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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cornell University
2022
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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. |
format | Online Article Text |
id | pubmed-9603820 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Cornell University |
record_format | MEDLINE/PubMed |
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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