An integrative approach for predicting interactions of protein regions

Schelhorn, Sven-Eric; Lengauer, Thomas; Albrecht, Mario

doi:10.1093/bioinformatics/btn290

Cited by 15 publications

(6 citation statements)

References 56 publications

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“…They formulated the problem as a linear program for which the objective function is to minimize the number of DDIs necessary to justify the underlying PPIs. There are also some computational methods to predict DDIs that could enhance the results of our approach [30-33]. …”

Section: Discussionmentioning

confidence: 99%

Protein complex prediction via verifying and reconstructing the topology of domain-domain interactions

et al. 2010

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BackgroundHigh-throughput methods for detecting protein-protein interactions enable us to obtain large interaction networks, and also allow us to computationally identify the associations of proteins as protein complexes. Although there are methods to extract protein complexes as sets of proteins from interaction networks, the extracted complexes may include false positives because they do not account for the structural limitations of the proteins and thus do not check that the proteins in the extracted complex can simultaneously bind to each other. In addition, there have been few searches for deeper insights into the protein complexes, such as of the topology of the protein-protein interactions or into the domain-domain interactions that mediate the protein interactions.ResultsHere, we introduce a combinatorial approach for prediction of protein complexes focusing not only on determining member proteins in complexes but also on the DDI/PPI organization of the complexes. Our method analyzes complex candidates predicted by the existing methods. It searches for optimal combinations of domain-domain interactions in the candidates based on an assumption that the proteins in a candidate can form a true protein complex if each of the domains is used by a single protein interaction. This optimization problem was mathematically formulated and solved using binary integer linear programming. By using publicly available sets of yeast protein-protein interactions and domain-domain interactions, we succeeded in extracting protein complex candidates with an accuracy that is twice the average accuracy of the existing methods, MCL, MCODE, or clustering coefficient. Although the configuring parameters for each algorithm resulted in slightly improved precisions, our method always showed better precision for most values of the parameters.ConclusionsOur combinatorial approach can provide better accuracy for prediction of protein complexes and also enables to identify both direct PPIs and DDIs that mediate them in complexes.

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Section: Discussionmentioning

confidence: 99%

Protein complex prediction via verifying and reconstructing the topology of domain-domain interactions

et al. 2010

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“…The CORUM dataset has been used for a number of bioinformatics analyses like tissue-specific expression of proteins (16), functional interpretation of high-throughput data (17–19) or to predict interactions of protein regions (20). In addition, the dataset contributes to web-based applications like the DICS database of functional modules (21) or the COFECO tool for composite function annotation (22).…”

Section: Introductionmentioning

confidence: 99%

CORUM: the comprehensive resource of mammalian protein complexes—2009

Ruepp¹,

Waegele²,

Lechner³

et al. 2009

Nucleic Acids Research

942

679

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CORUM is a database that provides a manually curated repository of experimentally characterized protein complexes from mammalian organisms, mainly human (64%), mouse (16%) and rat (12%). Protein complexes are key molecular entities that integrate multiple gene products to perform cellular functions. The new CORUM 2.0 release encompasses 2837 protein complexes offering the largest and most comprehensive publicly available dataset of mammalian protein complexes. The CORUM dataset is built from 3198 different genes, representing ∼16% of the protein coding genes in humans. Each protein complex is described by a protein complex name, subunit composition, function as well as the literature reference that characterizes the respective protein complex. Recent developments include mapping of functional annotation to Gene Ontology terms as well as cross-references to Entrez Gene identifiers. In addition, a ‘Phylogenetic Conservation’ analysis tool was implemented that analyses the potential occurrence of orthologous protein complex subunits in mammals and other selected groups of organisms. This allows one to predict the occurrence of protein complexes in different phylogenetic groups. CORUM is freely accessible at (http://mips.helmholtz-muenchen.de/genre/proj/corum/index.html).

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“…More information on these datasets is found in Ramírez et al (2007). In addition, several domain–domain interaction datasets are offered by DASMI servers: three experimental datasets derived from 3D structures obtained by X-ray crystallography or NMR spectroscopy [3did (Stein et al , 2009), iPfam (Finn et al , 2005) and PiNS (Bordner and Gorin, 2008)] and 11 predicted datasets (Chen and Liu, 2005; Guimarães et al , 2006; Jothi et al , 2006; Lee et al , 2006a; Liu et al , 2005; Ng et al , 2003; Pagel et al , 2008; Riley et al , 2005; Schelhorn et al , 2008; Wang et al , 2007; Wuchty, 2006), see Supplementary Data File 3. Moreover, we set up two confidence servers, FunSimMat (Schlicker and Albrecht, 2008; Schlicker et al , 2006) and Domain support (Finn et al , 2005; Ramírez et al , 2007), which can be used to assess the reliability of protein interactions.…”

Section: Methodsmentioning

confidence: 99%

“…However, IMEx and its member databases are restricted to experimentally determined protein interactions, which cover only a fraction of the estimated interactomes (Stumpf et al , 2008). Voluminous data on predicted protein and domain interactions that have been made publicly available by different research groups (Schelhorn et al , 2008; Schlicker et al , 2007; Shoemaker and Panchenko, 2007) are not included.…”

Section: Introductionmentioning

confidence: 99%

DASMI: exchanging, annotating and assessing molecular interaction data

et al. 2009

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Motivation: Ever increasing amounts of biological interaction data are being accumulated worldwide, but they are currently not readily accessible to the biologist at a single site. New techniques are required for retrieving, sharing and presenting data spread over the Internet.Results: We introduce the DASMI system for the dynamic exchange, annotation and assessment of molecular interaction data. DASMI is based on the widely used Distributed Annotation System (DAS) and consists of a data exchange specification, web servers for providing the interaction data and clients for data integration and visualization. The decentralized architecture of DASMI affords the online retrieval of the most recent data from distributed sources and databases. DASMI can also be extended easily by adding new data sources and clients. We describe all DASMI components and demonstrate their use for protein and domain interactions.Availability: The DASMI tools are available at http://www.dasmi.de/ and http://ipfam.sanger.ac.uk/graph. The DAS registry and the DAS 1.53E specification is found at http://www.dasregistry.org/.Contact: mario.albrecht@mpi-inf.mpg.deSupplementary information: Supplementary data and all figures in color are available at Bioinformatics online.

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An integrative approach for predicting interactions of protein regions

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 15 publications

References 56 publications

Protein complex prediction via verifying and reconstructing the topology of domain-domain interactions

Protein complex prediction via verifying and reconstructing the topology of domain-domain interactions

CORUM: the comprehensive resource of mammalian protein complexes—2009

DASMI: exchanging, annotating and assessing molecular interaction data

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