GPCR-2L: predicting G protein-coupled receptors and their types by hybridizing two different modes of pseudo amino acid compositions

G protein-coupled receptors (GPCRs) are among the most frequent targets of therapeutic drugs. With the avalanche of newly generated protein sequences in the post genomic age, to expedite the process of drug discovery, it is highly desirable to develop an automated method to rapidly identify GPCRs and their types. A new predictor was developed by hybridizing two different modes of pseudo-amino acid composition (PseAAC): the functional domain PseAAC and the low-frequency Fourier spectrum PseAAC. The new predictor is called GPCR-2L, where "2L" means that it is a two-layer predictor: the 1st layer prediction engine is to identify a query protein as GPCR or not; if it is, the prediction will be automatically continued to further identify it as belonging to one of the following six types: (1) rhodopsin-like (Class A), (2) secretin-like (Class B), (3) metabotropic glutamate/pheromone (Class C), (4) fungal pheromone (Class D), (5) cAMP receptor (Class E), or (6) frizzled/smoothened family (Class F). The overall success rate of GPCR-2L in identifying proteins as GPCRs or non-GPCRs is over 97.2%, while identifying GPCRs among their six types is over 97.8%. Such high success rates were derived by the rigorous jackknife cross-validation on a stringent benchmark dataset, in which none of the included proteins had ≥40% pairwise sequence identity to any other protein in a same subset. As a user-friendly web-server, GPCR-2L is freely accessible to the public at http://icpr.jci.edu.cn/, by which one can obtain the 2-level results in about 20 s for a query protein sequence of 500 amino acids. The longer the sequence is, the more time it may usually need. The high success rates reported here indicate that it is a quite effective approach to identify GPCRs and their types with the functional domain information and the low-frequency Fourier spectrum analysis. It is anticipated that GPCR-2L may become a useful tool for both basic research and drug development in the areas related to GPCRs.     

The G protein-coupled receptors in the pufferfish <Emphasis Type="Italic">Takifugu rubripes</Emphasis>

Background

Guanine protein-coupled receptors (GPCRs) constitute a eukaryotic transmembrane protein family and function as “molecular switches” in the second messenger cascades and are found in all organisms between yeast and humans. They form the single, biggest drug-target family due to their versatility of action and their role in several physiological functions, being active players in detecting the presence of light, a variety of smells and tastes, amino acids, nucleotides, lipids, chemicals etc. in the environment of the cell. Comparative genomic studies on model organisms provide information on target receptors in humans and their function. The Japanese teleost Fugu has been identified as one of the smallest vertebrate genomes and a compact model to study the human genome, owing to the great similarity in its gene repertoire with that of human and other vertebrates. Thus the characterization of the GPCRs of Fugu would provide insights to the evolution of the vertebrate genome.

Results

We classified the GPCRs in the Fugu genome and our analysis of its 316 membrane-bound receptors, available on the public databases as well as from literature, detected 298 GPCRs that were grouped into five main families according to the GRAFS classification system (namely, Glutamate, Rhodopsin, Adhesion, Frizzled and Secretin). We also identified 18 other GPCRs that could not be grouped under the GRAFS family and hence were classified as ‘Other 7TM’ receptors. On comparison of the GPCR information from the Fugu genome with those in the human and chicken genomes, we detected 96.83% (306/316) and 96.51% (305/316) orthology in GPCRs among the Fugu-human genomes and Fugu-chicken genomes, respectively.

Conclusions

This study reveals the position of pisces in vertebrate evolution from the GPCR perspective. Fugu can act as a reference model for the human genome for other protein families as well, going by the high orthology observed for GPCRs between Fugu and human. The evolutionary comparison of GPCR sequences between key vertebrate classes of mammals, birds and fish will help in identifying key functional residues and motifs so as to fill in the blanks in the evolution of GPCRs in vertebrates.

