Large-scale mapping of human protein–protein interactions by mass spectrometry

Rob M Ewing^1,2, Peter Chu^1,a, Fred Elisma³, Hongyan Li^1,a, Paul Taylor^1,a, Shane Climie^1,a, Linda McBroom-Cerajewski^1,a, Mark D Robinson^1,a, Liam O'Connor^1,a, Michael Li^1,a, Rod Taylor¹, Moyez Dharsee^1,2, Yuen Ho^1,a, Adrian Heilbut^1,a, Lynda Moore^1,a, Shudong Zhang¹, Olga Ornatsky^1,a, Yury V Bukhman^1,a, Martin Ethier³, Yinglun Sheng³, Julian Vasilescu³, Mohamed Abu-Farha³, Jean-Philippe Lambert³, Henry S Duewel^1,a, Ian I Stewart^1,2, Bonnie Kuehl^1,a, Kelly Hogue^1,a, Karen Colwill^1,a, Katharine Gladwish¹, Brenda Muskat^1,a, Robert Kinach^1,a, Sally-Lin Adams^1,a, Michael F Moran^1,a, Gregg B Morin^1,a, Thodoros Topaloglou^1,4 & Daniel Figeys^1,3

^aPresent address: Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada

^aPresent address: Department of Biology, York University, Toronto, Ontario, Canada

^aPresent address: Hospital for Sick Children and McLaughlin Centre for Molecular Medicine, and Department of Medical Genetics and Microbiology, University of Toronto, Toronto, Ontario, Canada

^aPresent address: Popper and Company LLC, Sarasota, FL, USA

^aPresent address: Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada

^aPresent address: Genetics and Bioinformatics, Walter and Eliza Hall Institute of Medical Research (WEHI), Parkville, Victoria, Australia

^aPresent address: Novartis Institutes for Biomedical Research, Cambridge, MA, USA

^aPresent address: Platform Computing, Markham, Ontario, Canada

^aPresent address: Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada

^aPresent address: CombinatoRx Inc, Cambridge, MA, USA

^aPresent address: Michael Smith Genome Sciences Centre, BC Cancer Agency Genome Sciences Centre, Vancouver, British Columbia, Canada

^aPresent address: Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada

^aPresent address: Campbell Family Institute for Breast Cancer Research, University Health Network, Toronto, Ontario, Canada

^aPresent address: Sigma-Aldrich Corporation, St Louis, MO, USA

^aPresent address: Scientific Insights Consulting Group Inc., Mississauga, Ontario, Canada

^aPresent address: Advanced Protein Technology Centre, Hospital for Sick Children, Toronto, Ontario, Canada

^aPresent address: Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada

^aPresent address: MDS Pharma Services, Mississauga, Ontario, Canada

^aPresent address: Division of Haematology/Oncology, Hospital for Sick Children, Toronto, Ontario, Canada

Article highlights

Synopsis

Understanding the roles and consequences of protein–protein interactions is a fundamental goal in cellular biology and a prerequisite for the development of molecular systems biology. The endeavor of cataloging protein interactions is primarily hindered by the throughput and reproducibility of existing technologies. Different techniques for mapping protein interactions are available, such as the two-hybrid approach (Chien et al, 1991) and the LUMIER approach (Barrios-Rodiles et al, 2005) and assay whether two proteins interact in a pair-wise fashion. We have developed a high-throughput platform combining immunoprecipitation and high-throughput mass spectrometry (IP-HTMS) to rapidly identify potentially novel protein interactions for a bait protein of interest. We (Ho et al, 2002) and others (Gavin et al, 2002) previously used this approach to map protein–protein interactions in yeast, creating invaluable data sets for yeast biology and extrapolation into mammalian biology.

Mapping protein interactions in human cells has its own set of challenges owing to the number of potentially expressed genes, the number of different cell types, and the numbers of internal and external factors that impact the cellular system. Although a complete mapping of the human interactome is still beyond current capabilities, more focused studies are possible. Here we report the first large-scale application of IP-HTMS to the mapping of protein–protein interactions in human cells using 338 human bait proteins of significant biomedical interest. The complete data set is available from the Intact database (http://www.ebi.ac.uk/intact/site/) (accession EBI-1059370) or as a table of bait–prey pairs with associated confidence values (Supplementary Table II).

