Quantitative structure-activity relationship studies of mushroom tyrosinase inhibitors.

Here, we report our results from quantitative structure-activity relationship studies on tyrosinase inhibitors. Interactions between benzoic acid derivatives and tyrosinase active sites were also studied using a molecular docking method. These studies indicated that one possible mechanism for the interaction between benzoic acid derivatives and the tyrosinase active site is the formation of a hydrogen-bond between the hydroxyl (aOH) and carbonyl oxygen atoms of Tyr98, which stabilized the position of Tyr98 and prevented Tyr98 from participating in the interaction between tyrosinase and ORF378. Tyrosinase, also known as phenoloxidase, is a key enzyme in animals, plants and insects that is responsible for catalyzing the hydroxylation of tyrosine into o-diphenols and the oxidation of o-diphenols into o-quinones. In the present study, the bioactivities of 48 derivatives of benzaldehyde, benzoic acid, and cinnamic acid compounds were used to construct three-dimensional quantitative structure-activity relationship (3D-QSAR) models using comparative molecular field (CoMFA) and comparative molecular similarity indices (CoMSIA) analyses. After superimposition using common substructure-based alignments, robust and predictive 3D-QSAR models were obtained from CoMFA (q2 = 0.855, r2 = 0.978) and CoMSIA (q2 = 0.841, r2 = 0.946), with 6 optimum components. Chemical descriptors, including electronic (Hammett sigma), hydrophobic (pi), and steric (MR) parameters, hydrogen bond acceptor (H-acc), and indicator variable (I), were used to construct a 2D-QSAR model. The results of this QSAR indicated that pi, MR, and H-acc account for 34.9, 31.6, and 26.7% of the calculated biological variance, respectively. The molecular interactions between ligand and target were studied using a flexible docking method (FlexX). The best scored candidates were docked flexibly, and the interaction between the benzoic acid derivatives and the tyrosinase active site was elucidated in detail. We believe that the QSAR models built here provide important information necessary for the design of novel tyrosinase inhibitors.