title: Predicting the linkage sites in glycoproteins using bio-basis function neural network.
abstract: BACKGROUND
  Although, it is known that O-glycosidically linked oligosaccharides are commonly conjugated to a serine, threonine or hydroxylysine residue of the polypeptide, the chemical nature of the anchoring monosaccharide and the size of the oligosaccharide unit varies. Among different types, O-linked or mucin-type oligosaccharides are intimately involved in the secretion of proteins, be they enzymes, hormones or structural glycoproteins. Knowledge of the linkage sites in glycoproteins is critical to the design of specific and efficient inhibitors against the enzyme to catalyse the formation of the carbohydrate-peptide linkage.
  
  
  RESULTS
  We present a method for predicting the linkage sites in O-linked glycoproteins using bio-basis function neural networks. The mean prediction accuracy of this method is 91.15 +/- 2.75% while it is 82.28 +/- 6.45% using back-propagation neural networks. Importantly, this method has significantly reduced the CPU time for modelling.
authors.affiliation: School of Engineering and Computer Science, Exeter University, Exeter EX4 4QF, UK. Z.R.Yang@exeter.ac.uk
authors.forename: Zheng Rong
authors.initials: ZR
authors.lastname: Yang
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D006023:Glycoproteins / Q000737:chemistry*; D008956:Models, Chemical; D008958:Models, Molecular; D016571:Neural Networks, Computer; D011485:Protein Binding; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D020539:Sequence Analysis, Protein / Q000379:methods*; D013329:Structure-Activity Relationship
chemical_list: D006023:Glycoproteins