COMPUTATIONAL APPROACHES TO PROTONATION AND DEPROTONATION REACTIONS FOR BIOLOGICAL MACROMOLECULES AND SUPRAMOLECULAR COMPLEXES

Abstract

<p>Understanding and predicting chemical phenomena is the main goal of computational chemistry. In this thesis I present my work on applying computational approaches to study chemical processes in biological and supramolecular systems.</p> <p>pH-responsive molecular tweezers have been proposed as an approach for targeting drug-delivery to tumors, which tend to have a lower pH than normal cells. In chapter 2 I present a computational study I performed on a pH-responsive molecular tweezer using <em>ab initio</em> quantum chemistry in the gas phase and molecular dynamics simulations in solution. The binding free energy in solution was calculated using Steered Molecular Dynamics. We observe, in atomistic detail, the pH-induced conformational switch of the tweezer and the resulting release of the drug molecule. Even when the tweezer opens, the drug molecule remains near a hydrophobic arm of the molecular tweezer. Drug release cannot occur, it seems, unless the tweezer is a hydrophobic environment with low pH.</p> <p>The protonation state of amino acid residues in proteins depends on their respective pK_a values. Computational methods are particularly important for estimating the pK_a values of buried and active site residues, where experimental data is scarce. In chapter 3 I used the cluster model approach to predict the pK_a of some challenging protein residues and for which methods based on the numerical solution of the Poisson-Boltzmann equation and empirical approaches fail. The ionizable residue and its close environment were treated quantum mechanically, while the rest of the protein was replaced by a uniform dielectric continuum. The approach was found to overestimate the electrostatic interaction leading to predicting lower pK_a values.</p>