BepiTBR: T-B reciprocity enhances B cell epitope prediction

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

doi:10.1016/j.isci.2022.103764

BepiTBR: T-B reciprocity enhances B cell epitope prediction

iScience. 2022 Jan 12;25(2):103764. doi: 10.1016/j.isci.2022.103764. eCollection 2022 Feb 18.

Authors

James Zhu¹, Anagha Gouru¹, Fangjiang Wu¹, Jay A Berzofsky², Yang Xie^{1

3}, Tao Wang^{1

4}

Affiliations

¹ Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
² Vaccine Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA.
³ Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
⁴ Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.

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.

Keywords: Bioinformatics; Immunology; Systems biology.

Abstract

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