Sulfated biomolecules are widespread in nature and play important roles in biological functions. Among the enzymes responsible for sulfation, ArylSulfate SulfoTransferases (ASSTs) are interesting biocatalysts as they use simple aromatic sulfates such as para-nitrophenyl sulfate as donors in comparison to PAPS-dependent sulfotransferases that use the complex and less stable PAPS as donor. However, very few is known about ASSTs (only one 3D-structure and its molecular mechanism described, tentative assignment into different classes according to their biochemistry or genomic context, only one natural donor and one acceptor substrate identified). According to our preliminary phylogenetic analysis on 2244 sequences of ASSTs genes, we identified 19 clades displaying reasonable boots-trap values. In analogy to CAZY or Sulfatlas databases, each of the actual 19 clades could correspond to a varying substrate specificity or/and mechanism. However, since biochemical and structural data are scarce, this hypothesis cannot be challenged by experimental data today. Moreover many of the branches (clades) coincide with taxonomy, which raises the obvious question that substrate specificity might be a trait which is linked to taxonomy. In the SulfASST project, we uses a combination of complementary approaches in bioinformatics, biochemistry, enzymology, structural biology, molecular modeling and protein engineering to obtain substantial information on the ASST enzymes. Based on the preliminary phylogenetic analysis, one representative of each of the 19 subfamilies (clades) will be expressed and screened for donor and acceptor substrates. Enzyme crystallography of 6-8 soundly selected representatives of ASSTs should provide precious details on molecular aspects of catalysis and selectivity (substrate, regiochemistry). Directed enzyme evolution (KnowVolution) and modeling will allow to obtain tailor-made biocatalysts for biotechnological purposes. Finally, this in-depth characterization of the ASSTs and rationalization of the obtained results will enable to: determine if substrate specificity is correlated to phylogeny; know if the genomic context of ASSTs genes is indicative of substrate or biological activity; decipher the structural determinants of substrate specificity/promiscuity and regioselectivity; define if enzyme mechanism is conserved throughout the different subfamilies (clades); predict substrate selectivity and regioselectivity by molecular modeling.