B‐EPIC: A Transformer‐Based Language Model for Decoding B Cell Immunodominance Patterns

Abstract Vaccine development for pathogens has faced significant challenges, contributing to a public health burden. B‐cell epitope (BCE) prediction is a crucial process in vaccine development, but is hindered by limited efficiency and accuracy. To address this, B‐Epic, the first pipeline applying Transformer to predict BCEs is independently developed. B‐Epic's robustness is validated through multiple testing datasets, including distinguishing clinically‐approved vaccine targets, identifying BCEs (the Immune Epitope Database testing dataset; n = 23,888) and immunoreactive peptides ( Trypanosoma cruzi peptidome; n = 239,575) with high AUCs of 0.882 and 0.945, respectively, outperforming widely used tools. Based on its superior performance, B‐Epic is applied to the prevention of carcinogenic pathogens. In the application to Helicobacter pylori , peptides screened by B‐Epic can activate B cells in experiments, suggesting their potential as vaccine targets. In another application to Epstein‐Barr virus, B‐Epic identifies pan‐immunoreactive peptides in a clinical cohort ( n = 899). These peptides exhibit higher reactogenicity in nasopharyngeal carcinoma patients than in healthy controls ( n = 140), indicating their viability as immunodiagnostic targets. Overall, B‐Epic utilizes self‐attention, high‐dimensional feature projection, and convolutional neural networks to autonomously extract complicated BCE features, enabling accurate BCE prediction and thereby facilitating efforts to prevent infectious diseases and cancers.