Metadynamics-Based Approaches for Modeling the Hypoxia-Inducible Factor 2α Ligand Binding Process
Copyright policy )Metadynamics-Based Approaches for Modeling the Hypoxia-Inducible Factor 2α Ligand Binding Process
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.
Models, Chemical, Molecular dynamics, Steered MD, Metadynamics, Path Collective Variables, free-energy surface, ligand binding, ligand unbinding, HIF-2α, Basic Helix-Loop-Helix Transcription Factors, Molecular Conformation, Cluster Analysis, Thermodynamics, Molecular Dynamics Simulation, Ligands, Protein Binding
Models, Chemical, Molecular dynamics, Steered MD, Metadynamics, Path Collective Variables, free-energy surface, ligand binding, ligand unbinding, HIF-2α, Basic Helix-Loop-Helix Transcription Factors, Molecular Conformation, Cluster Analysis, Thermodynamics, Molecular Dynamics Simulation, Ligands, Protein Binding
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).20 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
Citations provided by BIP!Popularity provided by BIP!