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

Molecular Pharmacological Phenotyping of EBI2

Epstein-Barr virus (EBV)-induced receptor 2 (EBI2) is an orphan seven-transmembrane (7TM) receptor originally identified as the most up-regulated gene (>200-fold) in EBV-infected cells. Here we show that EBI2 signals with constitutive activity through Galpha(i) as determined by a receptor-mediated inhibition of forskolin-induced cAMP production and an induction of the serum response element-driven transcriptional activity in a pertussis toxin-sensitive manner. Galpha(s) and Galpha(q) were not activated constitutively as determined by the lack of cAMP production, the lack of inositol phosphate turnover, and the lack of activities of the transcription factors: cAMP response element-binding protein and nuclear factor-kappaB. Immunohistochemistry and confocal microscopy of FLAG- and green fluorescent protein-tagged EBI2 revealed cell-surface expression. A putative N-terminal truncated version of EBI2, delta4-EBI2, showed similar expression and signaling through Galpha(i) as full-length EBI2. By using a 32P-labeled EBI2 probe we found a very high expression in lymphoid tissue (spleen and lymph node) and peripheral blood mononuclear cells and a high expression in lung tissue. Real-time PCR of EBV-infected cells showed high expression of EBI2 during latent and lytic infection, in contrast to the EBV-encoded 7TM receptor BILF1, which was induced during lytic infection. EBI2 clustered with the orphan GPR18 by alignment analysis as well as by close proximity in the chromosomal region 13q32.3. Based on the constitutive signaling and cellular expression pattern of EBI2, it is suggested that it may function in conjunction with BILF1 in the reprogramming of the cell during EBV infection.

Alignment-Free Classification of G-Protein-Coupled Receptors Using Self-Organizing Maps

Proteins are classified mainly on the basis of alignments of amino acid sequences. Drug discovery processes based on pharmacologically important proteins such as G-protein-coupled receptors (GPCRs) may be facilitated if more information is extracted directly from the primary sequences. Here, we investigate an alignment-free approach to protein classification using self-organizing maps (SOMs), a kind of artificial neural network, which needs only primary sequences of proteins and determines their relative locations in a two-dimensional lattice of neurons through an adaptive process. We first showed that a set of 1397 aligned samples of Class A GPCRs can be classified by our SOM program into 15 conventional categories with 99.2% accuracy. Similarly, a nonaligned raw sequence data set of 4116 samples was categorized into 15 conventional families with 97.8% accuracy in a cross-validation test. Orphan GPCRs were also classified appropriately using the result of the SOM learning. A supposedly diverse family of olfactory receptors formed the most distinctive cluster in the map, whereas amine and peptide families exhibited diffuse distributions. A feature of this kind in the map can be interpreted to reflect hierarchical family composition. Interestingly, some orphan receptors that were categorized as olfactory were somatosensory chemoreceptors. These results suggest the applicability and potential of the SOM program to classification prediction and knowledge discovery from protein sequences.

A chemogenomic analysis of the transmembrane binding cavity of human G-protein-coupled receptors

The amino acid sequences of 369 human nonolfactory G-protein-coupled receptors (GPCRs) have been aligned at the seven transmembrane domain (TM) and used to extract the nature of 30 critical residues supposed--from the X-ray structure of bovine rhodopsin bound to retinal--to line the TM binding cavity of ground-state receptors. Interestingly, the clustering of human GPCRs from these 30 residues mirrors the recently described phylogenetic tree of full-sequence human GPCRs (Fredriksson et al., Mol Pharmacol 2003;63:1256-1272) with few exceptions. A TM cavity could be found for all investigated GPCRs with physicochemical properties matching that of their cognate ligands. The current approach allows a very fast comparison of most human GPCRs from the focused perspective of the predicted TM cavity and permits to easily detect key residues that drive ligand selectivity or promiscuity.

An automated system for the analysis of G protein-coupled receptor transmembrane binding pockets: alignment, receptor-based pharmacophores, and their application

G protein-coupled receptors (GPCRs) share a common architecture consisting of seven transmembrane (TM) domains. Various lines of evidence suggest that this fold provides a generic binding pocket within the TM region for hosting agonists, antagonists, and allosteric modulators. Here, a comprehensive and automated method allowing fast analysis and comparison of these putative binding pockets across the entire GPCR family is presented. The method relies on a robust alignment algorithm based on conservation indices, focusing on pharmacophore-like relationships between amino acids. Analysis of conservation patterns across the GPCR family and alignment to the rhodopsin X-ray structure allows the extraction of the amino acids lining the TM binding pocket in a so-called ligand binding pocket vector (LPV). In a second step, LPVs are translated to simple 3D receptor pharmacophore models, where each amino acid is represented by a single spherical pharmacophore feature and all atomic detail is omitted. Applications of the method include the assessment of selectivity issues, support of mutagenesis studies, and the derivation of rules for focused screening to identify chemical starting points in early drug discovery projects. Because of the coarseness of this 3D receptor pharmacophore model, however, meaningful scoring and ranking procedures of large sets of molecules are not justified. The LPV analysis of the trace amine-associated receptor family and its experimental validation is discussed as an example. The value of the 3D receptor model is demonstrated for a class C GPCR family, the metabotropic glutamate receptors.

