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

A Web Server for GPCR-GPCR Interaction Pair Prediction

The GGIP web server (https://protein.b.dendai.ac.jp/GGIP/) provides a web application for GPCR-GPCR interaction pair prediction by a support vector machine. The server accepts two sequences in the FASTA format. It responds with a prediction that the input GPCR sequence pair either interacts or not. GPCRs predicted to interact with the monomers constituting the pair are also shown when query sequences are human GPCRs. The server is simple to use. A pair of amino acid sequences in the FASTA format is pasted into the text area, a PDB ID for a template structure is selected, and then the 'Execute' button is clicked. The server quickly responds with a prediction result. The major advantage of this server is that it employs the GGIP software, which is presently the only method for predicting GPCR-interaction pairs. Our web server is freely available with no login requirement. In this article, we introduce some application examples of GGIP for disease-associated mutation analysis.

PRECOG: PREdicting COupling probabilities of G-protein coupled receptors

G-protein coupled receptors (GPCRs) control multiple physiological states by transducing a multitude of extracellular stimuli into the cell via coupling to intra-cellular heterotrimeric G-proteins. Deciphering which G-proteins couple to each of the hundreds of GPCRs present in a typical eukaryotic organism is therefore critical to understand signalling. Here, we present PRECOG (precog.russelllab.org): a web-server for predicting GPCR coupling, which allows users to: (i) predict coupling probabilities for GPCRs to individual G-proteins instead of subfamilies; (ii) visually inspect the protein sequence and structural features that are responsible for a particular coupling; (iii) suggest mutations to rationally design artificial GPCRs with new coupling properties based on predetermined coupling features.

Understanding G Protein Selectivity of Muscarinic Acetylcholine Receptors Using Computational Methods

The neurotransmitter molecule acetylcholine is capable of activating five muscarinic acetylcholine receptors, M1 through M5, which belong to the superfamily of G-protein-coupled receptors (GPCRs). These five receptors share high sequence and structure homology; however, the M1, M3, and M5 receptor subtypes signal preferentially through the Gαq/11 subset of G proteins, whereas the M2 and M4 receptor subtypes signal through the Gαi/o subset of G proteins, resulting in very different intracellular signaling cascades and physiological effects. The structural basis for this innate ability of the M1/M3/M5 set of receptors and the highly homologous M2/M4 set of receptors to couple to different G proteins is poorly understood. In this study, we used molecular dynamics (MD) simulations coupled with thermodynamic analyses of M1 and M2 receptors coupled to both Gαi and Gαq to understand the structural basis of the M1 receptor’s preference for the Gαq protein and the M2 receptor’s preference for the Gαi protein. The MD studies showed that the M1 and M2 receptors can couple to both Gα proteins such that the M1 receptor engages with the two Gα proteins in slightly different orientations and the M2 receptor engages with the two Gα proteins in the same orientation. Thermodynamic studies of the free energy of binding of the receptors to the Gα proteins showed that the M1 and M2 receptors bind more strongly to their cognate Gα proteins compared to their non-cognate ones, which is in line with previous experimental studies on the M3 receptor. A detailed analysis of receptor–G protein interactions showed some cognate-complex-specific interactions for the M2:Gαi complex; however, G protein selectivity determinants are spread over a large overlapping subset of residues. Conserved interaction between transmembrane helices 5 and 6 far away from the G-protein-binding receptor interface was found only in the two cognate complexes and not in the non-cognate complexes. An analysis of residues implicated previously in G protein selectivity, in light of the cognate and non-cognate structures, shaded a more nuanced role of those residues in affecting G protein selectivity. The simulation of both cognate and non-cognate receptor–G protein complexes fills a structural gap due to difficulties in determining non-cognate complex structures and provides an enhanced framework to probe the mechanisms of G protein selectivity exhibited by most GPCRs.

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.

GGIP: Structure and sequence-based GPCR-GPCR interaction pair predictor

G Protein-Coupled Receptors (GPCRs) are important pharmaceutical targets. More than 30% of currently marketed pharmaceutical medicines target GPCRs. Numerous studies have reported that GPCRs function not only as monomers but also as homo- or hetero-dimers or higher-order molecular complexes. Many GPCRs exert a wide variety of molecular functions by forming specific combinations of GPCR subtypes. In addition, some GPCRs are reportedly associated with diseases. GPCR oligomerization is now recognized as an important event in various biological phenomena, and many researchers are investigating this subject. We have developed a support vector machine (SVM)-based method to predict interacting pairs for GPCR oligomerization, by integrating the structure and sequence information of GPCRs. The performance of our method was evaluated by the Receiver Operating Characteristic (ROC) curve. The corresponding area under the curve was 0.938. As far as we know, this is the only prediction method for interacting pairs among GPCRs. Our method could accelerate the analyses of these interactions, and contribute to the elucidation of the global structures of the GPCR networks in membranes. Proteins 2016; 84:1224-1233. © 2016 Wiley Periodicals, Inc.

