Systematic discovery of in vivo phosphorylation networks.

By Rune Linding, Lars Juhl Jensen, G. J. Ostheimer, M. V. van Vugt, Claus Jorgensen, Ioana Miron, Francesca Diella, K. Colwill, L. Taylor, Kelly D. Elder and 11 more. Cell, 2007

772 citations recorded by the source corpus.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/8e19e2bb1913e88c373bb99175ec6ff35bf6057c

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Citations in the corpus

Phospho.ELM: a database of phosphorylation sites—update 2008 (2007, background): “…a database of tissue and sub-cellular distribution of mammalian protein kinases and phosphatases (31), and NetworKin, a computational approach which combines consensus sequence motifs and contextual data to predict which kinases phosphorylate experimentally identiﬁed phosphorylation sites (6).”
STRING 8—a global view on proteins and their functional interactions in 630 organisms (2008, methodology): “A variety of other databases use STRING networks as a basis for further computations/annotations, for example by augmenting the networks with small molecules [STITCH, (31)], or by using the network to increase the power of kinase–substrate predictions [NetworKIN, (32)].”
GPS 2.0, a Tool to Predict Kinase-specific Phosphorylation Sites in Hierarchy *S (2008, background): “(9) developed NetworKIN and constructed a human phosphorylation network, which has gained diversified interest not only for human phosphorylation network prediction but also for general implication in cell biology.”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, methodology): “The bioinformatics resources NetworKIN (http://networkin.info; Linding et al., 2007) and NetPhorest (http://netphorest.info; Miller et al., 2008) can help in the analysis cancer-related signaling changes.”
Dynamic modularity in protein interaction networks predicts breast cancer outcome (2009, methodology): “These were either computed directly from the hub sequence or by mapping to the appropriate databas”
Decoding signalling networks by mass spectrometry-based proteomics (2010, background): “Integration of information about tissue- or cell type-specific kinase expression, cellular localization, substrate phosphorylation motifs and other functional interaction data with global phosphorylation data is useful in predicting kinase–substrate relationships in signalling network”
Cancer systems biology: a network modeling perspective (2009, background): “The biochemical resolution of interactome information has been recently enhanced by directly incorporating phosphoproteomic data to indicate not only physical interactions but also kinase/substrate interactions [28].”
Glycogen synthase kinase-3 (GSK3): regulation, actions, and diseases (2014, background): “A theoretical analysis found that GSK3β has more predicted substrates than any other kinase (Linding et al., 2007).”
Phospho.ELM: a database of phosphorylation sites—update 2011 (2010, background): “However, phosphorylation motifs are short, strongly dependent on the surrounding context (18) and often reside in unstructured and rapidly-evolving regions (19), hence they have been difﬁcult to trace evolutionarily and mixed conclusions have been reported (20,21).”
Kinase-selective enrichment enables quantitative phosphoproteomics of the kinome across the cell cycle. (2008, background): “We think that such experimental data in combination with advanced bioinformatics might significantly extend current knowledge of cellular signaling pathways and their underlying phosphorylation networks (Linding et al., 2007).”
Differential network biology (2012, background): “…levels or protein post-translational modiﬁcations under different conditions or species has provided unique insight into the regulation of the cell (Gygi et al , 1999; Ong et al , 2002; Aebersold and Mann, 2003; Linding et al , 2007; Beltrao et al , 2009; Holt et al , 2009; Tan et al , 2009).”