Robust Classification of Protein Variation Using Structural Modeling and Large-Scale Data Integration

BioRxiv : the Preprint Server for Biology
Evan H BaughRichard Bonneau

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 modeling (using the Rosetta protein modeling suite) to identify and interpret deleterious protein variants. To train VIPUR, we collected 9,477 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 highligh...Continue Reading

Related Concepts

Classification
Candidate Disease Gene
Protein Function
Site
Sequence Analysis
Proteome
Autism Spectrum Disorders
Diabetes
Organism
Structure

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