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Glycobiology Advance Access published online on June 8, 2006
Glycobiology, 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
Received February 15, 2006
Revised April 28, 2006
Accepted May 26, 2006

Article

Analysis and prediction of mammalian protein glycation

Morten Bo Johansen 1, Lars Kiemer 1, and Søren Brunak 1 *

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

* To whom correspondence should be addressed.
Søren Brunak, E-mail: brunak{at}cbs.dtu.dk


  Abstract

Glycation is a non-enzymatic 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 accumulation of glycation products cause side effects. In the present study, we statistically investigate glycation of {epsilon} 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 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 http://www.cbs.dtu.dk/services/NetGlycate-1.0.

Keywords: Glycation/Lysine/Machine learning prediction/Non-enzymatic glycosylation.
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