An ontology-based search engine for protein-protein interactions

<p>Abstract</p> <p>Background</p> <p>Keyword matching or ID matching is the most common searching method in a large database of protein-protein interactions. They are purely syntactic methods, and retrieve the records in the database that contain a keyword or ID specified in a query. Such syntactic search methods often retrieve too few search results or no results despite many potential matches present in the database.</p> <p>Results</p> <p>We have developed a new method for representing protein-protein interactions and the Gene Ontology (GO) using modified Gödel numbers. This representation is hidden from users but enables a search engine using the representation to efficiently search protein-protein interactions in a biologically meaningful way. Given a query protein with optional search conditions expressed in one or more GO terms, the search engine finds all the interaction partners of the query protein by unique prime factorization of the modified Gödel numbers representing the query protein and the search conditions.</p> <p>Conclusion</p> <p>Representing the biological relations of proteins and their GO annotations by modified Gödel numbers makes a search engine efficiently find all protein-protein interactions by prime factorization of the numbers. Keyword matching or ID matching search methods often miss the interactions involving a protein that has no explicit annotations matching the search condition, but our search engine retrieves such interactions as well if they satisfy the search condition with a more specific term in the ontology.</p

A self-organized model for cell-differentiation based on variations of molecular decay rates

Systemic properties of living cells are the result of molecular dynamics governed by so-called genetic regulatory networks (GRN). These networks capture all possible features of cells and are responsible for the immense levels of adaptation characteristic to living systems. At any point in time only small subsets of these networks are active. Any active subset of the GRN leads to the expression of particular sets of molecules (expression modes). The subsets of active networks change over time, leading to the observed complex dynamics of expression patterns. Understanding of this dynamics becomes increasingly important in systems biology and medicine. While the importance of transcription rates and catalytic interactions has been widely recognized in modeling genetic regulatory systems, the understanding of the role of degradation of biochemical agents (mRNA, protein) in regulatory dynamics remains limited. Recent experimental data suggests that there exists a functional relation between mRNA and protein decay rates and expression modes. In this paper we propose a model for the dynamics of successions of sequences of active subnetworks of the GRN. The model is able to reproduce key characteristics of molecular dynamics, including homeostasis, multi-stability, periodic dynamics, alternating activity, differentiability, and self-organized critical dynamics. Moreover the model allows to naturally understand the mechanism behind the relation between decay rates and expression modes. The model explains recent experimental observations that decay-rates (or turnovers) vary between differentiated tissue-classes at a general systemic level and highlights the role of intracellular decay rate control mechanisms in cell differentiation.Comment: 16 pages, 5 figure

Hepatitis C virus infection protein network

A proteome-wide mapping of interactions between hepatitis C virus (HCV) and human proteins was performed to provide a comprehensive view of the cellular infection. A total of 314 protein–protein interactions between HCV and human proteins was identified by yeast two-hybrid and 170 by literature mining. Integration of this data set into a reconstructed human interactome showed that cellular proteins interacting with HCV are enriched in highly central and interconnected proteins. A global analysis on the basis of functional annotation highlighted the enrichment of cellular pathways targeted by HCV. A network of proteins associated with frequent clinical disorders of chronically infected patients was constructed by connecting the insulin, Jak/STAT and TGFβ pathways with cellular proteins targeted by HCV. CORE protein appeared as a major perturbator of this network. Focal adhesion was identified as a new function affected by HCV, mainly by NS3 and NS5A proteins

A systematic analysis of host factors reveals a Med23-interferon-λ regulatory axis against herpes simplex virus type 1 replication