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 Matthe'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.

Improved approach for proteochemometrics modeling: application to organic compound--amine G protein-coupled receptor interactions

MOTIVATION

Proteochemometrics is a novel technology for the analysis of interactions of series of proteins with series of ligands. We have here customized it for analysis of large datasets and evaluated it for the modeling of the interaction of psychoactive organic amines with all the five known families of amine G protein-coupled receptors (GPCRs).

RESULTS

The model exploited data for the binding of 22 compounds to 31 amine GPCRs, correlating chemical descriptions and cross-descriptions of compounds and receptors to binding affinity using a novel strategy. A highly valid model (q2 = 0.76) was obtained which was further validated by external predictions using data for 10 other entirely independent compounds, yielding the high q2ext = 0.67. Interpretation of the model reveals molecular interactions that govern psychoactive organic amines overall affinity for amine GPCRs, as well as their selectivity for particular amine GPCRs. The new modeling procedure allows us to obtain fully interpretable proteochemometrics models using essentially unlimited number of ligand and protein descriptors.

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

We describe a novel system, GRIFFIN (G-protein and Receptor Interaction Feature Finding INstrument), that predicts G-protein coupled receptor (GPCR) and G-protein coupling selectivity based on a support vector machine (SVM) and a hidden Markov model (HMM) with high sensitivity and specificity. Based on our assumption that whole structural segments of ligands, GPCRs and G-proteins are essential to determine GPCR and G-protein coupling, various quantitative features were selected for ligands, GPCRs and G-protein complex structures, and those parameters that are the most effective in selecting G-protein type were used as feature vectors in the SVM. The main part of GRIFFIN includes a hierarchical SVM classifier using the feature vectors, which is useful for Class A GPCRs, the major family. For the opsins and olfactory subfamilies of Class A and other minor families (Classes B, C, frizzled and smoothened), the binding G-protein is predicted with high accuracy using the HMM. Applying this system to known GPCR sequences, each binding G-protein is predicted with high sensitivity and specificity (>85% on average). GRIFFIN () is freely available and allows users to easily execute this reliable prediction of G-proteins.

GPCRsclass: a web tool for the classification of amine type of G-protein-coupled receptors

The receptors of amine subfamily are specifically major drug targets for therapy of nervous disorders and psychiatric diseases. The recognition of novel amine type of receptors and their cognate ligands is of paramount interest for pharmaceutical companies. In the past, Chou and co-workers have shown that different types of amine receptors are correlated with their amino acid composition and are predictable on its basis with considerable accuracy [Elrod and Chou (2002) Protein Eng., 15, 713–715]. This motivated us to develop a better method for the recognition of novel amine receptors and for their further classification. The method was developed on the basis of amino acid composition and dipeptide composition of proteins using support vector machine. The method was trained and tested on 167 proteins of amine subfamily of G-protein-coupled receptors (GPCRs). The method discriminated amine subfamily of GPCRs from globular proteins with Matthew's correlation coefficient of 0.98 and 0.99 using amino acid composition and dipeptide composition, respectively. In classifying different types of amine receptors using amino acid composition and dipeptide composition, the method achieved an accuracy of 89.8 and 96.4%, respectively. The performance of the method was evaluated using 5-fold cross-validation. The dipeptide composition based method predicted 67.6% of protein sequences with an accuracy of 100% with a reliability index ≥5. A web server GPCRsclass has been developed for predicting amine-binding receptors from its amino acid sequence [ and (mirror site)].

Availability of short amino acid sequences in proteins

Much attention is being paid to protein databases as an important information source for proteome research. Although used extensively for similarity searches, protein databases themselves have not fully been characterized. In a systematic attempt to reveal protein-database characters that could contribute to revealing how protein chains are constructed, frequency distributions of all possible combinatorial sets of three, four, and five amino acids ("triplets," "quartets," and "pentats"; collectively called constituent sequences) have been examined in the nonredundant (nr) protein database, demonstrating the existence of nonrandom bias in their "availability" at the population level. Nonexistent short sequences of pentats were found that showed low availability in biological proteins against their expected probabilities of occurrence. Among them, six representative ones were successfully synthesized as peptides with reasonably high yields in a conventional Fmoc method, excluding the possibility that a putative physicochemical energy barrier in forming them could be a direct cause for the low availability. They were also expressed as soluble fusion proteins in a conventional Escherichia coli BL21Star(DE3) system with reasonably high yield, again excluding a possible difficulty in their biological synthesis. Together, these results suggest that information on three-dimensional structures and functions of proteins exists in the context of connections of short constituent sequences, and that proteins are composed of evolutionarily selected constituent sequences, which are reflected in their availability differences in the database. These results may have biological implications for protein structural studies.

