Improved prediction of lysine acetylation by support vector machines

Protein and Peptide Letters

Volumn 16, Issue 8, 2009, Pages 977-983

  Li, Songng ^a,c   Li, Hong ^b,c   Li, Mingfa ^a   Shyr, Yu ^d   Xie, Lu ^c   Li, Yixue ^a,b,c  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b Shanghai Jiao Tong University Affiliated Sixth People s Hospital   (China)

^c SHANGHAI CENTER FOR BIOINFORMATION TECHNOLOGY   (China)

^d VANDERBILT INGRAM CANCER CENTER   (United States)

Author keywords

Protein coupling pattern; Reversible lysine acetylation; Support vector machine

Indexed keywords

LYSINE; PROTEIN;

ACETYLATION; ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MODEL; METABOLISM;

ACETYLATION; ALGORITHMS; ARTIFICIAL INTELLIGENCE; LYSINE; MODELS, BIOLOGICAL; PROTEINS;

EID: 68949108329     PISSN: 09298665     EISSN: None     Source Type: Journal    
DOI: 10.2174/092986609788923338     Document Type: Article

References (67)

1. Polevoda B.; Sherman F.; Nalpha-terminal acetylation of eukaryotic proteins. J. Bio. Chem., 2000, 275(47), 36479-36482.
2. Allfrey V. G.; Mirsky A. E.; Structural modifications of histones and their possible role in the regulation of RNA synthesis. Science, 1964, 144(3618), 559.
3. Yang X. J.; The diverse superfamily of lysine acetyltransferases and their roles in leukemia and other diseases. Nucleic Acids Res., 2004, 32(3), 959-976.
4. Verdin E.; Dequiedt F.; Kasler H. G.; Class II histone deacetylases: versatile regulators. Trends Genet., 2003, 19(5), 286-293. (Pubitemid 36507011)
5. Strahl B. D.; Allis C. D.; The language of covalent histone modifications. Nature, 2000, 403(6765), 41-45.
6. Turner B. M.; Histone acetylation and an epigenetic code. Bioessays, 2000, 22(9), 836-845.
7. Kim S. C.; Sprung R.; Chen Y.; Xu Y.; Ball H.; Pei J.; Cheng T.; Kho Y.; Xiao H.; Xiao L.; Grishin N. V.; White M.; Yang X. J.; Zhao Y.; Substrate and functional diversity of lysine acetylation revealed by a proteomics survey. Mol. Cell, 2006, 23(4), 607-618. (Pubitemid 44205490)
8. Yang X. J.; Gregoire S.; Metabolism, cytoskeleton and cellular signalling in the grip of protein Nepsilon - and O-acetylation. EMBO Rep., 2007, 8(6), 556-562. (Pubitemid 47214709)
9. Polevoda B.; Sherman F.; The diversity of acetylated proteins. Genome Biol., 2002, 3(5), reviews0006. (Pubitemid 34578281)
10. Spange S.; Wagner T.; Heinzel T.; Kramer O. H.; Acetylation of non-histone proteins modulates cellular signalling at multiple levels. Int. J. Biochem. Cell Biol., 2008.
11. Cress W. D.; Seto E.; Histone deacetylases, transcriptional control, and cancer. J.; Cell. Physiol., 2000, 184(1), 1-16. (Pubitemid 30349623)
12. Mahlknecht U.; Hoelzer D.; Histone acetylation modifiers in the pathogenesis of malignant disease. Mol. Med., 2000, 6(8), 623-644.
13. Huang L.; Targeting histone deacetylases for the treatment of cancer and inflammatory diseases. J.; Cell. Physiol., 2006, 209(3), 611-616. (Pubitemid 44665677)
14. Mottet D.; Castronovo V.; Histone deacetylases: target enzymes for cancer therapy. Clin. Exp. Metastasis, 2008, 25(2), 183-189.
15. Balakin K. V.; Ivanenkov Y. A.; Kiselyov A. S.; Tkachenko S. E.; Histone deacetylase inhibitors in cancer therapy: latest developments, trends and medicinal chemistry perspective. Anticancer Agents Med. Chem., 2007, 7(5), 576-592.
16. Yang X. J.; Seto E.; HATs and HDACs: from structure, function and regulation to novel strategies for therapy and prevention. Oncogene, 2007, 26(37), 5310-5318. (Pubitemid 47255917)
17. Ott M.; Schnolzer M.; Garnica J.; Fischle W.; Emiliani S.; Rackwitz H. R.; Verdin E.; Acetylation of the HIV-1 Tat protein by p300 is important for its transcriptional activity. Curr. Biol., 1999, 9(24), 1489-1492. (Pubitemid 30023485)
18. Kiernan R. E.; Vanhulle C.; Schiltz L.; Adam E.; Xiao H.; Maudoux F.; Calomme C.; Burny A.; Nakatani Y.; Jeang K. T.; Benkirane M.; Van Lint C.; HIV-1 tat transcriptional activity is regulated by acetylation. EMBO J., 1999, 18(21), 6106-6118. (Pubitemid 29515686)
19. Deng Q.; Li Y.; Tedesco D.; Liao R.; Fuhrmann G.; Sun P.; The ability of E1A to rescue ras-induced premature senescence and confer transformation relies on inactivation of both p300/CBP and Rb family proteins. Cancer Res., 2005, 65(18), 8298-8307. (Pubitemid 41330594)
