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Nucleic Acids Res. 2016 Apr 7;44(6):2501-13. doi: 10.1093/nar/gkw120. Epub 2016 Feb 28.

Robust classification of protein variation using structural modelling and large-scale data integration.

Author information

1
Department of Biology, New York University, New York, NY 10003, USA New York University Center for Genomics and Systems Biology, New York, NY 10003, USA.
2
New York University Center for Genomics and Systems Biology, New York, NY 10003, USA Computer Science Department, New York University, New York, NY 10003, USA Simons Center for Data Analysis, Simons Foundation, New York, NY 10010, USA.
3
Carnegie Mellon University Department of Chemistry, 5000 Forbes Ave, Pittsburgh, PA 15289, USA Commack High School, Commack, NY 11725, USA.
4
Simons Foundation, New York, NY 10010, USA.
5
Department of Biology, New York University, New York, NY 10003, USA New York University Center for Genomics and Systems Biology, New York, NY 10003, USA Computer Science Department, New York University, New York, NY 10003, USA Simons Center for Data Analysis, Simons Foundation, New York, NY 10010, USA Simons Foundation, New York, NY 10010, USA rb133@nyu.edu.

Abstract

Existing methods for interpreting protein variation focus on annotating mutation pathogenicity rather than detailed interpretation of variant deleteriousness and frequently use only sequence-based or structure-based information. We present VIPUR, a computational framework that seamlessly integrates sequence analysis and structural modelling (using the Rosetta protein modelling suite) to identify and interpret deleterious protein variants. To train VIPUR, we collected 9477 protein variants with known effects on protein function from multiple organisms and curated structural models for each variant from crystal structures and homology models. VIPUR can be applied to mutations in any organism's proteome with improved generalized accuracy (AUROC .83) and interpretability (AUPR .87) compared to other methods. We demonstrate that VIPUR's predictions of deleteriousness match the biological phenotypes in ClinVar and provide a clear ranking of prediction confidence. We use VIPUR to interpret known mutations associated with inflammation and diabetes, demonstrating the structural diversity of disrupted functional sites and improved interpretation of mutations associated with human diseases. Lastly, we demonstrate VIPUR's ability to highlight candidate variants associated with human diseases by applying VIPUR to de novo variants associated with autism spectrum disorders.

PMID:
26926108
PMCID:
PMC4824117
DOI:
10.1093/nar/gkw120
[Indexed for MEDLINE]
Free PMC Article

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