AMS 3.0: Prediction of post-translational modifications

BMC Bioinformatics

Volumn 11, Issue , 2010, Pages

  Basu, Subhadip ^a   Plewczynski, Dariusz ^b  

^a JADAVPUR UNIVERSITY   (India)

^b UNIVERSITY OF WARSAW   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION SCHEME; PHYSICOCHEMICAL FEATURES; POST-TRANSLATIONAL MODIFICATIONS; PRE-COMPILED BINARIES; PRECISION AND RECALL; RECEIVER OPERATING CHARACTERISTIC CURVES; SEQUENCE INFORMATIONS; STAND-ALONE VERSION;

AMINO ACIDS; NEURAL NETWORKS; PROTEINS; QUERY PROCESSING;

TOOLS;

PROTEIN; PROTEOME;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CHEMISTRY; COMPUTER PROGRAM; METABOLISM; PROTEIN PROCESSING; SEQUENCE ANALYSIS;

NEURAL NETWORKS (COMPUTER); PROTEIN PROCESSING, POST-TRANSLATIONAL; PROTEINS; PROTEOME; SEQUENCE ANALYSIS, PROTEIN; SOFTWARE;

EID: 77951560949     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-11-210     Document Type: Article

References (32)

1. Bairoch A, Apweiler R. The SWISS-PROT protein sequence data bank and its supplement TrEMBL in 1999. Nucleic acids research 1999, 27(1):49-54. 10.1093/nar/27.1.49, 148094, 9847139.
2. Diella F, Cameron S, Gemund C, Linding R, Via A, Kuster B, Sicheritz-Ponten T, Blom N, Gibson TJ. Phospho.ELM: a database of experimentally verified phosphorylation sites in eukaryotic proteins. BMC bioinformatics 2004, 5:79. 10.1186/1471-2105-5-79, 449700, 15212693.
3. Diella F, Gould CM, Chica C, Via A, Gibson TJ. Phospho.ELM: a database of phosphorylation sites--update 2008. Nucleic acids research 2008, (36 Database):D240-244. 2238828, 17962309.
4. Puntervoll P, Linding R, Gemund C, Chabanis-Davidson S, Mattingsdal M, Cameron S, Martin DM, Ausiello G, Brannetti B, Costantini A, et al. ELM server: A new resource for investigating short functional sites in modular eukaryotic proteins. Nucleic acids research 2003, 31(13):3625-3630. 10.1093/nar/gkg545, 168952, 12824381.
5. Sigrist CJ, Cerutti L, Hulo N, Gattiker A, Falquet L, Pagni M, Bairoch A, Bucher P. PROSITE: a documented database using patterns and profiles as motif descriptors. Briefings in bioinformatics 2002, 3(3):265-274. 10.1093/bib/3.3.265, 12230035.
6. Yaffe MB, Leparc GG, Lai J, Obata T, Volinia S, Cantley LC. A motif-based profile scanning approach for genome-wide prediction of signaling pathways. Nat Biotechnol 2001, 19(4):348-353. 10.1038/86737, 11283593.
7. Xue Y, Zhou F, Zhu M, Ahmed K, Chen G, Yao X. GPS: a comprehensive www server for phosphorylation sites prediction. Nucleic acids research 2005, (33 Web Server):W184-187. 10.1093/nar/gki393, 1160154, 15980451.
8. Xue Y, Li A, Wang L, Feng H, Yao X. PPSP: prediction of PK-specific phosphorylation site with Bayesian decision theory. BMC bioinformatics 2006, 7:163. 10.1186/1471-2105-7-163, 1435943, 16549034.
9. Koenig M, Grabe N. Highly specific prediction of phosphorylation sites in proteins. Bioinformatics (Oxford, England) 2004, 20(18):3620-3627. 10.1093/bioinformatics/bth455, 15297298.
10. Blom N, Gammeltoft S, Brunak S. Sequence and structure-based prediction of eukaryotic protein phosphorylation sites. J Mol Biol 1999, 294(5):1351-1362. 10.1006/jmbi.1999.3310, 10600390.
11. Blom N, Sicheritz-Ponten T, Gupta R, Gammeltoft S, Brunak S. Prediction of post-translational glycosylation and phosphorylation of proteins from the amino acid sequence. Proteomics 2004, 4(6):1633-1649. 10.1002/pmic.200300771, 15174133.
12. Hjerrild M, Stensballe A, Rasmussen TE, Kofoed CB, Blom N, Sicheritz-Ponten T, Larsen MR, Brunak S, Jensen ON, Gammeltoft S. Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry. J Proteome Res 2004, 3(3):426-433. 10.1021/pr0341033, 15253423.
13. Julenius K, Molgaard A, Gupta R, Brunak S. Prediction, conservation analysis, and structural characterization of mammalian mucin-type O-glycosylation sites. Glycobiology 2005, 15(2):153-164. 10.1093/glycob/cwh151, 15385431.
