Connecting protein interaction data, mutations, and disease using bioinformatics.
Understanding how mutations lead to changes in protein function and/or protein interaction is critical to understanding the molecular causes of clinical phenotypes. In this method, we present a path toward integration of protein interaction data and mutation data and then demonstrate the identification of a subset of proteins and interactions that are important to a particular disease. We then build a statistical model of disease mutations in this disease-associated subset of proteins, and visualize these results. Using Alzheimer's disease (AD) as case implementation, we find that we are able to identify a subset of proteins involved in AD and discriminate disease-associated mutations from SNPs in these proteins with 83% accuracy. As the molecular causes of disease become more understood, models such as these will be useful for identifying candidate variants most likely to be causative.
Chen
Jake Y
JY
Informatics and Technology Complex (IT), Indiana University School of Informatics, IUPUI, Indianapolis, IN, USA.
Youn
Eunseog
E
Mooney
Sean D
SD
D000544:Alzheimer Disease / Q000235:genetics / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D004194:Disease / Q000209:etiology* / Q000235:genetics; D020022:Genetic Predisposition to Disease; D006801:Humans; D009154:Mutation / Q000502:physiology*; D011485:Protein Binding / Q000235:genetics; D025941:Protein Interaction Mapping / Q000379:methods*