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Multi-task learning for predicting SARS-CoV-2 antibody escape
The coronavirus pandemic has revolutionized our world, with vaccination proving to be a key tool in fighting the disease. However, a major threat to this line of attack are variants that can evade the vaccine. Thus, a fundamental problem of growing importance is the identification of mutations of co...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9403730/ https://www.ncbi.nlm.nih.gov/pubmed/36035121 http://dx.doi.org/10.3389/fgene.2022.886649 |
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author | Gross, Barak Sharan, Roded |
author_facet | Gross, Barak Sharan, Roded |
author_sort | Gross, Barak |
collection | PubMed |
description | The coronavirus pandemic has revolutionized our world, with vaccination proving to be a key tool in fighting the disease. However, a major threat to this line of attack are variants that can evade the vaccine. Thus, a fundamental problem of growing importance is the identification of mutations of concern with high escape probability. In this paper we develop a computational framework that harnesses systematic mutation screens in the receptor binding domain of the viral Spike protein for escape prediction. The framework analyzes data on escape from multiple antibodies simultaneously, creating a latent representation of mutations that is shown to be effective in predicting escape and binding properties of the virus. We use this representation to validate the escape potential of current SARS-CoV-2 variants. |
format | Online Article Text |
id | pubmed-9403730 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94037302022-08-26 Multi-task learning for predicting SARS-CoV-2 antibody escape Gross, Barak Sharan, Roded Front Genet Genetics The coronavirus pandemic has revolutionized our world, with vaccination proving to be a key tool in fighting the disease. However, a major threat to this line of attack are variants that can evade the vaccine. Thus, a fundamental problem of growing importance is the identification of mutations of concern with high escape probability. In this paper we develop a computational framework that harnesses systematic mutation screens in the receptor binding domain of the viral Spike protein for escape prediction. The framework analyzes data on escape from multiple antibodies simultaneously, creating a latent representation of mutations that is shown to be effective in predicting escape and binding properties of the virus. We use this representation to validate the escape potential of current SARS-CoV-2 variants. Frontiers Media S.A. 2022-08-11 /pmc/articles/PMC9403730/ /pubmed/36035121 http://dx.doi.org/10.3389/fgene.2022.886649 Text en Copyright © 2022 Gross and Sharan. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Gross, Barak Sharan, Roded Multi-task learning for predicting SARS-CoV-2 antibody escape |
title | Multi-task learning for predicting SARS-CoV-2 antibody escape |
title_full | Multi-task learning for predicting SARS-CoV-2 antibody escape |
title_fullStr | Multi-task learning for predicting SARS-CoV-2 antibody escape |
title_full_unstemmed | Multi-task learning for predicting SARS-CoV-2 antibody escape |
title_short | Multi-task learning for predicting SARS-CoV-2 antibody escape |
title_sort | multi-task learning for predicting sars-cov-2 antibody escape |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9403730/ https://www.ncbi.nlm.nih.gov/pubmed/36035121 http://dx.doi.org/10.3389/fgene.2022.886649 |
work_keys_str_mv | AT grossbarak multitasklearningforpredictingsarscov2antibodyescape AT sharanroded multitasklearningforpredictingsarscov2antibodyescape |