Advertisement

Amino Acids

, Volume 46, Issue 4, pp 1069–1078 | Cite as

Prediction of protein kinase-specific phosphorylation sites in hierarchical structure using functional information and random forest

  • Wenwen Fan
  • Xiaoyi Xu
  • Yi Shen
  • Huanqing Feng
  • Ao Li
  • Minghui WangEmail author
Original Article

Abstract

Reversible protein phosphorylation is one of the most important post-translational modifications, which regulates various biological cellular processes. Identification of the kinase-specific phosphorylation sites is helpful for understanding the phosphorylation mechanism and regulation processes. Although a number of computational approaches have been developed, currently few studies are concerned about hierarchical structures of kinases, and most of the existing tools use only local sequence information to construct predictive models. In this work, we conduct a systematic and hierarchy-specific investigation of protein phosphorylation site prediction in which protein kinases are clustered into hierarchical structures with four levels including kinase, subfamily, family and group. To enhance phosphorylation site prediction at all hierarchical levels, functional information of proteins, including gene ontology (GO) and protein–protein interaction (PPI), is adopted in addition to primary sequence to construct prediction models based on random forest. Analysis of selected GO and PPI features shows that functional information is critical in determining protein phosphorylation sites for every hierarchical level. Furthermore, the prediction results of Phospho.ELM and additional testing dataset demonstrate that the proposed method remarkably outperforms existing phosphorylation prediction methods at all hierarchical levels. The proposed method is freely available at http://bioinformatics.ustc.edu.cn/phos_pred/.

Keywords

Phosphorylation Hierarchical structure Functional information Random forest 

Notes

Acknowledgments

This work was supported by National Natural Science Foundation of China (61101061, 31100955), Fundamental Research Funds for the Central Universities (WK2100230011).

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

726_2014_1669_MOESM1_ESM.pdf (662 kb)
Supplementary material 1 (PDF 661 kb)

