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

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

Fast fourier transform-based support vector machine for prediction of G-protein coupled receptor subfamilies

Although the sequence information on G-protein coupled receptors （GPCRs） continues to grow, many GPCRs remain orphaned （i.e. ligand specificity unknown） or poorly characterized with little structural information available, so an automated and reliable method is badly needed to facilitate the identification of novel receptors. In this study, a method of fast Fourier transform-based support vector machine has been developed for predicting GPCR subfamilies according to protein＇s hydrophobicity. In classifying Class B, C, D and F subfamilies, the method achieved an overall Matthew＇s correlation coefficient and accuracy of 0.95 and 93.3%, respectively, when evaluated using the jackknife test. The method achieved an accuracy of 100% on the Class B independent dataset. The results show that this method can classify GPCR subfamilies as well as their functional classification with high accuracy. A web server implementing the prediction is available at http：//chem.scu.edu.cn/blast/Pred-GPCR.     

Ultrasonic velocity assay of extracellular invertase in living yeasts

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

Prediction of GPCRs with pseudo amino acid composition: employing composite features and grey incidence degree based classification

G-protein coupled receptor (GPCR) is a protein family that is found only in the Eukaryotes. They are used for the interfacing of cell to the outside world and are involved in many physiological processes. Their role in drug development is evident. Hence, the prediction of GPCRs is very much demanding. Because of the unavailability of 3D structures of most of the GPCRs; the statistical and machine learning based prediction of GPCRs is much demanding. The GPCRs are classified into family, sub family and sub-sub family levels in the proposed approach. We have extracted features using the hybrid combination of Pseudo amino acid, Fast Fourier Transform and Split amino acid techniques. The overall feature vector is then reduced using Principle component analysis. Mostly, GPCRs are composed of two or more sub units. The arrangement and number of sub units forming a GPCR are referred to as quaternary structure. The functions of GPCRs are closely related to their quaternary structure. The classification in the present research is performed using grey incidence degree (GID) measure, which can efficiently analyze the numerical relation between various components of GPCRs. The GID measure based classification has shown remarkable improvement in predicting GPCRs.     

In vitro expression and analysis of the 826 human G protein-coupled receptors

G protein-coupled receptors (GPCRs) are involved in all humanphysiological systems where they are responsible for transducing extracellular signals into cells. GPCRs signal in response to a diverse array of stimuli including light, hormones, and lipids, where these signals affect downstream cascades to impact both health and disease states. Yet, despite their importance as therapeutic targets, detailed molecular structures of only 30 GPCRs have been determined to date. A key challenge to their structure determination is adequate protein expression. Here we report the quantification of protein expression in an insect cell expression system for all 826humanGPCRs using two different fusion constructs. Expression characteristics are analyzed in aggregate and among each of the five distinct subfamilies. These data can be used to identify trends related to GPCR expression between different fusion constructs and between different GPCR families, and to prioritize lead candidates for future structure determination feasibility.     

Classifying G protein-coupled receptors and nuclear receptors on the basis of protein power spectrum from fast Fourier transform

Summary. As the potential drug targets, G-protein coupled receptors (GPCRs) and nuclear receptors (NRs) are the focuses in pharmaceutical research. It is of great practical significance to develop an automated and reliable method to facilitate the identification of novel receptors. In this study, a method of fast Fourier transform-based support vector machine was proposed to classify GPCRs and NRs from the hydrophobicity of proteins. The models for all the GPCR families and NR subfamilies were trained and validated using jackknife test and the results thus obtained are quite promising. Meanwhile, the performance of the method was evaluated on GPCR and NR independent datasets with good performance. The good results indicate the applicability of the method. Two web servers implementing the prediction are available at and .     

Identifying GPCRs and their types with Chou's pseudo amino acid composition: an approach from multi-scale energy representation and position specific scoring matrix

