High throughput screening (HTS) in identification new ligands and drugable targets of G protein-coupled receptors (GPCRs)

G protein-coupled receptors (GPCRs) which constitute one of the largest and most versatile families of cell surface receptors are involved in a wide spectrum of physiological functions, such as, neuronal transmission, chemotaxis, pacemaker activity and embryonic development. Therefore, in the past a few years GPCR families have become very important targets in pharmaceutical design. However, according to the human genome project, there are approximately 1000 genes encoding GPCRs, only about 200 of GPCRs have known ligands and functions. Searching for ligands of the unknown GPCRs and better modulators of known GPCRs are currently attracting lots of interest. High throughput screening (HTS), which is commonly defined as an automatic process of testing potential drug candidates efficiently, is widely used in drug discovery. In this review, the use of high throughput screening (HTS) in studying GPCRs and the choice of screening technology in different G-protein signaling pathways were summarized.     

New strategies in drug discovery for GPCRs: high throughput detection of cellular ERK phosphorylation

G-protein coupled receptors (GPCRs) are a large family of receptors for a wide range of stimulants, including hormones, neurotransmitters, and taste and olfactory chemicals. Due to their broad involvement in cellular responses, GPCRs affect many important body functions both in health and disease. Compared to other receptor families, the GPCRs have been a rich source of extracellularly-acting pharmaceuticals, due largely to the fact that many GPCR ligands are small molecules when compared with ligands for other receptors, such as the tyrosine kinase receptor family. This has allowed the development of small molecule modulators of receptor function that act on specific GPCRs, such as those involved in cardiovascular regulation. However, at several levels, current screening technologies of drug development for GPCRs are lacking. Firstly, responses from many GPCRs, such as the Gi-coupled GPCRs, are not easily measured in large screening programs by current techniques. Secondly, there are few options for detecting agonists of orphan GPCRs. Thirdly, it is now clear that the signaling from GPCRs is more complex than once thought, and the measurement of Ca(2+) and cAMP can account for only a fraction of the biological information emanating from an activated GPCR. Studies of the discrete and sometimes separable activation of the Ras/Raf/Mek/ERK cascade by many GPCRs is likely to offer development of new agonists and antagonists, contribute to new pharmacologies from receptors, and raise the potential for novel drug candidates in this important area of biology. Downstream activation of the ERK pathway, with or without transactivation of growth factor receptors, has not been measurable by high throughput methodologies. This article presents recent advances and associated applications for screening of GPCRs and other receptor species through the rapid measurement of protein phosphorylation events, such as ERK phosphorylation, as new readouts for drug discovery.     

Label-free cell-based assays for GPCR screening

G protein-coupled receptors (GPCRs) have been proven to be the largest family of druggable targets in the human genome. Given the importance of GPCRs as drug targets and the de-orphanization of novel targets, GPCRs are likely to remain the frequent targets of many drug discovery programs. With recent advances in instrumentation and understanding of cellular mechanisms for the signals measured, biosensor-centered label-free cell assay technologies become a very active area for GPCR screening. This article reviews the principles and potential of current label-free cell assay technologies in GPCR drug discovery.     

Small Molecule Tools to Study Cellular Target Engagement for the Intracellular Allosteric Binding Site of GPCRs

A conserved intracellular allosteric binding site (IABS) has recently been identified at several G protein-coupled receptors (GPCRs). Ligands targeting the IABS, so-called intracellular allosteric antagonists, are highly promising compounds for pharmaceutical intervention and currently evaluated in several clinical trials. Beside co-crystal structures that laid the foundation for the structure-based development of intracellular allosteric GPCR antagonists, small molecule tools that enable an unambiguous identification and characterization of intracellular allosteric GPCR ligands are of utmost importance for drug discovery campaigns in this field. Herein, we discuss recent approaches that leverage cellular target engagement studies for the IABS and thus play a critical role in the evaluation of IABS-targeted ligands as potential therapeutic agents.     

The complexity of G-protein coupled receptor-ligand interactions

The G-protein coupled receptors (GPCRs) play fundamental roles in the human biololgy and drug discovery. GPCRs function as signalling molecules that transduce extracellular signals into cells. The signalling transduction is generally triggered by interacting with ligands, including photons, ions, small organic compounds, peptides, proteins and lipids. In this review, we focus on interactions with diffusible ligands such as hormones and neurotransmitters. We discuss three aspects of the complexity of the GPCR-ligand interactions: functional selectivity of ligands, receptor subtype selectivity of ligands and orphan GPCRs.     

