Glycobiology, Vol 9, 1009-1022, Copyright © 1999 by Oxford University Press
R Gupta, E Jung, AA Gooley, KL Williams, S Brunak and J Hansen
Dictyostelium discoideum has been suggested as a eukaryotic model organism
for glycobiology studies. Presently, the characteristics of acceptor sites
for the N-acetylglucosaminyl-transferases in Dictyostelium discoideum,
which link GlcNAc in an alpha linkage to hydroxyl residues, are largely
unknown. This motivates the development of a species specific method for
prediction of O-linked GlcNAc glycosylation sites in secreted and membrane
proteins of D. discoideum. The method presented here employs a jury of
artificial neural networks. These networks were trained to recognize the
sequence context and protein surface accessibility in 39 experimentally
determined O-alpha- GlcNAc sites found in D. discoideum glycoproteins
expressed in vivo. Cross-validation of the data revealed a correlation in
which 97% of the glycosylated and nonglycosylated sites were correctly
identified. Based on the currently limited data set, an abundant
periodicity of two (positions-3, -1, +1, +3, etc.) in Proline residues
alternating with hydroxyl amino acids was observed upstream and downstream
of the acceptor site. This was a consequence of the spacing of the
glycosylated residues themselves which were peculiarly found to be situated
only at even positions with respect to each other, indicating that these
may be located within beta-strands. The method has been used for a rapid
and ranked scan of the fraction of the Dictyostelium proteome available in
public databases, remarkably 25-30% of which were predicted glycosylated.
The scan revealed acceptor sites in several proteins known experimentally
to be O-glycosylated at unmapped sites. The available proteome was
classified into functional and cellular compartments to study any
preferential patterns of glycosylation. A sequence based prediction server
for GlcNAc O-glycosylations in D. discoideum proteins has been made
available through the WWW at http:/(/)www.cbs.dtu.dk/services/DictyOGlyc/
and via E-mail to DictyOGlyc@cbs.dtu.dk.
ORIGINAL ARTICLES
Scanning the available Dictyostelium discoideum proteome for O-linked GlcNAc glycosylation sites using neural networks
Department of Biotechnology, Technical University of Denmark, Lyngby, Denmark.
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