title: Artificial neural network method for predicting the specificity of GalNAc-transferase.
abstract: The specificity of GalNAc-transferase is consistent with the existence of an extended site composed of nine subsites, denoted by R4, R3, R2, R1, R0, R1', R2', R3', and R4', where the acceptor at R0 is either Ser or Thr to which the reducing monosaccharide is anchored. To predict whether a peptide will react with the enzyme to form a Ser- or Thr-conjugated glycopeptide, a neural network method--Kohonen's self-organization model is proposed in this paper. Three hundred five oligopeptides are chosen for the training site, with another 30 oligopeptides for the test set. Because of its high correct prediction rate (26/30 = 86.7%) and stronger fault-tolerant ability, it is expected that the neural network method can be used as a technique for predicting O-glycosylation and designing effective inhibitors of GalNAc-transferase. It might also be useful for targeting drugs to specific sites in the body and for enzyme replacement therapy for the treatment of genetic disorders.
authors.affiliation: EMBL, Heidelberg, Germany.
authors.forename: Y D
authors.initials: YD
authors.lastname: Cai
authors.forename: H
authors.initials: H
authors.lastname: Yu
authors.forename: K C
authors.initials: KC
authors.lastname: Chou
mesh_terms: D006031:Glycosylation; D017350:N-Acetylgalactosaminyltransferases / Q000378:metabolism*; D016571:Neural Networks, Computer; D010455:Peptides / Q000378:metabolism*; D013379:Substrate Specificity
chemical_list: D010455:Peptides; D017350:N-Acetylgalactosaminyltransferases