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Predicting Physical Interactions between Protein Complexes*

  • Trevor Clancy
    Correspondence
    To whom correspondence should be addressed. Tel.: 47-414-312-42; Fax: 47-225-224-21;
    Affiliations
    Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital and Oslo University Hospital, Oslo
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  • Einar Andreas Rødland
    Affiliations
    Biomedical Research Group, Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo

    Center for Cancer Biomedicine, University of Oslo, Oslo
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  • Ståle Nygard
    Affiliations
    Bioinformatics Core Facility, Institute of Medical Informatics, University of Oslo, Oslo University Hospital, Oslo
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  • Eivind Hovig
    Affiliations
    Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital and Oslo University Hospital, Oslo

    Biomedical Research Group, Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo

    Institute of Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
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  • Author Footnotes
    * This work was supported by European Commission Grant FP7-2008, Number 223367-MultiMod.
    This article contains supplemental material.
Open AccessPublished:February 25, 2013DOI:https://doi.org/10.1074/mcp.O112.019828
      Protein complexes enact most biochemical functions in the cell. Dynamic interactions between protein complexes are frequent in many cellular processes. As they are often of a transient nature, they may be difficult to detect using current genome-wide screens. Here, we describe a method to computationally predict physical interactions between protein complexes, applied to both humans and yeast. We integrated manually curated protein complexes and physical protein interaction networks, and we designed a statistical method to identify pairs of protein complexes where the number of protein interactions between a complex pair is due to an actual physical interaction between the complexes. An evaluation against manually curated physical complex-complex interactions in yeast revealed that 50% of these interactions could be predicted in this manner. A community network analysis of the highest scoring pairs revealed a biologically sensible organization of physical complex-complex interactions in the cell. Such analyses of proteomes may serve as a guide to the discovery of novel functional cellular relationships.
      Protein complexes are central to nearly all biochemical processes in the cell (
      • Alberts B.
      The cell as a collection of protein machines: preparing the next generation of molecular biologists.
      ). In physiologically relevant states, their protein members assemble with varying degrees of stability, over time and under different cellular conditions, to carry out specific cellular functions (
      • Alberts B.
      The cell as a collection of protein machines: preparing the next generation of molecular biologists.
      ). Although it is a dynamic and tightly regulated process, there is much evidence to support the notion that protein complex assembly results in discrete signaling macromolecules (
      • Gibson T.J.
      Cell regulation: determined to signal discrete cooperation.
      ). According to the modular organization of molecular networks of the cell (
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      From molecular to modular cell biology.
      ), protein complexes cooperate in functional networks through dynamic physical interactions with other macromolecules, including other protein complexes (
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      ). These physical interactions between pairs of protein complexes may form the backbone of cellular processes (
      • Malovannaya A.
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      ), such as the recruitment of complexes by other complexes to sites of genome reorganization or in signaling networks. In this study, we attempted to predict these physical interactions between all pairs of known protein complexes, using the manually curated protein complex databases in CORUM and CYC2008 for humans and yeast, respectively.
      The physical protein interactions that may occur between pairs of complexes have been reported to be more transient, compared with the combination of both permanent and transient interactions that occur within complexes (
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      Characterization and prediction of protein-protein interactions within and between complexes.
      ). Indeed, the very stability of protein interactions within a protein complex lies between the two extremes of either transient or permanent states (
      • Devos D.
      • Russell R.B.
      A more complete, complexed, and structured interactome.
      ). Consequently, the experimental identification in a genome-wide manner of the physical interactions between pairs of complexes is very difficult. This challenge has recently been addressed (
      • Malovannaya A.
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      • Le N.T.
      • Chan D.W.
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      • Li C.
      • Chen R.
      • Li W.
      • Wang Y.
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      • Qin J.
      Analysis of the human endogenous coregulator complexome.
      ,
      • Malovannaya A.
      • Li Y.
      • Bulynko Y.
      • Jung S.Y.
      • Wang Y.
      • Lanz R.B.
      • O'Malley B.W.
      • Qin J.
      Streamlined analysis schema for high throughput identification of endogenous protein complexes.
      ) by experiments where the weak interactions were preserved during affinity purifications, followed by inference of the less stable interactions of proteins with the core proteins within the complex. Guided by a computational method to predict the list of protein members in the complexes (
      • Malovannaya A.
      • Li Y.
      • Bulynko Y.
      • Jung S.Y.
      • Wang Y.
      • Lanz R.B.
      • O'Malley B.W.
