A Customized Bayesian Algorithm to Optimize Enzyme-Catalyzed Reactions

Abstract

Design of experiments (DoE) plays an important role in optimizing the catalytic performance of chemical reactions. The most commonly used DoE relies on the response surface methodology (RSM) to model the variable space of experimental conditions with the fewest number of experiments. However, the RSM leads to an exponential increase in the number of required experiments as the number of variables increases. Herein we describe a Bayesian optimization algorithm (BOA) to optimize the continuous parameters (e.g., temperature, reaction time, reactant and enzyme concentrations, etc.) of enzyme-catalyzed reactions with the aim of maximizing performance. Compared to existing Bayesian optimization methods, we propose an improved algorithm that leads to better results under limited resources and time for experiments. To validate the versatility of the BOA, we benchmarked its performance with biocatalytic C−C bond formation and amination for the optimization of the turnover number. Gratifyingly, up to 80% improvement compared to RSM and up to 360% improvement vs previous Bayesian optimization algorithms were obtained. Importantly, this strategy enabled simultaneous optimization of both the enzyme's activity and selectivity for cross-benzoin condensation.

Balancing computational chemistry's potential with its environmental impact

“…Similarly, Iwama et al 92 utilized BO to optimize the conversion of CO 2 to CO via the reverse water–gas shift reaction. BO also helped to optimize the reaction conditions for biocatalytic C–C bond formation reactions, 93 synthesis of methanol from syngas, 94 and the CO 2 to methanol conversion using heterogeneous catalysis. 95 Several other studies show the reduction of the number of run experiments to achieve more optimal reaction conditions in also digital chemistry experiments, e.g.…”