Computational prediction of N-linked glycosylation incorporating structural properties and patterns

Bioinformatics

Volumn 28, Issue 17, 2012, Pages 2249-2255

  Chuang, Gwo Yu ^a   Boyington, Jeffrey C ^a   Gordon Joyce, M ^a   Zhu, Jiang ^a   Nabel, Gary J ^a   Kwong, Peter D ^a   Georgiev, Ivelin ^a  

^a NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AMINO ACID; GLYCOPROTEIN; PROTEIN;

ALGORITHM; AMINO ACID SEQUENCE; ARTICLE; BIOLOGICAL MODEL; CHEMISTRY; GENETICS; GLYCOSYLATION; METABOLISM; PROTEIN SECONDARY STRUCTURE; PROTEIN TERTIARY STRUCTURE; STRUCTURE ACTIVITY RELATION;

ALGORITHMS; AMINO ACID SEQUENCE; AMINO ACIDS; GLYCOPROTEINS; GLYCOSYLATION; MODELS, BIOLOGICAL; PROTEIN STRUCTURE, SECONDARY; PROTEIN STRUCTURE, TERTIARY; PROTEINS; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 84865534292     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/bts426     Document Type: Article

References (27)

1. Adams, P.D. et al. (2010) PHENIX: a comprehensive Python-based systemformacromolecular structure solution. Acta Crystallogr. D Biol. Crystallogr., 66, 213-221.
2. Apweiler, R. et al. (2004) UniProt: the Universal Protein knowledgebase. Nucleic Acids Res., 32, D115-D119.
3. Benjamini, Y. and Hochberg, Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc. B.Methodol., 57, 289-300.
4. Berman, H.M. et al. (2000) The Protein Data Bank. Nucleic Acids Res., 28, 235-242.
5. Breiman, L. (2001) Random forests. Mach. Learn., 45, 5-32.
6. Caragea, C. et al. (2007) Glycosylation site prediction using ensembles of Support Vector Machine classifiers. BMC Bioinformatics, 8, 438.
7. Culyba, E.K. et al. (2011) Protein native-state stabilization by placing aromatic side chains in N-glycosylated reverse turns. Science, 331, 571-575.
8. Emsley, P. et al. (2010) Features and development of Coot. Acta Crystallogr. D Biol. Crystallogr., 66, 486-501.
9. Gavel, Y. and von Heijne, G. (1990) Sequence differences between glycosylated and non-glycosylated Asn-X-Thr/Ser acceptor sites: implications for protein engineering. Protein Eng., 3, 433-442.
10. Gupta, R. et al. (2004) Prediction of N-glycosylation sites in human proteins. http://www.cbs.dtu.dk/services/NetNGlyc/.
11. Hall, M. et al. (2009) The WEKA data mining software: an update. SIGKDD Explorations, 11, 10-18.
12. Hamby, S.E. and Hirst, J.D. (2008) Prediction of glycosylation sites using random forests. BMC Bioinformatics, 9, 500.
13. Helenius, A. and Aebi, M. (2004) Roles of N-linked glycans in the endoplasmic reticulum. Annu. Rev. Biochem., 73, 1019-1049.
14. Henikoff, S. and Henikoff, J.G. (1992) Amino acid substitution matrices from protein blocks. Proc. Natl Acad. Sci. USA., 89, 10915-10919.
15. Hubbard, S. and Thorton, J. (1993) NACCESS. Software.
16. Kabsch, W. and Sander, C. (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers, 22, 2577-2637.
17. Kaji, H. et al. (2007) Proteomics reveals N-linked glycoprotein diversity in Caenorhabditis elegans and suggests an atypical translocation mechanism for integral membrane proteins. Mol. Cell Proteomics, 6, 2100-2109.
18. Karnik, S. et al. (2009) Identification of n-glycosylation sites with sequence and structural features employing random forests. In Santanu, C., Sushmita, M., Murthy, C.A., Sastry, P.S. and Pal, S.K. (eds.) Pattern Recognition and Machine Intelligence, Third International Conference, PReMI 2009. Springer, New Delhi, India, pp. 146-151.
19. Kleywegt, G.J. et al. (2004) The Uppsala electron-density server. Acta Crystallogr. D Biol. Crystallogr., 60, 2240-2249.
20. Kowarik, M. et al. (2006) N-linked glycosylation of folded proteins by the bacterial oligosaccharyltransferase. Science, 314, 1148-1150.
21. Pantophlet, R. et al. (2003) Hyperglycosylated mutants of human immunodeficiency virus (HIV) type 1 monomeric gp120 as novel antigens for HIV vaccine design. J. Virol., 77, 5889-5901.
22. Petrescu, A.J. et al. (2004) Statistical analysis of the protein environment of N-glycosylation sites: implications for occupancy, structure, and folding. Glycobiology, 14, 103-114.
23. R Development Core Team. (2005) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0.
24. Rudd, P.M. et al. (2001) Glycosylation and the immune system. Science, 291, 2370-2376.
25. Sasaki, K. et al. (2009) Support vector machine prediction of n- and o-glycosylation sites using whole sequence information and subcellular localization. IPSJ Trans. Bioinform., 2, 25-35.
26. Welch, B.L. (1947) The generalization of "Student's" problem when several different population variances are involved. Biometrika, 34, 28-35.
27. Zielinska, D.F. et al. (2010) Precision mapping of an in vivo N-glycoproteome reveals rigid topological and sequence constraints. Cell, 141, 897-907.