RESUMEN
Deoxyribonucleic acid (DNA) is an important molecular target for anti-cancer agents due to its involvement in gene expression and protein synthesis which are fundamental steps in cell division and growth. A number of antineoplastic agents interfere with DNA and hence disturb the cell cycle. Compounds including planar aromatic rings are privileged scaffolds in binding to DNA. This characteristic is mainly arisen from the fact that such structural feature may be appropriate to insert between the base pairs of the DNA double helix and produce relatively stable non-covalent complexes. Besides π-π stacking interactions, binding to the DNA molecule might be intensified through H-bond interactions of heterocyclic rings. In the present contribution, a series of experimentally validated cytotoxic indeno[1,2-b]quinoline-9,11-diones (1-12) and their aromatized analogues (13-21) developed in our group were subjected to docking and molecular dynamics simulations to elucidate their most probable binding modes with DNA.
RESUMEN
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.