A new crystal structure fragment-based pharmacophore method for G protein-coupled receptors (original) (raw)
Related papers
2009
G protein-coupled receptors (GPCRs) constitute a very large family of heptahelical, integral membrane proteins that mediate a wide variety of physiological processes, ranging from the transmission of the light and odorant signals to the mediation of neurotransmission and hormonal actions. GPCRs are dysfunctional or deregulated in several human diseases and are estimated to be the target of more than 40% of drugs used in clinical medicine today. The crystal structures of rhodopsin and the recent published crystal structures of beta-adrenergic receptors and human A2A Adrenergic Receptor provide the information of the three-dimensional structure of GPCRs, which supports homology modeling studies and structure-based drug-design approaches. Rhodopsin-based homology modeling has represented for many years a widely used approach to built GPCR three-dimensional models. Structural models can be used to describe the interatomic interactions between ligand and receptor and how the binding info...
Identification of Histamine H3 Receptor Ligands Using a New Crystal Structure Fragment-based Method
Scientific Reports, 2017
Virtual screening offers an efficient alternative to high-throughput screening in the identification of pharmacological tools and lead compounds. Virtual screening is typically based on the matching of target structures or ligand pharmacophores to commercial or in-house compound catalogues. This study provides the first proof-of-concept for our recently reported method where pharmacophores are instead constructed based on the inference of residue-ligand fragments from crystal structures. We demonstrate its unique utility for G protein-coupled receptors, which represent the largest families of human membrane proteins and drug targets. We identified five neutral antagonists and one inverse agonist for the histamine H 3 receptor with potencies of 0.7-8.5 μM in a recombinant receptor cell-based inositol phosphate accumulation assay and validated their activity using a radioligand competition binding assay. H 3 receptor antagonism is of large therapeutic value and our ligands could serve as starting points for further lead optimisation. The six ligands exhibit four chemical scaffolds, whereof three have high novelty in comparison to the known H 3 receptor ligands in the ChEMBL database. The complete pharmacophore fragment library is freely available through the GPCR database, GPCRdb, allowing the successful application herein to be repeated for most of the 285 class A GPCR targets. The method could also easily be adapted to other protein families. G protein-coupled receptors (GPCRs) are membrane proteins activated by many diverse ligands including photons, ions, neurotransmitters, lipids, carbohydrates, nucleotides, amino acids, peptides (e.g. hormones) and proteins 1. The GPCR family is with its ~800 genes among the largest families in the human genome 2 and involved in most physiological processes 3, 4. Their regulation of pathophysiology in diverse disease areas and accessibility at the cell surface have earned them a key role in medicine: more than 30% of the drug on the market target GPCRs 5. 59 GPCRs have been drugged with small molecules 6 ; the vast majority binding within a structurally conserved pocket within the transmembrane heptahelical bundle (7TM) 7. Modelling of ligand-receptor complexes can now be performed with higher accuracy as a result of the increasing number of GPCR crystal structures, and has been evaluated in three community-wide 'GPCR Dock' assessments 8-10. Virtual screening is an efficient alternative to high-throughput screening to identify new ligands. This entails a preceding screening in silico of commercial or in-house compound catalogues against a target structure 11-13 or ligand pharmacophore 14, 15. We recently described the development of a new chemogenomics method to generate pharmacophores for GPCRs 16. The method is based on the extraction of a reference library of crystal structure fragments, which are interacting moiety-residue pairs. The complete library of moiety-residue pairs has been made available through the GPCR database, GPCRdb 17, 18. Although specific for GPCR ligand discovery, this is analogous to general resources that extract the residue-ligand fragment information from crystal structures 19-21. Here, we describe the first application of this method to identify new histamine H 3 receptor ligands. The H 3 receptor is found mainly in the CNS and is implicated in cognition, sleep regulation, feeding, memory, nociception and the sleep/wake cycle 22-24. It functions both as a presynaptic autoreceptor, as well as a regulator of
G Protein Coupled Receptors - In Silico Drug Discovery and Design
Current Topics in Medicinal Chemistry, 2010
