Estimation of Drug-Target Residence Times by τ-Random Acceleration Molecular Dynamics Simulations

• DOI: 10.1021/acs.jctc.8b00230
• Publication Date: 2018-05-17T02:12:15Z
• Authors: Daria B. Kokh; Marta Amaral; Joerg Bomke; Ulrich Grädler; Djordje Musil; Hans-Peter Buchst...; Matthias K. Dreyer; Matthias Frech; Maryse Lowinski; Francois Vallee; Marc Bianciotto; Alexey Rak; Rebecca C. Wade
• Keywords: 0301 basic medicine; Kinetics; 03 medical and health sciences; Binding Sites; Protein Domains; Drug Discovery; Humans; HSP90 Heat-Shock Proteins; Molecular Dynamics Simulation; Ligands; Protein Binding

Drug-target residence time (τ), one of the main determinants drug efficacy, remains highly challenging to predict computationally and, therefore, is usually not considered in early stages design. Here, we present an efficient computational method, τ-random acceleration molecular dynamics (τRAMD), for ranking candidates by their and obtaining insights into ligand-target dissociation mechanisms. We assessed τRAMD on a data set 70 diverse drug-like ligands N-terminal domain HSP90α, pharmaceutically important target with flexible binding site, computed relative times accuracy about 2.3τ 78% compounds less than 2.0τ within congeneric series. Analysis trajectories reveals features that affect ligand unbinding rates, including transient polar interactions steric hindrance. These results suggest will be widely applicable as aid improving during lead optimization.