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

Proteome-wide analysis reveals G protein-coupled receptor-like proteins in rice (Oryza sativa)

G protein-coupled receptors (GPCRs) constitute the largest family of transmembrane proteins in metazoans that mediate the regulation of various physiological responses to discrete ligands through heterotrimeric G protein subunits. The existence of GPCRs in plant is contentious, but their comparable crucial role in various signaling pathways necessitates the identification of novel remote GPCR-like proteins that essentially interact with the plant G protein α subunit and facilitate the transduction of various stimuli. In this study, we identified three putative GPCR-like proteins (OsGPCRLPs) (LOC_Os06g09930.1, LOC_Os04g36630.1, and LOC_Os01g54784.1) in the rice proteome using a stringent bioinformatics workflow. The identified OsGPCRLPs exhibited a canonical GPCR 'type I' 7TM topology, patterns, and biologically significant sites for membrane anchorage and desensitization. Cluster-based interactome mapping revealed that the identified proteins interact with the G protein α subunit which is a characteristic feature of GPCRs. Computational results showing the interaction of identified GPCR-like proteins with G protein α subunit and its further validation by the membrane yeast-two-hybrid assay strongly suggest the presence of GPCR-like 7TM proteins in the rice proteome. The absence of a regulator of G protein signaling (RGS) box in the C- terminal domain, and the presence of signature motifs of canonical GPCR in the identified OsGPCRLPs strongly suggest that the rice proteome contains GPCR-like proteins that might be involved in signal transduction.

Stringent in-silico identification of putative G-protein-coupled receptors (GPCRs) of the entomopathogenic nematode Heterorhabditis bacteriophora

The infective juveniles (IJs) of entomopathogenic nematode (EPN) Heterorhabditis bacteriophora find and infect their host insects in heterogeneous soil ecosystems by sensing a universal host cue (CO2) or insect/plant-derived odorants, which bind to various sensory receptors, including G protein-coupled receptors (GPCRs). Nematode chemosensory GPCRs (NemChRs) bind to a diverse set of ligands, including odor molecules. However, there is a lack of information on the NemChRs in EPNs. Here we identified 21 GPCRs in the H. bacteriophora genome sequence in a triphasic manner, combining various transmembrane detectors and GPCR predictors based on different algorithms, and considering inherent properties of GPCRs. The pipeline was validated by reciprocal BLAST, InterProscan, GPCR-CA, and NCBI CDD search. Functional classification of predicted GPCRs using Pfam revealed the presence of four NemChRs. Additionally, GPCRs were classified into various families based on the reciprocal BLAST approach into a frizzled type, a secretin type, and 19 rhodopsin types of GPCRs. Gi/o is the most abundant kind of G-protein, having a coupling specificity to all the fetched GPCRs. As the 21 GPCRs identified are expected to play a crucial role in the host-seeking behavior, these might be targeted to develop novel insect-pest management strategies by tweaking EPN IJ behavior, or to design novel anthelminthic drugs. Our new and stringent GPCR detection pipeline may also be used to identify GPCRs from the genome sequence of other organisms.

Characterization of a new type of neuronal 5-HT G- protein coupled receptor in the cestode nervous system