     

GPCR-MPredictor: multi-level prediction of G protein-coupled receptors using genetic ensemble

G protein-coupled receptors (GPCRs) are transmembrane proteins, which transduce signals from extracellular ligands to intracellular G protein. Automatic classification of GPCRs can provide important information for the development of novel drugs in pharmaceutical industry. In this paper, we propose an evolutionary approach, GPCR-MPredictor, which combines individual classifiers for predicting GPCRs. GPCR-MPredictor is a web predictor that can efficiently predict GPCRs at five levels. The first level determines whether a protein sequence is a GPCR or a non-GPCR. If the predicted sequence is a GPCR, then it is further classified into family, subfamily, sub-subfamily, and subtype levels. In this work, our aim is to analyze the discriminative power of different feature extraction and classification strategies in case of GPCRs prediction and then to use an evolutionary ensemble approach for enhanced prediction performance. Features are extracted using amino acid composition, pseudo amino acid composition, and dipeptide composition of protein sequences. Different classification approaches, such as k-nearest neighbor (KNN), support vector machine (SVM), probabilistic neural networks (PNN), J48, Adaboost, and Naives Bayes, have been used to classify GPCRs. The proposed hierarchical GA-based ensemble classifier exploits the prediction results of SVM, KNN, PNN, and J48 at each level. The GA-based ensemble yields an accuracy of 99.75, 92.45, 87.80, 83.57, and 96.17% at the five levels, on the first dataset. We further perform predictions on a dataset consisting of 8,000 GPCRs at the family, subfamily, and sub-subfamily level, and on two other datasets of 365 and 167 GPCRs at the second and fourth levels, respectively. In comparison with the existing methods, the results demonstrate the effectiveness of our proposed GPCR-MPredictor in classifying GPCRs families. It is accessible at .     

Towards Improved Quality of GPCR Models by Usage of Multiple Templates and Profile-Profile Comparison

G-protein coupled receptors (GPCRs) are targets of nearly one third of the drugs at the current pharmaceutical market. Despite their importance in many cellular processes the crystal structures are available for less than 20 unique GPCRs of the Rhodopsin-like class. Fortunately, even though involved in different signaling cascades, this large group of membrane proteins has preserved a uniform structure comprising seven transmembrane helices that allows quite reliable comparative modeling. Nevertheless, low sequence similarity between the GPCR family members is still a serious obstacle not only in template selection but also in providing theoretical models of acceptable quality. An additional level of difficulty is the prediction of kinks and bulges in transmembrane helices. Usage of multiple templates and generation of alignments based on sequence profiles may increase the rate of success in difficult cases of comparative modeling in which the sequence similarity between GPCRs is exceptionally low. Here, we present GPCRM, a novel method for fast and accurate generation of GPCR models using averaging of multiple template structures and profile-profile comparison. In particular, GPCRM is the first GPCR structure predictor incorporating two distinct loop modeling techniques: Modeller and Rosetta together with the filtering of models based on the Z-coordinate. We tested our approach on all unique GPCR structures determined to date and report its performance in comparison with other computational methods targeting the Rhodopsin-like class. We also provide a database of precomputed GPCR models of the human receptors from that class.

Availability

GPCRM server and database: http://gpcrm.biomodellab.eu     

Classification of G proteins and prediction of GPCRs-G proteins coupling specificity using continuous wavelet transform and information theory

The coupling between G protein-coupled receptors (GPCRs) and guanine nucleotide-binding proteins (G proteins) regulates various signal transductions from extracellular space into the cell. However, the coupling mechanism between GPCRs and G proteins is still unknown, and experimental determination of their coupling specificity and function is both expensive and time consuming. Therefore, it is significant to develop a theoretical method to predict the coupling specificity between GPCRs and G proteins as well as their function using their primary sequences. In this study, a novel four-layer predictor (GPCRsG_CWTIT) based on support vector machine (SVM), continuous wavelet transform (CWT) and information theory (IT) is developed to classify G proteins and predict the coupling specificity between GPCRs and G proteins. SVM is used for construction of models. CWT and IT are used to characterize the primary structure of protein. Performance of GPCRsG_CWTIT is evaluated with cross-validation test on various working dataset. The overall accuracy of the G proteins at the levels of class and family is 98.23 and 85.42%, respectively. The accuracy of the coupling specificity prediction varies from 74.60 to 94.30%. These results indicate that the proposed predictor is an effective and feasible tool to predict the coupling specificity between GPCRs and G proteins as well as their functions using only the protein full sequence. The establishment of such an accurate prediction method will facilitate drug discovery by improving the ability to identify and predict protein-protein interactions. GPCRsG_CWTIT and dataset can be acquired freely on request from the authors.     

