PLoS ONE · 2016 · 178 citations · 105 references
Life SciencesBiochemistryMolecular BiologyComparative GenomicsEnzymologyGenomicsEnzyme ClassificationEnzyme-substrate RecognitionProtein Sequence AnalysisSequence Similarity SearchSulfur ChemistrySulfur MetabolismClassification System DesignSequence HomologySequence Homology AssessmentSequence Homology DetectionSulfatase Enzyme SpecificitySulfate ChemistrySulfur Metabolism Enzymes
Sulfatases cleave sulfate groups from various molecules and constitute a biologically and industrially important group of enzymes. However, the number of sulfatases whose substrate has been characterized is limited in comparison to the huge diversity of sulfated compounds, yielding functional annotations of sulfatases particularly prone to flaws and misinterpretations. In the context of the explosion of genomic data, a classification system allowing a better prediction of substrate specificity and for setting the limit of functional annotations is urgently needed for sulfatases. Here, after an overview on the diversity of sulfated compounds and on the known sulfatases, we propose a classification database, SulfAtlas (http://abims.sb-roscoff.fr/sulfatlas/), based on sequence homology and composed of four families of sulfatases. The formylglycine-dependent sulfatases, which constitute the largest family, are also divided by phylogenetic approach into 73 subfamilies, each subfamily corresponding to either a known specificity or to an uncharacterized substrate. SulfAtlas summarizes information about the different families of sulfatases. Within a family a web page displays the list of its subfamilies (when they exist) and the list of EC numbers. The family or subfamily page shows some descriptors and a table with all the UniProt accession numbers linked to the databases UniProt, ExplorEnz, and PDB.
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Koichiro Tamura, Daniel G. Peterson, Nora Peterson et al. · Molecular Biology and Evolution · 2011 · 40.3K citations · Full text
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