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Immunoinformatic identification of B cell and T cell epitopes in the SARS-CoV-2 proteome
A novel coronavirus (SARS-CoV-2) emerged from China in late 2019 and rapidly spread across the globe, infecting millions of people and generating societal disruption on a level not seen since the 1918 influenza pandemic. A safe and effective vaccine is desperately needed to prevent the continued spr...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7447814/ https://www.ncbi.nlm.nih.gov/pubmed/32843695 http://dx.doi.org/10.1038/s41598-020-70864-8 |
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author | Crooke, Stephen N. Ovsyannikova, Inna G. Kennedy, Richard B. Poland, Gregory A. |
author_facet | Crooke, Stephen N. Ovsyannikova, Inna G. Kennedy, Richard B. Poland, Gregory A. |
author_sort | Crooke, Stephen N. |
collection | PubMed |
description | A novel coronavirus (SARS-CoV-2) emerged from China in late 2019 and rapidly spread across the globe, infecting millions of people and generating societal disruption on a level not seen since the 1918 influenza pandemic. A safe and effective vaccine is desperately needed to prevent the continued spread of SARS-CoV-2; yet, rational vaccine design efforts are currently hampered by the lack of knowledge regarding viral epitopes targeted during an immune response, and the need for more in-depth knowledge on betacoronavirus immunology. To that end, we developed a computational workflow using a series of open-source algorithms and webtools to analyze the proteome of SARS-CoV-2 and identify putative T cell and B cell epitopes. Utilizing a set of stringent selection criteria to filter peptide epitopes, we identified 41 T cell epitopes (5 HLA class I, 36 HLA class II) and 6 B cell epitopes that could serve as promising targets for peptide-based vaccine development against this emerging global pathogen. To our knowledge, this is the first study to comprehensively analyze all 10 (structural, non-structural and accessory) proteins from SARS-CoV-2 using predictive algorithms to identify potential targets for vaccine development. |
format | Online Article Text |
id | pubmed-7447814 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-74478142020-08-26 Immunoinformatic identification of B cell and T cell epitopes in the SARS-CoV-2 proteome Crooke, Stephen N. Ovsyannikova, Inna G. Kennedy, Richard B. Poland, Gregory A. Sci Rep Article A novel coronavirus (SARS-CoV-2) emerged from China in late 2019 and rapidly spread across the globe, infecting millions of people and generating societal disruption on a level not seen since the 1918 influenza pandemic. A safe and effective vaccine is desperately needed to prevent the continued spread of SARS-CoV-2; yet, rational vaccine design efforts are currently hampered by the lack of knowledge regarding viral epitopes targeted during an immune response, and the need for more in-depth knowledge on betacoronavirus immunology. To that end, we developed a computational workflow using a series of open-source algorithms and webtools to analyze the proteome of SARS-CoV-2 and identify putative T cell and B cell epitopes. Utilizing a set of stringent selection criteria to filter peptide epitopes, we identified 41 T cell epitopes (5 HLA class I, 36 HLA class II) and 6 B cell epitopes that could serve as promising targets for peptide-based vaccine development against this emerging global pathogen. To our knowledge, this is the first study to comprehensively analyze all 10 (structural, non-structural and accessory) proteins from SARS-CoV-2 using predictive algorithms to identify potential targets for vaccine development. Nature Publishing Group UK 2020-08-25 /pmc/articles/PMC7447814/ /pubmed/32843695 http://dx.doi.org/10.1038/s41598-020-70864-8 Text en © The Author(s) 2020 Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Crooke, Stephen N. Ovsyannikova, Inna G. Kennedy, Richard B. Poland, Gregory A. Immunoinformatic identification of B cell and T cell epitopes in the SARS-CoV-2 proteome |
title | Immunoinformatic identification of B cell and T cell epitopes in the SARS-CoV-2 proteome |
title_full | Immunoinformatic identification of B cell and T cell epitopes in the SARS-CoV-2 proteome |
title_fullStr | Immunoinformatic identification of B cell and T cell epitopes in the SARS-CoV-2 proteome |
title_full_unstemmed | Immunoinformatic identification of B cell and T cell epitopes in the SARS-CoV-2 proteome |
title_short | Immunoinformatic identification of B cell and T cell epitopes in the SARS-CoV-2 proteome |
title_sort | immunoinformatic identification of b cell and t cell epitopes in the sars-cov-2 proteome |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7447814/ https://www.ncbi.nlm.nih.gov/pubmed/32843695 http://dx.doi.org/10.1038/s41598-020-70864-8 |
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