Response surface methodology in drug design: A case study on docking analysis of a potent antifungal fluconazole. (April 2017)

• Record Type: Journal Article
• Authors: Bohlooli, Fatemeh; Sepehri, Saghi; Razzaghi-Asl, Nima
• Is Part Of: Computational biology and chemistry. Volume 67(2017)
• Page Start: 158
• Page End: 173
• Publication Date: 2017-04
• Subjects: Antifungal -- CYP51 -- Azoles -- Box-Behnken -- Flexible docking; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2017.01.005
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: A multifactor response surface analysis was applied to model a docking of fluconazole against various CYP51 conformations. Significant and interactive effects of computational factors on docking output were elucidated. Grid spacing and number of energy evaluations per genetic algorithm run were the most effective factors. Torsion degrees of ligand was insignificant but showed significant interactive effect with number of genetic algorithm runs. Abstract: Molecular docking is a valuable in silico technique for discovery/design of bioactive compounds. A current challenge within docking simulations is the incorporation of receptor flexibility. A useful strategy toward solving such problem would be the docking of a typical ligand into the multiple conformations of the target. In this study, a multifactor response surface model was constructed to estimate the AutoDock based binding free energy of fluconazole within multiple conformations of 14α-demethylase (CYP51) (cross docking) as a validated antifungal target. On the basis of developed models, individual and interactive effects of important experimental parameters on cross docking of fluconazole were elucidated. For this purpose, a set of high-resolution holo crystallographic structures from CYP51 of human pathogen Trypanosoma cruzi were retrieved to statistically model the binding mode and affinity of fluconazole. The changes of AutoDock binding free energy for the complexes of CYP51-fluconazole were elucidated with the simultaneous variations of six independent variables including grid size, grid spacing, number of genetic algorithm (GA) runs, maximum number of energy evaluations, torsion degrees for ligand and quaternion degrees for ligand. It was revealed that grid spacing (distance between adjacent grid points) and maximum number of energy evaluations were two significant model terms. It was also revealed that grid size, torsion degrees for ligand and quaternion degrees for ligand had insignificant effects on estimated binding energy while the effect of GA runs was non-significant. The interactive effect of "torsion degrees for ligand" with number of GA runs was found to be the significant factor. Other important interactive effects were the interaction of "number of GA runs" with "grid spacing" and "number of energy evaluations" with "grid size". Furthermore; results of modeling studies within several CYP51 conformations exhibited that "number of GA runs" and "number of energy evaluations" were less sensitive to varied target conformations.