A customized Bayesian algorithm to optimize enzyme-catalyzed reactions

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 condi-tions with a minimal number of experiments. However, the RSM leads to an exponential increase in the number of required experiments to be evaluated 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 BOA for the optimization of the turnover number in enzyme-catalyzed reactions, we benchmarked its per-formance for a biocatalytic C-C bond-forming reaction as well as an amination reaction. Gratifyingly, up to 80% improvement compared to RSM and up to 360% improvement vs. previous Bayesian optimization algorithms was obtained. Importantly, this strategy enabled the simultaneous optimization of both the enzyme’s activity and chemoselectivity for a cross-benzoin condensation.