Molecular Docking And Visualization Of Selected Phytochemicals For Using Software Autodock Vina And Pymol
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Abstract
The accurate prediction of protein−ligand binding affinity is a central challenge in computational chemistry and insilico drug discovery. The free energy perturbation (FEP) method based on molecular dynamics (MD) simulation provides accurate results only if a reliable structure is available via high-resolution X-ray crystallography. To overcome the limitation, we propose a sequential prediction protocol using generalized replica exchange with solute tempering AutoDock Vina and PyMOL. At first, ligand binding poses are predicted using PyMOL, which weakens protein−ligand interactions at high temperatures to multiple binding poses. To avoid ligand dissociation at high temperatures, a flat-bottom restraint potential centered on the binding site is applied in the simulation. The binding affinity of the most reliable pose is then calculated using FEP. The protocol is applied to the bindings of ten ligands to FK506 binding proteins AutoDock Vina showing the excellent agreement between the calculated and experimental binding affinities. The present protocol, which is referred to as the AutoDock Vina and PyMOL method, would help to predict the binding affinities without high-resolution structural information on the ligand-bound state.
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