Cestodes are platyhelminth parasites with a wide range of hosts that cause neglected diseases. Neurotransmitter signaling is of critical importance for these parasites which lack circulatory, respiratory and digestive systems. For example, serotonin (5-HT) and serotonergic G-protein coupled receptors (5-HT GPCRs) play major roles in cestode motility, development and reproduction. In previous work, we deorphanized a group of 5-HT7 type GPCRs from cestodes. However, little is known about another type of 5-HT GPCR, the 5-HT1 clade, which has been studied in several invertebrate phyla but not in platyhelminthes. Three putative 5-HT GPCRs from Echinococcus canadensis, Mesocestoides vogae (syn. M. corti) and Hymenolepis microstoma were cloned, sequenced and bioinformatically analyzed. Evidence grouped these new sequences within the 5-HT1 clade of GPCRs but differences in highly conserved GPCR motifs were observed. Transcriptomic analysis, heterologous expression and immunolocalization studies were performed to characterize the E. canadensis receptor, called Eca-5-HT_1a. Functional heterologous expression studies showed that Eca-5-HT_1a is highly specific for serotonin. 5-Methoxytryptamine and α-methylserotonin, both known 5-HT GPCR agonists, give stimulatory responses whereas methysergide, a known 5-HT GPCR ligand, give an antagonist response in Eca-5-HT_1a. Mutants obtained by the substitution of key predicted residues resulted in severe impairment of receptor activity, confirming that indeed, these residues have important roles in receptor function. Immunolocalization studies on the protoscolex stage from E. canadensis, showed that Eca-5-HT_1a is localized in branched fibers which correspond to the nervous system of the parasite. The patterns of immunoreactive fibers for Eca-5-HT_1a and for serotonin were intimately intertwined but not identical, suggesting that they are two separate groups of fibers. These data provide the first functional, pharmacological and localization report of a serotonergic receptor that putatively belongs to the 5-HT1 type of GPCRs in cestodes. The serotonergic GPCR characterized here may represent a new target for antiparasitic intervention.

A neuroendocrine pathway modulating osmotic stress in Drosophila

Environmental factors challenge the physiological homeostasis in animals, thereby evoking stress responses. Various mechanisms have evolved to counter stress at the organism level, including regulation by neuropeptides. In recent years, much progress has been made on the mechanisms and neuropeptides that regulate responses to metabolic/nutritional stress, as well as those involved in countering osmotic and ionic stresses. Here, we identified a peptidergic pathway that links these types of regulatory functions. We uncover the neuropeptide Corazonin (Crz), previously implicated in responses to metabolic stress, as a neuroendocrine factor that inhibits the release of a diuretic hormone, CAPA, and thereby modulates the tolerance to osmotic and ionic stress. Both knockdown of Crz and acute injections of Crz peptide impact desiccation tolerance and recovery from chill-coma. Mapping of the Crz receptor (CrzR) expression identified three pairs of Capa-expressing neurons (Va neurons) in the ventral nerve cord that mediate these effects of Crz. We show that Crz acts to restore water/ion homeostasis by inhibiting release of CAPA neuropeptides via inhibition of cAMP production in Va neurons. Knockdown of CrzR in Va neurons affects CAPA signaling, and consequently increases tolerance for desiccation, ionic stress and starvation, but delays chill-coma recovery. Optogenetic activation of Va neurons stimulates excretion and simultaneous activation of Crz and CAPA-expressing neurons reduces this response, supporting the inhibitory action of Crz. Thus, Crz inhibits Va neurons to maintain osmotic and ionic homeostasis, which in turn affects stress tolerance. Earlier work demonstrated that systemic Crz signaling restores nutrient levels by promoting food search and feeding. Here we additionally propose that Crz signaling also ensures osmotic homeostasis by inhibiting release of CAPA neuropeptides and suppressing diuresis. Thus, Crz ameliorates stress-associated physiology through systemic modulation of both peptidergic neurosecretory cells and the fat body in Drosophila.

Diabetic nephropathy produces alterations in the tissue expression profile of the orphan receptors GPR149, GPR153, GPR176, TAAR3, TAAR5 and TAAR9 in Wistar rats

Diabetes mellitus is a debilitating health care problem affecting 382 million people around the world and one of the most common complications is diabetic nephropathy. For this reason, it is important to try to identify new mechanisms that could be involved in diabetes. A new class of receptors has been reported, called orphan receptors because the associated ligand and signaling cascades are unknown. These receptors could be an important source of targets for the treatment of many diseases such as diabetes and its associated complications like diabetic nephropathy. Therefore, the aim of this work was to study expression of the orphan receptors GPR149, GPR153, GPR176, TAAR3, TAAR5 and TAAR9 in the kidney of diabetic rats. We used male Wistar rats at 10-12 weeks of age. Diabetes was induced by a single dose of streptozotocin (60 mg/kg i.p.). After 4 weeks, tissues were obtained, and the expression of the mRNAs was measured by RT-PCR. Our results showed that the orphan receptors are expressed in a different way in the kidney. In conclusion, we suggest that orphan receptors could be involved in the development of diabetic nephropathy.

