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.2015 Aug;388(8):883-903.
doi: 10.1007/s00210-015-1111-8. Epub 2015 Mar 14.

GPCR structure, function, drug discovery and crystallography: report from Academia-Industry International Conference (UK Royal Society) Chicheley Hall, 1-2 September 2014

Affiliations

GPCR structure, function, drug discovery and crystallography: report from Academia-Industry International Conference (UK Royal Society) Chicheley Hall, 1-2 September 2014

Alexander Heifetz et al. Naunyn Schmiedebergs Arch Pharmacol.2015 Aug.

Abstract

G-protein coupled receptors (GPCRs) are the targets of over half of all prescribed drugs today. The UniProt database has records for about 800 proteins classified as GPCRs, but drugs have only been developed against 50 of these. Thus, there is huge potential in terms of the number of targets for new therapies to be designed. Several breakthroughs in GPCRs biased pharmacology, structural biology, modelling and scoring have resulted in a resurgence of interest in GPCRs as drug targets. Therefore, an international conference, sponsored by the Royal Society, with world-renowned researchers from industry and academia was recently held to discuss recent progress and highlight key areas of future research needed to accelerate GPCR drug discovery. Several key points emerged. Firstly, structures for all three major classes of GPCRs have now been solved and there is increasing coverage across the GPCR phylogenetic tree. This is likely to be substantially enhanced with data from x-ray free electron sources as they move beyond proof of concept. Secondly, the concept of biased signalling or functional selectivity is likely to be prevalent in many GPCRs, and this presents exciting new opportunities for selectivity and the control of side effects, especially when combined with increasing data regarding allosteric modulation. Thirdly, there will almost certainly be some GPCRs that will remain difficult targets because they exhibit complex ligand dependencies and have many metastable states rendering them difficult to resolve by crystallographic methods. Subtle effects within the packing of the transmembrane helices are likely to mask and contribute to this aspect, which may play a role in species dependent behaviour. This is particularly important because it has ramifications for how we interpret pre-clinical data. In summary, collaborative efforts between industry and academia have delivered significant progress in terms of structure and understanding of GPCRs and will be essential for resolving problems associated with the more difficult targets in the future.