Design of Drug Efficacy Guided by Free Energy Simulations of the β2-Adrenoceptor

G-protein-coupled receptors (GPCRs) play important roles in physiological processes and are modulated by drugs that either activate or block signaling. Rational design of the pharmacological efficacy profiles of GPCR ligands could enable the development of more efficient drugs, but is challenging even if high-resolution receptor structures are available. We performed molecular dynamics simulations of the β₂ adrenergic receptor in active and inactive conformations to assess if binding free energy calculations can predict differences in ligand efficacy for closely related compounds. Previously identified ligands were successfully classified into groups with comparable efficacy profiles based on the calculated shift in ligand affinity upon activation. A series of ligands were then predicted and synthesized, leading to the discovery of partial agonists with nanomolar potencies and novel scaffolds. Our results demonstrate that free energy simulations enable design of ligand efficacy and the same approach can be applied to other GPCR drug targets.     

High throughput screening for orphan and liganded GPCRs

GPCRs had significant representation in the drug discovery portfolios of most major commercial drug discovery organizations for many years. This is due in part to the diverse biological roles mediated by GPCRs as a class, as well as the empirical discovery that they have proven relatively tractable to the development of small molecule therapeutics. Publication of the human genome sequence in 2001 confirmed GPCRs as the largest single gene superfamily with more than 700 members, furthering the already strong appeal of addressing this target class using efficient and highly parallelized platform approaches. The GPCR research platform implemented at Amgen is used as a case study to review the evolution and implementation of available assays and technologies applicable to GPCR drug discovery. The strengths, weaknesses, and applications of assay technologies applicable to G alpha s, G alpha i and G alpha q-coupled receptors are described and their relative merits evaluated. Particular consideration is made of the role and practice of "de-orphaning" and signaling pathway characterization as a pre-requisite to establishing effective screens. In silico and in vitro methodology developed for rapid, parallel high throughput hit characterization and prioritization is also discussed extensively.     

Membrane interactive alpha-helices in GPCRs as a novel drug target

G-Protein Coupled Receptors (GPCRs) are one of the most important targets for pharmaceutical drug design. Over the past 30 years, mounting evidence has suggested the existence of homo and hetero dimers or higher-order complexes (oligomers) that are involved in signal transduction and some diseases. The number of reports describing GPCR oligomerization has increased, and in 2003, the organization of mouse rhodopsin into two-dimensional arrays of dimers was determined by an atomic force microscopic analysis. The analysis of the mouse rhodopsin complex has enabled us to discuss the oligomerization based on structural data. Although many unsolved problems still remains, the idea that GPCRs directly interact to form oligomers has been gradually accepted. One of the recent findings in the GPCR investigations is the clarification of the mechanisms of GPCR oligomerization at a molecular level. Most of these studies have suggested the importance of transmembrane alpha-helices for GPCR oligomerization. In this review, we will first summarize the importance of GPCR oligomerization and the functions of GPCRs. Then, we will explain the involvement of transmembrane alpha-helices in the oligomerization and a drug design strategy that targets these regions for GPCR oligomerization. Considering the current drug design methods, which are based on the modification of the protein-protein interactions of soluble regions of proteins, a "peptide mimic approach" that targets the transmembrane alpha-helices constituting the interfaces would be promising in drug discovery for GPCR oligomerization. For that purpose, we must know the positions of the interfaces. However, problems specific to membrane proteins have made it difficult to identify the positions of the interfaces experimentally. Therefore, information about the interfaces predicted by bioinformatics approaches is valuable. At the end of this review, several bioinformatics approaches toward interface prediction for oligomerization are introduced. The benefits and the pitfalls of these approaches are also discussed.     

Snooker: a structure-based pharmacophore generation tool applied to class A GPCRs

G-protein coupled receptors (GPCRs) are important drug targets for various diseases and of major interest to pharmaceutical companies. The function of individual members of this protein family can be modulated by the binding of small molecules at the extracellular side of the structurally conserved transmembrane (TM) domain. Here, we present Snooker, a structure-based approach to generate pharmacophore hypotheses for compounds binding to this extracellular side of the TM domain. Snooker does not require knowledge of ligands, is therefore suitable for apo-proteins, and can be applied to all receptors of the GPCR protein family. The method comprises the construction of a homology model of the TM domains and prioritization of residues on the probability of being ligand binding. Subsequently, protein properties are converted to ligand space, and pharmacophore features are generated at positions where protein ligand interactions are likely. Using this semiautomated knowledge-driven bioinformatics approach we have created pharmacophore hypotheses for 15 different GPCRs from several different subfamilies. For the beta-2-adrenergic receptor we show that ligand poses predicted by Snooker pharmacophore hypotheses reproduce literature supported binding modes for ～75% of compounds fulfilling pharmacophore constraints. All 15 pharmacophore hypotheses represent interactions with essential residues for ligand binding as observed in mutagenesis experiments and compound selections based on these hypotheses are shown to be target specific. For 8 out of 15 targets enrichment factors above 10-fold are observed in the top 0.5% ranked compounds in a virtual screen. Additionally, prospectively predicted ligand binding poses in the human dopamine D3 receptor based on Snooker pharmacophores were ranked among the best models in the community wide GPCR dock 2010.     

