Sulfonation and methylation in silico : Modelling studies on SULT1A3 and COMT

Phenolic compounds are ubiquitously encountered in all living organisms. Due to the versatile chemical properties of phenols, they can form various types of specific interactions with biological macromolecules and participate in different types of reactions. In humans, endogenous phenols act as important neurotransmitters, hormones, and building blocks of proteins. A multitude of potentially bioactive phenols is ingested from different sources every day. To modulate the activities of endogenous and xenobiotic phenols, several families of Phase II metabolic enzymes have evolved, which can eliminate phenolic compounds through conjugation. In humans, the most important Phase II enzymes for phenol metabolism are UDP-glucuronosyltransferases (UGTs), cytosolic sulfotransferases (SULTs) and catechol O-methyltransferase (COMT). These enzymes increase the solubility of phenolic substrates, making them less active and easier to excrete. Because many clinically applied drugs also possess phenolic functionalities, UGTs, SULTs, and COMT are potentially important for the pharmacokinetics, exposure, and efficacy of therapeutics. In this thesis, the substrate specificity of human SULT1A3 and COMT were studied computationally, using comparative molecular field analysis (CoMFA), which is a widely used 3-dimensional (3D) quantitative structure-activity relationship (QSAR) method, based on the molecular interaction fields of known substrate molecules and their activities. The CoMFA fields describe the shape, size, and electrostatic properties of the substrates, which are the most important determinants of molecular recognition by enzymes. In our CoMFA models, variations in the substrate fields were statistically correlated with changes in enzyme kinetic parameters such as the Michaelis-Menten constant (Km) or maximum velocity (Vmax), allowing the structural elements that are most important for activity to be extracted. The derived CoMFA models can be used to assess the probability that a new and unknown phenolic drug candidate may be a substrate of SULT1A3 or COMT. In the SULT1A3 models, we found a clear preference for structural elements typically found in catecholamines, in agreement with the results of site-directed mutagenesis studies, X-ray crystal structures and molecular dynamics (MD) simulations that have been published after our studies. When describing the electrostatic effects in the CoMFA models for COMT, semi-empirical atomic partial charges were preferred over empirically parametrized charges. In addition, the acid dissociation constant (pKa) value of the reacting catecholic hydroxyl was a critical factor that affected the affinities of the ligands. Replacing the electrostatic field in the CoMFA model with the predicted pKa value facilitated the development of less acidic COMT ligands that are capable of central nervous system (CNS) permeation. To improve the prediction of pKa values for certain privileged COMT ligand scaffolds, we developed a modified Hammett equation, by synthesizing a series of p-vinylphenols and measuring their pKa values. As increasing amounts of X-ray structural information, covering whole protein families, have become publicly available, we have attempted to extract relevant knowledge from the binding sites of several related proteins, to inspire ligand design. We created an automated data processing workflow, designed to process, combine, and analyze the electrostatic and knowledge-based contact preference fields of aligned binding sites. This analysis was performed and validated using the nuclear receptor (NR) family of proteins and was later tested for the prediction of SULT isoenzyme substrate affinities. The fieldbased protein binding pocket analysis has proven to be a useful tool, however, the intrinsic flexibility that has been observed among the cytosolic SULT family can be challenging to model without considering the dynamics of the system. In summary, the computational models that were developed in this study could be used in combination with other in silico approaches, especially MD simulations, to provide a better picture of the probable enzymes that may be relevant for the metabolism of a new phenolic drug or active metabolite. These models could be also used to design compounds with an improved affinity towards the studied enzymes, which may be clinically interesting due to the important roles played by SULT1A3 and COMT in the catecholamine-mediated neurotransmission pathways.