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Melicher P, Dvořák P, Šamaj J, Takáč T. Protein-protein interactions in plant antioxidant defense. FRONTIERS IN PLANT SCIENCE 2022; 13:1035573. [PMID: 36589041 PMCID: PMC9795235 DOI: 10.3389/fpls.2022.1035573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 11/14/2022] [Indexed: 06/17/2023]
Abstract
The regulation of reactive oxygen species (ROS) levels in plants is ensured by mechanisms preventing their over accumulation, and by diverse antioxidants, including enzymes and nonenzymatic compounds. These are affected by redox conditions, posttranslational modifications, transcriptional and posttranscriptional modifications, Ca2+, nitric oxide (NO) and mitogen-activated protein kinase signaling pathways. Recent knowledge about protein-protein interactions (PPIs) of antioxidant enzymes advanced during last decade. The best-known examples are interactions mediated by redox buffering proteins such as thioredoxins and glutaredoxins. This review summarizes interactions of major antioxidant enzymes with regulatory and signaling proteins and their diverse functions. Such interactions are important for stability, degradation and activation of interacting partners. Moreover, PPIs of antioxidant enzymes may connect diverse metabolic processes with ROS scavenging. Proteins like receptor for activated C kinase 1 may ensure coordination of antioxidant enzymes to ensure efficient ROS regulation. Nevertheless, PPIs in antioxidant defense are understudied, and intensive research is required to define their role in complex regulation of ROS scavenging.
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Jehle S, Kunowska N, Benlasfer N, Woodsmith J, Weber G, Wahl MC, Stelzl U. A human kinase yeast array for the identification of kinases modulating phosphorylation-dependent protein-protein interactions. Mol Syst Biol 2022; 18:e10820. [PMID: 35225431 PMCID: PMC8883442 DOI: 10.15252/msb.202110820] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/28/2022] [Accepted: 01/31/2022] [Indexed: 12/11/2022] Open
Abstract
Protein kinases play an important role in cellular signaling pathways and their dysregulation leads to multiple diseases, making kinases prime drug targets. While more than 500 human protein kinases are known to collectively mediate phosphorylation of over 290,000 S/T/Y sites, the activities have been characterized only for a minor, intensively studied subset. To systematically address this discrepancy, we developed a human kinase array in Saccharomyces cerevisiae as a simple readout tool to systematically assess kinase activities. For this array, we expressed 266 human kinases in four different S. cerevisiae strains and profiled ectopic growth as a proxy for kinase activity across 33 conditions. More than half of the kinases showed an activity-dependent phenotype across many conditions and in more than one strain. We then employed the kinase array to identify the kinase(s) that can modulate protein-protein interactions (PPIs). Two characterized, phosphorylation-dependent PPIs with unknown kinase-substrate relationships were analyzed in a phospho-yeast two-hybrid assay. CK2α1 and SGK2 kinases can abrogate the interaction between the spliceosomal proteins AAR2 and PRPF8, and NEK6 kinase was found to mediate the estrogen receptor (ERα) interaction with 14-3-3 proteins. The human kinase yeast array can thus be used for a variety of kinase activity-dependent readouts.
