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.     

TM-MOTIF: an alignment viewer to annotate predicted transmembrane helices and conserved motifs in aligned set of sequences

Multiple sequence alignments become biologically meaningful only if conserved and functionally important residues and secondary structural elements preserved can be identified at equivalent positions. This is particularly important for transmembrane proteins like G-protein coupled receptors (GPCRs) with seven transmembrane helices. TM-MOTIF is a software package and an effective alignment viewer to identify and display conserved motifs and amino acid substitutions (AAS) at each position of the aligned set of homologous sequences of GPCRs. The key feature of the package is to display the predicted membrane topology for seven transmembrane helices in seven colours (VIBGYOR colouring scheme) and to map the identified motifs on its respective helices /loop regions. It is an interactive package which provides options to the user to submit query or pre-aligned set of GPCR sequences to align with a reference sequence, like rhodopsin, whose structure has been solved experimentally. It also provides the possibility to identify the nearest homologue from the available inbuilt GPCR or Olfactory Receptor cluster dataset whose association is already known for its receptor type. AVAILABILITY: The database is available for free at mini@ncbs.res.in.     

Trends in application of advancing computational approaches in GPCR ligand discovery

G protein-coupled receptors (GPCRs) comprise the most important superfamily of protein targets in current ligand discovery and drug development. GPCRs are integral membrane proteins that play key roles in various cellular signaling processes. Therefore, GPCR signaling pathways are closely associated with numerous diseases, including cancer and several neurological, immunological, and hematological disorders. Computer-aided drug design (CADD) can expedite the process of GPCR drug discovery and potentially reduce the actual cost of research and development. Increasing knowledge of biological structures, as well as improvements on computer power and algorithms, have led to unprecedented use of CADD for the discovery of novel GPCR modulators. Similarly, machine learning approaches are now widely applied in various fields of drug target research. This review briefly summarizes the application of rising CADD methodologies, as well as novel machine learning techniques, in GPCR structural studies and bioligand discovery in the past few years. Recent novel computational strategies and feasible workflows are updated, and representative cases addressing challenging issues on olfactory receptors, biased agonism, and drug-induced cardiotoxic effects are highlighted to provide insights into future GPCR drug discovery.     

Amino acid recognition by Venus flytrap domains is encoded in an 8-residue motif

A motif foramino acid recognition by proteins or domains of the periplasmic binding protein-like I superfamily has been identified. An initial pattern of 5 residues was based on a multiple sequence alignment of selected proteins of that fold family and on common structural features observed in the crystal structure of some members of the family [leucine isoleucine valine binding protein (LIVBP), leucine binding protein (LBP), and metabotropic glutamate receptor type 1 (mGlu1R) amino terminal domain)]. This pattern was used against the PIR-NREF sequence database and further refined to retrieve all sequences of proteins that belong to the family and eliminate those that do not belong to it. A motif of 8 residues was finally selected to build up the general signature. A total of 232 sequences were retrieved. They were found to belong to only three families of proteins: bacterial periplasmic binding proteins (PBP, 71 sequences), family 3 (or C) of G-protein coupled receptor (GPCR) (146 sequences), and plant putative ionotropic glutamate receptors (iGluR, 15 sequences). PBPs are known to adopt a bilobate structure also named Venus flytrap domain, or LIVBP domain in the present case. Family 3/C GPCRs are also known to hold such a domain. However, for plant iGluRs, it was previously detected by classical similarity searches but not specifically described. Thus plant iGluRs carry two Venus flytrap domains, one that binds glutamate and an additional one that would be a modulatory LIVBP domain. In some cases, the modulator binding to that domain would be an amino acid.     

