tetano
Editor, Senior Moderator
Proc Natl Acad Sci U S A
. 2022 Jan 25;119(4):e2113118119.
doi: 10.1073/pnas.2113118119.
Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes
Juan Rodriguez-Rivas[SUP] 1 [/SUP], Giancarlo Croce[SUP] 2 3 [/SUP], Maureen Muscat[SUP] 1 [/SUP], Martin Weigt[SUP] 4 [/SUP]
Affiliations
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.
Keywords: SARS-CoV-2; data-driven models; direct coupling analysis; epistasis; mutability
. 2022 Jan 25;119(4):e2113118119.
doi: 10.1073/pnas.2113118119.
Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes
Juan Rodriguez-Rivas[SUP] 1 [/SUP], Giancarlo Croce[SUP] 2 3 [/SUP], Maureen Muscat[SUP] 1 [/SUP], Martin Weigt[SUP] 4 [/SUP]
Affiliations
- PMID: 35022216
- DOI: 10.1073/pnas.2113118119
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.
Keywords: SARS-CoV-2; data-driven models; direct coupling analysis; epistasis; mutability