Extension of an Atom-Atom Dispersion Function to Halogen Bonds and Its Use for Rational Design of Drugs and Biocatalysts

Organophosphate Hydrolysis by a Designed Metalloenzyme: Impact of Mutations Explained

The efficient design of novel enzymes has been attainable only by a combination of theoretical approaches and experimental refinement, suggesting inadequate performance of de novo design protocols. Based on the analysis of the evolutionary trajectory of a designed organophosphate hydrolase, this work aimed at developing and validating the improved theoretical models describing the catalytic activity of five enzyme variants (including wild-type as well as theoretically derived and experimentally refined enzymes) performing the hydrolysis of diethyl 7-hydroxycoumarinyl phosphate. The following aspects possibly important for enzyme design were addressed: the level of theory sufficient for a reliable description of enzyme-reactant interactions, the issue of ground state (GS) destabilization versus transition state (TS) stabilization, and the derivation of the proper side chain rotamers of amino acid residues. For enzyme variants analyzed herein, differential transition state stabilization (DTSS, i.e., preferential TS binding by an enzyme over the GS binding) calculated with a non-empirical model of the interaction energy (i.e., multipole electrostatic plus approximate dispersion terms, MED) displayed a superior performance in ranking the enzyme catalytic activity. The MED DTSS-based systematic rotamer refinement performed with an efficient scanning procedure and accounting for long-range interaction energy terms is an important step capable of unlocking the full potential impact of the given residue that could be otherwise overlooked with a conventional static approach featuring optimization to the nearby minimum. While TS stabilization is the main factor contributing to the increased catalytic activity of the de novo-designed variant studied in this work, directed evolution refinement appears to impact the catalytic activity of another enzyme variant analyzed herein via GS destabilization.

Catalytic Fields as a Tool to Analyze Enzyme Reaction Mechanism Variants and Reaction Steps

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.

Halogens in Acetophenones Direct the Hydrogen Bond Docking Preference of Phenol via Stacking Interactions

Phenol is added to acetophenone (methyl phenyl ketone) and to six of its halogenated derivatives in a supersonic jet expansion to determine the hydrogen bonding preference of the cold and isolated 1:1 complexes by linear infrared spectroscopy. Halogenation is found to have a pronounced effect on the docking site in this intermolecular ketone balance experiment. The spectra unambiguously decide between competing variants of phenyl group stacking due to their differences in hydrogen bond strength. Structures where the phenyl group interaction strongly distorts the hydrogen bond are more difficult to quantify in the experiment. For unsubstituted acetophenone, phenol clearly prefers the methyl side despite a predicted sub-kJ/mol advantage that is nearly independent of zero-point vibrational energy, turning this complex into a challenging benchmark system for electronic structure methods, which include long range dispersion interactions in some way.

Combining Molecular Dynamic Information and an Aspherical-Atom Data Bank in the Evaluation of the Electrostatic Interaction Energy in Multimeric Protein-Ligand Complex: A Case Study for HIV-1 Protease

Computational analysis of protein–ligand interactions is of crucial importance for drug discovery. Assessment of ligand binding energy allows us to have a glimpse of the potential of a small organic molecule to be a ligand to the binding site of a protein target. Available scoring functions, such as in docking programs, all rely on equations that sum each type of protein–ligand interactions in order to predict the binding affinity. Most of the scoring functions consider electrostatic interactions involving the protein and the ligand. Electrostatic interactions constitute one of the most important part of total interactions between macromolecules. Unlike dispersion forces, they are highly directional and therefore dominate the nature of molecular packing in crystals and in biological complexes and contribute significantly to differences in inhibition strength among related enzyme inhibitors. In this study, complexes of HIV-1 protease with inhibitor molecules (JE-2147 and darunavir) were analyzed by using charge densities from the transferable aspherical-atom University at Buffalo Databank (UBDB). Moreover, we analyzed the electrostatic interaction energy for an ensemble of structures, using molecular dynamic simulations to highlight the main features of electrostatic interactions important for binding affinity.