Identifying G-protein coupled receptors using weighted Levenshtein distance and the nearest neighbor method

G-protein coupled receptors （GPCRs） are a class of seven-helix transmembrane proteins that have been used in bioinformatics as the targets to facilitate drug discovery for human diseases. Although thousands of GPCR sequences have been collected, the ligand specificity of many GPCRs is still unknown and only one crystal structure of the rhodopsin-like family has been solved. Therefore, identifying GPCR types only from sequence data has become an important research issue. In this study, a novel technique for identifying GPCR types based on the weighted Levenshtein distance between two receptor sequences and the nearest neighbor method （NNM） is introduced, which can deal with receptor sequences with different lengths directly. In our experiments for classifying four classes （acetylcholine, adrenoceptor, dopamine, and serotonin） of the rhodopsin-like family of GPCRs, the error rates from the leave-one-out procedure and the leave-half-out procedure were 0.62% and 1.24%, respectively. These results are prior to those of the covariant discriminant algorithm, the support vector machine method, and the NNM with Euclidean distance.     

Identification of cholesterol recognition amino acid consensus (CRAC) motif in G-protein coupled receptors

G-protein coupled receptors (GPCRs) are the largest class of molecules involved in signal transduction across membranes, and represent major targets in the development of novel drug candidates in all clinical areas. Membrane cholesterol has been reported to have an important role in the function of a number of GPCRs. Several structural features of proteins, believed to result in preferential association with cholesterol, have been recognized. Cholesterol recognition/interaction amino acid consensus (CRAC) sequence represents such a motif. Many proteins that interact with cholesterol have been shown to contain the CRAC motif in their sequence. We report here the presence of CRAC motifs in three representative GPCRs, namely, rhodopsin, the β(2)-adrenergic receptor, and the serotonin(1A) receptor. Interestingly, the function of these GPCRs has been previously shown to be dependent on membrane cholesterol. The presence of CRAC motifs in GPCRs indicates that interaction of cholesterol with GPCRs could be specific in nature. Further analysis shows that CRAC motifs are inherent characteristic features of the serotonin(1A) receptor and are conserved over natural evolution. These results constitute the first report of the presence of CRAC motifs in GPCRs and provide novel insight in the molecular nature of GPCR-cholesterol interaction.     

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

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

Insights from the analysis of conserved motifs and permitted amino acid exchanges in the human, the fly and the worm GPCR clusters

G-protein coupled receptors (GPCRs) belong to biologically important and functionally diverse and largest super family of membrane proteins. GPCRs retain a characteristic membrane topology of seven alpha helices with three intracellular, three extracellular loops and flanking N' and C' terminal residues. Subtle differences do exist in the helix boundaries (TM-domain), loop lengths, sequence features such as conserved motifs, and substituting amino acid patterns and their physiochemical properties amongst these sequences (clusters) at intra-genomic and inter-genomic level (please re-phrase into 2 statements for clarity). In the current study, we employ prediction of helix boundaries and scores derived from amino acid substitution exchange matrices to identify the conserved amino acid residues (motifs) as consensus in aligned set of homologous GPCR sequences. Co-clustered GPCRs from human and other genomes, organized as 32 clusters, were employed to study the amino acid conservation patterns and species-specific or cluster-specific motifs. Critical analysis on sequence composition and properties provide clues to connect functional relevance within and across genome for vast practical applications such as design of mutations and understanding of disease-causing genetic abnormalities.     

A naive Bayes model to predict coupling between seven transmembrane domain receptors and G-proteins

