Abstract
Studies on phosphorylation are important but challenging for both wet-bench experiments and computational studies, and accurate non-kinase-specific prediction tools are highly desirable for whole-genome annotation in a wide variety of species. Here, we describe a phosphorylation site prediction webserver, PhosphoSVM, that employs Support Vector Machine to combine protein secondary structure information and seven other one-dimensional structural properties, including Shannon entropy, relative entropy, predicted protein disorder information, predicted solvent accessible area, amino acid overlapping properties, averaged cumulative hydrophobicity, and subsequence k-nearest neighbor profiles. This method achieved AUC values of 0.8405/0.8183/0.7383 for serine (S), threonine (T), and tyrosine (Y) phosphorylation sites, respectively, in animals with a tenfold cross-validation. The model trained by the animal phosphorylation sites was also applied to a plant phosphorylation site dataset as an independent test. The AUC values for the independent test data set were 0.7761/0.6652/0.5958 for S/T/Y phosphorylation sites, respectively. This algorithm with the optimally trained model was implemented as a webserver. The webserver, trained model, and all datasets used in the current study are available at http://sysbio.unl.edu/PhosphoSVM.
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References
Caenepeel S, Charydczak G, Sudarsanam S, Hunter T, Manning G (2004) The mouse kinome: discovery and comparative genomics of all mouse protein kinases. Proc Natl Acad Sci U S A 101(32):11707–11712. doi:10.1073/pnas.0306880101
Manning G, Whyte DB, Martinez R, Hunter T, Sudarsanam S (2002) The protein kinase complement of the human genome. Science 298(5600):1912–1934. doi:10.1126/science.1075762
Vlad F, Turk BE, Peynot P, Leung J, Merlot S (2008) A versatile strategy to define the phosphorylation preferences of plant protein kinases and screen for putative substrates. Plant J 55(1):104–117. doi:10.1111/j.1365-313X.2008.03488.x
Trost B, Kusalik A (2011) Computational prediction of eukaryotic phosphorylation sites. Bioinformatics 27(21):2927–2935. doi:10.1093/bioinformatics/btr525
Dou Y, Yao B, Zhang C (2014) PhosphoSVM: prediction of phosphorylation sites by integrating various protein sequence attributes with a support vector machine. Amino Acids 46(6):1459–1469. doi:10.1007/s00726-014-1711-5
Diella F, Gould CM, Chica C, Via A, Gibson TJ (2008) Phospho.ELM, a database of phosphorylation sites—update. Nucleic Acids Res 36(Database issue):D240–D244. doi:10.1093/nar/gkm772
Heazlewood JL, Durek P, Hummel J, Selbig J, Weckwerth W, Walther D, Schulze WX (2008) PhosPhAt: a database of phosphorylation sites in Arabidopsis thaliana and a plant-specific phosphorylation site predictor. Nucleic Acids Res 36(Database issue):D1015–D1021. doi:10.1093/nar/gkm812
Durek P, Schmidt R, Heazlewood JL, Jones A, MacLean D, Nagel A, Kersten B, Schulze WX (2010) PhosPhAt: the Arabidopsis thaliana phosphorylation site database. An update. Nucleic Acids Res 38(Database issue):D828–D834. doi:10.1093/nar/gkp810
Zulawski M, Braginets R, Schulze WX (2013) PhosPhAt goes kinases—searchable protein kinase target information in the plant phosphorylation site database PhosPhAt. Nucleic Acids Res 41(Database issue):D1176–D1184. doi:10.1093/nar/gks1081
Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25(17):3389–3402
Fan RE, Chen PH, Lin CJ (2005) Working set selection using second order information for training support vector machines. J Mach Learn Res 6:1889–1918
Iakoucheva LM, Radivojac P, Brown CJ, O'Connor TR, Sikes JG, Obradovic Z, Dunker AK (2004) The importance of intrinsic disorder for protein phosphorylation. Nucleic Acids Res 32(3):1037–1049. doi:10.1093/nar/gkh253
