Prediction of Protein–Ligand Binding Poses via a Combination of Induced Fit Docking and Metadynamics Simulations

Abstract

Ligand docking is a widely used tool for lead discovery and binding mode prediction based drug discovery. The greatest challenges in docking occur when the receptor significantly reorganizes upon small molecule binding, thereby requiring an induced fit docking (IFD) approach in which the receptor is allowed to move in order to bind to the ligand optimally. IFD methods have had some success but suffer from a lack of reliability. Complementing IFD with all-atom molecular dynamics (MD) is a straightforward solution in principle but not in practice due to the severe time scale limitations of MD. Here we introduce a metadynamics plus IFD strategy for accurate and reliable prediction of the structures of protein-ligand complexes at a practically useful computational cost. Our strategy allows treating this problem in full atomistic detail and in a computationally efficient manner and enhances the predictive power of IFD methods. We significantly increase the accuracy of the underlying IFD protocol across a large data set comprising 42 different ligand-receptor systems. We expect this approach to be of significant value in computationally driven drug design.

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes via Nonequilibrium Candidate Monte Carlo

“…However, in practice sampling transitions between binding modes using MD is typically inefficient because of large energy barriers (and hence slow timescales) separating binding modes. 1,20,31,32 …”

Resolving the problem of trapped water in binding cavities: prediction of host–guest binding free energies in the SAMPL5 challenge by funnel metadynamics

“…It is especially well suited for systems in which there are multiple binding poses [7, 8] or in which the binding pose is not known a priori [9]. In order to be a rigorous method, metadynamics requires a set of predefined collective variables (CVs) representing all the slow degrees of freedom of the system, but the method has also been used in more approximate ways to improve docking poses [10, 11]. In accurate metadynamics studies of protein–ligand binding, the collective variables typically include the protein–ligand distance, some descriptor of the orientation of the ligand relative to the protein, and sometimes an additional variable describing a conformational change in the protein [8].…”

Prediction of N-Methyl-D-Aspartate Receptor GluN1-Ligand Binding Affinity by a Novel SVM-Pose/SVM-Score Combinatorial Ensemble Docking Scheme

“…As such, the plastic nature of GluN1 cannot be fully addressed by a single GluN1 structure to accurately model the protein-ligand interaction except molecular dynamics that, in turn, will be less practically useful due to its low computational throughput33. Conversely, ensemble docking, which is carried out by placing a ligand into several target structures and selecting the best fit pose by score or root mean square deviation (RMSD) values if applicable34, seems to be a plausible alternative since it has been demonstrated that ensemble docking performs better than docking with a single protein structure35.…”