MOTIVATION: An understanding of the coupling between a G-protein coupled receptor (GPCR) and a specific class of heterotrimeric GTP-binding proteins (G-proteins) is vital for further comprehending the function of the receptor within a cell. However, predicting G-protein coupling based on the amino acid sequence of a receptor has been a daunting task. While experimental data for G-protein coupling exist, published models that rely on sequence based prediction are few. In this study, we have developed a Naive Bayes model to successfully predict G-protein coupling specificity by training over 80 GPCRs with known coupling. Each intracellular domain of GPCRs was treated as a discrete random variable, conditionally independent of one another. In order to determine the conditional probability distributions of these variables, ClustalW-generated phylogenetic trees were used as an approximation for the clustering of the intracellular domain sequences. The sampling of an intracellular domain sequence was achieved by identifying the cluster containing the homologue with the highest sequence similarity. RESULTS: Out of 55 GPCRs validated, the model yielded a correct classification rate of 72%. Our model also predicted multiple G-protein coupling for most of the GPCRs in the validation set. The Bayesian approach in this work offers an alternative to the experimental approach in order to answer the biological problem of GPCR/G-protein coupling selectivity. AVAILABILITY: Academic users should send their request for the perl program for calculating likelihood probabilities at jack.cao@astrazeneca.com. SUPPLEMENTARY INFORMATION: The materials can be viewed at http://www.astrazeneca-montreal.com/AZRDM_info/supporting_info.pdf.     

Structural biology of G protein‐coupled receptor signaling complexes

G protein‐coupled receptors (GPCRs) constitute the largest family of cell surface receptors that mediate numerous cell signaling pathways, and are targets of more than one‐third of clinical drugs. Thanks to the advancement of novel structural biology technologies, high‐resolution structures of GPCRs in complex with their signaling transducers, including G‐protein and arrestin, have been determined. These 3D complex structures have significantly improved our understanding of the molecular mechanism of GPCR signaling and provided a structural basis for signaling‐biased drug discovery targeting GPCRs. Here we summarize structural studies of GPCR signaling complexes with G protein and arrestin using rhodopsin as a model system, and highlight the key features of GPCR conformational states in biased signaling including the sequence motifs of receptor TM6 that determine selective coupling of G proteins, and the phosphorylation codes of GPCRs for arrestin recruitment. We envision the future of GPCR structural biology not only to solve more high‐resolution complex structures but also to show stepwise GPCR signaling complex assembly and disassembly and dynamic process of GPCR signal transduction.     

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 .     

One motif to bind them: A small-XXX-small motif affects transmembrane domain 1 oligomerization,function, localization,and cross-talk between two yeast GPCRs

G protein-coupled receptors (GPCRs) are the largest family of cell-surface receptors in mammals and facilitate a range of physiological responses triggered by a variety of ligands. GPCRs were thought to function as monomers, however it is now accepted that GPCR homo- and hetero-oligomers also exist and influence receptor properties. The Schizosaccharomyces pombe GPCR Mam2 is a pheromone-sensing receptor involved in mating and has previously been shown to form oligomers in vivo. The first transmembrane domain (TMD) of Mam2 contains a small-XXX-small motif, overrepresented in membrane proteins and well-known for promoting helix–helix interactions. An ortholog of Mam2 in Saccharomyces cerevisiae, Ste2, contains an analogous small-XXX-small motif which has been shown to contribute to receptor homo-oligomerization, localization and function. Here we have used experimental and computational techniques to characterize the role of the small-XXX-small motif in function and assembly of Mam2 for the first time. We find that disruption of the motif via mutagenesis leads to reduction of Mam2 TMD1 homo-oligomerization and pheromone-responsive cellular signaling of the full-length protein. It also impairs correct targeting to the plasma membrane. Mutation of the analogous motif in Ste2 yielded similar results, suggesting a conserved mechanism for assembly. Using co-expression of the two fungal receptors in conjunction with computational models, we demonstrate a functional change in G protein specificity and propose that this is brought about through hetero-dimeric interactions of Mam2 with Ste2 via the complementary small-XXX-small motifs. This highlights the potential of these motifs to affect a range of properties that can be investigated in other GPCRs.     

