A Global Protein Kinase and Phosphatase Interaction Network in Yeast

By Ashton Breitkreutz, Hyungwon Choi, J. Sharom, Lorrie Boucher, Victor Neduva, B. Larsen, Zhen-Yuan Lin, Bobby-Joe Breitkreutz, Chris Stark, Guomin Liu and 10 more. Science, 2010

716 citations recorded by the source corpus.

Budding Yeast Kinome Revealed Covalent modification of proteins by phosphorylation is a primary means by which cells control the biochemical activities and functions of proteins. To better understand the full spectrum of cellular control mechanisms mediated by phosphorylation, Breitkreutz et al. (p. 1043; see the Perspective by Levy et al.) used mass spectrometry to identify proteins that interacted with the complete set of protein kinases from budding yeast and with other molecules, including phosphatases, which influence phosphorylation reactions. The results reveal a network of interacting protein kinases and phosphatases, and analysis of other interacting proteins suggests previously undiscovered roles for many of these enzymes. Phosphorylation reactions in budding yeast reveal the regulatory architecture of a fundamental cellular control system. The interactions of protein kinases and phosphatases with their regulatory subunits and substrates underpin cellular regulation. We identified a kinase and phosphatase interaction (KPI) network of 1844 interactions in budding yeast by mass spectrometric analysis of protein complexes. The KPI network contained many dense local regions of interactions that suggested new functions. Notably, the cell cycle phosphatase Cdc14 associated with multiple kinases that revealed roles for Cdc14 in mitogen-activated protein kinase signaling, the DNA damage response, and metabolism, whereas interactions of the target of rapamycin complex 1 (TORC1) uncovered new effector kinases in nitrogen and carbon metabolism. An extensive backbone of kinase-kinase interactions cross-connects the proteome and may serve to coordinate diverse cellular responses.

Subfields: Cell Biology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7ba027e00fe85b29327712c75cc2022470232a02

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Citations in the corpus

The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions (2020, background): “21,22 Often these site assignments are condition-specific and/or supported by site-specific mutagenesis data.”
Nutritional Control of Growth and Development in Yeast (2012, background): “Finally, some rapamycin-induced changes in phosphorylation occurred independently of Sch9 and Tap42, suggesting either a limit in the sensitivity of the analysis or the existence of other pathways emanating from TORC1 (Breitkreutz et al. 2010).”
The Index-Based Subgraph Matching Algorithm with General Symmetries (ISMAGS): Exploiting Symmetry for Faster Subgraph Enumeration (2014, background): “The first network pertains to physical (P, undirected), genetic (G, undirected) and signalling (S, directed) interactions between kinases and phosphatases in yeast [19,20].”
iProphet: Multi-level Integrative Analysis of Shotgun Proteomic Data Improves Peptide and Protein Identification Rates and Error Estimates* (2011, methodology): “MS-based reconstruction of protein interaction networks,(14–16) or the comprehensive characterization of proteomes of model organisms such as Drosophila (17), C. elegans (18), and S. cerevisiae (19) via extensive fractionation of the proteome sample and the mass spectro-metric measurement of each…”
The BioGRID Interaction Database: 2011 update (2010, methodology): “These federated data sets allow the construction of interaction networks from various data sources, which can be used to explain biological processes, interpret different orthogonal data types and predict new biological functions (6,7).”
SAINTexpress: improvements and additional features in Significance Analysis of Interactome software (2013, methodology): “The method was originally developed for large-scale AP-MS experiments for which control purifications were not implicitly used for scoring [1].”
The BioGRID interaction database: 2015 update (2014, background): “The systematic experimental identification and characterization of protein and genetic interaction networks in major model organism species and humans has continued to grow in pace and scale (21,23,29–35).”
Target of Rapamycin (TOR) in Nutrient Signaling and Growth Control (2011, background): “…TOR complex) that control cell growth in different ways (Barbet et al. 1996; Schmidt et al. 1997, 1998; Bickle et al. 1998; Helliwell et al. 1998a; Loewith et al. 2002; Loewith and Hall 1180 R. Loewith and M. N. Hall 2004; De Virgilio and Loewith 2006; Breitkreutz et al. 2010; Kaizu et al. 2010).”
The BioGRID interaction database: 2019 update (2018, background): “This project began as a systematic HTP mass spectrometrybased study that reported 1844 interactions for all proteins in the kinome (85).”
Less label, more free: Approaches in label‐free quantitative mass spectrometry (2011, background): “Efforts to achieve a more detailed analysis of the human interactome have turned to localised studies, such as focusing on the kinase and phosphatase interactome [134] or deubiquinating enzyme interactions [135], and by investigating interactions quantitatively.”
The Index-Based Subgraph Matching Algorithm (ISMA): Fast Subgraph Enumeration in Large Networks Using Optimized Search Trees (2013, background): “The first is an integrated network of physical (P, undirected), genetic (G, undirected) and signaling (S, directed) interactions between kinases and phosphatases in yeast [32,33], also used in [26] (see left panel of Table 2 for basic network characteristics).”
Phospho.ELM: a database of phosphorylation sites—update 2011 (2010, background): “More and more authors are highlighting the importance of factors such as cooperativity, networking, redundancy and decision-making by in-complex molecular switching as we move away from overly linear pathway-based descriptions of cellular systems (8–14).”
A Protein Complex Network of Drosophila melanogaster (2011, methodology): “The coAP-MS data set was also analyzed using published scoring methods (Breitkreutz et al., 2010; Choi et al., 2011; Gavin et al., 2006; Hart et al., 2007; Sowa et al., 2009).”