      • Qin J.
      Streamlined analysis schema for high throughput identification of endogenous protein complexes.
      ), this allowed a screen of putative inter-complex relationships from human cell lines (
      • Malovannaya A.
      • Lanz R.B.
      • Jung S.Y.
      • Bulynko Y.
      • Le N.T.
      • Chan D.W.
      • Ding C.
      • Shi Y.
      • Yucer N.
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      • Kim B.-J.
      • Li C.
      • Chen R.
      • Li W.
      • Wang Y.
      • O'Malley B.W.
      • Qin J.
      Analysis of the human endogenous coregulator complexome.
      ). This adds to the many landmark developments in recent years to characterize protein complexes in a genome-wide manner (
      • Malovannaya A.
      • Lanz R.B.
      • Jung S.Y.
      • Bulynko Y.
      • Le N.T.
      • Chan D.W.
      • Ding C.
      • Shi Y.
      • Yucer N.
      • Krenciute G.
      • Kim B.-J.
      • Li C.
      • Chen R.
      • Li W.
      • Wang Y.
      • O'Malley B.W.
      • Qin J.
      Analysis of the human endogenous coregulator complexome.
      ,
      • Gavin A.-C.
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      • Krause R.
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      • Rau C.
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      • Drewes G.
      • Neubauer G.
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      • Kuster B.
      • Bork P.
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      • Superti-Furga G.
      Proteome survey reveals modularity of the yeast cell machinery.
      ,
      • Krogan N.J.
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      • Yu H.
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      • Emili A.
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      ,
      • Ewing R.M.
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      ). However, in these experiments it is not always easy to infer accurately what constitutes the protein members of a protein complex. Because of various experimental limitations (
      • Aloy P.
      • Russell R.B.
      Potential artefacts in protein-interaction networks.
      ) and the dynamic nature of complex assembly in the cell (
      • de Lichtenberg U.
      • Jensen L.J.
      • Brunak S.
      • Bork P.
      Dynamic complex formation during the yeast cell cycle.
      ), the protein members of the complexes must be predicted from thousands of purification measurements (
      • Malovannaya A.
      • Li Y.
      • Bulynko Y.
      • Jung S.Y.
      • Wang Y.
      • Lanz R.B.
      • O'Malley B.W.
      • Qin J.
      Streamlined analysis schema for high throughput identification of endogenous protein complexes.
      ,
      • Gavin A.-C.
      • Aloy P.
      • Grandi P.
      • Krause R.
      • Boesche M.
      • Marzioch M.
      • Rau C.
      • Jensen L.J.
      • Bastuck S.
      • Dümpelfeld B.
      • Edelmann A.
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      • Hoffman V.
      • Hoefert C.
      • Klein K.
      • Hudak M.
      • Michon A.-M.
      • Schelder M.
      • Schirle M.
      • Remor M.
      • Rudi T.
      • Hooper S.
      • Bauer A.
      • Bouwmeester T.
      • Casari G.
      • Drewes G.
      • Neubauer G.
      • Rick J.M.
      • Kuster B.
      • Bork P.
      • Russell R.B.
      • Superti-Furga G.
      Proteome survey reveals modularity of the yeast cell machinery.
      ,
      • Krogan N.J.
      • Cagney G.
      • Yu H.
      • Zhong G.
      • Guo X.
      • Ignatchenko A.
      • Li J.
      • Pu S.
      • Datta N.
      • Tikuisis A.P.
      • Punna T.
      • Peregrín-Alvarez J.M.
      • Shales M.
      • Zhang X.
      • Davey M.
      • Robinson M.D.
      • Paccanaro A.
      • Bray J.E.
      • Sheung A.
      • Beattie B.
      • Richards D.P.
      • Canadien V.
      • Lalev A.
      • Mena F.
      • Wong P.
      • Starostine A.
      • Canete M.M.
      • Vlasblom J.
      • Wu S.
      • Orsi C.
      • Collins S.R.
      • Chandran S.
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      • Gandi K.
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      • Musso G.
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      • Ghanny S.
      • Lam M.H.
      • Butland G.
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      • Kanaya S.
      • Shilatifard A.
      • O'Shea E.
      • Weissman J.S.
      • Ingles C.J.
      • Hughes T.R.
      • Parkinson J.
      • Gerstein M.
      • Wodak S.J.
      • Emili A.
      • Greenblatt J.F.
      Global landscape of protein complexes in the yeast Saccharomyces cerevisiae.
      ,
      • Choi H.
