Prediction of the coupling specificity of G protein coupled receptors to their G proteins

Comparative analysis of adipokinetic hormones and their receptors in Blattodea reveals novel patterns of gene evolution

Adipokinetic hormone (AKH) is a neuropeptide produced in the insect corpora cardiaca that plays an essential role in mobilising carbohydrates and lipids from the fat body to the haemolymph. AKH acts by binding to a rhodopsin-like G protein-coupled receptor (GPCR), the adipokinetic hormone receptor (AKHR). In this study, we tackle AKH ligand and receptor gene evolution as well as the evolutionary origins of AKH gene paralogues from the order Blattodea (termites and cockroaches). Phylogenetic analyses of AKH precursor sequences point to an ancient AKH gene duplication event in the common ancestor of Blaberoidea, yielding a new group of putative decapeptides. In total, 16 different AKH peptides from 90 species were obtained. Two octapeptides and seven putatively novel decapeptides are predicted for the first time. AKH receptor sequences from 18 species, spanning solitary cockroaches and subsocial wood roaches as well as lower and higher termites, were subsequently acquired using classical molecular methods and in silico approaches employing transcriptomic data. Aligned AKHR open reading frames revealed 7 highly conserved transmembrane regions, a typical arrangement for GPCRs. Phylogenetic analyses based on AKHR sequences support accepted relationships among termite, subsocial (Cryptocercus spp.) and solitary cockroach lineages to a large extent, while putative post-translational modification sites do not greatly differ between solitary and subsocial roaches and social termites. Our study provides important information not only for AKH and AKHR functional research but also for further analyses interested in their development as potential candidates for biorational pest control agents against invasive termites and cockroaches.

A Gq Biased Small Molecule Active at the TSH Receptor

G protein coupled receptors (GPCRs) can lead to G protein and non-G protein initiated signals. By virtue of its structural property, the TSH receptor (TSHR) has a unique ability to engage different G proteins making it highly amenable to selective signaling. In this study, we describe the identification and characterization of a novel small molecule agonist to the TSHR which induces primary engagement with G_αq/11. To identify allosteric modulators inducing selective signaling of the TSHR we used a transcriptional-based luciferase assay system with CHO-TSHR cells stably expressing response elements (CRE, NFAT, SRF, or SRE) that were capable of measuring signals emanating from the coupling of G_αs , G_αq/11, G_βγ, and G_α12/13, respectively. Using this system, TSH activated G_αs , G_αq/11, and G_α12/13 but not G_βγ. On screening a library of 50K molecules at 0.1,1.0 and 10 μM, we identified a novel G_q/11 agonist (named MSq1) which activated G_q/11 mediated NFAT-luciferase >4 fold above baseline and had an EC₅₀= 8.3 × 10^-9 M with only minor induction of G_αs and cAMP. Furthermore, MSq1 is chemically and structurally distinct from any of the previously reported TSHR agonist molecules. Docking studies using a TSHR transmembrane domain (TMD) model indicated that MSq1 had contact points on helices H1, H2, H3, and H7 in the hydrophobic pocket of the TMD and also with the extracellular loops. On co-treatment with TSH, MSq1 suppressed TSH-induced proliferation of thyrocytes in a dose-dependent manner but lacked the intrinsic ability to influence basal thyrocyte proliferation. This unexpected inhibitory property of MSq1 could be blocked in the presence of a PKC inhibitor resulting in derepressing TSH induced protein kinase A (PKA) signals and resulting in the induction of proliferation. Thus, the inhibitory effect of MSq1 on proliferation resided in its capacity to overtly activate protein kinase C (PKC) which in turn suppressed the proliferative signal induced by activation of the predomiant cAMP-PKA pathway of the TSHR. Treatment of rat thyroid cells (FRTL5) with MSq1 did not show any upregulation of gene expression of the key thyroid specific markers such as thyroglobulin(Tg), thyroid peroxidase (Tpo), sodium iodide symporter (Nis), and the TSH receptor (Tshr) further suggesting lack of involvement of MSq1 and G_αq/11 activation with cellular differentation. In summary, we identified and characterized a novel G_αq/11 agonist molecule acting at the TSHR and which showed a marked anti-proliferative ability. Hence, Gq biased activation of the TSHR is capable of ameliorating the proliferative signals from its orthosteric ligand and may offer a therapeutic option for thyroid growth modulation.

Non-canonical Wnt induces chondrocyte de-differentiation through Frizzled 6 and DVL-2/B-raf/CaMKIIα/syndecan 4 axis

Dysregulation of Wnt signaling has been implicated in developmental defects and in the pathogenesis of many diseases such as osteoarthritis; however, the underlying mechanisms are poorly understood. Here, we report that non-canonical Wnt signaling induced loss of chondrocyte phenotype through activation of Fz-6/DVL-2/SYND4/CaMKIIα/B-raf/ERK1/2 cascade. We show that in response to Wnt-3a, Frizzled 6 (Fz-6) triggers the docking of CaMKIIα to syndecan 4 (SYND4) and that of B-raf to DVL-2, leading to the phosphorylation of B-raf by CaMKIIα and activation of extracellular signal-regulated kinase 1 and 2 (ERK1/2) signaling, which leads to chondrocyte de-differentiation. We demonstrate that CaMKIIα associates and phosphorylates B-raf in vitro and in vivo. Our study reveals the mechanism by which non-canonical Wnt activates ERK1/2 signaling that induces loss of chondrocyte phenotype, and demonstrates a direct functional relationship between CaMKIIα and B-raf during chondrocyte de-differentiation. The identification of Fz-6, SYND4, and B-raf as novel physiological regulators of chondrocyte phenotype may provide new potential anti-osteoarthritic targets.

Computational Analysis of Missense Variants of G Protein-Coupled Receptors Involved in the Neuroendocrine Regulation of Reproduction

INTRODUCTION

Many missense variants in G protein-coupled receptors (GPCRs) involved in the neuroendocrine regulation of reproduction have been identified by phenotype-driven or large-scale exome sequencing. Computational functional prediction analysis is commonly performed to evaluate their impact on receptor function.

