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Review

Strategies towards high-quality binary proteininteractome maps

Irma Lemmens, Sam Lievens, Jan Tavernier⁎

Department of Medical Protein Research, VIB, Ghent, BelgiumDepartment of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium

A R T I C L E I N F O

⁎ Corresponding author. Department of Medfax: +32 92649492.

E-mail address: jan.tavernier@vib-ugent.b

1874-3919/$ – see front matter © 2010 Elsevidoi:10.1016/j.jprot.2010.02.001

A B S T R A C T

Article history:Received 11 December 2009Accepted 5 February 2010

Many processes in a cell depend on protein–protein interactions (PPIs) and perturbations ofthese interactions can lead to diseases. Comprehensive knowledge of PPI networks will notonly give us information on how the cell is organized, but will also provide new drug targets.Current binary PPI networks are mainly generated by high-throughput yeast two-hybrid.Due to the small overlap of thesemaps, it has long been assumed that thesemaps are of lowquality containing many false positives. However, by using an orthogonal two-hybridmethod, MAPPIT (mammalian protein–protein interaction trap), these maps were shown tobe of high quality suggesting that the limited overlap is likely due to low sensitivity and notto low specificity.

© 2010 Elsevier B.V. All rights reserved.

Keywords:Yeast two-hybridProtein–protein interaction networksMAPPITHigh-throughputValidationProtein interactome

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14152. Application of Y2H in interactome mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14163. Validation of HT-Y2H-based PPI networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14174. Towards high-confidence genome-wide protein interactome maps . . . . . . . . . . . . . . . . . . . . . . . . . . . 14185. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1419Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1419References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1419

1. Introduction

Now that the entire human genome has been sequenced, anew milestone yet to be achieved is the identification of the

ical Protein Research, Alb

e (J. Tavernier).

er B.V. All rights reserved

interactions between the encoded proteins. Protein–proteininteractions (PPIs) are at the heart of cellular function. Theorganization of sub-cellular compartments and filament net-works, the function ofmolecularmachines such as ribosomes,

ert Baertsoenkaai 3, B-9000 Ghent, Belgium. Tel.: +32 92649302;

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Fig. 1 – The yeast two-hybrid principle. Protein B (Bait) andProtein P (Prey) are fused to the DNA binding domain (DBD)and activation domain (AD) of a transcription factor,respectively. Only when protein B and P interact functionalreconstitution of the transcription factor will occur, leading totranscriptional activation of the reporter gene [10].

proteasomes, spliceosomes and the nuclear pore complex, orthe highly regulated intracellular signalling network andcontrol of gene expression: all rely on the correct assemblyof protein complexes and their organisation into higher-levelassemblies. The formidable advances in protein science overthe past decade have highlighted the extent of the PPInetwork. It appears that most proteins are, at some timepoint in a cell's lifespan, involved in complex formation withmultiple interaction partners. Although far from being com-plete, large-scale PPI networks for several model organismsare currently being generated and are starting to reveal thebasic wiring diagrams of complex biological systems. Highlyconnected, hierarchically organised PPI-modules appear todefine distinct biological processes [1–5].

It is not surprising that altered protein interaction profilescaused by aberrant expression patterns or by mutations cantrigger cellular dysfunction, eventually leading to disease.Moreover, many viral and bacterial pathogens rely on PPIs toexert their damaging effects. It can be expected that newinsights into the processes within PPI networks will help toexplain the pathophysiologies underlying major human dis-eases and will enable the better definition of drug targets [6].Moreover, establishing a detailed understanding of selectedinteracting protein domains, combined with highly efficientscreening tools may contribute significantly to the design andselection of novel therapeutics.

Strategies to analyse PPIs essentially comprise biochemicaland genetic approaches [7–9]. Biochemical methods generallyutilize purified protein (complexes). Examples include surfaceplasmon resonance, calorimetry and mass spectrometry-based approaches, just to name a few. In the genetic two-hybrid methods, interaction between genetically encodedhybrid bait and prey proteins leads to functional reconstitu-tion of a reporter system in live cells.

