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Developments in G protein-coupled receptor (GPCR) structural biology provide insights into GPCR-ligand binding. Compound 1 (4-(2-benzylphenoxy)piperidine) with high ligand efficiency for the histamine H 1 receptor (H 1 R) was used to design derivatives to investigate the roles of (i) the amine-binding region, (ii) the upper and lower aromatic region, and (iii) binding site solvation. SAR analysis showed that the amine-binding region serves as the primary binding hot spot, preferably binding small tertiary amines. In silico prediction of water network energetics and mutagenesis studies indicated that the displacement of a water molecule from the amine-binding region is most likely responsible for the increased affinity of the N-methylated analog of 1. Deconstruction of 1 showed that the lower aromatic region serves as a secondary binding hot spot. This study demonstrates that an X-ray structure in combination with tool compounds, assessment of water energetics, and mutagenesis studies enables SAR exploration to map GPCR-ligand binding hot spots.

X-ray structures of G protein-coupled receptors (GPCRs) have now been reported for more than 60 ligands and 20 receptors, including examples from GPCR classes A, B, C and F. The new structures show previously unobtainable details of interactions between GPCRs and ligands, including the roles of lipophilic regions and water molecules as key drivers of binding. In addition, the structures have revealed several surprising ligand-binding modes, including sites outside the orthosteric pocket. This new information is dramatically changing the way we approach GPCR drug discovery., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Class B GPCRs of the secretin family are important drug targets in many human diseases including diabetes, neurodegeneration, cardiovascular disease and psychiatric disorders. X-ray crystal structures for the glucagon receptor and corticotropin-releasing factor receptor 1 have now been published. In this review, we analyse the new structures and how they compare with each other and with Class A and F receptors. We also consider the differences in druggability and possible similarity in the activation mechanisms. Finally, we discuss the potential for the design of small-molecule modulators for these important targets in drug discovery. This new structural insight allows, for the first time, structure-based drug design methods to be applied to Class B GPCRs., (© 2014 The British Pharmacological Society.)

The recent availability of X-ray structures for diverse ligand-bound Family A G protein-coupled receptors (GPCRs) in multiple conformations (inactive form with an antagonist/inverse agonist bound and active form with an agonist bound) now enables rational drug design efforts that have historically been applied to soluble enzyme targets. Here, we review properties of these GPCR binding sites, using a unique combination of calculated physicochemical properties and water energetics (GRID, WaterMap and SZMAP) to provide a new perspective and rational assessment of druggability for each GPCR target binding site. Examples are described from several well-studied enzyme systems to support this advanced structure-based approach to assessing druggability and to contrast their properties with those of GPCRs. Changes in receptor conformations between the GPCR inactive and active forms evident from the protein structures are discussed, yielding important pointers for rational drug design of antagonists and agonists and a better understanding of GPCR activation., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

A new approach to generating information on ligand receptor interactions within the binding pocket of G protein-coupled receptors has been developed, called Biophysical Mapping (BPM). Starting from a stabilized receptor (StaR), minimally engineered for thermostability, additional single mutations are then added at positions that could be involved in small molecule interactions. The StaR and a panel of binding site mutants are captured onto Biacore chips to enable characterization of the binding of small molecule ligands using surface plasmon resonance (SPR) measurement. A matrix of binding data for a set of ligands versus each active site mutation is then generated, providing specific affinity and kinetic information (K(D), k(on), and k(off)) of receptor-ligand interactions. This data set, in combination with molecular modeling and docking, is used to map the small molecule binding site for each class of compounds. Taken together, the many constraints provided by these data identify key protein-ligand interactions and allow the shape of the site to be refined to produce a high quality three-dimensional picture of ligand binding, thereby facilitating structure based drug design. Results of biophysical mapping of the adenosine A(2A) receptor are presented.

Compound subsets, which may be screened where it is not feasible or desirable to screen all available compounds, may be designed using rational or random selection. Literature on the relative performance of random versus rational selection reports conflicting observations, possibly because some random subsets might be more representative than others and perform better than subsets designed by rational means, or vice versa. In order to address this likelihood, we simulated a large number of rationally designed subsets for evaluation against an equally large number of randomly generated subsets. We found that our rationally designed subsets give higher mean hit rates compared to those of the random ones. We also compared subsets comprising random plates with subsets of random compounds and found that, while the mean hit rate of both is the same, the former demonstrates more variation in the hit rate. The choice of compound file, rational subset method, and ratio of the subset size to the compound file size are key factors in the relative performance of random and rational selection, and statistical simulation is a viable way to identify the selection approach appropriate for a subset.