G-protein coupled receptor (GPCR) is a membrane protein family, which serves as an interface between cell and the outside world. They are involved in various physiological processes and are the targets of more than 50% of the marketed drugs. The function of GPCRs can be known by conducting Biological experiments. However, the rapid increase of GPCR sequences entering into databanks, it is very time consuming and expensive to determine their function based only on experimental techniques. Hence, the computational prediction of GPCRs is very much demanding for both pharmaceutical and educational research. Feature extraction of GPCRs in the proposed research is performed using three techniques i.e. Pseudo amino acid composition, Wavelet based multi-scale energy and Evolutionary information based feature extraction by utilizing the position specific scoring matrices. For classification purpose, a majority voting based ensemble method is used; whose weights are optimized using genetic algorithm. Four classifiers are used in the ensemble i.e. Nearest Neighbor, Probabilistic Neural Network, Support Vector Machine and Grey Incidence Degree. The performance of the proposed method is assessed using Jackknife test for a number of datasets. First, the individual performances of classifiers are assessed for each dataset using Jackknife test. After that, the performance for each dataset is improved by using weighted ensemble classification. The weights of ensemble are optimized using various runs of Genetic Algorithm. We have compared our method with various other methods. The significance in performance of the proposed method depicts it to be useful for GPCRs classification.     

gpDB: a database of GPCRs, G-proteins, effectors and their interactions

gpDB is a publicly accessible, relational database, containing information about G-proteins, G-protein coupled receptors (GPCRs) and effectors, as well as information concerning known interactions between these molecules. The sequences are classified according to a hierarchy of different classes, families and subfamilies based on literature search. The main innovation besides the classification of G-proteins, GPCRs and effectors is the relational model of the database, describing the known coupling specificity of GPCRs to their respective alpha subunits of G-proteins, and also the specific interaction between G-proteins and their effectors, a unique feature not available in any other database. AVAILABILITY: http://bioinformatics.biol.uoa.gr/gpDB CONTACT: shamodr@biol.uoa.gr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

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

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

Quantitative Measurement of GPCR Endocytosis via Pulse-Chase Covalent Labeling

G protein-coupled receptors (GPCRs) play a critical role in many physiological systems and represent one of the largest families of signal-transducing receptors. The number of GPCRs at the cell surface regulates cellular responsiveness to their cognate ligands, and the number of GPCRs, in turn, is dynamically controlled by receptor endocytosis. Recent studies have demonstrated that GPCR endocytosis, in addition to affecting receptor desensitization and resensitization, contributes to acute G protein-mediated signaling. Thus, endocytic GPCR behavior has a significant impact on various aspects of physiology. In this study, we developed a novel GPCR internalization assay to facilitate characterization of endocytic GPCR behavior. We genetically engineered chimeric GPCRs by fusing HaloTag (a catalytically inactive derivative of a bacterial hydrolase) to the N-terminal end of the receptor (HT-GPCR). HaloTag has the ability to form a stable covalent bond with synthetic HaloTag ligands that contain fluorophores or a high-affinity handle (such as biotin) and the HaloTag reactive linker. We selectively labeled HT-GPCRs at the cell surface with a HaloTag PEG ligand, and this pulse-chase covalent labeling allowed us to directly monitor the relative number of internalized GPCRs after agonist stimulation. Because the endocytic activities of GPCR ligands are not necessarily correlated with their agonistic activities, applying this novel methodology to orphan GPCRs, or even to already characterized GPCRs, will increase the likelihood of identifying currently unknown ligands that have been missed by conventional pharmacological assays.     

Proximity based GPCRs prediction in transform domain

In this work, we predict G-protein coupled receptors (GPCRs) using hydrophobicity of amino acid sequences and Fast Fourier Transform for feature generation. We analyze whether the GPCRs classification strategy depends on the way the feature space may be exploited. Consequently, we show that the sequence pattern based information could easily be exploited in the frequency domain using proximity rather than increasing margin of separation between the classes. We thus develop a simple proximity based approach known as nearest neighbor (NN) for classifying the 17 GPCRs subfamilies. The NN classifier has outperformed the one against all implementation of support vector machine using both Jackknife and independent dataset. The results validate the importance of the understanding and efficient exploitation of the feature space. It also shows that simple classification strategies may outperform complex ones because of the efficient exploitation of the feature space.     

A model for how Gβγ couples Gα to GPCR

Representing ∼5% of the human genome, G-protein-coupled receptors (GPCRs) are a primary target for drug discovery; however, the molecular details of how they couple to heterotrimeric G protein subunits are incompletely understood. Here, I propose a hypothetical initial docking model for the encounter between GPCR and Gβγ that is defined by transient interactions between the cytosolic surface of the GPCR and the prenyl moiety and the tripeptide motif, asparagine–proline–phenylalanine (NPF), in the C-terminus of the Gγ subunit. Analysis of class A GPCRs reveals a conserved NPF binding site formed by the interaction of the TM1 and H8. Functional studies using differentially prenylated proteins and peptides further suggest that the intracellular hydrophobic core of the GPCR is a prenyl binding site. Upon binding TM1 and H8 of GPCRs, the propensity of the C-terminal region of Gγ to convert into an α helix allows it to extend into the hydrophobic core of the GPCR, facilitating the GPCR active state. Conservation of the NPF motif in Gγ isoforms and interacting residues in TM1 and H8 suggest that this is a general mechanism of GPCR–G protein signaling. Analysis of the rhodopsin dimer also suggests that Gγ–rhodopsin interactions may facilitate GPCR dimer transactivation.     