There has been much focus and discussion over the last few years on the quality and reproducibility of interactions in high-throughput protein–protein interaction datasets (e.g. von Mering et al, 2002). A guiding principle in our study has therefore been to implement stringent quality controls. The final data set includes protein interactions for 338 human bait proteins (Supplementary Table I). For over half of these baits, two or more replicate immunoprecipitation experiments were performed, requiring a total of 1034 individual immunoprecipitation experiments with associated SDS–PAGE. These experiments yielded over 16 000 gel bands for which over 400 000 MS/MS spectra were assigned peptide sequences. Approximately 1/5 of our immunoprecipitation experiments were control (no-bait) experiments allowing us to build a comprehensive list of spurious and ubiquitously binding proteins that could then be filtered out of the interaction network. Another 1/5 of the experiments were directed towards a study of the reproducibility of prey protein identification using our platform. These 202 immunoprecipitation experiments, derived from 18 baits, were used to train a statistical model that associates interaction reproducibility with various observed experimental parameters, such as the number of peptides identified for the given prey protein. This model was used to assign confidence values (taking a value between 0 and 1) to each of the 6486 interactions in the data set.

As the interaction confidence score is calculated solely from IP-HTMS experimental parameters, an initial focus was to confirm that the confidence score was an accurate means of ranking the interactions for further study. We observed, for example, that known interactions in the data set have, on average, significantly higher interaction confidence scores. For example, the set of baits corresponding to core and regulatory components of the proteasome enabled reconstruction of a proteasome interaction network (Figure 6C), comprising many known proteasome components and enriched for high-scoring interactions.

Figure 6c

(C–F) Complete interaction networks (representing both baits and preys) for selected groups of baits. Nodes are colored according to cellular component or biological process as indicated on each figure. Baits are shown as large, labeled oval shapes, preys as small, labeled oval shapes. Arrow direction indicates a bait–prey relationship and line thickness indicates the interaction confidence score (see legend in panel C). Preys are grouped according to the baits with which they were identified (except panel E where they are grouped according to interaction confidence score). (C) Proteasome baits (corresponds to bait–bait cluster B (panel iv)). (D) Sumoylation pathway (corresponds to bait-bait cluster B (panel vi)). (E) Nek6. (F) Translation initiation and elongation (corresponds to bait–bait cluster B (panel iii)).

Full figure and legend (279K)Figures & Tables index

We also integrated the IP-HTMS data set with several other genomic-scale data including other protein–protein interaction data sets, gene co-expression data, and annotations from the gene ontology project. In the latter case, we analyzed the frequency of co-occurrence of both bait and prey protein in the same biological process or cellular component category (Figure 3). We find that there is significant enrichment of bait–prey pairs sharing the same annotation category, indicating a strong tendency for bait proteins to bind prey proteins with related functions. Integration with gene co-expression data showed that interaction data sets, this one included, are enriched for gene pairs that are co-expressed. This enabled identification of tightly clustered sets of protein interactors that are also co-expressed at the mRNA level. For example, the LYAR bait protein (Ly1 antibody reactive clone) is a nucleolar protein of unknown function (Su et al, 1993). This bait identified a set of nucleolar-localized prey proteins that are also very tightly co-expressed (Figure 5). These results along with the other protein–protein interaction data sources provided a powerful means of cross-validating the human IP-HTMS data set and associated methodology.

Figure 3

GO coincidence maps. Coincidence maps showing enrichment of bait–prey GO category combinations. Each bait–prey category combination is represented by a square in the matrix and colored according to the P-value from a pairwise statistical test (Fisher exact test) of association. (A) Bait–prey biological processes. (B) Randomly permuted bait–prey biological processes. (C) Cellular component categories. (D) Randomly permuted bait–prey cellular component categories.

Full figure and legend (341K)Figures & Tables index

Figure 5

LYAR interactors also show strong gene co-expression with LYAR. Box plot showing distribution of P-values for all genes coexpressed (in three or more studies) with LYAR. Red points indicate co-expression P-values for 12 LYAR IP-HTMS interactors. Interactor descriptions include known subcellular localizations in square brackets where available.

Full figure and legend (133K)Figures & Tables index

Our focus in this paper has been to prepare a quality-controlled, large-scale human protein interaction data set that will add significantly to our knowledge of the human protein interactome. Given the focus on baits of significant biomedical interest (through functional or disease associations), we anticipate that this data set alongside other sources of human protein–protein interactions will be an important starting point for functional characterization of disease-related interactions and complexes. The IP-HTMS platform utilized here shows great promise as an effective means of protein interaction discovery and we anticipate that future applications will include broadening to a larger set of disease associated proteins, to other cell lines and coupling with drug treatments.

MORE ARTICLES LIKE THIS

These links to content published by NPG are automatically generated.