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

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.

Review of computational methods for virus–host protein interaction prediction: a case study on novel Ebola–human interactions

“…Finally, a set of high-confidence rule was extracted to predict novel human protein interactions. As a further improvement to their work, Mukhopadhyay et al [36] introduced type and direction (host-to-virus and virus-to-host)-based bi-clustering to existing interactions to predict novel host proteins. Mondal et al [37] also proposed a HIV-human interaction prediction method using hierarchical bi-clustering and minimal covers of association rule mining.…”

DeepViral: infectious disease phenotypes improve prediction of novel virus–host interactions

“…Existing PHI prediction methods for novel viruses typically utilize protein sequence features of the interacting proteins (Eid et al, 2016;Zhou et al, 2018;Alguwaizani et al, 2018;Yang et al, 2020). While protein functions have been shown to predict intra-species (e.g., human) i i "output" -2020/8/12 -19:24 -page 2 -#2 i i i i i i PPIs (Guzzi et al, 2011;Jain and Bader, 2010;Pesquita et al, 2009) and such protein specific features exist for some extensively studied pathogens, such as Mycobacterium tuberculosis (Huo et al, 2015) and HIV (Mukhopadhyay et al, 2014), for most pathogens, these features are rare and expensive to obtain. As new virus species continue to be discovered (Woolhouse et al, 2012), a method is needed to rapidly identify candidate interactions from information that can be obtained quickly, such as the signs and symptoms of the host, which may be utilized as a proxy for the underlying molecular interactions between host and pathogen proteins.…”

Phenotypic, functional and taxonomic features predict host-pathogen interactions

“…Existing HPI prediction methods typically utilizes features of interacting protein pairs, such as PPI network topology, structural and sequential homology, or functional profiling such as Gene Ontology similarity and KEGG pathway analysis (Nourani et al, 2015). While such protein-specific features exist for some extensively studied pathogens, such as Mycobacterium tuberculosis (Huo et al, 2015), human immunodeficiency virus (Mukhopadhyay et al, 2014), Salmonella and Yersinia (Kshirsagar et al, 2012), for most of the other pathogens, especially the newly emerging ones, these features are scarce (or nonexistent) and expensive to obtain. As new virus species are discovered each year with potentially many more to come (Woolhouse et al, 2012), a method is needed to aggregate our existing phenotypic, functional and taxonomic knowledge about HPIs and predict for pathogens that are less well studied or novel.…”