دورية أكاديمية
Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain.
| العنوان: | Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain. |
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| المؤلفون: | Taft JM; Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland; Botnar Research Centre for Child Health, Basel 4058, Switzerland., Weber CR; Alloy Therapeutics (Switzerland) AG, Basel 4058, Switzerland., Gao B; Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland; Botnar Research Centre for Child Health, Basel 4058, Switzerland., Ehling RA; Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland., Han J; Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland; Botnar Research Centre for Child Health, Basel 4058, Switzerland., Frei L; Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland; Botnar Research Centre for Child Health, Basel 4058, Switzerland., Metcalfe SW; Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland., Overath MD; Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland., Yermanos A; Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland; Botnar Research Centre for Child Health, Basel 4058, Switzerland; Department of Biology, Institute of Microbiology and Immunology, ETH Zurich, Zurich 8093, Switzerland; Department of Pathology and Immunology, University of Geneva, Geneva 1211, Switzerland., Kelton W; Te Huataki Waiora School of Health, University of Waikato, Hamilton 3240, New Zealand., Reddy ST; Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland; Botnar Research Centre for Child Health, Basel 4058, Switzerland. Electronic address: sai.reddy@ethz.ch. |
| المصدر: | Cell [Cell] 2022 Oct 13; Vol. 185 (21), pp. 4008-4022.e14. Date of Electronic Publication: 2022 Aug 31. |
| نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't |
| اللغة: | English |
| بيانات الدورية: | Publisher: Cell Press Country of Publication: United States NLM ID: 0413066 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-4172 (Electronic) Linking ISSN: 00928674 NLM ISO Abbreviation: Cell Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: Cambridge, Ma : Cell Press Original Publication: Cambridge, MIT Press. |
| مواضيع طبية MeSH: | COVID-19* , SARS-CoV-2*/genetics, Angiotensin-Converting Enzyme 2/*metabolism , Spike Glycoprotein, Coronavirus/*metabolism, Angiotensin-Converting Enzyme 2/chemistry ; Angiotensin-Converting Enzyme 2/genetics ; Antibodies, Neutralizing ; Antibodies, Viral ; COVID-19 Vaccines ; Humans ; Mutation ; Pandemics ; Protein Binding ; Spike Glycoprotein, Coronavirus/chemistry ; Spike Glycoprotein, Coronavirus/genetics |
| مستخلص: | 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. Competing Interests: Declaration of interests ETH Zurich has filed for patent protection on the technology described herein, and J.M.T., C.R.W., B.G., R.A.E., and S.T.R. are named as co-inventors. C.R.W. is an employee of Alloy Therapeutics (Switzerland) AG. C.R.W. and S.T.R. may hold shares of Alloy Therapeutics. S.T.R. is on the scientific advisory board of Alloy Therapeutics. (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.) |
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| فهرسة مساهمة: | Keywords: artificial intelligence; deep learning; deep sequencing; directed evolution; machine learning; protein engineering; viral escape; yeast display |
| المشرفين على المادة: | 0 (Antibodies, Neutralizing) 0 (Antibodies, Viral) 0 (COVID-19 Vaccines) 0 (Spike Glycoprotein, Coronavirus) 0 (spike protein, SARS-CoV-2) EC 3.4.17.23 (Angiotensin-Converting Enzyme 2) |
| SCR Organism: | SARS-CoV-2 variants |
| تواريخ الأحداث: | Date Created: 20220923 Date Completed: 20221018 Latest Revision: 20240906 |
| رمز التحديث: | 20240906 |
| مُعرف محوري في PubMed: | PMC9428596 |
| DOI: | 10.1016/j.cell.2022.08.024 |
| PMID: | 36150393 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 1097-4172 |
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| DOI: | 10.1016/j.cell.2022.08.024 |