Identifying the immunodeficiency gateway proteins in humans and their involvement in microRNA regulation

Network-Based Analysis of OMICs Data to Understand the HIV-Host Interaction

The interaction of human immunodeficiency virus with human cells is responsible for all stages of the viral life cycle, from the infection of CD4+ cells to reverse transcription, integration, and the assembly of new viral particles. To date, a large amount of OMICs data as well as information from functional genomics screenings regarding the HIV–host interaction has been accumulated in the literature and in public databases. We processed databases containing HIV–host interactions and found 2910 HIV-1-human protein-protein interactions, mostly related to viral group M subtype B, 137 interactions between human and HIV-1 coding and non-coding RNAs, essential for viral lifecycle and cell defense mechanisms, 232 transcriptomics, 27 proteomics, and 34 epigenomics HIV-related experiments. Numerous studies regarding network-based analysis of corresponding OMICs data have been published in recent years. We overview various types of molecular networks, which can be created using OMICs data, including HIV–human protein–protein interaction networks, co-expression networks, gene regulatory and signaling networks, and approaches for the analysis of their topology and dynamics. The network-based analysis can be used to determine the critical pathways and key proteins involved in the HIV life cycle, cellular and immune responses to infection, viral escape from host defense mechanisms, and mechanisms mediating different susceptibility of humans to infection. The proteins and pathways identified in these studies represent a basis for developing new anti-HIV therapeutic strategies such as new drugs preventing infection of CD4+ cells and viral replication, effective vaccines, “shock and kill” and “block and lock” approaches to cure latent infection.

Biclustering-based association rule mining approach for predicting cancer-associated protein interactions

Protein-protein interactions (PPIs) have been widely used to understand different biological processes and cellular functions associated with several diseases like cancer. Although some cancer-related protein interaction databases are available, lack of experimental data and conflicting PPI data among different available databases have slowed down the cancer research. Therefore, in this study, the authors have focused on various proteins that are directly related to different types of cancer disease. They have prepared a PPI database between cancer-associated proteins with the rest of the human proteins. They have also incorporated the annotation type and direction of each interaction. Subsequently, a biclustering-based association rule mining algorithm is applied to predict new interactions with type and direction. This study shows the prediction power of association rule mining algorithm over the traditional classifier model without choosing a negative data set. The time complexity of the biclustering-based association rule mining is also analysed and compared to traditional association rule mining. The authors are able to discover 38 new PPIs which are not present in the cancer database. The biological relevance of these newly predicted interactions is analysed by published literature. Recognition of such interactions may accelerate a way of developing new drugs to prevent different cancer-related diseases.

Interactions of HIV-1 proteins as targets for developing anti-HIV-1 peptides

Protein–protein interactions (PPI) are essential in every step of the HIV replication cycle. Mapping the interactions between viral and host proteins is a fundamental target for the design and development of new therapeutics. In this review, we focus on rational development of anti-HIV-1 peptides based on mapping viral–host and viral–viral protein interactions all across the HIV-1 replication cycle. We also discuss the mechanism of action, specificity and stability of these peptides, which are designed to inhibit PPI. Some of these peptides are excellent tools to study the mechanisms of PPI in HIV-1 replication cycle and for the development of anti-HIV-1 drug leads that modulate PPI.

A review of in silico approaches for analysis and prediction of HIV-1-human protein-protein interactions

The computational or in silico approaches for analysing the HIV-1-human protein-protein interaction (PPI) network, predicting different host cellular factors and PPIs and discovering several pathways are gaining popularity in the field of HIV research. Although there exist quite a few studies in this regard, no previous effort has been made to review these works in a comprehensive manner. Here we review the computational approaches that are devoted to the analysis and prediction of HIV-1-human PPIs. We have broadly categorized these studies into two fields: computational analysis of HIV-1-human PPI network and prediction of novel PPIs. We have also presented a comparative assessment of these studies and proposed some methodologies for discussing the implication of their results. We have also reviewed different computational techniques for predicting HIV-1-human PPIs and provided a comparative study of their applicability. We believe that our effort will provide helpful insights to the HIV research community.

Network-based study reveals potential infection pathways of hepatitis-C leading to various diseases

Protein-protein interaction network-based study of viral pathogenesis has been gaining popularity among computational biologists in recent days. In the present study we attempt to investigate the possible pathways of hepatitis-C virus (HCV) infection by integrating the HCV-human interaction network, human protein interactome and human genetic disease association network. We have proposed quasi-biclique and quasi-clique mining algorithms to integrate these three networks to identify infection gateway host proteins and possible pathways of HCV pathogenesis leading to various diseases. Integrated study of three networks, namely HCV-human interaction network, human protein interaction network, and human proteins-disease association network reveals potential pathways of infection by the HCV that lead to various diseases including cancers. The gateway proteins have been found to be biologically coherent and have high degrees in human interactome compared to the other virus-targeted proteins. The analyses done in this study provide possible targets for more effective anti-hepatitis-C therapeutic involvement.

Incorporating the type and direction information in predicting novel regulatory interactions between HIV-1 and human proteins using a biclustering approach

BACKGROUND

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. 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.

RESULTS

Here 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.

CONCLUSIONS

We 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.

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

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.