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.