SUMMARY The function of G protein-coupled receptors (GPCRs) can be modulated by a number of endogenous allosteric molecules. In this study, we used molecular dynamics, radioligand binding and thermostability experiments to elucidate the role of the recently discovered sodium ion binding site in the allosteric modulation of the human A2A adenosine receptor, conserved among class A GPCRs. While the binding of antagonists and sodium ions to the receptor was non-competitive in nature, the binding of agonists and sodium ions appears to require mutually exclusive conformational states of the receptor. Amiloride analogs can also bind to the sodium binding pocket showing distinct patterns of agonist and antagonist modulation. These findings suggest that physiological concentrations of sodium ions affect functionally relevant conformational states of GPCRs, and can help to design novel synthetic allosteric modulators or bitopic ligands exploiting the sodium ion binding pocket.

We describe the discovery and optimization of 3,4-dihydropyrimidin-2(1H)-ones as a novel family of (nonxanthine) A2B receptor antagonists that exhibit an unusually high selectivity profile. The Biginelli-based hit optimization process enabled a thoughtful exploration of the structure-activity and structure-selectivity relationships for this chemotype, enabling the identification of ligands that combine structural simplicity with excellent hA2B AdoR affinity and remarkable selectivity profiles.

Three novel families of A2B adenosine receptor antagonists were identified in the context of the structural exploration of the 3,4-dihydropyrimidin-2(1H)-one chemotype. The most appealing series contain imidazole, 1,2,4-triazole, or benzimidazole rings fused to the 2,3-positions of the parent diazinone core. The optimization process enabled identification of a highly potent (3.49 nM) A2B ligand that exhibits complete selectivity toward A1, A2A, and A3 receptors. The results of functional cAMP experiments confirmed the antagonistic behavior of representative ligands. The main SAR trends identified within the series were substantiated by a molecular modeling study based on a receptor-driven docking model constructed on the basis of the crystal structure of the human A2A receptor.

Recently we identified a sodium ion binding pocket in a highresolution structure of the human adenosine A 2A receptor. In the present study we explored this binding site through site-directed mutagenesis and molecular dynamics simulations. Amino acids in the pocket were mutated to alanine, and their influence on agonist and antagonist affinity, allosterism by sodium ions and amilorides, and receptor functionality was explored. Mutation of the polar residues in the Na 1 pocket were shown to either abrogate (D52A 2.50 and N284A7.49

G protein-coupled receptors (GPCRs) are involved in numerous physiological processes and are the most frequent targets of approved drugs. The explosion in the number of new 3D molecular structures of GPCRs (3D-GPCRome) during the last decade has greatly advanced the mechanistic understanding and drug design opportunities for this protein family. Molecular dynamics (MD) simulations have become a widely established technique to explore the conformational landscape of proteins at an atomic level. However, the analysis and visualization of MD simulations require efficient storage resources and specialized software. Here we present GPCRmd (http://gpcrmd.org/), an online platform that incorporates web-based visualization capabilities as well as a comprehensive and user-friendly analysis toolbox that allows scientists from different disciplines to visualize, analyse and share GPCR MD data.GPCRmd originates from a community-driven effort to create the first open, interactive, and standardized database of GPCR MD simulations.However, static high-resolution structures provide little information on the intrinsic 71 flexibility of GPCRs, a key aspect to fully understand their function. Important advances 72

A broad range of computational methods exist for the estimation of ligand-protein binding affinities. In this chapter we will provide a guide to the linear interaction energy (LIE) method for binding free energy calculations, focusing on the drug design problem. The method is implemented in combination with molecular dynamics (MD) sampling of relevant conformations of the ligands and complexes under consideration. The detailed procedure for MD sampling is followed by key notes in order to properly analyze such sampling and obtain sufficiently accurate estimations of ligand-binding affinities.

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 A 2A 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.

Site-directed mutagenesis combined with binding affinity measurements is widely used to probe the nature of ligand interactions with GPCRs. Such experiments, as well as structure-activity relationships for series of ligands, are usually interpreted with computationally derived models of ligand binding modes. However, systematic approaches for accurate calculations of the corresponding binding free energies are still lacking. Here, we report a computational strategy to quantitatively predict the effects of alanine scanning and ligand modifications based on molecular dynamics free energy simulations. A smooth stepwise scheme for free energy perturbation calculations is derived and applied to a series of thirteen alanine mutations of the human neuropeptide Y1 receptor and series of eight analogous antagonists. The robustness and accuracy of the method enables univocal interpretation of existing mutagenesis and binding data. We show how these calculations can be used to validate structural models and demonstrate their ability to discriminate against suboptimal ones.