Reduced alphabet motif methodology for GPCR annotation

Identification and Classification of G-protein coupled receptors (GPCRs) using protein sequences is an important computational challenge, given that experimental screening of thousands of ligands is an expensive proposition. There are two distinct but complementary approaches to GPCR classification --machine learning and sequence motif analysis. Machine learning methodologies typically suffer from problems of class imbalance and lack of multi-class classification. Many sequence motif methods, meanwhile, are too dependent on the similarity of the primary sequence alignments. It is desirable to have a motif discovery and application methodology that is not strongly dependent on primary sequence similarity. It should also overcome limitations of machine learning. We propose and evaluate the effectiveness of a simple methodology that uses a reduced protein functional alphabet representation, where similar functional residues have similar symbols. Regular expression motifs can then be obtained by ClustalW based multiple sequence alignment, using an identity matrix. Since evolutionary matrices like BLOSUM, PAM are not used, this method can be useful for any set of sequences that do not necessarily share a common ancestry. Reduced alphabet motifs can accurately classify known GPCR proteins and the results are comparable to PRINTS and PROSITE. For well known GPCR proteins from SWISSPROT, there were no false negatives and only a few false positives. This methodology covers most currently known classes of GPCRs, even if there are very few representative sequences. It also predicts more than one class for certain sequences, thus overcoming the limitation of machine learning methods. We also annotated, 695 orphan receptors, and 121 were identified as belonging to Family A. A simple JavaScript based web interface has been developed to predict GPCR families and subfamilies (www.insilico-consulting.com/gpcrmotif.html).     

Classification of G-protein coupled receptors by alignment-independent extraction of principal chemical properties of primary amino acid sequences

We have developed an alignment-independent method for classification of G-protein coupled receptors (GPCRs) according to the principal chemical properties of their amino acid sequences. The method relies on a multivariate approach where the primary amino acid sequences are translated into vectors based on the principal physicochemical properties of the amino acids and transformation of the data into a uniform matrix by applying a modified autocross-covariance transform. The application of principal component analysis to a data set of 929 class A GPCRs showed a clear separation of the major classes of GPCRs. The application of partial least squares projection to latent structures created a highly valid model (cross-validated correlation coefficient, Q(2) = 0.895) that gave unambiguous classification of the GPCRs in the training set according to their ligand binding class. The model was further validated by external prediction of 535 novel GPCRs not included in the training set. Of the latter, only 14 sequences, confined in rapidly expanding GPCR classes, were mispredicted. Moreover, 90 orphan GPCRs out of 165 were tentatively identified to GPCR ligand binding class. The alignment-independent method could be used to assess the importance of the principal chemical properties of every single amino acid in the protein sequences for their contributions in explaining GPCR family membership. It was then revealed that all amino acids in the unaligned sequences contributed to the classifications, albeit to varying extent; the most important amino acids being those that could also be determined to be conserved by using traditional alignment-based methods.     

G protein-coupled receptors as regulators of pancreatic islet functionality

Glucose homeostasis is maintained by hormones secreted from different types of pancreatic islets and its dysregulation can result in diseases including diabetes mellitus. The secretion of hormones from pancreatic islets is highly complex and tightly controlled by G protein-coupled receptors (GPCRs). Moreover, GPCR signaling may play a role in enhancing islet cell replication and proliferation. Thus, targeting GPCRs offers a promising strategy for regulating the functionality of pancreatic islets. Here, available RNAseq datasets from human and mouse islets were used to identify the GPCR expression profile and the impact of GPCR signaling for normal islet functionality is discussed.     