      • Larsen B.
      • Lin Z.Y.
      • Breitkreutz A.
      • Mellacheruvu D.
      • Fermin D.
      • Qin Z.S.
      • Tyers M.
      • Gingras A.C.
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      SAINT: probabilistic scoring of affinity purification-mass spectrometry data.
      ). As a result, there are surprisingly large differences in the protein complexes inferred in these studies, depending on the algorithm used (
      • Gagneur J.
      • David L.
      • Steinmetz L.M.
      Capturing cellular machines by systematic screens of protein complexes.
      ,
      • Goll J.
      • Uetz P.
      The elusive yeast interactome.
      ). Hence, the inference of protein complexes from genome-wide screens (
      • Gavin A.-C.
      • Aloy P.
      • Grandi P.
      • Krause R.
      • Boesche M.
      • Marzioch M.
      • Rau C.
      • Jensen L.J.
      • Bastuck S.
      • Dümpelfeld B.
      • Edelmann A.
      • Heurtier M.-A.
      • Hoffman V.
      • Hoefert C.
      • Klein K.
      • Hudak M.
      • Michon A.-M.
      • Schelder M.
      • Schirle M.
      • Remor M.
      • Rudi T.
      • Hooper S.
      • Bauer A.
      • Bouwmeester T.
      • Casari G.
      • Drewes G.
      • Neubauer G.
      • Rick J.M.
      • Kuster B.
      • Bork P.
      • Russell R.B.
      • Superti-Furga G.
      Proteome survey reveals modularity of the yeast cell machinery.
      ,
      • Krogan N.J.
      • Cagney G.
      • Yu H.
      • Zhong G.
      • Guo X.
      • Ignatchenko A.
      • Li J.
      • Pu S.
      • Datta N.
      • Tikuisis A.P.
      • Punna T.
      • Peregrín-Alvarez J.M.
      • Shales M.
      • Zhang X.
      • Davey M.
      • Robinson M.D.
      • Paccanaro A.
      • Bray J.E.
      • Sheung A.
      • Beattie B.
      • Richards D.P.
      • Canadien V.
      • Lalev A.
      • Mena F.
      • Wong P.
      • Starostine A.
      • Canete M.M.
      • Vlasblom J.
      • Wu S.
      • Orsi C.
      • Collins S.R.
      • Chandran S.
      • Haw R.
      • Rilstone J.J.
      • Gandi K.
      • Thompson N.J.
      • Musso G.
      • St Onge P.
      • Ghanny S.
      • Lam M.H.
      • Butland G.
      • Altaf-Ul A.M.
      • Kanaya S.
      • Shilatifard A.
      • O'Shea E.
      • Weissman J.S.
      • Ingles C.J.
      • Hughes T.R.
      • Parkinson J.
      • Gerstein M.
      • Wodak S.J.
      • Emili A.
      • Greenblatt J.F.
      Global landscape of protein complexes in the yeast Saccharomyces cerevisiae.
      ) is likely to contain significant noise from false-positives resulting from methodological uncertainty (
      • Devos D.
      • Russell R.B.
      A more complete, complexed, and structured interactome.
      ). This noise would in turn cause ambiguity when attempting to predict, genome-wide, interactions that may occur between protein complexes. One solution to this problem, as applied in this study, is the use of comprehensive databases of the so-called “gold standard” community definitions of protein complexes (
      • Mewes H.W.
      • Frishman D.
      • Mayer K.F.
      • Münsterkötter M.
      • Noubibou O.
      • Pagel P.
      • Rattei T.
      • Oesterheld M.
      • Ruepp A.
      • Stümpflen V.
      MIPS: analysis and annotation of proteins from whole genomes in 2005.
      ,
      • Cherry J.M.
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      • Dwight S.S.
      • Hester E.T.
      • Jia Y.
      • Juvik G.
      • Roe T.
      • Schroeder M.
      • Weng S.
      • Botstein D.
      SGD: Saccharomyces Genome Database.
      ,
      • Ruepp A.
      • Waegele B.
      • Lechner M.
      • Brauner B.
      • Dunger-Kaltenbach I.
      • Fobo G.
      • Frishman G.
      • Montrone C.
      • Mewes H.
      CORUM: the comprehensive resource of mammalian protein complexes–2009.
      ,
      • Pu S.
      • Wong J.
      • Turner B.
      • Cho E.
      • Wodak S.J.
      Up-to-date catalogues of yeast protein complexes.