Discovery of novel regulatory peptides by reverse pharmacology: spotlight on chemerin and the RF-amide peptides metastin and QRFP

Reverse pharmacology is a screening technology that matches G protein-coupled receptors (GPCRs) with unknown cognate ligands in cell-based screening assays by detection of agonist-induced signaling pathways. One decade spent pursuing orphan GPCR screening by this technique assigned over 30 ligand/receptor pairs and revealed previously known or novel undescribed ligands, mostly of a peptidic nature. In this review, we describe the discovery, characterization of the structural composition, biological function, physiological role and therapeutic potential of three recently identified peptidic ligands. These are metastin, QRFP in a context of five RF-amide genes described in humans and the chemoattractant, chemerin. Metastin was initially characterized as a metastasis inhibitor. Investigations using ligand/receptor pairing revealed that metastin was involved in a variety of physiological processes, including endocrine function during pregnancy and gonad development. The novel RF-amide QRFP is implicated in food intake and aldosterone release from the adrenal cortex in the rat. Chemerin, first described as TIG2, is upregulated in tazarotene-treated psoriatic skin. By GPCR screening, bioactive chemerin was isolated from ovarial carcinoma fluid as well as hemofiltrate. Characterization as a chemoattractant for immature dendritic cells and analysis of the expression profile of metastin and its receptor suggested a physiological role of chemerin as a mediator of the immune response, inflammatory processes and bone development.     

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

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.     

High content screening for G protein-coupled receptors using cell-based protein translocation assays

G protein-coupled receptors (GPCRs) have been one of the most productive classes of drug targets for several decades, and new technologies for GPCR-based discovery promise to keep this field active for years to come. While molecular screens for GPCR receptor agonist- and antagonist-based drugs will continue to be valuable discovery tools, the most exciting developments in the field involve cell-based assays for GPCR function. Some cell-based discovery strategies, such as the use of beta-arrestin as a surrogate marker for GPCR function, have already been reduced to practice, and have been used as valuable discovery tools for several years. The application of high content cell-based screening to GPCR discovery has opened up additional possibilities, such as direct tracking of GPCRs, G proteins and other signaling pathway components using intracellular translocation assays. These assays provide the capability to probe GPCR function at the cellular level with better resolution than has previously been possible, and offer practical strategies for more definitive selectivity evaluation and counter-screening in the early stages of drug discovery. The potential of cell-based translocation assays for GPCR discovery is described, and proof-of-concept data from a pilot screen with a CXCR4 assay are presented. This chemokine receptor is a highly relevant drug target which plays an important role in the pathogenesis of inflammatory disease and also has been shown to be a co-receptor for entry of HIV into cells as well as to play a role in metastasis of certain cancer cells.     

Balancing focused combinatorial libraries based on multiple GPCR ligands

G-Protein coupled receptors (GPCRs) are important targets for drug discovery, and combinatorial chemistry is an important tool for pharmaceutical development. The absence of detailed structural information, however, limits the kinds of combinatorial design techniques that can be applied to GPCR targets. This is particularly problematic given the current emphasis on focused combinatorial libraries. By linking an incremental construction method (OptDesign) to the very fast shape-matching capability of ChemSpace, we have created an efficient method for designing targeted sublibraries that are topomerically similar to known actives. Multi-objective scoring allows consideration of multiple queries (actives) simultaneously. This can lead to a distribution of products skewed towards one particular query structure, however, particularly when the ligands of interest are quite dissimilar to one another. A novel pivoting technique is described which makes it possible to generate promising designs even under those circumstances. The approach is illustrated by application to some serotonergic agonists and chemokine antagonists.     

GPCR–drug interactions prediction using random forest with drug-association-matrix-based post-processing procedure

G-protein-coupled receptors (GPCRs) are important targets of modern medicinal drugs. The accurate identification of interactions between GPCRs and drugs is of significant importance for both protein function annotations and drug discovery. In this paper, a new sequence-based predictor called TargetGDrug is designed and implemented for predicting GPCR–drug interactions. In TargetGDrug, the evolutionary feature of GPCR sequence and the wavelet-based molecular fingerprint feature of drug are integrated to form the combined feature of a GPCR–drug pair; then, the combined feature is fed to a trained random forest (RF) classifier to perform initial prediction; finally, a novel drug-association-matrix-based post-processing procedure is applied to reduce potential false positive or false negative of the initial prediction. Experimental results on benchmark datasets demonstrate the efficacy of the proposed method, and an improvement of 15% in the Matthews correlation coefficient (MCC) was observed over independent validation tests when compared with the most recently released sequence-based GPCR–drug interactions predictor. The implemented webserver, together with the datasets used in this study, is freely available for academic use at http://csbio.njust.edu.cn/bioinf/TargetGDrug.     