The ‘classical’ yeast two-hybrid (Y2H) principle wasoriginally developed by Fields and Song in 1989, and is basedon the fact that a transcription factor can be split up into twoparts, a DNA binding domain (DBD) and an activation domain(AD) [10]. Most commonly, the yeast transcription factor Gal4is used. Its DBD is fused to protein X (bait) and its AD to proteinY (prey). Only when protein X and Y interact, complementa-tion of the DBD and AD occurs, reconstituting a functionaltranscription factor that induces transcriptional activation ofa reporter gene. Frequently used reporters generate a colori-metric or fluorescent read-out or allow growth on selectivemedia (Fig. 1).

Many variations on this basic concept were developed,including protein-fragment complementation assays (PCA)which are based on complementationof fluorescentmoleculesor enzymes, e.g. beta-lactamase, luciferase, or dihydrofolatereductase (DHFR). The folding of these complementary proteinfragments is proximity-dependent, so only if bait and preyinteract will both protein fragments assemble into a fluores-cent molecule or functional enzyme. A variant hereof is thesplit-TEV assay, where an interaction leads to the functionalreconstitution of the tobacco etch virus (TEV) protease, whichsubsequently releases a reporter molecule or a transcriptionfactor that activates transcription of a reporter gene. Two-hybrid variants that allow for real-time analysis are FRET andBRET (fluorescence — and bioluminescence resonance energy

transfer). These assays are not based on the reconstitution of aprotein but depend on the energy transfer between twoproteins, i.e. in FRET bait and prey are fused to two fluorescentmolecules with different emission and excitation spectra.Upon interaction, energy transfer between the excited ‘donor’(e.g. CFP) and ‘acceptor’ protein (e.g. YFP) leads to a shift of thefluorescence signal. In case of BRET, the energy donor is aluciferase enzyme instead of a fluorescent protein [9]. Yetanother two-hybrid systemwas developed by our group and istermedMAPPIT (Mammalian protein–protein interaction trap).Here the bait is fused to a signaling-deficient cytokine receptorand the prey to a part of the glycoprotein 130 receptor (gp130).Upon bait-prey interaction and cytokine stimulation, func-tional complementation of the receptor leads to recruitmentand activation of endogenous STAT (signal transducer andactivator of transcription) molecules. These STAT moleculessubsequently shuttle to thenucleus to activate transcription ofa reporter gene [11]. For a more detailed description of thesemethods we refer to Lievens et al. [9].

Although recently PCA was used to create a genome-widePPI network of Saccharomyces cerevisiae [12], large-scale binarymapping efforts still largely rely on the ‘classical’ Y2H system,as it is capable of screening large collections of PPIs in a cost-effective manner. Given the low overlap observed betweenlarge-scale PPI datasets generated by high-throughput (HT)-Y2H, the validity of such interactome maps can be criticized[13]. In this reviewwe discuss the approaches that can be usedto assess and improve thequality of current interactomemaps.

2. Application of Y2H in interactome mapping

The first complete interactome to be mapped was that of thebacteriophage T7 [14]. This effort sparked many (often still

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ongoing) projects that aim at charting genome-wide proteininteractomes, including those of bacteria (Helicobacter pylori,Campylobacter jejuni, Treponema pallidum) [15–18], S. cerevisiae[19–21], Arabidopsis thaliana [22], Plasmodium falciparum [23],Caenorhabditis elegans [24] and Drosophila melanogaster [25]. Inaddition, several focused interactome sub-networks have alsobeen generated, often underlying human diseases such asHuntington's disease [26] and human inherited ataxias [27] orintraviral and viral-host pathogen networks like the ones forhepatitis C [28] and herpes viruses. [29–31] Recently, twopapers have described a first draft of the human interactome[32,33].

