Accurate and reliable prediction of B cell epitopes holds critical importance in immunology and vaccine development. While traditional experimental methods offer high accuracy in identifying epitope regions, they are often laborious, time-consuming, and costly. Therefore, attempts are made to increase the efficiency of experimental characterization processes by using computational approaches. Since approximately 90% of epitopes are conformational, the prediction processes must account for three-dimensional protein structures and the geometric details of antigen-antibody interactions. In response to these requirements, our study introduces BepiCon, a two-stage geometric deep learning framework that models antigen proteins as graph structures, incorporating structural and physicochemical properties and protein language model embeddings to predict epitope regions on antigen proteins. In the first stage, the model was trained using a graph contrastive learning approach to learn high-quality representations of epitope and non-epitope residues. In the second stage, the pre-trained model was fine-tuned using supervised learning to perform conformational epitope prediction. The developed framework has demonstrated effective and generalizable performance when applied to both experimentally determined protein structures and predicted structures. Comparative analysis revealed that our approach distinguishes itself from existing B cell epitope prediction methods by exhibiting a lower false-positive rate and generating more reliable predictions. Our work contributes significantly to scientific research and therapeutic design processes by showcasing the advantages of geometric deep-learning approaches in B-cell epitope prediction.

Co-authors: Tunca Doğan