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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
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author Zhu, James
Gouru, Anagha
Wu, Fangjiang
Berzofsky, Jay A.
Xie, Yang
Wang, Tao
author_facet Zhu, James
Gouru, Anagha
Wu, Fangjiang
Berzofsky, Jay A.
Xie, Yang
Wang, Tao
author_sort Zhu, James
collection PubMed
description 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.
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spelling pubmed-88036162022-02-04 BepiTBR: T-B reciprocity enhances B cell epitope prediction Zhu, James Gouru, Anagha Wu, Fangjiang Berzofsky, Jay A. Xie, Yang Wang, Tao iScience Article 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. Elsevier 2022-01-12 /pmc/articles/PMC8803616/ /pubmed/35128358 http://dx.doi.org/10.1016/j.isci.2022.103764 Text en © 2022. https://creativecommons.org/licenses/by-nc-nd/3.0/igo/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/igo/).
spellingShingle Article
Zhu, James
Gouru, Anagha
Wu, Fangjiang
Berzofsky, Jay A.
Xie, Yang
Wang, Tao
BepiTBR: T-B reciprocity enhances B cell epitope prediction
title BepiTBR: T-B reciprocity enhances B cell epitope prediction
title_full BepiTBR: T-B reciprocity enhances B cell epitope prediction
title_fullStr BepiTBR: T-B reciprocity enhances B cell epitope prediction
title_full_unstemmed BepiTBR: T-B reciprocity enhances B cell epitope prediction
title_short BepiTBR: T-B reciprocity enhances B cell epitope prediction
title_sort bepitbr: t-b reciprocity enhances b cell epitope prediction
topic Article
url 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
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