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1.
Background
The amount of data on protein-protein interactions (PPIs) available in public databases and in the literature has rapidly expanded in recent years. PPI data can provide useful information for researchers in pharmacology and medicine as well as those in interactome studies. There is urgent need for a novel methodology or software allowing the efficient utilization of PPI data in pharmacology and medicine. 相似文献2.
Christian Frech Michael Kommenda Viktoria Dorfer Thomas Kern Helmut Hintner Johann W Bauer Kamil ?nder 《BMC bioinformatics》2009,10(1):21
Background
Protein-protein interaction (PPI) data sets generated by high-throughput experiments are contaminated by large numbers of erroneous PPIs. Therefore, computational methods for PPI validation are necessary to improve the quality of such data sets. Against the background of the theory that most extant PPIs arose as a consequence of gene duplication, the sensitive search for homologous PPIs, i.e. for PPIs descending from a common ancestral PPI, should be a successful strategy for PPI validation. 相似文献3.
Background
Recently, there has been much interest in relating domain-domain interactions (DDIs) to protein-protein interactions (PPIs) and vice versa, in an attempt to understand the molecular basis of PPIs. 相似文献4.
Background
Regulated protein-protein interactions (PPIs) are pivotal molecular switches that are important for the regulation of signaling processes within eukaryotic cells. Cellular signaling is altered in various disease conditions and offers interesting options for pharmacological interventions. Constitutive PPIs are usually mediated by large interaction domains. In contrast, stimulus-regulated PPIs often depend on small post-translational modifications and are thus better suited targets for drug development. However, the detection of modification-dependent PPIs with biochemical methods still remains a labour- and material-intensive task, and many pivotal PPIs that are potentially suited for pharmacological intervention most likely remain to be identified. The availability of methods to easily identify and quantify stimulus-dependent, potentially also transient interaction events, is therefore essential. The assays should be applicable to intact mammalian cells, optimally also to primary cells in culture. 相似文献5.
Harald E Vonkeman Louise MA Braakman-Jansen Rogier M Klok Maarten J Postma Jacobus RBJ Brouwers Mart AFJ van de Laar 《Arthritis research & therapy》2008,10(6):R144
Introduction
We estimated the cost effectiveness of concomitant proton pump inhibitors (PPIs) in relation to the occurrence of non-steroidal anti-inflammatory drug (NSAID) ulcer complications. 相似文献6.
Sheng-An Lee Chen-Hsiung Chan Tzu-Chi Chen Chia-Ying Yang Kuo-Chuan Huang Chi-Hung Tsai Jin-Mei Lai Feng-Sheng Wang Cheng-Yan Kao Chi-Ying F Huang 《BMC bioinformatics》2009,10(1):114-11
Background
Protein-protein interactions (PPIs) are critical to every aspect of biological processes. Expansion of all PPIs from a set of given queries often results in a complex PPI network lacking spatiotemporal consideration. Moreover, the reliability of available PPI resources, which consist of low- and high-throughput data, for network construction remains a significant challenge. Even though a number of software tools are available to facilitate PPI network analysis, an integrated tool is crucial to alleviate the burden on querying across multiple web servers and software tools. 相似文献7.
Background
One of the crucial steps toward understanding the biological functions of a cellular system is to investigate protein–protein interaction (PPI) networks. As an increasing number of reliable PPIs become available, there is a growing need for discovering PPIs to reconstruct PPI networks of interesting organisms. Some interolog-based methods and homologous PPI families have been proposed for predicting PPIs from the known PPIs of source organisms.Results
Here, we propose a multiple-strategy scoring method to identify reliable PPIs for reconstructing the mouse PPI network from two well-known organisms: human and fly. We firstly identified the PPI candidates of target organisms based on homologous PPIs, sharing significant sequence similarities (joint E-value ≤ 1 × 10−40), from source organisms using generalized interolog mapping. These PPI candidates were evaluated by our multiple-strategy scoring method, combining sequence similarities, normalized ranks, and conservation scores across multiple organisms. According to 106,825 PPI candidates in yeast derived from human and fly, our scoring method can achieve high prediction accuracy and outperform generalized interolog mapping. Experiment results show that our multiple-strategy score can avoid the influence of the protein family size and length to significantly improve PPI prediction accuracy and reflect the biological functions. In addition, the top-ranked and conserved PPIs are often orthologous/essential interactions and share the functional similarity. Based on these reliable predicted PPIs, we reconstructed a comprehensive mouse PPI network, which is a scale-free network and can reflect the biological functions and high connectivity of 292 KEGG modules, including 216 pathways and 76 structural complexes.Conclusions
Experimental results show that our scoring method can improve the predicting accuracy based on the normalized rank and evolutionary conservation from multiple organisms. Our predicted PPIs share similar biological processes and cellular components, and the reconstructed genome-wide PPI network can reflect network topology and modularity. We believe that our method is useful for inferring reliable PPIs and reconstructing a comprehensive PPI network of an interesting organism. 相似文献8.
