期刊
CARBOHYDRATE RESEARCH
卷 340, 期 14, 页码 2270-2278出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.carres.2005.07.012
关键词
computational glycomics; glycan classification; glycome informatics; glycan motif; support vector machine
There have been almost no standard methods for conducting computational analyses on glycan structures in comparison to DNA and proteins. In this paper, we present a novel method for extracting functional motifs from glycan structures using the KEGG/GLYCAN database. First, we developed a new similarity measure for comparing glycan structures taking into account the characteristic mechanisms of glycan biosynthesis, and we tested its ability to classify glycans of different blood components in the framework of support vector machines (SVMs), The results show that our method can successfully classify glycans from four types of human blood components: leukemic cells, erythrocyte, serum, and plasma. Next we extracted characteristic functional motifs of glycans considered to be specific to each blood component. We predicted the substruct the alpha-D-Neup5AC-(2-->3)-beta-D-Galp-(1-->4)-D-GlcpNAc as a leukemia specific glycan motif. Based on the fact that the Agrocybe cylindracea galectin (ACG) specifically binds to the same substructure, we conducted an experiment using Cell agglutination assay and confirmed that this fungal lectin specifically recognized human leukemic cells. (C) 2005 Elsevier Ltd. All rights reserved.
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