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.

A multi-fold string kernel for sequence classification

A novel framework is proposed to classify biological sequences using a kernel. It considers the topological information along with the primary structural information. The widely used string kernel for sequence classification does not take into account the structural information which might be available for biological sequences. The proposed kernels incorporate the additional structural information and thus make the features more informative.

NRfamPred: a proteome-scale two level method for prediction of nuclear receptor proteins and their sub-families

Nuclear receptor proteins (NRP) are transcription factor that regulate many vital cellular processes in animal cells. NRPs form a super-family of phylogenetically related proteins and divided into different sub-families on the basis of ligand characteristics and their functions. In the post-genomic era, when new proteins are being added to the database in a high-throughput mode, it becomes imperative to identify new NRPs using information from amino acid sequence alone. In this study we report a SVM based two level prediction systems, NRfamPred, using dipeptide composition of proteins as input. At the 1st level, NRfamPred screens whether the query protein is NRP or non-NRP; if the query protein belongs to NRP class, prediction moves to 2nd level and predicts the sub-family. Using leave-one-out cross-validation, we were able to achieve an overall accuracy of 97.88% at the 1st level and an overall accuracy of 98.11% at the 2nd level with dipeptide composition. Benchmarking on independent datasets showed that NRfamPred had comparable accuracy to other existing methods, developed on the same dataset. Our method predicted the existence of 76 NRPs in the human proteome, out of which 14 are novel NRPs. NRfamPred also predicted the sub-families of these 14 NRPs.

Protein sub-nuclear localization prediction using SVM and Pfam domain information

The nucleus is the largest and the highly organized organelle of eukaryotic cells. Within nucleus exist a number of pseudo-compartments, which are not separated by any membrane, yet each of them contains only a specific set of proteins. Understanding protein sub-nuclear localization can hence be an important step towards understanding biological functions of the nucleus. Here we have described a method, SubNucPred developed by us for predicting the sub-nuclear localization of proteins. This method predicts protein localization for 10 different sub-nuclear locations sequentially by combining presence or absence of unique Pfam domain and amino acid composition based SVM model. The prediction accuracy during leave-one-out cross-validation for centromeric proteins was 85.05%, for chromosomal proteins 76.85%, for nuclear speckle proteins 81.27%, for nucleolar proteins 81.79%, for nuclear envelope proteins 79.37%, for nuclear matrix proteins 77.78%, for nucleoplasm proteins 76.98%, for nuclear pore complex proteins 88.89%, for PML body proteins 75.40% and for telomeric proteins it was 83.33%. Comparison with other reported methods showed that SubNucPred performs better than existing methods. A web-server for predicting protein sub-nuclear localization named SubNucPred has been established at http://14.139.227.92/mkumar/subnucpred/. Standalone version of SubNucPred can also be downloaded from the web-server.

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.

An approach for identifying cytokines based on a novel ensemble classifier

Biology is meaningful and important to identify cytokines and investigate their various functions and biochemical mechanisms. However, several issues remain, including the large scale of benchmark datasets, serious imbalance of data, and discovery of new gene families. In this paper, we employ the machine learning approach based on a novel ensemble classifier to predict cytokines. We directly selected amino acids sequences as research objects. First, we pretreated the benchmark data accurately. Next, we analyzed the physicochemical properties and distribution of whole amino acids and then extracted a group of 120-dimensional (120D) valid features to represent sequences. Third, in the view of the serious imbalance in benchmark datasets, we utilized a sampling approach based on the synthetic minority oversampling technique algorithm and K-means clustering undersampling algorithm to rebuild the training set. Finally, we built a library for dynamic selection and circulating combination based on clustering (LibD3C) and employed the new training set to realize cytokine classification. Experiments showed that the geometric mean of sensitivity and specificity obtained through our approach is as high as 93.3%, which proves that our approach is effective for identifying cytokines.

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.

Classification of GPCRs using family specific motifs

The classification of G-Protein Coupled Receptor (GPCR) sequences is an important problem that arises from the need to close the gap between the large number of orphan receptors and the relatively small number of annotated receptors. Equally important is the characterization of GPCR Class A subfamilies and gaining insight into the ligand interaction since GPCR Class A encompasses a very large number of drug-targeted receptors. In this work, we propose a method for Class A subfamily classification using sequence-derived motifs which characterizes the subfamilies by discovering receptor-ligand interaction sites. The motifs that best characterize a subfamily are selected by the Distinguishing Power Evaluation (DPE) technique we propose. The experiments performed on GPCR sequence databases show that our method outperforms state-of-the-art classification techniques for GPCR Class A subfamily prediction. An important contribution of our work is to discover key receptor-ligand interaction sites which is very important for drug design.

