Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain.
The continual evolution of SARS-CoV-2 and the emergence of variants that show resistance to vaccines and neutralizing antibodies threaten to prolong the COVID-19 pandemic. Selection and emergence of SARS-CoV-2 variants are driven in part by mutations within the viral spike protein and in particular...
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Online Access: | https://hdl.handle.net/10289/15247 https://doi.org/10.1016/j.cell.2022.08.024 |
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ftunivwaikato:oai:researchcommons.waikato.ac.nz:10289/15247 2024-02-11T10:03:22+01:00 Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain. Taft, Joseph M. Weber, Cédric R. Gao, Beichen Ehling, Roy A. Han, Jiami Frei, Lester Metcalfe, Sean W. Overath, Max D. Yermanos, Alexander Kelton, William Reddy, Sai T. United States 2022 application/pdf https://hdl.handle.net/10289/15247 https://doi.org/10.1016/j.cell.2022.08.024 eng eng Cell https://hdl.handle.net/10289/15247 doi:10.1016/j.cell.2022.08.024 1097-4172 © 2022 The Author(s). Published by Elsevier Inc. artificial intelligence deep learning deep sequencing directed evolution machine learning protein engineering viral escape yeast display Journal Article 2022 ftunivwaikato https://doi.org/10.1016/j.cell.2022.08.024 2024-01-23T18:25:37Z The continual evolution of SARS-CoV-2 and the emergence of variants that show resistance to vaccines and neutralizing antibodies threaten to prolong the COVID-19 pandemic. 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 investigate 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, thus guiding the development of therapeutic antibody treatments and vaccines for COVID-19. Article in Journal/Newspaper DML The University of Waikato: Research Commons Cell 185 21 4008 4022.e14 |
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collection |
The University of Waikato: Research Commons |
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ftunivwaikato |
language |
English |
topic |
artificial intelligence deep learning deep sequencing directed evolution machine learning protein engineering viral escape yeast display |
spellingShingle |
artificial intelligence deep learning deep sequencing directed evolution machine learning protein engineering viral escape yeast display Taft, Joseph M. Weber, Cédric R. Gao, Beichen Ehling, Roy A. Han, Jiami Frei, Lester Metcalfe, Sean W. Overath, Max D. Yermanos, Alexander Kelton, William Reddy, Sai T. Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain. |
topic_facet |
artificial intelligence deep learning deep sequencing directed evolution machine learning protein engineering viral escape yeast display |
description |
The continual evolution of SARS-CoV-2 and the emergence of variants that show resistance to vaccines and neutralizing antibodies threaten to prolong the COVID-19 pandemic. 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 investigate 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, thus guiding the development of therapeutic antibody treatments and vaccines for COVID-19. |
format |
Article in Journal/Newspaper |
author |
Taft, Joseph M. Weber, Cédric R. Gao, Beichen Ehling, Roy A. Han, Jiami Frei, Lester Metcalfe, Sean W. Overath, Max D. Yermanos, Alexander Kelton, William Reddy, Sai T. |
author_facet |
Taft, Joseph M. Weber, Cédric R. Gao, Beichen Ehling, Roy A. Han, Jiami Frei, Lester Metcalfe, Sean W. Overath, Max D. Yermanos, Alexander Kelton, William Reddy, Sai T. |
author_sort |
Taft, Joseph M. |
title |
Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain. |
title_short |
Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain. |
title_full |
Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain. |
title_fullStr |
Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain. |
title_full_unstemmed |
Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain. |
title_sort |
deep mutational learning predicts ace2 binding and antibody escape to combinatorial mutations in the sars-cov-2 receptor-binding domain. |
publishDate |
2022 |
url |
https://hdl.handle.net/10289/15247 https://doi.org/10.1016/j.cell.2022.08.024 |
op_coverage |
United States |
genre |
DML |
genre_facet |
DML |
op_relation |
Cell https://hdl.handle.net/10289/15247 doi:10.1016/j.cell.2022.08.024 1097-4172 |
op_rights |
© 2022 The Author(s). Published by Elsevier Inc. |
op_doi |
https://doi.org/10.1016/j.cell.2022.08.024 |
container_title |
Cell |
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185 |
container_issue |
21 |
container_start_page |
4008 |
op_container_end_page |
4022.e14 |
_version_ |
1790599586706882560 |