Protein Interactions

Abstract

High throughput methods for detecting protein interactions require assessment of their accuracy. We present two forms of computational assessment. The first method is the expression profile reliability (EPR) index. The EPR index estimates the biologically relevant fraction of protein interactions detected in a high throughput screen. It does so by comparing the RNA expression profiles for the proteins whose interactions are found in the screen with expression profiles for known interacting and non-interacting pairs of proteins. The second form of assessment is the paralogous verification method (PVM). This method judges an interaction likely if the putatively interacting pair has paralogs that also interact. In contrast to the EPR index, which evaluates datasets of interactions, PVM scores individual interactions. On a test set, PVM identifies correctly 40% of true interactions with a false positive rate of ∼1%. EPR and PVM were applied to the Database of Interacting Proteins (DIP), a large and diverse collection of protein-protein interactions that contains over 8000 Saccharomyces cerevisiae pairwise protein interactions. Using these two methods, we estimate that ∼50% of them are reliable, and with the aid of PVM we identify confidently 3003 of them. Web servers for both the PVM and EPR methods are available on the DIP website (dip.doe-mbi.ucla.edu/Services.cgi). High throughput methods for detecting protein interactions require assessment of their accuracy. We present two forms of computational assessment. The first method is the expression profile reliability (EPR) index. The EPR index estimates the biologically relevant fraction of protein interactions detected in a high throughput screen. It does so by comparing the RNA expression profiles for the proteins whose interactions are found in the screen with expression profiles for known interacting and non-interacting pairs of proteins. The second form of assessment is the paralogous verification method (PVM). This method judges an interaction likely if the putatively interacting pair has paralogs that also interact. In contrast to the EPR index, which evaluates datasets of interactions, PVM scores individual interactions. On a test set, PVM identifies correctly 40% of true interactions with a false positive rate of ∼1%. EPR and PVM were applied to the Database of Interacting Proteins (DIP), a large and diverse collection of protein-protein interactions that contains over 8000 Saccharomyces cerevisiae pairwise protein interactions. Using these two methods, we estimate that ∼50% of them are reliable, and with the aid of PVM we identify confidently 3003 of them. Web servers for both the PVM and EPR methods are available on the DIP website (dip.doe-mbi.ucla.edu/Services.cgi). One thrust of post-genomic biology is the study of the networks of protein interactions that control the lives of cells and organisms. These networks have been reconstructed by detecting pairwise interactions of proteins. To store and manage this information in a systematic way, databases have been created (1.Bader G.D. Donaldson I. Wolting C. Ouellette B.F. Pawson T. Hogue C.W. BIND - The biomolecular interaction network database.Nucleic Acids Res. 2001; 29: 242-245Google Scholar, 2.Xenarios I. Rice D.W. Salwinski L. Baron M.K. Marcotte E.M. Eisenberg D. DIP: the database of interacting proteins.Nucleic Acids Res. 2000; 28: 289-291Google Scholar). These databases provide centralized access to curated experimental data. They have also emerged as resources for the investigation of the large scale properties of biological networks, in particular their functional and evolutionary aspects (3.Jeong H. Mason S.P. Barabási A.L. Oltvai Z.N. Lethality and centrality in protein networks.Nature. 2001; 411: 41-42Google Scholar). In this paper we explore the usefulness of the Database of Interacting Proteins (DIP) 1The abbreviations used are: DIP, Database of Interacting Proteins; EPR, expression profile reliability; IST, interaction sequence tag; PVM, paralogous verification method; Y2H, yeast 2 hybrid; GY2H, genome-wide Y2H; YPD, Yeast Protein Database. 1The abbreviations used are: DIP, Database of Interacting Proteins; EPR, expression profile reliability; IST, interaction sequence tag; PVM, paralogous verification method; Y2H, yeast 2 hybrid; GY2H, genome-wide Y2H; YPD, Yeast Protein Database. for assessing the reliability of measurement of protein interaction. Until two years ago, when high throughput screens of protein interaction were developed, the information within interaction databases was collected from the small scale screens in hundreds of individual research papers. The biological relevance of each interaction had often been investigated thoroughly, sometimes with a repertoire of experimental techniques and often with multiple controls (4.Xenarios I. Eisenberg D. Protein interaction databases.Curr. Opin. Biotechnol. 2001; 12: 334-339Google Scholar, 5.Golemis E.A. Protein-Protein Interactions: A Molecular Cloning Manual. