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

• Published in: PLoS computational biology 2024-06, Vol.20 (6), p.e1012215
• Main Authors: Rojas Chávez, Roberth Anthony, Fili, Mohammad, Han, Changze, Rahman, Syed A, Bicar, Isaiah G L, Gregory, Sullivan, Helverson, Annika, Hu, Guiping, Darbro, Benjamin W, Das, Jishnu, Brown, Grant D, Haim, Hillel
• Format: Article
• Language: English
• Subjects: Amino acid sequence; Amino acids; Antibodies; Antiviral Agents - therapeutic use; Biology and life sciences; Computational Biology - methods; Computer and Information Sciences; COVID-19; COVID-19 - genetics; COVID-19 - virology; COVID-19 Drug Treatment; Datasets; Disease resistance; Drug Resistance, Viral - genetics; Evolution, Molecular; Glycoproteins; Humans; Medicine and health sciences; Mutation; Pandemics; Phylogenetics; Proteins; Research and Analysis Methods; SARS-CoV-2 - genetics; Severe acute respiratory syndrome coronavirus 2; Spike glycoprotein; Spike Glycoprotein, Coronavirus - chemistry; Spike Glycoprotein, Coronavirus - genetics; Stochasticity; Subpopulations
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1012215

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.