References

  1. Aponte AM, Phillips D, Harris RA, Blinova K, French S, Johnson DT, Balaban RS (2009) <sup> 32 </sup> P labeling of protein phosphorylation and metabolite association in the mitochondria matrix. Methods Enzymol 457:63–80PubMedCentralPubMedCrossRefGoogle Scholar
  2. Beausoleil SA, Villén J, Gerber SA, Rush J, Gygi SP (2006) A probability-based approach for high-throughput protein phosphorylation analysis and site localization. Nat Biotechnol 24(10):1285–1292PubMedCrossRefGoogle Scholar
  3. Blom N, Gammeltoft S, Brunak S (1999) Sequence and structure-based prediction of eukaryotic protein phosphorylation sites. J Mol Biol 294(5):1351–1362PubMedCrossRefGoogle Scholar
  4. Blom N, Sicheritz-Pontén T, Gupta R, Gammeltoft S, Brunak S (2004) Prediction of post-translational glycosylation and phosphorylation of proteins from the amino acid sequence. Proteomics 4(6):1633–1649PubMedCrossRefGoogle Scholar
  5. Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRefGoogle Scholar
  6. Dinkel H, Chica C, Via A, Gould CM, Jensen LJ, Gibson TJ, Diella F (2011) Phospho. ELM: a database of phosphorylation sites—update 2011. Nucleic Acids Res 39 (suppl 1):D261–D267Google Scholar
  7. Dondoshansky I, Wolf Y (2002) Blastclust (NCBI Software Development Toolkit). NCBI, BethesdaGoogle Scholar
  8. Fang B, Haura EB, Smalley KS, Eschrich SA, Koomen JM (2010) Methods for investigation of targeted kinase inhibitor therapy using chemical proteomics and phosphorylation profiling. Biochem Pharmacol 80(5):739–747PubMedCentralPubMedCrossRefGoogle Scholar
  9. Gao J, Thelen JJ, Dunker AK, Xu D (2010) Musite, a tool for global prediction of general and kinase-specific phosphorylation sites. Mol Cell Proteomics 9(12):2586–2600PubMedCentralPubMedCrossRefGoogle Scholar
  10. Gastwirth JL (1972) The estimation of the Lorenz curve and Gini index. Review Econ Stat 54(3):306–316CrossRefGoogle Scholar
  11. Harris M, Clark J, Ireland A, Lomax J, Ashburner M, Foulger R, Eilbeck K, Lewis S, Marshall B, Mungall C (2004) The Gene Ontology (GO) database and informatics resource. Nucleic Acids Research 32 (Database issue):D258–2D61Google Scholar
  12. Huang Y, Niu B, Gao Y, Fu L, Li W (2010) CD-HIT suite: a web server for clustering and comparing biological sequences. Bioinformatics 26(5):680–682PubMedCentralPubMedCrossRefGoogle Scholar
  13. Jung H-J, Kim Y-J, Eggert S, Chung KC, Choi KS, Park SA (2013) Age-dependent increases in tau phosphorylation in the brains of type 2 diabetic rats correlate with a reduced expression of p62. Exp Neurol 248:441–450PubMedCrossRefGoogle Scholar
  14. Lagranha CJ, Deschamps A, Aponte A, Steenbergen C, Murphy E (2010) Sex differences in the phosphorylation of mitochondrial proteins result in reduced production of reactive oxygen species and cardioprotection in females. Circ Res 106(11):1681–1691PubMedCentralPubMedCrossRefGoogle Scholar
  15. Li T, Du P, Xu N (2010) Identifying human kinase-specific protein phosphorylation sites by integrating heterogeneous information from various sources. PLoS One 5(11):e15411PubMedCentralPubMedCrossRefGoogle Scholar
  16. Lou Y, Yao J, Zereshki A, Dou Z, Ahmed K, Wang H, Hu J, Wang Y, Yao X (2004) NEK2A interacts with MAD1 and possibly functions as a novel integrator of the spindle checkpoint signaling. J Biol Chem 279(19):20049–20057PubMedCrossRefGoogle Scholar
  17. Ma L, Chen Z, Erdjument-Bromage H, Tempst P, Pandolfi PP (2005) Phosphorylation and functional inactivation of TSC2 by Erk: implications for tuberous sclerosis and cancer pathogenesis. Cell 121(2):179–193PubMedCrossRefGoogle Scholar
  18. Maeshima Y, Fukatsu K, Kang W, Ueno C, Moriya T, Saitoh D, Mochizuki H (2007) Lack of enteral nutrition blunts extracellular-regulated kinase phosphorylation in gut-associated lymphoid tissue. Shock 27(3):320–325PubMedCrossRefGoogle Scholar
  19. Manning G, Whyte DB, Martinez R, Hunter T, Sudarsanam S (2002) The protein kinase complement of the human genome. Science 298(5600):1912–1934PubMedCrossRefGoogle Scholar
  20. Newman RH, Hu J, Rho H-S, Xie Z, Woodard C, Neiswinger J, Cooper C, Shirley M, Clark HM, Hu S (2013) Construction of human activity-based phosphorylation networks. Mol Syst Biol 9(1):655. doi: 10.1038/msb.2013.12 Google Scholar
