Classification Of G-protein Coupled Receptors Research Articles

Optimizing amino acid groupings for GPCR classification

There is much interest in reducing the complexity inherent in the representation of the 20 standard amino acids within bioinformatics algorithms by developing a so-called reduced alphabet. Although there is no universally applicable residue grouping, there are numerous physiochemical criteria upon which one can base groupings. Local descriptors are a form of alignment-free analysis, the efficiency of which is dependent upon the correct selection of amino acid groupings. Within the context of G-protein coupled receptor (GPCR) classification, an optimization algorithm was developed, which was able to identify the most efficient grouping when used to generate local descriptors. The algorithm was inspired by the relatively new computational intelligence paradigm of artificial immune systems. A number of amino acid groupings produced by this algorithm were evaluated with respect to their ability to generate local descriptors capable of providing an accurate classification algorithm for GPCRs.

A Novel and Efficient Technique for Identification and Classification of GPCRs

G-protein coupled receptors (GPCRs) play a vital role in different biological processes, such as regulation of growth, death, and metabolism of cells. GPCRs are the focus of significant amount of current pharmaceutical research since they interact with more than 50% of prescription drugs. The dipeptide-based support vector machine (SVM) approach is the most accurate technique to identify and classify the GPCRs. However, this approach has two major disadvantages. First, the dimension of dipeptide-based feature vector is equal to 400. The large dimension makes the classification task computationally and memory wise inefficient. Second, it does not consider the biological properties of protein sequence for identification and classification of GPCRs. In this paper, we present a novel-feature-based SVM classification technique. The novel features are derived by applying wavelet-based time series analysis approach on protein sequences. The proposed feature space summarizes the variance information of seven important biological properties of amino acids in a protein sequence. In addition, the dimension of the feature vector for proposed technique is equal to 35. Experiments were performed on GPCRs protein sequences available at GPCRs Database. Our approach achieves an accuracy of 99.9%, 98.06%, 97.78%, and 94.08% for GPCR superfamily, families, subfamilies, and subsubfamilies (amine group), respectively, when evaluated using fivefold cross-validation. Further, an accuracy of 99.8%, 97.26%, and 97.84% was obtained when evaluated on unseen or recall datasets of GPCR superfamily, families, and subfamilies, respectively. Comparison with dipeptide-based SVM technique shows the effectiveness of our approach.

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. http://bioinformatics.biol.uoa.gr/gpDB CONTACT: shamodr@biol.uoa.gr Supplementary data are available at Bioinformatics online.

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.

Whole proteome identification of plant candidate G-protein coupled receptors in Arabidopsis, rice, and poplar: computational prediction and in-vivo protein coupling

Computational prediction and in vivo protein coupling experiments identify candidate plant G-protein coupled receptors in Arabidopsis, rice and poplar.

GPCRTree: online hierarchical classification of GPCR function

BackgroundG 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.FindingsUsing 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.ConclusionA 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: .

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 SWIS-SPROT, 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).

On the hierarchical classification of G protein-coupled receptors

G protein-coupled receptors (GPCRs) play an important role in many physiological systems by transducing an extracellular signal into an intracellular response. Over 50% of all marketed drugs are targeted towards a GPCR. There is considerable interest in developing an algorithm that could effectively predict the function of a GPCR from its primary sequence. Such an algorithm is useful not only in identifying novel GPCR sequences but in characterizing the interrelationships between known GPCRs. An alignment-free approach to GPCR classification has been developed using techniques drawn from data mining and proteochemometrics. A dataset of over 8000 sequences was constructed to train the algorithm. This represents one of the largest GPCR datasets currently available. A predictive algorithm was developed based upon the simplest reasonable numerical representation of the protein's physicochemical properties. A selective top-down approach was developed, which used a hierarchical classifier to assign sequences to subdivisions within the GPCR hierarchy. The predictive performance of the algorithm was assessed against several standard data mining classifiers and further validated against Support Vector Machine-based GPCR prediction servers. The selective top-down approach achieves significantly higher accuracy than standard data mining methods in almost all cases.

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.

A combined approach for the classification of G protein-coupled receptors and its application to detect GPCR splice variants

G protein-coupled receptors (GPCRs) constitute the largest family of cell surface receptors and play a central role in cellular signaling pathways. The importance of GPCRs has led to their becoming the targets of more than 50% of prescription drugs. However, drug compounds that do not differentiate between receptor subtypes can have considerable side effects and efficacy problems. An accurate classification of GPCRs can solve the side effect problems and raise the efficacy of drugs. Here, we introduce an approach that combines a fingerprint method, statistical profiles and physicochemical properties of transmembrane (TM) domains for a highly accurate classification of the receptors. The approach allows both the recognition and classification for GPCRs at the subfamily and subtype level, and allows the identification of splice variants. We found that the approach demonstrates an overall accuracy of 97.88% for subfamily classification, and 94.57% for subtype classification.