      ). In these resources, critical reading of the scientific literature by trained experts leads to definitions of the lists of protein members that are experimentally verified to form complexes. Each of these manually curated protein complexes are assigned functional annotations and a unique identifier. It is our assumption that this approach will allow for a more accurate resolution of the physical interactions between protein complexes.
      Based on this reasoning, we utilized all protein complex pairs from 1216 human protein complexes in CORUM (
      • Ruepp A.
      • Waegele B.
      • Lechner M.
      • Brauner B.
      • Dunger-Kaltenbach I.
      • Fobo G.
      • Frishman G.
      • Montrone C.
      • Mewes H.
      CORUM: the comprehensive resource of mammalian protein complexes–2009.
      ) and 471 in the yeast CYC2008 databases (
      • Pu S.
      • Wong J.
      • Turner B.
      • Cho E.
      • Wodak S.J.
      Up-to-date catalogues of yeast protein complexes.
      ,
      • Wang H.
      • Kakaradov B.
      • Collins S.R.
      • Karotki L.
      • Fiedler D.
      • Shales M.
      • Shokat K.M.
      • Walther T.C.
      • Krogan N.J.
      • Koller D.
      A complex-based reconstruction of the Saccharomyces cerevisiae interactome.
      ), and we attempted to predict physical interactions between them.
      To this end, we integrated only binary physical protein interactions that were experimentally verified and supported by Medline references, from the iRefIndex database (
      • Razick S.
      • Magklaras G.
      • Donaldson I.M.
      iRefIndex: a consolidated protein interaction database with provenance.
      ,
      • Turner B.
      • Razick S.
      • Turinsky A.L.
      • Vlasblom J.
      • Crowdy E.K.
      • Cho E.
      • Morrison K.
      • Donaldson I.M.
      • Wodak S.J.
      iRefWeb: interactive analysis of consolidated protein interaction data and their supporting evidence.
      ), and we developed a statistical method that compared the number of observed physical protein interactions between pairs of protein complexes versus the number of protein interactions expected to be present in pairs of randomized protein complexes. The highest scoring predicted pairs formed a network that was analyzed to identify communities of physically interacting protein complexes. Such higher order perspectives of cellular proteomes may aid discovery of novel functional relationships and lead to an improved understanding of cellular behavior.
      One recent study utilized manually curated protein complexes-complex interactions in yeast (
      • Wang H.
      • Kakaradov B.
      • Collins S.R.
      • Karotki L.
      • Fiedler D.
      • Shales M.
      • Shokat K.M.
      • Walther T.C.
      • Krogan N.J.
      • Koller D.
      A complex-based reconstruction of the Saccharomyces cerevisiae interactome.
      ) as part of a machine learning strategy to identify complex-complex interactions. However, they added to the training data complex pairs enriched with protein interactions under the assumption that these were likely to contain complex-complex interactions but without a clear statistical argument to assess the reliability of these. Our aim has been to provide a more rigorous statistical approach applied to yeast and human, in which the main confounding factors, protein degrees and protein similarities within the complexes, have been taken into account.
      We used only the manually curated yeast complex-complex interactions from Ref.
      • Wang H.
      • Kakaradov B.
      • Collins S.R.
      • Karotki L.
      • Fiedler D.
      • Shales M.
      • Shokat K.M.
      • Walther T.C.
      • Krogan N.J.
      • Koller D.
      A complex-based reconstruction of the Saccharomyces cerevisiae interactome.
      as the reference set to evaluate our method after verifying with the authors that the manual curation had not been guided by enrichment in the protein network. Of these interactions, we predicted half at a 10% false discovery rate. Thus, although improvements in data as well as methods are still required for a more complete prediction of complex-complex interactions, a fair portion of these interactions can be reliably predicted now by using our method.

      DISCUSSION

      Capturing a higher order perspective of the complex molecular networks in a cell has been demonstrated previously to offer valuable new insights (
      • Kim J.R.
      • Kim J.
      • Kwon Y.K.
      • Lee H.Y.
      • Heslop-Harrison P.
      • Cho K.H.
      Reduction of complex signaling networks to a representative kernel.
      ). It has also been suggested that cells could be interpreted as higher order networks of protein molecular machines, transforming and passing information to each other (
      • Brenner S.
      Sequences and consequences.
      ). In turn, it has been proposed that these discrete, yet dynamic, protein complexes form the backbone of cell regulation (
      • Gibson T.J.
      Cell regulation: determined to signal discrete cooperation.