METHODS

To assess the performance and outcome of functional prediction analyses for these GPCRs, we performed a statistical analysis of the prediction performance of SIFT and PolyPhen-2 for variants with documented biological function as well as variants retrieved from Ensembl. We obtained missense variants with documented biological function testing from patients with reproductive disorders from a comprehensive literature search. Missense variants from individuals with known reproductive disorders were retrieved from the Human Gene Mutation Database. Missense variants from the general population were retrieved from the Ensembl genome database.

RESULTS

The accuracies of SIFT and PolyPhen-2 were 83 and 85%, respectively. The performance of both prediction tools was greater in predicting loss-of-function variants (SIFT: 92%; PolyPhen-2: 95%) than in predicting variants that did not affect function (SIFT: 54%; PolyPhen-2: 57%). Concordance between SIFT and PolyPhen-2 did not improve accuracy. Surprisingly, approximately half of the variants retrieved from Ensembl were predicted as loss-of-function variants by SIFT (47%) and PolyPhen-2 (54%).

CONCLUSION

Our findings provide new guidance for interpreting the results and limitations of computational functional prediction analyses for GPCRs and will help to determine which variants require biological function testing. In addition, our findings raise important questions regarding the link between genotype and phenotype in the general population.

Bioinformatics tools for predicting GPCR gene functions

The automatic classification of GPCRs by bioinformatics methodology can provide functional information for new GPCRs in the whole 'GPCR proteome' and this information is important for the development of novel drugs. Since GPCR proteome is classified hierarchically, general ways for GPCR function prediction are based on hierarchical classification. Various computational tools have been developed to predict GPCR functions; those tools use not simple sequence searches but more powerful methods, such as alignment-free methods, statistical model methods, and machine learning methods used in protein sequence analysis, based on learning datasets. The first stage of hierarchical function prediction involves the discrimination of GPCRs from non-GPCRs and the second stage involves the classification of the predicted GPCR candidates into family, subfamily, and sub-subfamily levels. Then, further classification is performed according to their protein-protein interaction type: binding G-protein type, oligomerized partner type, etc. Those methods have achieved predictive accuracies of around 90 %. Finally, I described the future subject of research of the bioinformatics technique about functional prediction of GPCR.

Novel insights on thyroid-stimulating hormone receptor signal transduction

The TSH receptor (TSHR) is a member of the glycoprotein hormone receptors, a subfamily of family A G protein-coupled receptors. The TSHR is of great importance for the growth and function of the thyroid gland. The TSHR and its endogenous ligand TSH are pivotal proteins with respect to a variety of physiological functions and malfunctions. The molecular events of TSHR regulation can be summarized as a process of signal transduction, including signal reception, conversion, and amplification. The steps during signal transduction from the extra- to the intracellular sites of the cell are not yet comprehensively understood. However, essential new insights have been achieved in recent years on the interrelated mechanisms at the extracellular region, the transmembrane domain, and intracellular components. This review contains a critical summary of available knowledge of the molecular mechanisms of signal transduction at the TSHR, for example, the key amino acids involved in hormone binding or in the structural conformational changes that lead to G protein activation or signaling regulation. Aspects of TSHR oligomerization, signaling promiscuity, signaling selectivity, phenotypes of genetic variations, and potential extrathyroidal receptor activity are also considered, because these are relevant to an understanding of the overall function of the TSHR, including physiological, pathophysiological, and pharmacological perspectives. Directions for future research are discussed.

Camps 2.0: exploring the sequence and structure space of prokaryotic, eukaryotic, and viral membrane proteins

Structural bioinformatics of membrane proteins is still in its infancy, and the picture of their fold space is only beginning to emerge. Because only a handful of three-dimensional structures are available, sequence comparison and structure prediction remain the main tools for investigating sequence-structure relationships in membrane protein families. Here we present a comprehensive analysis of the structural families corresponding to α-helical membrane proteins with at least three transmembrane helices. The new version of our CAMPS database (CAMPS 2.0) covers nearly 1300 eukaryotic, prokaryotic, and viral genomes. Using an advanced classification procedure, which is based on high-order hidden Markov models and considers both sequence similarity as well as the number of transmembrane helices and loop lengths, we identified 1353 structurally homogeneous clusters roughly corresponding to membrane protein folds. Only 53 clusters are associated with experimentally determined three-dimensional structures, and for these clusters CAMPS is in reasonable agreement with structure-based classification approaches such as SCOP and CATH. We therefore estimate that ∼1300 structures would need to be determined to provide a sufficient structural coverage of polytopic membrane proteins. CAMPS 2.0 is available at http://webclu.bio.wzw.tum.de/CAMPS2.0/.

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.

Yellow submarine of the Wnt/Frizzled signaling: submerging from the G protein harbor to the targets

The Wnt/Frizzled signaling pathway plays multiple functions in animal development and, when deregulated, in human disease. The G-protein coupled receptor (GPCR) Frizzled and its cognate heterotrimeric Gi/o proteins initiate the intracellular signaling cascades resulting in cell fate determination and polarization. In this review, we summarize the knowledge on the ligand recognition, biochemistry, modifications and interacting partners of the Frizzled proteins viewed as GPCRs. We also discuss the effectors of the heterotrimeric Go protein in Frizzled signaling. One group of these effectors is represented by small GTPases of the Rab family, which amplify the initial Wnt/Frizzled signal. Another effector is the negative regulator of Wnt signaling Axin, which becomes deactivated in response to Go action. The discovery of the GPCR properties of Frizzled receptors not only provides mechanistic understanding to their signaling pathways, but also paves new avenues for the drug discovery efforts.

