Computational Systems Biology

Volume 541 of the series Methods in Molecular Biology pp 449-461


Connecting Protein Interaction Data, Mutations, and Disease Using Bioinformatics

  • Jake Y. ChenAffiliated withInformatics and Technology Complex (IT), Indiana University School of Informatics, IUPUI, Indianapolis
  • , Eunseog YounAffiliated withDepartment of Computer Science, Texas Tech University
  • , Sean D. MooneyAffiliated withDepartment of Medical and Molecular Genetics, Center for Computational Biology and Bioinformatics, IUPUI, Indianapolis

* Final gross prices may vary according to local VAT.

Get Access


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.

Key words

Protein interaction SNP mutation bioinformatics data integration