Herpes simplex virus type 1 (HSV-1) is a neurotropic virus causing vesicular oral or genital skin lesions, meningitis and other diseases particularly harmful in immunocompromised individuals. To comprehensively investigate the complex interaction between HSV-1 and its host we combined two genome-scale screens for host factors (HFs) involved in virus replication. A yeast two-hybrid screen for protein interactions and a RNA interference (RNAi) screen with a druggable genome small interfering RNA (siRNA) library confirmed existing and identified novel HFs which functionally influence HSV-1 infection. Bioinformatic analyses found the 358 HFs were enriched for several pathways and multi-protein complexes. Of particular interest was the identification of Med23 as a strongly anti-viral component of the largely pro-viral Mediator complex, which links specific transcription factors to RNA polymerase II. The anti-viral effect of Med23 on HSV-1 replication was confirmed in gain-of-function gene overexpression experiments, and this inhibitory effect was specific to HSV-1, as a range of other viruses including Vaccinia virus and Semliki Forest virus were unaffected by Med23 depletion. We found Med23 significantly upregulated expression of the type III interferon family (IFN-λ) at the mRNA and protein level by directly interacting with the transcription factor IRF7. The synergistic effect of Med23 and IRF7 on IFN-λ induction suggests this is the major transcription factor for IFN-λ expression. Genotypic analysis of patients suffering recurrent orofacial HSV-1 outbreaks, previously shown to be deficient in IFN-λ secretion, found a significant correlation with a single nucleotide polymorphism in the IFN-λ3 (IL28b) promoter strongly linked to Hepatitis C disease and treatment outcome. This paper describes a link between Med23 and IFN-λ, provides evidence for the crucial role of IFN-λ in HSV-1 immune control, and highlights the power of integrative genome-scale approaches to identify HFs critical for disease progression and outcome

Constructing the HBV-human protein interaction network to understand the relationship between HBV and hepatocellular carcinoma

<p>Abstract</p> <p>Background</p> <p>Epidemiological studies have clearly validated the association between hepatitis B virus (HBV) infection and hepatocellular carcinoma (HCC). Patients with chronic HBV infection are at increased risk of HCC, in particular those with active liver disease and cirrhosis.</p> <p>Methods</p> <p>We catalogued all published interactions between HBV and human proteins, identifying 250 descriptions of HBV and human protein interactions and 146 unique human proteins that interact with HBV proteins by text mining.</p> <p>Results</p> <p>Integration of this data set into a reconstructed human interactome showed that cellular proteins interacting with HBV are made up of core proteins that are interconnected with many pathways. A global analysis based on functional annotation highlighted the enrichment of cellular pathways targeted by HBV.</p> <p>Conclusions</p> <p>By connecting the cellular proteins targeted by HBV, we have constructed a central network of proteins associated with hepatocellular carcinoma, which might be to regard as the basis of a detailed map for tracking new cellular interactions, and guiding future investigations.</p

The Overlap of Small Molecule and Protein Binding Sites within Families of Protein Structures

Protein–protein interactions are challenging targets for modulation by small molecules. Here, we propose an approach that harnesses the increasing structural coverage of protein complexes to identify small molecules that may target protein interactions. Specifically, we identify ligand and protein binding sites that overlap upon alignment of homologous proteins. Of the 2,619 protein structure families observed to bind proteins, 1,028 also bind small molecules (250–1000 Da), and 197 exhibit a statistically significant (p<0.01) overlap between ligand and protein binding positions. These “bi-functional positions”, which bind both ligands and proteins, are particularly enriched in tyrosine and tryptophan residues, similar to “energetic hotspots” described previously, and are significantly less conserved than mono-functional and solvent exposed positions. Homology transfer identifies ligands whose binding sites overlap at least 20% of the protein interface for 35% of domain–domain and 45% of domain–peptide mediated interactions. The analysis recovered known small-molecule modulators of protein interactions as well as predicted new interaction targets based on the sequence similarity of ligand binding sites. We illustrate the predictive utility of the method by suggesting structural mechanisms for the effects of sanglifehrin A on HIV virion production, bepridil on the cellular entry of anthrax edema factor, and fusicoccin on vertebrate developmental pathways. The results, available at http://pibase.janelia.org, represent a comprehensive collection of structurally characterized modulators of protein interactions, and suggest that homologous structures are a useful resource for the rational design of interaction modulators

Integrative network analysis identified key genes and pathways in the progression of hepatitis C virus induced hepatocellular carcinoma