Chemogenomic analysis of G-protein coupled receptors and their ligands deciphers locks and keys governing diverse aspects of signalling

Understanding the molecular mechanism of signalling in the important super-family of G-protein-coupled receptors (GPCRs) is causally related to questions of how and where these receptors can be activated or inhibited. In this context, it is of great interest to unravel the common molecular features of GPCRs as well as those related to an active or inactive state or to subtype specific G-protein coupling. In our underlying chemogenomics study, we analyse for the first time the statistical link between the properties of G-protein-coupled receptors and GPCR ligands. The technique of mutual information (MI) is able to reveal statistical inter-dependence between variations in amino acid residues on the one hand and variations in ligand molecular descriptors on the other. Although this MI analysis uses novel information that differs from the results of known site-directed mutagenesis studies or published GPCR crystal structures, the method is capable of identifying the well-known common ligand binding region of GPCRs between the upper part of the seven transmembrane helices and the second extracellular loop. The analysis shows amino acid positions that are sensitive to either stimulating (agonistic) or inhibitory (antagonistic) ligand effects or both. It appears that amino acid positions for antagonistic and agonistic effects are both concentrated around the extracellular region, but selective agonistic effects are cumulated between transmembrane helices (TMHs) 2, 3, and ECL2, while selective residues for antagonistic effects are located at the top of helices 5 and 6. Above all, the MI analysis provides detailed indications about amino acids located in the transmembrane region of these receptors that determine G-protein signalling pathway preferences.

Regulation of stem cell pluripotency and differentiation by G protein coupled receptors

Stem cell-based therapeutics have the potential to effectively treat many terminal and debilitating human diseases, but the mechanisms by which their growth and differentiation are regulated are incompletely defined. Recent data from multiple systems suggest major roles for G protein coupled receptor (GPCR) pathways in regulating stem cell function in vivo and in vitro. The goal of this review is to illustrate common ground between the growing field of stem cell therapeutics and the long-established field of G protein coupled receptor signaling. Herein, we briefly introduce basic stem cell biology and discuss how several conserved pathways regulate pluripotency and differentiation in mouse and human stem cells. We further discuss general mechanisms by which GPCR signaling may impact these pluripotency and differentiation pathways, and summarize specific examples of receptors from each of the major GPCR subfamilies that have been shown to regulate stem cell function. Finally, we discuss possible therapeutic implications of GPCR regulation of stem cell function.

Aminergic control and modulation of honeybee behaviour

Biogenic amines are important messenger substances in the central nervous system and in peripheral organs of vertebrates and of invertebrates. The honeybee, Apis mellifera, is excellently suited to uncover the functions of biogenic amines in behaviour, because it has an extensive behavioural repertoire, with a number of biogenic amine receptors characterised in this insect.In the honeybee, the biogenic amines dopamine, octopamine, serotonin and tyramine modulate neuronal functions in various ways. Dopamine and serotonin are present in high concentrations in the bee brain, whereas octopamine and tyramine are less abundant. Octopamine is a key molecule for the control of honeybee behaviour. It generally has an arousing effect and leads to higher sensitivity for sensory inputs, better learning performance and increased foraging behaviour. Tyramine has been suggested to act antagonistically to octopamine, but only few experimental data are available for this amine. Dopamine and serotonin often have antagonistic or inhibitory effects as compared to octopamine.Biogenic amines bind to membrane receptors that primarily belong to the large gene-family of GTP-binding (G) protein coupled receptors. Receptor activation leads to transient changes in concentrations of intracellular second messengers such as cAMP, IP(3) and/or Ca(2+). Although several biogenic amine receptors from the honeybee have been cloned and characterised more recently, many genes still remain to be identified. The availability of the completely sequenced genome of Apis mellifera will contribute substantially to closing this gap.In this review, we will discuss the present knowledge on how biogenic amines and their receptor-mediated cellular responses modulate different behaviours of honeybees including learning processes and division of labour.

Principles and determinants of G-protein coupling by the rhodopsin-like thyrotropin receptor

In this study we wanted to gain insights into selectivity mechanisms between G-protein-coupled receptors (GPCR) and different subtypes of G-proteins. The thyrotropin receptor (TSHR) binds G-proteins promiscuously and activates both Gs (cAMP) and Gq (IP). Our goal was to dissect selectivity patterns for both pathways in the intracellular region of this receptor. We were particularly interested in the participation of poorly investigated receptor parts.

We systematically investigated the amino acids of intracellular loop (ICL) 1 and helix 8 using site-directed mutagenesis alongside characterization of cAMP and IP accumulation. This approach was guided by a homology model of activated TSHR in complex with heterotrimeric Gq, using the X-ray structure of opsin with a bound G-protein peptide as a structural template.

We provide evidence that ICL1 is significantly involved in G-protein activation and our model suggests potential interactions with subunits Gα as well as Gβγ. Several amino acid substitutions impaired both IP and cAMP accumulation. Moreover, we found a few residues in ICL1 (L440, T441, H443) and helix 8 (R687) that are sensitive for Gq but not for Gs activation. Conversely, not even one residue was found that selectively affects cAMP accumulation only.

Together with our previous mutagenesis data on ICL2 and ICL3 we provide here the first systematically completed map of potential interfaces between TSHR and heterotrimeric G-protein. The TSHR/Gq-heterotrimer complex is characterized by more selective interactions than the TSHR/Gs complex. In fact the receptor interface for binding Gs is a subset of that for Gq and we postulate that this may be true for other GPCRs coupling these G-proteins. Our findings support that G-protein coupling and preference is dominated by specific structural features at the intracellular region of the activated GPCR but is completed by additional complementary recognition patterns between receptor and G-protein subtypes.

Computational overview of GPCR gene universe to support reverse chemical genomics study

In order to support high-throughput screening for ligands of G-protein coupled receptors (GPCRs) by using bioinformatics technology, we introduce a database (SEVENS) with genome-scale annotation and software (GRIFFIN) that can simulate GPCR function. SEVENS ( http://sevens.cbrc.jp/ ) is an integrated database that includes GPCR genes that are identified with high accuracy (99.4% sensitivity and 96.6% specificity) from various types of genomes, by a pipeline that integrates such software as a gene finder, a sequence alignment tool, a motif and domain assignment tool, and a transmembrane helix (TMH) predictor. SEVENS provides the user a genome-scale overview of the "GPCR universe" with detailed information of chromosomal mapping, phylogenetic tree, protein sequence and structure, and experimental evidence, all of which are accessible via a user-friendly interface. GRIFFIN ( http://griffin.cbrc.jp/ ) can predict GPCR and G-protein coupling selectivity induced by ligand binding with high sensitivity and specificity (more than 87% on average), based on the support vector machine (SVM) and hidden Markov Model (HMM). SEVENS and GRIFFIN are expected to contribute to revealing the function of orphan and unknown GPCRs.

