Hit-to-lead and lead optimization binding free energy calculations for G protein-coupled receptors.

Shunzhou Wan,Maciej Malawski,Andrea Townsend-Nicholson,Peter V Coveney,Alexander Heifetz,David W Wright,Fouad S Husseini,Andrew Potterton

doi:10.1098/rsfs.2019.0128

Abstract

We apply the hit-to-lead ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) and lead-optimization TIES (thermodynamic integration with enhanced sampling) methods to compute the binding free energies of a series of ligands at the A1 and A2A adenosine receptors, members of a subclass of the GPCR (G protein-coupled receptor) superfamily. Our predicted binding free energies, calculated using ESMACS, show a good correlation with previously reported experimental values of the ligands studied. Relative binding free energies, calculated using TIES, accurately predict experimentally determined values within a mean absolute error of approximately 1 kcal mol−1. Our methodology may be applied widely within the GPCR superfamily and to other small molecule–receptor protein systems.

Highlights

There is an urgent need for approaches and tools that permit the prediction of rapid, accurate and reliable properties of systems across science as a whole
We have introduced a method, TIES [25], that makes use of ensemble techniques to ensure reproducibility, accuracy and precision in the calculation of relative binding free energies and to control the errors associated with alchemical predictions; it compares favourably with commercially offered solutions based on free energy perturbation (FEP) [8]
Predictions of binding free energy (ΔG), using the ESMACS protocol, were carried out on 14 and 17 ligands of the A1 and A2A receptors, respectively

Summary

Introduction

There is an urgent need for approaches and tools that permit the prediction of rapid, accurate and reliable properties of systems across science as a whole. A pathway can be either a physical binding path or an alchemical path The former is usually defined by a suitable collective variable with which simulation is driven and free energy change is derived. The approach is valuable to explore the pathways of ligand binding, to find the binding site(s), to predict the binding poses and to estimate binding free energies. Such simulations require a timescale of microseconds and need to run for days if not weeks on high performance supercomputers. Alchemical and endpoint approaches, on the other hand, are being increasingly promoted by pharmaceutical companies collectively [7] as they can be implemented in a rapid, accurate and reliable manner [8]

Methods

Results

Conclusion