ArticleProceedings of the National Academy of Sciences of the United States of America2022
Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
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Who cites it
59 citing papers in PubMed.
- Rethinking non-human primate models for emerging viral infections after COVID-19.Laboratory animal research · 2026Review
- Constrained evolutionary funnels shape viral immune escape.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Functional bottlenecks can emerge from non-epistatic underlying traits.PLoS computational biology · 2026Article
- D-LIM: A neural network for interpretable gene-gene interactions.PLoS computational biology · 2026Article
- From single-sequences to evolutionary trajectories: protein language models capture the evolutionary potential of SARS-CoV-2.Nature communications · 2026Article
- Epistatic drift in protein evolution.Current opinion in genetics & development · 2025Review
- Constrained Evolutionary Funnels Shape Viral Immune Escape.bioRxiv : the preprint server for biology · 2025Article
- The mutational landscape of SARS-CoV-2 provides new insight into viral evolution and fitness.Nature communications · 2025Article
- Human herpesvirus 6B glycoprotein B postfusion structure, vulnerability mapping, and receptor recognition.PLoS pathogens · 2025Article
- Deciphering the Code of Viral-Host Adaptation Through Maximum-Entropy Nucleotide Bias Models.Molecular biology and evolution · 2025Article
- Review
- Paying attention to the SARS-CoV-2 dialect : a deep neural network approach to predicting novel protein mutations.Communications biology · 2025Article
- Predicting pathogen evolution and immune evasion in the age of artificial intelligence.Computational and structural biotechnology journal · 2025Review
- Emergent time scales of epistasis in protein evolution.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Understanding epistatic networks in the B1 β-lactamases through coevolutionary statistical modeling and deep mutational scanning.Nature communications · 2024Article
- Impact of phylogeny on the inference of functional sectors from protein sequence data.PLoS computational biology · 2024Article
- Real-time identification of epistatic interactions in SARS-CoV-2 from large genome collections.Genome biology · 2024Article
- Machine learning in biological physics: From biomolecular prediction to design.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Mapping the Evolutionary Space of SARS-CoV-2 Variants to Anticipate Emergence of Subvariants Resistant to COVID-19 Therapeutics.PLoS computational biology · 2024Article
- Article
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4 authors.
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Abstract
The emergence of new variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major concern given their potential impact on the transmissibility and pathogenicity of the virus as well as the efficacy of therapeutic interventions. Here, we predict the mutability of all positions in SARS-CoV-2 protein domains to forecast the appearance of unseen variants. Using sequence data from other coronaviruses, preexisting to SARS-CoV-2, we build statistical models that not only capture amino acid conservation but also more complex patterns resulting from epistasis. We show that these models are notably superior to conservation profiles in estimating the already observable SARS-CoV-2 variability. In the receptor binding domain of the spike protein, we observe that the predicted mutability correlates well with experimental measures of protein stability and that both are reliable mutability predictors (receiver operating characteristic areas under the curve ∼0.8). Most interestingly, we observe an increasing agreement between our model and the observed variability as more data become available over time, proving the anticipatory capacity of our model. When combined with data concerning the immune response, our approach identifies positions where current variants of concern are highly overrepresented. These results could assist studies on viral evolution and future viral outbreaks and, in particular, guide the exploration and anticipation of potentially harmful future SARS-CoV-2 variants.
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