These PPI maps are not perfect and contain false negatives,PPIs that are not detected by the method used, and falsepositives. The latter can be split up into technical andbiological artifacts. In the first category, an interaction signalis caused by events other than a PPI, in the latter the PPIactually takes place but cannot occur in the normal in vivosituation, for example because the proteins are never co-expressed in time or in space. To reduce the amount of falsepositives in Y2H screens many aspects have been optimized,such as the expression level of the hybrid proteins, the use ofdifferent reporter gene constructs and extra controls for auto-activation of the reporter genes. To eliminate technicalartifacts, the PPIs are tested at least twice and only reprodu-cibly detected interactions are retained. In addition, ‘sticky’proteins that bind non-specifically can be identified byincorporating a cut-off at the level of the promiscuity of PPIs[34]. If only a small number of interacting partners is allowedand the promiscuous proteins are filtered-out, the falsepositive rate decreases but at the expense of an increasednumber of false negatives. Especially the so-called ‘hubs’,proteins that have many binding partners, can be missed.When the network is large enough or can be integrated withexternal data, computationalmethods can partially correct forthat, for instance by taking into account if the binding partnersof a protein are connected to each other. Finally, integratingexternal datasets such as expression profiles or paralogous orhomologous interacting proteins can enhance the reliability ofan interaction network.

3. Validation of HT-Y2H-based PPI networks

The first large-scale Y2H study in human reported 755interactions in a focused analysis of TGFβ-regulated SMADsignaling pathways [35]. Because it was not feasible to studyeach interaction individually, only a handful of interactionswere validated by siRNA-mediated knockdown experimentsusing functional pathway-specific assays, thereby confirming8 out of the 14 selected interactions. Stelzl et al. [32] and Rualet al. [33], who published the first drafts of the humaninteractome on a genome-wide scale, used biochemical pull-down experiments to increase confidence of their datasets,with verification rates of 60% and 78%, respectively. Stelzlet al. also developed a bioinformatics scoring system thatgrouped the 3000 putative interactions into sets with high,medium and low confidence. Criteria that contributed to theconfidence score of a given PPI included signal consistencyacross different reporter genes, evolutionary conservation,

functional annotation and network topology. Although thisscoring entailed a bias towards well studied interactions, itappeared very useful for the prioritization of functionalfollow-up studies. An illustration of the biological significanceof this network included the validation of two novel Axin-1interactions, demonstrating that ANP32A and CRMP1 act asmodulators of Wnt signaling [32].

A more specific PPI network focused on inherited humanataxias, a group of diseases characterized by degeneration ofcerebellar Purkinje cells, showed that cerebellar ataxias sharecommon processes and pathways [27]. Data quality wasverified by performing a biochemical pull-down assay on aPPI subset, revealing a success rate of about 83%. The directbiological significance of this map was demonstrated by theidentification of Puratrophin-1 as an indirect binding partnerof the ataxia causing protein Ataxin-1, a finding that wassupported by concurrent evidence implicating Puratrophin-1in cerebellar ataxia [36]. Likewise, the biological relevance ofan interactome subset linked to Huntington's disease wasunderscored by the identification of the huntingtin interactingprotein GIT1, a G protein-coupled receptor kinase-interactingprotein which appeared to enhance huntingtin aggregation,the causative mechanism underlying the disease. [26]. Theseexamples illustrate the biological relevance of building HT-Y2H PPI networks. Although false positives and false negativesremain an issue, the wealth of useful information obtainedfrom these PPI networks largely surpasses this limitation.

Retesting of a subset of PPIs using orthogonal techniquesprovides a good estimation of the quality of HT-Y2H generatedinteractome maps since different methods are unlikely togenerate the same technical false positives. However, everyvalidation method has its own limitations and an intrinsic setof false positives and false negatives. To take this into accountstandardized, species-specific, positive — (PRS) and negativereference sets (RRS) were created by Vidal and coworkers.Positive reference sets were selected frommanually recuratedinformation taken from literature and databases. The negativereference sets contain randomly chosen protein pairs that areunlikely to interact. By comparing the success rate of retestinga PPI subset of a certain network to the detection rate of thepositive reference set under conditions that minimize thedetection of false positives (almost or no positives in the RRS),one compensates automatically for the shortcomings of thevalidation method, thereby getting reliable information aboutthe quality of the PPI network.

