Literature DB >> 22723110

Identifying protein kinase target preferences using mass spectrometry.

Jacqueline Douglass1, Ruwan Gunaratne, Davis Bradford, Fahad Saeed, Jason D Hoffert, Peter J Steinbach, Mark A Knepper, Trairak Pisitkun.   

Abstract

A general question in molecular physiology is how to identify candidate protein kinases corresponding to a known or hypothetical phosphorylation site in a protein of interest. It is generally recognized that the amino acid sequence surrounding the phosphorylation site provides information that is relevant to identification of the cognate protein kinase. Here, we present a mass spectrometry-based method for profiling the target specificity of a given protein kinase as well as a computational tool for the calculation and visualization of the target preferences. The mass spectrometry-based method identifies sites phosphorylated in response to in vitro incubation of protein mixtures with active recombinant protein kinases followed by standard phosphoproteomic methodologies. The computational tool, called "PhosphoLogo," uses an information-theoretic algorithm to calculate position-specific amino acid preferences and anti-preferences from the mass-spectrometry data (http://helixweb.nih.gov/PhosphoLogo/). The method was tested using protein kinase A (catalytic subunit α), revealing the well-known preference for basic amino acids in positions -2 and -3 relative to the phosphorylated amino acid. It also provides evidence for a preference for amino acids with a branched aliphatic side chain in position +1, a finding compatible with known crystal structures of protein kinase A. The method was also employed to profile target preferences and anti-preferences for 15 additional protein kinases with potential roles in regulation of epithelial transport: CK2, p38, AKT1, SGK1, PKCδ, CaMK2δ, DAPK1, MAPKAPK2, PKD3, PIM1, OSR1, STK39/SPAK, GSK3β, Wnk1, and Wnk4.

Entities:  

Mesh:

Substances:

Year:  2012        PMID: 22723110      PMCID: PMC3774550          DOI: 10.1152/ajpcell.00166.2012

Source DB:  PubMed          Journal:  Am J Physiol Cell Physiol        ISSN: 0363-6143            Impact factor:   4.249


  39 in total

Review 1.  The renaissance of GSK3.

Authors:  P Cohen; S Frame
Journal:  Nat Rev Mol Cell Biol       Date:  2001-10       Impact factor: 94.444

Review 2.  The protein kinase complement of the human genome.

Authors:  G Manning; D B Whyte; R Martinez; T Hunter; S Sudarsanam
Journal:  Science       Date:  2002-12-06       Impact factor: 47.728

3.  A curated compendium of phosphorylation motifs.

Authors:  Ramars Amanchy; Balamurugan Periaswamy; Suresh Mathivanan; Raghunath Reddy; Sudhir Gopal Tattikota; Akhilesh Pandey
Journal:  Nat Biotechnol       Date:  2007-03       Impact factor: 54.908

4.  A systematic MS-based approach for identifying in vitro substrates of PKA and PKG in rat uteri.

Authors:  Sheng-Yu Huang; Mei-Ling Tsai; Guan-Yuan Chen; Chin-Jen Wu; Shu-Hui Chen
Journal:  J Proteome Res       Date:  2007-06-12       Impact factor: 4.466

5.  Proteomic profiling of nuclei from native renal inner medullary collecting duct cells using LC-MS/MS.

Authors:  Dmitry Tchapyjnikov; Yuedan Li; Trairak Pisitkun; Jason D Hoffert; Ming-Jiun Yu; Mark A Knepper
Journal:  Physiol Genomics       Date:  2009-12-08       Impact factor: 3.107

6.  The Elk-1 ETS-domain transcription factor contains a mitogen-activated protein kinase targeting motif.

Authors:  S H Yang; P R Yates; A J Whitmarsh; R J Davis; A D Sharrocks
Journal:  Mol Cell Biol       Date:  1998-02       Impact factor: 4.272

7.  Exceptional disfavor for proline at the P + 1 position among AGC and CAMK kinases establishes reciprocal specificity between them and the proline-directed kinases.