14. Gupta R, Jung E, Gooley AA, Williams KL, Brunak S, Hansen J. Scanning the available Dictyostelium discoideum proteome for O-linked GlcNAc glycosylation sites using neural networks. Glycobiology 1999, 9(10):1009-1022. 10.1093/glycob/9.10.1009, 10521537.
15. Gupta R, Brunak S. Prediction of glycosylation across the human proteome and the correlation to protein function. Pac Symp Biocomput 2002, 310-322.
16. Blom N, Hansen J, Blaas D, Brunak S. Cleavage site analysis in picornaviral polyproteins: discovering cellular targets by neural networks. Protein Sci 1996, 5(11):2203-2216. 10.1002/pro.5560051107, 2143287, 8931139.
17. Duckert P, Brunak S, Blom N. Prediction of proprotein convertase cleavage sites. Protein Eng Des Sel 2004, 17(1):107-112. 10.1093/protein/gzh013, 14985543.
18. Kiemer L, Lund O, Brunak S, Blom N. Coronavirus 3CLpro proteinase cleavage sites: possible relevance to SARS virus pathology. BMC bioinformatics 2004, 5:72. 10.1186/1471-2105-5-72, 442122, 15180906.
19. Bologna G, Yvon C, Duvaud S, Veuthey AL. N-Terminal myristoylation predictions by ensembles of neural networks. Proteomics 2004, 4(6):1626-1632. 10.1002/pmic.200300783, 15174132.
20. Shao J, Xu D, Tsai SN, Wang Y, Ngai SM. Computational identification of protein methylation sites through bi-profile Bayes feature extraction. PLoS One 2009, 4(3):e4920. 10.1371/journal.pone.0004920, 2654709, 19290060.
21. Li S, Li H, Li M, Shyr Y, Xie L, Li Y. Improved prediction of lysine acetylation by support vector machines. Protein Pept Lett 2009, 16(8):977-983. 10.2174/092986609788923338, 19689425.
22. Kim JH, Lee J, Oh B, Kimm K, Koh I. Prediction of phosphorylation sites using SVMs. Bioinformatics (Oxford, England) 2004, 20(17):3179-3184. 10.1093/bioinformatics/bth382, 15231530.
23. Plewczynski D, Tkacz A, Wyrwicz LS, Rychlewski L. AutoMotif server: prediction of single residue post-translational modifications in proteins. Bioinformatics (Oxford, England) 2005, 21(10):2525-2527. 10.1093/bioinformatics/bti333, 15728119.
24. Wong YH, Lee TY, Liang HK, Huang CM, Wang TY, Yang YH, Chu CH, Huang HD, Ko MT, Hwang JK. KinasePhos 2.0: a web server for identifying protein kinase-specific phosphorylation sites based on sequences and coupling patterns. Nucleic acids research 2007, (35 Web Server):W588-594. 10.1093/nar/gkm322, 1933228, 17517770.
25. Monigatti F, Gasteiger E, Bairoch A, Jung E. The Sulfinator: predicting tyrosine sulfation sites in protein sequences. Bioinformatics (Oxford, England) 2002, 18(5):769-770. 10.1093/bioinformatics/18.5.769, 12050077.
26. Hamby SE, Hirst JD. Prediction of glycosylation sites using random forests. BMC bioinformatics 2008, 9:500. 10.1186/1471-2105-9-500, 2651179, 19038042.
27. Wan J, Kang S, Tang C, Yan J, Ren Y, Liu J, Gao X, Banerjee A, Ellis LB, Li T. Meta-prediction of phosphorylation sites with weighted voting and restricted grid search parameter selection. Nucleic acids research 2008, 36(4):e22. 10.1093/nar/gkm848, 2275094, 18234718.
28. Lee TY, Huang HD, Hung JH, Huang HY, Yang YS, Wang TH. dbPTM: an information repository of protein post-translational modification. Nucleic acids research 2006, (34 Database):D622-627. 10.1093/nar/gkj083, 1347446, 16381945.
29. Miller ML, Blom N. Kinase-specific prediction of protein phosphorylation sites. Methods Mol Biol 2009, 527:299-310. full_text, 19241022.
30. Plewczynski D, Tkacz A, Wyrwicz LS, Rychlewski L, Ginalski K. AutoMotif Server for prediction of phosphorylation sites in proteins using support vector machine: 2007 update. J Mol Model 2008, 14(1):69-76. 10.1007/s00894-007-0250-3, 17994256.
31. Fawcett T. An introduction to ROC analysis. Pattern Recognition Letters 2006, 27:861-874.
32. Breiman L. Heuristics of instability and stabilization in model selection. Annals of Statistics 1996, 24:2350-2383.