An experimentally derived confidence score for binary protein-protein interactions

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Abstract

Information on protein-protein interactions is of central importance for many areas of biomedical research. Currently no method exists to systematically and experimentally assess the quality of individual interactions reported in interaction mapping experiments. To provide a standardized confidence-scoring method that can be applied to tens of thousands of protein interactions we have developed an interaction tool-kit consisting of four complementary high-throughput (HT) protein interaction assays. These assays were benchmarked against positive and random reference sets (PRS and RRS) consisting of well documented human interaction pairs and randomly chosen protein pairs, respectively. A logistic regression model was trained using the PRS/RRS data to combine the assay outputs and calculate the probability that any novel interaction pair is a true biophysical interaction once it has been tested in the tool-kit. This general approach will allow a systematic and empirical assignment of confidence scores to all individual protein-protein interactions in interactome networks.

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Most cited references 27

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Protein-protein interactions play crucial roles in the execution of various biological functions. Accordingly, their comprehensive description would contribute considerably to the functional interpretation of fully sequenced genomes, which are flooded with novel genes of unpredictable functions. We previously developed a system to examine two-hybrid interactions in all possible combinations between the approximately 6,000 proteins of the budding yeast Saccharomyces cerevisiae. Here we have completed the comprehensive analysis using this system to identify 4,549 two-hybrid interactions among 3,278 proteins. Unexpectedly, these data do not largely overlap with those obtained by the other project [Uetz, P., et al. (2000) Nature (London) 403, 623-627] and hence have substantially expanded our knowledge on the protein interaction space or interactome of the yeast. Cumulative connection of these binary interactions generates a single huge network linking the vast majority of the proteins. Bioinformatics-aided selection of biologically relevant interactions highlights various intriguing subnetworks. They include, for instance, the one that had successfully foreseen the involvement of a novel protein in spindle pole body function as well as the one that may uncover a hitherto unidentified multiprotein complex potentially participating in the process of vesicular transport. Our data would thus significantly expand and improve the protein interaction map for the exploration of genome functions that eventually leads to thorough understanding of the cell as a molecular system.

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Siming Li, Christopher Armstrong, Nicolas Bertin … (2004)

To initiate studies on how protein-protein interaction (or "interactome") networks relate to multicellular functions, we have mapped a large fraction of the Caenorhabditis elegans interactome network. Starting with a subset of metazoan-specific proteins, more than 4000 interactions were identified from high-throughput, yeast two-hybrid (HT=Y2H) screens. Independent coaffinity purification assays experimentally validated the overall quality of this Y2H data set. Together with already described Y2H interactions and interologs predicted in silico, the current version of the Worm Interactome (WI5) map contains approximately 5500 interactions. Topological and biological features of this interactome network, as well as its integration with phenome and transcriptome data sets, lead to numerous biological hypotheses.

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The Biomolecular Interaction Network Database (BIND: http://bind.ca) archives biomolecular interaction, complex and pathway information. A web-based system is available to query, view and submit records. BIND continues to grow with the addition of individual submissions as well as interaction data from the PDB and a number of large-scale interaction and complex mapping experiments using yeast two hybrid, mass spectrometry, genetic interactions and phage display. We have developed a new graphical analysis tool that provides users with a view of the domain composition of proteins in interaction and complex records to help relate functional domains to protein interactions. An interaction network clustering tool has also been developed to help focus on regions of interest. Continued input from users has helped further mature the BIND data specification, which now includes the ability to store detailed information about genetic interactions. The BIND data specification is available as ASN.1 and XML DTD.

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Author and article information

Journal

Journal ID (nlm-journal-id): 101215604

Journal ID (pubmed-jr-id): 32338

Journal ID (nlm-ta): Nat Methods

Title: Nature methods

ISSN (Print): 1548-7091

ISSN (Electronic): 1548-7105

Publication date Nihms-submitted: 18 October 2010

Publication date (Electronic): 7 December 2008

Publication date (Print): January 2009

Publication date PMC-release: 9 November 2010

Volume: 6

Issue: 1

Pages: 91-97

Affiliations

[1 ]Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, 44 Binney Street, Boston, Massachusetts 02115, USA.

[2 ]Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, Massachusetts 02115, USA.

[3 ]Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 250 Longwood Avenue, Boston, Massachusetts 02115, USA.

[4 ]Facultés Universitaires Notre-Dame de la Paix, 61 Rue de Bruxelles, 5000 Namur, Belgium.

[5 ]Centre for Systems Biology, Samuel Lunenfeld Research Institute, Mount Sinai Hospital, 600 University Avenue, Toronto Ontario M5G 1X5.

[6 ]Department of Medical Protein Research, VIB, and Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium.

Author notes

Correspondence and requests for materials should be addressed to M.V. ( marc_vidal@ 123456dfci.harvard.edu ), P.B. ( pascal_braun@ 123456dfci.harvard.edu ), J.L.W. ( wrana@ 123456mshri.on.ca ), or J.T. ( jan.tavernier@ 123456ugent.be )

[7]

Present address: Harvard Medical School, Department of Cell Biology, 240 Longwood Avenue, Boston, Massachusetts 02115, USA.

[8]

These authors contributed equally to this work.

Article

Manuscript ID: nihpa79432

DOI: 10.1038/nmeth.1281

PMC ID: 2976677

PubMed ID: 19060903

SO-VID: ece7551e-66bc-46ff-ba16-c9fc76308480