Qiongming Liu Qing Chen Jian Wang Ying Zhang Ying Zhou Cong Lin Wei He Fuchu He Danke Xu 《BMC biotechnology》2010,10(1):78
Background
Analysis of protein-protein interactions (PPIs) is a valuable approach for the characterization of huge networks of protein complexes or proteins of unknown function. Co-immunoprecipitation (coIP) using affinity resins coupled to protein A/G is the most widely used method for PPI detection. However, this traditional large scale resin-based coIP is too laborious and time consuming. To overcome this problem, we developed a miniaturized sandwich immunoassay platform (MSIP) by combining antibody array technology and coIP methods. 相似文献9.
Background
Experimentally verified protein-protein interactions (PPIs) cannot be easily retrieved by researchers unless they are stored in PPI databases. The curation of such databases can be facilitated by employing text-mining systems to identify genes which play the interactor role in PPIs and to map these genes to unique database identifiers (interactor normalization task or INT) and then to return a list of interaction pairs for each article (interaction pair task or IPT). These two tasks are evaluated in terms of the area under curve of the interpolated precision/recall (AUC iP/R) score because the order of identifiers in the output list is important for ease of curation. 相似文献10.
11.
Background
Signaling pathways can be reconstructed by identifying ‘effect types’ (i.e. activation/inhibition) of protein-protein interactions (PPIs). Effect types are composed of ‘directions’ (i.e. upstream/downstream) and ‘signs’ (i.e. positive/negative), thereby requiring directions as well as signs of PPIs to predict signaling events from PPI networks. Here, we propose a computational method for systemically annotating effect types to PPIs using relations between functional information of proteins.Results
We used regulates, positively regulates, and negatively regulates relations in Gene Ontology (GO) to predict directions and signs of PPIs. These relations indicate both directions and signs between GO terms so that we can project directions and signs between relevant GO terms to PPIs. Independent test results showed that our method is effective for predicting both directions and signs of PPIs. Moreover, our method outperformed a previous GO-based method that did not consider the relations between GO terms. We annotated effect types to human PPIs and validated several highly confident effect types against literature. The annotated human PPIs are available in Additional file 2 to aid signaling pathway reconstruction and network biology research.Conclusions
We annotated effect types to PPIs by using regulates, positively regulates, and negatively regulates relations in GO. We demonstrated that those relations are effective for predicting not only signs, but also directions of PPIs. The usefulness of those relations suggests their potential applications to other types of interactions such as protein-DNA interactions.12.