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York2001Google Scholar). These independent, often repeated observations, coupled with controls and curation in the peer-review process, enhanced the reliability of the published data. In the past two years, high throughput, genome-wide detections of protein interactions by yeast two hybrid (Y2H) and mass spectrometric analysis of protein complexes have increased tremendously the experimental coverage. The new methods can generate rapidly more information than was collected by traditional means in more than a decade (6.Fromont-Racine M. Mayes A.E. Brunet-Simon A. Rain J.C. Colley A. Dix I. Decourty L. Joly N. Richard F. Beggs J.D. Legrain P. Genome-wide protein interaction screens reveal functional networks involving Sm-like proteins.Yeast. 2000; 17: 95-110Google Scholar–10.Newman J.R. Wolf E. Kim P.S. From the cover: A computationally directed screen identifying interacting coiled coils from Saccharomyces cerevisiae.Proc. Natl. Acad. Sci. U. S. A. 2000; 97: 13203-13208Google Scholar). However, the large size of such datasets makes it impractical to verify individual interactions by the same methods used previously in small scale experiments (11.Walhout A.J. Vidal M. High-throughput yeast two-hybrid assays for large-scale protein interaction mapping.Methods. 2001; 24: 297-306Google Scholar, 12.Hazbun T.R. Fields S. Networking proteins in yeast.Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 4277-4278Google Scholar). The question then arises, Do these new, high throughput methods of detecting interactions provide information as reliable as the small scale experiments? Verifying the interactions from these high throughput methods is vital (11.Walhout A.J. Vidal M. High-throughput yeast two-hybrid assays for large-scale protein interaction mapping.Methods. 2001; 24: 297-306Google Scholar–15.Schwikowski B. Uetz P. Fields S. A network of protein-protein interactions in yeast.Nat. Biotechnol. 2000; 18: 1257-1261Google Scholar), because only then can the large and small scale data be combined into one self-consistent interaction network useful for further studies. To address these issues we have analyzed the complete set of 8063 protein-protein interactions identified in yeast, Saccharomyces cerevisiae, that are described in DIP as of November 2001. We demonstrate that the subset of interactions obtained through the high throughput Y2H screens differs in several respects from the subset based only on the small scale or multiple, redundant experiments. Most notably, analysis of the coexpression profiles of the interacting partners leads to the conclusion that, overall, only about 30% of the high throughput dataset possesses the same characteristic mRNA expression features as the dataset based on the small scale experiments. To further pinpoint the interactions within the dataset that are likely to be correct, interactions were analyzed between protein pairs that are paralogs of the tested proteins. This resulted in the identification of ∼1400 interactions likely to be correct. A reliable, self-consistent set of interactions totaling ∼3000 is extracted when these ∼1400 are combined with the small experiment datasets and with interactions verified by more than one experiment. The protein-protein interaction datasets analyzed in this work are listed in Table I. They are all, except for the RND sets, subsets of the S. cerevisiae protein-protein interaction network (DIP-YEAST; 8063 distinct interactions) extracted from the DIP database on November 19, 2001. The INT set contains all the interactions determined by one or more small scale experiment (defined as an experiment described in a published article listing no more than 100 distinct protein-protein interactions) whereas sets EC2 and EC3 contain interactions determined by, respectively, at least two or three independent experiments. The GY2H set contains all the interactions reported in high throughput protein-protein interaction screens (6.Fromont-Racine M. Mayes A.E. Brunet-Simon A. Rain J.C. Colley A. Dix I. Decourty L. Joly N. Richard F. Beggs J.D. Legrain P. Genome-wide protein interaction screens reveal functional networks involving Sm-like proteins.Yeast. 2000; 17: 95-110Google Scholar–8.Uetz P. Giot L. Cagney G. Mansfield T.A. Judson R.S. Knight J.R. Lockshon D. Narayan V. Srinivasan M. Pochart P. Qureshi-Emili 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, 16.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, 17.Fromont-Racine M. Rain J.C. Legrain P. Toward a functional analysis of the yeast genome through exhaustive two-hybrid screens.Nat. Genet. 1997; 16: 277-282Google Scholar), and GY2H′ is a subset of GY2H that excludes interactions occurring only in the ITO1 set. The ITO1, ITO2, … ITO8 are subsets of GY2H that contain all the interactions reported by Ito et al. (7.