Functional G protein-coupled receptor assays for primary and secondary screening

Screening technologies for G protein-coupled receptors comprise two major approaches; homogeneous assays, conducted in microtiter plate formats, and protein redistribution assays that require imaged-based analysis using automated confocal systems. Generally, the former are used in primary screening campaigns for lead identification, while the latter are used in secondary screens for lead optimization. Homogeneous assays measure changes in G protein-coupled receptor second messengers such as cAMP, Ins P3 or calcium. Protein redistribution assays assess internalization of the receptor ligand complex or translocation of G-protein coupled receptor-associated proteins, such as beta-arrestin. In the present review functional cell based approaches are discussed, emphasizing the variety of non radiometric technologies now in use for HTS.     

Self-organizing neural networks for screening and development of novel artificial sweetener candidates

The use of Kohonen feature maps for the visualization of various aspects of molecular similarity is briefly reviewed and illustrated. It is shown that a specific feature of self-organizing maps (SOM) makes them of special interest for the screening of compounds. In particular, these methods were used to design candidates for new sweeteners, which were then synthesized.     

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.     

Biomimetic screening of class-B G protein-coupled receptors

The 41-amino acid peptide corticotropin releasing factor (CRF) is a major modulator of the mammalian stress response. Upon stressful stimuli, it binds to the corticotropin releasing factor receptor 1 (CRF(1)R), a typical member of the class-B G-protein-coupled receptors (GPCRs) and a prime target in the treatment of mood disorders. To chemically probe the molecular interaction of CRF with the transmembrane domain of its cognate receptor, we developed a high-throughput conjugation approach that mimics the natural activation mechanism of class-B GPCRs. An acetylene-tagged peptide library was synthesized and conjugated to an azide-modified high-affinity carrier peptide derived from the CRF C-terminus using copper-catalyzed dipolar cycloaddition. The resulting conjugates reconstituted potent agonists and were tested in situ for activation of the CRF(1) receptor in a cell-based assay. By use of this approach we (i) defined the minimal sequence motif that is required for full receptor activation, (ii) identified the critical functional groups and structure-activity relationships, (iii) developed an optimized, highly modified peptide probe with high potency (EC(50) = 4 nM) that is specific for the activation domain of the receptor, and (iv) probed the behavioral role of CRF receptors in living mice. The membrane recruitment by a high-affinity carrier enhanced the potency of the tethered peptides by >4 orders of magnitude and thus allowed the testing of very weak initial fragments that otherwise would have been inactive on their own. As no chromatography purification of the test peptides was necessary, a substantial increase in screening throughput was achieved. Importantly, the peptide conjugates can be used to probe the endogenous receptor in its native environment in vivo.     

G protein-coupled receptor mediated sensing of TMA

A new approach for the detection of trimethylamine (TMA) using recombinant Xenopus laevis melanophores was developed. The cells were genetically modified to express the mouse trace amine-associated receptor 5 (mTAAR5), a G protein-coupled receptor from the olfactory epithelium, which conferred high sensitivity to TMA. A focused chemical screen allowed the discovery of additional, previously unknown stimuli of mTAAR5. The cell-based sensor demonstrated no sensitivity to trimethylamine N-oxide (TMAO), making it suitable for a convenient evaluation of TMA levels in fish tissue extracts. The developed gas measurement platform was able to detect TMA from 1 to 100 ppm within thirty-five minutes.     

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.     

Inverse in silico screening for identification of kinase inhibitor targets

Protein kinases are clinically relevant, attractive drug targets for cancer. One major problem with kinase inhibitors is broad promiscuity, causing off-target actions and side effects. In silico prediction of targets of a compound would immensely facilitate and accelerate drug development. Using a virtual "inverse" screening approach, where single compounds are docked into protein structures from a database, we identify among known targets of indirubin derivatives phosphoinositide-dependent kinase 1 (PDK1) as a target of one derivative (6BIO) in particular. This prediction is functionally supported by an in vitro kinase assay, inhibition of intracellular phosphorylation of PDK1-substrates, and inhibition of endothelial cell migration, which highly depends on PDK1. Virtual inverse screening combined with biological tests, thus, is proposed as a valuable tool for the drug discovery process and re-examination of already established kinase inhibitors.     

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.     

Toward deciphering the code to aminergic G protein-coupled receptor drug design

The trace amine-associated receptor 1 (TAAR(1)) is a biogenic amine G protein-coupled receptor (GPCR) that is potently activated by 3-iodothyronamine (1, T(1)AM) in vitro. Compound 1 is an endogenous derivative of the thyroid hormone thyroxine which rapidly induces hypothermia, anergia, and bradycardia when administered to mice. To explore the role of TAAR(1) in mediating the effects of 1, we rationally designed and synthesized rat TAAR(1) superagonists and lead antagonists using the rotamer toggle switch model of aminergic GPCR activation. The functional activity of a ligand is proposed to be correlated to its probable interactions with the rotamer switch residues; agonists allow the rotamer switch residues to toggle to their active conformation, whereas antagonists interfere with this conformational transition. These agonist and antagonist design principles provide a conceptual model for understanding the relationship between the molecular structure of a drug and its pharmacological properties.     

