Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information

BMC Bioinformatics

Volumn 11, Issue , 2010, Pages

  Biswas, Ashis K ^a   Noman, Nasimul ^a   Sikder, Abdur R ^a  

^a UNIVERSITY OF DHAKA   (Bangladesh)

Author keywords

[No Author keywords available]

Indexed keywords

EVOLUTIONARY INFORMATION; MACHINE LEARNING APPROACHES; PHOSPHORYLATION SITES; POST-TRANSLATIONAL MODIFICATIONS; PREDICTION PERFORMANCE; PREDICTION SYSTEMS; PRIMARY SEQUENCES; PROTEIN PHOSPHORYLATION;

BIOINFORMATICS; ENZYMES; FORECASTING; INFORMATION USE; LEARNING SYSTEMS; PHOSPHORYLATION; PROTEINS;

CLASSIFICATION (OF INFORMATION);

PROTEIN; PROTEIN KINASE;

AMINO ACID SEQUENCE; ARTICLE; ARTIFICIAL INTELLIGENCE; BINDING SITE; CHEMISTRY; COMPUTER PROGRAM; METABOLISM; PHOSPHORYLATION; PROTEIN DATABASE; SEQUENCE ANALYSIS;

AMINO ACID SEQUENCE; ARTIFICIAL INTELLIGENCE; BINDING SITES; DATABASES, PROTEIN; PHOSPHORYLATION; PROTEIN KINASES; PROTEINS; SEQUENCE ANALYSIS, PROTEIN; SOFTWARE;

EID: 77952464216     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-11-273     Document Type: Article

References (32)

