Steroidal sulfate esters play a central role in many physiological processes. They serve as the reservoir for endogenous sex hormones and form a significant fraction of the steroid metabolite pool. The analysis of steroid sulfates is thus essential in fields such as medical science and sports drug testing. Although the direct detection of steroid sulfates can be readily achieved using liquid chromatography-mass spectrometry, many analytical approaches, including gas chromatography-mass spectrometry, are hampered due to the lack of suitable enzymatic or chemical methods for sulfate ester hydrolysis prior to analysis. Enhanced methods of steroid sulfate hydrolysis would expand analytical possibilities for the study of these widely occurring metabolites. The arylsulfatase from Pseudomonas aeruginosa (PaS) is a purified enzyme capable of hydrolysing steroid sulfates. However, this enzyme requires improvement to hydrolytic activity and substrate scope in order to be useful in analytical applications. These improvements were sought by applying semi-rational design to mutate amino acid residues neighbouring the enzyme active site. Mutagenesis was implemented on both single and multiple residue sites. Screening by UPLC-MS was performed to test the steroid sulfate hydrolysis activity of these mutant libraries against testosterone sulfate. This approach revealed the steroid sulfate binding pocket and resulted in three mutants that showed an improvement in catalytic efficiency (Vmax/KM) of more than 150 times that of wild-type PaS. The substrate scope of PaS was expanded and a modest increase in thermostability was observed. Finally, molecular dynamics simulations of enzyme-substrate complexes were used to provide qualitative insight into the structural origin of the observed effects.The authors thank the World Anti-Doping Agency’s Science Research Grants (13A13MM and 16A06MM), the Swedish Research Council (VR, Grant 2015-04928), as well as the Knut and Alice Wallenberg and Wenner-Gren foundations for financial support as well as fellowships to SCLK and AP respectively. All computational work in this paper was supported by computational resources provided by the Swedish National Infrastructure for Computing (SNIC, grants 2016-34-27 and 2017-12-11)