Classification of G-protein coupled receptors at four levels

G-protein coupled receptors (GPCRs) are transmembrane proteins which via G-proteins initiate some of the important signaling pathways in a cell and are involved in various physiological processes. Thus, computational prediction and classification of GPCRs can supply significant information for the development of novel drugs in pharmaceutical industry. In this paper, a nearest neighbor method has been introduced to discriminate GPCRs from non-GPCRs and subsequently classify GPCRs at four levels on the basis of amino acid composition and dipeptide composition of proteins. Its performance is evaluated on a non-redundant dataset consisted of 1406 GPCRs for six families and 1406 globular proteins using the jackknife test. The present method based on amino acid composition achieved an overall accuracy of 96.4% and Matthew's correlation coefficient (MCC) of 0.930 for correctly picking out the GPCRs from globular proteins. The overall accuracy and MCC were further enhanced to 99.8% and 0.996 by dipeptide composition-based method. On the other hand, the present method has successfully classified 1406 GPCRs into six families with an overall accuracy of 89.6 and 98.8% using amino acid composition and dipeptide composition, respectively. For the subfamily prediction of 1181 GPCRs of rhodopsin-like family, the present method achieved an overall accuracy of 76.7 and 94.5% based on the amino acid composition and dipeptide composition, respectively. Finally, GPCRs belonging to the amine subfamily and olfactory subfamily of rhodopsin-like family were further analyzed at the type level. The overall accuracy of dipeptide composition-based method for the classification of amine type and olfactory type of GPCRs reached 94.5 and 86.9%, respectively, while the overall accuracy of amino acid composition-based method was very low for both subfamilies. In comparison with existing methods in the literature, the present method also displayed great competitiveness. These results demonstrate the effectiveness of our method on identifying and classifying GPCRs correctly. GPCRsIdentifier, a corresponding stand-alone executable program for GPCR identification and classification was also developed, which can be acquired freely on request from the authors for academic purposes.

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 .

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.

Genome wide survey of G protein-coupled receptors in Tetraodon nigroviridis

BackgroundThe G-protein-coupled receptors (GPCRs) constitute one of the largest and most ancient superfamilies of membrane proteins. They play a central role in physiological processes affecting almost all aspects of the life cycle of an organism. Availability of the complete sets of putative members of a family from diverse species provides the basis for cross genome comparative studies.ResultsWe have defined the repertoire of GPCR superfamily of Tetraodon complement with the availability of complete sequence of the freshwater puffer fish Tetraodon nigroviridis. Almost all 466 Tetraodon GPCRs (Tnig-GPCRs) identified had a clear human homologue. 189 putative human and Tetraodon GPCR orthologous pairs could be identified. Tetraodon GPCRs are classified into five GRAFS families, by phylogenetic analysis, concurrent with human GPCR classification.ConclusionDirect comparison of GPCRs in Tetraodon and human genomes displays a high level of orthology and supports large-scale gene duplications in Tetraodon. Examples of lineage specific gene expansions were also observed in opsin and odorant receptors. The human and Tetraodon GPCR sequences are analogous in terms of GPCR subfamilies but display disproportionate numbers of receptors at the subfamily level. The teleost genome with its expanded set of GPCRs provides additional and interesting comparators to study both evolution and function of these receptors.

Prediction of G-Protein-Coupled Receptor Classes

Being the largest family of cell surface receptors, G-protein-coupled receptors (GPCRs) are among the most frequent targets of therapeutic drugs. The functions of many of GPCRs are unknown, and it is both time-consuming and expensive to determine their ligands and signaling pathways. This forces us to face a critical challenge: how to develop an automated method for classifying the family of GPCRs so as to help us in classifying drugs and expedite the process of drug discovery. Owing to their highly divergent nature, it is difficult to predict the classification of GPCRs by means of conventional sequence alignment approaches. To cope with such a situation, the CD (Covariant Discriminant) predictor was introduced to predict the families of GPCRs. The overall success rate thus obtained by jack-knife test for 1238 GPCRs classified into three main families, i.e., class A-"rhodopsin like", class B-"secretin like", and class C-"metabotrophic/glutamate/pheromone", was over 97%. The high success rate suggests that the CD predictor holds very high potential to become a useful tool for understanding the actions of drugs that target GPCRs and designing new medications with fewer side effects and greater efficacy.

