Exploring genomes for glycosyltransferases

Abstract

Glycosyltransferases are one of the largest and most diverse enzyme groups in Nature. They catalyse the synthesis of glycosidic linkages by the transfer of a sugar residue from a donor to an acceptor substrate. These enzymes have been classified into families on the basis of amino acid sequence similarity that are kept updated in the Carbohydrate Active enZyme database (CAZy, ). The repertoire of glycosyltransferases in genomes is believed to determine the diversity of cellular glycan structures, and current estimates suggest that for most genomes about 1% of the coding regions are glycosyltransferases. However, plants tend to have far more glycosyltransferase genes than any other organism sequenced to date, and this can be explained by the highly complex polysaccharide network that form the cell wall and also by the numerous glycosylated secondary metabolites. In recent years, various bioinformatics strategies have been used to search bacterial and plant genomes for new glycosyltransferase genes. These are based on the use of remote homology detection methods that act at the 1D, 2D, and 3D level. The combined use of methods such as profile Hidden Markov Model (HMM) and fold recognition appears to be appropriate for this class of enzyme. Chemometric tools are also particularly well suited for obtaining an overview of multivariate data and revealing hidden latent information when dealing with large and highly complex datasets.