The process of ligand binding to a biological target can be represented as the equilibrium between the relevant solvated and bound states of the ligand. This which is the basis of structure-based, rigorous methods such as the estimation of relative binding affinities by free energy perturbation (FEP). Despite the growing capacity of computing power and the development of more accurate force fields, a high throughput application of FEP is currently hampered due to the need, in the current schemes, of an expert user definition of the “alchemical” transformations between molecules in the series explored. Here, we present QligFEP, a solution to this problem using an automated workflow for FEP calculations based on a dual topology approach. In this scheme, the starting poses of each of the two ligands, for which the relative affinity is to be calculated, are explicitly present in the MD simulations associated with the (dual topology) FEP transformation, making the perturbation pathway between the two ligands univocal. We show that this generalized method can be applied to accurately estimate solvation free energies for amino acid sidechain mimics, as well as the binding affinity shifts due to the chemical changes typical of lead optimization processes. This is illustrated in a number of protein systems extracted from other FEP studies in the literature: inhibitors of CDK2 kinase and a series of A2A adenosine G protein-coupled receptor antagonists, where the results obtained with QligFEP are in excellent agreement with experimental data. In addition, our protocol allows for scaffold hopping perturbations to identify the binding affinities between different core scaffolds, which we illustrate with a series of Chk1 kinase inhibitors. QligFEP is implemented in the open-source MD package Q, and works with the most common family of force fields: OPLS, CHARMM and AMBER.

Infrared spectroscopy can provide significant insight into the structures and dynamics of molecules of all sizes. The information that is contained in the spectrum is, however, often not easily extracted without the aid of theoretical calculations or simulations. We present here the calculation of the infrared spectra of a database of 366 gas phase compounds with four different force fields (CGenFF, GAFF-BCC, GAFF-ESP, OPLS) using normal mode analysis. Modern force fields increasingly use virtual sites to describe e.g. lone-pair electrons or the sigma-holes on halogen atoms. This requires some adaptation of code to perform normal mode analysis of such compounds, the implementation of which into the GROMACS software is briefly described as well. For the quantitative comparison of the obtained spectra with experimental reference data, we discuss the application of two different statistical correlation coefficients, Pearson and Spearman. The advantages and drawbacks of the different methods of comparison are discussed and we find that both methods of comparison give the same overall picture, showing that present force field methods cannot match the performance of quantum chemical methods for the calculation of infrared spectra.

G protein-coupled receptors (GPCRs) constitute a functionally diverse protein family and are targets for a broad spectrum of pharmaceuticals. Technological progress in X-ray crystallography and cryogenic electron microscopy has enabled extensive, high-resolution structural characterisation of GPCRs in different conformational states. However, as highly dynamic events underlie GPCR signalling, a complete understanding of GPCR functionality requires insights into their conformational dynamics. Here, we present a large dataset of molecular dynamics simulations covering 60% of currently available GPCR structures. Our analysis reveals extensive local “breathing” motions of the receptor on a nano- to microsecond timescale and provides access to numerous previously unexplored receptor conformational states. Furthermore, we reveal that receptor flexibility impacts the shape of allosteric drug binding sites, which frequently adopt partially or completely closed states in the absence of a molecular modulator. We demonstrate that exploring membrane lipid dynamics and their interaction with GPCRs is an efficient approach to expose such hidden allosteric sites and even lateral ligand entrance gateways. The obtained insights and generated dataset on conformations, allosteric sites and lateral entrance gates in GPCRs allows us to better understand the functionality of these receptors and opens new therapeutic avenues for drug-targeting strategies.