Molecular Dynamics Simulations and Kinetic Measurements to Estimate and Predict Protein–Ligand Residence Times

Abstract

Ligand-target residence time is emerging as a key drug discovery parameter because it can reliably predict drug efficacy in vivo. Experimental approaches to binding and unbinding kinetics are nowadays available, but we still lack reliable computational tools for predicting kinetics and residence time. Most attempts have been based on brute-force molecular dynamics (MD) simulations, which are CPU-demanding and not yet particularly accurate. We recently reported a new scaled-MD-based protocol, which showed potential for residence time prediction in drug discovery. Here, we further challenged our procedure's predictive ability by applying our methodology to a series of glucokinase activators that could be useful for treating type 2 diabetes mellitus. We combined scaled MD with experimental kinetics measurements and X-ray crystallography, promptly checking the protocol's reliability by directly comparing computational predictions and experimental measures. The good agreement highlights the potential of our scaled-MD-based approach as an innovative method for computationally estimating and predicting drug residence times.

Impact of protein–ligand solvation and desolvation on transition state thermodynamic properties of adenosine A2A ligand binding kinetics

“…This is the case for ligand unbinding, where the time scale reaches up to several hours or days, and is thus too computationally expensive for a single unbiased MD simulation starting from the ligand bound conformation; nowadays it is possible to reach the millisecond time scale on specialized machines (Shaw et al 2009). Kinetic descriptions of binding and unbinding have been addressed by several diferent approaches, including, but not limited to, methods introducing an energy bias as a scalar in the potential energy equation of the system (Fukunishi et al 2002;Hamelberg et al 2004;Luitz and Zacharias 2014;Mollica et al 2015Mollica et al , 2016Pierce et al 2012;Sinko et al 2013;Wang et al 2013) and methods requiring a preliminary deinition of a set of collective variables (CVs) to be biased during the simulation (Barducci et al 2011;Bui et al 2003;Gervasio et al 2005;Guo et al 2016;Isralewitz et al 2001;Laio and Parrinello 2002;Laio et al 2005;Li 2005;Patel et al 2014;Torrie and Valleau 1977;Yu et al 2016). CVs can be for example intermolecular or interatomic distances, angles formed by atoms or group of atoms, coordination numbers, degree of solvation and they are used in order to drive the binding/unbinding transition and to map the corresponding energy proile.…”

Ligand binding free energy and kinetics calculation in 2020

“…In addition, the preservation of the protein tridimensional structure is necessary through the application of harmonic constraints especially in the region external to the binding site. Scaled MD has been employed with success to rank ligands of HSP90, Grp78, adenosine A2A GPCR and glucokinase, based on their residence time …”

Kinetics for Drug Discovery: an industry-driven effort to target drug residence time

“…Results indicate that the ligand shape might influence induced fit and thereby have an impact on the residence time. Additionally, specific residues influencing residence time were identified [91].…”