Structural Alphabets for Protein Structure Classification: A Comparison Study

Finding structural similarities between proteins often helps reveal shared functionality, which otherwise might not be detected by native sequence information alone. Such similarity is usually detected and quantified by protein structure alignment. Determining the optimal alignment between two protein structures, however, remains a hard problem. An alternative approach is to approximate each three-dimensional protein structure using a sequence of motifs derived from a structural alphabet. Using this approach, structure comparison is performed by comparing the corresponding motif sequences or structural sequences. In this article, we measure the performance of such alphabets in the context of the protein structure classification problem. We consider both local and global structural sequences. Each letter of a local structural sequence corresponds to the best matching fragment to the corresponding local segment of the protein structure. The global structural sequence is designed to generate the best possible complete chain that matches the full protein structure. We use an alphabet of 20 letters, corresponding to a library of 20 motifs or protein fragments having four residues. We show that the global structural sequences approximate well the native structures of proteins, with an average coordinate root mean square of 0.69 Å over 2225 test proteins. The approximation is best for all α-proteins, while relatively poorer for all β-proteins. We then test the performance of four different sequence representations of proteins (their native sequence, the sequence of their secondary-structure elements, and the local and global structural sequences based on our fragment library) with different classifiers in their ability to classify proteins that belong to five distinct folds of CATH. Without surprise, the primary sequence alone performs poorly as a structure classifier. We show that addition of either secondary-structure information or local information from the structural sequence considerably improves the classification accuracy. The two fragment-based sequences perform better than the secondary-structure sequence but not well enough at this stage to be a viable alternative to more computationally intensive methods based on protein structure alignment.     

On the hierarchical classification of G protein-coupled receptors

MOTIVATION: 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. RESULTS: 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.     

Composite structural motifs of binding sites for delineating biological functions of proteins

Most biological processes are described as a series of interactions between proteins and other molecules, and interactions are in turn described in terms of atomic structures. To annotate protein functions as sets of interaction states at atomic resolution, and thereby to better understand the relation between protein interactions and biological functions, we conducted exhaustive all-against-all atomic structure comparisons of all known binding sites for ligands including small molecules, proteins and nucleic acids, and identified recurring elementary motifs. By integrating the elementary motifs associated with each subunit, we defined composite motifs that represent context-dependent combinations of elementary motifs. It is demonstrated that function similarity can be better inferred from composite motif similarity compared to the similarity of protein sequences or of individual binding sites. By integrating the composite motifs associated with each protein function, we define meta-composite motifs each of which is regarded as a time-independent diagrammatic representation of a biological process. It is shown that meta-composite motifs provide richer annotations of biological processes than sequence clusters. The present results serve as a basis for bridging atomic structures to higher-order biological phenomena by classification and integration of binding site structures.     

G-protein-coupled receptor prediction using pseudo-amino-acid composition and multiscale energy representation of different physiochemical properties

G-protein-coupled receptors (GPCRs) are the largest family of cell surface receptors that, via trimetric guanine nucleotide-binding proteins (G-proteins), initiate some signaling pathways in the eukaryotic cell. Many diseases involve malfunction of GPCRs making their role evident in drug discovery. Thus, the automatic prediction of GPCRs can be very helpful in the pharmaceutical industry. However, prediction of GPCRs, their families, and their subfamilies is a challenging task. In this article, GPCRs are classified into families, subfamilies, and sub-subfamilies using pseudo-amino-acid composition and multiscale energy representation of different physiochemical properties of amino acids. The aim of the current research is to assess different feature extraction strategies and to develop a hybrid feature extraction strategy that can exploit the discrimination capability in both the spatial and transform domains for GPCR classification. Support vector machine, nearest neighbor, and probabilistic neural network are used for classification purposes. The overall performance of each classifier is computed individually for each feature extraction strategy. It is observed that using the jackknife test the proposed GPCR–hybrid method provides the best results reported so far. The GPCR–hybrid web predictor to help researchers working on GPCRs in the field of biochemistry and bioinformatics is available at http://111.68.99.218/GPCR.     