Dimerization in aminergic G-protein-coupled receptors: application of a hidden-site class model of evolution

G-Protein-coupled receptors (GPCRs) are an important superfamily of transmembrane proteins involved in cellular communication. Recently, it has been shown that dimerization is a widely occurring phenomenon in the GPCR superfamily, with likely important physiological roles. Here we use a novel hidden-site class model of evolution as a sequence analysis tool to predict possible dimerization interfaces in GPCRs. This model aims to simulate the evolution of proteins at the amino acid level, allowing the analysis of their sequences in an explicitly evolutionary context. Applying this model to aminergic GPCR sequences, we first validate the general reasoning behind the model. We then use the model to perform a family specific analysis of GPCRs. Accounting for the family structure of these proteins, this approach detects different evolutionarily conserved and accessible patches on transmembrane (TM) helices 4-6 in different families. On the basis of these findings, we propose an experimentally testable dimerization mechanism, involving interactions among different combinations of these helices in different families of aminergic GPCRs.     

Phylogenetic analysis of 277 human G-protein-coupled receptors as a tool for the prediction of orphan receptor ligands

Background

G-protein-coupled receptors (GPCRs) are the largest and most diverse family of transmembrane receptors. They respond to a wide range of stimuli, including small peptides, lipid analogs, amino-acid derivatives, and sensory stimuli such as light, taste and odor, and transmit signals to the interior of the cell through interaction with heterotrimeric G proteins. A large number of putative GPCRs have no identified natural ligand. We hypothesized that a more complete knowledge of the phylogenetic relationship of these orphan receptors to receptors with known ligands could facilitate ligand identification, as related receptors often have ligands with similar structural features.

Results

A database search excluding olfactory and gustatory receptors was used to compile a list of accession numbers and synonyms of 81 orphan and 196 human GPCRs with known ligands. Of these, 241 sequences belonging to the rhodopsin receptor-like family A were aligned and a tentative phylogenetic tree constructed by neighbor joining. This tree and local alignment tools were used to define 19 subgroups of family A small enough for more accurate maximum-likelihood analyses. The secretin receptor-like family B and metabotropic glutamate receptor-like family C were directly subjected to these methods.

Conclusions

Our trees show the overall relationship of 277 GPCRs with emphasis on orphan receptors. Support values are given for each branch. This approach may prove valuable for identification of the natural ligands of orphan receptors as their relation to receptors with known ligands becomes more evident.     

Delaunay triangulation with partial least squares projection to latent structures: a model for G-protein coupled receptors classification and fast structure recognition

As an important transmembrane protein family in eukaryon, G-protein coupled receptors (GPCRs) play a significant role in cellular signal transduction and are important targets for drug design. However, it is very difficult to resolve their tertiary structure by X-ray crystallography. In this study, we have developed a Delaunay model, which constructs a series of simplexes with latent variables to classify the families of GPCRs and projects unknown sequences to principle component space (PC-space) to predict their topology. Computational results show that, for the classification of GPCRs, the method achieves the accuracy of 91.0 and 87.6% for Class A, more than 80% for the other three classes in differentiating GPCRs from non-GPCRs and 70% for discriminating between four major classes of GPCR, respectively. When recognizing the structure of GPCRs, all the N-terminals of sequences can be determined correctly. The maximum accuracy of predicting transmembrane segments is achieved in the 7th transmembrane segment of Rhodopsin, which is 99.4%, and the average error is 2.1 amino acids, which is the lowest in all of the segments prediction. This method could provide structural information of a novel GPCR as a tool for experiments and other algorithms of structure prediction of GPCRs. Academic users should send their request for the MATLAB program for classifying GPCRs and predicting the topology of them at liml@scu.edu.cn .     

