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Emerging dominant SARS-CoV-2 variants

Accurate and reliable forecasting of emerging dominant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants enables policymakers and vaccine makers to get prepared for future waves of infections. The last three waves of SARS-CoV-2 infections caused by dominant variants Omicron (BA.1...

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Autores principales: Chen, Jiahui, Wang, Rui, Hozumi, Yuta, Liu, Gengzhuo, Qiu, Yuchi, Wei, Xiaoqi, Wei, Guo-Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cornell University 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9603820/
https://www.ncbi.nlm.nih.gov/pubmed/36299737
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author Chen, Jiahui
Wang, Rui
Hozumi, Yuta
Liu, Gengzhuo
Qiu, Yuchi
Wei, Xiaoqi
Wei, Guo-Wei
author_facet Chen, Jiahui
Wang, Rui
Hozumi, Yuta
Liu, Gengzhuo
Qiu, Yuchi
Wei, Xiaoqi
Wei, Guo-Wei
author_sort Chen, Jiahui
collection PubMed
description Accurate and reliable forecasting of emerging dominant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants enables policymakers and vaccine makers to get prepared for future waves of infections. The last three waves of SARS-CoV-2 infections caused by dominant variants Omicron (BA.1), BA.2, and BA.4/BA.5 were accurately foretold by our artificial intelligence (AI) models built with biophysics, genotyping of viral genomes, experimental data, algebraic topology, and deep learning. Based on newly available experimental data, we analyzed the impacts of all possible viral spike (S) protein receptor-binding domain (RBD) mutations on the SARS-CoV-2 infectivity. Our analysis sheds light on viral evolutionary mechanisms, i.e., natural selection through infectivity strengthening and antibody resistance. We forecast that BA.2.10.4, BA.2.75, BQ.1.1, and particularly, BA.2.75+R346T, have high potential to become new dominant variants to drive the next surge.
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spelling pubmed-96038202022-10-27 Emerging dominant SARS-CoV-2 variants Chen, Jiahui Wang, Rui Hozumi, Yuta Liu, Gengzhuo Qiu, Yuchi Wei, Xiaoqi Wei, Guo-Wei ArXiv Article Accurate and reliable forecasting of emerging dominant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants enables policymakers and vaccine makers to get prepared for future waves of infections. The last three waves of SARS-CoV-2 infections caused by dominant variants Omicron (BA.1), BA.2, and BA.4/BA.5 were accurately foretold by our artificial intelligence (AI) models built with biophysics, genotyping of viral genomes, experimental data, algebraic topology, and deep learning. Based on newly available experimental data, we analyzed the impacts of all possible viral spike (S) protein receptor-binding domain (RBD) mutations on the SARS-CoV-2 infectivity. Our analysis sheds light on viral evolutionary mechanisms, i.e., natural selection through infectivity strengthening and antibody resistance. We forecast that BA.2.10.4, BA.2.75, BQ.1.1, and particularly, BA.2.75+R346T, have high potential to become new dominant variants to drive the next surge. Cornell University 2022-10-18 /pmc/articles/PMC9603820/ /pubmed/36299737 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Chen, Jiahui
Wang, Rui
Hozumi, Yuta
Liu, Gengzhuo
Qiu, Yuchi
Wei, Xiaoqi
Wei, Guo-Wei
Emerging dominant SARS-CoV-2 variants
title Emerging dominant SARS-CoV-2 variants
title_full Emerging dominant SARS-CoV-2 variants
title_fullStr Emerging dominant SARS-CoV-2 variants
title_full_unstemmed Emerging dominant SARS-CoV-2 variants
title_short Emerging dominant SARS-CoV-2 variants
title_sort emerging dominant sars-cov-2 variants
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9603820/
https://www.ncbi.nlm.nih.gov/pubmed/36299737
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