Identification and characterisation of the dopamine receptor II from the cat flea Ctenocephalides felis (CfDopRII)

G protein-coupled receptors (GPCRs) represent a protein family with a wide range of functions. Approximately 30% of human drug targets are GPCRs, illustrating their pharmaceutical relevance. In contrast, the knowledge about invertebrate GPCRs is limited and is mainly restricted to model organisms like Drosophila melanogaster and Caenorhabditis elegans. Especially in ectoparasites like ticks and fleas, only few GPCRs are characterised. From the cat flea Ctenocephalides felis, a relevant parasite of cats and dogs, no GPCRs are known so far. Thus, we performed a bioinformatic analysis of available insect GPCR sequences from the honeybee Apis mellifera, the mosquito Anopheles gambiae, the fruit fly Drosophila melanogaster and genomic sequences from insect species. Aim of this analysis was the identification of highly conserved GPCRs in order to clone orthologs of these candidates from Ctenocephalides felis. It was found that the dopamine receptor family revealed highest conservation levels and thus was chosen for further characterisation. In this work, the identification, full-length cloning and functional expression of the first GPCR from Ctenocephalides felis, the dopamine receptor II (CfDopRII), are described.     

The role of cholesterol on the activity and stability of neurotensin receptor 1

Understanding the role of specific bilayer components in controlling the function of G-protein coupled receptors (GPCRs) will be a key factor in the development of novel pharmaceuticals. Cholesterol-dependence in particular has become an area of keen interest with respect to GPCR function; not least since the 2.6? crystal structure of the β2 adrenergic receptor revealed a putative cholesterol binding motif conserved throughout class-A GPCRs. Furthermore, experimental evidence for cholesterol-dependent GPCR function has been demonstrated in a limited number of cases. This modulation of receptor function has been attributed to both direct interactions between cholesterol and receptor, and indirect effects caused by the influence of cholesterol on bilayer order and lateral pressure. Despite the widespread occurrence of cholesterol binding motifs, available experimental data on the functional involvement of cholesterol on GPCRs are currently limited to a small number of receptors. Here we investigate the role of cholesterol in the function of the neurotensin receptor 1 (NTS1) a class-A GPCR. Specifically we show how cholesterol, and the analogue cholesteryl hemisuccinate, influence activity, stability, and oligomerisation of both purified and reconstituted NTS1. The results caution against using such motifs as indicators of cholesterol-dependent GPCR activity.     

Independent HHsearch, Needleman--Wunsch-based, and motif analyses reveal the overall hierarchy for most of the G protein-coupled receptor families

Several families of G protein-coupled receptors (GPCRs) show no significant sequence similarities to each other, and it has been debated which of them share a common origin. We developed and performed integrated and independent HHsearch, Needleman--Wunsch-based and motif analyses on more than 6,600 unique GPCRs from 12 species. Moreover, we mined the evolutionary important Trichoplax adhaerens, Nematostella vectensis, Thalassiosira pseudonana, and Strongylocentrotus purpuratus genomes, revealing remarkably rich vertebrate-like GPCR repertoires already in the early Metazoan species. We found strong evidence that the Adhesion and Frizzled families are children to the cyclic AMP (cAMP) family with HHsearch homology probabilities of 99.8% and 99.4%, respectively, also supported by the Needleman--Wunsch analysis and several motifs. We also found that the large Rhodopsin family is likely a child of the cAMP family with an HHsearch homology probability of 99.4% and conserved motifs. Therefore, we suggest that the Adhesion and Frizzled families originated from the cAMP family in an event close to that which gave rise to the Rhodopsin family. We also found convincing evidence that the Rhodopsin family is parent to the important sensory families; Taste 2 and Vomeronasal type 1 as well as the Nematode chemoreceptor families. The insect odorant, gustatory, and Trehalose receptors, frequently referred to as GPCRs, form a separate cluster without relationship to the other families, and we propose, based on these and others' results, that these families are ligand-gated ion channels rather than GPCRs. Overall, we suggest common descent of at least 97% of the GPCRs sequences found in humans.