20. Augen J.; Wold F.; How much sequence information is needed for the regulation of amino-terminal acetylation of eukaryotic proteins? Trends Biochem. Sci., 1896, 11(12), 494-497.
21. Li A.; Xue Y.; Jin C.; Wang M.; Yao X.; Prediction of Nepsilonacetylation on internal lysines implemented in Bayesian Discriminant Method. Biochem. Biophys. Res. Commun., 2006, 350(4), 818-824. (Pubitemid 44584171)
22. Chen H.; Xue Y.; Huang N.; Yao X.; Sun Z.; MeMo: a web tool for prediction of protein methylation modifications. Nucleic Acids Res., 2006, 34 (Web Server issue), W249-253. (Pubitemid 44529773)
23. Xue Y.; Zhou F.; Fu C.; Xu Y.; Yao X.; SUMOsp: a web server for sumoylation site prediction. Nucleic Acids Res., 2006, 34 (Web Server issue), W254-257. (Pubitemid 44529774)
24. Kim J. H.; Lee J.; Oh B.; Kimm K.; Koh I.; Prediction of phosphorylation sites using SVMs. Bioinformatics, 2004, 20(17), 3179-3184. (Pubitemid 39619207)
25. 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.; KinasePhos 2.0: a web server for identifying protein kinasespecific phosphorylation sites based on sequences and coupling patterns. Nucleic Acids Res., 2007, 35 (Web Server issue), W588-594.
26. Cohen T.; Yao T. P.; AcK-knowledge reversible acetylation. Sci. STKE, 2004, 2004(245), pe42.
27. Kimura A.; Matsubara K.; Horikoshi M. A decade of histone acetylation: marking eukaryotic chromosomes with specific codes. J. Biochem., 2005, 138(6), 647-662. (Pubitemid 43258858)
28. Roth S. Y.; Denu J. M.; Allis C. D.; Histone acetyltransferases. Annu. Rev. Biochem., 2001, 70, 81-120.
29. Li W.; Godzik A.; Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics, 2006, 22(13), 1658-1659. (Pubitemid 43985301)
30. Thompson J. D.; Higgins D. G.; Gibson T. J.; CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res., 1994, 22(22), 4673-4680.
31. Pang C. N.; Hayen A.; Wilkins M. R.; Surface accessibility of protein post-translational modifications. J.; Proteome Res., 2007, 6(5), 1833-1845. (Pubitemid 46814507)
32. Plewczynski D.; Tkacz A.; Godzik A.; Rychlewski L. A support vector machine approach to the identification of phosphorylation sites. Cell Mol. Biol. Lett., 2005, 10(1), 73-89. (Pubitemid 40559061)
33. Ian H. W.; Frank E.; Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed. Morgan Kaufmann: San Francisco, 2005.
34. Hall M. A.; Correlation-based Feature Subset Selection for Machine Learning, Hamilton: New Zealand, 1998.
35. Pirooznia M.; Yang J. Y.; Yang M. Q.; Deng Y. A comparative study of different machine learning methods on microarray gene expression data. BMC Genomics, 2008, 9 (Suppl 1), S13. (Pubitemid 351837303)
36. Joachims T.; Making large-scale SVM learning practical. In Advances in Kernel Methods - Support Vector Learning; Schölkopf, B., Burges, C. J. C., Smola, A., Eds.; MIT Press: Cambridge, MA, 1999; pp. 169-184.
37. Muller K. R.; Mika S.; Ratsch G.; Tsuda K.; Scholkopf B.; An introduction to kernel-based learning algorithms. IEEE Trans. Neural Netw., 2001, 12(2), 181-201. (Pubitemid 32371478)
38. Chou K. C.; Zhang C. T.; Prediction of protein structural classes. Crit. Rev. Biochem. Mol. Biol., 1995, 30(4), 275-349.
39. Chou K. C.; Shen H. B.; Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms. Nat. Protoc., 2008, 3(2), 153-162.
40. Chou K. C.; Shen H. B.; Recent progress in protein subcellular location prediction. Anal. Biochem., 2007, 370(1), 1-16. (Pubitemid 47404494)
41. Chou K. C.; Shen H. B.; Hum-PLoc: a novel ensemble classifier for predicting human protein subcellular localization. Biochem. Biophys. Res. Commun., 2006, 347(1), 150-157.
42. Zhou X. B.; Chen C.; Li Z. C.; Zou X. Y.; Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes. J.; Theor. Biol., 2007, 248(3), 546-551. (Pubitemid 47368372)
43. Chou K. C.; Shen H. B.; Euk-mPLoc: a fusion classifier for largescale eukaryotic protein subcellular location prediction by incorporating multiple sites. J.; Proteome Res., 2007, 6(5), 1728-1734. (Pubitemid 46814497)
44. Lin H.; The modified Mahalanobis Discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition. J.; Theor. Biol., 2008, 252(2), 350-356.
