Biocatalysis has become an important aspect of modern organic synthesis, both in academia and across the chemical and pharmaceutical industries. Its success has been largely due to a rapid expansion of the range of chemical reactions accessible, made possible by advanced tools for enzyme discovery coupled with high-throughput laboratory evolution techniques for biocatalyst optimization. A wide range of tailor-made enzymes with high efficiencies and selectivities can now be produced quickly and on a gram to kilogram scale, with dedicated databases and search tools aimed at making these biocatalysts accessible to a broader scientific community. This Primer discusses the current state-of-the-art methodology in the field, including route design, enzyme discovery, protein engineering and the implementation of biocatalysis in industry. We highlight recent advances, such as de novo design and directed evolution, and discuss parameters that make a good reproducible biocatalytic process for industry. The general concepts will be illustrated by recent examples of applications in academia and industry, including the development of multistep enzyme cascades.

Enzymes exist as ensembles of conformations that are important for function. Tuning these populations of conformational states through mutation enables evolution toward additional activities. Here we computationally evaluate the population shifts induced by distal and active site mutations in a family of computationally designed and experimentally optimized retro-aldolases. The conformational landscape of these enzymes was significantly altered during evolution, as pre-existing catalytically active conformational substates became major states in the most evolved variants. We further demonstrate that key residues responsible for these substate conversions can be predicted computationally. Significantly, the identified residues coincide with those positions mutated in the laboratory evolution experiments. This study establishes that distal mutations that affect enzyme catalytic activity can be predicted computationally and thus provides the enzyme (re)design field with a rational strategy to determine promising sites for enhancing activity through mutation.

Natural enzymes have evolved to perform their cellular functions under complex selective pressures, which often require their catalytic activities to be regulated by other proteins. We contrasted a natural enzyme, LovD, which acts on a protein-bound (LovF) acyl substrate, with a laboratory-generated variant that was transformed by directed evolution to accept instead a small free acyl thioester, and no longer requires the acyl carrier protein. The resulting 29-mutant variant is 1000-fold more efficient in the synthesis of the drug simvastatin than the wild-type LovD. This is the first non-patent report of the enzyme currently used for the manufacture of simvastatin, as well as the intermediate evolved variants. Crystal structures and microsecond molecular dynamics simulations revealed the mechanism by which the laboratory-generated mutations free LovD from dependence on protein-protein interactions. Mutations dramatically altered conformational dynamics of the catalytic residues, obviating the need for allosteric modulation by the acyl carrier LovF.