A novel biclustering approach to association rule mining for predicting HIV-1-human protein interactions

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.

Figure 1. Flowchart of the process of predicting new interactions.

Figure 2. Final Set of 15 Association Rules among HIV-1 Proteins Generated from HV Matrix.

Figure 3. Final Set of 36 Association Rules among HIV-1 Proteins Generated from VH Matrix.

Figure 4. Distribution of the number of predicted interactions at different confidence levels.

Figure 5. The predicted bipartite network involving 17 HIV-1 proteins and 140 human proteins.

HIV-1 proteins are represented by large red circles and human proteins are represented by small blue circles. The interactions predicted from VH matrix are represented by black lines and the interactions predicted from HV matrix are represented by green lines. Line widths are proportional to the confidence of the predictions.

Figure 6. Venn diagram showing the overlaps of predicted interactions provided by the proposed method, by Tastan et al. and by Doolittle et al.