Abstract
Knowledge of B cell immunodominance is important for designing vaccines that may elicit effective immune responses. However, the prevalence and characteristics of B cell immunodominance remain poorly understood. In this study, we introduced an immunodominance score through novel data processing methods and identified statistically significant characteristics of B cell immunodominance at the residue and patch levels. Based on these findings, we developed BIDpred, a B cell ImmunoDominance predictor, that learns newly discovered features by leveraging protein language model embeddings and graph attention network to predict the immunodominance scores. BIDpred demonstrates superior performance in predicting immunodominance scores compared to existing methods while maintaining competitive accuracy with state-of-the-art methods for conventional B cell epitope prediction. To the best of our knowledge, this is the first study to systematically analyze and predict B cell immunodominance patterns, marking a significant advancement in vaccine design research.