PhosphoSVM: Prediction of phosphorylation sites by integrating various protein sequence attributes with a support vector machine

Amino Acids

Volumn 46, Issue 6, 2014, Pages 1459-1469

  Dou, Yongchao ^a   Yao, Bo ^a   Zhang, Chi ^a  

^a UNIVERSITY OF NEBRASKA   (United States)

Author keywords

Non kinase specific tool; Phosphorylation site prediction; Support vector machine

Indexed keywords

THREONINE; TYROSINE; PROTEIN KINASE;

AMINO ACID SEQUENCE; ARTICLE; CONTROLLED STUDY; ENTROPY; HYDROPHOBICITY; PREDICTION; PRIORITY JOURNAL; PROTEIN PHOSPHORYLATION; PROTEIN SECONDARY STRUCTURE; SUPPORT VECTOR MACHINE; BINDING SITE; BIOLOGY; CHEMICAL PHENOMENA; METABOLISM; PHOSPHORYLATION; PROCEDURES;

ANIMALIA; EUKARYOTA;

AMINO ACID SEQUENCE; BINDING SITES; COMPUTATIONAL BIOLOGY; HYDROPHOBIC AND HYDROPHILIC INTERACTIONS; PHOSPHORYLATION; PROTEIN KINASES; SUPPORT VECTOR MACHINES;

EID: 84901445385     PISSN: 09394451     EISSN: 14382199     Source Type: Journal    
DOI: 10.1007/s00726-014-1711-5     Document Type: Article

References (53)