Constraints on GPCR Heterodimerization Revealed by the Type-4 Induced-Association BRET Assay

G-protein-coupled receptors (GPCRs) comprise the largest and most pharmacologically important family of cell-surface receptors encoded by the human genome. In many instances, the distinct signaling behavior of certain GPCRs has been explained in terms of the formation of heteromers with, for example, distinct signaling properties and allosteric cross-regulation. Confirmation of this has, however, been limited by the paucity of reliable methods for probing heteromeric GPCR interactions in situ. The most widely used assays for GPCR stoichiometry, based on resonance energy transfer, are unsuited to reporting heteromeric interactions. Here, we describe a targeted bioluminescence resonance energy transfer (BRET) assay, called type-4 BRET, which detects both homo- and heteromeric interactions using induced multimerization of protomers within such complexes, at constant expression. Using type-4 BRET assays, we investigate heterodimerization among known GPCR homodimers: the CXC chemokine receptor 4 and sphingosine-1-phosphate receptors. We observe that CXC chemokine receptor 4 and sphingosine-1-phosphate receptors can form heterodimers with GPCRs from their immediate subfamilies but not with more distantly related receptors. We also show that heterodimerization appears to disrupt homodimeric interactions, suggesting the sharing of interfaces. Broadly, these observations indicate that heterodimerization results from the divergence of homodimeric receptors and will therefore likely be restricted to closely related homodimeric GPCRs.     

G protein-coupled receptor dimers: look like their parents,but act like teenagers!

Abstract

G protein-coupled receptors (GPCRs) represent the largest group of cell surface receptors and an important pharmacological target. Though originally thought to act in a one receptor–one effector fashion, it is now known that these receptors are capable of oligomerization and can function as dimers or higher order oligomers in native tissue. They do not only assemble with identical receptors as homodimers, but also associate with different GPCRs to form heterodimers. We discuss here how heterodimeric GPCRs can assemble, traffic and signal in a manner distinct from their individual receptor components or from homodimers. These receptor pairs are also demonstrated to be regulated by different chaperones, Rabs and scaffolding proteins, further emphasizing their potential as unique targets. We believe in the importance of investigating each GPCR heterodimer as an individual signaling complex, as they appear to act differently from each monomer constituting them. Just as teenagers may resemble their parents and share their genetic makeup, they can still act in a manner that is entirely unique!     

Structural genomics on membrane proteins: comparison of more than 100 GPCRs in 3 expression systems

Production of recombinant receptors has been one of the major bottlenecks in structural biology on G protein-coupled receptors (GPCRs). The MePNet (Membrane Protein Network) was established to overexpress a large number of GPCRs in three major expression systems, based on Escherichia coli, Pichia pastoris and Semliki Forest virus (SFV) vectors. Evaluation by immunodetection demonstrated that 50% of a total of 103 GPCRs were expressed in bacterial inclusion bodies, 94% in yeast cell membranes and 95% in SFV-infected mammalian cells. The expression levels varied from low to high and the various GPCR families and subtypes were analyzed for their expressability in each expression system. More than 60% of the GPCRs were expressed at milligram levels or higher in one or several systems, compatible to structural biology applications. Functional activity was determined by binding assays in yeast and mammalian cells and the correlation between immunodetection and binding activity was analyzed.     

Classifying G-protein-coupled receptors to the finest subtype level

G-protein-coupled receptors (GPCRs) constitute a remarkable protein family of receptors that are involved in a broad range of biological processes. A large number of clinically used drugs elicit their biological effect via a GPCR. Thus, developing a reliable computational method for predicting the functional roles of GPCRs would be very useful in the pharmaceutical industry. Nowadays, researchers are more interested in functional roles of GPCRs at the finest subtype level. However, with the accumulation of many new protein sequences, none of the existing methods can completely classify these GPCRs to their finest subtype level. In this paper, a pioneer work was performed trying to resolve this problem by using a hierarchical classification method. The first level determines whether a query protein is a GPCR or a non-GPCR. If it is considered as a GPCR, it will be finally classified to its finest subtype level. GPCRs are characterized by 170 sequence-derived features encapsulating both amino acid composition and physicochemical features of proteins, and support vector machines are used as the classification engine. To test the performance of the present method, a non-redundant dataset was built which are organized at seven levels and covers more functional classes of GPCRs than existing datasets. The number of protein sequences in each level is 5956, 2978, 8079, 8680, 6477, 1580 and 214, respectively. By 5-fold cross-validation test, the overall accuracy of 99.56%, 93.96%, 82.81%, 85.93%, 94.1%, 95.38% and 92.06% were observed at each level. When compared with some previous methods, the present method achieved a consistently higher overall accuracy. The results demonstrate the power and effectiveness of the proposed method to accomplish the classification of GPCRs to the finest subtype level.     

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

Background

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

Results

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

Conclusions

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