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Affiliation(s)
- Stefanie Jehle
- Otto-Warburg-Laboratory, Max-Planck-Institute for Molecular Genetics (MPIMG), Berlin, Germany
| | - Natalia Kunowska
- Institute of Pharmaceutical Sciences, University of Graz, Graz, Austria
| | - Nouhad Benlasfer
- Otto-Warburg-Laboratory, Max-Planck-Institute for Molecular Genetics (MPIMG), Berlin, Germany
| | - Jonathan Woodsmith
- Otto-Warburg-Laboratory, Max-Planck-Institute for Molecular Genetics (MPIMG), Berlin, Germany
- Institute of Pharmaceutical Sciences, University of Graz, Graz, Austria
| | - Gert Weber
- Institut für Chemie und Biochemie, Freie Universität, Berlin, Germany
- Helmholtz-Zentrum Berlin für Materialien und Energie, Macromolecular Crystallography, Berlin, Germany
| | - Markus C Wahl
- Institut für Chemie und Biochemie, Freie Universität, Berlin, Germany
| | - Ulrich Stelzl
- Otto-Warburg-Laboratory, Max-Planck-Institute for Molecular Genetics (MPIMG), Berlin, Germany
- Institute of Pharmaceutical Sciences, University of Graz, Graz, Austria
- Field of Excellence BioHealth, University of Graz and BioTechMed-Graz, Graz, Austria
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Cafarelli TM, Desbuleux A, Wang Y, Choi SG, De Ridder D, Vidal M. Mapping, modeling, and characterization of protein-protein interactions on a proteomic scale. Curr Opin Struct Biol 2017; 44:201-210. [PMID: 28575754 DOI: 10.1016/j.sbi.2017.05.003] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 04/24/2017] [Accepted: 05/02/2017] [Indexed: 12/14/2022]
Abstract
Proteins effect a number of biological functions, from cellular signaling, organization, mobility, and transport to catalyzing biochemical reactions and coordinating an immune response. These varied functions are often dependent upon macromolecular interactions, particularly with other proteins. Small-scale studies in the scientific literature report protein-protein interactions (PPIs), but slowly and with bias towards well-studied proteins. In an era where genomic sequence is readily available, deducing genotype-phenotype relationships requires an understanding of protein connectivity at proteome-scale. A proteome-scale map of the protein-protein interaction network provides a global view of cellular organization and function. Here, we discuss a summary of methods for building proteome-scale interactome maps and the current status and implications of mapping achievements. Not only do interactome maps serve as a reference, detailing global cellular function and organization patterns, but they can also reveal the mechanisms altered by disease alleles, highlight the patterns of interaction rewiring across evolution, and help pinpoint biologically and therapeutically relevant proteins. Despite the considerable strides made in proteome-wide mapping, several technical challenges persist. Therefore, future considerations that impact current mapping efforts are also discussed.
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Affiliation(s)
- T M Cafarelli
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA USA; Department of Genetics, Harvard Medical School, Boston, MA, USA.
| | - A Desbuleux
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA USA; Department of Genetics, Harvard Medical School, Boston, MA, USA; GIGA-R, University of Liège, Liège, Belgium
| | - Y Wang
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA USA; Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - S G Choi
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA USA; Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - D De Ridder
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA USA; Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - M Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Genetics, Harvard Medical School, Boston, MA, USA
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Apelt L, Knockenhauer KE, Leksa NC, Benlasfer N, Schwartz TU, Stelzl U. Systematic Protein-Protein Interaction Analysis Reveals Intersubcomplex Contacts in the Nuclear Pore Complex. Mol Cell Proteomics 2016; 15:2594-606. [PMID: 27194810 PMCID: PMC4974338 DOI: 10.1074/mcp.m115.054627] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2015] [Revised: 05/17/2016] [Indexed: 11/06/2022] Open
Abstract
The nuclear pore complex (NPC) enables transport across the nuclear envelope. It is one of the largest multiprotein assemblies in the cell, built from about 30 proteins called nucleoporins (Nups), organized into distinct subcomplexes. Structure determination of the NPC is a major research goal. The assembled ∼40-112 MDa NPC can be visualized by cryoelectron tomography (cryo-ET), while Nup subcomplexes are studied crystallographically. Docking the crystal structures into the cryo-ET maps is difficult because of limited resolution. Further, intersubcomplex contacts are not well characterized. Here, we systematically investigated direct interactions between Nups. In a comprehensive, structure-based, yeast two-hybrid interaction matrix screen, we mapped protein-protein interactions in yeast and human. Benchmarking against crystallographic and coaffinity purification data from the literature demonstrated the high coverage and accuracy of the data set. Novel intersubcomplex interactions were validated biophysically in microscale thermophoresis experiments and in intact cells through protein fragment complementation. These intersubcomplex interaction data provide direct experimental evidence toward possible structural arrangements of architectural elements within the assembled NPC, or they may point to assembly intermediates. Our data favors an assembly model in which major architectural elements of the NPC, notably the Y-complex, exist in different structural contexts within the scaffold.
