An Integrated Markov State Model and Path Metadynamics Approach to Characterize Drug Binding Processes

Unveiling the mechanistic features of drug-target binding is of central interest in biophysics and drug discovery. Herein, we address this challenge by combining two major computational approaches, namely, Molecular Dynamics (MD) simulations and Markov State Models (MSM), with a Path Collective Variables (PCVs) description coupled with metadynamics. We apply our methodology to reconstruct the binding process of the antagonist alprenolol to the beta(2)-adrenergic receptor, a well-established pharmaceutical target. The devised protocol allowed us to estimate the binding free energy and identify the minimum free energy path leading to the protein-ligand complex. In summary, we show that MSM and PCVs can be efficiently integrated to shed light upon mechanistic and energetic details underlying complex recognition processes in biological systems.