Journal article
Mapping the Evolutionary Space of SARS-CoV-2 Variants to Anticipate Emergence of Subvariants Resistant to COVID-19 Therapeutics
PLoS computational biology, Vol.20(6), e1012215
06/10/2024
DOI: 10.1371/journal.pcbi.1012215
PMCID: PMC11192331
PMID: 38857308
Abstract
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.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.
Details
- Title: Subtitle
- Mapping the Evolutionary Space of SARS-CoV-2 Variants to Anticipate Emergence of Subvariants Resistant to COVID-19 Therapeutics
- Creators
- Roberth Anthony Rojas Chávez - University of IowaMohammad Fili - Iowa State UniversityChangze Han - University of IowaSyed A Rahman - University of PittsburghIsaiah G L Bicar - University of IowaSullivan Gregory - University of IowaAnnika Helverson - University of IowaGuiping Hu - Iowa State UniversityBenjamin W Darbro - University of IowaJishnu Das - University of PittsburghGrant D Brown - University of IowaHillel Haim - University of Iowa
- Resource Type
- Journal article
- Publication Details
- PLoS computational biology, Vol.20(6), e1012215
- DOI
- 10.1371/journal.pcbi.1012215
- PMID
- 38857308
- PMCID
- PMC11192331
- ISSN
- 1553-7358
- eISSN
- 1553-7358
- Language
- English
- Date published
- 06/10/2024
- Academic Unit
- Microbiology and Immunology; Stead Family Department of Pediatrics; Biostatistics; Medical Genetics and Genomics
- Record Identifier
- 9984643658302771
Metrics
6 Record Views