This approach has recently been applied by Yu et al. whoreported a Saccharomyces cerevisiae PPI network generated byHT-Y2H, the quality of which was confirmed by retesting asubset of PPIs by a yellow fluorescent PCA inmammalian cells,while additionally testing a Saccharomyces cerevisiae specificPRS and RRS set [21]. In this study, PCA was also used to test asubset of interactions from the previously reported HT-Y2H-based yeast PPI networks by Ito et al. [20] andUetz et al. [19]. Itoet al. split up their data in core and non-core interactions. Thecore interactions are more confident since they were pickedup three times or more during the screen, whereas the non-core interactions were identified only once or twice. Althoughthe overlap between the Ito and Uetz data is low (only 19% ofUetz and 15% of Ito-core interactionswere present in the otherdata set) the retest with PCA revealed that both the Uetz and

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Ito-core data are of high quality, since the success rate ofdetection (22–25%)was comparable to the PRS set (18%). This isin contrast to the Ito-non-core set of which only about 6% ofthe PPIs were validated suggesting the Ito-non-core set isindeed of lower quality as already pointed out previously [20].The small overlap between the Ito and Uetz datasets mostlikely stems from low sensitivity (i.e. many false-negatives)rather than from low specificity (i.e. many false positives) [21].

Our group retested a subset of the yeast PPI map generatedby Yu et al. using MAPPIT. Comparison with the MAPPIT retestscores of the yeast-specific PRS and RRS set further confirmedthe high quality of this protein interaction network [21]. Nextto the yeast PPImap,MAPPITwas also used as a validation toolfor newly generated HT-Y2H maps in C. elegans [24] andhuman [37]. In each of these studies, a species-specific PRSand RRS was tested and compared to a subset of interactionspresent in the protein interaction network generated by HT-Y2H. An overview of the verification data is shown in Fig. 2.These experiments indicated that the PPI maps are of highquality as opposed to previous suggestions. Likewise, afragmentome network involving 200 C. elegans proteinsinvolved in early embryogenesis was validated by MAPPIT[38]. For the generation of this network not a full-length openreading frame (ORF) library was used but instead for each

Fig. 2 – Validation of HT-Y2H generated PPI maps by MAPPIT. MAPPInetworks generated by high-throughput yeast two-hybrid (HT-YBesides a subset of the different PPI networks, a species-specifictime included. Data are taken from Venkatesan et al. [37] (humaC. elegans, in addition to theMAPPIT retest of theWI-2007 networBoxem et al. [38], covering 200 C. elegans proteins involved in eardivided in PPIs detectable with full-length (FL) proteins in the HTwhen using domains of the proteins. For the MAPPIT retest only

protein several small fragments were designed and tested byHT-Y2H. MAPPIT retests again confirmed the high quality ofthe data and, in addition revealed the complementaritybetween both methods since interactions not detectablewith full-length proteins in Y2H could be measured with theMAPPIT technology (Fig. 2).

The success rates of around 25–30% reported in thesevalidation studies seem low in contrast to the previousdetection rates of about 60–80% obtained with biochemicalpull-down assays, as mentioned above. This discrepancy canbe explained by the fact that the validation assays (PCA andMAPPIT) were calibrated against a PRS and RRS set, imposingstringent criteria to minimize the detection of false positivesby the validation method itself.

