BepiTBR: T-B reciprocity enhances B cell epitope prediction

James Zhu, Anagha Gouru, Fangjiang Wu, Jay A. Berzofsky, Yang Xie, Tao Wang

Research output: Contribution to journalArticlepeer-review

Abstract

The ability to predict B cell epitopes is critical for biomedical research and many clinical applications. Investigators have observed the phenomenon of T-B reciprocity, in which candidate B cell epitopes with nearby CD4+ T cell epitopes have higher chances of being immunogenic. To our knowledge, existing B cell epitope prediction algorithms have not considered this interesting observation. We developed a linear B cell epitope prediction model, BepiTBR, based on T-B reciprocity. We showed that explicitly including the enrichment of putative CD4+ T cell epitopes (predicted HLA class II epitopes) in the model leads to significant enhancement in the prediction of linear B cell epitopes. Curiously, the positive impact on B cell epitope generation is specific to the enrichment of DQ allele binders. Overall, our work provides interesting mechanistic insights into the generation of B cell epitopes and points to a new avenue to improve B cell epitope prediction for the field.

Original languageEnglish (US)
Article number103764
JournaliScience
Volume25
Issue number2
DOIs
StatePublished - Feb 18 2022

Keywords

  • Bioinformatics
  • Immunology
  • Systems biology

ASJC Scopus subject areas

  • General

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