Abstract
As a critical post-translational modification, phosphorylation plays important roles in regulating various biological processes, while recent studies suggest that phosphorylation in bacteria is also critical for functional signaling transduction. Since identification of phosphorylation substrates and sites is fundamental for understanding the phosphorylation mediated regulatory mechanism, a number of studies have been contributed to this area. Since experimental identification of phosphorylation sites is time-consuming and labor-intensive, computational predictions attract much attention for its convenience to provide helpful information. However, although there are a large number of computational studies in eukaryotes, predictions in bacteria are still rare. In this study, we present a new predictor of cPhosBac to predict phosphorylation serine/threonine in bacteria proteins. The predictor is developed with CKSAAP algorithm, which was combined with motif length selection to optimize the prediction, which achieves promising performance. The online service of cPhosBac is available at: http://netalign.ustc.edu.cn/cphosbac/.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Raju TN (2000) The nobel chronicles 1992: Fischer EH (b 1920), Krebs EG (b 1918). Lancet, 355:2004
Hunter T (2009) Tyrosine phosphorylation: 30 years and counting. Curr Opin Cell Biol 21:140–146
Johnson LN (2009) The regulation of protein phosphorylation. Biochem Soc Trans 37:627–641
Pawson T, Scott JD (2005) Protein phosphorylation in signaling—50 years and counting. Trends Biochem Sci 30:286–290
Cousin C, Derouiche A, Shi L, Pagot Y, Poncet S, Mijakovic I (2013) Protein-serine/threonine/tyrosine kinases in bacterial signaling and regulation. FEMS Microbiol Lett 346(1):11–19
Cozzone AJ (1988) Protein-phosphorylation in prokaryotes. Annu Rev Microbiol 42:97–125
Ohlsen K, Donat S (2010) The impact of serine/threonine phosphorylation in staphylococcus aureus. Int J Med Microbiol 300:137–141
Deutscher J, Francke C, Postma PW (2006) How phosphotransferase system-related protein phosphorylation regulates carbohydrate metabolism in bacteria. Microbiol Mol Biol Rev 70:939–1031
Hoch JA (2000) Two-component and phosphorelay signal transduction. Curr Opin Microbiol 3:165–170
Song C, Ye M, Liu Z, Cheng H, Jiang X, Han G, Songyang Z, Tan Y, Wang H, Ren J et al (2012) Systematic analysis of protein phosphorylation networks from phosphoproteomic data. Mol cell proteomics: MCP 11:1070–1083
Macek B, Mann M, Olsen JV (2009) Global and site-specific quantitative phosphoproteomics: principles and applications. Annu Rev Pharmacol Toxicol 49:199–221
Macek B, Gnad F, Soufi B, Kumar C, Olsen JV, Mijakovic I, Mann M (2008) Phosphoproteome analysis of E. coli reveals evolutionary conservation of bacterial Ser/Thr/Tyr phosphorylation. Mol cell proteomics: MCP 7:299–307
Macek B, Mijakovic I, Olsen JV, Gnad F, Kumar C, Jensen PR, Mann M (2007) The serine/threonine/tyrosine phosphoproteome of the model bacterium bacillus subtilis. Mol cell proteomics: MCP 6:697–707
Ren J, Gao X, Liu Z, Cao J, Ma Q, Xue Y (2011) Computational analysis of phosphoproteomics: progresses and perspectives. Curr Protein Pept Sci 12:591–601
Xue Y, Gao X, Cao J, Liu Z, Jin C, Wen L, Yao X, Ren J (2010) A summary of computational resources for protein phosphorylation. Curr Protein Pept Sci 11:485–496
Miller ML, Soufi B, Jers C, Blom N, Macek B, Mijakovic I (2009) NetPhosBac—a predictor for Ser/Thr phosphorylation sites in bacterial proteins. Proteomics 9:116–125
Chen K, Jiang Y, Du L, Kurgan L (2009) Prediction of integral membrane protein type by collocated hydrophobic amino acid pairs. J Comput Chem 30:163–172
Chen K, Kurgan LA, Ruan J (2007) Prediction of flexible/rigid regions from protein sequences using k-spaced amino acid pairs. BMC Struct Biol 7:25
Chen YZ, Tang YR, Sheng ZY, Zhang Z (2008) Prediction of mucin-type O-glycosylation sites in mammalian proteins using the composition of k-spaced amino acid pairs. BMC Bioinformatics 9:101
Chen Z, Chen YZ, Wang XF, Wang C, Yan RX, Zhang Z (2011) Prediction of ubiquitination sites by using the composition of k-spaced amino acid pairs. PLoS ONE 6:e22930
Chen Z, Zhou Y, Song J (1834) Zhang Z (2013) hCKSAAP_UbSite: improved prediction of human ubiquitination sites by exploiting amino acid pattern and properties. Biochim Biophys Acta 8:1461–1467
Vacic V, Iakoucheva LM, Radivojac P (2006) Two sample logo: a graphical representation of the differences between two sets of sequence alignments. Bioinformatics 22:1536–1537
Xue Y, Liu Z, Cao J, Ma Q, Gao X, Wang Q, Jin C, Zhou Y, Wen L, Ren J (2011) GPS 2.1: enhanced prediction of kinase-specific phosphorylation sites with an algorithm of motif length selection. Protein Eng Des Sel 24:255–260
He Z, Yang C, Guo G, Li N, Yu W (2011) Motif-All: discovering all phosphorylation motifs. BMC Bioinformatics 12(Suppl 1):S22
Team RC (2012) R: a Language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria
Acknowledgments
This work was supported, in whole or in part, by Provincial Key Research Program of Universities in Anhui (KJ2012A063), Innovation Foundation of USTC for Young Scientists (WK2070000028).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Shanghai Jiaotong University Press, Shanghai and Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Li, Z., Wu, P., Zhao, Y., Liu, Z., Zhao, W. (2015). Prediction of Serine/Threonine Phosphorylation Sites in Bacteria Proteins. In: Wei, D., Xu, Q., Zhao, T., Dai, H. (eds) Advance in Structural Bioinformatics. Advances in Experimental Medicine and Biology, vol 827. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9245-5_16
Download citation
DOI: https://doi.org/10.1007/978-94-017-9245-5_16
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-017-9244-8
Online ISBN: 978-94-017-9245-5
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)