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.

翻译：准确预测抗原上的抗体结合位点（表位）对于疫苗设计、免疫诊断、治疗性抗体开发、抗体工程、自身免疫及过敏性疾病研究，以及深化对免疫应答的理解至关重要。尽管已有多种计算方法被提出用于预测线性（连续）和构象（非连续）表位，但它们在预测构象表位方面始终表现不佳。在本工作中，我们提出了基于Conformer的模型，分别在AlphaFold预测的结构和实验确定的结构上进行训练，利用卷积神经网络（CNN）提取局部特征，并利用Transformer捕捉抗原序列内的长程依赖关系。消融研究表明，CNN增强了对线性表位的预测能力，而Transformer模块则提升了对构象表位的预测性能。实验结果表明，我们的模型在线性和构象表位的预测上，在MCC、ROC-AUC、PR-AUC和F1分数方面均优于现有基线方法。