Abstract
A quantitative structure-activity relationship (QSAR) was used to streamline re-design of a model environmental catalyst, horseradish peroxidase (HRP), for enhanced reactivity towards a target pollutant, steroid hormone 17β-estradiol. This QSAR, embodying relationship between reaction rate and intermolecular binding distance, was used in silico to screen for mutations improving enzyme reactivity. Eight mutations mediating significant reductions in binding distances were expressed in Saccharomyces cerevisiae, and resulting recombinant HRP strains were analyzed to determine Michaelis-Menten parameters during reaction with the target substrate. Enzyme turnover rate, ln(kCAT), exhibited inverse relationship with model-predicted binding distances (R2=0.81), consistent with the QSAR. Additional analysis of native substrate degradation by selected mutants yielded unexpected increases in ln(kCAT) that were also inversely correlated (R2=1.00) with model-predicted binding distances. This suggests that the mechanism of improvement comprises a nonspecific "opening up" of the active site such that it better accommodates environmental estrogens of any size. The novel QSAR-assisted approach described herein offers specific advantages compared to conventional design strategies, most notably targeting an entire class of pollutants at one time and a flexible hybridization of benefits associated with rational design and directed evolution. Thus, this approach is a promising tool for improving enzyme-mediated environmental remediation.