Prediction of O-glycosylation of mammalian proteins: specificity patterns of UDP-GalNAc:polypeptide N-acetylgalactosaminyltransferase

• Published in: Biochemical journal Vol. 308 ( Pt 3); no. 3; pp. 801 - 813
• Main Authors: Hansen, J E, Lund, O, Engelbrecht, J, Bohr, H, Nielsen, J O
• Format: Journal Article
• Language: English
• Published: England 15.06.1995
• Subjects: Algorithms; Animals; Computer Communication Networks; Glycoproteins - chemistry; Glycosylation; Information Systems; Mammals - metabolism; Molecular Sequence Data; N-Acetylgalactosaminyltransferases - chemistry; N-Acetylgalactosaminyltransferases - metabolism; Neural Networks, Computer; Polypeptide N-acetylgalactosaminyltransferase; Protein Structure, Secondary; Serine - chemistry; Serine - metabolism; Threonine - chemistry; Threonine - metabolism
• ISSN: 0264-6021; 1470-8728
• DOI: 10.1042/bj3080801

The specificity of the enzyme(s) catalysing the covalent link between the hydroxyl side chains of serine or threonine and the sugar moiety N-acetylgalactosamine (GalNAc) is unknown. Pattern recognition by artificial neural networks and weight matrix algorithms was performed to determine the exact position of in vivo O-linked GalNAc-glycosylated serine and threonine residues from the primary sequence exclusively. The acceptor sequence context for O-glycosylation of serine was found to differ from that of threonine and the two types were therefore treated separately. The context of the sites showed a high abundance of proline, serine and threonine extending far beyond the previously reported region covering positions -4 through +4 relative to the glycosylated residue. The O-glycosylation sites were found to cluster and to have a high abundance in the N-terminal part of the protein. The sites were also found to have an increased preference for three different classes of beta-turns. No simple consensus-like rule could be deduced for the complex glycosylation sequence acceptor patterns. The neural networks were trained on the hitherto largest data material consisting of 48 carefully examined mammalian glycoproteins comprising 264 O-glycosylation sites. For detection neural network algorithms were much more reliable than weight matrices. The networks correctly found 60-95% of the O-glycosylated serine/threonine residues and 88-97% of the non-glycosylated residues in two independent test sets of known glycoproteins. A computer server using E-mail for prediction of O-glycosylation sites has been implemented and made publicly available. The Internet address is NetOglyc@cbs.dtu.dk.