A novel functional cross-interaction between opioid and pheromone signaling may be involved in stress avoidance in Caenorhabditis elegans

Upon sensing starvation stress, Caenorhabditis elegans larvae (L2d) elicit two seemingly opposing behaviors to escape from the stressful condition: food-seeking roaming mediated by the opioid peptide NLP-24 and dauer formation mediated by pheromones. Because opioid and pheromone signals both originate in ASI chemosensory neurons, we hypothesized that they might act sequentially or competitively to avoid starvation stress. Our data shows that NPR-17 opioid receptor signaling suppressed pheromone biosynthesis and the overexpression of opioid genes disturbed dauer formation. Likewise, DAF-37 pheromone receptor signaling negatively modulated nlp-24 expression in the ASI neurons. Under short-term starvation (STS, 3 h), both pheromone and opioid signaling were downregulated in gpa-3 mutants. Surprisingly, the gpa-3;nlp-24 double mutants exhibited much higher dauer formation than seen in either of the single mutants. Under long-term starvation (LTS, >24 h), the stress-activated SKN-1a downregulated opioid signaling and then enhanced dauer formation. Both insulin and serotonin stimulated opioid signaling, whereas NHR-69 suppressed opioid signaling. Thus, GPA-3 and SKN-1a are proposed to regulate cross-antagonistic interaction between opioids and pheromones in a cell-specific manner. These regulatory functions are suggested to be exerted via the selective interaction of GPA-3 with NPR-17 and site-specific SKN-1 binding to the promoter of nlp-24 to facilitate stress avoidance.

Expression Profiling, Downstream Signaling, and Inter-subunit Interactions of GPA2/GPB5 in the Adult Mosquito Aedes aegypti

GPA2/GPB5 and its receptor constitute a glycoprotein hormone-signaling system native to the genomes of most vertebrate and invertebrate organisms. Unlike the well-studied gonadotropins and thyrotropin, the exact function of GPA2/GPB5 remains elusive, and whether it elicits its functions as heterodimers, homodimers or as independent monomers remains unclear. Here, the glycoprotein hormone signaling system was investigated in adult mosquitoes, where GPA2 and GPB5 subunit expression was mapped and modes of its signaling were characterized. In adult Aedes aegypti mosquitoes, GPA2 and GPB5 transcripts co-localized to bilateral pairs of neuroendocrine cells, positioned within the first five abdominal ganglia of the central nervous system. Unlike GPA2/GPB5 homologs in human and fly, GPA2/GPB5 subunits in A. aegypti lacked evidence of heterodimerization. Rather, cross-linking analysis to determine subunit interactions revealed A. aegypti GPA2 and GPB5 subunits may form homodimers, although treatments with independent subunits did not demonstrate receptor activity. Since mosquito GPA2/GPB5 heterodimers were not evident by heterologous expression, a tethered fusion construct was generated for expression of the subunits as a single polypeptide chain to mimic heterodimer formation. Our findings revealed A. aegypti LGR1 elicited constitutive activity with elevated levels of cAMP. However, upon treatment with recombinant tethered GPA2/GPB5, an inhibitory G protein (Gi/o) signaling cascade is initiated and forskolin-induced cAMP production is inhibited. These results further support the notion that heterodimerization is a requirement for glycoprotein hormone receptor activation and provide novel insight to how signaling is achieved for GPA2/GPB5, an evolutionary ancient neurohormone.

