Accurate Computation of Thermodynamic Activation Parameters in the Chorismate Mutase Reaction from Empirical Valence Bond Simulations

Investigation of Electrostatic Effects on Enyzme Catalysis: Insights from Computational Simulations of Monoamine Oxidase A Pathological Variants Leading to the Brunner Syndrome

Brunner syndrome is a rare genetic disorder characterized by impulsive aggressiveness and intellectual disability, which is linked to impaired function of the monoamine oxidase A (MAO-A) enzyme. Patients with specific point mutations in the MAOA gene have been reported to exhibit these symptoms, along with notably elevated serotonin levels, which suggest a decreased catalytic performance of the mutated MAO-A enzymes. In this study, we present multiscale molecular simulations focusing on the rate-limiting step of MAO-A-catalyzed serotonin degradation for the C266F and V244I variants that are reportedly associated with pathologies characteristic of the Brunner syndrome. We found that the C266F mutation causes an approximately 18,000-fold slowdown of enzymatic function, which is equivalent to a MAOA gene knockout. For the V244I mutant, a somewhat smaller, yet still significant 300-fold slowdown has been estimated. Furthermore, we conducted a comprehensive comparison of the impact of enzyme electrostatics on the catalytic function of the wild-type (WT) MAO-A and both aforementioned mutants (C266F and V244I), as well as on the E446K mutant investigated in one of our earlier studies. The results have shown that the mutation induces a noteworthy change in electrostatic interactions between the reacting moiety and its enzymatic surroundings, leading to a decreased catalytic performance in all of the considered MAO-A variants. An analysis of mutation effects supported by geometry comparison of mutants and the wild-type enzyme at a residue level suggests that a principal driving force behind the altered catalytic performance of the mutants is subtle structural changes scattered along the entire enzyme. These shifts in geometry also affect domains most relevant to catalysis, where structural offsets of few tenths of an Å can significantly change contribution to the barrier of the involved residues. These results are in full agreement with the reasoning derived from clinical observations and biochemical data. Our research represents a step forward in the attempts of using fundamental principles of chemical physics in order to explain genetically driven pathologies. In addition, our results support the view that the catalytic function of enzymes is crucially driven by electrostatic interactions.

Computer Simulations of the Temperature Dependence of Enzyme Reactions

In this review we discuss the development of methodology for calculating the temperature dependence and thermodynamic activation parameters for chemical reactions in solution and in enzymes, from computer simulations. We outline how this is done by combining the empirical valence bond method with molecular dynamics free energy simulations. In favorable cases it turns out that such simulations can even capture temperature optima for the catalytic rate. The approach turns out be very useful both for addressing questions regarding the roles of enthalpic and entropic effects in specific enzymes and also for attacking evolutionary problems regarding enzyme adaptation to different temperature regimes. In the latter case, we focus on cold-adaptation of enzymes from psychrophilic species and show how computer simulations have revealed the basic mechanisms behind such adaptation. Understanding these mechanisms also opens up the possibility of designing the temperature dependence, and we highlight a recent example of this.

Biochemical and Computational Characterization of Haloalkane Dehalogenase Variants Designed by Generative AI: Accelerating the SN2 Step

Generative artificial intelligence (AI) models trained on natural protein sequences have been used to design functional enzymes. However, their ability to predict individual reaction steps in enzyme catalysis remains unclear, limiting the potential use of sequence information for enzyme engineering. In this study, we demonstrated that sequence information can predict the rate of the SN2 step of a haloalkane dehalogenase using a generative maximum-entropy (MaxEnt) model. We then designed lower-order protein variants of haloalkane dehalogenase using the model. Kinetic measurements confirmed the successful design of protein variants that enhance catalytic activity, above that of the wild type, in the overall reaction and in particular in the SN2 step. On the simulation side, we provided molecular insights into these designs for the SN2 step using the empirical valence bond (EVB) and metadynamics simulations. The EVB calculations showed activation barriers consistent with experimental reaction rates, while examining the effect of amino acid replacements on the electrostatic effect on the activation barrier and the consequence of water penetration, as well as the extent of ground state destabilization/stabilization. Metadynamics simulations emphasize the importance of the substrate positioning in enzyme catalysis. Overall, our AI-guided approach successfully enabled the design of a variant with a faster rate for the SN2 step than the wild-type enzyme, despite haloalkane dehalogenase being extensively optimized through natural evolution.