1. Cohen P. The origins of protein phosphorylation. Nat Cell Biol 2002, 4(5):E127-30. 10.1038/ncb0502-e127, 11988757.
2. Lawlor M, Alessi D. PKB/Akt a key mediator of cell proliferation, survival and insulin responses?. Journal of Cell Science 2001, 114(16):2903-2910.
3. Hunter T. The Croonian Lecture 1997. The phosphorylation of proteins on tyrosine: its role in cell growth and disease. Philosophical Transactions of the Royal Society B: Biological Sciences 1998, 353(1368):583.
4. Pinna L, Ruzzene M. How do protein kinases recognize their substrates?. BBA-Molecular Cell Research 1996, 1314(3):191-225.
5. Gnad F, Ren S, Cox J, Olsen J, Macek B, Oroshi M, Mann M. PHOSIDA (phosphorylation site database): management, structural and evolutionary investigation, and prediction of phosphosites. Genome biology 2007, 8(11):R250. 10.1186/gb-2007-8-11-r250, 2258193, 18039369.
6. 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.
7. Diella F, Cameron S, Gemünd C, Linding R, Via A, Kuster B, Sicheritz-Pontén T, Blom N, Gibson T. 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.
8. Iakoucheva L, Radivojac P, Brown C, O'Connor T, Sikes J, Obradovic Z, Dunker A. The importance of intrinsic disorder for protein phosphorylation. Nucleic acids research 2004, 32(3):1037. 10.1093/nar/gkh253, 373391, 14960716.
9. Kreegipuu A, Blom N, Brunak S. PhosphoBase, a database of phosphorylation sites: release 2.0. Nucleic Acids Research 1999, 27:237-239. 10.1093/nar/27.1.237, 148144, 9847189.
10. 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(Weinheim. Print) 2004, 4(6):1633-1649.
11. Huang H, Lee T, Tzeng S, Horng J. KinasePhos: a web tool for identifying protein kinase-specific phosphorylation sites. Nucleic Acids Research 2005, 33:W226. 10.1093/nar/gki471, 1160232, 15980458.
12. Obenauer J, Cantley L, Yaffe M. Scansite 2.0: proteome-wide prediction of cell signaling interactions using short sequence motifs. Article. Nucleic Acids Research 2003, 31(13):3635-3641. 10.1093/nar/gkg584, 168990, 12824383.
13. Blom N, Gammeltoft S, Brunak S. Sequence and structure-based prediction of eukaryotic protein phosphorylation sites. Journal of Molecular Biology 1999, 294(5):1351-1362. 10.1006/jmbi.1999.3310, 10600390.
14. Plewczynski D, Tkacz A, Wyrwicz L, Rychlewski L. AutoMotif server: prediction of single residue post-translational modifications in proteins. Bioinformatics 2005, 21(10):2525. 10.1093/bioinformatics/bti333, 15728119.
15. Xue Y, Ren J, Gao X, Jin C, Wen L, Yao X. GPS 2.0, a tool to predict kinase-specific phosphorylation sites in hierarchy. Molecular & Cellular Proteomics 2008, 7(9):1598.
16. Ptacek J, Devgan G, Michaud G, Zhu H, Zhu X, Fasolo J, Guo H, Jona G, Breitkreutz A, Sopko R, et al. Global analysis of protein phosphorylation in yeast. Nature 2005, 438(7068):679-684. 10.1038/nature04187, 16319894.
17. Sikder AR, Zomaya AY. Analysis of protein phosphorylation site predictors with an independent dataset. International Journal of Bioinformatics Research and Applications 2009, 5:20-37. 10.1504/IJBRA.2009.022461, 19136362.
18. Altschul S, Madden T, Schaffer A, Zhang J, Zhang Z, Miller W, Lipman D. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic acids research 1997, 25(17):3389. 10.1093/nar/25.17.3389, 146917, 9254694.
19. Altschul S, Wootton J, Gertz E, Agarwala R, Morgulis A, Schäffer A, Yu Y. Protein database searches using compositionally adjusted substitution matrices. The FEBS journal 2005, 272(20):5101. 10.1111/j.1742-4658.2005.04945.x, 1343503, 16218944.
20. Schaffer A, Aravind L, Madden T, Shavirin S, Spouge J, Wolf Y, Koonin E, Altschul S. Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements. Nucleic Acids Research 2001, 29(14):2994. 10.1093/nar/29.14.2994, 55814, 11452024.
21. Kaur H, Raghava G. Prediction of β-turns in proteins from multiple alignment using neural network. Protein Science 2003, 12(3):627-634. 10.1110/ps.0228903, 2312433, 12592033.
22. Kaur H, Raghava G. Prediction of-Turns in Proteins Using PSI-BLAST Profiles and Secondary Structure Information. Proteins: Structure, Function, and Bioinformatics 2004, 55:83-90.
23. Diella F, Gould C, Chica C, Via A, Gibson T. Phospho.ELM: a database of phosphorylation sites update 2008. Nucleic Acids Research 2008, (36 Database):D240-D244. 2238828, 17962309.
24. Fawcett T. An introduction to ROC analysis. Pattern recognition letters 2006, 27(8):861-874.
25. Boeckmann B, Bairoch A, Apweiler R, Blatter M, Estreicher A, Gasteiger E, Martin M, Michoud K, O'Donovan C, Phan I, et al. The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Research 2003, 31:365. 10.1093/nar/gkg095, 165542, 12520024.
26. Kakuta M, Nakamura S, Shimizu K. Prediction of Protein-Protein Interaction Sites Using Only Sequence Information and Using Both Sequence and Structural Information. Information and Media Technologies 2008, 3(2):351-361.
27. Ahmad S, Sarai A. PSSM-based prediction of DNA binding sites in proteins. BMC bioinformatics 2005, 6:33. 10.1186/1471-2105-6-33, 550660, 15720719.
28. Hertzberg L, Zuk O, Getz G, Domany E. Finding motifs in promoter regions. Journal of Computational Biology 2005, 12(3):314-330. 10.1089/cmb.2005.12.314, 15857245.
29. Cortes C, Vapnik V. Support-vector networks. Machine Learning 1995, 20(3):273-297.
30. Vapnik V. Statistical learning theory. 1998, John Wiley & Sons, New York.
31. Chih-Chung C, Chih-Jen L. LIBSVM: a library for support vector machines. 2001, http://www.csie.ntu.edu.tw/~cjlin/libsvm/
32. Fan R, Chen P, Lin C. Working set selection using second order information for training support vector machines. The Journal of Machine Learning Research 2005, 6:1889-1918.