2021

Ahinko, Mira

Computational drug design aids to lower the costs and amount of experimental testing required to identify potent bioactive lead molecules for biological target macromolecules, usually proteins. Computational prediction and analysis of cytochrome P450 (CYP) enzyme mediated metabolism can be used to assess bioavailability, potential drug-drug interactions and metabolic reaction products, and thus to abandon or re-design potentially harmful lead compounds, improve drug candidate bioavailability, and to design prodrugs that are activated at a metabolic event. Virtual screening (VS), in turn, is used to find novel bioactive compounds from a large virtual molecular database, filtering the number of compounds subjected to experimental testing. In this doctoral thesis, protein structure-based methods were utilized for computational prediction and analysis of CYP metabolism and VS. Metrics of binding free energy, ligand stability and accessibility for metabolic reaction in the CYP ligand binding site are suggested for future prediction and analysis protocols of CYP metabolism using molecular dynamics (MD) simulations. Using these metrics and expert analysis, MD simulations offered rationalization of catalytic and inhibitory activities of novel CYP ligands. Novel profluorescent tool molecules are presented for experimental CYP assays. Molecular modelling and docking aided to identify the most potent target CYP enzymes for these compounds. Moreover, further MD simulations suggested an essential role of water interactions and access channel composition for the fluorescent catalysis of the tool molecules in the CYP1 enzyme family. Finally, a workflow and practical discussion for a priorly developed protein binding site negative image-based (NIB) VS methodology, Panther, is presented. The presented results, computational methods, and tool molecules offer potent tools for drug development and ideas for the further development of the methods.