STRING: Known and predicted protein-protein associations, integrated and transferred across organisms

European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany.
Nucleic Acids Research (Impact Factor: 9.11). 02/2005; 33(Database issue):D433-7. DOI: 10.1093/nar/gki005
Source: PubMed


A full description of a protein's function requires knowledge of all partner proteins with which it specifically associates. From a functional perspective, 'association' can mean direct physical binding, but can also mean indirect interaction such as participation in the same metabolic pathway or cellular process. Currently, information about protein association is scattered over a wide variety of resources and model organisms. STRING aims to simplify access to this information by providing a comprehensive, yet quality-controlled collection of protein-protein associations for a large number of organisms. The associations are derived from high-throughput experimental data, from the mining of databases and literature, and from predictions based on genomic context analysis. STRING integrates and ranks these associations by benchmarking them against a common reference set, and presents evidence in a consistent and intuitive web interface. Importantly, the associations are extended beyond the organism in which they were originally described, by automatic transfer to orthologous protein pairs in other organisms, where applicable. STRING currently holds 730,000 proteins in 180 fully sequenced organisms, and is available at

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Available from: Markus Krupp,
    • "The default cutoff for confident interactions is 0.4 [11] [17]. Chemical– protein interactions were transferred among species based on the sequence similarity of the proteins [17]. The chemical–protein interactions have also been successfully utilized to predict nongenotoxic hepatocarcinogenicity [18] [19]. "
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    ABSTRACT: Maleic acid is a multi-functional chemical widely applied in the manufacturing of polymer products including food packaging. However, the contamination of maleic acid in modified starch has raised the concerns about the effects of chronic exposure to maleic acid on human health. This study proposed a novel toxicogenomics approach for inferring functions, pathways and diseases potentially affected by maleic acid on humans by using known interactions between maleic acid and proteins. Neuronal signal transmission and cell metabolism were identified to be most influenced by maleic acid in this study. The top disease categories inferred to be associated with maleic acid were mental disorder, nervous system diseases, cardiovascular diseases, and cancers. The results from an in silico analysis showed that maleic acid could penetrate the blood-brain barrier to affect the nervous system. Several functions and pathways were further analyzed and identified to give insights into the mechanisms of maleic acid-associated diseases. The toxicogenomics approach may offer both a better understanding of the potential risks of maleic-acid exposure to humans and a direction for future toxicological investigation.
    Chemico-Biological Interactions 09/2014; 223. DOI:10.1016/j.cbi.2014.09.004 · 2.58 Impact Factor
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    • "Gene-gene interactions (GGIs) were extracted from STRING 9.0 [24]. We started from protein-protein interactions present in the H. sapiens STRING database and for each of them we recalculated the score as described in [56] but discarded text mining contributions. We then extracted 285,096 protein-protein interactions with scores of ≥ 0.7. "
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    ABSTRACT: Background Cilia are microtubule-based organelles protruding from almost all mammalian cells which, when dysfunctional, result in genetic disorders called “ciliopathies”. High-throughput studies have revealed that cilia are composed of thousands of proteins. However, despite many efforts, much remains to be determined regarding the biological functions of this increasingly important complex organelle. Results We have derived an online tool, from a systematic network-based approach to dissect the cilia/centrosome complex interactome (CCCI). The tool integrates all current available data into a model which provides an “interaction” perspective on ciliary function. We generated a network of interactions between human proteins organized into functionally relevant “communities”, which can be defined as groups of genes that are both highly inter-connected and strongly co-expressed. We then combined sequence and co-expression data in order to identify the transcription factors responsible for regulating genes within their respective communities. Our analyses have discovered communities significantly specialized for delegating specific biological functions such as mRNA processing, protein translation, folding and degradation processes that had never been associated with ciliary proteins until now. Conclusions CCCI will allow us to clarify the roles of previously unknown ciliary functions, elucidate the molecular mechanisms underlying ciliary-associated phenotypes, and apply our knowledge of the functional roles of relatively uncharacterized molecular entities to disease phenotypes and new clinical applications. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-658) contains supplementary material, which is available to authorized users.
    BMC Genomics 08/2014; 15(1):658. DOI:10.1186/1471-2164-15-658 · 3.99 Impact Factor
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    • "The STRING database ( was used as the source of functional networks [14, 31]. We interrogated STRING using as queries our predicted GTs from both L. rhamnosus GG and C. jejuni NCTC 11168 to retrieve the network of functional partners associated to each query (query-based subnetwork). "
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    ABSTRACT: Background Bacterial interactions with the environment- and/or host largely depend on the bacterial glycome. The specificities of a bacterial glycome are largely determined by glycosyltransferases (GTs), the enzymes involved in transferring sugar moieties from an activated donor to a specific substrate. Of these GTs their coding regions, but mainly also their substrate specificity are still largely unannotated as most sequence-based annotation flows suffer from the lack of characterized sequence motifs that can aid in the prediction of the substrate specificity. Results In this work, we developed an analysis flow that uses sequence-based strategies to predict novel GTs, but also exploits a network-based approach to infer the putative substrate classes of these predicted GTs. Our analysis flow was benchmarked with the well-documented GT-repertoire of Campylobacter jejuni NCTC 11168 and applied to the probiotic model Lactobacillus rhamnosus GG to expand our insights in the glycosylation potential of this bacterium. In L. rhamnosus GG we could predict 48 GTs of which eight were not previously reported. For at least 20 of these GTs a substrate relation was inferred. Conclusions We confirmed through experimental validation our prediction of WelI acting upstream of WelE in the biosynthesis of exopolysaccharides. We further hypothesize to have identified in L. rhamnosus GG the yet undiscovered genes involved in the biosynthesis of glucose-rich glycans and novel GTs involved in the glycosylation of proteins. Interestingly, we also predict GTs with well-known functions in peptidoglycan synthesis to also play a role in protein glycosylation. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-349) contains supplementary material, which is available to authorized users.
    BMC Genomics 05/2014; 15(1):349. DOI:10.1186/1471-2164-15-349 · 3.99 Impact Factor
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