How Ligands Illuminate GPCR Molecular Pharmacology

G protein-coupled receptors (GPCRs), which are modulated by a variety of endogenous and synthetic ligands, represent the largest family of druggable targets in the human genome. Recent structural and molecular studies have both transformed and expanded classical concepts of receptor pharmacology and have begun to illuminate the distinct mechanisms by which structurally, chemically, and functionally diverse ligands modulate GPCR function. These molecular insights into ligand engagement and action have enabled new computational methods and accelerated the discovery of novel ligands and tool compounds, especially for understudied and orphan GPCRs. These advances promise to streamline the development of GPCR-targeted medications. © 2017 Elsevier Inc

Synthesis and Characterisation of Dual CCR7/CXCR4 Antagonists

Metastasis is a major cause of death in cancer patients but currently there are no drugs available for its treatment. Hence there is an urgent clinical need for identifying and developing anti-metastatic drugs. The activation of CC chemokine receptor 7 (CCR7) and C-X-C chemokine receptor type 4 (CXCR4) plays an important role in lymph node metastasis in a variety of cancers. Indeed, in patients with tumours which are positive for CCR7 and/or CXCR4 expression, prognosis and survival are poorer than those whose tumours are negative for these receptors. CCR7 and/or CXCR4 activation, in addition to being involved in inducing invasive phenotypes in cancer cells, promotes tumour cell growth and survival. Our group has previously identified a series of sulfonamides as CCR7 antagonists. This project aims to extend on those studies and to develop a dual CCR7 and CXCR4 antagonist to reduce metastasis in cancer. Novel potent biaryl sulfonamide CCR7 antagonists were synthesised and assessed by calcium flux assay. Several potential dual CCR7 and CXCR4 biaryl sulfonamide antagonists have been synthesised, these are hybrid compounds incorporating features from CCR7 antagonists of this project, and from known sulfonamide CXCR4 antagonists. The most potent of such compound was able to inhibit CCR7 activation in calcium flux assay (95% inhibition at 1 µM), however, the relative potency of these compounds as CXCR4 antagonists was low. Molecular docking was used to investigate the binding mode of the synthesised compounds in CCR7 and CXCR4. The generated docking poses were able to rationalise some of the calcium flux assay results

Structure-based exploration and pharmacological evaluation of N-substituted piperidin-4-yl-methanamine CXCR4 chemokine receptor antagonists

Using the available structural information of the chemokine receptor CXCR4, we present hit finding and hit exploration studies that make use of virtual fragment screening, design, synthesis and structure-activity relationship (SAR) studies. Fragment 2 was identified as virtual screening hit and used as a starting point for the exploration of 31 N-substituted piperidin-4-yl-methanamine derivatives to investigate and improve the interactions with the CXCR4 binding site. Additionally, subtle structural ligand changes lead to distinct interactions with CXCR4 resulting in a full to partial displacement of CXCL12 binding and competitive and/or non-competitive antagonism. Three-dimensional quantitative structure-activity relationship (3D-QSAR) and binding model studies were used to identify important hydrophobic interactions that determine binding affinity and indicate key ligand-receptor interactions

Chemokine Receptors CXCR3 and CXCR7: Allosteric Ligand Binding, Biased Signaling, and Receptor Regulation

