Abstract
BackgroundDiscovering novel interactions between HIV-1 and human proteins would greatly contribute to different areas of HIV research. Identification of such interactions leads to a greater insight into drug target prediction. Some recent studies have been conducted for computational prediction of new interactions based on the experimentally validated information stored in a HIV-1-human protein-protein interaction database. However, these techniques do not predict any regulatory mechanism between HIV-1 and human proteins by considering interaction types and direction of regulation of interactions.ResultsHere we present an association rule mining technique based on biclustering for discovering a set of rules among human and HIV-1 proteins using the publicly available HIV-1-human PPI database. These rules are subsequently utilized to predict some novel interactions among HIV-1 and human proteins. For prediction purpose both the interaction types and direction of regulation of interactions, (i.e., virus-to-host or host-to-virus) are considered here to provide important additional information about the regulation pattern of interactions. We have also studied the biclusters and analyzed the significant GO terms and KEGG pathways in which the human proteins of the biclusters participate. Moreover the predicted rules have also been analyzed to discover regulatory relationship between some human proteins in course of HIV-1 infection. Some experimental evidences of our predicted interactions have been found by searching the recent literatures in PUBMED. We have also highlighted some human proteins that are likely to act against the HIV-1 attack.ConclusionsWe pose the problem of identifying new regulatory interactions between HIV-1 and human proteins based on the existing PPI database as an association rule mining problem based on biclustering algorithm. We discover some novel regulatory interactions between HIV-1 and human proteins. Significant number of predicted interactions has been found to be supported by recent literature.
Highlights
Discovering novel interactions between Human immunodeficiency virus-1 (HIV-1) and human proteins would greatly contribute to different areas of HIV research
For the purpose of illustrating those rules we find out biological importance of these rules that give an insight view into the regulation pattern of human proteins during the HIV-1 infection
Here we have posed the problem of identifying new regulatory interactions between HIV-1 and human proteins based on the existing Protein-Protein Interaction (PPI) database as an association rule mining problem based on Binary inclusionMaximal (BiMax) biclustering algorithm
Summary
Discovering novel interactions between HIV-1 and human proteins would greatly contribute to different areas of HIV research. Identification of such interactions leads to a greater insight into drug target prediction. Some recent studies have been conducted for computational prediction of new interactions based on the experimentally validated information stored in a HIV-1-human protein-protein interaction database. These techniques do not predict any regulatory mechanism between HIV-1 and human proteins by considering interaction types and direction of regulation of interactions. In [3] a Bayesian classification based approach is proposed for predicting PPIs in yeast. Afterwards an approach called Mixture-of-FeatureExperts (mixture of classifiers) [6], some kernel based methods [7] and a decision tree based method [8] have been constructed to predict the set of interacting proteins in yeast and human cells
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