HCVpro: Hepatitis C virus protein interaction database

https://doi.org/10.1016/j.meegid.2011.09.001Get rights and content

Abstract

It is essential to catalog characterized hepatitis C virus (HCV) protein–protein interaction (PPI) data and the associated plethora of vital functional information to augment the search for therapies, vaccines and diagnostic biomarkers. In furtherance of these goals, we have developed the hepatitis C virus protein interaction database (HCVpro) by integrating manually verified hepatitis C virus–virus and virus–human protein interactions curated from literature and databases. HCVpro is a comprehensive and integrated HCV-specific knowledgebase housing consolidated information on PPIs, functional genomics and molecular data obtained from a variety of virus databases (VirHostNet, VirusMint, HCVdb and euHCVdb), and from BIND and other relevant biology repositories. HCVpro is further populated with information on hepatocellular carcinoma (HCC) related genes that are mapped onto their encoded cellular proteins. Incorporated proteins have been mapped onto Gene Ontologies, canonical pathways, Online Mendelian Inheritance in Man (OMIM) and extensively cross-referenced to other essential annotations. The database is enriched with exhaustive reviews on structure and functions of HCV proteins, current state of drug and vaccine development and links to recommended journal articles. Users can query the database using specific protein identifiers (IDs), chromosomal locations of a gene, interaction detection methods, indexed PubMed sources as well as HCVpro, BIND and VirusMint IDs. The use of HCVpro is free and the resource can be accessed via http://apps.sanbi.ac.za/hcvpro/ or http://cbrc.kaust.edu.sa/hcvpro/.

Highlights

► Experimentally verified hepatitis C virus protein interactions. ► Archives hepatocellular carcinoma related genes. ► Provides reviews on structure and functions of HCV proteins, current state of drug and vaccine development. ► Easy exploration of integrated data to facilitate the generation of therapeutic baseline research clues.

Introduction

Currently, about 3% of the global population is infected with hepatitis C virus (HCV), a leading risk factor and key causative agent for the development of acute and chronic hepatic infections. Patients with chronic hepatitis may eventually develop cirrhosis, hepatocellular carcinoma (HCC) and hepatic impairment. Gaining insight into the development of novel diagnostic or therapeutic targets for HCV related infections requires a vigorous understanding of (i) structure and functions of its encoded proteins, (ii) role of host cellular proteins, and (iii) HCV implicated immunologic pathways and biomolecular networks. Recently, the search for HCV therapy has robustly focused on the development of anti-viral drugs targeting host cellular proteins and also protease/polymerase inhibitors specifically designed to disrupt the role of the HCV proteins during viral propagation and replication (Tencate et al., 2010).

Protein–protein interactions (PPIs) are vital in orchestrating molecular cellular events and constitute the basis for multitudes of signal transduction pathways and transcriptional regulatory networks (Raman, 2010). Efforts towards elucidating HCV protein interactions through the use of proteome-wide mapping techniques and the computational construction of the entire HCV–human protein interactome map have been reported (de Chassey et al., 2008, Flajolet et al., 2000). Additionally, several other HCV research groups have published large amounts of information on HCV PPIs and related enriched functionally analyzed data vital for the understanding of HCV infection mechanisms. This progress has been facilitated by the advent of highly innovative low and high throughput techniques essential for the elucidation and characterization of protein interactions. Various experimental methods have been widely used to either screen or confirm HCV protein–protein interactions, including yeast two-hybrid (Ahn et al., 2004, Dimitrova et al., 2003, Huang et al., 2005), confocal microscopy (Lan et al., 2003, Tong et al., 2002), coimmunoprecipitation (Dimitrova et al., 2003, Wang et al., 2005), surface plasmon resonance studies (Jennings et al., 2008, Sabile et al., 1999), spectroscopy (Kang et al., 2005, Masumi et al., 2005), immunoblotting (Tan et al., 1999), cross-linking (Wang et al., 2002), affinity chromatography (Masumi et al., 2005) and 3-dimensional protein structures (Op De Beeck et al., 2000). The use of several types of methods in elucidating PPIs has led to the availability of interaction data in heterogeneous formats thus requiring standardization. In response, numerous efforts have focused on formatting the ever-increasing protein–protein interactions and associated meta-data as well as integrating them into public online repositories. The proteomics standards initiative-molecular interaction (PSI-MI) format (Hermjakob et al., 2004), a community standard data model for the representation and exchange of protein interaction data, and MIMIx, the minimum information required for reporting a molecular interaction experiment guidelines (Orchard et al., 2007) are platforms for harnessing information from structured PPI data.

