Metadynamics-Based Approaches for Modeling the Hypoxia-Inducible Factor 2α Ligand Binding Process

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 hypoxiainducible 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.

PathDetect-SOM: A Neural Network Approach for the Identification of Pathways in Ligand Binding Simulations

“…59−62 In a recent work, we investigated the unbinding of the THS-020 ligand from the HIF-2α PAS-B domain through SMD simulations. 63 50 constant velocity SMD replicas of 25 ns each were used to pull the ligand along the selected CV, namely, the distance between the center of mass of the amino acid atoms lining the cavity and the center of mass of the ligand. The simulations analyzed in this work are those along the preferred entrance to the cavity identified in the previous work (reported as "path 1").…”

“…For the sake of comparison with the free-energy landscape previously identified by the MetaD calculation, 63 we mapped the frames belonging to each free-energy basin on the SOM (Figure S10). We found that conformations belonging to each of these basins generally map in few close neurons, belonging to the same cluster on the map.…”

The Conformational Transition Pathways and Hidden Intermediates in DFG-Flip Process of c-Met Kinase Revealed by Metadynamics Simulations

“…Metadynamics with Path Collective Variables. The PCV metadynamics simulation 48,49 is performed to provide a more accurate estimation of the free energy change with respect to the DFG flipping process along the two discovered translation paths. The parameter settings are the same as described in the Metadynamics Setting subsection.…”

Exploring aspartic protease inhibitor binding to design selective antimalarials

“…Currently, however, there are no experimental methods that would enable such dynamic process characterisation at a molecular scale. Lately, in-silico techniques have emerged as a viable alternative to experimental methods and are becoming increasingly effective in providing atomic-level description of protein-ligand interactions [38][39][40][41][42][43][44][45][46][47] . Dynamic processes like these are typically studied using molecular dynamics (MD) simulations, however, ligand binding/unbinding is a rare event that can be hard to sample within a reasonable time [48,49] .…”

“…This approach leads to a significantly improved exploration of the simulation space, and, once the exploration is complete, an efficient reconstruction of the free energy surface (FES) as a function of the CVs used [57,58] . MetaD in its various modifications has been applied in numerous ligand-protein binding studies [38][39][40][41][42][43][44][45][46][47] . One of these MetaD variations is funnel metadynamics (FMetaD), where a funnel shaped restraint potential is applied to the system to reduce the space explored by the ligand once it is unbound, thus reducing computational cost even more [46,[59][60][61][62][63] .…”