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Bioinformatic prediction of potential T cell epitopes for SARS-Cov-2
To control and prevent the current COVID-19 pandemic, the development of novel vaccines is an emergent issue. In addition, we need to develop tools that can measure/monitor T-cell and B-cell responses to know how our immune system is responding to this deleterious virus. However, little information...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Singapore
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7200206/ https://www.ncbi.nlm.nih.gov/pubmed/32372051 http://dx.doi.org/10.1038/s10038-020-0771-5 |
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author | Kiyotani, Kazuma Toyoshima, Yujiro Nemoto, Kensaku Nakamura, Yusuke |
author_facet | Kiyotani, Kazuma Toyoshima, Yujiro Nemoto, Kensaku Nakamura, Yusuke |
author_sort | Kiyotani, Kazuma |
collection | PubMed |
description | To control and prevent the current COVID-19 pandemic, the development of novel vaccines is an emergent issue. In addition, we need to develop tools that can measure/monitor T-cell and B-cell responses to know how our immune system is responding to this deleterious virus. However, little information is currently available about the immune target epitopes of novel coronavirus (SARS-CoV-2) to induce host immune responses. Through a comprehensive bioinformatic screening of potential epitopes derived from the SARS-CoV-2 sequences for HLAs commonly present in the Japanese population, we identified 2013 and 1399 possible peptide epitopes that are likely to have the high affinity (<0.5%- and 2%-rank, respectively) to HLA class I and II molecules, respectively, that may induce CD8(+) and CD4(+) T-cell responses. These epitopes distributed across the structural (spike, envelope, membrane, and nucleocapsid proteins) and the nonstructural proteins (proteins corresponding to six open reading frames); however, we found several regions where high-affinity epitopes were significantly enriched. By comparing the sequences of these predicted T cell epitopes to the other coronaviruses, we identified 781 HLA-class I and 418 HLA-class II epitopes that have high homologies to SARS-CoV. To further select commonly-available epitopes that would be applicable to larger populations, we calculated population coverages based on the allele frequencies of HLA molecules, and found 2 HLA-class I epitopes covering 83.8% of the Japanese population. The findings in the current study provide us valuable information to design widely-available vaccine epitopes against SARS-CoV-2 and also provide the useful information for monitoring T-cell responses. |
format | Online Article Text |
id | pubmed-7200206 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer Singapore |
record_format | MEDLINE/PubMed |
spelling | pubmed-72002062020-05-06 Bioinformatic prediction of potential T cell epitopes for SARS-Cov-2 Kiyotani, Kazuma Toyoshima, Yujiro Nemoto, Kensaku Nakamura, Yusuke J Hum Genet Article To control and prevent the current COVID-19 pandemic, the development of novel vaccines is an emergent issue. In addition, we need to develop tools that can measure/monitor T-cell and B-cell responses to know how our immune system is responding to this deleterious virus. However, little information is currently available about the immune target epitopes of novel coronavirus (SARS-CoV-2) to induce host immune responses. Through a comprehensive bioinformatic screening of potential epitopes derived from the SARS-CoV-2 sequences for HLAs commonly present in the Japanese population, we identified 2013 and 1399 possible peptide epitopes that are likely to have the high affinity (<0.5%- and 2%-rank, respectively) to HLA class I and II molecules, respectively, that may induce CD8(+) and CD4(+) T-cell responses. These epitopes distributed across the structural (spike, envelope, membrane, and nucleocapsid proteins) and the nonstructural proteins (proteins corresponding to six open reading frames); however, we found several regions where high-affinity epitopes were significantly enriched. By comparing the sequences of these predicted T cell epitopes to the other coronaviruses, we identified 781 HLA-class I and 418 HLA-class II epitopes that have high homologies to SARS-CoV. To further select commonly-available epitopes that would be applicable to larger populations, we calculated population coverages based on the allele frequencies of HLA molecules, and found 2 HLA-class I epitopes covering 83.8% of the Japanese population. The findings in the current study provide us valuable information to design widely-available vaccine epitopes against SARS-CoV-2 and also provide the useful information for monitoring T-cell responses. Springer Singapore 2020-05-06 2020 /pmc/articles/PMC7200206/ /pubmed/32372051 http://dx.doi.org/10.1038/s10038-020-0771-5 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Kiyotani, Kazuma Toyoshima, Yujiro Nemoto, Kensaku Nakamura, Yusuke Bioinformatic prediction of potential T cell epitopes for SARS-Cov-2 |
title | Bioinformatic prediction of potential T cell epitopes for SARS-Cov-2 |
title_full | Bioinformatic prediction of potential T cell epitopes for SARS-Cov-2 |
title_fullStr | Bioinformatic prediction of potential T cell epitopes for SARS-Cov-2 |
title_full_unstemmed | Bioinformatic prediction of potential T cell epitopes for SARS-Cov-2 |
title_short | Bioinformatic prediction of potential T cell epitopes for SARS-Cov-2 |
title_sort | bioinformatic prediction of potential t cell epitopes for sars-cov-2 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7200206/ https://www.ncbi.nlm.nih.gov/pubmed/32372051 http://dx.doi.org/10.1038/s10038-020-0771-5 |
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