Can predicted protein 3D-structures provide reliable insights into whether missense variants are disease-associated?
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Published version
Author(s)
Type
Journal Article
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
Date Issued
2019-05-17
Date Acceptance
2019-04-07
Citation
Journal of Molecular Biology, 2019, 431 (11), pp.2197-2212
ISSN
0022-2836
Publisher
Elsevier
Start Page
2197
End Page
2212
Journal / Book Title
Journal of Molecular Biology
Volume
431
Issue
11
Copyright Statement
© 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Sponsor
Wellcome Trust
Biotechnology and Biological Sciences Research Council (BBSRC)
Grant Number
WT/104955/Z/14/Z
BB/P011705/1
Subjects
Phyre2 protein structure prediction
missense variants
protein structure prediction
structure-based prediction
variant effect prediction
Biochemistry & Molecular Biology
0601 Biochemistry and Cell Biology
Publication Status
Published
Date Publish Online
2019-04-14