Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes.

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 mut

SUBMITTER: Rodriguez-Rivas J 

PROVIDER: S-EPMC8795541 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature