Predictive profiling of SARS-CoV-2 variants by deep mutational learning
The continual evolution of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and the emergence of variants that show resistance to vaccines and neutralizing antibodies (1–4) threaten to prolong the coronavirus disease 2019 (COVID-19) pandemic (5). Selection and emergence of SARS-CoV-2...
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Cold Spring Harbor Laboratory
2021
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ftethz:oai:www.research-collection.ethz.ch:20.500.11850/528562 2024-02-27T08:39:59+00:00 Predictive profiling of SARS-CoV-2 variants by deep mutational learning Taft, Joseph M. Weber, Cédric id_orcid:0 000-0003-4802-8996 Gao, Beichen Ehling, Roy A. Han, Jiami Frei, Lester Metcalfe, Sean id_orcid:0 000-0003-0165-2425 Yermanos, Alexander Kelton, William Reddy, Sai T. 2021-12-09 application/application/pdf https://hdl.handle.net/20.500.11850/528562 https://doi.org/10.3929/ethz-b-000528562 en eng Cold Spring Harbor Laboratory info:eu-repo/semantics/altIdentifier/doi/10.1101/2021.12.07.471580 http://hdl.handle.net/20.500.11850/528562 doi:10.3929/ethz-b-000528562 info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc/4.0/ Creative Commons Attribution-NonCommercial 4.0 International bioRxiv info:eu-repo/semantics/workingPaper 2021 ftethz https://doi.org/20.500.11850/52856210.3929/ethz-b-00052856210.1101/2021.12.07.471580 2024-01-29T00:51:04Z The continual evolution of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and the emergence of variants that show resistance to vaccines and neutralizing antibodies (1–4) threaten to prolong the coronavirus disease 2019 (COVID-19) pandemic (5). Selection and emergence of SARS-CoV-2 variants are driven in part by mutations within the viral spike protein and in particular the ACE2 receptor-binding domain (RBD), a primary target site for neutralizing antibodies. Here, we develop deep mutational learning (DML), a machine learning-guided protein engineering technology, which is used to interrogate a massive sequence space of combinatorial mutations, representing billions of RBD variants, by accurately predicting their impact on ACE2 binding and antibody escape. A highly diverse landscape of possible SARS-CoV-2 variants is identified that could emerge from a multitude of evolutionary trajectories. DML may be used for predictive profiling on current and prospective variants, including highly mutated variants such as omicron (B.1.1.529), thus supporting decision making for public heath as well as guiding the development of therapeutic antibody treatments and vaccines for COVID-19. Report DML ETH Zürich Research Collection |
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ETH Zürich Research Collection |
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ftethz |
language |
English |
description |
The continual evolution of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and the emergence of variants that show resistance to vaccines and neutralizing antibodies (1–4) threaten to prolong the coronavirus disease 2019 (COVID-19) pandemic (5). Selection and emergence of SARS-CoV-2 variants are driven in part by mutations within the viral spike protein and in particular the ACE2 receptor-binding domain (RBD), a primary target site for neutralizing antibodies. Here, we develop deep mutational learning (DML), a machine learning-guided protein engineering technology, which is used to interrogate a massive sequence space of combinatorial mutations, representing billions of RBD variants, by accurately predicting their impact on ACE2 binding and antibody escape. A highly diverse landscape of possible SARS-CoV-2 variants is identified that could emerge from a multitude of evolutionary trajectories. DML may be used for predictive profiling on current and prospective variants, including highly mutated variants such as omicron (B.1.1.529), thus supporting decision making for public heath as well as guiding the development of therapeutic antibody treatments and vaccines for COVID-19. |
format |
Report |
author |
Taft, Joseph M. Weber, Cédric id_orcid:0 000-0003-4802-8996 Gao, Beichen Ehling, Roy A. Han, Jiami Frei, Lester Metcalfe, Sean id_orcid:0 000-0003-0165-2425 Yermanos, Alexander Kelton, William Reddy, Sai T. |
spellingShingle |
Taft, Joseph M. Weber, Cédric id_orcid:0 000-0003-4802-8996 Gao, Beichen Ehling, Roy A. Han, Jiami Frei, Lester Metcalfe, Sean id_orcid:0 000-0003-0165-2425 Yermanos, Alexander Kelton, William Reddy, Sai T. Predictive profiling of SARS-CoV-2 variants by deep mutational learning |
author_facet |
Taft, Joseph M. Weber, Cédric id_orcid:0 000-0003-4802-8996 Gao, Beichen Ehling, Roy A. Han, Jiami Frei, Lester Metcalfe, Sean id_orcid:0 000-0003-0165-2425 Yermanos, Alexander Kelton, William Reddy, Sai T. |
author_sort |
Taft, Joseph M. |
title |
Predictive profiling of SARS-CoV-2 variants by deep mutational learning |
title_short |
Predictive profiling of SARS-CoV-2 variants by deep mutational learning |
title_full |
Predictive profiling of SARS-CoV-2 variants by deep mutational learning |
title_fullStr |
Predictive profiling of SARS-CoV-2 variants by deep mutational learning |
title_full_unstemmed |
Predictive profiling of SARS-CoV-2 variants by deep mutational learning |
title_sort |
predictive profiling of sars-cov-2 variants by deep mutational learning |
publisher |
Cold Spring Harbor Laboratory |
publishDate |
2021 |
url |
https://hdl.handle.net/20.500.11850/528562 https://doi.org/10.3929/ethz-b-000528562 |
genre |
DML |
genre_facet |
DML |
op_source |
bioRxiv |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1101/2021.12.07.471580 http://hdl.handle.net/20.500.11850/528562 doi:10.3929/ethz-b-000528562 |
op_rights |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc/4.0/ Creative Commons Attribution-NonCommercial 4.0 International |
op_doi |
https://doi.org/20.500.11850/52856210.3929/ethz-b-00052856210.1101/2021.12.07.471580 |
_version_ |
1792047073810448384 |