Accurate in silico identification of species-specific acetylation sites by integrating protein sequence-derived and functional features

Scientific Reports

Volumn 4, Issue , 2014, Pages

  Li, Yuan ^a   Wang, Mingjun ^a   Wang, Huilin ^a   Tan, Hao ^b   Zhang, Ziding ^c   Webb, Geoffrey I ^b   Song, Jiangning ^a,b  

^a Tianjin Airport Economic Area   (China)

^b MONASH UNIVERSITY   (Australia)

^c CHINA AGRICULTURAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

PROTEOME;

ACETYLATION; AMINO ACID SEQUENCE; ANIMAL; AREA UNDER THE CURVE; CHEMISTRY; COMPUTER SIMULATION; HUMAN; METABOLISM; PROTEIN PROCESSING; RECEIVER OPERATING CHARACTERISTIC; SEQUENCE ANALYSIS; SPECIES DIFFERENCE;

ACETYLATION; AMINO ACID SEQUENCE; ANIMALS; AREA UNDER CURVE; COMPUTER SIMULATION; HUMANS; PROTEIN PROCESSING, POST-TRANSLATIONAL; PROTEOME; ROC CURVE; SEQUENCE ANALYSIS, PROTEIN; SPECIES SPECIFICITY;

EID: 84904632581     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep05765     Document Type: Article

References (60)