Objective
To investigate whether there is an increased risk of cardiac events in diabetic patients with a combined therapy of clopidogrel (CLO) and proton pump inhibitors (PPIs) after drug-eluting stent (DES) deployment.Methods
By using National Health Insurance Research Database, all patients who received CLO with or without PPI therapy within 90 days after undergoing DES (limus-eluting or paclitaxel-eluting stents) deployment were enrolled. Endpoints were acute coronary syndrome (ACS) and readmission for revascularization (percutaneous coronary intervention or coronary artery bypass graft surgery) after 3, 6, and 12 months.Results
A total of 6,603 diabetic patients received LESs (5,933 in the CLO subgroup and 670 in the CLO plus PPIs subgroup), and 3,202 patients received PESs (2,923 in the CLO subgroup and 279 in the CLO plus PPIs subgroup). The patients who received CLO plus PPIs were at higher risk of ACS than those receiving CLO within 1 year after DES deployment (LESs: 6-month hazard ratio [HR] = 1.63, and 1-year HR = 1.37; PESs: 3-month HR = 1.72). Patients with a history of ACS who received CLO plus PPIs were at higher risk of ACS after LES implantation (HR = 1.55) than those in the CLO group.Conclusion
In “real-world” diabetic patients with LES deployment, the combination of PPIs and CLO is associated with higher rates of ACS after 6 months and 1 year. Even after correction for confounding factors, concomitant PPI use remained an independent predictor of cardiac events, emphasizing the clinical importance of this drug—drug interaction. 相似文献13.
Background
The small subunit (SSU) processome is a large ribonucleoprotein complex involved in small ribosomal subunit assembly. It consists of the U3 snoRNA and ∼72 proteins. While most of its components have been identified, the protein-protein interactions (PPIs) among them remain largely unknown, and thus the assembly, architecture and function of the SSU processome remains unclear.Methodology
We queried PPI databases for SSU processome proteins to quantify the degree to which the three genome-wide high-throughput yeast two-hybrid (HT-Y2H) studies, the genome-wide protein fragment complementation assay (PCA) and the literature-curated (LC) datasets cover the SSU processome interactome.Conclusions
We find that coverage of the SSU processome PPI network is remarkably sparse. Two of the three HT-Y2H studies each account for four and six PPIs between only six of the 72 proteins, while the third study accounts for as little as one PPI and two proteins. The PCA dataset has the highest coverage among the genome-wide studies with 27 PPIs between 25 proteins. The LC dataset was the most extensive, accounting for 34 proteins and 38 PPIs, many of which were validated by independent methods, thereby further increasing their reliability. When the collected data were merged, we found that at least 70% of the predicted PPIs have yet to be determined and 26 proteins (36%) have no known partners. Since the SSU processome is conserved in all Eukaryotes, we also queried HT-Y2H datasets from six additional model organisms, but only four orthologues and three previously known interologous interactions were found. This provides a starting point for further work on SSU processome assembly, and spotlights the need for a more complete genome-wide Y2H analysis. 相似文献14.
Background
The identification of potentially relevant biomarkers and a deeper understanding of molecular mechanisms related to heart failure (HF) development can be enhanced by the implementation of biological network-based analyses. To support these efforts, here we report a global network of protein-protein interactions (PPIs) relevant to HF, which was characterized through integrative bioinformatic analyses of multiple sources of "omic" information. 相似文献15.
Bill Andreopoulos Christof Winter Dirk Labudde Michael Schroeder 《BMC bioinformatics》2009,10(1):196-20
Background
A lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies. Second-level neighbors separated by two protein-protein interactions (PPIs) were previously used for predicting protein function and finding complexes in high-error PPINs. We retrieve second level neighbors in PPINs, and complement these with structural domain-domain interactions (SDDIs) representing binding evidence on proteins, forming PPI-SDDI-PPI triangles. 相似文献16.
Background
Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI) is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles. 相似文献17.
18.
Background
Although protein-protein interaction (PPI) networks have been explored by various experimental methods, the maps so built are still limited in coverage and accuracy. To further expand the PPI network and to extract more accurate information from existing maps, studies have been carried out to integrate various types of functional relationship data. A frequently updated database of computationally analyzed potential PPIs to provide biological researchers with rapid and easy access to analyze original data as a biological network is still lacking. 相似文献19.
Background
Protein-protein interactions (PPIs) play fundamental roles in nearly all biological processes, and provide major insights into the inner workings of cells. A vast amount of PPI data for various organisms is available from BioGRID and other sources. The identification of communities in PPI networks is of great interest because they often reveal previously unknown functional ties between proteins. A large number of global clustering algorithms have been applied to protein networks, where the entire network is partitioned into clusters. Here we take a different approach by looking for local communities in PPI networks. 相似文献20.