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Roles for the Two-hybrid System in Exploration of the Yeast Protein Interactome

2002; Elsevier BV; Volume: 1; Issue: 8 Linguagem: Inglês

10.1074/mcp.r200005-mcp200

1535-9484

Takashi Ito, Kazuhisa Ota, Hiroyuki Kubota, Yoshihiro Yamaguchi, Tomoko Chiba, Kazumi Sakuraba, Mikio Yoshida,

Fungal and yeast genetics research

Comprehensive analysis of protein-protein interactions is a challenging endeavor of functional proteomics and has been best explored in the budding yeast. The yeast protein interactome analysis was achieved first by using the yeast two-hybrid system in a proteome-wide scale and next by large-scale mass spectrometric analysis of affinity-purified protein complexes. While these interaction data have led to a number of novel findings and the emergence of a single huge network containing thousands of proteins, they suffer many false signals and fall short of grasping the entire interactome. Thus, continuous efforts are necessary in both bioinformatics and experimentation to fully exploit these data and to proceed another step forward to the goal. Computational tools to integrate existing biological knowledge buried in literature and various functional genomic data with the interactome data are required for biological interpretation of the huge protein interaction network. Novel experimental methods have to be developed to detect weak, transient interactions involving low abundance proteins as well as to obtain clues to the biological role for each interaction. Since the yeast two-hybrid system can be used for the mapping of the interaction domains and the isolation of interaction-defective mutants, it would serve as a technical basis for the latter purpose, thereby playing another important role in the next phase of protein interactome research. Comprehensive analysis of protein-protein interactions is a challenging endeavor of functional proteomics and has been best explored in the budding yeast. The yeast protein interactome analysis was achieved first by using the yeast two-hybrid system in a proteome-wide scale and next by large-scale mass spectrometric analysis of affinity-purified protein complexes. While these interaction data have led to a number of novel findings and the emergence of a single huge network containing thousands of proteins, they suffer many false signals and fall short of grasping the entire interactome. Thus, continuous efforts are necessary in both bioinformatics and experimentation to fully exploit these data and to proceed another step forward to the goal. Computational tools to integrate existing biological knowledge buried in literature and various functional genomic data with the interactome data are required for biological interpretation of the huge protein interaction network. Novel experimental methods have to be developed to detect weak, transient interactions involving low abundance proteins as well as to obtain clues to the biological role for each interaction. Since the yeast two-hybrid system can be used for the mapping of the interaction domains and the isolation of interaction-defective mutants, it would serve as a technical basis for the latter purpose, thereby playing another important role in the next phase of protein interactome research. WHY PROTEIN INTERACTOME?Proteins rarely work by themselves. They almost always interact with other biomolecules to execute their functions. Networks of such biomolecular interactions constitute the basis for life, and those occurring between proteins play extremely important roles. Thus deciphering of entire protein interaction networks or protein interactome is vital to our understanding of life as a system of molecules. Although identification of novel protein interactions is an integral part of conventional or target-oriented studies, these are severely skewed to the neighbors of proteins with current popularity. It is thus necessary to perform a complementary, hypothesis-free approach in protein interactome analysis to obtain more unbiased representation. Such an analysis would also help us guess the functions of numerous novel proteins revealed by the genome projects, which are currently lacking any clue as to their specific functions. If a novel protein is found to bind a well