The application of structure-based in silico methods to drug discovery is still considered a major challenge, especially when the x-ray structure of the target protein is unknown. Such is the case with human G protein-coupled receptors (GPCRs), one of the most important families of drug targets, where in the absence of x-ray structures, one has to rely on in silico 3D models. We report repeated success in using ab initio in silico GPCR models, generated by the PREDICT method, for blind in silico screening when applied to a set of five different GPCR drug targets. More than 100,000 compounds were typically screened in silico for each target, leading to a selection of <100 ''virtual hit'' compounds to be tested in the lab. In vitro binding assays of the selected compounds confirm high hit rates, of 12-21% (full dose-response curves, K i < 5 M). In most cases, the best hit was a novel compound (New Chemical Entity) in the 1-to 100-nM range, with very promising pharmacological properties, as measured by a variety of in vitro and in vivo assays. These assays validated the quality of the hits as lead compounds for drug discovery. The results demonstrate the usefulness and robustness of ab initio in silico 3D models and of in silico screening for GPCR drug discovery. modeling ͉ in silico screening ͉ structure-based G protein-coupled receptors (GPCRs) are membraneembedded proteins, responsible for communication between the cell and its environment (1). As a consequence, many major diseases, such as hypertension, cardiac dysfunction, depression, anxiety, obesity, inflammation, and pain, involve malfunction of these receptors (2), making them among the most important drug targets for pharmacological intervention (3-5). Thus, whereas GPCRs are only a small subset of the human genome, they are the targets for Ϸ50% of all recently launched drugs (6). As targets of paramount importance, it is expected that drug discovery for GPCRs would benefit from the introduction of computational methodologies (7), especially as these methods can be used in conjunction with such experimental methods as high-throughput screening (8, 9), NMR, and crystallography (10). Unfortunately, GPCRs, like other membrane-embedded proteins, have characteristics that make their 3D structure extremely difficult to determine experimentally. To date, the only GPCR for which a 3D structure was determined by x-ray crystallography is bovine rhodopsin (11), which is unique among GPCRs in that its ligand, retinal, is covalently bound and that it responds to light rather than to ligand binding. Hence, in the case of GPCRs, the limited availability of structural data has forced the computational design of ligands to heavily rely on ligand-based techniques. Indeed, for many GPCRs, the natural ligand can provide a good starting point, leading to useful pharmacophore models that can be used for identifying lead structures with novel scaffolds (6). These methods have been successfully applied for the discovery of peptide agonists to the somatostatin receptor (12) and for the discovery of nonpeptidic antagonists to the urotensin II receptor (13). Nonetheless, structure-based drug discovery remains highly desirable for GPCRs. It is known that all GPCRs structurally consist of seven transmembrane (TM) helices joined together by three extracellular and three intracellular loops. Of particular interest to
Journal of Computer-Aided Molecular Design, 2009
Accurate in silico models for the quantitative prediction of the activity of G protein-coupled receptor (GPCR) ligands would greatly facilitate the process of drug discovery and development. Several methodologies have been developed based on the properties of the ligands, the direct study of the receptor-ligand interactions, or a combination of both approaches. Ligand-based three-dimensional quantitative structure-activity relationships (3D-QSAR) techniques, not requiring knowledge of the receptor structure, have been historically the first to be applied to the prediction of the activity of GPCR ligands. They are generally endowed with robustness and good ranking ability; however they are highly dependent on training sets. Structure-based techniques generally do not provide the level of accuracy necessary to yield meaningful rankings when applied to GPCR homology models. However, they are essentially independent from training sets and have a sufficient level of accuracy to allow an effective discrimination between binders and nonbinders, thus qualifying as viable lead discovery tools. The combination of ligand and structure-based methodologies in the form of receptor-based 3D-QSAR and ligand and structure-based consensus models results in robust and accurate quantitative predictions. The contribution of the structure-based component to these combined approaches is expected to become more substantial and effective in the future, as more sophisticated scoring functions are developed and more detailed structural information on GPCRs is gathered.
Direct and indirect quantitative structureactivity relationship (QSAR) analyses are the two major modelling strategies used in drug design. Both approaches are based on detailed analysis of the structural properties of the molecules concerned, and are interpreted in terms of complementary size-shape and electronic features with a known (direct or receptor fitting) or hypothetical (indirect or molecular fitting) three-dimensional (3D) biological target. In order to extend these procedures, we propose here the heuristic-direct QSAR approach, applicable when the atomic resolved structure of the receptor is unknown but predictable by means of the available experimental information. The first step of this iteractive procedure consists in the building of a 3D model of the receptor using computer-aided model building techniques and appropriate assessment criteria [ 11. The second involves docking simulations with selected ligands, maximizing the complementarity between ligands and receptor. The evaluation of the adequacy of the model obtained for reproducing the trend of the experimental affinity data constitutes the third iteractive step and it is accomplished by correlation analysis. To set complex molecular modelling exercises in the conceptual * Corresponding author.
