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Years ago, we identified a rapid vibrational motion  termed a rate-promoting vibration (RPV) as central to the reaction coordinate of some enzymes. This study addresses two key questions for one example enzyme we have studied, lactate dehydrogenase (LDH): first, what is a lower bound on the RPV’s contribution to catalytic efficiency, and second, what is the mechanism of RPV formation via allosteric transmission. The goal is to understand how we can artificially create such a system. LDH catalyzes the interconversion of pyruvate and lactate via hydride and proton transfer. Altering the motion range between Val31 and Arg106, central residues in the promoting vibration, with a modest constraint reduces the reaction rate (through a raising of the free energy barrier) by over 3 orders of magnitude. Committor analysis shows that shorter distances in the constrained system shift the transition state toward proton transfer, while natural or longer distances favor a transition state formed in hydride transfer. PCA confirms the anticorrelated motion between Val31 and Arg106, aligning with vibrational modes to optimize the reaction path. Critically, we find that a breathing motion among alpha helices is used to create the necessary distance over which a rapid and short RPV can be effective. Network analysis reveals that Val31, Ala34, and Cys35 have higher eigenvector centrality in reactive trajectories, indicating enhanced inter-residue communication. These findings underscore RPVs as crucial modulators of enzymatic function through dynamic and allosteric mechanisms and suggest an approach to the generation of nonbiologic protein catalysts that include a promoting vibration. 

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.) 

β-Lactamases are one of the primary enzymes responsible for antibiotic resistance and have existed for billions of years. The structural differences between a modern class A TEM-1 β-lactamase compared to a sequentially reconstructed Gram-negative bacteria β- lactamase are minor. Despite the similar structures and mechanisms, there are different functions between the two enzymes. We recently identified differences in dynamics effects that result from evolutionary changes that could potentially account for the increase in substrate specificity and catalytic rate. In this study, we used transition path sampling-based calculations of free energies to identify how evolutionary changes found between an ancestral β-lactamase, and its extant counterpart TEM-1 β-lactamase affect rate. 

β-Lactamases are a class of well-studied enzymes that are known to have existed since billions of years ago, starting as a defense mechanism to stave off competitors and are now enzymes responsible for antibiotic resistance. Using ancestral sequence reconstruction, it is possible to study the crystal structure of a laboratory resurrected 2−3 billion year-old β-lactamase. Comparing the ancestral enzyme to its modern counterpart, a TEM-1 β-lactamase, the structural changes are minor, and it is probable that dynamic effects play an important role in the evolution of function. We used molecular dynamics simulations and employed transition path sampling methods to identify the presence of rate-enhancing dynamics at the femtosecond level in both systems, found that these fast motions are more efficiently coordinated in the modern enzyme, and examined how specific dynamics can pinpoint evolutionary effects that are essential for improving enzymatic catalysis.