Mapping the Evolutionary Space of SARS-CoV-2 Variants to Anticipate Emergence of Subvariants Resistant to COVID-19 Therapeutics.

New sublineages of SARS-CoV-2 variants-of-concern (VOCs) continuously emerge with mutations in the spike glycoprotein. In most cases, the sublineage-defining mutations vary between the VOCs. It is unclear whether these differences reflect lineage-specific likelihoods for mutations at each spike posi...

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Main Authors: Roberth Anthony Rojas Chávez, Mohammad Fili, Changze Han, Syed A Rahman, Isaiah G L Bicar, Sullivan Gregory, Annika Helverson, Guiping Hu, Benjamin W Darbro, Jishnu Das, Grant D Brown, Hillel Haim
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2024-06-01
Series:PLoS Computational Biology
Online Access:https://doi.org/10.1371/journal.pcbi.1012215
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author Roberth Anthony Rojas Chávez
Mohammad Fili
Changze Han
Syed A Rahman
Isaiah G L Bicar
Sullivan Gregory
Annika Helverson
Guiping Hu
Benjamin W Darbro
Jishnu Das
Grant D Brown
Hillel Haim
author_facet Roberth Anthony Rojas Chávez
Mohammad Fili
Changze Han
Syed A Rahman
Isaiah G L Bicar
Sullivan Gregory
Annika Helverson
Guiping Hu
Benjamin W Darbro
Jishnu Das
Grant D Brown
Hillel Haim
author_sort Roberth Anthony Rojas Chávez
collection DOAJ
description New sublineages of SARS-CoV-2 variants-of-concern (VOCs) continuously emerge with mutations in the spike glycoprotein. In most cases, the sublineage-defining mutations vary between the VOCs. It is unclear whether these differences reflect lineage-specific likelihoods for mutations at each spike position or the stochastic nature of their appearance. Here we show that SARS-CoV-2 lineages have distinct evolutionary spaces (a probabilistic definition of the sequence states that can be occupied by expanding virus subpopulations). This space can be accurately inferred from the patterns of amino acid variability at the whole-protein level. Robust networks of co-variable sites identify the highest-likelihood mutations in new VOC sublineages and predict remarkably well the emergence of subvariants with resistance mutations to COVID-19 therapeutics. Our studies reveal the contribution of low frequency variant patterns at heterologous sites across the protein to accurate prediction of the changes at each position of interest.
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institution OA Journals
issn 1553-734X
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publishDate 2024-06-01
publisher Public Library of Science (PLoS)
record_format Article
series PLoS Computational Biology
spelling doaj-art-830f0adf314c4501b60e2b80d32e83892025-08-20T02:28:12ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-06-01206e101221510.1371/journal.pcbi.1012215Mapping the Evolutionary Space of SARS-CoV-2 Variants to Anticipate Emergence of Subvariants Resistant to COVID-19 Therapeutics.Roberth Anthony Rojas ChávezMohammad FiliChangze HanSyed A RahmanIsaiah G L BicarSullivan GregoryAnnika HelversonGuiping HuBenjamin W DarbroJishnu DasGrant D BrownHillel HaimNew sublineages of SARS-CoV-2 variants-of-concern (VOCs) continuously emerge with mutations in the spike glycoprotein. In most cases, the sublineage-defining mutations vary between the VOCs. It is unclear whether these differences reflect lineage-specific likelihoods for mutations at each spike position or the stochastic nature of their appearance. Here we show that SARS-CoV-2 lineages have distinct evolutionary spaces (a probabilistic definition of the sequence states that can be occupied by expanding virus subpopulations). This space can be accurately inferred from the patterns of amino acid variability at the whole-protein level. Robust networks of co-variable sites identify the highest-likelihood mutations in new VOC sublineages and predict remarkably well the emergence of subvariants with resistance mutations to COVID-19 therapeutics. Our studies reveal the contribution of low frequency variant patterns at heterologous sites across the protein to accurate prediction of the changes at each position of interest.https://doi.org/10.1371/journal.pcbi.1012215
spellingShingle Roberth Anthony Rojas Chávez
Mohammad Fili
Changze Han
Syed A Rahman
Isaiah G L Bicar
Sullivan Gregory
Annika Helverson
Guiping Hu
Benjamin W Darbro
Jishnu Das
Grant D Brown
Hillel Haim
Mapping the Evolutionary Space of SARS-CoV-2 Variants to Anticipate Emergence of Subvariants Resistant to COVID-19 Therapeutics.
PLoS Computational Biology
title Mapping the Evolutionary Space of SARS-CoV-2 Variants to Anticipate Emergence of Subvariants Resistant to COVID-19 Therapeutics.
title_full Mapping the Evolutionary Space of SARS-CoV-2 Variants to Anticipate Emergence of Subvariants Resistant to COVID-19 Therapeutics.
title_fullStr Mapping the Evolutionary Space of SARS-CoV-2 Variants to Anticipate Emergence of Subvariants Resistant to COVID-19 Therapeutics.
title_full_unstemmed Mapping the Evolutionary Space of SARS-CoV-2 Variants to Anticipate Emergence of Subvariants Resistant to COVID-19 Therapeutics.
title_short Mapping the Evolutionary Space of SARS-CoV-2 Variants to Anticipate Emergence of Subvariants Resistant to COVID-19 Therapeutics.
title_sort mapping the evolutionary space of sars cov 2 variants to anticipate emergence of subvariants resistant to covid 19 therapeutics
url https://doi.org/10.1371/journal.pcbi.1012215
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