4. Towards high-confidence genome-wideprotein interactome maps

High-confidence, high-coverage genome-wide PPI maps willrequire concerted efforts that go beyond HT-Y2H. First, not allPPIs can be detected by Y2H. Intrinsic false negatives includeinteractions that are triggeredbypost-translationalmodificationsthat do not occur in yeast. Although heterologous expression of

T retest scores are shown for protein–protein interaction (PPI)2H) from different species (human, yeast and C. elegans).positive – (PRS) and negative reference set (RRS) was eachn), Yu et al. [21] (yeast) and Simonis et al. [24] (C. elegans). Fork, also the validation scores of a domain-based PPI network byly embryogenesis are included. This dataset was additionally-Y2H assay versus PPIs only detectable in the HT-Y2H assayFL proteins were used.

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amodifying enzymecanovercome this, such enzymes, e.g. activetyrosine kinases, can be toxic to yeast cells. In addition, severalprotein classes are difficult to detect with Y2H. Membraneproteins for example are likely to be inappropriately assayed asa fusionwith a reconstituted transcription factor. Also, proteinswith transcriptional activity can lead to auto-activation of thereporter genes rendering them unsuitable as bait. Consequent-ly, the number of PPIs that aremissed by HT-Y2H is substantial,implying that othermethodswill be needed if one aims atmorecompletely covering protein interaction networks. Efficientbiochemical and alternative two-hybrid techniques that aresuitable for this task are emerging [12,39–41]. In addition, falsepositives need to be filtered out of the PPI dataset. As previouslymentioned, false positive rates canbemeasuredby reanalysis of(selected) PPI subsets using orthogonal assays. However,increased efficiency and throughput of these alternativetechnologies now makes it possible to consistently retest allPPIs observed in a HT-Y2H screen, which will considerablyimprove the overall quality of HT-Y2H datasets. Moreover,retesting PPIs by multiple orthogonal assays also allows theassignment of a confidence score to each interaction. Thisapproachhas been recently proposed by the teamofMarcVidal,who has set out to generate a genome-wide map of the humanprotein interactome. Data fromHT-Y2H primary screenswill beretested by a panel of four orthogonal methods, includingMAPPIT, PCA, LUMIER (LUminescence-basedMammalian IntER-actome) and wNAPPA (Nucleic Acid Programmable ProteinArray) [42]. The performance of each of these methods hasbeen assessed using a human PRS and RRS. Any of thetechnologies appeared to detect between 20 and 35% of the setof true interactingproteins, under conditionswhere thenumberof false positives showingup in theRRSwas kept to aminimum.In addition, a closer look at the identity of the PPIs observed,learned that the different methods detect complementarysubsets of the PRS. This highlights the need of using comple-mentary methods for screening, taking into account theirintrinsic features and limitations, such as the cellular environ-ment in which the assay is carried out (e.g. yeast, human), thesite of interaction (e.g. nucleus, cytoplasm), the effect of thefusion (e.g. steric hindrance) and the read-outmethod (e.g. needfor cell lysis). Thisalso implies that PPIs that areonlydetectedbyonemethod can still represent true interactions, although witha higher risk of being a technical artifact. Nevertheless, thegeneration of a genome-wide PPI network, and the implemen-tation of a confidence score that increases its reliability, willyield a wealth of useful information. Integration with otherdatasets like expression and localization data and studying thebehavior of naturally occurring and disease-related mutationson the networkwill further increase the biological accuracy andvalue of PPI networks.

5. Conclusion

The quality of HT-Y2H interactomemaps is often criticized butstudies using orthogonal assays contradict this. The syste-matic retesting by multiple assay formats will allow addingconfidence scores for all individual PPIs enabling the gener-ation of high-quality PPI networks. Since intrinsic negativesare inherent to every technique, including HT-Y2H, optimal

coverage of an interactome will require complementarytechniques. Ultimately, such high-quality PPI networks willbecome invaluable resources for a better understanding ofcellular processes and pathologies at the systems level.

Acknowledgements

We thank the Marc Vidal Lab for helpful discussions andassistance in the MAPPIT constructs. Our work is supported bygrants from the Fund for Scientific Research — Flanders(G.0031.06N), Ghent University (GOA 12051401), and the IUAP-6(No. P6:28). IL is a postdoctoral fellow with the Fund forScientific Research — Flanders.

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