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Can Predicted Protein 3D Structures Provide Reliable Insights into whether Missense Variants Are Disease Associated?


ABSTRACT: Knowledge of protein structure can be used to predict the phenotypic consequence of a missense variant. Since structural coverage of the human proteome can be roughly tripled to over 50% of the residues if homology-predicted structures are included in addition to experimentally determined coordinates, it is important to assess the reliability of using predicted models when analyzing missense variants. Accordingly, we assess whether a missense variant is structurally damaging by using experimental and predicted structures. We considered 606 experimental structures and show that 40% of the 1965 disease-associated missense variants analyzed have a structurally damaging change in the mutant structure. Only 11% of the 2134 neutral variants are structurally damaging. Importantly, similar results are obtained when 1052 structures predicted using Phyre2 algorithm were used, even when the model shares low (<40%) sequence identity to the template. Thus, structure-based analysis of the effects of missense variants can be effectively applied to homology models. Our in-house pipeline, Missense3D, for structurally assessing missense variants was made available at http://www.sbg.bio.ic.ac.uk/~missense3d.

SUBMITTER: Ittisoponpisan S 

PROVIDER: S-EPMC6544567 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Can Predicted Protein 3D Structures Provide Reliable Insights into whether Missense Variants Are Disease Associated?

Ittisoponpisan Sirawit S   Islam Suhail A SA   Khanna Tarun T   Alhuzimi Eman E   David Alessia A   Sternberg Michael J E MJE  

Journal of molecular biology 20190414 11


Knowledge of protein structure can be used to predict the phenotypic consequence of a missense variant. Since structural coverage of the human proteome can be roughly tripled to over 50% of the residues if homology-predicted structures are included in addition to experimentally determined coordinates, it is important to assess the reliability of using predicted models when analyzing missense variants. Accordingly, we assess whether a missense variant is structurally damaging by using experimenta  ...[more]

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