SEC–MS as an Approach to Isolate and Directly Identifying Small Molecular GPCR–Ligands from Complex Mixtures Without Labeling

G protein coupled receptors (GPCRs) belong to the most successful targets in drug discovery. However, the development of assays with an appropriately labeled high affinity reporter compound is laborious. In the present study an MS-based binding assay is described using the rat histamine receptor 2 (rH2) as a model GPCR system. Instead of using a purified receptor it is demonstrated that it is possible to use an unpurified receptor to extract active compounds from a solution or small mixture of compounds. By using SEC it is possible to separate the bound ligand from the unbound ligand. The major advantage of this approach is that there is no labeling of ligands required (direct monitoring based on the appropriate m/z values).     

An ontology for pharmaceutical ligands and its application for in silico screening and library design

Annotation efforts in biosciences have focused in past years mainly on the annotation of genomic sequences. Only very limited effort has been put into annotation schemes for pharmaceutical ligands. Here we propose annotation schemes for the ligands of four major target classes, enzymes, G protein-coupled receptors (GPCRs), nuclear receptors (NRs), and ligand-gated ion channels (LGICs), and outline their usage for in silico screening and combinatorial library design. The proposed schemes cover ligand functionality and hierarchical levels of target classification. The classification schemes are based on those established by the EC, GPCRDB, NuclearDB, and LGICDB. The ligands of the MDL Drug Data Report (MDDR) database serve as a reference data set of known pharmacologically active compounds. All ligands were annotated according to the schemes when attribution was possible based on the activity classification provided by the reference database. The purpose of the ligand-target classification schemes is to allow annotation-based searching of the ligand database. In addition, the biological sequence information of the target is directly linkable to the ligand, hereby allowing sequence similarity-based identification of ligands of next homologous receptors. Ligands of specified levels can easily be retrieved to serve as comprehensive reference sets for cheminformatics-based similarity searches and for design of target class focused compound libraries. Retrospective in silico screening experiments within the MDDR01.1 database, searching for structures binding to dopamine D2, all dopamine receptors and all amine-binding class A GPCRs using known dopamine D2 binding compounds as a reference set, have shown that such reference sets are in particular useful for the identification of ligands binding to receptors closely related to the reference system. The potential for ligand identification drops with increasing phylogenetic distance. The analysis of the focus of a tertiary amine based combinatorial library compared to known amine binding class A GPCRs, peptide binding class A GPCRs, and LGIC ligands constitutes a second application scenario which illustrates how the focus of a combinatorial library can be treated quantitatively. The provided annotation schemes, which bridge chem- and bioinformatics by linking ligands to sequences, are expected to be of key utility for further systematic chemogenomics exploration of previously well explored target families.     

Virtual screen for ligands of orphan G protein-coupled receptors

This paper describes a virtual screening methodology that generates a ranked list of high-binding small molecule ligands for orphan G protein-coupled receptors (oGPCRs), circumventing the requirement for receptor three-dimensional structure determination. Features representing the receptor are based only on physicochemical properties of primary amino acid sequence, and ligand features use the two-dimensional atomic connection topology and atomic properties. An experimental screen comprised nearly 2 million hypothetical oGPCR-ligand complexes, from which it was observed that the top 1.96% predicted affinity scores corresponded to "highly active" ligands against orphan receptors. Results representing predicted high-scoring novel ligands for many oGPCRs are presented here. Validation of the method was carried out in several ways: (1) A random permutation of the structure-activity relationship of the training data was carried out; by comparing test statistic values of the randomized and nonshuffled data, we conclude that the value obtained with nonshuffled data is unlikely to have been encountered by chance. (2) Biological activities linked to the compounds with high cross-target binding affinity were analyzed using computed log-odds from a structure-based program. This information was correlated with literature citations where GPCR-related pathways or processes were linked to the bioactivity in question. (3) Anecdotal, out-of-sample predictions for nicotinic targets and known ligands were performed, with good accuracy in the low-to-high "active" binding range. (4) An out-of-sample consistency check using the commercial antipsychotic drug olanzapine produced "active" to "highly-active" predicted affinities for all oGPCRs in our study, an observation that is consistent with documented findings of cross-target affinity of this compound for many different GPCRs. It is suggested that this virtual screening approach may be used in support of the functional characterization of oGPCRs by identifying potential cognate ligands. Ultimately, this approach may have implications for pharmaceutical therapies to modulate the activity of faulty or disease-related cellular signaling pathways. In addition to application to cell surface receptors, this approach is a generalized strategy for discovery of small molecules that may bind intracellular enzymes and involve protein-protein interactions.