Discrimination of Golgi type II membrane proteins based on their hydropathy profiles and the amino acid propensities of their transmembrane regions

Membrane proteins in the Golgi apparatus play important roles in biological functions, predominantly as catalysts related to post-translational modification of protein oligosaccharides. We succeeded in extracting the characteristics of Golgi type II membrane proteins computationally by comparison with those of Golgi no retention proteins, which are mainly localized in the plasma membrane. Golgi type II membrane proteins were detected by combining hydropathy alignment and a position-specific score matrix of the amino acid propensities around the transmembrane region. We achieved 96.2% sensitivity, 93.5% specificity, and a 0.949 success rate in a self-consistency test. In a 5-fold cross-validation test, 88.0% sensitivity, 85.5% specificity, and a 0.867 success rate were achieved.

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

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

Prediction of functional class of proteins and peptides irrespective of sequence homology by support vector machines

Various computational methods have been used for the prediction of protein and peptide function based on their sequences. A particular challenge is to derive functional properties from sequences that show low or no homology to proteins of known function. Recently, a machine learning method, support vector machines (SVM), have been explored for predicting functional class of proteins and peptides from amino acid sequence derived properties independent of sequence similarity, which have shown promising potential for a wide spectrum of protein and peptide classes including some of the low- and non-homologous proteins. This method can thus be explored as a potential tool to complement alignment-based, clustering-based, and structure-based methods for predicting protein function. This article reviews the strategies, current progresses, and underlying difficulties in using SVM for predicting the functional class of proteins. The relevant software and web-servers are described. The reported prediction performances in the application of these methods are also presented.

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: .

Prediction of antibiotic resistance proteins from sequence-derived properties irrespective of sequence similarity

Increasing antibiotic resistance has become a worldwide challenge to the clinical treatment of infectious diseases. The identification of antibiotic resistance proteins (ARPs) would be helpful in the discovery of new therapeutic targets and the design of novel drugs to control the potential spread of antibiotic resistance. In this work, a support vector machine (SVM)-based ARP prediction system was developed using 1308 ARPs and 15587 non-ARPs. Its performance was evaluated using 313 ARPs and 7156 non-ARPs. The computed prediction accuracy was 88.5% for ARPs and 99.2% for non-ARPs. A potential application of this method is the identification of ARPs non-homologous to proteins of known function. Further genome screening found that ca. 3.5% and 3.2% of proteins in Escherichia coli and Staphylococcus aureus, respectively, are potential ARPs. These results suggest the usefulness of SVMs for facilitating the identification of ARPs. The software can be accessed at SARPI (Server for Antibiotic Resistance Protein Identification).

Alternative splicing of the G protein-coupled receptor superfamily in human airway smooth muscle diversifies the complement of receptors

G protein-coupled receptors (GPCRs) are the largest signaling family in the genome, serve an expansive array of functions, and are targets for approximately 50% of current therapeutics. In many tissues, such as airway smooth muscle (ASM), complex, unexpected, or paradoxical responses to agonists/antagonists occur without known mechanisms. We hypothesized that ASM express many more GPCRs than predicted, and that these undergo substantial alternative splicing, creating a highly diversified receptor milieu. Transcript arrays were designed detecting 434 GPCRs and their predicted splice variants. In this cell type, 353 GPCRs were detected (including 111 orphans), with expression levels varying by approximately 900-fold. Receptors used for treating airway disease were expressed lower than others with similar signaling properties, indicating potentially more effective targets. A disproportionate number of Class-A peptide-group receptors, and those coupling to G(q)/(11) or G(s) (vs. G(i)), was found. Importantly, 192 GPCRs had, on average, five different expressed receptor isoforms because of splicing events, including alternative splice donors and acceptors, novel introns, intron retentions, exon(s) skips, and novel exons, with the latter two events being most prevalent. The consequences of splicing were further investigated with the leukotriene B4 receptor, known for its aberrant responsiveness in lung. We found transcript expression of three variants because of alternative donor and acceptor splice sites, representing in-frame deletions of 38 and 100 aa, with protein expression of all three isoforms. Thus, alternative splicing, subject to conditional, temporal, and cell-type regulation, is a major mechanism that diversifies the GPCR superfamily, creating local recepteromes with specialized environments.

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.

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.

Recent progresses in the application of machine learning approach for predicting protein functional class independent of sequence similarity

Protein sequence contains clues to its function. Functional prediction from sequence presents a challenge particularly for proteins that have low or no sequence similarity to proteins of known function. Recently, machine learning methods have been explored for predicting functional class of proteins from sequence-derived properties independent of sequence similarity, which showed promising potential for low- and non-homologous proteins. These methods can thus be explored as potential tools to complement alignment- and clustering-based methods for predicting protein function. This article reviews the strategies, current progresses, and underlying difficulties in using machine learning methods for predicting the functional class of proteins. The relevant software and web-servers are described. The reported prediction performances in the application of these methods are also presented, which need to be interpreted with caution as they are dependent on such factors as datasets used and choice of parameters.

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