Transmembrane topology and signal peptide prediction using dynamic bayesian networks

Hidden Markov models (HMMs) have been successfully applied to the tasks of transmembrane protein topology prediction and signal peptide prediction. In this paper we expand upon this work by making use of the more powerful class of dynamic Bayesian networks (DBNs). Our model, Philius, is inspired by a previously published HMM, Phobius, and combines a signal peptide submodel with a transmembrane submodel. We introduce a two-stage DBN decoder that combines the power of posterior decoding with the grammar constraints of Viterbi-style decoding. Philius also provides protein type, segment, and topology confidence metrics to aid in the interpretation of the predictions. We report a relative improvement of 13% over Phobius in full-topology prediction accuracy on transmembrane proteins, and a sensitivity and specificity of 0.96 in detecting signal peptides. We also show that our confidence metrics correlate well with the observed precision. In addition, we have made predictions on all 6.3 million proteins in the Yeast Resource Center (YRC) database. This large-scale study provides an overall picture of the relative numbers of proteins that include a signal-peptide and/or one or more transmembrane segments as well as a valuable resource for the scientific community. All DBNs are implemented using the Graphical Models Toolkit. Source code for the models described here is available at http://noble.gs.washington.edu/proj/philius. A Philius Web server is available at http://www.yeastrc.org/philius, and the predictions on the YRC database are available at http://www.yeastrc.org/pdr.

An olfactory receptor pseudogene whose function emerged in humans: a case study in the evolution of structure-function in GPCRs

Human olfactory receptor, hOR17-210, is identified as a pseudogene in the human genome. Experimental data has shown however, that the gene product of frame-shifted, cloned hOR17-210 cDNA was able to bind an odorant-binding protein and is narrowly tuned for excitation by cyclic ketones. Supported by experimental results, we used the bioinformatics methods of sequence analysis (genome-wide and pair-wise), computational protein modeling and docking, to show that functionality in this receptor is retained due to sequence-structure features not previously observed in mammalian ORs. This receptor does not possess the first two transmembrane helical domains (of seven typically seen in GPCRs). It however, possesses an additional TM that has not been observed in other human olfactory receptors. By incorporating these novel structural features, we created two putative models for this receptor. We also docked odor ligands that were experimentally shown to bind hOR17-210. We show how and why structural modifications of OR17-210 do not hinder this receptor's functionality. Our studies reveal that novel gene rearrangements that result in sequence and structural diversity may have a bearing on OR and GPCR function and evolution.

The orphan G protein-coupled receptor 161 is required for left-right patterning

Gpr161 (also known as RE2) is an orphan G protein-coupled receptor (GPCR) that is expressed during embryonic development in zebrafish. Determining its biological function has proven difficult due to lack of knowledge regarding its natural or synthetic ligands. Here, we show that targeted knockdown of gpr161 disrupts asymmetric gene expression in the lateral plate mesoderm, resulting in aberrant looping of the heart tube. This is associated with elevated Ca(2+) levels in cells lining the Kupffer's vesicle and normalization of Ca(2+) levels, by over-expression of ncx1 or pmca-RNA, is able to partially rescue the cardiac looping defect in gpr161 knockdown embryos. Taken together, these data support a model in which gpr161 plays an essential role in left-right (L-R) patterning by modulating Ca(2+) levels in the cells surrounding the Kupffer's vesicle.

An intracellular loop (IL2) residue confers different basal constitutive activities to the human lutropin receptor and human thyrotropin receptor through structural communication between IL2 and helix 6, via helix 3

The human lutropin receptor (hLHR) and human TSH receptor (hTSHR) are G protein-coupled receptors that play key roles in reproductive and thyroid physiology, respectively. We show using a quantitative assessment of cAMP production as a function of cell surface receptor expression that the hTSHR possesses greater basal constitutive activity than the hLHR. Further studies were undertaken to test the hypothesis that different potential Gs-coupling motifs identified in IL2 of the hTSHR and hLHR contribute to their different basal constitutive activities. Although mutating the receptors to interchange their potential Gs-coupling motifs reversed their relative activities, we show this to be due to the swapping of one IL2 residue (Q476 in the hLHR; R531 in the hTSHR). Molecular dynamics simulations show that the effect of the hLHR(Q476R) mutation, switching the structural features of the hLHR toward those of the hTSHR, is greater than the switching effect of the hTSHR(R531Q) mutant toward the hLHR. The structural model of the hLHR(Q476R) mutant can be considered as a hybrid of wild-type (wt) hTSHR and constitutively active mutant hLHR forms. In this hLHR(Q476R) mutant, IL2 adopts a structure similar to IL2 of the wt hTSHR, but it shares with the hLHR constitutively active mutant the solvent exposure and the reciprocal arrangement of helices 3, 5, and 6, including the weakening of the wt native R3.50-D6.30 interaction. Our results suggest a H3-mediated structural connection between IL2 and the cytosolic extension of H6. Thus, IL2 contributes significantly to the inactive and active state ensembles of these G protein-coupled receptors.

Regulation of G protein-coupled receptor signalling: focus on the cardiovascular system and regulator of G protein signalling proteins

G protein-coupled receptors (GPCRs) are involved in many biological processes. Therefore, GPCR function is tightly controlled both at receptor level and at the level of signalling components. Well-known mechanisms by which GPCR function can be regulated comprise desensitization/resensitization processes and GPCR up- and downregulation. GPCR function can also be regulated by several proteins that directly interact with the receptor and thereby modulate receptor activity. An additional mechanism by which receptor signalling is regulated involves an emerging class of proteins, the so-called regulators of G protein signalling (RGS). In this review we will describe some of these control mechanisms in more detail with some specific examples in the cardiovascular system. In addition, we will provide an overview on RGS proteins and the involvement of RGS proteins in cardiovascular function.