Current Topics in Medicinal Chemistry, 2011
G protein-coupled receptors (GPCRs) share a common architecture consisting of seven transmembrane (TM) domains. Various lines of evidence suggest that this fold provides a generic binding pocket within the TM region for hosting agonists, antagonists, and allosteric modulators. Hence, an automated method was developed that allows a fast analysis and comparison of these generic ligand binding pockets across the entire GPCR family by providing the relevant information for all GPCRs in the same format. This methodology compiles amino acids lining the TM binding pocket including parts of the ECL2 loop in a so-called 1D ligand binding pocket vector and translates these 1D vectors in a second step into 3D receptor pharmacophore models. It aims to support various aspects of GPCR drug discovery in the pharmaceutical industry. Applications of pharmacophore similarity analysis of these 1D LPVs include definition of receptor subfamilies, prediction of species differences within subfamilies in regard to in vitro pharmacology and identification of nearest neighbors for GPCRs of interest to generate starting points for GPCR lead identification programs. These aspects of GPCR research are exemplified in the field of melanopsins, trace amine-associated receptors and somatostatin receptor subtype 5. In addition, it is demonstrated how 3D pharmacophore models of the LPVs can support the prediction of amino acids involved in ligand recognition, the understanding of mutational data in a 3D context and the elucidation of binding modes for GPCR ligands and their evaluation. Furthermore, guidance through 3D receptor pharmacophore modeling for the synthesis of subtype-specific GPCR ligands will be reported. Illustrative examples are taken from the GPCR family class C, metabotropic glutamate receptors 1 and 5 and sweet taste receptors, and from the GPCR class A, e.g. nicotinic acid and 5-hydroxytryptamine 5A receptor.
The impact of GPCR structures on pharmacology and structure-based drug design
British Journal of Pharmacology, 2010
After many years of effort, recent technical breakthroughs have enabled the X-ray crystal structures of three G-protein-coupled receptors (GPCRs) (b1 and b2 adrenergic and adenosine A2a) to be solved in addition to rhodopsin. GPCRs, like other membrane proteins, have lagged behind soluble drug targets such as kinases and proteases in the number of structures available and the level of understanding of these targets and their interaction with drugs. The availability of increasing numbers of structures of GPCRs is set to greatly increase our understanding of some of the key issues in GPCR biology. In particular, what constitutes the different receptor conformations that are involved in signalling and the molecular changes which occur upon receptor activation. How future GPCR structures might alter our views on areas such as agonist-directed signalling and allosteric regulation as well as dimerization is discussed. Knowledge of crystal structures in complex with small molecules will enable techniques in drug discovery and design, which have previously only been applied to soluble targets, to now be used for GPCR targets. These methods include structure-based drug design, virtual screening and fragment screening. This review considers how these methods have been used to address problems in drug discovery for kinase and protease targets and therefore how such methods are likely to impact GPCR drug discovery in the future. 159 986-996
G protein-coupled receptors: structure- and function-based drug discovery
Signal Transduction and Targeted Therapy
As one of the most successful therapeutic target families, G protein-coupled receptors (GPCRs) have experienced a transformation from random ligand screening to knowledge-driven drug design. We are eye-witnessing tremendous progresses made recently in the understanding of their structure–function relationships that facilitated drug development at an unprecedented pace. This article intends to provide a comprehensive overview of this important field to a broader readership that shares some common interests in drug discovery.
Modern Homology Modeling of G-Protein Coupled Receptors: Which Structural Template to Use?
Journal of Medicinal Chemistry, 2009
The recent availability in the literature of new crystal structures of inactive G-protein coupled receptors (GPCRs) prompted us to study the extent to which these crystal structures constitute an advantage over the former prototypic rhodopsin template for homology modeling of the transmembrane (TM) region of human class A GPCRs. Our results suggest that better templates than those currently available are required by the majority of these GPCRs to generate homology models that are accurate enough for simple virtual screening aimed at computer-aided drug discovery. Thus, we investigated: 1) which class A GPCRs would have the highest impact as potential templates for homology modeling of other GPCRs, if their structures were solved; and 2) the extent to which multiple-template homology modeling (using all currently available GPCR crystal structures) provides an improvement over single-template homology modeling, as evaluated by the accuracy of rigid protein-flexible ligand docking on these models.