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This study presents a transition path sampling (TPS) procedure to create an ensemble of trajectories describing a chemical transformation from a reactant to a product state, augmented with a computer vision technique. A 3D convolutional neural network (CNN) sorts the slices of the TPS trajectories into reactant or product state categories, which aids in automatically accepting or rejecting a newly generated trajectory. Furthermore, information about the geometrical configuration of each slice enables one to calculate the percentage of reactant and product states within a specific shooting range. These statistics are used to determine the most appropriate shooting range and, if needed, to improve a shooting acceptance rate. To test the automated 3D CNN TPS technique, we applied it to collect an ensemble of the transition paths for the rate-limiting step of the Morita−Bayliss−Hillman (MBH) reaction. 

Natural enzymes are powerful catalysts, reducing the apparent activation energy for reaction, enabling chemistry to proceed as much as 1015 times faster than the corresponding solution reaction. It has been suggested for some time that in some cases quantum tunneling can contribute to this rate enhancement by offering pathways through a barrier inaccessible to activated events. A central question of interest to both physical chemists and biochemists is the extent to which evolution introduces below the barrier or tunneling mechanisms. In view of the rapidly expanding chemistries for which artificial enzymes have now been created, it is of interest to see how quantum tunneling has been used in these reactions. In this paper, we study the evolution of possible proton tunneling during C-H bond cleavage in enzymes that catalyze the Morita-Baylis-Hillman (MBH) reaction. The enzymes were generated by theoretical design followed by laboratory evolution. We employ classical and centroid molecular dynamics approaches in path sampling computations to determine if there is a quantum contribution to lowering the free energy of the proton transfer for various experimentally generated protein and substrate combinations. This data is compared to experiments reporting on the observed kinetic isotope effect (KIE) for the relevant reactions. Our results indicate modest involvement of tunneling when laboratory evolution has resulted in a system with a higher classical free energy barrier to chemistry (that is when optimization of processes other than chemistry result in a higher chemical barrier.) 

It is hoped that artificial enzymes designed in laboratories can be efficient alternatives to chemical catalysts that have been used to synthesize organic molecules. However, the design of artificial enzymes is challenging and requires a detailed molecular-level analysis to understand the mechanism they promote in order to design efficient variants. In this study, we computationally investigate the mechanism of proficient Morita-Baylis-Hillman enzymes developed using a combination of computational design and directed evolution. The powerful transition path sampling method coupled with in-depth post-processing analysis has been successfully used to elucidate the different chemical pathways, transition states, protein dynamics, and free energy barriers of reactions catalyzed by such laboratory-optimized enzymes. This research provides an explanation for how different chemical modifications in an enzyme affect its catalytic activity in ways that are not predictable by static design algorithms.