Human immunodeficiency virus type 1, human protein interaction database at NCBI

The ‘Human Immunodeficiency Virus Type 1 (HIV-1), Human Protein Interaction Database’, available through the National Library of Medicine at www.ncbi.nlm.nih.gov/RefSeq/HIVInteractions, was created to catalog all interactions between HIV-1 and human proteins published in the peer-reviewed literature. The database serves the scientific community exploring the discovery of novel HIV vaccine candidates and therapeutic targets. To facilitate this discovery approach, the following information for each HIV-1 human protein interaction is provided and can be retrieved without restriction by web-based downloads and ftp protocols: Reference Sequence (RefSeq) protein accession numbers, Entrez Gene identification numbers, brief descriptions of the interactions, searchable keywords for interactions and PubMed identification numbers (PMIDs) of journal articles describing the interactions. Currently, 2589 unique HIV-1 to human protein interactions and 5135 brief descriptions of the interactions, with a total of 14 312 PMID references to the original articles reporting the interactions, are stored in this growing database. In addition, all protein–protein interactions documented in the database are integrated into Entrez Gene records and listed in the ‘HIV-1 protein interactions’ section of Entrez Gene reports. The database is also tightly linked to other databases through Entrez Gene, enabling users to search for an abundance of information related to HIV pathogenesis and replication

Finding Bicliques in Digraphs: Application into Viral-host Protein Interactome

We provide the first formalization true to the best of our knowledge to the problem of finding bicliques in a directed graph. The problem is addressed employing a two-stage approach based on an existing biclustering algorithm. This novel problem is useful in several biological applications of which we focus only on analyzing the viral-host protein interaction graphs. Strong and significant bicliques of HIV-1 and human proteins are derived using the proposed methodology, which provides insights into some novel regulatory functionalities in case of the acute immunodeficiency syndrome in human

Iron overload down-regulates the expression of the HIV-1 Rev cofactor eIF5A in infected T lymphocytes

Background Changes in iron metabolism frequently accompany HIV-1 infection. However, while many clinical and in vitro studies report iron overload exacerbates the development of infection, many others have found no correlation. Therefore, the multi-faceted role of iron in HIV-1 infection remains enigmatic. Methods RT-qPCR targeting the LTR region, gag, Tat and Rev were performed to measure the levels of viral RNAs in response to iron overload. Spike-in SILAC proteomics comparing i) iron-treated, ii) HIV-1-infected and iii) HIV-1-infected/iron treated T lymphocytes was performed to define modifications in the host cell proteome. Data from quantitative proteomics were integrated with the HIV-1 Human Interaction Database for assessing any viral cofactors modulated by iron overload in infected T lymphocytes. Results Here, we demonstrate that the iron overload down-regulates HIV-1 gene expression by decreasing the levels of viral RNAs. In addition, we found that iron overload modulates the expression of many viral cofactors. Among them, the downregulation of the REV cofactor eIF5A may correlate with the iron-induced inhibition of HIV-1 gene expression. Therefore, we demonstrated that eiF5A downregulation by shRNA resulted in a significant decrease of Nef levels, thus hampering HIV-1 replication. Conclusions Our study indicates that HIV-1 cofactors influenced by iron metabolism represent potential targets for antiretroviral therapy and suggests eIF5A as a selective target for drug development

HIV-1 host interactions: integration of large-scale datasets

HIV-1 replication and viral pathogenesis are dependent on numerous host factors. A series of recent papers apply genome-wide and large-scale approaches to map host-virus interactions and to identify host proteins capable of restricting (that is, controlling) the virus. Strategies include genome-wide association studies, small interfering RNA screens, genome-wide transcriptome profiling, proteome studies, and the assessment of the role of host-encoded microRNAs in infection. The various layers of large-scale data are brought together through meta-analytical procedures

CAPIH: A Web interface for comparative analyses and visualization of host-HIV protein-protein interactions

<p>Abstract</p> <p>Background</p> <p>The Human Immunodeficiency Virus type one (HIV-1) is the major causing pathogen of the Acquired Immune Deficiency Syndrome (AIDS). A large number of HIV-1-related studies are based on three non-human model animals: chimpanzee, rhesus macaque, and mouse. However, the differences in host-HIV-1 interactions between human and these model organisms have remained unexplored.</p> <p>Description</p> <p>Here we present CAPIH (Comparative Analysis of Protein Interactions for HIV-1), the first web-based interface to provide comparative information between human and the three model organisms in the context of host-HIV-1 protein interactions. CAPIH identifies genetic changes that occur in HIV-1-interacting host proteins. In a total of 1,370 orthologous protein sets, CAPIH identifies ~86,000 amino acid substitutions, ~21,000 insertions/deletions, and ~33,000 potential post-translational modifications that occur only in one of the four compared species. CAPIH also provides an interactive interface to display the host-HIV-1 protein interaction networks, the presence/absence of orthologous proteins in the model organisms in the networks, the genetic changes that occur in the protein nodes, and the functional domains and potential protein interaction hot sites that may be affected by the genetic changes. The CAPIH interface is freely accessible at <url>http://bioinfo-dbb.nhri.org.tw/capih</url>.</p> <p>Conclusion</p> <p>CAPIH exemplifies that large divergences exist in disease-associated proteins between human and the model animals. Since all of the newly developed medications must be tested in model animals before entering clinical trials, it is advisable that comparative analyses be performed to ensure proper translations of animal-based studies. In the case of AIDS, the host-HIV-1 protein interactions apparently have differed to a great extent among the compared species. An integrated protein network comparison among the four species will probably shed new lights on AIDS studies.</p

Prediction of virus-host protein-protein interactions mediated by short linear motifs

Table S4. Supplement information describing the previous files. (Supplement - Prediction of Virus-Host Protein-Protein interactions based on Short Linear Motifs.pdf) available at https://figshare.com/articles/Supplement_-_Prediction_of_Virus-Host_Protein-Protein_interactions_based_on_Short_Linear_Motifs/4667461 . (PDF 166 kb

Modular composition predicts kinase/substrate interactions

<p>Abstract</p> <p>Background</p> <p>Phosphorylation events direct the flow of signals and metabolites along cellular protein networks. Current annotations of kinase-substrate binding events are far from complete. In this study, we scanned the entire human protein sequences using the PROSITE domain annotation tool to identify patterns of domain composition in kinases and their substrates. We identified statistically enriched pairs of strings of domains (signature pairs) in kinase-substrate couples presented in the 2006 version of the PTM database.</p> <p>Results</p> <p>The signature pairs enriched in kinase - substrate binding interactions turned out to be highly specific to kinase subtypes. The resulting list of signature pairs predicted kinase-substrate interactions in validation dataset not used in learning with high statistical accuracy.</p> <p>Conclusions</p> <p>The method presented here produces predictions of protein phosphorylation events with high accuracy and mid-level coverage. Our method can be used in expanding the currently available drafts of cell signaling pathways and thus will be an important tool in the development of combination drug therapies targeting complex diseases.</p