characterized one, the former is likely involved in the same functional category as the latter (i.e. guilt by association). At the same time, the novel protein indicates a previously unrecognized aspect of the molecular pathways involving the known one, thereby expanding our knowledge on that pathway. Pioneering works toward this goal were first undertaken using the two-hybrid system to analyze the yeast protein interactome.THE PRINCIPLE OF THE YEAST TWO-HYBRID SYSTEMThe yeast two-hybrid (Y2H) 1The abbreviations used are: Y2H, yeast two-hybrid; ORF, open reading frame; IST, interaction sequence tag; MS, mass spectrometric or mass spectrometry. system was developed by Stanley Fields (1.Fields S. Song O. A novel genetic system to detect protein-protein interactions.Nature. 1989; 340: 245-246Google Scholar) on the basis of modular domain structure of the transcription factor GAL4, comprised of a DNA binding domain and transcription activation domain. In the Y2H system, one of the proteins of one's interest, termed X, is expressed as a hybrid protein with the GAL4 DNA binding domain, whereas the other, termed Y, is expressed with the activation domain. If X and Y interact, the two hybrid proteins, often coined as "bait" and "prey," respectively, are assembled onto GAL4 binding sites in the yeast genome. The assembly functionally reconstitutes the GAL4 transcription factor and induces the expression of reporter genes integrated in the region downstream of the GAL4 binding sites.The Y2H system enables highly sensitive detection of protein-protein interactions in vivo without handling any protein molecules. It also allows one to screen a library of activation domain fusions or preys for the binding partners of one's favorite protein expressed as a DNA binding domain fusion or bait, and it can be used to pinpoint protein regions mediating the interactions.On the other hand, the Y2H system has limitations. First, in principle, it cannot detect interactions requiring three or more proteins and those depending on posttranslational modifications. However, note that, when applied to the budding yeast itself, it can occasionally detect interactions involving three proteins or posttranslational modification by the aid of endogenous third proteins or modifying enzymes. Second, the Y2H system is not suitable for the detection of interactions involving membrane proteins, although a substantial number of such interactions have been detected via an unexplained mechanism. Finally, the Y2H interaction does not guarantee that the inferred interactions are of physiological relevance. Despite these and other limitations, the power of the Y2H system is so tremendous that it is now established as a standard technique in molecular biology.The Y2H system has been successfully used to examine an interaction between the two proteins of one's interest and also to screen for unknown binding partners of one's favorite protein. It can be, in principle, used in a more comprehensive fashion to examine all possible binary combinations between the proteins encoded by any single genome. Three groups (ours, CuraGen's, and Fields's) launched such ambitious projects using the budding yeast as the target.GENOME-WIDE Y2H ANALYSIS OF THE BUDDING YEASTWe amplified all open reading frames (ORFs) of the budding yeast by means of PCR and cloned them into two types of vectors, one expressing each ORF as bait and the other as prey (2.Ito T. Tashiro K. Muta S. Ozawa R. Chiba T. Nishizawa M. Yamamoto K. Kuhara S. Sakaki Y. Toward a protein-protein interaction map of the budding yeast: a comprehensive system to examine two-hybrid interactions in all possible combinations between the yeast proteins.Proc. Natl. Acad. Sci. U. S. A. 2000; 97: 1143-1147Google Scholar). The bait and prey plasmids were introduced into Y2H host strains one by one: the former and the latter were transformed to Y2H hosts bearing mating type a and α, respectively. Bearing opposite mating types, bait clone and prey clone can mate to form diploid cells. Consequently each diploid cell has a unique combination of bait and prey. If they interact, the reporter genes are activated to allow the cells to survive the selection. In other words, each survivor should bear a pair of mutually interacting bait and prey, which can be revealed by tag sequencing of the cohabiting plasmids to generate an interaction sequence tag (IST). These ISTs can then be used for the data base search to decode the inferred protein-protein interactions.We prepared pools for screening, each containing 96 bait or prey clones, performed the mating-based screening described above in all possible combinations between the pools, and finally revealed 4,549 independent two-hybrid interactions (3.Ito T. Chiba T. Ozawa R. Yoshida M. Hattori M. Sakaki Y. A comprehensive two-hybrid analysis to explore the yeast protein interactome.Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 4569-4574Google Scholar). Of these, 841 were detected more than three times and were assumed to be of high relevance. Hence we call these interactions as our "core" data. Notably more than 80% of these interactions were the ones never described before. A similar IST project was conducted by CuraGen (4.Uetz P. Giot L. Cagney G. Mansfield T.A. Judson R.S. Knight J.R. Lockshon D. Narayan V. Srinivasan M. Pochart P. Qureshi-Emilli A. Li Y. Godwin B. Conover D. Kalbfleisch T. Vijayadamodar G. Yang M. Johnston M. Fields S. Rothberg J.M. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae.Nature. 2000; 403: 623-627Google Scholar), who screened a pool of ∼6,000 preys with each unique bait. They revealed 691 interactions in total, most of which were also novel.Comparison between the two data sets revealed an unexpectedly small overlap: they share 141 interactions, which correspond to ∼10% of the total independent interactions (Fig. 1) (3.Ito T. Chiba T. Ozawa R. Yoshida M. Hattori M. Sakaki Y. A comprehensive two-hybrid analysis to explore the yeast protein interactome.Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 4569-4574Google Scholar). There would be a number of plausible reasons for the small overlap. The systems used by the two groups were different: we used multicopy vectors in the host bearing multiple reporter genes, whereas they used single-copy vectors but used only a single reporter gene. Since both groups PCR-amplified the ORFs, some would inevitably bear mutations that affect interactions. Although both groups pooled clones, the screen does not seem saturated: two-thirds of our 4,549 interactions (3.Ito T. Chiba T. Ozawa R. Yoshida M. Hattori M. Sakaki Y. A comprehensive two-hybrid analysis to explore the yeast protein interactome.Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 4569-4574Google Scholar), and one-third of CuraGen's 691 interactions were identified only once (4.Uetz P. Giot L. Cagney G. Mansfield T.A. Judson R.S. Knight J.R. Lockshon D. Narayan V. Srinivasan M. Pochart P. Qureshi-Emilli A. Li Y. Godwin B. Conover D. Kalbfleisch T. Vijayadamodar G. Yang M. Johnston M. Fields S. Rothberg J.M. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae.Nature. 2000; 403: 623-627Google Scholar). Of course, any two-hybrid screen contains false signals (see below). These and other unidentified factors are assumed to contribute to the small overlap observed between the two IST projects.The group led by Fields (4.Uetz P. Giot L. Cagney G. Mansfield T.A. Judson R.S. Knight J.R. Lockshon D. Narayan V. Srinivasan M. Pochart P. Qureshi-Emilli A. Li Y. Godwin B. Conover D. Kalbfleisch T. Vijayadamodar G. Yang M. Johnston M. Fields S. Rothberg J.M. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae.Nature. 2000; 403: 623-627Google Scholar) took a different approach in which an array of ∼6,000 prey clones was mated with each unique bait strain, and the diploid cells formed were replica-plated onto the selection medium to decode interactions from the coordinates of the survivors. This approach is rather slow and tedious but is highly sensitive and free from the problem of unsaturated screening. They examined 142 baits to reveal 281 interactions, which again failed to largely overlap with those by IST approaches.FALSE POSITIVESOne of the major concerns for the Y2H system is so-called "false positives," which actually include two different categories, namely technical and biological ones. The technical false positive is an apparent two-hybrid interaction that is not based on the assembly of two hybrid proteins: expression of some baits or preys seems to induce unexplained events leading to artificial induction of reporter genes. Use of multiple reporter genes driven by different GAL4-responsive promoters, as we did in our project (3.Ito T. Chiba T. Ozawa R. Yoshida M. Hattori M. Sakaki Y. A comprehensive two-hybrid analysis to explore the yeast protein interactome.Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 4569-4574Google Scholar), was reported to minimize such technical false positives. The biological false positive means a bona fide two-hybrid interaction with no physiological relevance and is discussed below.What fraction of the genome-wide Y2H data is biologically relevant? To estimate the reliability of our data, we inspected a subset of our core data composed of 415 interactions because these interactions occur between two known proteins and hence can be, more or less, evaluated for their biological relevance. This analysis indicated that ∼50% of the interactions can be assumed to be biologically relevant (3.Ito T. Chiba T. Ozawa R. Yoshida M. Hattori M. Sakaki Y. A comprehensive two-hybrid analysis to explore the yeast protein interactome.Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 4569-4574Google Scholar).More recently an interesting method was developed to evaluate the validity of interaction data based on the similarity of the gene expression profile between the genes for the bait and prey displaying a two-hybrid interaction (5.Deane C.M. Salwinski L. Xenarios I. Eisenberg D. Protein interactions: two methods for assessment of the reliability of high throughput observations.Mol. Cell. Proteomics. 2002; 1: 349-356Google Scholar). The analysis of our data by this method indicates that interactions with more than three IST hits, or our core data, are expected to be ∼60% reliable (5.Deane C.M. Salwinski L. Xenarios I. Eisenberg D. Protein interactions: two methods for assessment of the reliability of high throughput observations.Mol. Cell. Proteomics. 2002; 1: 349-356Google Scholar).While these two independent estimates may illustrate the overall quality of genome-wide two-hybrid data, users of these data still have to evaluate each interaction of their interest. Even in our non-core data, one does find a number of intriguing interactions. On the other hand, those with high IST hits may well contain a substantial number of biologically meaningless interactions. Therefore, bioinformatics tools to assist such evaluation are critical to fully exploit these genome-wide data (see below).FALSE NEGATIVESIt should be also noted that the genome-wide Y2H projects missed most (as much as 90%) of known interactions (i.e. false negatives) (3.Ito T. Chiba T. Ozawa R. Yoshida M. Hattori M. Sakaki Y. A comprehensive two-hybrid analysis to explore the yeast protein interactome.Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 4569-4574Google Scholar). Recently an interesting result was reported by Vidal's group (6.Matthews L.R. Vaglio P. Reboul J. Ge H. Davis B.P. Garrels J. Vincent S. Vidal M. Identification of potential interaction networks using sequence-based searches for conserved protein-protein interactions or "interologs.".Genome Res. 2001; 11: 1771-1775Google Scholar), who tried to recapitulate two-hybrid interactions reported by the three groups (i.e. Ito, CuraGen, and Fields). They amplified yeast ORFs by themselves, cloned them into their own two-hybrid vectors, and examined the interactions in their own Y2H system. Of the 72 interactions examined, 19 (26%) were recapitulated in their study.We further analyzed their data by examining the origin of each interaction examined in their study. The analysis revealed that 9 of the 19 interactions reproduced were originally detected by at least two of the three groups, whereas more than 90% of the interactions that they failed to recapitulate were those detected only by a single group. Although such irreproducible interactions may well be technical false positives discussed above, some interactions seem to be sensitive to subtle difference in the constructs and Y2H system used, whereas others are largely insensitive and easily reproduced by anyone. Such a tendency may become more prominent when using full-length ORFs in the Y2H system because it is known that full-length proteins often show much weaker signals than the appropriately trimmed protein regions containing the interaction domains. These features are inherent to the Y2H system and seem to have contributed to the small overlap observed between the different genome-wide screen data.Y2H AND OTHER INTERACTOME DATATwo impressive studies were published to report large-scale mass spectrometric (MS) analyses of affinity-purified yeast protein complexes to demonstrate the power of proteomics (7.Gavin A.