Improving the apo-state detergent stability of NTS1 with CHESS for pharmacological and structural studies

The largest single class of drug targets is the G protein-coupled receptor (GPCR) family. Modern high-throughput methods for drug discovery require working with pure protein, but this has been a challenge for GPCRs, and thus the success of screening campaigns targeting soluble, catalytic protein domains has not yet been realized for GPCRs. Therefore, most GPCR drug screening has been cell-based, whereas the strategy of choice for drug discovery against soluble proteins is HTS using purified proteins coupled to structure-based drug design. While recent developments are increasing the chances of obtaining GPCR crystal structures, the feasibility of screening directly against purified GPCRs in the unbound state (apo-state) remains low. GPCRs exhibit low stability in detergent micelles, especially in the apo-state, over the time periods required for performing large screens. Recent methods for generating detergent-stable GPCRs, however, offer the potential for researchers to manipulate GPCRs almost like soluble enzymes, opening up new avenues for drug discovery. Here we apply cellular high-throughput encapsulation, solubilization and screening (CHESS) to the neurotensin receptor 1 (NTS₁) to generate a variant that is stable in the apo-state when solubilized in detergents. This high stability facilitated the crystal structure determination of this receptor and also allowed us to probe the pharmacology of detergent-solubilized, apo-state NTS₁ using robotic ligand binding assays. NTS₁ is a target for the development of novel antipsychotics, and thus CHESS-stabilized receptors represent exciting tools for drug discovery.     

G-protein-coupled receptor prediction using pseudo-amino-acid composition and multiscale energy representation of different physiochemical properties

G-protein-coupled receptors (GPCRs) are the largest family of cell surface receptors that, via trimetric guanine nucleotide-binding proteins (G-proteins), initiate some signaling pathways in the eukaryotic cell. Many diseases involve malfunction of GPCRs making their role evident in drug discovery. Thus, the automatic prediction of GPCRs can be very helpful in the pharmaceutical industry. However, prediction of GPCRs, their families, and their subfamilies is a challenging task. In this article, GPCRs are classified into families, subfamilies, and sub-subfamilies using pseudo-amino-acid composition and multiscale energy representation of different physiochemical properties of amino acids. The aim of the current research is to assess different feature extraction strategies and to develop a hybrid feature extraction strategy that can exploit the discrimination capability in both the spatial and transform domains for GPCR classification. Support vector machine, nearest neighbor, and probabilistic neural network are used for classification purposes. The overall performance of each classifier is computed individually for each feature extraction strategy. It is observed that using the jackknife test the proposed GPCR–hybrid method provides the best results reported so far. The GPCR–hybrid web predictor to help researchers working on GPCRs in the field of biochemistry and bioinformatics is available at http://111.68.99.218/GPCR.     

How do G-protein-coupled receptors work? The case of metabotropic glutamate and GABA receptors

G-protein coupled receptors (GPCRs) represent the largest membrane proteins family in animal genomes. Being the receptors for most hormones and neurotransmitters, these proteins play a central role in intercellular communication. GPCRs can be classified into several groups based on the sequence similarity of their common structural feature: the heptahelical domain. The metabotropic receptors for the main neurotransmitters glutamate and gamma-aminobutyric acid (GABA) belong to the class III of GPCRs, together with others receptors for Ca2+, for sweet and amino acid taste compounds and for some pheromones, as well as for odorants in fish. Besides their transmembrane heptahelical domain responsible for G-protein activation, most of class III receptors possess a large extracellular domain responsible for ligand recognition. The recent resolution of the structure of this binding domain of one of these receptors, the mGlu1 receptor, together with the recent demonstration that these receptors are dimers, revealed an original mechanism of activation for these GPCRs. Such data open new possibilities to develop drugs aimed at modulating these receptors, and raised a number of interesting questions on the activation mechanism of other GPCRs.     

