Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes.
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| Title: | Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes. |
|---|---|
| Authors: | Rodriguez-Rivas, Juan1, Croce, Giancarlo2,3, Muscat, Maureen1, Weigt, Martin1 martin.weigt@sorbonne-universite.fr |
| Source: | Proceedings of the National Academy of Sciences of the United States of America. 1/25/2022, Vol. 119 Issue 4, p1-10. 10p. |
| Subjects: | SARS-CoV-2, Receiver operating characteristic curves, Epitopes, Protein domains, Protein stability |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Proceedings of the National Academy of Sciences of the United States of America is the property of National Academy of Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 154939864 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Rodriguez-Rivas%2C+Juan%22">Rodriguez-Rivas, Juan</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Croce%2C+Giancarlo%22">Croce, Giancarlo</searchLink><relatesTo>2,3</relatesTo><br /><searchLink fieldCode="AR" term="%22Muscat%2C+Maureen%22">Muscat, Maureen</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Weigt%2C+Martin%22">Weigt, Martin</searchLink><relatesTo>1</relatesTo><i> martin.weigt@sorbonne-universite.fr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Proceedings+of+the+National+Academy+of+Sciences+of+the+United+States+of+America%22">Proceedings of the National Academy of Sciences of the United States of America</searchLink>. 1/25/2022, Vol. 119 Issue 4, p1-10. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22SARS-CoV-2%22">SARS-CoV-2</searchLink><br /><searchLink fieldCode="DE" term="%22Receiver+operating+characteristic+curves%22">Receiver operating characteristic curves</searchLink><br /><searchLink fieldCode="DE" term="%22Epitopes%22">Epitopes</searchLink><br /><searchLink fieldCode="DE" term="%22Protein+domains%22">Protein domains</searchLink><br /><searchLink fieldCode="DE" term="%22Protein+stability%22">Protein stability</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Proceedings of the National Academy of Sciences of the United States of America is the property of National Academy of Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1073/pnas.2113118119 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 1 Subjects: – SubjectFull: SARS-CoV-2 Type: general – SubjectFull: Receiver operating characteristic curves Type: general – SubjectFull: Epitopes Type: general – SubjectFull: Protein domains Type: general – SubjectFull: Protein stability Type: general Titles: – TitleFull: Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Rodriguez-Rivas, Juan – PersonEntity: Name: NameFull: Croce, Giancarlo – PersonEntity: Name: NameFull: Muscat, Maureen – PersonEntity: Name: NameFull: Weigt, Martin IsPartOfRelationships: – BibEntity: Dates: – D: 25 M: 01 Text: 1/25/2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 00278424 Numbering: – Type: volume Value: 119 – Type: issue Value: 4 Titles: – TitleFull: Proceedings of the National Academy of Sciences of the United States of America Type: main |
| ResultId | 1 |
