A multi-layered approach to protein data integration for diabetes research.

OBJECTIVE

Recent advances in high-throughput experimental techniques have enabled many protein-protein interactions to be identified and stored in large databases. Understanding protein interactions is fundamental to the advancement of science and medical knowledge, unfortunately the scale of the requires an automated approach to analysis. We describe our graph-mining techniques to identify important structures within protein-protein interaction networks to aid in human comprehension and computerised analysis.

METHODS AND MATERIALS

We describe our techniques for characterizing graph type and associated properties which is constructed from data collated from the Human Protein Reference Database. Using random graph rewiring comparative techniques and cross-validation with other identification methods a further analysis of the identified essential proteins is presented to illustrate the accuracy of these measures. We argue for using techniques based upon graph structure for separating and encapsulating proteins based upon functionality.

RESULTS

We demonstrate how rational Erdos numbers may be used as a method to identify collaborating proteins based solely upon network structure. Further, by using dynamic cut-off limit it demonstrates how collaboration subgraphs can be generated for each protein within the network, and how graph containment can be used as a means of identifying which of many possible graphs are likely to be actual protein complexes. The demonstration protein interaction network built for diabetes is found to be a scale-free, small-world graph with a power-law degree distribution of interactions on nodes. These findings are consistent with many other protein interaction networks.