用自采样Conformer模型统一预测抗体结合位点，显著提升构象表位预测精度
BConformeR: A Conformer Based on Mutual Sampling for Unified Prediction of Continuous and Discontinuous Antibody Binding Sites
- 基于AlphaFold和实验结构分别训练Conformer模型，融合卷积与注意力机制
- 在连续与非连续表位上均超越现有方法，尤其对构象表位提升明显
- 适合疫苗设计与抗体药物研发人员参考，尤其关注构象表位的场景
准确预测抗原上的抗体结合位点（表位）对疫苗设计、免疫诊断、治疗性抗体开发、抗体工程以及自身免疫和过敏性疾病研究至关重要。尽管已有计算方法尝试预测线性（连续）和构象（非连续）表位，但其在预测构象表位方面始终表现不佳。本文提出基于自采样的Conformer模型，分别在AlphaFold预测结构和实验确定结构上进行训练，利用卷积神经网络提取局部特征，通过Transformer捕捉序列中的长程依赖关系。消融实验表明，卷积模块有助于提升线性表位预测，而Transformer模块显著改善了构象表位的预测性能。实验结果表明，本模型在线性与构象表位的MCC、ROC-AUC、PR-AUC和F1得分上均优于现有基线方法。
原文摘要 · Abstract (English)
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 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 Conformer-based models trained separately on AlphaFold-predicted structures and experimentally determined structures, 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 MCC, ROC-AUC, PR-AUC, and F1 scores on both linear and conformational epitopes.
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