Abstract
Identification of potential viral-host protein interactions is a vital and useful approach towards development of new drugs targeting those interactions. In recent days, computational tools are being utilized for predicting viral-host interactions. Recently a database containing records of experimentally validated interactions between a set of HIV-1 proteins and a set of human proteins has been published. The problem of predicting new interactions based on this database is usually posed as a classification problem. However, posing the problem as a classification one suffers from the lack of biologically validated negative interactions. Therefore it will be beneficial to use the existing database for predicting new viral-host interactions without the need of negative samples. Motivated by this, in this article, the HIV-1–human protein interaction database has been analyzed using association rule mining. The main objective is to identify a set of association rules both among the HIV-1 proteins and among the human proteins, and use these rules for predicting new interactions. In this regard, a novel association rule mining technique based on biclustering has been proposed for discovering frequent closed itemsets followed by the association rules from the adjacency matrix of the HIV-1–human interaction network. Novel HIV-1–human interactions have been predicted based on the discovered association rules and tested for biological significance. For validation of the predicted new interactions, gene ontology-based and pathway-based studies have been performed. These studies show that the human proteins which are predicted to interact with a particular viral protein share many common biological activities. Moreover, literature survey has been used for validation purpose to identify some predicted interactions that are already validated experimentally but not present in the database. Comparison with other prediction methods is also discussed.
Highlights
Interactions between proteins are important biochemical reactions which determine different biological processes
An entry of 1 in the matrices denotes the presence of interaction between the corresponding pair of human and Human immunodeficiency virus (HIV)-1 proteins, and an entry of 0 represents the absence of any information regarding the interaction of the corresponding human and viral proteins
We found that some HIV-1 proteins interact with multiple human proteins and biological relevance study based on gene ontology reveals that the human proteins interacting with a single viral protein share common biological activities and participate in common cellular pathways
Summary
Interactions between proteins are important biochemical reactions which determine different biological processes. Analysis of the regulation between viral and host proteins in different organisms is an important step to uncover the underlying mechanism of various viral diseases. HIV-1 is a species of the HIV virus that relies on human host cell proteins in virtually every phase of its life cycle. One of the main goals in research of Protein-Protein Interaction (PPI) is to predict possible viral-host interactions. This is aimed at assisting drug developers targeting protein interactions for the development of specially designed small molecules to inhibit potential HIV-1–human PPIs. Targeting protein-protein interactions has relatively recently been established to be a promising alternative to the conventional approach to drug design [2,3]
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