A Supervised Molecular Dynamics Approach to Unbiased Ligand-Protein Unbinding

The recent paradigm shift toward the use of the kinetics parameters in place of thermodynamic constants is leading the computational chemistry community to develop methods for studying the mechanisms of drug binding and unbinding. From this standpoint, molecular dynamics (MD) plays an important role in delivering insight at the molecular scale. However, a known limitation of MD is that the time scales are usually far from those involved in ligand-receptor unbinding events. Here, we show that the algorithm behind supervised MD (SuMD) can simulate the dissociation mechanism of druglike small molecules while avoiding the input of any energy bias to facilitate the transition. SuMD was tested on seven different intermolecular complexes, covering four G protein-coupled receptors: the A2A and A1 adenosine receptors, the orexin 2 and the muscarinic 2 receptors, and the soluble globular enzyme epoxide hydrolase. SuMD well-described the multistep nature of ligand-receptor dissociation, rationalized previous experimental data and produced valuable working hypotheses for structure-kinetics relationships.