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Glycobiology Advance Access originally published online on June 8, 2006
Glycobiology 2006 16(9):844-853; doi:10.1093/glycob/cwl009
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org

Analysis and prediction of mammalian protein glycation

Morten Bo Johansen, Lars Kiemer and Søren Brunak1

Center for Biological Sequence Analysis, BioCentrum-DTU, Technical University of Denmark, Building 208, DK-2800 Lyngby, Denmark

1 To whom correspondence should be addressed; e-mail: brunak{at}cbs.dtu.dk

Received on February 15, 2006; revised on April 28, 2006; accepted on May 26, 2006

Glycation is a nonenzymatic process in which proteins react with reducing sugar molecules and thereby impair the function and change the characteristics of the proteins. Glycation is involved in diabetes and aging where the accumulation of glycation products causes side effects. In this study, we statistically investigate the glycation of {varepsilon} amino groups of lysines and also train a sequence-based predictor. The statistical analysis suggests that acidic amino acids, mainly glutamate, and lysine residues catalyze the glycation of nearby lysines. The catalytic acidic amino acids are found mainly C-terminally from the glycation site, whereas the basic lysine residues are found mainly N-terminally. The predictor was made by combining 60 artificial neural networks in a balloting procedure. The cross-validated Matthews correlation coefficient for the predictor is 0.58, which is quite impressive given the relatively small amount of experimental data available. The method is made available at www.cbs.dtu.dk/services/NetGlycate-1.0.

Key words: glycation / lysine / machine learning prediction / nonenzymatic glycosylation


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