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Affiliation(s)
- Luise Apelt
- From the ‡Otto-Warburg Laboratory, Max-Planck Institute for Molecular Genetics (MPIMG), Berlin, Germany
| | | | - Nina C Leksa
- §Department of Biology, Massachusetts Institute of Technology (MIT), Cambridge
| | - Nouhad Benlasfer
- From the ‡Otto-Warburg Laboratory, Max-Planck Institute for Molecular Genetics (MPIMG), Berlin, Germany
| | - Thomas U Schwartz
- §Department of Biology, Massachusetts Institute of Technology (MIT), Cambridge
| | - Ulrich Stelzl
- From the ‡Otto-Warburg Laboratory, Max-Planck Institute for Molecular Genetics (MPIMG), Berlin, Germany; ¶Institute of Pharmaceutical Sciences, Pharmaceutical Chemistry, University of Graz, Graz, Austria
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Kim I, Lee H, Han SK, Kim S. Linear motif-mediated interactions have contributed to the evolution of modularity in complex protein interaction networks. PLoS Comput Biol 2014; 10:e1003881. [PMID: 25299147 PMCID: PMC4191887 DOI: 10.1371/journal.pcbi.1003881] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2014] [Accepted: 08/29/2014] [Indexed: 02/06/2023] Open
Abstract
The modular architecture of protein-protein interaction (PPI) networks is evident in diverse species with a wide range of complexity. However, the molecular components that lead to the evolution of modularity in PPI networks have not been clearly identified. Here, we show that weak domain-linear motif interactions (DLIs) are more likely to connect different biological modules than strong domain-domain interactions (DDIs). This molecular division of labor is essential for the evolution of modularity in the complex PPI networks of diverse eukaryotic species. In particular, DLIs may compensate for the reduction in module boundaries that originate from increased connections between different modules in complex PPI networks. In addition, we show that the identification of biological modules can be greatly improved by including molecular characteristics of protein interactions. Our findings suggest that transient interactions have played a unique role in shaping the architecture and modularity of biological networks over the course of evolution. Modular architecture is important for the evolution of cellular systems. Modular rearrangements facilitate functional innovations and modular insulations provide robustness to perturbations. However, molecular-level understanding of the mechanisms underlying modular network evolution is currently not well understood. Here we show that strong domain-domain interactions (DDIs) and weak domain-linear motif interactions (DLIs) made different contributions to the evolution of the modular architecture of PPI networks. Especially, DLIs mediate between-module interactions, and that their relative abundance has dramatically increased in metazoan species. Linear motifs have been identified as evolutionary interaction switches since subtle amino acid changes can cause the short sequences in linear motifs to appear and disappear. Our results suggest that subtle changes in linear motifs have contributed to the rewiring of functional modules and, consequently, to functional innovations in metazoan species.