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Figures

Fig. 1
Fig. 1
Attendees of the conference (listed from left to right): front row—Isabel Moraes, Fiona Marshall, Gebhard Schertler and Patrick Sexton; second row—Shweta Singh, Irina Tikhonova, Tom Ceska, Roland Seifert, Simon Hodgson, Daniel Fourmy and Alexander Heifetz; back row—Ian Storer, Mike Bodkin, Vadim Cherezov, Christofer Tautermann, Christopher Tate, Vsevolod Gurevich and Peter Hunt. Chicheley Hall itself can be seen in the background (the photograph was taken by Richard Law)
Fig. 2
Fig. 2
Crystal structures of β1AR bound to novel chemotypes developed by fragment screening and hit optimisation. Structures of the ligand binding pocket are depicted with only portions of selected transmembrane helices (H3, H5, H6 and H7) shown with the side chains (green) and ligands (yellow) depicted in sticks:a cyanopindolol, PDB code 2VT4;b compound 19, PDB code 3ZPQ;c compound 20, PDB code 3ZPR—figure adapted from the publication (Christopher et al. 2013)
Fig. 3
Fig. 3
Schematic summary illustrating serial femtosecond crystallography of GPCRs with using lipidic cubic phase for microcrystal growth and delivery
Fig. 4
Fig. 4
a Multidimensional analysis of the β2AR in native and recombinant systems. Study design. The concept of functional selectivity requires that a given GPCR is analysed in multiple different assays. GPCR-Ga fusion proteins ensure a defined 1:1 stoichiometry of receptor and G-protein and allow analysis of GDP/GTP turnover, measured in the GTPase activity assay, and effector system activation, measured in the adenylyl cyclase (AC) assay, with high sensitivity. The measurement of β-arrestin recruitment is accomplished in HEK cells stably transfected with fusion proteins of the β2AR linked to a luciferase fragment and β-arrestin linked to the complementary luciferase fragment. Upon binding of an agonist, β-arrestin binds to the GPCR, and luciferase activity is reconstituted. Human neutrophilic granulocytes constitute a physiologically relevant model system for the β2AR. In these cells, the β2AR couples to AC (isoform 9), resulting in an increase in the second messenger cAMP. The β2AR inhibits formyl peptide receptor (FPR)-mediated activation of the neutrophilic NADPH oxidase (NOX) that generates reactive oxygen species (ROS). FPR-mediated NOX activation is inhibited by the β2AR. It is generally assumed that this inhibition is mediated via cAMP, but an increasing number of studies indicate that the inhibition is actually cAMP independent.b Functional and genetic analysis of β2AR polymorphisms in 60 healthy volunteers. Study design. A major goal of current pharmacological research is the development of individualised pharmacotherapy that takes into account individual genetic polymorphisms. At the level of GPCRs, very little research has been performed in this field so far. Therefore, as a model receptor, the β2AR was analysed because for this GPCR several polymorphisms are already known, but assignment of specific polymorphisms to defined disease entities is controversial. After obtaining consent from volunteers and completing a questionnaire, a small sample of blood (4–8 ml) was drawn from healthy male and female subjects. A fraction of the blood was used to sequence the β2AR gene to identify known (and unknown) β2AR polymorphisms. The remainder of the blood was used to isolate human neutrophils and assess the pharmacological profile of the β2AR with several standard ligands according to the signalling paradigm shown in (a). Ligands were characterised with regard to potency and efficacy. The data sets have now been completed. The pharmacological profile of each individual was assessed several times. Currently, data are analysed in multiple ways. Specifically, the impact of specific β2AR polymorphisms on the pharmacological profile is assessed. Additionally, the impact of sex, age, smoking and allergy history on β2AR pharmacology is evaluated. The study fills a gap in the field because it provides data on the pharmacological properties of GPCR polymorphisms in a physiologically relevant context. The results of the study will be submitted for publication to a peer-reviewed journal in spring 2015
Fig. 5
Fig. 5
Schematic representation of the CCK2R which can adopt two distinct conformational states upon CCK activation. The CCK2RG state couples to phospholipase-C activation and the CCK2Rβ state recruits β-arrestins. This figure also shows that GV150013X, a competitive antagonist on CCK2RG, is inefficient to inhibit recruitment of β-arrestins by CCK2Rβ because of steric hindrance at the orthosteric binding site
Fig. 6
Fig. 6
Crystal structure of the class C mGlu5-mavoglurant receptor (Bennett et al. 2014) complex with CP-376395 from the class B receptor CRF1 and overlays of a selection of ligands from class A receptor structures present in the PDB. The observed ligand binding positions demonstrate the spectrum of binding modes across GPCR classes ranging from extracellular orthosteric to deeper intracellular allosteric sites
Fig. 7
Fig. 7
Crystal structure of H1R bound to highly selective second- and third-generation antihistamines: Cetirizine (left) and Fexofenadine (right)
Fig. 8
Fig. 8
Modelling of binding and activation of 5-HT2C (Storer et al. 2014) receptor by pyrimido[4,5-d]azepines
Fig. 9
Fig. 9
Homology model of the CCR4 receptor, with putative multiple binding sites for peptide agonists and small molecule allosteric ligands
Fig. 10
Fig. 10
One of the low-energy conformations of the extended dual H1H3R ligands docked into the homology model of H1
Fig. 11
Fig. 11
Structure-based computational protocol for selective polypharmacology—figure adapted from a recent publication (Selvam et al. 2013)
Fig. 12
Fig. 12
a HGMP workflow andb a model of 5-HT2C (inred) produced by the HGMP workflow. The ligand is shown ingreen and the whole complex (Tye et al. 2011) is embedded in a membrane (grey). The water molecules and ions are omitted from the figure for clarity
Fig. 13
Fig. 13
Left—comparison of the water (oxygen) densities in the tiotropium and the methyl ligand. The water densities of the tiotropium MD are displayed asblue solid surfaces; densities in methyl-ligand MDs are shown asgreen mesh. The significant extra density in the methyl-ligand MD is marked by agreen ellipse. Top right—chemical structure of tiotropium (R=OH) and the methyl-ligand (R=CH3).Middle right—binding and dissociation constants of the ligands at the hM3R.Bottom right—a snapshot of the MD simulation with the methyl ligand, where water inserts into the ligand-protein hydrogen bond (in contrast to the tiotropium MD, where such water-mediated hydrogen bonds are never observed)—figure adapted from the recent publication (Tautermann et al. 2015)
Fig. 14
Fig. 14
a The proposed binding modes for the quinoxaline (yellow carbons) and urea (cyan carbons) series with the influential mutated residues in CXCR2 shown in CPK. The consistent influence of K320 locates the acidic functionality in the antagonists, yet the varying effects of D143 on representatives of these two series suggest that the hydrophobic groups are located differently.b Overlay of the CXCR1 NMR structures from the PDB (code 2LNL) N320 is shown ingreen CPK and influential mutants from our experiments inpurple CPK. The protein backbones, in ribbon representation, are coloured from N (blue) to C (red) termini
Fig. 15
Fig. 15
Automated multi-objective compound design using reaction vectors (26K Reaction Db and 93K Reagents) starting from piperidine and using four objectives: similarity to haloperidol and Ziprasidone pharmacophores, Dopamine D2, α1B Adrenergic and Histamine QSAR models. The tri-cyclics generated appeared similar to known anti-pyschotics, Chlorpromazine and Fluphenazine
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