CARTnet: Template-guided B-cell Epitope Prediction with Contact-aware Attention

B-cell epitope prediction remains challenging due to the limited availability of experimentally characterized data and the incomplete understanding of antibody-antigen recognition. Here, we present CARTnet, a novel contact-aware deep learning framework that integrates multimodal features and template-derived epitope information. CARTnet demonstrated superior performance when compared to the existing state-of-the-art methods across most evaluation metrics on both the internal and external test datasets. In particular, CARTnet achieved an AUPRC of 0.243 on the internal test set, compared with 0.220 for DiscoTope-3, 0.140 for BepiPred-2, and 0.198 for BepiPred-3. This improvement was also observed on the external test set, where CARTnet achieved an AUPRC of 0.267, compared with 0.192, 0.111, and 0.215 for DiscoTope-3, BepiPred-2, and BepiPred-3, respectively, supporting its generalizability. CARTnet also supports template-guided prediction, in which limited prior epitope information can improve the identification of additional epitope residues; a single template residue increased the AUPRC of the template-based variant from 0.225 to 0.403 on the internal test set. Furthermore, we demonstrated that epitope information from closely related viruses can be transferred across the genus Alphavirus to improve epitope prediction accuracy. These results establish CARTnet as an accurate framework for both de novo and template-guided B-cell epitope prediction.