Authors:  Guozhi Zhu; Koichi Fujii; Natalya Belkina; Yin Liu; Michael James; Juan Herrero; Stephen Shaw
Journal:  J Biol Chem       Date:  2005-01-12       Impact factor: 5.157

8.  Linear motif atlas for phosphorylation-dependent signaling.

Authors:  Martin Lee Miller; Lars Juhl Jensen; Francesca Diella; Claus Jørgensen; Michele Tinti; Lei Li; Marilyn Hsiung; Sirlester A Parker; Jennifer Bordeaux; Thomas Sicheritz-Ponten; Marina Olhovsky; Adrian Pasculescu; Jes Alexander; Stefan Knapp; Nikolaj Blom; Peer Bork; Shawn Li; Gianni Cesareni; Tony Pawson; Benjamin E Turk; Michael B Yaffe; Søren Brunak; Rune Linding
Journal:  Sci Signal       Date:  2008-09-02       Impact factor: 8.192

9.  NetworKIN: a resource for exploring cellular phosphorylation networks.

Authors:  Rune Linding; Lars Juhl Jensen; Adrian Pasculescu; Marina Olhovsky; Karen Colwill; Peer Bork; Michael B Yaffe; Tony Pawson
Journal:  Nucleic Acids Res       Date:  2007-11-02       Impact factor: 16.971

10.  Characterizing the microenvironment surrounding phosphorylated protein sites.

Authors:  Shi Cai Fan; Xue Gong Zhang
Journal:  Genomics Proteomics Bioinformatics       Date:  2005-11       Impact factor: 7.691

View more
  33 in total

1.  Proteome-wide measurement of protein half-lives and translation rates in vasopressin-sensitive collecting duct cells.

Authors:  Pablo C Sandoval; Dane H Slentz; Trairak Pisitkun; Fahad Saeed; Jason D Hoffert; Mark A Knepper
Journal:  J Am Soc Nephrol       Date:  2013-09-12       Impact factor: 10.121

2.  CaMKII Phosphorylation of Na(V)1.5: Novel in Vitro Sites Identified by Mass Spectrometry and Reduced S516 Phosphorylation in Human Heart Failure.

Authors:  Anthony W Herren; Darren M Weber; Robert R Rigor; Kenneth B Margulies; Brett S Phinney; Donald M Bers
Journal:  J Proteome Res       Date:  2015-04-13       Impact factor: 4.466

3.  An Integrative Analysis of Tumor Proteomic and Phosphoproteomic Profiles to Examine the Relationships Between Kinase Activity and Phosphorylation.

Authors:  Osama A Arshad; Vincent Danna; Vladislav A Petyuk; Paul D Piehowski; Tao Liu; Karin D Rodland; Jason E McDermott
Journal:  Mol Cell Proteomics       Date:  2019-06-21       Impact factor: 5.911

4.  PTM-Logo: a program for generation of sequence logos based on position-specific background amino-acid probabilities.

Authors:  Thammakorn Saethang; Kenneth Hodge; Chin-Rang Yang; Yue Zhao; Ingorn Kimkong; Mark A Knepper; Trairak Pisitkun
Journal:  Bioinformatics       Date:  2019-12-15       Impact factor: 6.937

Review 5.  Systems biology in physiology: the vasopressin signaling network in kidney.

Authors:  Mark A Knepper
Journal:  Am J Physiol Cell Physiol       Date:  2012-08-29       Impact factor: 4.249

6.  Letter to the editor: "Systems biology versus reductionism in cell physiology".

Authors:  Mark A Knepper; Viswanathan Raghuram; Davis Bradford; Chung-Lin Chou; Jason D Hoffert; Trairak Pisitkun
Journal:  Am J Physiol Cell Physiol       Date:  2014-08-01       Impact factor: 4.249

7.  Quantitative phosphoproteomics in nuclei of vasopressin-sensitive renal collecting duct cells.

Authors:  Steven J Bolger; Patricia A Gonzales Hurtado; Jason D Hoffert; Fahad Saeed; Trairak Pisitkun; Mark A Knepper
Journal:  Am J Physiol Cell Physiol       Date:  2012-09-19       Impact factor: 4.249

Review 8.  Vasopressin-2 receptor signaling and autosomal dominant polycystic kidney disease: from bench to bedside and back again.

Authors:  Markus M Rinschen; Bernhard Schermer; Thomas Benzing
Journal:  J Am Soc Nephrol       Date:  2014-02-20       Impact factor: 10.121

Review 9.  Vasopressin and the regulation of aquaporin-2.

Authors:  Justin L L Wilson; Carlos A Miranda; Mark A Knepper
Journal:  Clin Exp Nephrol       Date:  2013-04-13       Impact factor: 2.801

10.  A knowledge base of vasopressin actions in the kidney.

Authors:  Akshay Sanghi; Matthew Zaringhalam; Callan C Corcoran; Fahad Saeed; Jason D Hoffert; Pablo Sandoval; Trairak Pisitkun; Mark A Knepper
Journal:  Am J Physiol Renal Physiol       Date:  2014-07-23
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.