Abstract
BackgroundPredicting novel interactions between HIV-1 and human proteins contributes most promising area in HIV research. Prediction is generally guided by some classification and inference based methods using single biological source of information.ResultsIn this article we have proposed a novel framework to predict protein-protein interactions (PPIs) between HIV-1 and human proteins by integrating multiple biological sources of information through non negative matrix factorization (NMF). For this purpose, the multiple data sets are converted to biological networks, which are then utilized to predict modules. These modules are subsequently combined into meta-modules by using NMF based clustering method. The integrated meta-modules are used to predict novel interactions between HIV-1 and human proteins. We have analyzed the significant GO terms and KEGG pathways in which the human proteins of the meta-modules participate. Moreover, the topological properties of human proteins involved in the meta modules are investigated. We have also performed statistical significance test to evaluate the predictions.ConclusionsHere, we propose a novel approach based on integration of different biological data sources, for predicting PPIs between HIV-1 and human proteins. Here, the integration is achieved through non negative matrix factorization (NMF) technique. Most of the predicted interactions are found to be well supported by the existing literature in PUBMED. Moreover, human proteins in the predicted set emerge as ‘hubs’ and ‘bottlenecks’ in the analysis. Low p-value in the significance test also suggests that the predictions are statistically significant.
Highlights
Predicting novel interactions between Human Immunodeficiency Virus-1 (HIV-1) and human proteins contributes most promising area in HIV research
We have proposed a framework where three sources of information, namely, gene expression, proteinprotein interactions (PPIs) and Gene Ontology based similarity, are integrated through negative matrix factorization (NMF) based clustering
Results and discussions we describe the results of our proposed method for predicting interactions between HIV-1 and human proteins
Summary
Predicting novel interactions between HIV-1 and human proteins contributes most promising area in HIV research. Interaction between proteins is considered to be an important biochemical reactions which controls different biological processes. Analysis and prediction of proteinprotein interactions (PPIs) between viral and host proteins is an important step to uncover the underlying mechanism of viral infection in host cell machinery. Human Immunodeficiency Virus-1 (HIV-1) belongs to a special class of viruses called retrovirus, in which it is placed in the subgroup of lentiviruses. It consists of a single stranded RNA which encodes 19 proteins. HIV-1 virus relies on the human cellular machinery for its replication.
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