A virtual screen for diverse ligands: discovery of selective G protein-coupled receptor antagonists

Virtual screening has become a major focus of bioactive small molecule lead identification, and reports of agonists and antagonists discovered via virtual methods are becoming more frequent. G protein-coupled receptors (GPCRs) are the one class of protein targets for which success with this approach has been limited. This is likely due to the paucity of detailed experimental information describing GPCR structure and the intrinsic function-associated structural flexibility of GPCRs which present major challenges in the application of receptor-based virtual screening. Here we describe an in silico methodology that diminishes the effects of structural uncertainty, allowing for more inclusive representation of a potential docking interaction with exogenous ligands. Using this approach, we screened one million compounds from a virtual database, and a diverse subgroup of 100 compounds was selected, leading to experimental identification of five structurally diverse antagonists of the thyrotropin-releasing hormone receptors (TRH-R1 and TRH-R2). The chirality of the most potent chemotype was demonstrated to be important in its binding affinity to TRH receptors; the most potent stereoisomer was noted to have a 13-fold selectivity for TRH-R1 over TRH-R2. A comprehensive mutational analysis of key amino acid residues that form the putative binding pocket of TRH receptors further verified the binding modality of these small molecule antagonists. The described virtual screening approach may prove applicable in the search for novel small molecule agonists and antagonists of other GPCRs.     

Inverse screening of Simvastatin kinase targets from glioblastoma druggable kinome

The statin drug Simvastatin is a HMG-CoA reductase inhibitor that has been widely used to lower blood lipid. However, the drug is clinically observed to reposition a significant suppressing potency on glioblastoma (GBM) by unexpectedly targeting diverse kinase pathways involved in GBM tumorigensis. Here, an inverse screening strategy is described to discover potential kinase targets of Simvastatin. Various human protein kinases implicated in GBM are enriched to define a druggable kinome; the binding behavior of Simvastatin to the kinome is profiled systematically via an integrative computational approach, from which most kinases have only low or moderate binding potency to Simvastatin, while only few are identified as promising kinase hits. It is revealed that Simvastatin can potentially interact with certain known targets or key regulators of GBM such as ErbB, c-Src and FGFR signaling pathways, but exhibit low affinity to the well-established GBM target of PI3K/Akt/mTOR pathway. Further assays determine that Simvastatin can inhibit kinase hits EGFR, MET, SRC and HER2 at nanomolar level, which are comparable with those of cognate kinase inhibitors. Structural analyses reveal that the sophisticated T790 M gatekeeper mutation can considerably reduce Simvastatin sensitivity to EGFR by inducing the ligand change between different binding modes.     

Solution NMR spectroscopy of the human vasopressin V2 receptor, a G protein-coupled receptor

The seven-transmembrane-spanning G protein-coupled receptor (GPCR) superfamily plays many important roles in basic biology, human health, and human disease. Here, well-resolved solution NMR spectra are presented for a human GPCR, the vasopressin V2 receptor in detergent micelles. The quality of the NMR spectra indicates that backbone resonance assignments for a majority of resonances are feasible. The key to obtaining high quality spectra appears to be the coupling of methods for expressing the receptor into membranes rather than into inclusion bodies, with use of a biochemically mild lysolipid detergent for membrane extraction, protein purification, and NMR sample preparation.     

Class identity assignment for amphetamines using neural networks and GC-FTIR data

An exploratory analysis was performed in order to evaluate the feasibility of building of neural network (NN) systems automating the identification of amphetamines necessary in the investigation of drugs of abuse for epidemiological, clinical and forensic purposes. A first neural network system was built to distinguish between amphetamines and nonamphetamines. A second, more refined system, aimed to the recognition of amphetamines according to their toxicological activity (stimulant amphetamines, hallucinogenic amphetamines, nonamphetamines). Both systems proved that discrimination between amphetamines and nonamphetamines, as well as between stimulants, hallucinogens and nonamphetamines is possible (83.44% and 85.71% correct classification rate, respectively). The spectroscopic interpretation of the 40 most important input variables (GC-FTIR absorption intensities) shows that the modeling power of an input variable seems to be correlated with the stability and not with the intensity of the spectral interaction. Thus, discarding variables only because they correspond to spectral windows with weak absorptions does not seem be not advisable.     

Ligand and decoy sets for docking to G protein-coupled receptors

We compiled a G protein-coupled receptor (GPCR) ligand library (GLL) for 147 targets, selecting for each ligand 39 decoy molecules, collected in the GPCR Decoy Database (GDD). Decoys were chosen ensuring a ligand-decoy similarity of six physical properties, while enforcing ligand-decoy chemical dissimilarity. The performance in docking of the GDD was evaluated on 19 GPCRs, showing a marked decrease in enrichment compared to bias-uncorrected decoy sets. Both the GLL and GDD are freely available for the scientific community.     

QSAR modelling using combined simple competitive learning networks and RBF neural networks

The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.     

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.