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Predicting Epitope Candidates for SARS-CoV-2
Epitopes are short amino acid sequences that define the antigen signature to which an antibody or T cell receptor binds. In light of the current pandemic, epitope analysis and prediction are paramount to improving serological testing and developing vaccines. In this paper, known epitope sequences fr...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9416013/ https://www.ncbi.nlm.nih.gov/pubmed/36016459 http://dx.doi.org/10.3390/v14081837 |
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author | Agarwal, Akshay Beck, Kristen L. Capponi, Sara Kunitomi, Mark Nayar, Gowri Seabolt, Edward Mahadeshwar, Gandhar Bianco, Simone Mukherjee, Vandana Kaufman, James H. |
author_facet | Agarwal, Akshay Beck, Kristen L. Capponi, Sara Kunitomi, Mark Nayar, Gowri Seabolt, Edward Mahadeshwar, Gandhar Bianco, Simone Mukherjee, Vandana Kaufman, James H. |
author_sort | Agarwal, Akshay |
collection | PubMed |
description | Epitopes are short amino acid sequences that define the antigen signature to which an antibody or T cell receptor binds. In light of the current pandemic, epitope analysis and prediction are paramount to improving serological testing and developing vaccines. In this paper, known epitope sequences from SARS-CoV, SARS-CoV-2, and other Coronaviridae were leveraged to identify additional antigen regions in 62K SARS-CoV-2 genomes. Additionally, we present epitope distribution across SARS-CoV-2 genomes, locate the most commonly found epitopes, and discuss where epitopes are located on proteins and how epitopes can be grouped into classes. The mutation density of different protein regions is presented using a big data approach. It was observed that there are 112 B cell and 279 T cell conserved epitopes between SARS-CoV-2 and SARS-CoV, with more diverse sequences found in Nucleoprotein and Spike glycoprotein. |
format | Online Article Text |
id | pubmed-9416013 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94160132022-08-27 Predicting Epitope Candidates for SARS-CoV-2 Agarwal, Akshay Beck, Kristen L. Capponi, Sara Kunitomi, Mark Nayar, Gowri Seabolt, Edward Mahadeshwar, Gandhar Bianco, Simone Mukherjee, Vandana Kaufman, James H. Viruses Article Epitopes are short amino acid sequences that define the antigen signature to which an antibody or T cell receptor binds. In light of the current pandemic, epitope analysis and prediction are paramount to improving serological testing and developing vaccines. In this paper, known epitope sequences from SARS-CoV, SARS-CoV-2, and other Coronaviridae were leveraged to identify additional antigen regions in 62K SARS-CoV-2 genomes. Additionally, we present epitope distribution across SARS-CoV-2 genomes, locate the most commonly found epitopes, and discuss where epitopes are located on proteins and how epitopes can be grouped into classes. The mutation density of different protein regions is presented using a big data approach. It was observed that there are 112 B cell and 279 T cell conserved epitopes between SARS-CoV-2 and SARS-CoV, with more diverse sequences found in Nucleoprotein and Spike glycoprotein. MDPI 2022-08-21 /pmc/articles/PMC9416013/ /pubmed/36016459 http://dx.doi.org/10.3390/v14081837 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Agarwal, Akshay Beck, Kristen L. Capponi, Sara Kunitomi, Mark Nayar, Gowri Seabolt, Edward Mahadeshwar, Gandhar Bianco, Simone Mukherjee, Vandana Kaufman, James H. Predicting Epitope Candidates for SARS-CoV-2 |
title | Predicting Epitope Candidates for SARS-CoV-2 |
title_full | Predicting Epitope Candidates for SARS-CoV-2 |
title_fullStr | Predicting Epitope Candidates for SARS-CoV-2 |
title_full_unstemmed | Predicting Epitope Candidates for SARS-CoV-2 |
title_short | Predicting Epitope Candidates for SARS-CoV-2 |
title_sort | predicting epitope candidates for sars-cov-2 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9416013/ https://www.ncbi.nlm.nih.gov/pubmed/36016459 http://dx.doi.org/10.3390/v14081837 |
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