The Secretin GPCRs descended from the family of Adhesion GPCRs

The Adhesion G-protein-coupled receptors (GPCRs) are the most complex gene family among GPCRs with large genomic size, multiple introns, and a fascinating flora of functional domains, though the evolutionary origin of this family has been obscure. Here we studied the evolution of all class B (7tm2)-related genes, including the Adhesion, Secretin, and Methuselah families of GPCRs with a focus on nine genomes. We found that the cnidarian genome of Nematostella vectensis has a remarkably rich set of Adhesion GPCRs with a broad repertoire of N-terminal domains although this genome did not have any Secretin GPCRs. Moreover, the single-celled and colony-forming eukaryotes Monosiga brevicollis and Dictyostelium discoideum contain Adhesion-like GPCRs although these genomes do not have any Secretin GPCRs suggesting that the Adhesion types of GPCRs are the most ancient among class B GPCRs. Phylogenetic analysis found Adhesion group V (that contains GPR133 and GPR144) to be the closest relative to the Secretin family in the Adhesion family. Moreover, Adhesion group V sequences in N. vectensis share the same splice site setup as the Secretin GPCRs. Additionally, one of the most conserved motifs in the entire Secretin family is only found in group V of the Adhesion family. We suggest therefore that the Secretin family of GPCRs could have descended from group V Adhesion GPCRs. We found a set of unique Adhesion-like GPCRs in N. vectensis that have long N-termini containing one Somatomedin B domain each, which is a domain configuration similar to that of a set of Adhesion-like GPCRs found in Branchiostoma floridae. These sequences show slight similarities to Methuselah sequences found in insects. The extended class B GPCRs have a very complex evolutionary history with several species-specific expansions, and we identified at least 31 unique N-terminal domains originating from other protein classes. The overall N-terminal domain structure, however, concurs with the phylogenetic analysis of the transmembrane domains, thus enabling us to track the origin of most of the subgroups.     

iGPCR-Drug: A Web Server for Predicting Interaction between GPCRs and Drugs in Cellular Networking

Involved in many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, G-protein-coupled receptors (GPCRs) are among the most frequent targets of therapeutic drugs. It is time-consuming and expensive to determine whether a drug and a GPCR are to interact with each other in a cellular network purely by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most GPCRs are still unknown. To overcome the situation, a sequence-based classifier, called “iGPCR-drug”, was developed to predict the interactions between GPCRs and drugs in cellular networking. In the predictor, the drug compound is formulated by a 2D (dimensional) fingerprint via a 256D vector, GPCR by the PseAAC (pseudo amino acid composition) generated with the grey model theory, and the prediction engine is operated by the fuzzy K-nearest neighbour algorithm. Moreover, a user-friendly web-server for iGPCR-drug was established at http://www.jci-bioinfo.cn/iGPCR-Drug/. For the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in this paper just for its integrity. The overall success rate achieved by iGPCR-drug via the jackknife test was 85.5%, which is remarkably higher than the rate by the existing peer method developed in 2010 although no web server was ever established for it. It is anticipated that iGPCR-Drug may become a useful high throughput tool for both basic research and drug development, and that the approach presented here can also be extended to study other drug – target interaction networks.     

Ultrasonic velocity assay of extracellular invertase in living yeasts

Being the largest family of cell surface receptors, G-protein-coupled receptors (GPCRs) are among the most frequent targets. The functions of many GPCRs are unknown, and it is both time-consuming and expensive to determine their ligands and signaling pathways by experimental methods. It is of great practical significance to develop an automated and reliable method for classification of GPCRs. In this study, a novel method based on the concept of Chou’s pseudo amino acid composition has been developed for predicting and recognizing GPCRs. The discrete wavelet transform was used to extract feature vectors from the hydrophobicity scales of amino acid to construct pseudo amino acid (PseAA) composition for training support vector machine. The prediction accuracies by the current method among the major families of GPCRs, subfamilies of class A, and types of amine receptors were 99.72%, 97.64%, and 99.20%, respectively, showing 9.4% to 18.0% improvement over other existing methods and indicating that the proposed method is a useful automated tool in identifying GPCRs.     

