Structural Determinants of Substrate Selection by the Human Insulin‐Receptor Protein‐Tyrosine Kinase

Keane, Noeleen E.; Chavanieu, Alain; Quirk, Philip G.; Evans, James S.; Levine, Barry A.; Calas, Bernard; Wei, Lei; Ellis, Leland

doi:10.1111/j.1432-1033.1994.tb20077.x

Cited by 10 publications

(7 citation statements)

References 36 publications

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“…Structural information is particularly limited, due in part to the difficulty of obtaining sufficient purified protein in a specific modification state. X-ray crystallography has determined atomic-resolution structures for a few tens of phosphorylated proteins [3], whereas nuclear magnetic resonance (NMR) experiments have elucidated structure and dynamics of a similar number of phosphorylated peptides and small proteins in solution [4–10]. Electron paramagnetic resonance (EPR) and circular dichroism experiments can also provide information on conformational change due to phosphorylation [11], but do not provide atomic detail.…”

Section: Introductionmentioning

confidence: 99%

Conformational Changes in Protein Loops and Helices Induced by Post-Translational Phosphorylation

2006

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Post-translational phosphorylation is a ubiquitous mechanism for modulating protein activity and protein-protein interactions. In this work, we examine how phosphorylation can modulate the conformation of a protein by changing the energy landscape. We present a molecular mechanics method in which we phosphorylate proteins in silico and then predict how the conformation of the protein will change in response to phosphorylation. We apply this method to a test set comprised of proteins with both phosphorylated and non-phosphorylated crystal structures, and demonstrate that it is possible to predict localized phosphorylation-induced conformational changes, or the absence of conformational changes, with near-atomic accuracy in most cases. Examples of proteins used for testing our methods include kinases and prokaryotic response regulators. Through a detailed case study of cyclin-dependent kinase 2, we also illustrate how the computational methods can be used to provide new understanding of how phosphorylation drives conformational change, why substituting Glu or Asp for a phosphorylated amino acid does not always mimic the effects of phosphorylation, and how a phosphatase can “capture” a phosphorylated amino acid. This work illustrates how computational methods can be used to elucidate principles and mechanisms of post-translational phosphorylation, which can ultimately help to bridge the gap between the number of known sites of phosphorylation and the number of structures of phosphorylated proteins.

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Section: Introductionmentioning

confidence: 99%

Conformational Changes in Protein Loops and Helices Induced by Post-Translational Phosphorylation

2006

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“…Structural information is particularly limited, due in part to the difficulty of obtaining sufficient purified protein in a specific modification state. X-ray crystallography has determined atomic-resolution structures for a few tens of phosphorylated proteins [3], whereas nuclear magnetic resonance (NMR) experiments have elucidated structure and dynamics of a similar number of phosphorylated peptides and small proteins in solution [4][5][6][7][8][9][10]. Electron paramagnetic resonance (EPR) and circular dichroism experiments can also provide information on conformational change due to phosphorylation [11], but do not provide atomic detail.…”

Section: Introductionmentioning

confidence: 99%

Conformational Changes in Protein Loops and Helices Induced by Post-Translational Phosphorylation

Groban¹,

Narayanan²,

Jacobson³

2005

PLoS Comp Biol

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Post-translational phosphorylation is a ubiquitous mechanism for modulating protein activity and protein-protein interactions. In this work, we examine how phosphorylation can modulate the conformation of a protein by changing the energy landscape. We present a molecular mechanics method in which we phosphorylate proteins in silico and then predict how the conformation of the protein will change in response to phosphorylation. We apply this method to a test set comprised of proteins with both phosphorylated and non-phosphorylated crystal structures, and demonstrate that it is possible to predict localized phosphorylation-induced conformational changes, or the absence of conformational changes, with near-atomic accuracy in most cases. Examples of proteins used for testing our methods include kinases and prokaryotic response regulators. Through a detailed case study of cyclin-dependent kinase 2, we also illustrate how the computational methods can be used to provide new understanding of how phosphorylation drives conformational change, why substituting Glu or Asp for a phosphorylated amino acid does not always mimic the effects of phosphorylation, and how a phosphatase can ''capture'' a phosphorylated amino acid. This work illustrates how computational methods can be used to elucidate principles and mechanisms of post-translational phosphorylation, which can ultimately help to bridge the gap between the number of known sites of phosphorylation and the number of structures of phosphorylated proteins.

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“…A recent mass spectrometry study of 162 cytosolic phosphoproteins provided an experimental confirmation: out of 512 phosphorylation sites, 97% occurred outside of structured domains, and 86% occurred in regions of protein disorder (Collins, et al, 2008). Nonetheless, there are examples of ordered phosphorylation sites (Johnson and Lewis, 2001; Keane, et al, 1994; Quirk, et al, 1996; Tholey, et al, 2001) and a number of structures containing phosphorylated sites have been deposited in PDB (see Data Sets section). In addition, several studies addressed conformational changes in proteins following the addition of the phosphate group (e.g.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Graphlet Kernels for Prediction of Functional Residues in Protein Structures

Vacic

Iakoucheva²,

Lonardi³

et al. 2010

Journal of Computational Biology

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We introduce a novel graph-based kernel method for annotating functional residues in protein structures. A structure is first modeled as a protein contact graph, where nodes correspond to residues and edges connect spatially neighboring residues. Each vertex in the graph is then represented as a vector of counts of labeled non-isomorphic subgraphs (graphlets), centered on the vertex of interest. A similarity measure between two vertices is expressed as the inner product of their respective count vectors and is used in a supervised learning framework to classify protein residues. We evaluated our method on two function prediction problems: identification of catalytic residues in proteins, which is a well-studied problem suitable for benchmarking, and a much less explored problem of predicting phosphorylation sites in protein structures. The performance of the graphlet kernel approach was then compared against two alternative methods, a sequence-based predictor and our implementation of the FEATURE framework. On both tasks the graphlet kernel performed favorably; however, the margin of difference was considerably higher on the problem of phosphorylation site prediction. While there is data that phosphorylation sites are preferentially positioned in intrinsically disordered regions, we provide evidence that for the sites that are located in structured regions, neither the surface accessibility alone nor the averaged measures calculated from the residue microenvironments utilized by FEATURE were sufficient to achieve high accuracy. The key benefit of the graphlet representation is its ability to capture neighborhood similarities in protein structures via enumerating the patterns of local connectivity in the corresponding labeled graphs.

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Structural Determinants of Substrate Selection by the Human Insulin‐Receptor Protein‐Tyrosine Kinase

Cited by 10 publications

References 36 publications

Conformational Changes in Protein Loops and Helices Induced by Post-Translational Phosphorylation

Conformational Changes in Protein Loops and Helices Induced by Post-Translational Phosphorylation

Conformational Changes in Protein Loops and Helices Induced by Post-Translational Phosphorylation

Graphlet Kernels for Prediction of Functional Residues in Protein Structures

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