Computational Studies of Enzyme Thermodynamics and Protein Stability

Enzymes adapt to varying environmental temperatures through a process often involving a fine balance between molecular flexibility and rigidity to maintain optimal catalytic activity. This thesis investigates the intricate mechanisms underlying enzyme temperature adaptation and protein stability using advanced computational methods. In Paper I, the aim was to investigate the general notion that psychrophilic (cold-adapted) enzymes exhibit enhanced catalytic activity at lower temperatures. Computational simulations of lactate dehydrogenases from fish species with differing thermal adaptations revealed that their increased low-temperature activity stems from a redistribution of action enthalpy and entropy favourable in low temperature environments. This advantageous thermodynamic profile was shown to be directly linked to enhanced conformational flexibility in specific surface regions of the enzyme. Paper II then explored whether enzyme oligomerization could serve as an additional strategy for temperature adaptation. Investigating a cold-adapted (R)-3-hydroxybutyrate dehydrogenase, empirical valence bond (EVB) simulations demonstrated that while oligomerization affects the mobility of intersubunit surface residues, it does not significantly alter the enzyme's thermodynamic activation parameters. This suggests that, for this system, the assembly of subunits does not impede cold adaptation. Moving beyond catalytic rates, Paper III addressed how temperature influences the diffusive process of substrate binding, focusing on heat capacity changes. Computational analysis of human thrombin inhibitors indicated that the observed negative heat capacity changes upon ligand binding are primarily due to the suppression of conformational equilibria present in the free inhibitors in solution. This occurred as opposed to being solely from changes in enzyme flexibility upon binding and highlights a crucial factor influencing the temperature dependence of binding processes. Finally, Paper IV focuses on developing a robust and efficient computational approach for predicting protein mutational effects on stability and ligand binding. This work presents QresFEP-2, a novel hybrid-topology free energy perturbation (FEP) protocol. Through rigorous benchmarking, QresFEP-2 demonstrated state-of-the-art accuracy compared to experimental data, while significantly improving computational efficiency. This tool provides a highly efficient and competitive open-source alternative for high-throughput in silico mutagenesis studies, relevant for protein engineering and drug design.