-C. Bosche M. Krause R. Grandi P. Marzioch M. Bauer A. Schultz J. Rick J.M. Michon A.M. Cruciat C.M. Remor M. Hofert C. Schelder M. Brajenovic M. Ruffner H. Merino A. Klein K. Hudak M. Dickson D. Rudi T. Gnau V. Bauch A. Bastuck S. Huhse B. Leutwein C. Heurtier M.A. Copley R.R. Edelmann A. Querfurth E. Rybin V. Drewes G. Raida M. Bouwmeester T. Bork P. Seraphin B. Kuster B. Neubauer G. Superti-Furga G. Functional organization of the yeast proteome by systematic analysis of protein complexes.Nature. 2002; 415: 141-147Google Scholar, 8.Ho Y. Gruhler A. Heilbut A. Bader G.D. Moore L. Adams S.L. Millar A. Taylor P. Bennett K. Boutilier K. Yang L. Wolting C. Donaldson I. Schandorff S. Shewnarane J. Vo M. Taggart J. Goudreault M. Muskat B. Alfarano C. Dewar D. Lin Z. Michalickova K. Willems A.R. Sassi H. Nielsen P.A. Rasmussen K.J. Andersen J.R. Johansen L.E. Hansen L.H. Jespersen H. Podtelejnikov A. Nielsen E. Crawford J. Poulsen V. Sorensen B.D. Matthiesen J. Hendrickson R.C. Gleeson F. Pawson T. Moran M.F. Durocher D. Mann M. Hogue C.W. Figeys D. Tyers M. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry.Nature. 2002; 415: 180-183Google Scholar). They, however, also illustrate the difficulty and limitations of the approach.For instance, Gavin et al. (7.Gavin A.-C. Bosche M. Krause R. Grandi P. Marzioch M. Bauer A. Schultz J. Rick J.M. Michon A.M. Cruciat C.M. Remor M. Hofert C. Schelder M. Brajenovic M. Ruffner H. Merino A. Klein K. Hudak M. Dickson D. Rudi T. Gnau V. Bauch A. Bastuck S. Huhse B. Leutwein C. Heurtier M.A. Copley R.R. Edelmann A. Querfurth E. Rybin V. Drewes G. Raida M. Bouwmeester T. Bork P. Seraphin B. Kuster B. Neubauer G. Superti-Furga G. Functional organization of the yeast proteome by systematic analysis of protein complexes.Nature. 2002; 415: 141-147Google Scholar) and Ho et al. (8.Ho Y. Gruhler A. Heilbut A. Bader G.D. Moore L. Adams S.L. Millar A. Taylor P. Bennett K. Boutilier K. Yang L. Wolting C. Donaldson I. Schandorff S. Shewnarane J. Vo M. Taggart J. Goudreault M. Muskat B. Alfarano C. Dewar D. Lin Z. Michalickova K. Willems A.R. Sassi H. Nielsen P.A. Rasmussen K.J. Andersen J.R. Johansen L.E. Hansen L.H. Jespersen H. Podtelejnikov A. Nielsen E. Crawford J. Poulsen V. Sorensen B.D. Matthiesen J. Hendrickson R.C. Gleeson F. Pawson T. Moran M.F. Durocher D. Mann M. Hogue C.W. Figeys D. Tyers M. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry.Nature. 2002; 415: 180-183Google Scholar) purified 589 and 493 complexes, respectively, of which 93 were purified by both groups using the same proteins as baits. Comparison of MS analysis on these 93 complexes between the two groups revealed that 48 complexes (52%) contain at least one protein detected by both groups, whereas the other 45 (48%) failed to share any. With respect to the entire proteins detected in these complexes, Gavin et al. (7.Gavin A.-C. Bosche M. Krause R. Grandi P. Marzioch M. Bauer A. Schultz J. Rick J.M. Michon A.M. Cruciat C.M. Remor M. Hofert C. Schelder M. Brajenovic M. Ruffner H. Merino A. Klein K. Hudak M. Dickson D. Rudi T. Gnau V. Bauch A. Bastuck S. Huhse B. Leutwein C. Heurtier M.A. Copley R.R. Edelmann A. Querfurth E. Rybin V. Drewes G. Raida M. Bouwmeester T. Bork P. Seraphin B. Kuster B. Neubauer G. Superti-Furga G. Functional organization of the yeast proteome by systematic analysis of protein complexes.Nature. 2002; 415: 141-147Google Scholar) and Ho et al. (8.Ho Y. Gruhler A. Heilbut A. Bader G.D. Moore L. Adams S.L. Millar A. Taylor P. Bennett K. Boutilier K. Yang L. Wolting C. Donaldson I. Schandorff S. Shewnarane J. Vo M. Taggart J. Goudreault M. Muskat B. Alfarano C. Dewar D. Lin Z. Michalickova K. Willems A.R. Sassi H. Nielsen P.A. Rasmussen K.J. Andersen J.R. Johansen L.E. Hansen L.H. Jespersen H. Podtelejnikov A. Nielsen E. Crawford J. Poulsen V. Sorensen B.D. Matthiesen J. Hendrickson R.C. Gleeson F. Pawson T. Moran M.F. Durocher D. Mann M. Hogue C.W. Figeys D. Tyers M. