Linear combinations of docking affinities explain quantitative differences in RTK signaling

Gordus, Andrew; Krall, Jordan A.; Beyer, Elsa M.; Kaushansky, Alexis; Wolf-Yadlin, Alejandro; Sevecka, Mark; Chang, Bryan H; Rush, A. John; MacBeath, Gavin

doi:10.1038/msb.2008.72

Cited by 56 publications

(71 citation statements)

References 26 publications

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“…Some of these can be seen in the variation between known phosphorylated motifs and the same motifs genome-wide. Indeed, phosphorylated sites generally differ from their non-phosphorylated orthologs as has been noted in recent studies examining distinct and significant bias for the PTB domain of Shc1 and the SH2 domains of PI3K (54).…”

Section: Discussionmentioning

confidence: 79%

SH2 Domains Recognize Contextual Peptide Sequence Information to Determine Selectivity

Liu¹,

Jablonowski²,

Shah³

et al. 2010

Molecular & Cellular Proteomics

106

137

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Selective ligand recognition by modular protein interaction domains is a primary determinant of specificity in signaling pathways. Src homology 2 (SH2) domains fulfill this capacity immediately downstream of tyrosine kinases, acting to recruit their host polypeptides to ligand proteins harboring phosphorylated tyrosine residues. The degree to which SH2 domains are selective and the mechanisms underlying selectivity are fundamental to understanding phosphotyrosine signaling networks. An examination of interactions between 50 SH2 domains and a set of 192 phosphotyrosine peptides corresponding to physiological motifs within FGF, insulin, and IGF-1 receptor pathways indicates that individual SH2 domains have distinct recognition properties and exhibit a remarkable degree of selectivity beyond that predicted by previously described binding motifs. The underlying basis for such selectivity is the ability of SH2 domains to recognize both permissive amino acid residues that enhance binding and non-permissive amino acid residues that oppose binding in the vicinity of the essential phosphotyrosine. Neighboring positions affect one another so local sequence context matters to SH2 domains. This complex linguistics allows SH2 domains to distinguish subtle differences in peptide ligands. This newly appreciated contextual dependence substantially increases the accessible information content embedded in the peptide ligands that can be effectively integrated to determine binding. This concept may serve more broadly as a paradigm for subtle recognition of physiological ligands by protein interaction domains. Molecular & Cellular Proteomics 9:2391-2404, 2010. The Src homology 2 (SH2)1 domain is the classic archetype for the large family of modular protein interaction domains that serve to organize a diverse array of cellular processes. As its name suggests, the SH2 domain was identified in the regulatory regions of non-receptor protein-tyrosine kinases (PTKs) of the Src family (1). The human genome encodes 110 SH2 domain proteins (2) that represent the primary mechanism for cellular signal transduction immediately downstream of PTKs. SH2 domains interact with phosphorylated tyrosinecontaining peptide sequences (3-6). In doing so, they couple activated PTKs to intracellular pathways that regulate many aspects of cellular communication in metazoans (7,8). In this manner, SH2 domains function alongside PTKs and proteintyrosine phosphatases to direct specificity in phosphotyrosine signaling. SH2 domain proteins play a critical role in development and have been linked to a wide range of human diseases including cancers, diabetes, and immunodeficiencies (2).Activation and recruitment of PTKs and protein-tyrosine phosphatases can control the spatial and temporal organization of phosphotyrosine signaling. However, signaling specificity in terms of downstream targets is entirely determined by phosphotyrosine-mediated recruitment events for which SH2 domains are responsible. Therefore, the binding selectivity of SH2 domains is critical for ...

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Section: Discussionmentioning

confidence: 79%

SH2 Domains Recognize Contextual Peptide Sequence Information to Determine Selectivity

Liu¹,

Jablonowski²,

Shah³

et al. 2010

Molecular & Cellular Proteomics

106

137

View full text Add to dashboard Cite

show abstract

“…This approach also assumed that other kinases with similar functions would not completely take over SIRK1 functions and phosphorylation patterns. For mammalian receptor tyrosine kinases, it was described that different kinases can actually phosphorylate the same target proteins but differ in their quantitative degree of phosphorylation (84). A large-scale and systematic comparative analysis of protein phosphorylation patterns in wild-type and kinase-or phosphatase-deletion mutants of yeast (85) revealed specific phosphorylation patterns for 78% of the kinases analyzed.…”

Section: Discussionmentioning

confidence: 99%

Sucrose-induced Receptor Kinase SIRK1 Regulates a Plasma Membrane Aquaporin in Arabidopsis