45. Zhang G. Y.; Fang B. S.; Predicting the cofactors of oxidoreductases based on amino acid composition distribution and Chou's amphiphilic pseudo-amino acid composition. J.; Theor. Biol., 2008, 253(2), 310-315.
46. Zhang G. Y.; Li H. C.; Fang B. S.; Predicting lipase types by improved Chou's pseudo-amino acid composition. Protein Pept. Lett., 2008, 15(10), 1132-1137.
47. Jiang X.; Wei R.; Zhang T.; Gu Q.; Using the concept of Chou's pseudo amino acid composition to predict apoptosis proteins subcellular location: an approach by approximate entropy. Protein Pept. Lett., 2008, 15(4), 392-396. (Pubitemid 351679342)
48. Li F. M.; Li Q. Z.; Predicting protein subcellular location using Chou's pseudo amino acid composition and improved hybrid approach. Protein Pept. Lett., 2008, 15(6), 612-616.
49. Lin H.; Ding H.; Guo F. B.; Zhang A. Y.; Huang J.; Predicting subcellular localization of mycobacterial proteins by using Chou's pseudo amino acid composition. Protein Pept. Lett., 2008, 15(7), 739-744.
50. Wang T.; Yang J.; Shen H. B.; Chou K. C.; Predicting membrane protein types by the LLDA algorithm. Protein Pept. Lett., 2008, 15(9), 915-921.
51. Ding Y. S.; Zhang T. L.; Using Chou's pseudo amino acid composition to predict subcellular localization of apoptosis proteins: an approach with immune genetic algorithm-based ensemble classifier. Pattern Recognit. Lett., 2008, 29(13), 1887-1892.
52. Hsu C. W.; Chang C. C.; Lin C. J. A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University, 2003.
53. Chou K. C.; Structural bioinformatics and its impact to biomedical science. Curr. Med. Chem., 2004, 11(16), 2105-2134.
54. Chou K. C.; Wei D. Q.; Zhong W. Z.; Binding mechanism of coronavirus main proteinase with ligands and its implication to drug design against SARS. Biochem. Biophys. Res. Commun., 2003, 308(1), 148-151.
55. Wang J. F.; Wei D. Q.; Chen C.; Li Y.; Chou K. C.; Molecular modeling of two CYP2C19 SNPs and its implications for personalized drug design. Protein Pept. Lett., 2008, 15(1), 27-32. (Pubitemid 351206472)
56. Sirois S.; Wei D. Q.; Du Q. S.; Chou K. C.; Virtual screening for SARS-CoV protease based on KZ7088 pharmacophore points. J.; Chem. Inf. Comput. Sci., 2004, 44(3), 1111-1122.
57. Du Q. S.; Huang R. B.; Wei Y. T.; Du L. Q.; Chou K. C.; Multiple field three-dimensional quantitative structure-activity relationship (MF-3D-QSAR). J.; Chem. Inf. Comput. Sci., 2008, 29(2), 211-219.
58. Prado-Prado F. J.; Gonzalez-Diaz H.; de la Vega O. M.; Ubeira F. M.; Chou K. C.; Unified QSAR approach to antimicrobials. Part 3: First multi-tasking QSAR model for input-coded prediction, structural back-projection, and complex networks clustering of antiprotozoal compounds. Bioorg. Med. Chem., 2008, 16(11), 5871-5880. (Pubitemid 351766720)
59. Chou K. C.; Shen H. B.; Review: Recent progresses in protein subcellular location prediction. Anal. Biochem., 2007, 370(1), 1-16. (Pubitemid 47404494)
60. Chou K. C.; Shen H. B.; MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. Biochem. Biophys. Res. Commun., 2007, 360(2), 339-345.
61. Shen H. B.; Chou K. C.; EzyPred: A top-down approach for predicting enzyme functional classes and subclasses. Biochem. Biophys. Res. Commun., 2007, 364(1), 53-59.
62. Chou K. C.; Shen H. B.; ProtIdent: a web server for identifying proteases and their types by fusing functional domain and sequential evolution information. Biochem. Biophys. Res. Commun., 2008, 376(2), 321-325.
63. Chou K. C. A vectorized sequence-coupling model for predicting HIV protease cleavage sites in proteins. J. Biol. Chem., 1993, 268(23), 16938-16948. (Pubitemid 23236917)
64. Chou K. C.; Review: prediction of HIV protease cleavage sites in proteins. Anal. Biochem., 1996, 233(1), 1-14.
65. Shen H. B.; Chou K. C.; HIVcleave: a web-server for predicting HIV protease cleavage sites in proteins. Anal. Biochem., 2008, 375(2), 388-390.
66. Chou K. C.; Shen H. B.; Signal-CF: a subsite-coupled and windowfusing approach for predicting signal peptides. Biochem. Biophys. Res. Commun., 2007, 357(3), 633-640. (Pubitemid 46653826)
67. Shen H. B.; Chou K. C.; Signal-3L: a 3-layer approach for predicting signal peptide. Biochem. Biophys. Res. Commun., 2007, 363(2), 297-303. (Pubitemid 47484331)