Linear Motif Atlas for Phosphorylation-Dependent Signaling

By Martin L. Miller, Lars Juhl Jensen, Francesca Diella, Claus Jorgensen, M. Tinti, Lei Li, Marilyn S. Hsiung, Sirlester A. Parker, J. Bordeaux, Thomas Sicheritz-Pontén and 13 more. Science Signaling, 2008

465 citations recorded by the source corpus.

Created with both in vitro and in vivo data, NetPhorest is an atlas of consensus sequence motifs for 179 kinases and 104 phosphorylation-dependent binding domains and reveals new insight into phosphorylation-dependent signaling. An Atlas of Phosphorylation NetPhorest is a community resource that uses phylogenetic trees to organize data from both in vivo and in vitro experiments to derive sequence specificities for 179 kinases and 104 domains (SH2, PTB, BRCT, WW, and 14–3–3) that bind to phosphorylated sites. The resulting atlas of linear motifs revealed that oncogenic kinases tend to be less specific in the target sequences they phosphorylate than their non-oncogenic counterparts, that autophosphorylation sites tend to be more variable than other substrates of a given kinase, and that coupling interaction domains with kinase domains may allow phosphorylation site specificity to be low while still maintaining substrate specificity. Systematic and quantitative analysis of protein phosphorylation is revealing dynamic regulatory networks underlying cellular responses to environmental cues. However, matching these sites to the kinases that phosphorylate them and the phosphorylation-dependent binding domains that may subsequently bind to them remains a challenge. NetPhorest is an atlas of consensus sequence motifs that covers 179 kinases and 104 phosphorylation-dependent binding domains [Src homology 2 (SH2), phosphotyrosine binding (PTB), BRCA1 C-terminal (BRCT), WW, and 14–3–3]. The atlas reveals new aspects of signaling systems, including the observation that tyrosine kinases mutated in cancer have lower specificity than their non-oncogenic relatives. The resource is maintained by an automated pipeline, which uses phylogenetic trees to structure the currently available in vivo and in vitro data to derive probabilistic sequence models of linear motifs. The atlas is available as a community resource (http://netphorest.info).

Subfields: Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/b780e7c7427a7f1ad7da17b7283a37b44e8c3001

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Citations in the corpus

Using graph theory to analyze biological networks (2011, unknown): “ELM [31], NetPhorest [32] or PHOSIDA [33].”
A tissue-specific atlas of mouse protein phosphorylation and expression. (2010, unknown): “Recently, algorithms have been developed to predict phosphorylation sites andmotifs in uncharacterized proteins based on amino acid sequence and other properties (Miller et al., 2008; Schwartz et al., 2009).”
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.”
PhosphoSitePlus, 2014: mutations, PTMs and recalibrations (2014, result): “This stands in contrast to resources that generate KSIs based on in vitro reactions using short peptides only (6,7) and modified variously with predictions or adjustments of one sort or another (26).”
Small molecule inhibition of the autophagy kinase ULK1 and identification of ULK1 substrates (2015, background): “Unlike other Ser/Thr kinases (Miller et al., 2008; Turk, 2008) that phosphorylate sites near charged residues or proline, ULK1 had an unusual preference for hydrophobic residues at multiple positions surrounding the phosphorylation site.”
The crucial role of protein phosphorylation in cell signaling and its use as targeted therapy (Review) (2017, unknown): “Most kinases act on both serine and threonine (serine/threonine kinases; STKs), others act on tyrosine (tyrosine kinases; TKs), and a number act on all three (dual-specificity kinases; DSKs) (16).”
Decoding signalling networks by mass spectrometry-based proteomics (2010, background): “Connecting linear sequence kinase motifs and phosphorylation-dependent binding domains with in vivo phosphorylation data further helps in matching phosphorylation sites to their putative upstream kinase”