Emerging concepts of guanine nucleotide-binding protein-coupled receptor (GPCR) function and implications for high throughput screening

Guanine nucleotide binding protein (G protein) coupled receptors (GPCRs) comprise one of the largest families of proteins in the human genome and are a target for 40% of all approved drugs. GPCRs have unique structural motifs that allow them to interact with a wide and diverse series of extracellular ligands, as well as intracellular proteins, G proteins, receptor activity-modifying proteins, arrestins, and indeed other receptors. This distinctive structure has led to numerous efforts to discover drugs against GPCRs with targeted therapeutic uses. Such "designer" drugs currently include allosteric regulators, inverse agonists, and drugs targeting hetero-oligomeric complexes. Moreover, the large family of orphan GPCRs provides a rich and novel field of targets to discover drugs with unique therapeutic properties. The numerous technologies to discover GPCR drugs have also greatly advanced over the years, facilitating compound screening against known and orphan GPCRs, as well as in the identification of unique designer GPCR drugs. Indeed, high throughput screening (HTS) technologies employing functional cell-based approaches are now widely used. These include measurement of second messenger accumulation such as cyclic AMP, calcium ions, and inositol phosphates, as well as mitogen-activated protein kinase activation, protein-protein interactions, and GPCR oligomerization. This review focuses on how the improved understanding of the molecular pharmacology of GPCRs, coupled with a plethora of novel HTS technologies, is leading to the discovery and development of an entirely new generation of GPCR-based therapeutics.

Proteomic applications of automated GPCR classification

The G-protein coupled receptor (GPCR) superfamily fulfils various metabolic functions and interacts with a diverse range of ligands. There is a lack of sequence similarity between the six classes that comprise the GPCR superfamily. Moreover, most novel GPCRs found have low sequence similarity to other family members which makes it difficult to infer properties from related receptors. Many different approaches have been taken towards developing efficient and accurate methods for GPCR classification, ranging from motif-based systems to machine learning as well as a variety of alignment-free techniques based on the physiochemical properties of their amino acid sequences. This review describes the inherent difficulties in developing a GPCR classification algorithm and includes techniques previously employed in this area.

Computational prediction of the coupling specificity of g protein-coupled receptors

G protein-coupled receptors (GPCRs) represent one of the most important categories of membrane proteins that play important roles in signaling pathways. GPCRs transduce the extracellular stimuli into intracellular second messengers via their coupling to specific class of heterotrimeric GTP-binding proteins (G proteins) and the subsequent regulation of a diverse variety of effectors. Understanding the coupling specificity of GPCRs is critical for further comprehending their function, and is of tremendous clinical significance because GPCRs are the most successful drug targets. This minireview addresses the computational approaches that have been created for the prediction of coupling specificity of GPCRs and highlights the perspective of bioinformatics strategies that may be used to tackle this important task. In addition, some of the important resources of this field are also provided.

Prediction of GPCR-G protein coupling specificity using features of sequences and biological functions

Understanding the coupling specificity between G protein-coupled receptors (GPCRs) and specific classes of G proteins is important for further elucidation of receptor functions within a cell. Increasing information on GPCR sequences and the G protein family would facilitate prediction of the coupling properties of GPCRs. In this study, we describe a novel approach for predicting the coupling specificity between GPCRs and G proteins. This method uses not only GPCR sequences but also the functional knowledge generated by natural language processing, and can achieve 92.2% prediction accuracy by using the C4.5 algorithm. Furthermore, rules related to GPCR-G protein coupling are generated. The combination of sequence analysis and text mining improves the prediction accuracy for GPCR-G protein coupling specificity, and also provides clues for understanding GPCR signaling.

Prediction and classification of human G-protein coupled receptors based on support vector machines

A computational system for the prediction and classification of human G-protein coupled receptors (GPCRs) has been developed based on the support vector machine (SVM) method and protein sequence information. The feature vectors used to develop the SVM prediction models consist of statistically significant features selected from single amino acid, dipeptide, and tripeptide compositions of protein sequences. Furthermore, the length distribution difference between GPCRs and non-GPCRs has also been exploited to improve the prediction performance. The testing results with annotated human protein sequences demonstrate that this system can get good performance for both prediction and classification of human GPCRs.

Predicting the coupling specificity of GPCRs to G-proteins by support vector machines

G-protein coupled receptors (GPCRs) represent one of the most important classes of drug targets for pharmaceutical industry and play important roles in cellular signal transduction. Predicting the coupling specificity of GPCRs to G-proteins is vital for further understanding the mechanism of signal transduction and the function of the receptors within a cell, which can provide new clues for pharmaceutical research and development. In this study, the features of amino acid compositions and physiochemical properties of the full-length GPCR sequences have been analyzed and extracted. Based on these features, classifiers have been developed to predict the coupling specificity of GPCRs to G-proteins using support vector machines. The testing results show that this method could obtain better prediction accuracy.

Predicting G-protein coupled receptors-G-protein coupling specificity based on autocross-covariance transform

Determining G-protein coupled receptors (GPCRs) coupling specificity is very important for further understanding the functions of receptors. A successful method in this area will benefit both basic research and drug discovery practice. Previously published methods rely on the transmembrane topology prediction at training step, even at prediction step. However, the transmembrane topology predicted by even the best algorithm is not of high accuracy. In this study, we developed a new method, autocross-covariance (ACC) transform based support vector machine (SVM), to predict coupling specificity between GPCRs and G-proteins. The primary amino acid sequences are translated into vectors based on the principal physicochemical properties of the amino acids and the data are transformed into a uniform matrix by applying ACC transform. SVMs for nonpromiscuous coupled GPCRs and promiscuous coupled GPCRs were trained and validated by jackknife test and the results thus obtained are very promising. All classifiers were also evaluated by the test datasets with good performance. Besides the high prediction accuracy, the most important feature of this method is that it does not require any transmembrane topology prediction at either training or prediction step but only the primary sequences of proteins. The results indicate that this relatively simple method is applicable. Academic users can freely download the prediction program at http://www.scucic.net/group/database/Service.asp.

