QSAR and molecular docking for the search of AOX inhibitors: a rational drug discovery approach

Abstract

AbstractThe alternative oxidase (AOX) is a monotopic diiron carboxylate protein that catalyses the oxidation of ubiquinol and the reduction of oxygen to water. Although a number of AOX inhibitors have been discovered, little is still known about the ligand–protein interaction and essential chemical characteristics of compounds required for a potent inhibition. Furthermore, owing to the rapidly growing resistance to existing inhibitors, new compounds with improved potency and pharmacokinetic properties are urgently required. In this study we used two computational approaches, ligand–protein docking and Quantitative Structure–Activity Relationships (QSAR) to investigate binding of AOX inhibitors to the enzyme and the molecular characteristics required for inhibition. Docking studies followed by protein–ligand interaction fingerprint (PLIF) analysis using the AOX enzyme and the mutated analogues revealed the importance of the residues Leu 122, Arg 118 and Thr 219 within the hydrophobic cavity. QSAR analysis, using stepwise regression analysis with experimentally obtained IC50 values as the response variable, resulted in a multiple regression model with a good prediction accuracy. The model highlighted the importance of the presence of hydrogen bonding acceptor groups on specific positions of the aromatic ring of ascofuranone derivatives, acidity of the compounds, and a large linker group on the compounds on the inhibitory effect of AOX.