EpiCluster: end-to-end deep learning model for B cell epitope prediction designed to capture epitope clustering property

Abstract Knowledge of B cell epitopes is crucial for vaccine design, diagnostics, and therapeutics. Many in silico tools have been developed to computationally predict the B cell epitope. However, most methods have shown inconsistent performance, thereby degrading the reliability of the predictions. To address this challenge, we developed EpiCluster, an end-to-end deep learning model that significantly outperforms existing methods by a large margin. Our model’s performance is consistent with several benchmark datasets, including the most recent one on which all existing methods performed very poorly. EpiCluster achieves this mainly through two ways. First, it effectively combines the structural and evolutionary features of epitopes. Second, it has the model architecture that exploits the clustering property of epitopes. More importantly, we have demonstrated that an end-to-end learning model architecture enforcing the clustering property of epitopes was critically important for building an accurate epitope prediction model. The source code and implementation are available at https://github.com/sj584/EpiCluster.