Computational Prediction of HCV-Human Protein-Protein Interaction via Topological Analysis of HCV Infected PPI Modules

In this paper, we have developed a framework for detection of protein-protein interactions (PPI) between Hepatitis-C virus (HCV) and human proteins based on PPI and gene ontology based information of the HCV infected proteins. First, a bipartite interaction network is formed between HCV proteins and human host proteins. Next, we have analyzed different topological properties of the interaction network and observed that degree of HCV-interacting proteins is significantly higher than non-interacting host proteins. We have also observed that the HCV interacted protein pairs are functionally similar with each other than the non-interacting pairs. Following the observations, we have applied an inference mechanism to predict novel interactions between HCV and human protein. The inference mechanism is based on partitioning the network formed by HCV interacted human proteins and their first neighbors in dense and functionally similar groups using a PPI network clustering algorithm. The groups are then analyzed to predict PPIs. The predicted interaction pairs are validated using literature search in PUBMED. Experimental evidence of over 50% of the predicted pairs are found in existing literatures by searching PUBMED. A Gene Ontology and pathway based analysis is also carried out to validate the identified modules biologically.