The Repertoire of G-Protein–Coupled Receptors in Fully Sequenced Genomes

The superfamily of G-protein-coupled receptors (GPCRs) is one of the largest and most studied families of proteins. We created Hidden Markov Models derived from sorted groups of GPCRs from our previous detailed phylogenetic classification of human GPCRs and added several other models derived from receptors not found in mammals. We used these models to search entire Genscan data sets from 13 species whose genomes are nearly completely sequenced. We found more than 5000 unique GPCRs that were divided into 15 main groups, and the largest one, the Rhodopsin family, was subdivided into 13 subclasses. The results show that the main families in the human genome, Glutamate, Rhodopsin, Adhesion, Frizzled, and Secretin, arose before the split of nematodes from the chordate lineage. Moreover, several of the subgroups of the Rhodopsin family arose before the split of the linage leading to vertebrates. We also searched expressed sequence tag (EST) databases and identified more than 20,000 sequences that match GPCRs. Although the GPCRs represent typically 1 to 2% of the Genscan predictions, the ESTs that match GPCRs are typically only 0.01 to 0.001%, indicating that GPCRs in most of the groups are expressed at low levels. We also provide searchable data sets that may be used for annotation and further detailed analysis of the GPCR family. This study provides an extensive overview of the expansion of the gene repertoire for families and subgroups of GPCRs.

Proteome-wide classification and identification of mammalian-type GPCRs by binary topology pattern

G protein-coupled receptors (GPCRs), a large eukaryotic protein family, have proved difficult to comprehensively detect and functionally identify by homology searches and domain detection, because they are highly divergent and their sequences share strikingly little similarity. Transmembrane (TM) topology pattern analysis has been used to classify TM proteins, and such patterns are conserved within GPCRs of similar function. Here, we developed a stepwise binary topology pattern (BTP) method for GPCR classification and identification and used it to identify and classify mammalian-type GPCRs in the genomes of 10 different eukaryotic species. A binary topology pattern was obtained for each functional class or group by assigning binary loop threshold lengths of ‘0’ (short loop) or ‘1’ (long loop). The GPCR-classification ability of the BTP method had quite high accuracies for classifying GPCR functions at the class level (Classes A, B, C, Frizzled/Smoothened, Non-GPCR, based on the GPCRDB classification scheme), with many classes being classified with 100% accuracy. Sufficiently high accuracies were also maintained at the functional group level, 0.945 over 15 functional groups. Proteome-wide mammalian-type GPCR searches in 10 eukaryotic genomes ( H. sapiens, M. musculus, F. rubripes, C. intestinalis, A. thaliana, D. melanogaster, A. gambiae, C. elegans, P. falciparum, S. cerevisiae) using the BTP method showed much higher classification/identification in non-mammalian genomes than typical BLAST searches, in which a higher number of sequences were classified as Non-GPCR. This stepwise BTP method should prove useful for the identification and functional classification of GPCRs from the genomes of a wide range of species.

PRED-GPCR: GPCR recognition and family classification server.

The vast cell-surface receptor family of G-protein coupled receptors (GPCRs) is the focus of both academic and pharmaceutical research due to their key role in cell physiology along with their amenability to drug intervention. As the data flow rate from the various genome and proteome projects continues to grow, so does the need for fast, automated and reliable screening for new members of the various GPCR families. PRED-GPCR is a free Internet service for GPCR recognition and classification at the family level. A submitted sequence or set of sequences, is queried against the PRED-GPCR library, housing 265 signature profile HMMs corresponding to 67 well-characterized GPCR families. Users query the server through a web interface and results are presented in HTML output format. The server returns all single-motif matches along with the combined results for the corresponding families. The service is available online since October 2003 at http://bioinformatics.biol.uoa.gr/PRED-GPCR.

Depicting a protein’s two faces: GPCR classification by phylogenetic tree-based HMMs

Related proteins with similar biological functions generally share common features, allowing us to extract the common sequence features. These common features enable us to build statistical models that can be used to classify proteins, to predict new members, and to study the sequence–function relationship of this protein function group. Although evolution underlies the basis of multiple sequence analysis methods, most methods ignore phylogenetic relationships and the evolutionary process in building these statistical models. Previously we have shown that a phylogenetic tree-based profile hidden Markov model (T-HMM) is superior in generating a profile for a group of similar proteins. In this study we used the method to generate common features of G protein-coupled receptors (GPCRs). The profile generated by T-HMM gives high accuracy in GPCR function classification, both by ligand and by coupled G protein.

Identification and Classification of Eukaryotic GPCRs by Binary Topology Pattern

G-protein coupled receptors (GPCRs) belong to one of the largest superfamilies in mammals, and important to target for drug design in pharmacology field. The heptahelical fold is a common characteristic to various functional groups (or families) of GPCR with eight loop regions protruding at out sides of the lipid bilayer, of which lengths have a large variation from group to group, indicating the function of each GPCR candidate sequence is inferable from its transmembrane (TM) topology [4, 6]. In this study, a TM topology is treated as a binary topology pattern (BTP, a string ‘0’ and ‘1’) by defining threshold lengths for individual eight loops, where the length of each loop is expressed with a binary number: ‘0’ for short loop or ‘1’ for long loop. By applying the BTPs assigned to individual functional groups, we have detected and classified GPCRs encoded in eukaryotic genomes, i.e., of human, mouse, fugu, fly and worm.