McGuffin LJ, Bryson K, Jones DT (2000) The PSIPRED protein structure prediction server. Bioinformatics 16(4):404–405
Ward JJ, Sodhi JS, McGuffin LJ, Buxton BF, Jones DT (2004) Prediction and functional analysis of native disorder in proteins from the three kingdoms of life. J Mol Biol 337(3):635–645. doi:10.1016/j.jmb.2004.02.002
Ahmad S, Gromiha MM, Sarai A (2003) RVP-net: online prediction of real valued accessible surface area of proteins from single sequences. Bioinformatics 19(14):1849–1851
Taylor WR (1986) The classification of amino acid conservation. J Theor Biol 119(2):205–218
Sweet RM, Eisenberg D (1983) Correlation of sequence hydrophobicities measures similarity in three-dimensional protein structure. J Mol Biol 171(4):479–488
Biswas AK, Noman N, Sikder AR (2010) Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information. BMC Bioinformatics 11:273. doi:10.1186/1471-2105-11-273
Capra JA, Singh M (2007) Predicting functionally important residues from sequence conservation. Bioinformatics 23(15):1875–1882. doi:10.1093/bioinformatics/btm270
Mihalek I, Res I, Lichtarge O (2004) A family of evolution-entropy hybrid methods for ranking protein residues by importance. J Mol Biol 336(5):1265–1282. doi:10.1016/j.jmb.2003.12.078
Johansson F, Toh H (2010) A comparative study of conservation and variation scores. BMC Bioinformatics 11:388. doi:10.1186/1471-2105-11-388
Wu TD, Brutlag DL (1995) Identification of protein motifs using conserved amino acid properties and partitioning techniques. Proc Int Conf Intell Syst Mol Biol 3:402–410
Gok M, Ozcerit AT (2012) Prediction of MHC class I binding peptides with a new feature encoding technique. Cell Immunol 275(1–2):1–4. doi:10.1016/j.cellimm.2012.04.005
Wu CY, Hwa YH, Chen YC, Lim C (2012) Hidden relationship between conserved residues and locally conserved phosphate-binding structures in NAD(P)-binding proteins. J Phys Chem B. doi:10.1021/jp3014332
Dou Y, Zheng X, Yang J, Wang J (2010) Prediction of catalytic residues based on an overlapping amino acid classification. Amino Acids 39(5):1353–1361. doi:10.1007/s00726-010-0587-2
Dou Y, Wang J, Yang J, Zhang C (2012) L1pred: a sequence-based prediction tool for catalytic residues in enzymes with the L1-logreg classifier. PLoS One 7(4):e35666. doi:10.1371/journal.pone.0035666
Zhang T, Zhang H, Chen K, Shen S, Ruan J, Kurgan L (2008) Accurate sequence-based prediction of catalytic residues. Bioinformatics 24(20):2329–2338. doi:10.1093/bioinformatics/btn433
Wang L, Brown SJ (2006) BindN: a web-based tool for efficient prediction of DNA and RNA binding sites in amino acid sequences. Nucleic Acids Res 34(Web Server issue):W243–W248. doi:10.1093/nar/gkl298
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):2586–2600. doi:10.1074/mcp.M110.001388
Acknowledgement
This project was supported by funding under CZ’s startup funds from University of Nebraska, Lincoln, NE. This work was completed utilizing the Holland Computing Center of the University of Nebraska.
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Dou, Y., Yao, B., Zhang, C. (2017). Prediction of Protein Phosphorylation Sites by Integrating Secondary Structure Information and Other One-Dimensional Structural Properties. In: Zhou, Y., Kloczkowski, A., Faraggi, E., Yang, Y. (eds) Prediction of Protein Secondary Structure. Methods in Molecular Biology, vol 1484. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6406-2_18
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DOI: https://doi.org/10.1007/978-1-4939-6406-2_18
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