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Affiliation(s)
- Inhae Kim
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, Korea
| | - Heetak Lee
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, Korea
| | - Seong Kyu Han
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, Korea
| | - Sanguk Kim
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, Korea
- School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, Pohang, Korea
- * E-mail:
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Wang J, Peng X, Peng W, Wu FX. Dynamic protein interaction network construction and applications. Proteomics 2014; 14:338-52. [DOI: 10.1002/pmic.201300257] [Citation(s) in RCA: 72] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2013] [Revised: 10/23/2013] [Accepted: 11/27/2013] [Indexed: 12/22/2022]
Affiliation(s)
- Jianxin Wang
- School of Information Science and Engineering; Central South University; Changsha P. R. China
| | - Xiaoqing Peng
- School of Information Science and Engineering; Central South University; Changsha P. R. China
| | - Wei Peng
- School of Information Science and Engineering; Central South University; Changsha P. R. China
| | - Fang-Xiang Wu
- Department of Mechanical Engineering and Division of Biomedical Engineering; University of Saskatchewan; Saskatoon Canada
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Woodsmith J, Stelzl U. Studying post-translational modifications with protein interaction networks. Curr Opin Struct Biol 2014; 24:34-44. [DOI: 10.1016/j.sbi.2013.11.009] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2013] [Revised: 11/15/2013] [Accepted: 11/22/2013] [Indexed: 12/14/2022]
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Vinayagam A, Stelzl U, Foulle R, Plassmann S, Zenkner M, Timm J, Assmus HE, Andrade-Navarro MA, Wanker EE. A directed protein interaction network for investigating intracellular signal transduction. Sci Signal 2011; 4:rs8. [PMID: 21900206 DOI: 10.1126/scisignal.2001699] [Citation(s) in RCA: 245] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Cellular signal transduction is a complex process involving protein-protein interactions (PPIs) that transmit information. For example, signals from the plasma membrane may be transduced to transcription factors to regulate gene expression. To obtain a global view of cellular signaling and to predict potential signal modulators, we searched for protein interaction partners of more than 450 signaling-related proteins by means of automated yeast two-hybrid interaction mating. The resulting PPI network connected 1126 proteins through 2626 PPIs. After expansion of this interaction map with publicly available PPI data, we generated a directed network resembling the signal transduction flow between proteins with a naïve Bayesian classifier. We exploited information on the shortest PPI paths from membrane receptors to transcription factors to predict input and output relationships between interacting proteins. Integration of directed PPI with time-resolved protein phosphorylation data revealed network structures that dynamically conveyed information from the activated epidermal growth factor and extracellular signal-regulated kinase (EGF/ERK) signaling cascade to directly associated proteins and more distant proteins in the network. From the model network, we predicted 18 previously unknown modulators of EGF/ERK signaling, which we validated in mammalian cell-based assays. This generic experimental and computational approach provides a framework for elucidating causal connections between signaling proteins and facilitates the identification of proteins that modulate the flow of information in signaling networks.
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Affiliation(s)
- Arunachalam Vinayagam
- AG Neuroproteomics and Computational Biology and Data Mining Group, Max Delbrück Centrum for Molecular Medicine, Robert-Rössle-Strasse 10, D-13125 Berlin-Buch, Germany
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Shahravan SH, Li ITS, Truong K, Shin JA. FRep: A Fluorescent Protein-Based Bioprobe for in Vivo Detection of Protein–DNA Interactions. Anal Chem 2011; 83:9643-50. [DOI: 10.1021/ac2024602] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- S. Hesam Shahravan
- Department of Chemical and Physical Sciences, University of Toronto at Mississauga, Mississauga, Ontario, Canada L5L 1C6
- Department of Chemistry, University of Toronto, Toronto, Ontario, Canada M5S 3H6
| | - Isaac T. S. Li
- Department of Chemistry, University of Toronto, Toronto, Ontario, Canada M5S 3H6
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada M5S 3G9
| | - Kevin Truong
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada M5S 3G9
| | - Jumi A. Shin
- Department of Chemical and Physical Sciences, University of Toronto at Mississauga, Mississauga, Ontario, Canada L5L 1C6
- Department of Chemistry, University of Toronto, Toronto, Ontario, Canada M5S 3H6
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada M5S 3G9
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Acuner Ozbabacan SE, Engin HB, Gursoy A, Keskin O. Transient protein-protein interactions. Protein Eng Des Sel 2011; 24:635-48. [DOI: 10.1093/protein/gzr025] [Citation(s) in RCA: 170] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
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Perkins JR, Diboun I, Dessailly BH, Lees JG, Orengo C. Transient protein-protein interactions: structural, functional, and network properties. Structure 2011; 18:1233-43. [PMID: 20947012 DOI: 10.1016/j.str.2010.08.007] [Citation(s) in RCA: 366] [Impact Index Per Article: 28.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2010] [Revised: 07/13/2010] [Accepted: 08/02/2010] [Indexed: 11/28/2022]
Abstract
Transient interactions, which involve protein interactions that are formed and broken easily, are important in many aspects of cellular function. Here we describe structural and functional properties of transient interactions between globular domains and between globular domains, short peptides, and disordered regions. The importance of posttranslational modifications in transient interactions is also considered. We review techniques used in the detection of the different types of transient protein-protein interactions. We also look at the role of transient interactions within protein-protein interaction networks and consider their contribution to different aspects of these networks.
