Predicting the linkage sites in glycoproteins using bio-basis function neural network

Authors:
Zheng Rong Yang;Kuo-Chen Chou
Affiliations:
School of Engineering and Computer Science, Exeter University, Exeter EX4 4QF, UK,;Gordon Life Science Institute, Kalamazoo, MI 49009, USA
Venue:
Bioinformatics
Year:
2004

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Abstract

Motivation: 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. Availability: The software and the data used in this study can be downloaded from http://www.dcs.ex.ac.uk/~zryang for free academic use.