The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Can Predicted Protein 3D Structures Provide Reliable Insights into whether Missense Variants Are Disease Associated?
Open accessAbstract
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.
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Cites
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“Missense variants were curated from Humsavar (from UniProt [61] release 4 Feb 2015), ClinVar [39] (release 7 Jan 2015) and ExAC [40] (version 0.”
BackgroundMethodInfluentialMutationTaster evaluates disease-causing potential of sequence alterations
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Cited by
Accurate proteome-wide missense variant effect prediction with AlphaMissense
Cheng, J., Novati, G., Pan, J., Bycroft, C., Žemgulytė, A., Applebaum, T., Pritzel, A., Wong, L. H., Zielinski, M., Sargeant, T., et al. (2023) · Science · doi:10.1126/science.adg7492 · Cited by 2,163
Challenges in bridging the gap between protein structure prediction and functional interpretation
Varadi, M., Tsenkov, M., & Velankar, S. (2025) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26614 · Cited by 36
Enhancing missense variant pathogenicity prediction with protein language models using VariPred
Lin, W., Wells, J., Wang, Z., Orengo, C., & Martin, A. C. R. (2024) · Scientific Reports · doi:10.1038/s41598-024-51489-7 · Cited by 34
Best templates outperform homology models in predicting the impact of mutations on protein stability
Pak, M. A., & Ivankov, D. N. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac515 · Cited by 26
Pennica, C., Hanna, G., Islam, S. A., Sternberg, M. J. E., & David, A. (2023) · Journal of Molecular Biology · doi:10.1016/j.jmb.2023.168060 · Cited by 25
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Metadata
- Authors
- Ittisoponpisan, S., Islam, S. A., Khanna, T., Alhuzimi, E., David, A., & Sternberg, M. J. E.
- Venue
- Journal of Molecular Biology
- Publisher
- Elsevier BV
- Published
- 2019-05
- Volume
- 431
- Issue
- 11
- Pages
- 2197-2212
- ISSN
- 0022-2836 (ISSN-L)
- OpenAlex
- W2936956377
- Cited by
- 489
- References
- 13
- FWCI
- 29.53
- Type
- journal article
- Language
- en
- Open access
- hybrid
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