De novo design of metalloenzymes using Metal Mer

Abstract

De novo enzyme design provides a route to create biocatalysts for target reactions, yet achieving high activity and selectivity directly from computation remains challenging. How best to design catalytic sites remains an open question, and enzyme design-metalloenzyme design for new-to-nature transformations in particular-is still limited in success rate, in substrate scope, and in the range of reactions accessible. Here we present a pipeline that produces efficient metalloenzymes directly from computation. It uses Metal Mer, a structural unit mapping backbone geometry to statistically preferred metal coordination modes, to build metal-binding sites with high confidence, and then constructs the catalytic core around the reaction product with geometric restrictions. Using the pipeline, we designed zinc-dependent hydride-transfer enzymes, obtaining a highly selective variant that exhibits enantiomeric excess (ee) >99% on 13 substrates without directed evolution. For steroid substrates bearing multiple carbonyl groups, redesigned variants achieved high regio- and diastereo- selectivity greater than 19:1. Applying this framework to iron-catalyzed alkene difunctionalization, designed variants were active out of the box (10% yield, 70:30 enantiomeric ratio (e.r.)), exceeding a previously reported natural functional analogs (<5% yield, <60:40 e.r.); a single active-site residue mutation further raised selectivity to 96:4 e.r. for several substrates, on par with extensively evolved natural enzymes. The crystal structure of an active design matches the computational model with a backbone RMSD of 0.4 Angstrom, supporting the accuracy of the approach. These results establish our design framework as a general strategy for the de novo design of metalloenzyme series with diverse coordination geometries and coordinating residues, enabling efficient and selective catalysis of challenging chemical transformations.