1. 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 (Pubitemid 37220479)
2. 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 (Pubitemid 27359211)
3. Basu S, Plewczynski D (2010) AMS 3.0: prediction of posttranslational modifications. BMC Bioinforma 11:210. doi:10.1186/1471-2105-11-210
4. Biswas AK, Noman N, Sikder AR (2010) Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information. BMC Bioinforma 11:273. doi:10.1186/1471-2105-11-273
5. Blom N, Hansen J, Blaas D, Brunak S (1996) Cleavage site analysis in picornaviral polyproteins: discovering cellular targets by neural networks. Protein Sci 5(11):2203-2216. doi:10.1002/pro.5560051107 (Pubitemid 26384459)
6. Blom N, Gammeltoft S, Brunak S (1999) Sequence and structure-based prediction of eukaryotic protein phosphorylation sites. J Mol Biol 294(5):1351-1362. doi:10.1006/jmbi1999.3310 (Pubitemid 30008805)
7. Bologna G, Yvon C, Duvaud S, Veuthey AL (2004) N-terminal myristoylation predictions by ensembles of neural networks. Proteomics 4(6):1626-1632. doi:10.1002/pmic.200300783 (Pubitemid 38738321)
8. 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 USA 101(32):11707-11712. doi:10.1073/pnas.0306880101 (Pubitemid 39095360)
9. Capra JA, Singh M (2007) Predicting functionally important residues from sequence conservation. Bioinformatics 23(15):1875-1882. doi:10.1093/ bioinformatics/btm270 (Pubitemid 47299827)
10. Chou PY, Fasman GD (1974) Conformational parameters for amino acids in helical, beta-sheet, and random coil regions calculated from proteins. Biochemistry 13(2):211-222
11. DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44(3):837-845
12. Diella F, Gould CM, Chica C, Via A, Gibson TJ (2008) Phospho.ELM: a database of phosphorylation sites - update 2008. Nucleic Acids Res 36(Database issue):D240-D244. doi:10.1093/nar/gkm772 (Pubitemid 351149729)
13. 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
14. 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
15. Duckert P, Brunak S, Blom N (2004) Prediction of proprotein convertase cleavage sites. Protein Eng Des Sel 17(1):107-112. doi:10.1093/protein/gzh013 (Pubitemid 38173174)
16. 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
17. 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
18. 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
19. 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
20. Gupta R, Brunak S (2002) Prediction of glycosylation across the human proteome and the correlation to protein function. Pac Symp Biocomput 7:310-322
21. Hamby SE, Hirst JD (2008) Prediction of glycosylation sites using random forests. BMC Bioinforma 9:500. doi:10.1186/1471-2105-9-500
22. 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 (Pubitemid 351149861)
23. Hjerrild M, Stensballe A, Rasmussen TE, Kofoed CB, Blom N, Sicheritz-Ponten T, Larsen MR, Brunak S, Jensen ON, Gammeltoft S (2004) Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry. J Proteome Res 3(3):426-433 (Pubitemid 39207346)
24. 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 (Pubitemid 38854704)
25. Johansson F, Toh H (2010) A comparative study of conservation and variation scores. BMC Bioinforma 11:388. doi:10.1186/1471-2105-11-388
26. Julenius K, Molgaard A, Gupta R, Brunak S (2005) Prediction, conservation analysis, and structural characterization of mammalian mucin-type O-glycosylation sites. Glycobiology 15(2):153-164. doi:10.1093/glycob/cwh151 (Pubitemid 40227920)
27. Kim JH, Lee J, Oh B, Kimm K, Koh I (2004) Prediction of phosphorylation sites using SVMs. Bioinformatics 20(17):3179-3184. doi:10.1093/bioinformatics/ bth382 (Pubitemid 39619207)
28. Kreegipuu A, Blom N, Brunak S, Jarv J (1998) Statistical analysis of protein kinase specificity determinants. FEBS Lett 430(1-2):45-50 (Pubitemid 28307029)
29. Kreegipuu A, Blom N, Brunak S (1999) PhosphoBase, a database of phosphorylation sites: release 2.0. Nucleic Acids Res 27(1):237-239 (Pubitemid 29209448)
30. Lasko TA, Bhagwat JG, Zou KH, Ohno-Machado L (2005) The use of receiver operating characteristic curves in biomedical informatics. J Biomed Inform 38(5):404-415. doi:10.1016/j.jbi.2005.02.008 (Pubitemid 41405811)
31. Li S, Li H, Li M, Shyr Y, Xie L, Li Y (2009) Improved prediction of lysine acetylation by support vector machines. Protein Pept Lett 16(8):977-983
32. Mackintosh RW, Davies SP, Clarke PR, Weekes J, Gillespie JG, Gibb BJ, Hardie DG (1992) Evidence for a protein kinase cascade in higher plants. 3-Hydroxy-3-methylglutaryl-CoA reductase kinase. Eur J Biochem 209(3):923-931
33. 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 (Pubitemid 35425239)
34. McGuffin LJ, Bryson K, Jones DT (2000) The PSIPRED protein structure prediction server. Bioinformatics 16(4):404-405 (Pubitemid 30417087)
35. 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 (Pubitemid 38229712)
36. Shao J, Xu D, Tsai SN, Wang Y, Ngai SM (2009) Computational identification of protein methylation sites through bi-profile Bayes feature extraction. PLoS One 4(3):e4920. doi:10.1371/journal.pone.0004920
37. Swaminathan K, Adamczak R, Porollo A, Meller J (2010) Enhanced prediction of conformational flexibility and phosphorylation in proteins. Adv Exp Med Biol 680:307-319. doi:10.1007/978-1-4419-5913-3-35
38. Sweet RM, Eisenberg D (1983) Correlation of sequence hydrophobicities measures similarity in three-dimensional protein structure. J Mol Biol 171(4):479-488
39. Taylor WR (1986) The classification of amino acid conservation. J Theor Biol 119(2):205-218
40. Trost B, Kusalik A (2011) Computational prediction of eukaryotic phosphorylation sites. Bioinformatics 27(21):2927-2935. doi:10.1093/ bioinformatics/btr525
41. Vapnik VN (1998) Statistical learning theory. Adaptive and learning systems for signal processing, communications, and control. Wiley, New York
42. Vapnik VN (2000) The nature of statistical learning theory. Statistics for engineering and information science, 2nd edn. Springer, New York
43. Vergara IA, Norambuena T, Ferrada E, Slater AW, Melo F (2008) StAR: a simple tool for the statistical comparison of ROC curves. BMC Bioinforma 9:265. doi:10.1186/1471-2105-9-265
44. 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 (Pubitemid 351990935)
45. 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
46. 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 (Pubitemid 38326883)
47. 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
48. 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
49. Xue Y, Li A, Wang L, Feng H, Yao X (2006) PPSP: prediction of PK-specific phosphorylation site with Bayesian decision theory. BMC Bioinforma 7:163. doi:10.1186/1471-2105-7-163
50. 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(6):485-496
51. 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(3):255-260. doi:10.1093/protein/gzq094
52. 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
53. 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