Abstract
Several methods based on enhanced-sampling molecular dynamics have been proposed for studying ligand binding processes. Here, we developed a protocol that combines the advantages of steered molecular dynamics (SMD) and metadynamics. While SMD is proposed for investigating possible unbinding pathways of the ligand and identifying the preferred one, metadynamics, with the path collective variable (PCV) formalism, is suggested to explore the binding processes along the pathway defined on the basis of SMD, by using only two CVs. We applied our approach to the study of binding of two known ligands to the hypoxia-inducible factor 2α, where the buried binding cavity makes simulation of the process a challenging task. Our approach allowed identification of the preferred entrance pathway for each ligand, highlighted the features of the bound and intermediate states in the free-energy surface, and provided a binding affinity scale in agreement with experimental data. Therefore, it seems to be a suitable tool for elucidating ligand binding processes of similar complex systems.
Highlights
Understanding the thermodynamic principles behind the mechanism of ligand-protein binding is very important for the development of a successful drug design campaign.Experimental techniques are able to estimate binding thermodynamic and kinetic properties but cannot provide the atomistic insight that forms the basis of rational approaches to drug design
Molecular dynamics (MD) simulations have been used to study processes happening on timescales that range from nanoseconds to milliseconds and beyond,[8] making them attractive for the study of ligand binding
Among the methods for studying ligand binding based on enhanced-sampling molecular dynamics (MD),[11−17] in this work, we focused on steered MD18 (SMD) and metadynamics[19] (MetaD)
Summary
Understanding the thermodynamic principles behind the mechanism of ligand-protein binding is very important for the development of a successful drug design campaign. Experimental techniques are able to estimate binding thermodynamic and kinetic properties but cannot provide the atomistic insight that forms the basis of rational approaches to drug design. In this context, in silico methods are becoming increasingly effective in complementing experiments and providing atomic-level descriptions of ligand binding. Molecular dynamics (MD) simulations have been used to study processes happening on timescales that range from nanoseconds to milliseconds and beyond,[8] making them attractive for the study of ligand binding. Enhanced-sampling techniques are used to speed up the simulation of the binding/unbinding events.[9,10] Most of these techniques make use of a bias potential that forces the system to sample higher-energy regions, speeding up the crossing of energy barriers
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