Multidimensional scaling reveals the main evolutionary pathways of class A G-protein-coupled receptors

Class A G-protein-coupled receptors (GPCRs) constitute the largest family of transmembrane receptors in the human genome. Understanding the mechanisms which drove the evolution of such a large family would help understand the specificity of each GPCR sub-family with applications to drug design. To gain evolutionary information on class A GPCRs, we explored their sequence space by metric multidimensional scaling analysis (MDS). Three-dimensional mapping of human sequences shows a non-uniform distribution of GPCRs, organized in clusters that lay along four privileged directions. To interpret these directions, we projected supplementary sequences from different species onto the human space used as a reference. With this technique, we can easily monitor the evolutionary drift of several GPCR sub-families from cnidarians to humans. Results support a model of radiative evolution of class A GPCRs from a central node formed by peptide receptors. The privileged directions obtained from the MDS analysis are interpretable in terms of three main evolutionary pathways related to specific sequence determinants. The first pathway was initiated by a deletion in transmembrane helix 2 (TM2) and led to three sub-families by divergent evolution. The second pathway corresponds to the differentiation of the amine receptors. The third pathway corresponds to parallel evolution of several sub-families in relation with a covarion process involving proline residues in TM2 and TM5. As exemplified with GPCRs, the MDS projection technique is an important tool to compare orthologous sequence sets and to help decipher the mutational events that drove the evolution of protein families.     

A novel, generic and effective method for the rapid purification of G protein-coupled receptors

G protein-coupled receptors (GPCRs) constitute the largest family of membrane receptors and are of major therapeutic importance. Structure determination of G protein-coupled receptors and other applications require milligram quantities of purified receptor proteins on a regular basis. Recombinant GPCRs fused to a heterologous biotinylation domain were produced in the yeast Pichia pastoris. We describe an efficient method for their rapid purification that relies on the capture of these receptors with streptavidin immobilized on agarose beads, and their subsequent release by enzymatic digestion with TEV protease. This method has been applied to several GPCRs belonging to the class A rhodopsin subfamily, leading to high yields of purified proteins; it represents a method of choice for biochemical and biophysical studies when large quantities of purified GPCRs are needed.     

Polymer-based cell-free expression of ligand-binding family B G-protein coupled receptors without detergents

G-protein coupled receptors (GPCRs) constitute the largest family of intercellular signaling molecules and are estimated to be the target of more than 50% of all modern drugs. As with most integral membrane proteins (IMPs), a major bottleneck in the structural and biochemical analysis of GPCRs is their expression by conventional expression systems. Cell-free (CF) expression provides a relatively new and powerful tool for obtaining preparative amounts of IMPs. However, in the case of GPCRs, insufficient homogeneity of the targeted protein is a problem as the in vitro expression is mainly done with detergents, in which aggregation and solubilization difficulties, as well as problems with proper folding of hydrophilic domains, are common. Here, we report that using CF expression with the help of a fructose-based polymer, NV10 polymer (NVoy), we obtained preparative amounts of homogeneous GPCRs from the three GPCR families. We demonstrate that two GPCR B family members, corticotrophin-releasing factor receptors 1 and 2β are not only solubilized in NVoy but also have functional ligand-binding characteristics with different agonists and antagonists in a detergent-free environment as well. Our findings open new possibilities for functional and structural studies of GPCRs and IMPs in general.     

Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features

Background

Study of drug-target interaction networks is an important topic for drug development. It is both time-consuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction methods can provide us with very useful information in a timely manner.

