Bayesian Optimization for an ATP-Regenerating In Vitro Enzyme Cascade

Abstract

Enzyme cascades are an emerging synthetic tool for the synthesis of various molecules, combining the advantages of biocatalysis and of one-pot multi-step reactions. However, the more complex the enzyme cascade is, the more difficult it is to achieve adequate productivities and product concentrations. Therefore, the whole process must be optimized to account for synergistic effects. One way to deal with this challenge involves data-driven models in combination with experimental validation. Here, Bayesian optimization was applied to an ATP-producing and -regenerating enzyme cascade consisting of polyphosphate kinases. The enzyme and co-substrate concentrations were adjusted for an ATP-dependent reaction, catalyzed by mevalonate kinase (MVK). With a total of 16 experiments, we were able to iteratively optimize the initial concentrations of the components used in the one-pot synthesis to improve the specific activity of MVK with 10.2 U mg−1. The specific activity even exceeded the results of the reference reaction with stoichiometrically added ATP amounts, with which a specific activity of 8.8 U mg−1 was reached. At the same time, the product concentrations were also improved so that complete yields were achieved.