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) as identified by at least 1,2, … 8 interaction sequence tags (ISTs) in a genome-wide Y2H protein-protein interaction screen. These datasets (ITO1, etc.) contain fewer interactions than the numbers reported in the original paper because of some redundancy of the original dataset (interactions were reported in both directions, P-P′ and P′-P). Also, some of the open reading frames could not be traced unambiguously to a unique SWISS-PROT, PIR (Protein Information Resource), or interaction of data reported by and interactions from each of the datasets by PVM as with reported by and G. 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A of the GY2H dataset to the described by to the The is as for the GY2H that of the reported pairs in this set in false To verify that the of the experimental subsets of the GY2H to of were as reported by Ito et al. (7.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). Ito et al. (7.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) created these sets by identifying interactions with at least … 8 as ITO1 to the of the as by with increased This that the EPR index can be used to the of large scale protein-protein interaction and to the fraction of pairs that is However, the on rapidly with dataset the of EPR in to large in a new The high throughput Y2H screens can be by the least reliable protein pairs that only in the ITO1 set. In the reliability of the GY2H′ set to as by the EPR index However, this reliability at the of size by The reliability of a protein interaction can be by the of paralogous interactions. The for this is that if two proteins are paralogs then the proteins that are to with are often also This is to the of by Vidal and A.J. Vidal M. Yeast two-hybrid and protein interaction for yeast and 2000; 17: Scholar). 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We the datasets of non-interacting proteins computationally because of the in such a set from within the The three sets of protein interactions described were used as the non-interacting these sets not be of interactions, the be small sets of protein interactions were used as true interaction sets, the and EC3 sets The EC2 and EC3 sets are than the INT set and can be used by PVM are not as datasets for EPR, because the in is large for such small datasets The of the PVM method can be by a known as a characteristic in It that a that false is to of the true interactions. the method high a This of in the of paralogs of some proteins. interactions if the and EC2 sets are to only pairs at least one of each of the pairs has more than one an in of is The is not by the of paralogs is because of both the of experimental data in a of a of paralogous interactions. is possible of in PVM because of the identification of interactions in Y2H experiments. if and are paralogs of and a it is possible that in only the and interactions However, Y2H interactions between and and and as as the true interactions and The rate of PVM of that this is However, as with computational the be in the of data such as or of the proteins. 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PVM, on the is to the of individual protein-protein interactions. However, it can also estimate the of biologically relevant interactions within a This is based on the that in the subsets of and INT with ∼50% of the interactions are identified by PVM PVM identify ∼50% of the biologically relevant interactions within The of true interactions within a set be as the by In the set only of the interactions that could the of true interactions is of the subset with This that of interactions are an rate for the of This with the EPR of in Table The of PVM to identify the true interactions within a dataset means that it can also be used to the of a by means of the of identified interactions. The Ito et al. (7.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) subsets described and were PVM, and it was found that as the of independent of the interactions increased from to 8 the of the dataset identified as by PVM increased as the EPR index The of PVM can also be by the EPR of the subset of by It that this dataset within experimental the INT set are about interactions within the dataset identified in the genome-wide Y2H These interactions that were reported by Ito et al. (7.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) as based on only these interactions are to contain false M. Legrain P. Yeast and Acids Res. Scholar) the in and demonstrate that contain a of true and the method such as PVM is to identify at least some of them. A subset of the interactions to be can be identified by the PVM INT and EC2 sets this a of 3003 interactions. This set is as the and is available on the DIP website of the interactions are identified by PVM and as such could not be by 2 that this set of interactions has a of that is to the sets to be and The of interactions to be based on the EPR index of is PVM is to identify putatively interactions with high it is with the of INT and EC2 to from all interactions, which are to be by We and for