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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...

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Detalles Bibliográficos
Autores principales: Gross, Barak, Sharan, Roded
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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.
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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
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