Computational Identification of Protein Kinases and Kinase-Specific Substrates in Plants
The protein phosphorylation catalyzed by protein kinases (PKs) plays an essential role in almost all biological progresses in plants. Thus, the identification of PKs and kinase-specific substrates is fundamental for understanding the regulatory mechanisms of protein phosphorylation especially in controlling plant growth and development. In this chapter, we describe the computational methods and protocols for the identification of PKs and kinase-specific substrates in plants, by using Vitis vinifera as an example. First, the proteome sequences and experimentally identified phosphorylation sites (p-sites) in Vitis vinifera were downloaded. The potential PKs were computationally identified based on preconstructed Hidden Markov Model (HMM) profiles and ortholog searches, whereas the kinase-specific p-sites, or site-specific kinase–substrate relations (ssKSRs) were initially predicted by the software package of Group-based Prediction System (GPS) and further processed by the iGPS algorithm (in vivo GPS) to filter potentially false positive hits. All primary data sets and prediction results of Vitis vinifera are available at: http://ekpd.biocuckoo.org/protocol.php.
Key wordsProtein kinase Phosphorylation Kinase-specific substrate Hidden Markov Model GPS Site-specific kinase–substrate relation
This work was supported by grants from the National Basic Research Program (973 project) (2013CB933900 and 2012CB910101), Natural Science Foundation of China (31171263, and 81272578), and International Science & Technology Cooperation Program of China (2014DFB30020).
- 4.Levene PA, Alsberg CL (1906) The cleavage products of vitellin. J Biol Chem 2:127–133Google Scholar
- 5.Lipmann FA, Levene PA (1932) Prokaryotic elongation factor Tu is phosphorylated in vivo. J Biol Chem 98:109–114Google Scholar
- 16.Yu Xue ZL, Jun Cao, Jian Ren (2011) Computational prediction of post-translational modification sites in proteins. Systems and computational biology—molecular and cellular experimental systems, Ning-Sun Yang (Ed), ISBN: 978-953-307-280-7, InTech, DOI:105772/18559Google Scholar
- 19.Flicek P, Ahmed I, Amode MR, Barrell D, Beal K, Brent S, Carvalho-Silva D, Clapham P, Coates G, Fairley S, Fitzgerald S, Gil L, Garcia-Giron C, Gordon L, Hourlier T, Hunt S, Juettemann T, Kahari AK, Keenan S, Komorowska M, Kulesha E, Longden I, Maurel T, McLaren WM, Muffato M, Nag R, Overduin B, Pignatelli M, Pritchard B, Pritchard E, Riat HS, Ritchie GR, Ruffier M, Schuster M, Sheppard D, Sobral D, Taylor K, Thormann A, Trevanion S, White S, Wilder SP, Aken BL, Birney E, Cunningham F, Dunham I, Harrow J, Herrero J, Hubbard TJ, Johnson N, Kinsella R, Parker A, Spudich G, Yates A, Zadissa A, Searle SM (2013) Ensembl 2013. Nucleic Acids Res 41(Database issue):D48–D55. doi: 10.1093/nar/gks1236 CrossRefPubMedCentralPubMedGoogle Scholar
- 21.Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C, Jensen LJ (2013) STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res 41(Database issue):D808–D815. doi: 10.1093/nar/gks1094 CrossRefPubMedCentralPubMedGoogle Scholar