Sulfated biomolecules are widespread in Nature and highly diverse in chemical structure and biological function. For instance steroid sulfate, cerebroside sulfate or heparin play vital roles in human and animals. Plants produce numerous sulfated secondary metabolites acting as defense or communication molecules. Sulfated biomolecules are also ubiquitous in marine environment. Indeed all marine animals, plants and algae synthesize sulfated polysaccharides as major compounds of their extracellular matrix.
Sulfatases catalyze the cleavage of sulfate groups from such molecules and are thus essential enzymes in the biomedical field, but also in general biology, in environmental processes and in biotechnology. These proteins have been essentially studied in the context of severe genetic diseases in human and the number of characterized sulfatases is thus limited in comparison to the huge diversity of sulfated compounds. In the context of the explosion of genomic data, the prediction of the function of new sulfatases is therefore particularly prone to misinterpretations. A classification system allowing a better prediction of substrate specificity and for setting the limit of functional annotations is therefore urgently needed for sulfatases.
To answer this issue the Marine Glycobiology group, in close collaboration with the bioinformatics platform ABIMS, has created a general database, SulfAtlas, to classify all available sulfatases based on sequence homology. The details of this collaborative work has been published in the journal PLOS One the 17th of October 2016 ( http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0164846). The SulfAtlas database will be regularly updated and is in free access at the following address: http://abims.sb-roscoff.fr/sulfatlas/.
Reference: Tristan BARBEYRON, Loraine BRILLET-GUÉGUEN, Wilfrid CARRÉ, Cathelène CARRIÈRE, Christophe CARON, Mirjam CZJZEK, Mark HOEBEKE and Gurvan MICHEL. (2016) Matching the diversity of sulfated biomolecules: creation of a classification database for sulfatases reflecting their substrate specificity (2016) PLOS One, 11:e0164846