  21. Pawson T (2004) Specificity in signal transduction: from phosphotyrosine-SH2 domain interactions to complex cellular systems. Cell 116(2):191–203PubMedCrossRefGoogle Scholar
  22. Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. Pattern Anal Mach Intell IEEE Trans 27(8):1226–1238CrossRefGoogle Scholar
  23. Peng C, Wang M, Shen Y, Feng H, Li A (2013) Reconstruction and analysis of transcription factor–miRNA co-regulatory feed-forward loops in human cancers using filter-wrapper feature selection. PLoS One 8(10). doi: 10.1371/journal.pone.0078197
  24. Schafmeier T, Haase A, Káldi K, Scholz J, Fuchs M, Brunner M (2005) Transcriptional feedback of neurospora circadian clock gene by phosphorylation-dependent inactivation of its transcription factor. Cell 122(2):235–246PubMedCrossRefGoogle Scholar
  25. Singh CR, Curtis C, Yamamoto Y, Hall NS, Kruse DS, He H, Hannig EM, Asano K (2005) Eukaryotic translation initiation factor 5 is critical for integrity of the scanning preinitiation complex and accurate control of GCN4 translation. Mol Cell Biol 25(13):5480–5491PubMedCentralPubMedCrossRefGoogle Scholar
  26. Teng S, Luo H, Wang L (2012) Predicting protein sumoylation sites from sequence features. Amino Acids 43(1):447–455PubMedCrossRefGoogle Scholar
  27. Trost B, Kusalik A (2013) Computational phosphorylation site prediction in plants using random forests and organism-specific instance weights. Bioinformatics 29(6):686–694PubMedCrossRefGoogle Scholar
  28. Von Mering C, Huynen M, Jaeggi D, Schmidt S, Bork P, Snel B (2003) STRING: a database of predicted functional associations between proteins. Nucleic Acids Res 31(1):258–261CrossRefGoogle Scholar
  29. Waddick KG, Chae HP, Tuel-Ahlgren L, Jarvis LJ, Dibirdik I, Myers DE, Uckun FM (1993) Engagement of the CD19 receptor on human B-lineage leukemia cells activates LCK tyrosine kinase and facilitates radiation-induced apoptosis. Radiat Res 136(3):313–319PubMedCrossRefGoogle Scholar
  30. Wang M, Chen X, Zhang M, Zhu W, Cho K, Zhang H (2009) Detecting significant single-nucleotide polymorphisms in a rheumatoid arthritis study using random forests. In: BMC proceedings. BioMed Central Ltd, p S69Google Scholar
  31. Wang M, Chen X, Zhang H (2010) Maximal conditional Chi square importance in random forests. Bioinformatics 26(6):831–837PubMedCentralPubMedCrossRefGoogle Scholar
  32. Wong Y-H, Lee T-Y, Liang H-K, Huang C-M, Wang T-Y, Yang Y-H, Chu C-H, Huang H-D, Ko M-T, Hwang J-K (2007) KinasePhos 2.0: a web server for identifying protein kinase-specific phosphorylation sites based on sequences and coupling patterns. Nucleic acids research 35 (suppl 2):W588–W594Google Scholar
  33. Wood CD, Thornton TM, Sabio G, Davis RA, Rincon M (2009) Nuclear localization of p38 MAPK in response to DNA damage. Int J Biol Sci 5(5):428PubMedCentralPubMedCrossRefGoogle Scholar
  34. Xue Y, Li A, Wang L, Feng H, Yao X (2006) PPSP: prediction of PK-specific phosphorylation site with Bayesian decision theory. BMC Bioinform 7(1):163CrossRefGoogle Scholar
  35. Xue Y, Ren J, Gao X, Jin C, Wen L, Yao X (2008) GPS 2.0, a tool to predict kinase-specific phosphorylation sites in hierarchy. Mol Cell Proteomics 7(9):1598–1608PubMedCentralPubMedCrossRefGoogle Scholar
  36. Yang ZR (2009) Predicting sulfotyrosine sites using the random forest algorithm with significantly improved prediction accuracy. BMC Bioinform 10(1):361CrossRefGoogle Scholar
  37. Zhang H, Wang M, Chen X (2009) Willows: a memory efficient tree and forest construction package. BMC Bioinform 10(1):130CrossRefGoogle Scholar
  38. Zou L, Huang Q, Li A, Wang M (2012) A genome-wide association study of Alzheimer’s disease using random forests and enrichment analysis. Sci China Life Sci 55(7):618–625PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Wien 2014

Authors and Affiliations

  • Wenwen Fan
    • 1
  • Xiaoyi Xu
    • 1
  • Yi Shen
    • 1
  • Huanqing Feng
    • 1
  • Ao Li
    • 1
    • 2
  • Minghui Wang
    • 1
    • 2
    Email author
  1. 1.School of Information Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina
  2. 2.Research Centers for Biomedical EngineeringUniversity of Science and Technology of ChinaHefeiChina

Personalised recommendations