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Mutational analysis of SARS-CoV-2 variants of concern reveals key tradeoffs between receptor affinity and antibody escape

SARS-CoV-2 variants with enhanced transmissibility represent a serious threat to global health. Here we report machine learning models that can predict the impact of receptor-binding domain (RBD) mutations on receptor (ACE2) affinity, which is linked to infectivity, and escape from human serum antib...

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Autores principales: Makowski, Emily K., Schardt, John S., Smith, Matthew D., Tessier, Peter M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9223403/
https://www.ncbi.nlm.nih.gov/pubmed/35639784
http://dx.doi.org/10.1371/journal.pcbi.1010160
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author Makowski, Emily K.
Schardt, John S.
Smith, Matthew D.
Tessier, Peter M.
author_facet Makowski, Emily K.
Schardt, John S.
Smith, Matthew D.
Tessier, Peter M.
author_sort Makowski, Emily K.
collection PubMed
description SARS-CoV-2 variants with enhanced transmissibility represent a serious threat to global health. Here we report machine learning models that can predict the impact of receptor-binding domain (RBD) mutations on receptor (ACE2) affinity, which is linked to infectivity, and escape from human serum antibodies, which is linked to viral neutralization. Importantly, the models predict many of the known impacts of RBD mutations in current and former Variants of Concern on receptor affinity and antibody escape as well as novel sets of mutations that strongly modulate both properties. Moreover, these models reveal key opposing impacts of RBD mutations on transmissibility, as many sets of RBD mutations predicted to increase antibody escape are also predicted to reduce receptor affinity and vice versa. These models, when used in concert, capture the complex impacts of SARS-CoV-2 mutations on properties linked to transmissibility and are expected to improve the development of next-generation vaccines and biotherapeutics.
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spelling pubmed-92234032022-06-24 Mutational analysis of SARS-CoV-2 variants of concern reveals key tradeoffs between receptor affinity and antibody escape Makowski, Emily K. Schardt, John S. Smith, Matthew D. Tessier, Peter M. PLoS Comput Biol Research Article SARS-CoV-2 variants with enhanced transmissibility represent a serious threat to global health. Here we report machine learning models that can predict the impact of receptor-binding domain (RBD) mutations on receptor (ACE2) affinity, which is linked to infectivity, and escape from human serum antibodies, which is linked to viral neutralization. Importantly, the models predict many of the known impacts of RBD mutations in current and former Variants of Concern on receptor affinity and antibody escape as well as novel sets of mutations that strongly modulate both properties. Moreover, these models reveal key opposing impacts of RBD mutations on transmissibility, as many sets of RBD mutations predicted to increase antibody escape are also predicted to reduce receptor affinity and vice versa. These models, when used in concert, capture the complex impacts of SARS-CoV-2 mutations on properties linked to transmissibility and are expected to improve the development of next-generation vaccines and biotherapeutics. Public Library of Science 2022-05-31 /pmc/articles/PMC9223403/ /pubmed/35639784 http://dx.doi.org/10.1371/journal.pcbi.1010160 Text en © 2022 Makowski et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Makowski, Emily K.
Schardt, John S.
Smith, Matthew D.
Tessier, Peter M.
Mutational analysis of SARS-CoV-2 variants of concern reveals key tradeoffs between receptor affinity and antibody escape
title Mutational analysis of SARS-CoV-2 variants of concern reveals key tradeoffs between receptor affinity and antibody escape
title_full Mutational analysis of SARS-CoV-2 variants of concern reveals key tradeoffs between receptor affinity and antibody escape
title_fullStr Mutational analysis of SARS-CoV-2 variants of concern reveals key tradeoffs between receptor affinity and antibody escape
title_full_unstemmed Mutational analysis of SARS-CoV-2 variants of concern reveals key tradeoffs between receptor affinity and antibody escape
title_short Mutational analysis of SARS-CoV-2 variants of concern reveals key tradeoffs between receptor affinity and antibody escape
title_sort mutational analysis of sars-cov-2 variants of concern reveals key tradeoffs between receptor affinity and antibody escape
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9223403/
https://www.ncbi.nlm.nih.gov/pubmed/35639784
http://dx.doi.org/10.1371/journal.pcbi.1010160
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