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 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.

Complexity of Viral Epitope Surfaces as Evasive Targets for Vaccines and Therapeutic Antibodies

“…Recently, Starr et al repeated their RBD DMS workflow employing the backbones of a number of SARS-CoV-2 variants to describe how the effect of each point mutation varies depending on the mutational context of RBD (147). Further, statistical models have found success in predicting SARS-CoV-2 site mutability from sequence data (148), and these epistatic interactions likely constrain the evolutionary landscape to drive future SARS-CoV-2 evolution. While these epistatic relationships may be driven largely by selection for enhanced fitness, such as the mutational pair Q498R and N501Y which together enhance ACE-2 binding (149), such epistatic effects are still relevant to EPI analyses as these residues are involved in numerous antibody epitopes.…”

Identifying SARS-CoV-2 Variants of Concern through Saliva-Based RT-qPCR by Targeting Recurrent Mutation Sites

“…Custom probes are more expensive but can reduce the complexity of the workflow. New predictive computational tools can identify recurring mutation sites correlated to emerging strains ( 44 , 45 ), which can expedite RT-qPCR test development for real-time monitoring. Following presumptive identification, whole genome sequencing of select samples should still be performed to ensure the most accurate surveillance strategy.…”

Antigen–Antibody Complex-Guided Exploration of the Hotspots Conferring the Immune-Escaping Ability of the SARS-CoV-2 RBD

“…In addition to our research, there are approaches conducted to predict the possible mutations in SARS-CoV-2. Rodriguez-Rivas et al used the epistatic models to predict the mutable sites of proteins and epitopes in SARS-CoV-2 ( Rodriguez-Rivas et al, 2022 ). In their sequence-based predictions, the predicted mutability of SARS-CoV-2 RBD was observed to be well correlated with experimentally determined protein stability.…”