Silencing of GPR82 with Interference RNA Improved Metabolic Profiles in Rats with High Fructose Intake

Metabolic syndrome (MS) is a clinical condition, constituted by alterations that lead to the onset of type II diabetes and cardiovascular disease. It has been reported that orphan G-protein-coupled receptor 82 (GPR82) participates in metabolic processes. The aim of this study was to evaluate the function of GPR82 in MS using a small interfering RNA (siRNA) against this receptor. We used Wistar rats of 10-12 weeks of age fed with a high-fructose solution (70%) for 9 weeks to induce MS. Subsequently, the rats were treated with an intrajugular dose of an siRNA against GPR82 and the effects were evaluated on day 3 and 7 after administration. On day 3 the siRNA had a transient effect on decreasing blood pressure and triglycerides and increasing high-density lipoprotein cholesterol, which recovered to the MS control on day 7. Decreased gene expressions of GPR82 mRNA in the aorta and heart were observed on day 3; moreover, decreased gene expression was maintained in the aorta on day 7. Therefore, we conclude that the orphan receptor GPR82 participates in the development of MS induced by fructose and the silencing of this receptor could ameliorate metabolic components.

Illuminating G-Protein-Coupling Selectivity of GPCRs

Heterotrimetic G proteins consist of four subfamilies (G_s, G_i/o, G_q/11, and G_12/13) that mediate signaling via G-protein-coupled receptors (GPCRs), principally by receptors binding Gα C termini. G-protein-coupling profiles govern GPCR-induced cellular responses, yet receptor sequence selectivity determinants remain elusive. Here, we systematically quantified ligand-induced interactions between 148 GPCRs and all 11 unique Gα subunit C termini. For each receptor, we probed chimeric Gα subunit activation via a transforming growth factor-α (TGF-α) shedding response in HEK293 cells lacking endogenous G_q/11 and G_12/13 proteins, and complemented G-protein-coupling profiles through a NanoBiT-G-protein dissociation assay. Interrogation of the dataset identified sequence-based coupling specificity features, inside and outside the transmembrane domain, which we used to develop a coupling predictor that outperforms previous methods. We used the predictor to engineer designer GPCRs selectively coupled to G₁₂. This dataset of fine-tuned signaling mechanisms for diverse GPCRs is a valuable resource for research in GPCR signaling.

Honey Bee Allatostatins Target Galanin/Somatostatin-Like Receptors and Modulate Learning: A Conserved Function?

Sequencing of the honeybee genome revealed many neuropeptides and putative neuropeptide receptors, yet functional characterization of these peptidic systems is scarce. In this study, we focus on allatostatins, which were first identified as inhibitors of juvenile hormone synthesis, but whose role in the adult honey bee (Apis mellifera) brain remains to be determined. We characterize the bee allatostatin system, represented by two families: allatostatin A (Apime-ASTA) and its receptor (Apime-ASTA-R); and C-type allatostatins (Apime-ASTC and Apime-ASTCC) and their common receptor (Apime-ASTC-R). Apime-ASTA-R and Apime-ASTC-R are the receptors in bees most closely related to vertebrate galanin and somatostatin receptors, respectively. We examine the functional properties of the two honeybee receptors and show that they are transcriptionally expressed in the adult brain, including in brain centers known to be important for learning and memory processes. Thus we investigated the effects of exogenously applied allatostatins on appetitive olfactory learning in the bee. Our results show that allatostatins modulate learning in this insect, and provide important insights into the evolution of somatostatin/allatostatin signaling.

CMKLR1 and GPR1 mediate chemerin signaling through the RhoA/ROCK pathway

Chemerin is an adipose-derived hormone that regulates immunity and energy homesotasis. To date, all known chemerin functions have been attributed to activation of the G protein-coupled receptor chemokine-like receptor-1 (CMKLR1). Chemerin is also the only known ligand for a second receptor, G protein-coupled receptor-1 (GPR1), whose signaling and function remains unknown. This study investigated the in vitro signal transduction mechanisms of CMKLR1 and GPR1 using a panel of luciferase-reporters and pathway-specific inhibitors. Herein we report the novel finding that chemerin signals through a RhoA and rho-associated protein kinase (ROCK)-dependent pathway for activation of the transcriptional regulator serum-response factor (SRF). Despite similarities in RhoA/ROCK, Gαi/o, and MAPK signaling, we also demonstrate species-specific and receptor-dependent variations in GPR1 and CMKLR1 signaling and expression of the SRF target genes EGR1, FOS and VCL. Moreover, we demonstrate that signaling through p38, Gαi/o, RhoA, and ROCK is required for chemerin-mediated chemotaxis of L1.2 lymphocytes and AGS gastric adenocarcinoma cells. These results provide, to our knowledge, the first empirical evidence that GPR1 is a functional chemerin receptor and identify RhoA/SRF as a novel chemerin-signaling axis via both CMKLR1 and GPR1.

