Topological Inquisition into the PPI Networks Associated with Human Diseases Through Graphlet Frequency Distribution

Abstract

In this article, we have proposed a new framework to compare topological structure of protein-protein interaction (PPI) networks constructed from disease associated proteins. Here, similarity of local topological structure between networks is discovered through the analysis of frequent sub-pattern occurred in them using a novel similarity measure based on graphlet frequency distribution. Graphlets are small connected non-isomorphic induced subgraphs in a network which provides detailed topological statistics of it. We have analyzed pairwise similarity of 22 disease associated PPI networks and compared topological and biological characteristics. It has been observed that the PPI networks associated with disease classes ‘metabolic’ and ‘neurological’ have the highest similarity scores. Higher similarity has also been observed for networks of disease classes ‘bone’ and ‘skeletal’; ‘endocrine’ and ‘multiple’; and ‘gastrointestinal and respiratory’. Topological analysis of the networks also reveals that degree and betweenness centrality of proteins is strongly correlated for the network pairs with high similarity scores. We have also performed gene ontology and pathway based analysis of the proteins involved in the disease associated networks.