      ). One genome-wide study estimated there to be ∼3000 protein complexes in humans and ∼800 in yeast (
      • Gavin A.-C.
      • Aloy P.
      • Grandi P.
      • Krause R.
      • Boesche M.
      • Marzioch M.
      • Rau C.
      • Jensen L.J.
      • Bastuck S.
      • Dümpelfeld B.
      • Edelmann A.
      • Heurtier M.-A.
      • Hoffman V.
      • Hoefert C.
      • Klein K.
      • Hudak M.
      • Michon A.-M.
      • Schelder M.
      • Schirle M.
      • Remor M.
      • Rudi T.
      • Hooper S.
      • Bauer A.
      • Bouwmeester T.
      • Casari G.
      • Drewes G.
      • Neubauer G.
      • Rick J.M.
      • Kuster B.
      • Bork P.
      • Russell R.B.
      • Superti-Furga G.
      Proteome survey reveals modularity of the yeast cell machinery.
      ) involved in all processes of cellular life. Here, we took 1216 and 471 manually curated protein complexes in humans (
      • Ruepp A.
      • Waegele B.
      • Lechner M.
      • Brauner B.
      • Dunger-Kaltenbach I.
      • Fobo G.
      • Frishman G.
      • Montrone C.
      • Mewes H.
      CORUM: the comprehensive resource of mammalian protein complexes–2009.
      ) and yeast (
      • Pu S.
      • Wong J.
      • Turner B.
      • Cho E.
      • Wodak S.J.
      Up-to-date catalogues of yeast protein complexes.
      ,
      • Wang H.
      • Kakaradov B.
      • Collins S.R.
      • Karotki L.
      • Fiedler D.
      • Shales M.
      • Shokat K.M.
      • Walther T.C.
      • Krogan N.J.
      • Koller D.
      A complex-based reconstruction of the Saccharomyces cerevisiae interactome.
      ), respectively, and we attempted to predict the physical interactions between these molecular entities.
      In the absence of having the three-dimensional structures for all the protein complexes, it was unknown which binding sites are available for interaction with proteins in other assembled complexes. The ICE and BSO protein exclusion rules were devised to consider the availability of binding sites between potential physical complex-complex interactions. These exclusion rules had a strong impact on predictions, despite excluding only a small percentage of the physical protein interactions, as they primarily took effect under rather specific conditions. The differences in the behavior of both rules within and between humans and yeast can be attributed to the observation of protein complex members overlapping much less in the yeast protein complexes than in human. The subsequent permutation and statistical analysis resulted in predictions of physically interacting complex-complex networks that corresponded to a sensible functional organization of the cell, as observed by the network community analysis
      Evaluating the predictions in yeast, we found that approximately half of the physical complex-complex interactions could be predicted in this manner. However, the method is limited to detecting protein complex pairs that have multiple possible protein interactions between them. In addition, many physically interacting complex pairs were possibly not detected, which is likely due to the current incompleteness of the manually curated protein complex and protein interaction databases.
      It was the goal of this study to predict physical interactions between two protein complexes, in an attempt to identify complex pairs that coordinate with each other to execute a cellular process. These interactions are likely to be transient and dependent on certain biochemical conditions at a specific time and cellular location. However, some of the predictions may not be transient physical interactions between two complexes, perhaps pointing to the assembly of larger stable complexes. The incomplete annotation in the curated databases means the larger assembly would not yet be considered as a unique entity and therefore results in an incorrect classification by the prediction method. These false-positives can be identified and resolved in future iterations of the method and improved by increased manual curation and more accurate experimental detection of intact protein complexes.
      Another class of inter-complex relationships has been comprehensively mapped in so-called “complexome” studies, which have constructed networks of protein complexes, containing shared protein members (
      • Li S.S.
      • Xu K.
      • Wilkins M.R.
      Visualization and analysis of the complexome network of Saccharomyces cerevisiae.
      ,
      • Ding C.
      • He X.
      • Meraz R.F.
      • Holbrook S.R.
      A unified representation of multiprotein complex data for modeling interaction networks.
      ,
      • Lee S.H.
      • Kim P.-J.
      • Jeong H.
      Global organization of protein complexome in the yeast Saccharomyces cerevisiae.
      ,
      • Wilhelm T.
      • Nasheuer H.-P.
      • Huang S.
      Physical and functional modularity of the protein network in yeast.
      ,
      • Mashaghi A.
      • Ramezanpour A.
      • Karimipour V.
      Investigation of a protein complex network.
