Identification of Pharmacological Targets Combining Docking and Molecular Dynamics Simulations

Abstract

Studies that include both experimental data and computational simulations ( in silico ) have increased in number because the techniques are complementary. In silico methodologies are currently an essential component of drug design; moreover, identification and optimization of the best ligand based on the structures of biomolecules are common scientific ch allenges. Geometric structural properties of biomolecules explain their behavior and interaction s and when this information is used by a combinatio n of algorithms, a dynamic model based on atomic details can be produced. Docking studies enable researcher s to determine the best position for a ligand to bind on a macromolecule, whereas Molecular Dynamics (MD) simulations describe the relevant interactions that maintain this binding. MD simulations have the advantage of illustrating the macromolecule movements in more detail. In the case of a protein, the si de chain, backbone and domain movements can explain how ligands are trapped during different conformational states. Additionally, MD simulations can depict several binding sites of ligands that c an be explored by docking studies, sampling many protein conformations. Following the previously mentioned strategy, it is possible to identify each binding s ite that might be able to accommodate different lig ands through atomic motion. Another important advantage of MD is to explore the movement of side chains of key catalytic residues, which could provide informa tion about the formation of transition states of a protein. All this information can be used to propose ligands and their most probable site of interaction, which are daily tasks of drug design. In this review, the mos t frequent criteria that are considered when determ ining pharmacological targets are gathered, particularly when docking and MD are combined.