Abstract

Catalytic fields representing the topology of the optimal molecular environment charge distribution that reduces the activation barrier have been used to examine alternative reaction variants and to determine the role of conserved catalytic residues for two consecutive reactions catalyzed by the same enzyme. Until now, most experimental and conventional top-down theoretical studies employing QM/MM or ONIOM methods have focused on the role of enzyme electric fields acting on broken bonds of reactants. In contrast, our bottom-up approach dealing with a small reactant and transition-state model allows the analysis of the opposite effects: how the catalytic field resulting from the charge redistribution during the enzyme reaction acts on conserved amino acid residues and contributes to the reduction of the activation barrier. This approach has been applied to the family of histidyl tRNA synthetases involved in the translation of the genetic code into the protein amino acid sequence. Activation energy changes related to conserved charged amino acid residues for 12 histidyl tRNA synthetases from different biological species allowed to compare on equal footing the catalytic residues involved in ATP aminoacylation and tRNA charging reactions and to analyze different reaction mechanisms proposed in the literature. A scan of the library of atomic multipoles for amino acid side-chain rotamers within the catalytic field pointed out the change in the Glu83 conformation as the critical catalytic effect, providing, at low computational cost, insight into the electrostatic preorganization of the enzyme catalytic site at a level of detail that has not yet been accessible in conventional experimental or theoretical methods. This opens the way for rational reverse biocatalyst design at a very limited computational cost without resorting to empirical methods.

Highlights

  • Detailed knowledge of enzyme reaction mechanisms, the role of amino acid residues essential for catalytic activity, and the structure of corresponding transition states at atomic resolution could provide invaluable information on the process of the rational catalyst[1,2] or drug[3] design, as many enzyme inhibitors are transition-state analogs

  • They are usually the starting point for tedious trial-and-error searches to locate stationary points on the vast multidimensional energy hypersurface using hybrid quantum mechanics/molecular mechanics (QM/MM)[5] or ONIOM6 techniques. In this Article, we present an alternative way to analyze enzyme reaction variants based on perturbational intermolecular interaction theory, allowing the partitioning of activation energy changes into well-defined physical components, the analytical representation of which could be the subject of gradual approximations leading to simpler nonempirical models applicable to large molecular systems

  • Catalytic fields derived from substrate and transition-state wave functions enable us to determine the optimal charge distribution of the catalytic environment, which may be naturally confronted with the topology of all conserved charged enzyme amino acids

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Summary

Introduction

Detailed knowledge of enzyme reaction mechanisms, the role of amino acid residues essential for catalytic activity, and the structure of corresponding transition states at atomic resolution could provide invaluable information on the process of the rational catalyst[1,2] or drug[3] design, as many enzyme inhibitors are transition-state analogs. The available experimental structural data for enzymes typically determined in X-ray diffraction studies are most frequently of low resolution and lack accurate hydrogen atom positions.[4] They are usually the starting point for tedious trial-and-error searches to locate stationary points on the vast multidimensional energy hypersurface using hybrid quantum mechanics/molecular mechanics (QM/MM)[5] or ONIOM (our own n-layered integrated molecular orbital and molecular mechanics)[6] techniques In this Article, we present an alternative way to analyze enzyme reaction variants based on perturbational intermolecular interaction theory, allowing the partitioning of activation energy changes into well-defined physical components, the analytical representation of which could be the subject of gradual approximations leading to simpler nonempirical models applicable to large molecular systems. In the case in which such data were available, more comprehensive methods integrating structural and kinetic data could be applied.[7]

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