Structure-based drug design is now used widely in modern medicinal chemistry. The application of structural biology to medicinal chemistry has heralded the "rational drug design" vision of discovering exquisitely selective ligands. However, recent advances in post-genomic biology are indicating that polypharmacology may be a necessary trait for the efficacy of many drugs, therefore questioning the "one drug, one target" assumption of current rational drug design. By combining advances in chemoinformatics and structural biology, it might be possible to rationally design the next generation of promiscuous drugs with polypharmacology.

The three-dimensional (3D) binary pharmacophore fingerprints find wide application as descriptors in applications ranging from virtual screening through library design. While the 3D content they capture is an intuitively attractive feature of such measures, maximizing their signal to noise ratio has proven to be a tricky balancing act. This issue surfaces primarily due to the potential of such fingerprints to create an explosion of pharmacophores as molecular complexity and flexibility increases. In this article, we describe a modification to the fingerprint generation process that normalizes pharmacophore occurrence frequency by the conformational ensemble size used to derive the descriptor. By including pharmacophore frequency and conformational count, the importance of a given pharmacophore is weighted by the probability of its existence within a given conformational ensemble, rather than treating each pharmacophore equally. In addition, a number of filters have been added to permit the removal of unwanted pharmacophores from the descriptor set. These filters are based on pharmacophore composition (e.g. permutations made up primarily of lipophilic and/or aromatic centers), and size (pharmacophore perimeter length relative to the largest perimeter length found in the molecule). The highly uneven nature of pharmacophore distributions across the conformational ensemble used to generate them is highlighted, as are enrichment comparisons with their binary fingerprint peers. In addition, the limitations in descriptor comparison validation are high-lighted as an illustration of the need for more extensive validation experiments.

An important element of any structure-based virtual screening (SVS) technique is the method used to orient the ligands in the target active site. This has been a somewhat overlooked issue in recent SVS validation studies, with the assumption being made that the performance of an algorithm for a given set of orientation sampling settings will be representative for the general behavior of said technique. Here, we analyze five different SVS targets using a variety of sampling paradigms within the DOCK, GOLD and PROMETHEUS programs over a data set of approximately 10,000 noise compounds, combined with data sets containing multiple active compounds. These sets have been broken down by chemotype, with chemotype hit rate used to provide a measure of enrichment with a potentially improved relevance to real world SVS experiments. The variability in enrichment results produced by different sampling paradigms is illustrated, as is the utility of using pharmacophores to constrain sampling to regions that reflect known structural biology. The difference in results when comparing chemotype with compound hit rates is also highlighted.

Virtual screening was performed against experimentallyenabledhomology models of the adenosine A2Areceptor, identifyinga diverse range of ligand efficient antagonists (hit rate 9%). Byuse of ligand docking and Biophysical Mapping (BPM), hits 1and 5were optimized to potent and selective lead molecules(11–13from 5, pKI= 7.5–8.5, 13- to >100-fold selectiveversus adenosine A1; 14–16from 1, pKI= 7.9–9.0,19- to 59-fold selective). [ABSTRACT FROM AUTHOR]

Potent, ligand efficient, selective, and orally efficacious1,2,4-triazinederivatives have been identified using structure based drug designapproaches as antagonists of the adenosine A2Areceptor.The X-ray crystal structures of compounds 4eand 4gbound to the GPCR illustrate that the molecules bind deeplyinside the orthosteric binding cavity. In vivo pharmacokinetic andefficacy data for compound 4kare presented, demonstratingthe potential of this series of compounds for the treatment of Parkinson’sdisease. [ABSTRACT FROM AUTHOR]

A series of novel allosteric antagonists of the GLP-1 receptor (GLP-1R), exemplified by HTL26119, are described. A series of novel allosteric antagonists of the GLP-1 receptor (GLP-1R), exemplified by HTL26119, are described. SBDD approaches were employed to identify HTL26119, exploiting structural understanding of the allosteric binding site of the closely related Glucagon receptor (GCGR) (Jazayeri et al., 2016) and the homology relationships between GCGR and GLP-1R. The region around residue C3476.36b of the GLP-1R receptor represents a key difference from GCGR and was targeted for selectivity for GLP-1R. [ABSTRACT FROM AUTHOR]