mon, 01 Sep 2025 08:40:36 GMT
• Title: BConformeR: A Conformer Based on Mutual Sampling for Unified Prediction of Continuous and Discontinuous Antibody Binding Sites
• Authors: Zhangyu You, Jiahao Ma, Hongzong Li, Ye-Fan Hu, Jian-Dong Huang,
• Abstract summary: In this work, we propose a conformer-based model trained on antigen sequences derived from 1,080 antigen-antibody complexes.<n>CNN enhances the prediction of linears, and the Transformer module improves the prediction of conformationals.<n> Experimental results show that our model outperforms existing baselines in terms of PCC, ROC-AUC, PR-AUC, and F1 scores on both linear and conformationals.
• Score: 3.947298454012977
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate prediction of antibody-binding sites (epitopes) on antigens is crucial for vaccine design, immunodiagnostics, therapeutic antibody development, antibody engineering, research into autoimmune and allergic diseases, and for advancing our understanding of immune responses. Despite in silico methods that have been proposed to predict both linear (continuous) and conformational (discontinuous) epitopes, they consistently underperform in predicting conformational epitopes. In this work, we propose a conformer-based model trained on antigen sequences derived from 1,080 antigen-antibody complexes, leveraging convolutional neural networks (CNNs) to extract local features and Transformers to capture long-range dependencies within antigen sequences. Ablation studies demonstrate that CNN enhances the prediction of linear epitopes, and the Transformer module improves the prediction of conformational epitopes. Experimental results show that our model outperforms existing baselines in terms of PCC, ROC-AUC, PR-AUC, and F1 scores on both linear and conformational epitopes.

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