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Affiliation(s)
- James R Perkins
- Department of Structural and Molecular Biology, University College of London, Gower Street, WC1E 6BT London, UK.
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12
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Vazquez A, Rual JF, Venkatesan K. Quality control methodology for high-throughput protein-protein interaction screening. Methods Mol Biol 2011; 781:279-94. [PMID: 21877286 DOI: 10.1007/978-1-61779-276-2_13] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Protein-protein interactions are key to many aspects of the cell, including its cytoskeletal structure, the signaling processes in which it is involved, or its metabolism. Failure to form protein complexes or signaling cascades may sometimes translate into pathologic conditions such as cancer or neurodegenerative diseases. The set of all protein interactions between the proteins encoded by an organism constitutes its protein interaction network, representing a scaffold for biological function. Knowing the protein interaction network of an organism, combined with other sources of biological information, can unravel fundamental biological circuits and may help better understand the molecular basics of human diseases. The protein interaction network of an organism can be mapped by combining data obtained from both low-throughput screens, i.e., "one gene at a time" experiments and high-throughput screens, i.e., screens designed to interrogate large sets of proteins at once. In either case, quality controls are required to deal with the inherent imperfect nature of experimental assays. In this chapter, we discuss experimental and statistical methodologies to quantify error rates in high-throughput protein-protein interactions screens.
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Affiliation(s)
- Alexei Vazquez
- Department of Radiation Oncology, The Cancer Institute of New Jersey and UMDNJ-Robert Wood Johnson Medical School, New Brunswick, NJ, USA.
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Sambourg L, Thierry-Mieg N. New insights into protein-protein interaction data lead to increased estimates of the S. cerevisiae interactome size. BMC Bioinformatics 2010; 11:605. [PMID: 21176124 PMCID: PMC3023808 DOI: 10.1186/1471-2105-11-605] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2010] [Accepted: 12/21/2010] [Indexed: 11/29/2022] Open
Abstract
Background As protein interactions mediate most cellular mechanisms, protein-protein interaction networks are essential in the study of cellular processes. Consequently, several large-scale interactome mapping projects have been undertaken, and protein-protein interactions are being distilled into databases through literature curation; yet protein-protein interaction data are still far from comprehensive, even in the model organism Saccharomyces cerevisiae. Estimating the interactome size is important for evaluating the completeness of current datasets, in order to measure the remaining efforts that are required. Results We examined the yeast interactome from a new perspective, by taking into account how thoroughly proteins have been studied. We discovered that the set of literature-curated protein-protein interactions is qualitatively different when restricted to proteins that have received extensive attention from the scientific community. In particular, these interactions are less often supported by yeast two-hybrid, and more often by more complex experiments such as biochemical activity assays. Our analysis showed that high-throughput and literature-curated interactome datasets are more correlated than commonly assumed, but that this bias can be corrected for by focusing on well-studied proteins. We thus propose a simple and reliable method to estimate the size of an interactome, combining literature-curated data involving well-studied proteins with high-throughput data. It yields an estimate of at least 37, 600 direct physical protein-protein interactions in S. cerevisiae. Conclusions Our method leads to higher and more accurate estimates of the interactome size, as it accounts for interactions that are genuine yet difficult to detect with commonly-used experimental assays. This shows that we are even further from completing the yeast interactome map than previously expected.
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Affiliation(s)
- Laure Sambourg
- Laboratoire TIMC-IMAG, BCM, CNRS UMR5525, Faculté de médecine, La Tronche, France
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