Analysis and prediction of mammalian protein glycation

被引:147
作者
Johansen, Morten Bo [1 ]
Kiemer, Lars [1 ]
Brunak, Soren [1 ]
机构
[1] Tech Univ Denmark, Ctr Biol Sequence Anal, Bioctr, DK-2800 Lyngby, Denmark
关键词
glycation; lysine; machine learning prediction; nonenzymatic glycosylation;
D O I
10.1093/glycob/cwl009
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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 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 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.
引用
收藏
页码:844 / 853
页数:10
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