title: Glycosylation site prediction using ensembles of Support Vector Machine classifiers.
abstract: BACKGROUND
  Glycosylation is one of the most complex post-translational modifications (PTMs) of proteins in eukaryotic cells. Glycosylation plays an important role in biological processes ranging from protein folding and subcellular localization, to ligand recognition and cell-cell interactions. Experimental identification of glycosylation sites is expensive and laborious. Hence, there is significant interest in the development of computational methods for reliable prediction of glycosylation sites from amino acid sequences.
  
  
  RESULTS
  We explore machine learning methods for training classifiers to predict the amino acid residues that are likely to be glycosylated using information derived from the target amino acid residue and its sequence neighbors. We compare the performance of Support Vector Machine classifiers and ensembles of Support Vector Machine classifiers trained on a dataset of experimentally determined N-linked, O-linked, and C-linked glycosylation sites extracted from O-GlycBase version 6.00, a database of 242 proteins from several different species. The results of our experiments show that the ensembles of Support Vector Machine classifiers outperform single Support Vector Machine classifiers on the problem of predicting glycosylation sites in terms of a range of standard measures for comparing the performance of classifiers. The resulting methods have been implemented in EnsembleGly, a web server for glycosylation site prediction.
  
  
  CONCLUSIONS
  Ensembles of Support Vector Machine classifiers offer an accurate and reliable approach to automated identification of putative glycosylation sites in glycoprotein sequences.
authors.affiliation: Artificial Intelligence Research Laboratory, Computer Science Department, Iowa State University, USA. cornelia@cs.iastate.edu
authors.forename: Cornelia
authors.initials: C
authors.lastname: Caragea
authors.forename: Jivko
authors.initials: J
authors.lastname: Sinapov
authors.forename: Adrian
authors.initials: A
authors.lastname: Silvescu
authors.forename: Drena
authors.initials: D
authors.lastname: Dobbs
authors.forename: Vasant
authors.initials: V
authors.lastname: Honavar
mesh_terms: D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D000818:Animals; D001185:Artificial Intelligence; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D016002:Discriminant Analysis; D006031:Glycosylation; D006801:Humans; D020407:Internet; D010363:Pattern Recognition, Automated / Q000379:methods; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D012372:ROC Curve; D020539:Sequence Analysis, Protein / Q000379:methods; D017386:Sequence Homology, Amino Acid; D012984:Software
chemical_list: D011506:Proteins