Leurs, R. [Promotor]Smit, M.J. [Promotor

Computational assessment of pharmacological similarity between Class A GPCRs

Though G protein-coupled receptors (GPCRs) are the target of 30% of the clinically-approved drug market, these drugs target just ~25% of the non-olfactory GPCR superfamily (108). Almost half of the remaining untargeted GPCRs are “orphans” with no known endogenous ligand(s), representing a rich and underutilised source of pharmacotherapeutic targets. To facilitate ligand identification, the Kufareva and Smith research groups previously developed GPCR-Contact-Informed Neighbouring Pocket (GPCR-CoINPocket). It is a metric of pharmacological similarity between Class A GPCRs, incorporating sequence similarity and structurally-observed ligand interaction patterns derived from liganded GPCRs in the Pocketome, an annotated encyclopaedia of liganded protein structures. In this thesis, I re-evaluate previous GPCR-CoINPocket predictions and develop an upgraded and improved version of GPCR-CoINPocket. In Chapter 2, I re-investigated the pharmacology of the orphan receptor, GPR37L1. In the previous iteration of GPCR-CoINPocket, GPR37L1 was used as a prototypical orphan for the prospective validation of the metric. However, previous retractions from our lab meant that clear evidence was once again lacking for the signalling pathways and activity of GPCR-CoINPocket-predicted ligands at GPR37L1. In the present study, I found no evidence of ligand-dependent or constitutive Gαi- or GαS-directed agonism in HEK293 or CHO-K1 cells, leaving the pharmacological toolbox of GPR37L1 once again empty. In Chapter 3, I developed a method for quantifying the similarity of ligand sets and generated a benchmark of pharmacologically similar and dissimilar Class A GPCRs for the assessment of updates and upgrades to GPCR-CoINPocket. My method formalises the intuitive concept of pharmacological similarity by deriving receptor similarity scores from high quality ChEMBL-annotated receptor:ligand binding data via an approximation of the number of shared unique chemotypes. In Chapter 4, I used my benchmarking set to evaluate four areas of potential improvement for GPCR-CoINPocket: the orthosteric contact fingerprints defining receptor pockets, the inclusion (or exclusion) of ECL2 fingerprints, the logic of score aggregation, and the amino acid similarity matrix used to score sequence similarity. The evaluation of these upgrades led to the development of GPCR-CoINPocket v2, which outperformed transmembrane similarity and GPCRdb-defined orthosteric pocket similarity in the discrimination of pharmacologically similar from dissimilar Class A GPCRs. The primary upgrade responsible for this improvement was the replacement of the Gonnet amino acid similarity matrix with a chemically-informed matrix developed in this thesis that directly (and solely) reflected structurally-observed ligand binding pattern similarities between amino acids. In Chapter 5, I prospectively validated GPCR-CoINPocket v2 using the human β2-adrenoceptor as the prototypical target. I identified 4 compounds typically binding to the OT, MCH1, and SST2 receptors with nanomolar affinity/potency that unexpectedly had affinity for the β2-adrenoceptor in competitive radioligand binding assays. The key output of this thesis is GPCR-CoINPocket v2, a direct upgrade to the original orthosteric pocket-based metric of pharmacological similarity between Class A GPCRs. I have validated it both retrospectively and prospectively, illustrating one use of the method that allows it to complement virtual and physical high-throughput screening technologies. Further applications and limitations are discussed in Chapter 6, but it is hoped that the methods and tools I have developed here can be easily adopted and aid in elucidating orphan GPCR physiology and pharmacology

Discovery of new GPCR ligands to illuminate new biology

Although a plurality of drugs target G-protein-coupled receptors (GPCRs), most have emerged from classical medicinal chemistry and pharmacology programs and resemble one another structurally and functionally. Though effective, these drugs are often promiscuous. With the realization that GPCRs signal via multiple pathways, and with the emergence of crystal structures for this family of proteins, there is an opportunity to target GPCRs with new chemotypes and confer new signaling modalities. We consider structure-based and physical screening methods that have led to the discovery of new reagents, focusing particularly on the former. We illustrate their use against previously untargeted or orphan GPCRs, against allosteric sites, and against classical orthosteric sites that selectively activate one downstream pathway over others. The ligands that emerge are often chemically novel, which can lead to new biological effects. © 2017 Nature America, Inc., part of Springer Nature. All rights

Advances in therapeutic peptides targeting G protein-coupled receptors.

Dysregulation of peptide-activated pathways causes a range of diseases, fostering the discovery and clinical development of peptide drugs. Many endogenous peptides activate G protein-coupled receptors (GPCRs) - nearly 50 GPCR peptide drugs have been approved to date, most of them for metabolic disease or oncology, and more than 10 potentially first-in-class peptide therapeutics are in the pipeline. The majority of existing peptide therapeutics are agonists, which reflects the currently dominant strategy of modifying the endogenous peptide sequence of ligands for peptide-binding GPCRs. Increasingly, novel strategies are being employed to develop both agonists and antagonists, to both introduce chemical novelty and improve drug-like properties. Pharmacodynamic improvements are evolving to allow biasing ligands to activate specific downstream signalling pathways, in order to optimize efficacy and reduce side effects. In pharmacokinetics, modifications that increase plasma half-life have been revolutionary. Here, we discuss the current status of the peptide drugs targeting GPCRs, with a focus on evolving strategies to improve pharmacokinetic and pharmacodynamic properties.Wellcome Trust [WT107715/Z/15/Z], Cambridge Biomedical Research Centre Biomedical Resources Gran