Adipokinetic hormone signalling system in the Chagas disease vector, Rhodnius prolixus

Neuropeptides and their G protein-coupled receptors are widespread throughout Metazoa and in several cases, clear orthologues can be identified in both protostomes and deuterostomes. One such neuropeptide is the insect adipokinetic hormone (AKH), which is related to the mammalian gonadotropin-releasing hormone. AKH has been studied extensively and is known to mobilize lipid, carbohydrates and proline for energy-consuming activities such as flight. In order to determine the possible roles for this signalling system in Rhodnius prolixus, we isolated the cDNA sequences encoding R. prolixus AKH (Rhopr-AKH) and its receptor (Rhopr-AKHR). We also examined their spatial expression pattern using quantitative PCR. Our expression analysis indicates that Rhopr-AKH is only expressed in the corpus cardiacum of fifth-instars and adults. Rhopr-AKHR, by contrast, is expressed in several peripheral tissues including the fat body. The expression of the receptor in the fat body suggests that AKH is involved in lipid mobilization, which was confirmed by knockdown of Rhopr-AKHR via RNA interference. Adult males that had been injected with double-stranded RNA (dsRNA) for Rhopr-AKHR exhibited increased lipid content in the fat body and decreased lipid levels in the haemolymph. Moreover, injection of Rhopr-AKH in Rhopr-AKHR dsRNA-treated males failed to elevate haemolymph lipid levels, confirming that this is indeed the receptor for Rhopr-AKH.

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.

Identification of novel arthropod vector G protein-coupled receptors

BACKGROUND

The control of vector-borne diseases, such as malaria, dengue fever, and typhus fever is often achieved with the use of insecticides. Unfortunately, insecticide resistance is becoming common among different vector species. There are currently no chemical alternatives to these insecticides because new human-safe classes of molecules have yet to be brought to the vector-control market. The identification of novel targets offer opportunities for rational design of new chemistries to control vector populations. One target family, G protein-coupled receptors (GPCRs), has remained relatively under explored in terms of insecticide development.

METHODS

A novel classifier, Ensemble*, for vector GPCRs was developed. Ensemble* was validated and compared to existing classifiers using a set of all known GPCRs from Aedes aegypti, Anopheles gambiae, Apis Mellifera, Drosophila melanogaster, Homo sapiens, and Pediculus humanus. Predictions for unidentified sequences from Ae. aegypti, An. gambiae, and Pe. humanus were validated. Quantitative RT-PCR expression analysis was performed on previously-known and newly discovered Ae. aegypti GPCR genes.

RESULTS

We present a new analysis of GPCRs in the genomes of Ae, aegypti, a vector of dengue fever, An. gambiae, a primary vector of Plasmodium falciparum that causes malaria, and Pe. humanus, a vector of epidemic typhus fever, using a novel GPCR classifier, Ensemble*, designed for insect vector species. We identified 30 additional putative GPCRs, 19 of which we validated. Expression of the newly discovered Ae. aegypti GPCR genes was confirmed via quantitative RT-PCR.

CONCLUSION

A novel GPCR classifier for insect vectors, Ensemble*, was developed and GPCR predictions were validated. Ensemble* and the validation pipeline were applied to the genomes of three insect vectors (Ae. aegypti, An. gambiae, and Pe. humanus), resulting in the identification of 52 GPCRs not previously identified, of which 11 are predicted GPCRs, and 19 are predicted and confirmed GPCRs.