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry.Nature. 2002; 415: 180-183Google Scholar) revealed 577 and 877, respectively. The overlap between these proteins was only 133, thereby comprising ∼10% of the 1,321 proteins collectively reported by the two groups (Fig. 1). Even in the 48 complexes described above, the proteins detected in both studies comprise 14% of the total.Thus the rate of overlap is similar to the one observed in the two-hybrid projects. Although the strategies of the two groups are different and the comparison at the level of protein nexuses revealed by several different baits improves the overlap, it should be noted that even these proteomic studies contain substantial false signals.It is also interesting to note that the interactions revealed by these approaches are somewhat complementary to those by the Y2H system. The Y2H projects essentially detect binary interactions including those of rather weak or transient nature. On the other hand, the MS studies reveal more complex interactions, which are inevitably biased toward those with high abundance and stability (9.von Mering C. Krause R. Snel B. Cornell M. Oliver S.G. Fields S. Bork P. Comparative assessment of large-scale data sets of protein-protein interactions.Nature. 2002; 417: 399-403Google Scholar). Novel analytical platforms are thus required for the detection of weak or fast interactions by means of MS. One of the promising approaches would be an integration of MS with biomolecular interaction analysis based on the principle of surface plasmon resonance (10.Natsume T. Nakayama H. Isobe T. BIA-MS-MS: biomolecular interaction analysis for functional proteomics.Trends Biotechnol. 2001; 19: S28-S33Abstract Full Text Full Text PDF Google Scholar).Intriguing features of these data sets are also revealed by the integration of gene expression data (11.Kemmeren P. van Berkum N.L. Vilo J. Bijma T. Donders R. Brazma A. Holstege F.C. Protein interaction verification and functional annotation by integrated analysis of genome-scale data.Mol. Cell. 2002; 9: 1133-1143Google Scholar). The data set by Gavin et al. (7.Gavin A.-C. Bosche M. Krause R. Grandi P. Marzioch M. Bauer A. Schultz J. Rick J.M. Michon A.M. Cruciat C.M. Remor M. Hofert C. Schelder M. Brajenovic M. Ruffner H. Merino A. Klein K. Hudak M. Dickson D. Rudi T. Gnau V. Bauch A. Bastuck S. Huhse B. Leutwein C. Heurtier M.A. Copley R.R. Edelmann A. Querfurth E. Rybin V. Drewes G. Raida M. Bouwmeester T. Bork P. Seraphin B. Kuster B. Neubauer G. Superti-Furga G. Functional organization of the yeast proteome by systematic analysis of protein complexes.Nature. 2002; 415: 141-147Google Scholar) based on genomic integration of tandem affinity purification tags displays strong co-expression among the genes encoding the identified proteins. In contrast, those by Ho et al. (8.Ho Y. Gruhler A. Heilbut A. Bader G.D. Moore L. Adams S.L. Millar A. Taylor P. Bennett K. Boutilier K. Yang L. Wolting C. Donaldson I. Schandorff S. Shewnarane J. Vo M. Taggart J. Goudreault M. Muskat B. Alfarano C. Dewar D. Lin Z. Michalickova K. Willems A.R. Sassi H. Nielsen P.A. Rasmussen K.J. Andersen J.R. Johansen L.E. Hansen L.H. Jespersen H. Podtelejnikov A. Nielsen E. Crawford J. Poulsen V. Sorensen B.D. Matthiesen J. Hendrickson R.C. Gleeson F. Pawson T. Moran M.F. Durocher D. Mann M. Hogue C.W. Figeys D. Tyers M. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry.Nature. 2002; 415: 180-183Google Scholar) based on episomal overexpression of epitope-tagged proteins shows rather weak co-expression patterns similar to those of Y2H projects. Recent analysis of accumulated protein interaction data provides further detail on the various aspects of both Y2H and MS data sets (9.von Mering C. Krause R. Snel B. Cornell M. Oliver S.G. Fields S. Bork P. Comparative assessment of large-scale data sets of protein-protein interactions.Nature. 2002; 417: 399-403Google Scholar).HUGE PROTEIN INTERACTION NETWORKThe Y2H projects led to the explosion of yeast protein interaction data, and integration of binary interaction data in silico has generated a single huge nexus of proteins including up to ∼4,000 proteins (3.Ito T. Chiba T. Ozawa R. Yoshida M. Hattori M. Sakaki Y. A comprehensive two-hybrid analysis to explore the yeast protein interactome.Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 4569-4574Google Scholar, 12.Fellenberg M. Albermann K. Zollner A. Mewes H.W. Hani J. Integrative analysis of protein interaction data.Proc. Int. Conf. Intell. Syst. Mol. Biol. 2000; 8: 152-161Google Scholar, 13.Schwikowski B. Uetz P. Fields S. A network of protein-protein interactions in yeast.Nat. Biotechnol. 2000; 18: 1257-1261Google Scholar). The additional interaction data by co-precipitation/MS studies would further expand the largest network. The entire network is obviously too complex for the human brain to understand. We need a method to extract biologically meaningful clusters or subnetworks from the huge nexus to formulate a novel hypothesis for further experimentation.However, one should note that the network has become too complex due to the lack of spatial and temporal resolution. For instance, while RNA polymerase I, II, and III are distinct entities, they would be linked into a single huge nexus in silico because of common subunits shared by the three. Thus, we have to integrate existing knowledge on yeast proteins with the massive interactome data. In addition, we should evaluate the relevance of each interaction provided by any large-scale projects. Ideally each edge of the complex graph should be "weighed" to help the evaluation of each interaction. As discussed above, the number of IST hits may serve as a good measure for the reliability of Y2H data (3.Ito T. Chiba T. Ozawa R. Yoshida M. Hattori M. Sakaki Y. A comprehensive two-hybrid analysis to explore the yeast protein interactome.Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 4569-4574Google Scholar, 5.Deane C.M. Salwinski L. Xenarios I. Eisenberg D. Protein interactions: two methods for assessment of the reliability of high throughput observations.Mol. Cell. Proteomics. 2002; 1: 349-356Google Scholar). The independent lines of evidence for the interaction, such as coincidence between Y2H and MS data, presence of genetic interaction, similar mutant phenotype, shared subcellular localization, and co-expression of the genes, would be more important.Even provided with these valuable data, construction of the protein interaction network model is still a tedious task that requires many trials and errors. We thus developed a bioinformatics tool to visualize and help one estimate the structure of networks by referring to existing knowledge and other data (Fig. 2) (3.Ito T. Chiba T. Ozawa R. Yoshida M. Hattori M. Sakaki Y. A comprehensive two-hybrid analysis to explore the yeast protein interactome.Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 4569-4574Google Scholar, 14.Ito T. Chiba T. Yoshida M. Exploring the protein interactome using comprehensive two-hybrid projects.Trends Biotechnol. 2001; 19: S23-S27Abstract Full Text Full Text PDF Google Scholar). Such tools would become critical to fully exploit interactome data as well as a plethora of other functional genomic data.Fig. 2Tools for analyzing protein interactome data. Analysis of protein interaction networks is substantially facilitated by the use of specialized bioinformatics tools such as WebGenNet (genome.c.kanazawa-u.ac.jp/~webgen/webgen.html). In this system, one can select and display the proteins of one's interest with their interaction partners (left windows). Note that interactions are indicated by arrows with different colors and thickness according to their origins and reliability (e.g. numbers of IST hits), respectively (left upper window). One can also retrieve information on each protein or node of the graph (right window). The system can be used for integrative analysis with other functional genomic data. For instance, red and green circles surrounding each node in this figure indicate the expression profile of the gene encoding each protein at a particular time point of the cell cycle.View Large Image Figure ViewerDownload (PPT)TOP-DOWN APPROACH TO INTERPRET HUGE INTERACTION NETWORKThe approach described above is a knowledge-based, bottom-up approach to construct biologically meaningful subnetworks around the protein of one's interest. However, a totally different approach might be undertaken to interpret the huge network. Rece