Characterization of Gpr101 expression and G-protein coupling selectivity

This report describes the identification and characterization of the murine orphan GPCR, Gpr101. Both human and murine genes were localized to chromosome X. Similar to its human ortholog, murine Gpr101 mRNA was detected predominantly in the brain within discrete nuclei. A knowledge-restricted hidden Markov model-based algorithm, capable of accurately predicting G-protein coupling selectivity, indicated that both human and murine GPR101 were likely coupled to Gs. This prediction was supported by the elevation of cyclic AMP levels and the activation of a cyclic AMP response element-luciferase reporter gene in HEK293 cells over-expressing human GPR101. Consistent with this, over-expression of human GPR101 in a yeast-based system yielded an elevated, agonist-independent reporter gene response in the presence of a yeast chimeric Galphas protein. These results indicate that GPR101 participates in a potentially wide range of activities in the CNS via modulation of cAMP levels.

Structure-function analysis of Frizzleds

Frizzleds, cell surface receptors that mediate the actions of Wnt ligands on early development, are heptahelical (based upon hydropathy analysis) and couple to heterotrimeric G proteins. The primary structure of all ten mammalian Frizzleds display many landmarks observed in virtually all G protein-coupled receptors, including an exofacial N-terminus that is N-glycosylated, the presence of seven hydrophobic transmembrane segments predicted to form alpha-helixes, and three intracellular loops as well as a cytoplasmic, C-terminal tail that harbor suspected sites for protein phosphorylation. Prediction of the G proteins to which Frizzleds mediate signaling based upon a bioinformatic analysis of the primary sequence of the intracellular domains are in good agreement with functional screens in Drosophila, zebrafish, and mouse models of development, e.g., predicting Frizzled-1 to interact with members of the Gi/Go protein family. Likewise various Wnt signaling pathways are sensitive to treatment with pertussis toxin and knock-down of specific G protein alpha-subunits. Homology among the sequences encoding the cytoplasmic domains of human Frizzleds is high and the various Frizzleds can be segregated into subsets predicted to share some common downstream signaling elements. Among different species, homologies can reveal conservation of signaling to cognate G protein partners. Additionally, cytoplasmic domains of the prototypic beta2-adrenergic receptor can be substituted with those from either Frizzled-1 or Frizzled-2 to create chimeric receptors that are activated by beta-adrenergic agonists, yet signal with high fidelity to the Wnt/beta-catenin and Wnt/Ca2+, cyclic GMP pathways, respectively, regulating key aspects of early development. The nature of Frizzled-based signaling complexes, their temporal assembly, and spatial distribution via scaffold protein remains to be elucidated, as does whether or not these Wnt receptors display agonist-induced desensitization, internalization, and re-cycling to the cell membrane.

Melanopsin: Another Way of Signaling Light

A subset of melanopsin-expressing retinal ganglion cells has been identified to be directly photosensitive (pRGCs), modulating a range of behavioral and physiological responses to light. Recent expression studies of melanopsin have provided compelling evidence that melanopsin is the photopigment of the pRGCs. However, the mechanism by which melanopsin transduces light information remains an open question. This review discusses the signaling pathways that may underlie melanopsin-dependent phototransduction in native pRGCs, as well as the many exciting challenges ahead.

A general model of G protein-coupled receptor sequences and its application to detect remote homologs

G protein-coupled receptors (GPCRs) constitute a large superfamily involved in various types of signal transduction pathways triggered by hormones, odorants, peptides, proteins, and other types of ligands. The superfamily is so diverse that many members lack sequence similarity, although they all span the cell membrane seven times with an extracellular N and a cytosolic C terminus. We analyzed a divergent set of GPCRs and found distinct loop length patterns and differences in amino acid composition between cytosolic loops, extracellular loops, and membrane regions. We configured GPCRHMM, a hidden Markov model, to fit those features and trained it on a large dataset representing the entire superfamily. GPCRHMM was benchmarked to profile HMMs and generic transmembrane detectors on sets of known GPCRs and non-GPCRs. In a cross-validation procedure, profile HMMs produced an error rate nearly twice as high as GPCRHMM. In a sensitivity-selectivity test, GPCRHMM's sensitivity was about 15% higher than that of the best transmembrane predictors, at comparable false positive rates. We used GPCRHMM to search for novel members of the GPCR superfamily in five proteomes. All in all we detected 120 sequences that lacked annotation and are potentially novel GPCRs. Out of those 102 were found in Caenorhabditis elegans, four in human, and seven in mouse. Many predictions (65) belonged to Pfam domains of unknown function. GPCRHMM strongly rejected a family of arthropod-specific odorant receptors believed to be GPCRs. A detailed analysis showed that these sequences are indeed very different from other GPCRs. GPCRHMM is available at http://gpcrhmm.cgb.ki.se.

Cellular and molecular biology of orphan G protein-coupled receptors

The superfamily of G protein-coupled receptors (GPCRs) is the largest and most diverse group of membrane-spanning proteins. It plays a variety of roles in pathophysiological processes by transmitting extracellular signals to cells via heterotrimeric G proteins. Completion of the human genome project revealed the presence of approximately 168 genes encoding established nonsensory GPCRs, as well as 207 genes predicted to encode novel GPCRs for which the natural ligands remained to be identified, the so-called orphan GPCRs. Eighty-six of these orphans have now been paired to novel or previously known molecules, and 121 remain to be deorphaned. A better understanding of the GPCR structures and classification; knowledge of the receptor activation mechanism, either dependent on or independent of an agonist; increased understanding of the control of GPCR-mediated signal transduction; and development of appropriate ligand screening systems may improve the probability of discovering novel ligands for the remaining orphan GPCRs.