Towards consolidating viral PPI data, VirusMint (Chatr-aryamontri et al., 2009) and VirHostNet (Navratil et al., 2009) databases have been developed to house viral PPI data. VirHostNet represents more than 180 distinct viral species with unique taxonomy identifications as well as a broad diversity spanning about 36 distinct viral families. VirusMint represents more than 110 different viral strains and provides PPI information on viruses involved in human infections and cancer, including adenovirus, Simian virus 40 (SV40), human papilloma viruses, Epstein–Barr virus (EBV), hepatitis B virus (HBV), hepatitis C virus (HCV), herpes viruses, influenza A virus, vaccinia virus and human immunodeficiency virus (HIV). Primarily, both VirusMint and VirHostNet are geared towards online network visualization and analysis of integrated human viral interactome maps. A trend is emerging where databases solely dedicated to single organisms are constructed instead of multi-organism PPI databases, and a typical example is HIV-1 Human Protein Interactions Database (Fu et al., 2009). It is our belief that a knowledgebase solely focused or customized to answer pertinent biological questions related to HCV has the potential to provide detailed and exhaustive information suitable to generate functional hypotheses in silico. In this regard, we describe the development of a new online hepatitis C virus protein interaction database (HCVpro), an integrated and comprehensive warehouse for storing and managing manually verified HCV virus–virus and virus–human protein interactions curated from literature and databases. HCVpro contains interaction information on human and viral interacting partners as well as meta-data, including interaction detection methods and journal articles reporting the interactions. Furthermore, it contains information on HCC related genes and other valuable contextual data on both viral and human proteins.

The diverse integrated information in HCVpro can accelerate the understanding of HCV mediated molecular mechanisms and also enable postulation of appropriate hypothesis to guide discovery of new knowledge of potential use in drug discovery. Additionally, HCVpro can augment efforts towards uncovering novel HCV-related tumorigenic biomarkers with therapeutic potential by harnessing integrated data composed of PPIs, canonical pathways, and HCC-related gene expression information on the genes encoding the host cellular proteins.

Section snippets

Protein interaction data curation and integration

Hepatitis C virus–virus and virus–human protein interactions were manually curated from published peer-reviewed journal articles and database sources including BIND, VirusMint and VirHostNet. Protein interactions were annotated with associated meta-data such as experiment types used in inferring interactions and PubMed identification numbers (PMIDs) of journal articles reporting the interactions. The Entrez gene symbols of the human genes encoding the interacting cellular proteins were used as

Database usage

We have developed a biological repository known as HCV protein interaction database (HCVpro) that integrates a multitude of enriched data from a variety of protein and genomic platforms relevant to HCV research. This knowledgebase is intended to serve as a “one stop shop” for retrieval of comprehensive data on HCV–HCV and HCV–human protein interactions, and associated vital information relevant for functional characterization experiments. The search menu on the index page of HCVpro enables

Future prospects

A content curator and database administrator will constantly be improving the database based on user comments and updates will be released twice every year to accommodate the ever-growing published protein interactions. We also intend exploring the possibility of incorporating features that compute the druggability of the integrated interactions, and also allow the online screening of potential drugs and cellular drug targets of interest in HCV research into HCVpro. Furthermore, data on

Conclusions

We have developed HCV protein interaction knowledgebase (HCVpro), a relational database dedicated to HCV protein interactions. It contains manually verified hepatitis C virus–virus and virus–human host cellular protein interactions obtained from other databases and curated literature. We have also incorporated a multitude of contextualized and functional genomic data pertaining to human and HCV proteins and cross-referenced to enriched biological databases. From the aforementioned, the reported

Competing interests

The authors declared no competing interest.

Authors’ contributions

S.K.K. conceptualized the project, participated in data curation, designed and co-developed the database as well as writing the manuscript. U.S. was the principal developer of the database. V.S.S. provided technical support. A.C. and V.B.B. supervised the project. All authors read and approved the manuscript.

Acknowledgments

This work was partly supported by grants from the National Research Foundation (South Africa), National Bioinformatics Network (to S.K.K.) and DST/NRF Research Chair (to A.C. and V.B.B.).

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