Benchmarking and Developing Novel Methods for G Protein-coupled Receptor Ligand Discovery

G protein-coupled receptors (GPCR) are integral membrane proteins mediating responses from extracellular effectors that regulate a diverse set of physiological functions. Consequently, GPCR are the targets of ~34% of current FDA-approved drugs.3 Although it is clear that GPCR are therapeutically significant, discovery of novel drugs for these receptors is often impeded by a lack of known ligands and/or experimentally determined structures for potential drug targets. However, computational techniques have provided paths to overcome these obstacles. As such, this work discusses the development and application of novel computational methods and workflows for GPCR ligand discovery. Chapter 1 provides an overview of current obstacles faced in GPCR ligand discovery and defines ligand- and structure-based computational methods of overcoming these obstacles. Furthermore, chapter 1 outlines methods of hit list generation and refinement and provides a GPCR ligand discovery workflow incorporating computational techniques. In chapter 2, a workflow for modeling GPCR structure incorporating template selection via local sequence similarity and refinement of the structurally variable extracellular loop 2 (ECL2) region is benchmarked. Overall, findings in chapter 2 support the use of local template homology modeling in combination with de novo ECL2 modeling in the presence of a ligand from the template crystal structure to generate GPCR models intended to study ligand binding interactions. Chapter 3 details a method of generating structure-based pharmacophore models via the random selection of functional group fragments placed with Multiple Copy Simultaneous Search (MCSS) that is benchmarked in the context of 8 GPCR targets. When pharmacophore model performance was assessed with enrichment factor (EF) and goodness-of-hit (GH) scoring metrics, pharmacophore models possessing the theoretical maximum EF value were produced in both resolved structures (8 of 8 cases) and homology models (7 of 8 cases). Lastly, chapter 4 details a method of structure-based pharmacophore model generation using MCSS that is applicable to targets with no known ligands. Additionally, a method of pharmacophore model selection via machine learning is discussed. Overall, the work in chapter 4 led to the development of pharmacophore models exhibiting high EF values that were able to be accurately selected with machine learning classifiers

Biased and g protein-independent signaling of chemokine receptors

Biased signaling or functional selectivity occurs when a 7TM receptor preferentially activates one of several available pathways. It can be divided into three distinct forms: ligand bias, receptor bias, and tissue or cell bias, where it is mediated by different ligands (on the same receptor), different receptors (with the same ligand) or different tissues or cells (for the same ligand-receptor pair). Most often biased signaling is differentiated into G protein-dependent and β-arrestin-dependent signaling. Yet, it may also cover signaling differences within these groups. Moreover, it may not be absolute, i.e. full versus no activation. Here we discuss biased signaling in the chemokine system, including the structural basis for biased signaling in chemokine receptors, as well as in class A 7TM receptors in general. This includes overall helical movements and the contributions of micro-switches based on recently published 7TM crystals and molecular dynamics studies. All three forms of biased signaling are abundant in the chemokine system. This challenges our understanding of classic redundancy inevitably ascribed to this system, where multiple chemokines bind to the same receptor and where a single chemokine may bind to several receptors – in both cases with the same functional outcome. The ubiquitous biased signaling confer a hitherto unknown specificity to the chemokine system with a complex interaction pattern that is better described as promiscuous with context-defined roles and different functional outcomes in a ligand-, receptor- or cell/tissue-defined manner. As the low number of successful drug development plans implies, there are great difficulties in targeting chemokine receptors; in particular with regard to receptor antagonists as anti-inflammatory drugs. Un-defined and putative non-selective targeting of the complete cellular signaling system could be the underlying cause of lack of success. Therefore, biased ligands could be the solution