The role of gene duplication and unconstrained selective pressures in the melanopsin gene family evolution and vertebrate circadian rhythm regulation

Melanopsin is a photosensitive cell protein involved in regulating circadian rhythms and other non-visual responses to light. The melanopsin gene family is represented by two paralogs, OPN4x and OPN4m, which originated through gene duplication early in the emergence of vertebrates. Here we studied the melanopsin gene family using an integrated gene/protein evolutionary approach, which revealed that the rhabdomeric urbilaterian ancestor had the same amino acid patterns (DRY motif and the Y and E conterions) as extant vertebrate species, suggesting that the mechanism for light detection and regulation is similar to rhabdomeric rhodopsins. Both OPN4m and OPN4x paralogs are found in vertebrate genomic paralogons, suggesting that they diverged following this duplication event about 600 million years ago, when the complex eye emerged in the vertebrate ancestor. Melanopsins generally evolved under negative selection (ω = 0.171) with some minor episodes of positive selection (proportion of sites = 25%) and functional divergence (θ(I) = 0.349 and θ(II) = 0.126). The OPN4m and OPN4x melanopsin paralogs show evidence of spectral divergence at sites likely involved in melanopsin light absorbance (200F, 273S and 276A). Also, following the teleost lineage-specific whole genome duplication (3R) that prompted the teleost fish radiation, type I divergence (θ(I) = 0.181) and positive selection (affecting 11% of sites) contributed to amino acid variability that we related with the photo-activation stability of melanopsin. The melanopsin intracellular regions had unexpectedly high variability in their coupling specificity of G-proteins and we propose that Gq/11 and Gi/o are the two G-proteins most-likely to mediate the melanopsin phototransduction pathway. The selection signatures were mainly observed on retinal-related sites and the third and second intracellular loops, demonstrating the physiological plasticity of the melanopsin protein group. Our results provide new insights on the phototransduction process and additional tools for disentangling and understanding the links between melanopsin gene evolution and the specializations observed in vertebrates, especially in teleost fish.

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.

Defining reference sequences for Nocardia species by similarity and clustering analyses of 16S rRNA gene sequence data

Background

The intra- and inter-species genetic diversity of bacteria and the absence of ‘reference’, or the most representative, sequences of individual species present a significant challenge for sequence-based identification. The aims of this study were to determine the utility, and compare the performance of several clustering and classification algorithms to identify the species of 364 sequences of 16S rRNA gene with a defined species in GenBank, and 110 sequences of 16S rRNA gene with no defined species, all within the genus Nocardia.

Methods

A total of 364 16S rRNA gene sequences of Nocardia species were studied. In addition, 110 16S rRNA gene sequences assigned only to the Nocardia genus level at the time of submission to GenBank were used for machine learning classification experiments. Different clustering algorithms were compared with a novel algorithm or the linear mapping (LM) of the distance matrix. Principal Components Analysis was used for the dimensionality reduction and visualization.

Results

The LM algorithm achieved the highest performance and classified the set of 364 16S rRNA sequences into 80 clusters, the majority of which (83.52%) corresponded with the original species. The most representative 16S rRNA sequences for individual Nocardia species have been identified as ‘centroids’ in respective clusters from which the distances to all other sequences were minimized; 110 16S rRNA gene sequences with identifications recorded only at the genus level were classified using machine learning methods. Simple kNN machine learning demonstrated the highest performance and classified Nocardia species sequences with an accuracy of 92.7% and a mean frequency of 0.578.

Conclusion

The identification of centroids of 16S rRNA gene sequence clusters using novel distance matrix clustering enables the identification of the most representative sequences for each individual species of Nocardia and allows the quantitation of inter- and intra-species variability.

The accurate prediction of protein family from amino acid sequence by measuring features of sequence fragments

The rapid advances in proteomic analyses coupled with the completion of multiple genomes have led to an increased demand for determining protein functions. The first step is classification or prediction into families. A method was developed for the prediction of protein family based only on protein sequence using support vector machine (SVM) models. In these models, the amino acids were classified into three categories (apolar, polar, and charged). Consecutive fragments ranging from one to five were annotated by amino acid type to define the protein features of each protein. SVM models were constructed based on the protein features of a training set of proteins and then examined with an independent set of proteins. The approach was tested for 20 protein families from the iProClass database of Protein Information Resources (PIR). For two-class SVM models, an average prediction accuracy of 0.9985 was achieved, while for multi-class SVM models an accuracy of 0.9941 was achieved. This study demonstrates that SVM based methods can accurately recognize and predict the protein family to which a sequence belongs based solely on its primary amino acid sequence.