GRIFFIN: a system for predicting GPCR-G-protein coupling selectivity using a support vector machine and a hidden Markov model

We describe a novel system, GRIFFIN (G-protein and Receptor Interaction Feature Finding INstrument), that predicts G-protein coupled receptor (GPCR) and G-protein coupling selectivity based on a support vector machine (SVM) and a hidden Markov model (HMM) with high sensitivity and specificity. Based on our assumption that whole structural segments of ligands, GPCRs and G-proteins are essential to determine GPCR and G-protein coupling, various quantitative features were selected for ligands, GPCRs and G-protein complex structures, and those parameters that are the most effective in selecting G-protein type were used as feature vectors in the SVM. The main part of GRIFFIN includes a hierarchical SVM classifier using the feature vectors, which is useful for Class A GPCRs, the major family. For the opsins and olfactory subfamilies of Class A and other minor families (Classes B, C, frizzled and smoothened), the binding G-protein is predicted with high accuracy using the HMM. Applying this system to known GPCR sequences, each binding G-protein is predicted with high sensitivity and specificity (>85% on average). GRIFFIN () is freely available and allows users to easily execute this reliable prediction of G-proteins.

A homolog of the human chemokine receptor CXCR1 is expressed in the mouse

Two distinct genes are present in the human genome encoding receptors for human interleukin-8 (hCXCL8), referred to as hCXCR1 and hCXCR2. While it seems clear that orthologous genes are present in the genomes of several mammals, the existence of a gene encoding an ortholog of hCXCR1 in the mouse has thus far been controversial. We have isolated a cDNA that is highly similar to the cDNAs of hCXCR1 and hCXCR2, but is clearly distinct from the cDNA encoding mouse CXCR2 (mCXCR2). The encoded protein, designated mouse CXCR1-like (mCXCR1-like), shares 64, 57, 57, and 89% identical amino acids with hCXCR1, hCXCR2, mCXCR2, and rCXCR1-like, respectively. The gene encoding mCXCR1-like was mapped to mouse chromosome 1 and its genomic organization was determined to be very similar to the organization of the gene encoding hCXCR1. Like hCXCR1, mCXCR1-like was found to be expressed at the mRNA level in neutrophils. In addition, mRNA encoding mCXCR1-like was detected in liver, kidney, and spleen. In spleen, mCXCR1-like transcripts were predominantly found in CD4+ T cells. In liver, mCXCR1-like transcripts were identified in residual CD3+ T cells and macrophages, suggesting that mCXCR1-like may regulate inflammatory and immunological processes in the liver. When expressed as a recombinant protein, mCXCR1-like was not activated by a large panel of known CXC chemokines of human and murine origin. These findings suggest that a homolog or ortholog of hCXCR1 is expressed in the mouse to be activated by a hitherto unknown CXC chemokine of the mouse.

The study of G-protein coupled receptor oligomerization with computational modeling and bioinformatics

To achieve a structural context for the analysis of G-protein coupled receptor (GPCR) oligomers, molecular modeling must be used to predict the corresponding interaction interfaces. The task is complicated by the paucity of detailed structural data at atomic resolution, and the large number of possible modes in which the bundles of seven transmembrane (TM) segments of the interacting GPCR monomers can be packed together into dimers and/or higher-order oligomers. Approaches and tools offered by bioinformatics can be used to reduce the complexity of this task and, combined with computational modeling, can serve to yield testable predictions for the structural properties of oligomers. Most of the bioinformatics methods take advantage of the evolutionary relation that exists among GPCRs, as expressed in their sequences and measurable in the common elements of their structural and functional features. These common elements are responsible for the presence of detectable patterns of motifs and correlated mutations evident from the alignment of the sequences of these complex biological systems. The decoding of these patterns in terms of structural and functional determinants can provide indications about the most likely interfaces of dimerization/oligomerization of GPCRs. We review here the main approaches from bioinformatics, enhanced by computational molecular modeling, that have been used to predict likely interfaces of dimerization/oligomerization of GPCRs, and compare results from their application to rhodopsin-like GPCRs. A compilation of the most frequently predicted GPCR oligomerization interfaces points to specific regions of TMs 4-6.

A method for the prediction of GPCRs coupling specificity to G-proteins using refined profile Hidden Markov Models

BACKGROUND

G- Protein coupled receptors (GPCRs) comprise the largest group of eukaryotic cell surface receptors with great pharmacological interest. A broad range of native ligands interact and activate GPCRs, leading to signal transduction within cells. Most of these responses are mediated through the interaction of GPCRs with heterotrimeric GTP-binding proteins (G-proteins). Due to the information explosion in biological sequence databases, the development of software algorithms that could predict properties of GPCRs is important. Experimental data reported in the literature suggest that heterotrimeric G-proteins interact with parts of the activated receptor at the transmembrane helix-intracellular loop interface. Utilizing this information and membrane topology information, we have developed an intensive exploratory approach to generate a refined library of statistical models (Hidden Markov Models) that predict the coupling preference of GPCRs to heterotrimeric G-proteins. The method predicts the coupling preferences of GPCRs to Gs, Gi/o and Gq/11, but not G12/13 subfamilies.

RESULTS

Using a dataset of 282 GPCR sequences of known coupling preference to G-proteins and adopting a five-fold cross-validation procedure, the method yielded an 89.7% correct classification rate. In a validation set comprised of all receptor sequences that are species homologues to GPCRs with known coupling preferences, excluding the sequences used to train the models, our method yields a correct classification rate of 91.0%. Furthermore, promiscuous coupling properties were correctly predicted for 6 of the 24 GPCRs that are known to interact with more than one subfamily of G-proteins.

CONCLUSION

Our method demonstrates high correct classification rate. Unlike previously published methods performing the same task, it does not require any transmembrane topology prediction in a preceding step. A web-server for the prediction of GPCRs coupling specificity to G-proteins available for non-commercial users is located at http://bioinformatics.biol.uoa.gr/PRED-COUPLE.

A hidden Markov model with molecular mechanics energy-scoring function for transmembrane helix prediction

A range of methods has been developed to predict transmembrane helices and their topologies. Although most of these algorithms give good predictions, no single method consistently outperforms the others. However, combining different algorithms is one approach that can potentially improve the accuracy of the prediction. We developed a new method that initially uses a hidden Markov model to predict alternative models for membrane spanning helices in proteins. The algorithm subsequently identifies the best among models by ranking them using a novel scoring function based on the folding energy of transmembrane helical fragments. This folding of helical fragments and the incorporation into membrane is modeled using CHARMm, extended with the Generalized Born surface area solvent model (GBSA/IM) with implicit membrane. The combined method reported here, TMHGB significantly increases the accuracy of the original hidden Markov model-based algorithm.