Background: Incidence of hepatitis C virus (HCV) induced hepatocellular carcinoma (HCC) has been increasing in the United States and Europe during recent years. Although HCV-associated HCC shares many pathological characteristics with other types of HCC, its molecular mechanisms of progression remain elusive. Methods: To investigate the underlying pathology, we developed a systematic approach to identify deregulated biological networks in HCC by integrating gene expression profiles with high-throughput protein-protein interaction data. We examined five stages including normal (control) liver, cirrhotic liver, dysplasia, early HCC and advanced HCC. Results: Among the five consecutive pathological stages, we identified four networks including precancerous networks (Normal-Cirrhosis and Cirrhosis-Dysplasia) and cancerous networks (Dysplasia-Early HCC, Early-Advanced HCC). We found little overlap between precancerous and cancerous networks, opposite to a substantial overlap within precancerous or cancerous networks. We further found that the hub proteins interacted with HCV proteins, suggesting direct interventions of these networks by the virus. The functional annotation of each network demonstrates a high degree of consistency with current knowledge in HCC. By assembling these functions into a module map, we could depict the stepwise biological functions that are deregulated in HCV-induced hepatocarcinogenesis. Additionally, these networks enable us to identify important genes and pathways by developmental stage, such as LCK signalling pathways in cirrhosis, MMP genes and TIMP genes in dysplastic liver, and CDC2-mediated cell cycle signalling in early and advanced HCC. CDC2 (alternative symbol CDK1), a cell cycle regulatory gene, is particularly interesting due to its topological position in temporally deregulated networks. Conclusions: Our study uncovers a temporal spectrum of functional deregulation and prioritizes key genes and pathways in the progression of HCV induced HCC. These findings present a wealth of information for further investigation

Dengue-2 Structural Proteins Associate with Human Proteins to Produce a Coagulation and Innate Immune Response Biased Interactome

<p>Abstract</p> <p>Background</p> <p>Dengue virus infection is a public health threat to hundreds of millions of individuals in the tropical regions of the globe. Although Dengue infection usually manifests itself in its mildest, though often debilitating clinical form, dengue fever, life-threatening complications commonly arise in the form of hemorrhagic shock and encephalitis. The etiological basis for the virus-induced pathology in general, and the different clinical manifestations in particular, are not well understood. We reasoned that a detailed knowledge of the global biological processes affected by virus entry into a cell might help shed new light on this long-standing problem.</p> <p>Methods</p> <p>A bacterial two-hybrid screen using DENV2 structural proteins as bait was performed, and the results were used to feed a manually curated, global dengue-human protein interaction network. Gene ontology and pathway enrichment, along with network topology and microarray meta-analysis, were used to generate hypothesis regarding dengue disease biology.</p> <p>Results</p> <p>Combining bioinformatic tools with two-hybrid technology, we screened human cDNA libraries to catalogue proteins physically interacting with the DENV2 virus structural proteins, Env, cap and PrM. We identified 31 interacting human proteins representing distinct biological processes that are closely related to the major clinical diagnostic feature of dengue infection: haemostatic imbalance. In addition, we found dengue-binding human proteins involved with additional key aspects, previously described as fundamental for virus entry into cells and the innate immune response to infection. Construction of a DENV2-human global protein interaction network revealed interesting biological properties suggested by simple network topology analysis.</p> <p>Conclusions</p> <p>Our experimental strategy revealed that dengue structural proteins interact with human protein targets involved in the maintenance of blood coagulation and innate anti-viral response processes, and predicts that the interaction of dengue proteins with a proposed human protein interaction network produces a modified biological outcome that may be behind the hallmark pathologies of dengue infection.</p

Structural similarity-based predictions of protein interactions between HIV-1 and Homo sapiens

Abstract Background In the course of infection, viruses such as HIV-1 must enter a cell, travel to sites where they can hijack host machinery to transcribe their genes and translate their proteins, assemble, and then leave the cell again, all while evading the host immune system. Thus, successful infection depends on the pathogen's ability to manipulate the biological pathways and processes of the organism it infects. Interactions between HIV-encoded and human proteins provide one means by which HIV-1 can connect into cellular pathways to carry out these survival processes. Results We developed and applied a computational approach to predict interactions between HIV and human proteins based on structural similarity of 9 HIV-1 proteins to human proteins having known interactions. Using functional data from RNAi studies as a filter, we generated over 2000 interaction predictions between HIV proteins and 406 unique human proteins. Additional filtering based on Gene Ontology cellular component annotation reduced the number of predictions to 502 interactions involving 137 human proteins. We find numerous known interactions as well as novel interactions showing significant functional relevance based on supporting Gene Ontology and literature evidence. Conclusions Understanding the interplay between HIV-1 and its human host will help in understanding the viral lifecycle and the ways in which this virus is able to manipulate its host. The results shown here provide a potential set of interactions that are amenable to further experimental manipulation as well as potential targets for therapeutic intervention