The impact of window functions on NMR-based paramagnetic relaxation enhancement measurements in membrane proteins

Though challenging, solution NMR spectroscopy allows fundamental interrogation of the structure and dynamics of membrane proteins. One major technical hurdle in studies of helical membrane proteins by NMR is the difficulty of obtaining sufficient long range NOEs to determine tertiary structure. For this reason, long range distance information is sometimes sought through measurement of paramagnetic relaxation enhancements (PRE) of NMR nuclei as a function of distance from an introduced paramagnetic probe. Current PRE interpretation is based on the assumption of Lorentzian resonance lineshapes. However, in order to optimize spectral resolution, modern multidimensional NMR spectra are almost always subjected to resolution-enhancement, leading to distortions in the Lorentizian peak shape. Here it is shown that when PREs are derived using peak intensities (i.e., peak height) and linewidths from both real and simulated spectra that were produced using a wide range of apodization/window functions, that there is little variation in the distances determined (<1 A at the extremes). This indicates that the high degree of resolution enhancement required to obtain well-resolved spectra from helical membrane proteins is compatible with the use of PRE data as a source of distance restraints. While these conclusions are particularly important for helical membrane proteins, they are generally applicable to all PRE measurements made using resolution-enhanced data.

A computational approach for the classification of protein tyrosine kinases

Protein tyrosine kinases (PTKs) play a central role in the modulation of a wide variety of cellular events such as differentiation, proliferation and metabolism, and their unregulated activation can lead to various diseases including cancer and diabetes. PTKs represent a diverse family of proteins including both receptor tyrosine kinases (RTKs) and non-receptor tyrosine kinases (NRTKs). Due to the diversity and important cellular roles of PTKs, accurate classification methods are required to better understand and differentiate different PTKs. In addition, PTKs have become important targets for drugs, providing a further need to develop novel methods to accurately classify this set of important biological molecules. Here, we introduce a novel statistical model for the classification of PTKs that is based on their structural features. The approach allows for both the recognition of PTKs and the classification of RTKs into their subfamilies. This novel approach had an overall accuracy of 98.5% for the identification of PTKs, and 99.3% for the classification of RTKs.

GPCRTree: online hierarchical classification of GPCR function

Background

G protein-coupled receptors (GPCRs) play important physiological roles transducing extracellular signals into intracellular responses. Approximately 50% of all marketed drugs target a GPCR. There remains considerable interest in effectively predicting the function of a GPCR from its primary sequence.

Findings

Using techniques drawn from data mining and proteochemometrics, an alignment-free approach to GPCR classification has been devised. It uses a simple representation of a protein's physical properties. GPCRTree, a publicly-available internet server, implements an algorithm that classifies GPCRs at the class, sub-family and sub-subfamily level.

Conclusion

A selective top-down classifier was developed which assigns sequences within a GPCR hierarchy. Compared to other publicly available GPCR prediction servers, GPCRTree is considerably more accurate at every level of classification. The server has been available online since March 2008 at URL: .

Advances in the Development and Application of Computational Methodologies for Structural Modeling of G-Protein Coupled Receptors

BACKGROUND: Despite the large amount of experimental data accumulated in the past decade on G-protein coupled receptor (GPCR) structure and function, understanding of the molecular mechanisms underlying GPCR signaling is still far from being complete, thus impairing the design of effective and selective pharmaceuticals. OBJECTIVE: Understanding of GPCR function has been challenged even further by more recent experimental evidence that several of these receptors are organized in the cell membrane as homo- or hetero-oligomers, and that they may exhibit unique pharmacological properties. Given the complexity of these new signaling systems, researcher's efforts are turning increasingly to molecular modeling, bioinformatics and computational simulations for mechanistic insights of GPCR functional plasticity. METHODS: We review here current advances in the development and application of computational approaches to improve prediction of GPCR structure and dynamics, thus enhancing current understanding of GPCR signaling. RESULTS/CONCLUSIONS: Models resulting from use of these computational approaches further supported by experiments are expected to help elucidate the complex allosterism that propagates through GPCR complexes, ultimately aiming at successful structure-based rational drug design.