Computational Analysis of Heat Capacity Effects in Protein-Ligand Binding

Heat capacity effects in protein-ligand binding as measured by calorimetric experiments have recently attracted considerable attention, particularly in the field of enzyme inhibitor design. A significant negative heat capacity change upon ligand binding implies a marked temperature dependence of the binding enthalpy, which is of high relevance for attempts to optimize protein-ligand interactions. In this work, we address the question of how well such heat capacity changes can be predicted by computer simulations. We examine a series of human thrombin inhibitors that all bind with ΔCp values of about -0.4 kcal/mol/K and calculate heat capacity changes from plain molecular dynamics simulations of the bound and free states of the enzyme and ligand. The results show that accurate ΔCp estimates within a few tenths of a kcal/mol/K of the experimental values can be obtained with this approach. This allows us to address the structural and energetic origin of the negative heat capacity changes for the thrombin inhibitors, and it is found that conformational equilibria of the free ligands in solution make a major contribution to the observed effect.

Why Do Empirical Valence Bond Simulations Yield Accurate Arrhenius Plots?

Computer simulations of the temperature dependence of enzyme reactions using the empirical valence bond (EVB) method have proven to give very accurate results in terms of the thermodynamic activation parameters. Here, we analyze the reasons for why such simulations are able to correctly capture activation enthalpies and entropies and how sensitive these quantities are to parametrization of the reactive potential energy function. We examine first the solution reference reaction for the enzyme ketosteroid isomerase, which corresponds to the acetate catalyzed deprotonation of the steroid in water. The experimentally determined activation parameters for this reaction turn out to be remarkably well reproduced by the calculations. By modifying the EVB potential so that the activation and reaction free energies become significantly shifted, we show that the activation entropy is basically invariant to such changes and that ΔS⧧ is instead determined by the specific mixture of the underlying force fields in the transition state region. The coefficients of this mixture do not change appreciably when the EVB potential is modified within reasonable limits, and hence, the estimate of ΔS⧧ becomes very robust. This is further verified by examining a more complex concerted hydride and proton transfer reaction in the enzyme hydroxybutyrate dehydrogenase.

Enthalpies and entropies of hydration from Monte Carlo simulations

The changes in free energy, enthalpy, and entropy for transfer of a solute from the gas phase into solution are the fundamental thermodynamic quantities that characterize the solvation process. Owing to the development of methods based on free-energy perturbation theory, computation of free energies of solvation has become routine in conjunction with Monte Carlo (MC) statistical mechanics and molecular dynamics (MD) simulations. Computation of the enthalpy change and by inference the entropy change is more challenging. Two methods are considered in this work corresponding to direct averaging for the solvent and solution and to computing the temperature derivative of the free energy in the van't Hoff approach. The application is for neutral organic solutes in TIP4P water using long MC simulations to improve precision. Definitive results are also provided for pure TIP4P water. While the uncertainty in computed free energies of hydration is ca. 0.05 kcal mol-1, it is ca. 0.4 kcal mol-1 for the enthalpy changes from either van't Hoff plots or the direct method with sampling for 5 billion MC configurations. Partial molar volumes of hydration are also computed by the direct method; they agree well with experimental data with an average deviation of 3 cm3 mol-1. In addition, the results permit breakdown of the errors in the free energy changes from the OPLS-AA force field into their enthalpic and entropic components. The excess hydrophobicity of organic solutes is enthalpic in origin.