MutPred

MutPred predicts the molecular impact of amino acid substitutions in human proteins to assess their potential association with disease.

Key Features:

• Protein-sequence-based modeling: Leverages protein sequence information to assess changes in structural features and functional sites between wild-type and mutant sequences.
• Probabilistic scoring: Quantifies alterations as probabilities indicating potential gains or losses of structure and function.
• SIFT-derived approach: Builds upon the SIFT method and enhances classification accuracy specifically for human disease mutations.
• Conservative thresholding: Applies conservative thresholds to predicted disruptions in molecular function to generate reliable hypotheses.
• Performance on inherited mutations: Can generate accurate molecular-basis hypotheses for approximately 11% of known inherited disease-causing mutations.
• Cancer relevance: Reports that cancer-associated somatic mutations exhibit a higher proportion of changes in functionally relevant residues compared to inherited lesions documented in the Human Gene Mutation Database.
• High-throughput data applicability: Addresses variation data arising from high-throughput genotyping and next-generation sequencing technologies.

Scientific Applications:

• Molecular mechanism hypothesis generation: Provides probabilistic hypotheses about gains or losses of structural and functional properties underlying disease-associated substitutions.
• Interpretation of inherited variants: Improves classification and interpretation of inherited disease-causing missense mutations.
• Analysis of somatic mutations in cancer: Enables comparison of functional impact patterns between cancer-associated somatic mutations and inherited lesions.
• Variant prioritization from sequencing projects: Prioritizes amino acid substitutions identified by high-throughput genotyping and next-generation sequencing for downstream study.

Methodology:

Uses a computational model that compares wild-type and mutant protein sequences to predict changes in structural features and functional sites, quantifies those alterations as probabilities of gains or losses of structure/function, and builds upon SIFT with conservative thresholds for classifying human disease mutations.