      ). In those primarily yeast studies, two experimentally detected or predicted complexes are connected if they shared one or more protein members. These studies have revealed key insights into the pattern of protein complex organization in the cell. They can be considered different from the approach taken in this study, which focused on predicting physical interactions between protein complexes by identifying the complex pairs that are significantly enriched for physical protein interactions between them. With respect to this goal, one yeast study by Wang et al. (
      • Wang H.
      • Kakaradov B.
      • Collins S.R.
      • Karotki L.
      • Fiedler D.
      • Shales M.
      • Shokat K.M.
      • Walther T.C.
      • Krogan N.J.
      • Koller D.
      A complex-based reconstruction of the Saccharomyces cerevisiae interactome.
      ) used a machine learning approach to detect signatures of complex-complex interactions. That particular study used a combination of manually curated complex-complex interactions and complex pairs enriched with reliable protein interactions between their members to train their model. However, these “enriched” complex pairs were used to augment their training data and, unlike this study, were not a result of statistical predictions under a complete randomized model (see under “Materials and Methods”). The cross-validation reported in that study was carried out using a merged reference set of enriched and curated complex-complex interactions. This is not directly comparable with our results, as the enriched interactions are selected based on our computation of the complex-complex degree. To avoid circular reasoning in our evaluation, we used only the manually curated complex pairs from the study by Wang et al. (
      • Wang H.
      • Kakaradov B.
      • Collins S.R.
      • Karotki L.
      • Fiedler D.
      • Shales M.
      • Shokat K.M.
      • Walther T.C.
      • Krogan N.J.
      • Koller D.
      A complex-based reconstruction of the Saccharomyces cerevisiae interactome.
      ). When our method was applied to their enriched and predicted complex pairs, some major differences were clearly due to factors that we correct for, in particular the protein degree of the complexes (see supplemental Table 6). Another yeast study used logistic regression trained on nine different experimental and computational parameters of interacting and noninteracting protein pairs to infer interactions between stable protein interactions within complexes and transient interactions between complexes (
      • Sprinzak E.
      • Altuvia Y.
      • Margalit H.
      Characterization and prediction of protein-protein interactions within and between complexes.
      ). There also has been a study in yeast to predict transient interactions between predicted protein complexes, based on similarity of protein members from pulldown assays (
      • Valente A.X.
      • Roberts S.B.
      • Buck G.A.
      • Gao Y.
      Functional organization of the yeast proteome by a yeast interactome map.
      ). To the best of our knowledge, there were no previous attempts to predict physical complex-complex interactions restricted to manually curated protein complexes in human.
      As more complete proteomes with characterized protein complexes (
      • Malovannaya A.
      • Lanz R.B.
      • Jung S.Y.
      • Bulynko Y.
      • Le N.T.
      • Chan D.W.
      • Ding C.
      • Shi Y.
      • Yucer N.
      • Krenciute G.
      • Kim B.-J.
      • Li C.
      • Chen R.
      • Li W.
      • Wang Y.
      • O'Malley B.W.
      • Qin J.
      Analysis of the human endogenous coregulator complexome.
      ) and their three-dimensional structures are fast approaching (
      • Stein A.
      • Mosca R.
      • Aloy P.
      Three-dimensional modeling of protein interactions and complexes is going 'omics.
      ), the in silico mapping of the physically interacting relationships between protein complexes will be of greater importance. Genome-wide technologies are now evolving to capture these physical complex-complex interactions (
      • Stein A.
      • Mosca R.
      • Aloy P.
      Three-dimensional modeling of protein interactions and complexes is going 'omics.
      ). The method described in this study could be used as a complementary tool in proteomics experiments, guiding the discovery of higher order interactions between protein complexes. For example, the application of this method, integrated with clustering algorithms that attempt to identify protein complexes from large protein networks (
      • Nepusz T.
      • Yu H.
      • Paccanaro A.
      Detecting overlapping protein complexes in protein-protein interaction networks.
      ), may guide the identification of higher order relationships in proteomics data (the SAS code is freely available by request from the authors). The prediction of complex-complex interactions will complement experimental advances and overall contribute to building complete molecular maps of the cell.

      Acknowledgments

      We thank Timothy J. Lavelle for helpful discussions and the Daphne Koller Laboratory for providing reference interactions. Author Contributions: T.C., E.H., and E.A.R. designed the research; T.C., E.A.R., and S.N. analyzed the data; and T.C., E.H., and E.A.R. wrote the paper.

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