Methods/Principal Findings

To realize this, drug compounds are encoded with functional groups and proteins encoded by biological features including biochemical and physicochemical properties. The optimal feature selection procedures are adopted by means of the mRMR (Maximum Relevance Minimum Redundancy) method. Instead of classifying the proteins as a whole family, target proteins are divided into four groups: enzymes, ion channels, G-protein- coupled receptors and nuclear receptors. Thus, four independent predictors are established using the Nearest Neighbor algorithm as their operation engine, with each to predict the interactions between drugs and one of the four protein groups. As a result, the overall success rates by the jackknife cross-validation tests achieved with the four predictors are 85.48%, 80.78%, 78.49%, and 85.66%, respectively.

Conclusion/Significance

Our results indicate that the network prediction system thus established is quite promising and encouraging.     

Differential affinities of visual arrestin, beta arrestin1, and beta arrestin2 for G protein-coupled receptors delineate two major classes of receptors

Visual arrestin, betaarrestin1, and betaarrestin2 comprise a family of intracellular proteins that desensitize G protein-coupled receptors (GPCRs). In addition, betaarrestin1 and betaarrestin2 target desensitized receptors to clathrin-coated pits for endocytosis. Whether arrestins differ in their ability to interact with GPCRs in cells is not known. In this study, we visualize the interaction of arrestin family members with GPCRs in real time and in live cells using green fluorescent protein-tagged arrestins. In the absence of agonist, visual arrestin and betaarrestin1 were found in both the cytoplasm and nucleus of HEK-293 cells, whereas betaarrestin2 was found only in the cytoplasm. Analysis of agonist-mediated arrestin translocation to multiple GPCRs identified two major classes of receptors. Class A receptors (beta2 adrenergic receptor, mu opioid receptor, endothelin type A receptor, dopamine D1A receptor, and alpha1b adrenergic receptor) bound betaarrestin2 with higher affinity than betaarrestin1 and did not interact with visual arrestin. In contrast, class B receptors (angiotensin II type 1A receptor, neurotensin receptor 1, vasopressin V2 receptor, thyrotropin-releasing hormone receptor, and substance P receptor) bound both betaarrestin isoforms with similar high affinities and also interacted with visual arrestin. Switching the carboxyl-terminal tails of class A and class B receptors completely reversed the affinity of each receptor for the visual and non-visual arrestins. In addition, exchanging the betaarrestin1 and betaarrestin2 carboxyl termini reversed their extent of binding to class A receptors as well as their subcellular distribution. These results reveal for the first time marked differences in the ability of arrestin family members to bind GPCRs at the plasma membrane. Moreover, they show that visual arrestin can interact in cells with GPCRs other than rhodopsin. These findings suggest that GPCR signaling may be differentially regulated depending on the cellular complement of arrestin isoforms and the ability of arrestins to interact with other cellular proteins.     

Multiplex GPCR assay in reverse transfection cell microarrays

G protein-coupled receptors (GPCRs) are a superfamily of proteins that include some of the most important drug targets in the pharmaceutical industry. Despite the success of this group of drugs, there remains a need to identify GPCR-targeted drugs with greater selectivity, to develop screening assays for validated targets, and to identify ligands for orphan receptors. To address these challenges, the authors have created a multiplexed GPCR assay that measures greater than 3000 receptor: ligand interactions in a single microplate. The multiplexed assay is generated by combining reverse transfection in a 96-well plate format with a calcium flux readout. This assay quantitatively measures receptor activation and inhibition and permits the determination of compound potency and selectivity for entire families of GPCRs in parallel. To expand the number of GPCR targets that may be screened in this system, receptors are cotransfected with plasmids encoding a promiscuous G protein, permitting the analysis of receptors that do not normally mobilize intracellular calcium upon activation. The authors demonstrate the utility of reverse transfection cell microarrays to GPCR-targeted drug discovery with examples of ligand selectivity screening against a panel of GPCRs as well as dose-dependent titrations of selected agonists and antagonists.