Global landscape of protein complexes in the yeast Saccharomyces cerevisiae

By Nevan J. Krogan, G. Cagney, Haiyuan Yu, Gouqing Zhong, Xinghua Guo, A. Ignatchenko, Joyce Li, S. Pu, Nira Datta, Aaron Tikuisis and 43 more. 2006

3,074 citations recorded by the source corpus.

Subfields: Molecular Biology

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Citations in the corpus

Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XII (2017, background): “29,30 Not surprisingly, mutations at protein-protein interfaces with structural effects destabilizing or altering the interactions are often associated with diseases 31 and structure information has been shown to be valuable in interpreting the functional effects of mutations on protein…”
Modeling protein quaternary structure of homo- and hetero-oligomers beyond binary interactions by homology (2017, background): “w(min(d , d )) ( ) ( ) (6) non shared A A non shared B B shared(A,B) A B d d”
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization (2019, background): “Graphs have proven to be an effective way to represent very diverse types of data including social networks [38], biological reaction networks[46], protein-protein interactions [29], the quantum mechanical properties of individual molecules [59, 20], and many more.”
The BioPlex Network: A Systematic Exploration of the Human Interactome (2015, positive): “AP-MS has previously mapped a substantial fraction of yeast (Gavin et al., 2002; Ho et al., 2002; Krogan et al., 2006) and Drosophila (Guruharsha et al.”
Less label, more free: Approaches in label‐free quantitative mass spectrometry (2011, methodology): “[132].”
An experimentally derived confidence score for binary protein-protein interactions (2008, positive): “…several groups have calculated a confidence score based on their experimental data, e.g. how often a given interaction was recovered in Y2H screens8,9, how reliably a protein was identified in an AP/MS experiment10,11 or combinations of such primary experimental data with secondary data12.”
How complete are current yeast and human protein-interaction networks? (2006, methodology): “In yeast, however, there appears to be some: the most recent yeast dataset from Krogan et al. [28] discovers 66% new interactions, on a par with the estimated false-positive rate of the dataset.”
Link communities reveal multiscale complexity in networks (2009, background): “6,7,9,10,14,15 Pervasive overlap in real networks is distinct from ‘fuzzy’ community overlap with relaxed interfaces, 19–21 because overlap can exist for each and every node (Fig.”
Mass spectrometry supported determination of protein complex structure. (2013, positive): “On the basis of large-scale AP-MS studies (reviewed in [5]), comprehensive protein–protein interaction data are available for the Saccharomyces cerevisiae proteome ( 6600 ORFs [6]) [7,8].”
Global Functional Atlas of Escherichia coli Encompassing Previously Uncharacterized Proteins (2009, methodology): “] and a genome-scale dataset of 7,123 PI (median confidence of 0.69) generated using an analogous affinity purification schema in yeast [”
Overlapping Communities Explain Core–Periphery Organization of Networks (2014, background): “Nodes in networks organize into communities [1], which often correspond to groups of nodes that share a common property, role or function, such as functionally related proteins [2], social communities [3], or topically related webpages [4].”
Recent progress and future directions in protein-protein docking. (2008, neutral): “1389-2037/08 $55.00+.00 © 2008 Bentham Science Publishers Ltd. Keywords: Protein-protein docking, protein-protein interactions, docking algorithms, data-driven docking, molecular dynamics, protein structure databases, protein interface prediction, CAPRI.”
Identification of functional modules using network topology and high-throughput data (2007, background): “We found a clear enrichment for interactions from recent experiments, such as [39] and [40], opposed to an underrepresentation of interactions from older works, such as [41,42] and [43] (see supplementary table).”
Up-to-date catalogues of yeast protein complexes (2008, methodology): “cerevisiae protein interactions networks built by applying the Purification Enrichment (PE) score (1) to the two most recent genome-wide purification studies in yeast (2,10).”
CORUM: the comprehensive resource of mammalian protein complexes—2019 (2018, background): “The first large-scale screens for protein complexes were performed in budding yeast (4,5) and discovered 491 and 547 complexes, respectively.”
clusterMaker: a multi-algorithm clustering plugin for Cytoscape (2011, methodology): “These algorithms are generally used for finding modules and complexes within protein-protein interaction networks [4,33,35,36] and for identifying functionally related groups of proteins within large protein-protein similarity networks [7,24,25,37].”
Anatomical profiling of G protein-coupled receptor expression (2008, positive): “Such correlation maps are a powerful tool for organizing and analyzing gene-gene and protein-protein interactions on a global scale (Collins et al., 2007a;Collins et al., 2007b;Krogan et al., 2006;Schuldiner et al., 2005;Segre et al., 2005).”