Enzyme enhancement through computational stability design targeting NMR-determined catalytic hotspots

Enzymes are the quintessential green catalysts, but realizing their full potential for biotechnology typically requires improvement of their biomolecular properties. Catalysis enhancement, however, is often accompanied by impaired stability. Here, we show how the interplay between activity and stability in enzyme optimization can be efficiently addressed by coupling two recently proposed methodologies for guiding directed evolution. We first identify catalytic hotspots from chemical shift perturbations induced by transition-state-analogue binding and then use computational/phylogenetic design (FuncLib) to predict stabilizing combinations of mutations at sets of such hotspots. We test this approach on a previously designed de novo Kemp eliminase, which is already highly optimized in terms of both activity and stability. Despite this, using our current approach, we were able to obtain 50-fold modulation in catalysis, with most variants displaying substantially increased denaturation temperatures and purification yields. Notably, our most efficient engineered variant (kcat 1700 s-1, kcat/KM 4.3·10^5 M-1s-1) is the most proficient proton-abstraction Kemp eliminase designed to date, with a catalytic efficiency on a par with naturally occurring enzymes. Molecular simulations pinpoint the origin of this catalytic enhancement as being due to the progressive elimination of a catalytically inefficient substrate conformation that is present in the original design. A large modulation in catalysis linked to substrate conformational selection illustrates the potential of our approach for the engineering of enzyme regio- and stereo-selectivity. Overall, our work showcases the power of dynamically guided enzyme engineering as a design principle for obtaining novel biocatalysts with tailored physicochemical properties, towards even anthropogenic reactions.