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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ftethz:oai:www.research-collection.ethz.ch:20.500.11850/578199 2024-02-27T08:39:59+00:00 Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain Taft, Joseph M. Weber, Close Cédric R. Gao, Beichen Ehling, Roy A. Han, Jiami Frei, Lester Metcalfe, Sean W. Overath, Max D. Yermanos, Alexander Kelton, William Reddy, Sai T. 2022-10-13 application/application/pdf https://hdl.handle.net/20.500.11850/578199 https://doi.org/10.3929/ethz-b-000578199 en eng Elsevier info:eu-repo/semantics/altIdentifier/doi/10.1016/j.cell.2022.08.024 info:eu-repo/semantics/altIdentifier/wos/000880437100004 http://hdl.handle.net/20.500.11850/578199 doi:10.3929/ethz-b-000578199 info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc/4.0/ Creative Commons Attribution-NonCommercial 4.0 International Cell, 185 (21) directed evolution protein engineering machine learning deep learning artificial intelligence viral escape deep sequencing yeast display info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2022 ftethz https://doi.org/20.500.11850/57819910.3929/ethz-b-00057819910.1016/j.cell.2022.08.024 2024-01-29T00:51:16Z 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. ISSN:0092-8674 ISSN:1097-4172 Article in Journal/Newspaper DML ETH Zürich Research Collection |
institution |
Open Polar |
collection |
ETH Zürich Research Collection |
op_collection_id |
ftethz |
language |
English |
topic |
directed evolution protein engineering machine learning deep learning artificial intelligence viral escape deep sequencing yeast display |
spellingShingle |
directed evolution protein engineering machine learning deep learning artificial intelligence viral escape deep sequencing yeast display Taft, Joseph M. Weber, Close 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 |
directed evolution protein engineering machine learning deep learning artificial intelligence viral escape deep sequencing 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. ISSN:0092-8674 ISSN:1097-4172 |
format |
Article in Journal/Newspaper |
author |
Taft, Joseph M. Weber, Close 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, Close 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 |
publisher |
Elsevier |
publishDate |
2022 |
url |
https://hdl.handle.net/20.500.11850/578199 https://doi.org/10.3929/ethz-b-000578199 |
genre |
DML |
genre_facet |
DML |
op_source |
Cell, 185 (21) |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.cell.2022.08.024 info:eu-repo/semantics/altIdentifier/wos/000880437100004 http://hdl.handle.net/20.500.11850/578199 doi:10.3929/ethz-b-000578199 |
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/20.500.11850/57819910.3929/ethz-b-00057819910.1016/j.cell.2022.08.024 |
_version_ |
1792047074006532096 |