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SARS-CoV-2 Omicron BA.2.75 Variant May Be Much More Infective than Preexisting Variants Based on In Silico Model
Previously, we developed a mathematical model via molecular simulation analysis to predict the infectivity of six SARS-CoV-2 variants. In this report, we aimed to predict the relative risk of the recent new variants of SARS-CoV-2 based on our previous research. We subjected Omicron BA.4/5 and BA.2.7...
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/PMC9607331/ https://www.ncbi.nlm.nih.gov/pubmed/36296366 http://dx.doi.org/10.3390/microorganisms10102090 |
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author | Sugano, Aki Takaoka, Yutaka Kataguchi, Haruyuki Ohta, Mika Kimura, Shigemi Araki, Masatake Morinaga, Yoshitomo Yamamoto, Yoshihiro |
author_facet | Sugano, Aki Takaoka, Yutaka Kataguchi, Haruyuki Ohta, Mika Kimura, Shigemi Araki, Masatake Morinaga, Yoshitomo Yamamoto, Yoshihiro |
author_sort | Sugano, Aki |
collection | PubMed |
description | Previously, we developed a mathematical model via molecular simulation analysis to predict the infectivity of six SARS-CoV-2 variants. In this report, we aimed to predict the relative risk of the recent new variants of SARS-CoV-2 based on our previous research. We subjected Omicron BA.4/5 and BA.2.75 variants of SARS-CoV-2 to the analysis to determine the evolutionary distance of the spike protein gene (S gene) of the variants from the Wuhan variant so as to appreciate the changes in the spike protein. We performed molecular docking simulation analyses of the spike proteins with human angiotensin-converting enzyme 2 (ACE2) to understand the docking affinities of these variants. We then compared the evolutionary distances and the docking affinities of these variants with those of the variants that we had analyzed in our previous research. As a result, BA.2.75 has both the highest docking affinity (ratio per Wuhan variant) and the longest evolutionary distance of the S gene from the Wuhan variant. These results suggest that BA.2.75 infection can spread farther than can infections of preexisting variants. |
format | Online Article Text |
id | pubmed-9607331 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96073312022-10-28 SARS-CoV-2 Omicron BA.2.75 Variant May Be Much More Infective than Preexisting Variants Based on In Silico Model Sugano, Aki Takaoka, Yutaka Kataguchi, Haruyuki Ohta, Mika Kimura, Shigemi Araki, Masatake Morinaga, Yoshitomo Yamamoto, Yoshihiro Microorganisms Brief Report Previously, we developed a mathematical model via molecular simulation analysis to predict the infectivity of six SARS-CoV-2 variants. In this report, we aimed to predict the relative risk of the recent new variants of SARS-CoV-2 based on our previous research. We subjected Omicron BA.4/5 and BA.2.75 variants of SARS-CoV-2 to the analysis to determine the evolutionary distance of the spike protein gene (S gene) of the variants from the Wuhan variant so as to appreciate the changes in the spike protein. We performed molecular docking simulation analyses of the spike proteins with human angiotensin-converting enzyme 2 (ACE2) to understand the docking affinities of these variants. We then compared the evolutionary distances and the docking affinities of these variants with those of the variants that we had analyzed in our previous research. As a result, BA.2.75 has both the highest docking affinity (ratio per Wuhan variant) and the longest evolutionary distance of the S gene from the Wuhan variant. These results suggest that BA.2.75 infection can spread farther than can infections of preexisting variants. MDPI 2022-10-21 /pmc/articles/PMC9607331/ /pubmed/36296366 http://dx.doi.org/10.3390/microorganisms10102090 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 | Brief Report Sugano, Aki Takaoka, Yutaka Kataguchi, Haruyuki Ohta, Mika Kimura, Shigemi Araki, Masatake Morinaga, Yoshitomo Yamamoto, Yoshihiro SARS-CoV-2 Omicron BA.2.75 Variant May Be Much More Infective than Preexisting Variants Based on In Silico Model |
title | SARS-CoV-2 Omicron BA.2.75 Variant May Be Much More Infective than Preexisting Variants Based on In Silico Model |
title_full | SARS-CoV-2 Omicron BA.2.75 Variant May Be Much More Infective than Preexisting Variants Based on In Silico Model |
title_fullStr | SARS-CoV-2 Omicron BA.2.75 Variant May Be Much More Infective than Preexisting Variants Based on In Silico Model |
title_full_unstemmed | SARS-CoV-2 Omicron BA.2.75 Variant May Be Much More Infective than Preexisting Variants Based on In Silico Model |
title_short | SARS-CoV-2 Omicron BA.2.75 Variant May Be Much More Infective than Preexisting Variants Based on In Silico Model |
title_sort | sars-cov-2 omicron ba.2.75 variant may be much more infective than preexisting variants based on in silico model |
topic | Brief Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9607331/ https://www.ncbi.nlm.nih.gov/pubmed/36296366 http://dx.doi.org/10.3390/microorganisms10102090 |
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