Sánchez-Rodríguez

Pertl-Obermeyer

et al. 2013

Molecular & Cellular Proteomics

106

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The transmembrane receptor kinase family is the largest protein kinase family in Arabidopsis, and it contains the highest fraction of proteins with yet uncharacterized functions. Here, we present functions of SIRK1, a receptor kinase that was previously identified with rapid transient phosphorylation after sucrose resupply to sucrosestarved seedlings. SIRK1 was found to be an active kinase with increasing activity in the presence of an external sucrose supply. In sirk1 T-DNA insertional mutants, the sucrose-induced phosphorylation patterns of several membrane proteins were strongly reduced; in particular, pore-gating phosphorylation sites in aquaporins were affected. SIRK1-GFP fusions were found to directly interact with aquaporins in affinity pull-down experiments on microsomal membrane vesicles. Furthermore, protoplast swelling assays of sirk1 mutants and SIRK1-GFP expressing lines confirmed a direct functional interaction of receptor kinase SIRK1 and aquaporins as substrates for phosphorylation. A lack of SIRK1 expression resulted in the failure of mutant protoplasts to control water channel activity upon changes in external sucrose concentrations. We propose that SIRK1 is involved in the regulation of sucrosespecific osmotic responses through direct interaction with and activation of an aquaporin via phosphorylation and that the duration of this response is controlled by phosphorylation-dependent receptor internalization. Molecular & Cellular Proteomics

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“…model output) can be achieved with the free parameters. We now know that this assumption is generally untrue, as most individual parameters are not leveraged to define the computed network response (Gutenkunst et al, 2007). Indeed, one routinely finds that most rate parameters can change over several orders of magnitude without substantially affecting the model output (Bentele et al, 2004;Chen et al, 2009;Nakakuki et al, 2010).…”

Section: Box 1 From Toy Models To Chemical-kinetic Modelsmentioning

confidence: 99%

“…For signal transduction, these combinations point to 'hidden dimensions' within a network, where multiple signalling proteins may be coordinately regulated to execute a common function (Jensen and Janes, 2012). Such models have proved to be remarkably versatile for signalling networks, capturing adaptors, effectors, cell-fate control and cytokine-release profiles in different settings (Beyer and MacBeath, 2012;Cosgrove et al, 2010;Gordus et al, 2009;Janes et al, 2005;Janes et al, 2008;Kemp et al, 2007;Kumar et al, 2007a;Kumar et al, 2007b;Lau et al, 2011;Lee et al, 2012;Miller-Jensen et al, 2007;Tentner et al, 2012). Therefore, the question is no longer whether these model-based simplifications of signalling networks are effective but, rather, why they work so well as often as they do.…”

Section: Hidden Dimensions In Complex Networkmentioning

confidence: 99%

Models of signalling networks – what cell biologists can gain from them and give to them

Janes

Lauffenburger

2013

Journal of Cell Science

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SummaryComputational models of cell signalling are perceived by many biologists to be prohibitively complicated. Why do math when you can simply do another experiment? Here, we explain how conceptual models, which have been formulated mathematically, have provided insights that directly advance experimental cell biology. In the past several years, models have influenced the way we talk about signalling networks, how we monitor them, and what we conclude when we perturb them. These insights required wet-lab experiments but would not have arisen without explicit computational modelling and quantitative analysis. Today, the best modellers are crosstrained investigators in experimental biology who work closely with collaborators but also undertake experimental work in their own laboratories. Biologists would benefit by becoming conversant in core principles of modelling in order to identify when a computational model could be a useful complement to their experiments. Although the mathematical foundations of a model are useful to appreciate its strengths and weaknesses, they are not required to test or generate a worthwhile biological hypothesis computationally. Key words: Cell signalling, Computational biology, Systems biologyIntroduction A decade ago, we welcomed the first signalling-network models that were strongly grounded in wet-lab experiments (Hoffmann et al., 2002;Schoeberl et al., 2002). Excellent models now exist for many canonical signalling circuits in a variety of biological settings. However, such models should not be viewed as an end product but rather as a tool for addressing systems-level challenges in cell biology . Have models fulfilled this role and have they provided biological insights that experimentalists should bother to care about? Here, we answer 'Yes' to both questions and predict that signallingnetwork models will soon become indispensable for modern research in the field. Fortunately, the current wealth of dataintensive methods has primed today's cell biologists to embrace modelling, even though they may lack formal training in the underlying mathematics.In this Opinion, we propose that empirical cell biologists have much to gain from signalling-network models, and much to give by ensuring that these models stay in touch with reality. We begin with a brief primer on how computational models can be critically assessed from a biological standpoint. Then, we walk through a series of important insights about cell signalling that have stemmed from computational-systems modelling. We conclude with future perspectives on where signalling-network models are just beginning to have an impact and will continue to do so in the coming years.

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Linear combinations of docking affinities explain quantitative differences in RTK signaling

Cited by 56 publications

References 26 publications

SH2 Domains Recognize Contextual Peptide Sequence Information to Determine Selectivity

SH2 Domains Recognize Contextual Peptide Sequence Information to Determine Selectivity

Sucrose-induced Receptor Kinase SIRK1 Regulates a Plasma Membrane Aquaporin in Arabidopsis

Models of signalling networks – what cell biologists can gain from them and give to them

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