1. Sadoul, K., Wang, J., Diagouraga, B. & Khochbin, S. The tale of protein lysine acetylation in the cytoplasm. J. Biomed. Biotechnol. 2011, 970382 (2011).
2. Allfrey, V. G., Pogo, B. G., Littau, V. C., Gershey, E. L. & Mirsky, A. E. Histone acetylation in insect chromosomes. Science 159, 314-316 (1968).
3. Allfrey, V. G., Faulkner, R. & Mirsky, A. E. Acetylation and methylation of histones and their possible role in the regulation of RNA synthesis. Proc. Natl. Acad. Sci. USA 51, 786-794 (1964).
4. Phillips, D. M. The presence of acetyl groups of histones. Biochem. J. 87, 258-263 (1963).
5. Zhao, S. et al. Regulation of cellular metabolism by protein lysine acetylation. Science 327, 1000-1004 (2010).
6. Choudhary, C. et al. Lysine acetylation targets protein complexes and co-regulates major cellular functions. Science 325, 834-840 (2009).
7. Kim, S. C. et al. Substrate and functional diversity of lysine acetylation revealed by a proteomics survey. Mol. Cell 23, 607-618 (2006). (Pubitemid 44205490)
8. Xiong, Y. & Guan, K. L. Mechanistic insights into the regulation of metabolic enzymes by acetylation. J. Cell Biol. 198, 155-164 (2012).
9. Welsch, D. J. & Nelsestuen, G. L. Amino-terminal alanine functions in a calciumspecific process essential for membrane binding by prothrombin fragment 1. Biochemistry 27, 4939-4945 (1988).
10. Medzihradszky, K. F. Peptide sequence analysis. Methods Enzymol. 402, 209-244 (2005). (Pubitemid 43053129)
11. Umlauf, D., Goto, Y. & Feil, R. Site-specific analysis of histone methylation and acetylation. Methods Mol. Biol. 287, 99-120 (2004).
12. Basu, A. et al. Proteome-wide prediction of acetylation substrates. Proc. Natl. Acad. Sci. USA 106, 13785-13790 (2009).
13. Choudhary, C. & Mann, M. Decoding signalling networks bymass spectrometrybased proteomics. Nat. Rev. Mol. Cell Biol. 11, 427-439 (2010).
14. Suo, S. B. et al. Proteome-wide analysis of amino acid variations that influence protein lysine acetylation. J. Proteome Res. 12, 949-958 (2013).
15. Wang, L., Du, Y., Lu, M. & Li, T. ASEB: a web server for KAT-specific acetylation site prediction. Nucleic Acids Res. 40, W376-379 (2012).
16. Suo, S. B. et al. Position-specific analysis and prediction for protein lysine acetylation based on multiple features. PLoS One 7, e49108 (2012).
17. Shi, S. P. et al. PLMLA: prediction of lysine methylation and lysine acetylation by combining multiple features. Mol. Biosyst. 8, 1520-1527 (2012).
18. Xu, Y., Wang, X. B., Ding, J., Wu, L. Y. & Deng, N. Y. Lysine acetylation sites prediction using an ensemble of support vector machine classifiers. J. Theor. Biol. 264, 130-135 (2010).
19. Lee, T. Y. et al. N-Ace: using solvent accessibility and physicochemical properties to identify protein N-acetylation sites. J. Comput. Chem. 31, 2759-2771 (2010).
20. Li, S. et al. Improved prediction of lysine acetylation by support vector machines. Protein Peptide Lett. 16, 977-983 (2009).
21. Cai, Y. D. & Lu, L. Predicting N-terminal acetylation based on feature selection method. Biochem. Biophys. Res. Commun. 372, 862-865 (2008).
22. Li, A., Xue, Y., Jin, C., Wang, M. & Yao, X. Prediction of Nepsilon-acetylation on internal lysines implemented in Bayesian Discriminant Method. Biochem. Biophys. Res. Commun. 350, 818-824 (2006). (Pubitemid 44584171)
23. Gnad, F., Ren, S., Choudhary, C., Cox, J.&Mann, M. Predicting post-translational lysine acetylation using support vector machines. Bioinformatics 26, 1666-1668 (2010).
24. Shao, J. et al. Systematic analysis of human lysine acetylation proteins and accurate prediction of human lysine acetylation through bi-relative adapted binomial score Bayes feature representation. Mol. Biosyst. 8, 2964-2973 (2012).
25. Jones, J. D. & O'Connor, C. D. Protein acetylation in prokaryotes. Proteomics 11, 3012-3022 (2011).
26. Colaert, N., Helsens, K., Martens, L., Vandekerckhove, J. & Gevaert, K. Improved visualization of protein consensus sequences by iceLogo. Nat. Methods 6, 786-787 (2009).
27. Saeys, Y., Inza, I. & Larranaga, P. A review of feature selection techniques in bioinformatics. Bioinformatics 23, 2507-2517 (2007). (Pubitemid 350048351)
28. Wang, M. et al. Cascleave 2.0, a new approach for predicting caspase and granzyme cleavage targets. Bioinformatics 30, 71-80 (2014).
29. Peng, H., Long, F. & Ding, C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1226-1238 (2005). (Pubitemid 41245053)
30. Zheng, C. et al. An integrative computational framework based on a two-step random forest algorithm improves prediction of zinc-binding sites in proteins. PLoS One 7, e49716 (2012).
31. Wang, M. et al. FunSAV: predicting the functional effect of single amino acid variants using a two-stage random forest model. PLoS One 7, e43847 (2012).
32. Song, J. et al. PROSPER: an integrated feature-based tool for predicting protease substrate cleavage sites. PLoS One 7, e50300 (2012).
33. Li, T., Du, P. & Xu, N. Identifying human kinase-specific protein phosphorylation sites by integrating heterogeneous information from various sources. PLoS One 5, e15411 (2010).
34. Liu, Z. et al. CPLA 1.0: an integrated database of protein lysine acetylation. Nucleic Acids Res. 39, D1029-1034 (2011).
35. Gnad, F., Gunawardena, J. & Mann, M. PHOSIDA 2011: the posttranslational modification database. Nucleic Acids Res. 39, D253-260 (2011).
36. Hornbeck, P. V. et al. Phospho Site Plus: a comprehensive resource for investigating the structure and function of experimentally determined posttranslational modifications in man and mouse. Nucleic Acids Res. 40, D261-270 (2012).
37. The Uniprot Consortium. Reorganizing the protein space at the Universal Protein Resource (Uni Prot). Nucleic Acids Res. 40, D71-75 (2012).
38. Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658-1659 (2006). (Pubitemid 43985301)
39. Song, J., Tan, H., Wang, M., Webb, G. I. & Akutsu, T. TANGLE: two-level support vector regression approach for protein backbone torsion angle prediction from primary sequences. PLoS One 7, e30361 (2012).
40. Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389-3402 (1997). (Pubitemid 27359211)
41. Kawashima, S. et al. AAindex: amino acid index database, progress report 2008. Nucleic Acids Res. 36, D202-205 (2008). (Pubitemid 351149722)
42. Wagner, M., Adamczak, R., Porollo, A. & Meller, J. Linear regression models for solvent accessibility prediction in proteins. J. Comput. Biol. 12, 355-369 (2005). (Pubitemid 40586438)
43. Song, J., Burrage, K., Yuan, Z. & Huber, T. Prediction of cis/trans isomerization in proteins using PSI-BLAST profiles and secondary structure information. BMC Bioinformatics 7, 124 (2006).
44. Faraggi, E., Zhang, T., Yang, Y., Kurgan, L. & Zhou, Y. SPINE X: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles. J. Comput. Chem. 33, 259-267 (2012).
45. Dunker, A. K. & Obradovic, Z. The protein trinity-linking function and disorder. Nat. Biotechnol. 19, 805-806 (2001).
46. Dunker, A. K. et al. The unfoldomics decade: an update on intrinsically disordered proteins. BMC Genomics 9 Suppl 2, S1 (2008).
47. Iakoucheva, L. M. et al. The importance of intrinsic disorder for protein phosphorylation. Nucleic Acids Res. 32, 1037-1049 (2004). (Pubitemid 38854704)
48. Gnad, F. et al. PHOSIDA (phosphorylation site database): management, structural and evolutionary investigation, and prediction of phosphosites. Genome Biol. 8, R250 (2007).
49. Ward, J. J., McGuffin, L. J., Bryson, K., Buxton, B. F. & Jones, D. T. The DISOPRED server for the prediction of protein disorder. Bioinformatics 20, 2138-2139 (2004). (Pubitemid 39236567)
50. Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25-29 (2000). (Pubitemid 30257031)
51. Hunter, S. et al. Inter Pro in 2011: new developments in the family and domain prediction database. Nucleic Acids Res. 40, D306-312 (2012).
52. Kanehisa, M.&Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27-30 (2000). (Pubitemid 30047706)
53. Punta, M. et al. The Pfam protein families database. Nucleic Acids Res. 40, D290-301 (2012).
54. Jensen, L. J. et al. STRING 8-a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res. 37, D412-416 (2009).
55. Liaw, A. & Wiener, M. Classification and regression by random forest. R news 2, 18-22 (2002).
56. Breiman, L. Random forests. Mach. Learn. 45, 5-32 (2001). (Pubitemid 32933532)
57. Segura, J., Jones, P. F. & Fernandez-Fuentes, N. A holistic in silico approach to predict functional sites in protein structures. Bioinformatics 28, 1845-1850 (2012).
58. Wang, X. F. et al. Predicting residue-residue contacts and helix-helix interactions in transmembrane proteins using an integrative feature-based random forest approach. PLoS One 6, e26767 (2011).
59. Liu, Z. P., Wu, L. Y., Wang, Y., Zhang, X. S. & Chen, L. Prediction of protein-RNA binding sites by a random forest method with combined features. Bioinformatics 26, 1616-1622 (2010).
60. Wu, J. et al. Prediction of DNA-binding residues in proteins from amino acid sequences using a random forest model with a hybrid feature. Bioinformatics 25, 30-35 (2009).