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BepiTBR: T-B reciprocity enhances B cell epitope prediction

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,...

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Detalles Bibliográficos
Autores principales: Zhu, James, Gouru, Anagha, Wu, Fangjiang, Berzofsky, Jay A., Xie, Yang, Wang, Tao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8803616/
https://www.ncbi.nlm.nih.gov/pubmed/35128358
http://dx.doi.org/10.1016/j.isci.2022.103764
Descripción
Sumario: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.