Deep learning-guided selection of antibody therapies with enhanced resistance to current and prospective SARS-CoV-2 Omicron variants ...
Most COVID-19 antibody therapies rely on binding the SARS-CoV-2 receptor binding domain (RBD). However, heavily mutated variants such as Omicron and its sublineages, which are characterized by an ever increasing number of mutations in the RBD, have rendered prior antibody therapies ineffective, leav...
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ftdatacite:10.3929/ethz-b-000652262 2024-04-28T08:17:08+00:00 Deep learning-guided selection of antibody therapies with enhanced resistance to current and prospective SARS-CoV-2 Omicron variants ... Frei, Lester Gao, Beichen Han, Jiami Taft, Joseph M. Irvine, Edward B. Weber, Cédric Kumar, Rachita Eisinger, Benedikt N. Reddy, Sai T. 2023 application/pdf https://dx.doi.org/10.3929/ethz-b-000652262 http://hdl.handle.net/20.500.11850/652262 en eng ETH Zurich article-journal Text ScholarlyArticle Working Paper 2023 ftdatacite https://doi.org/10.3929/ethz-b-000652262 2024-04-02T12:32:08Z Most COVID-19 antibody therapies rely on binding the SARS-CoV-2 receptor binding domain (RBD). However, heavily mutated variants such as Omicron and its sublineages, which are characterized by an ever increasing number of mutations in the RBD, have rendered prior antibody therapies ineffective, leaving no clinically approved antibody treatments for SARS-CoV-2. Therefore, the capacity of therapeutic antibody candidates to bind and neutralize current and prospective SARS-CoV-2 variants is a critical factor for drug development. Here, we present a deep learning-guided approach to identify antibodies with enhanced resistance to SARS-CoV-2 evolution. We apply deep mutational learning (DML), a machine learning-guided protein engineering method to interrogate a massive sequence space of combinatorial RBD mutations and predict their impact on angiotensin-converting enzyme 2 (ACE2) binding and antibody escape. A high mutational distance library was constructed based on the full-length RBD of Omicron BA.1, which was ... : bioRxiv ... Text DML DataCite Metadata Store (German National Library of Science and Technology) |
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Most COVID-19 antibody therapies rely on binding the SARS-CoV-2 receptor binding domain (RBD). However, heavily mutated variants such as Omicron and its sublineages, which are characterized by an ever increasing number of mutations in the RBD, have rendered prior antibody therapies ineffective, leaving no clinically approved antibody treatments for SARS-CoV-2. Therefore, the capacity of therapeutic antibody candidates to bind and neutralize current and prospective SARS-CoV-2 variants is a critical factor for drug development. Here, we present a deep learning-guided approach to identify antibodies with enhanced resistance to SARS-CoV-2 evolution. We apply deep mutational learning (DML), a machine learning-guided protein engineering method to interrogate a massive sequence space of combinatorial RBD mutations and predict their impact on angiotensin-converting enzyme 2 (ACE2) binding and antibody escape. A high mutational distance library was constructed based on the full-length RBD of Omicron BA.1, which was ... : bioRxiv ... |
format |
Text |
author |
Frei, Lester Gao, Beichen Han, Jiami Taft, Joseph M. Irvine, Edward B. Weber, Cédric Kumar, Rachita Eisinger, Benedikt N. Reddy, Sai T. |
spellingShingle |
Frei, Lester Gao, Beichen Han, Jiami Taft, Joseph M. Irvine, Edward B. Weber, Cédric Kumar, Rachita Eisinger, Benedikt N. Reddy, Sai T. Deep learning-guided selection of antibody therapies with enhanced resistance to current and prospective SARS-CoV-2 Omicron variants ... |
author_facet |
Frei, Lester Gao, Beichen Han, Jiami Taft, Joseph M. Irvine, Edward B. Weber, Cédric Kumar, Rachita Eisinger, Benedikt N. Reddy, Sai T. |
author_sort |
Frei, Lester |
title |
Deep learning-guided selection of antibody therapies with enhanced resistance to current and prospective SARS-CoV-2 Omicron variants ... |
title_short |
Deep learning-guided selection of antibody therapies with enhanced resistance to current and prospective SARS-CoV-2 Omicron variants ... |
title_full |
Deep learning-guided selection of antibody therapies with enhanced resistance to current and prospective SARS-CoV-2 Omicron variants ... |
title_fullStr |
Deep learning-guided selection of antibody therapies with enhanced resistance to current and prospective SARS-CoV-2 Omicron variants ... |
title_full_unstemmed |
Deep learning-guided selection of antibody therapies with enhanced resistance to current and prospective SARS-CoV-2 Omicron variants ... |
title_sort |
deep learning-guided selection of antibody therapies with enhanced resistance to current and prospective sars-cov-2 omicron variants ... |
publisher |
ETH Zurich |
publishDate |
2023 |
url |
https://dx.doi.org/10.3929/ethz-b-000652262 http://hdl.handle.net/20.500.11850/652262 |
genre |
DML |
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DML |
op_doi |
https://doi.org/10.3929/ethz-b-000652262 |
_version_ |
1797581907071860736 |