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.

Ligand screening system using fusion proteins of G protein-coupled receptors with G protein alpha subunits

G protein-coupled receptors (GPCRs) constitute one of the largest families of genes in the human genome, and are the largest targets for drug development. Although a large number of GPCR genes have recently been identified, ligands have not yet been identified for many of them. Various assay systems have been employed to identify ligands for orphan GPCRs, but there is still no simple and general method to screen for ligands of such GPCRs, particularly of G(i)-coupled receptors. We have examined whether fusion proteins of GPCRs with G protein alpha subunit (Galpha) could be utilized for ligand screening and showed that the fusion proteins provide an effective method for the purpose. This article focuses on the followings: (1) characterization of GPCR genes and GPCRs, (2) identification of ligands for orphan GPCRs, (3) characterization of GPCR-Galpha fusion proteins, and (4) identification of ligands for orphan GPCRs using GPCR-Galpha fusion proteins.

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.

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.

Differential Receptor Activation by Cockroach Adipokinetic Hormones Produces Differential Effects on Ion Currents, Neuronal Activity, and Locomotion

Adipokinetic hormone (AKH) peptides in insects serve the endocrine control of energy supply. They also produce, however, neuronal, vegetative, and motor effects, suggesting that AKHs orchestrate adaptive behavior by multiple actions. We have cloned, for Periplaneta americana, the AKH receptor to determine its localization and, based on current measurements in neurons and heterologous expression systems, the mechanisms of AKH actions. Apart from fat body, various neurons express the AKH receptor, among them abdominal dorsal unpaired median (DUM) neurons, which release the biogenic amine octopamine. They are part of the arousal system and are involved in the control of circulation and respiration. Both the two Periplaneta AKHs activate the Gspathway, and AKH I also potently activates Gq. AKH I and—with much less efficacy—AKH II accelerate spiking of DUM neurons through an increase of the pacemaking Ca2+current. Because the AKHs are released from the corpora cardiaca into the hemolymph, they must penetrate the blood-brain barrier for acting on neurons. That this happens was shown electrophysiologically by applying AKH I to an intact ganglion. Systemically injected AKH I stimulates locomotion potently in striking contrast to AKH II. This behavioral difference can be traced back conclusively to the different effectiveness of the AKHs on the level of G proteins. Our findings also show that AKHs act through the same basic mechanisms on neuronal and nonneuronal cells, and they support an integration of metabolic and neuronal effects in homoeostatic mechanisms.

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.

Prediction of the coupling specificity of GPCRs to four families of G-proteins using hidden Markov models and artificial neural networks

MOTIVATION

G-protein coupled receptors are a major class of eukaryotic cell-surface receptors. A very important aspect of their function is the specific interaction (coupling) with members of four G-protein families. A single GPCR may interact with members of more than one G-protein families (promiscuous coupling). To date all published methods that predict the coupling specificity of GPCRs are restricted to three main coupling groups G(i/o), G(q/11) and G(s), not including G(12/13)-coupled or other promiscuous receptors.

RESULTS

We present a method that combines hidden Markov models and a feed-forward artificial neural network to overcome these limitations, while producing the most accurate predictions currently available. Using an up-to-date curated dataset, our method yields a 94% correct classification rate in a 5-fold cross-validation test. The method predicts also promiscuous coupling preferences, including coupling to G(12/13), whereas unlike other methods avoids overpredictions (false positives) when non-GPCR sequences are encountered.

AVAILABILITY

A webserver for academic users is available at http://bioinformatics.biol.uoa.gr/PRED-COUPLE2