A database for G proteins and their interaction with GPCRs

Background

G protein-coupled receptors (GPCRs) transduce signals from extracellular space into the cell, through their interaction with G proteins, which act as switches forming hetero-trimers composed of different subunits (α,β,γ). The α subunit of the G protein is responsible for the recognition of a given GPCR. Whereas specialised resources for GPCRs, and other groups of receptors, are already available, currently, there is no publicly available database focusing on G Proteins and containing information about their coupling specificity with their respective receptors.

Description

gpDB is a publicly accessible G proteins/GPCRs relational database. Including species homologs, the database contains detailed information for 418 G protein monomers (272 Gα, 87 Gβ and 59 Gγ) and 2782 GPCRs sequences belonging to families with known coupling to G proteins. The GPCRs and the G proteins are classified according to a hierarchy of different classes, families and sub-families, based on extensive literature searchs. The main innovation besides the classification of both G proteins and GPCRs is the relational model of the database, describing the known coupling specificity of the GPCRs to their respective α subunit of G proteins, a unique feature not available in any other database. There is full sequence information with cross-references to publicly available databases, references to the literature concerning the coupling specificity and the dimerization of GPCRs and the user may submit advanced queries for text search. Furthermore, we provide a pattern search tool, an interface for running BLAST against the database and interconnectivity with PRED-TMR, PRED-GPCR and TMRPres2D.

Conclusions

The database will be very useful, for both experimentalists and bioinformaticians, for the study of G protein/GPCR interactions and for future development of predictive algorithms. It is available for academics, via a web browser at the URL:

Molecular mechanisms of ligand binding, signaling, and regulation within the superfamily of G-protein-coupled receptors: molecular modeling and mutagenesis approaches to receptor structure and function

The superfamily of G-protein-coupled receptors (GPCRs) could be subclassified into 7 families (A, B, large N-terminal family B-7 transmembrane helix, C, Frizzled/Smoothened, taste 2, and vomeronasal 1 receptors) among mammalian species. Cloning and functional studies of GPCRs have revealed that the superfamily of GPCRs comprises receptors for chemically diverse native ligands including (1) endogenous compounds like amines, peptides, and Wnt proteins (i.e., secreted proteins activating Frizzled receptors); (2) endogenous cell surface adhesion molecules; and (3) photons and exogenous compounds like odorants. The combined use of site-directed mutagenesis and molecular modeling approaches have provided detailed insight into molecular mechanisms of ligand binding, receptor folding, receptor activation, G-protein coupling, and regulation of GPCRs. The vast majority of family A, B, C, vomeronasal 1, and taste 2 receptors are able to transduce signals into cells through G-protein coupling. However, G-protein-independent signaling mechanisms have also been reported for many GPCRs. Specific interaction motifs in the intracellular parts of these receptors allow them to interact with scaffold proteins. Protein engineering techniques have provided information on molecular mechanisms of GPCR-accessory protein, GPCR-GPCR, and GPCR-scaffold protein interactions. Site-directed mutagenesis and molecular dynamics simulations have revealed that the inactive state conformations are stabilized by specific interhelical and intrahelical salt bridge interactions and hydrophobic-type interactions. Constitutively activating mutations or agonist binding disrupts such constraining interactions leading to receptor conformations that associates with and activate G-proteins.

G Protein-Coupled Receptors in Invertebrates: A State of the Art

G protein-coupled receptors (GPCRs) constitute one of the largest and most ancient superfamilies of membrane-spanning proteins. We focus on neuropeptide GPCRs, in particular on those of invertebrates. In general, such receptors mediate the responses of signaling molecules that constitute the highest hierarchical position in the regulation of physiological processes. Until recently, only a few of these receptors were identified in invertebrates. However, the availability of a plethora of genomic information has boosted the discovery of novel members in several invertebrate species, such as Drosophila, in which 18 neuropeptide GPCRs have been characterized. The finalization of genomic projects in other invertebrates will lead to a similar expansion of GPCR understanding. Many new insights regarding neuropeptide regulation have followed from the discovery of their cognate receptors. Furthermore, information on GPCR signaling is still fragmentary and the elucidation of these pathways in model insects such as Drosophila will lead to further insights in other species, including mammals. In this review we present the current status of what is known about invertebrate GPCRs, discuss some novel perceptions that follow from the identified members, and, finally, present some future prospects.

Automated detection of remote homology

The classification of a newly identified protein as a member of a superfamily is important for focusing experiments on its most likely functions. Such classification, often performed by hand, has now been fully automated. This sophisticated new approach takes into account not only alignment scores but also a number of other computable attributes, such as functional sites deduced from sequence conservation patterns.

Regulatory sequence analysis: application to the interpretation of gene expression

Microarray technologies for measuring mRNA abundances in cells allow monitoring of gene expression levels for tens of thousands of genes in parallel. By measuring expression responses across hundreds of different conditions or timepoints a relatively detailed gene expression map starts to emerge. Using cluster analysis techniques, it is possible to identify genes that are consistently coexpressed under several different conditions or treatments. These sets of coexpressed genes can then be compared to existing knowledge about biochemical or signalling pathways, the function of unknown genes can be hypothesised by comparing them to other genes with characterised function, or from trends in expression profiles in general - why cell needs to transcribe or silence the genes during particular treatment. The regulation of genes on the DNA level is largely guided by particular sequence features, the transcription factor binding sites, and other signals encaptured in DNA. By analyzing the regulatory regions of the DNA of the genes consistently coexpressed, we can discover the potential signals hidden in DNA by computational analysis methods. The prerequisite for this kind of analysis is the existence of genomic DNA sequence, knowledge about gene locations, and experimental gene expression measurements for a variety of conditions. This article surveys some of the analysis methods and studies for such a computational discovery approach for yeast Saccharomyces cerevisiae.