Descriptor-based protein remote homology identification

Here, we report a novel protein sequence descriptor-based remote homology identification method, able to infer fold relationships without the explicit knowledge of structure. In a first phase, we have individually benchmarked 13 different descriptor types in fold identification experiments in a highly diverse set of protein sequences. The relevant descriptors were related to the fold class membership by using simple similarity measures in the descriptor spaces, such as the cosine angle. Our results revealed that the three best-performing sets of descriptors were the sequence-alignment-based descriptor using PSI-BLAST e-values, the descriptors based on the alignment of secondary structural elements (SSEA), and the descriptors based on the occurrence of PROSITE functional motifs. In a second phase, the three top-performing descriptors were combined to obtain a final method with improved performance, which we named DescFold. Class membership was predicted by Support Vector Machine (SVM) learning. In comparison with the individual PSI-BLAST-based descriptor, the rate of remote homology identification increased from 33.7% to 46.3%. We found out that the composite set of descriptors was able to identify the true remote homolog for nearly every sixth sequence at the 95% confidence level, or some 10% more than a single PSI-BLAST search. We have benchmarked the DescFold method against several other state-of-the-art fold recognition algorithms for the 172 LiveBench-8 targets, and we concluded that it was able to add value to the existing techniques by providing a confident hit for at least 10% of the sequences not identifiable by the previously known methods.

Using Bioinformatics for Drug Target Identification from the Genome

Genomics and proteomics technologies have created a paradigm shift in the drug discovery process, with bioinformatics having a key role in the exploitation of genomic, transcriptomic, and proteomic data to gain insights into the molecular mechanisms that underlie disease and to identify potential drug targets. We discuss the current state of the art for some of the bioinformatic approaches to identifying drug targets, including identifying new members of successful target classes and their functions, predicting disease relevant genes, and constructing gene networks and protein interaction networks. In addition, we introduce drug target discovery using the strategy of systems biology, and discuss some of the data resources for the identification of drug targets. Although bioinformatics tools and resources can be used to identify putative drug targets, validating targets is still a process that requires an understanding of the role of the gene or protein in the disease process and is heavily dependent on laboratory-based work.

GPCRpred: an SVM-based method for prediction of families and subfamilies of G-protein coupled receptors

G-protein coupled receptors (GPCRs) belong to one of the largest superfamilies of membrane proteins and are important targets for drug design. In this study, a support vector machine (SVM)-based method, GPCRpred, has been developed for predicting families and subfamilies of GPCRs from the dipeptide composition of proteins. The dataset used in this study for training and testing was obtained from http://www.soe.ucsc.edu/research/compbio/gpcr/. The method classified GPCRs and non-GPCRs with an accuracy of 99.5% when evaluated using 5-fold cross-validation. The method is further able to predict five major classes or families of GPCRs with an overall Matthew's correlation coefficient (MCC) and accuracy of 0.81 and 97.5% respectively. In recognizing the subfamilies of the rhodopsin-like family, the method achieved an average MCC and accuracy of 0.97 and 97.3% respectively. The method achieved overall accuracy of 91.3% and 96.4% at family and subfamily level respectively when evaluated on an independent/blind dataset of 650 GPCRs. A server for recognition and classification of GPCRs based on multiclass SVMs has been set up at http://www.imtech.res.in/raghava/gpcrpred/. We have also suggested subfamilies for 42 sequences which were previously identified as unclassified ClassA GPCRs. The supplementary information is available at http://www.imtech.res.in/raghava/gpcrpred/info.html.

The RAIG Family Member, GPRC5D, Is Associated with Hard-Keratinized Structures

Retinoic acid-inducible gene-1 was originally identified as an orphan G-protein coupled receptor induced by retinoic acid. Three highly homologous oGPCR (GPRC5B, GPRC5C, and GPRC5D) have since been classified into the RAIG1 family. We describe here, the unique tissue distribution of GPRC5D and its mechanism of expression. Hybridization in situ has shown that GPRC5D is expressed in differentiating cells that produce hard keratin, including cortical cells of the hair shaft, the keratogenous zone of the nail, and in a central region of the filiform papillae of the tongue. The GPRC5D transcript is expressed in hair follicles during mid- and late anagen, and catagen but not at telogen and early anagen phases. The differentiation-inducer, all-trans retinoic acid, induces GPRC5D expression in cultured hair bulb cells. Because the tissue distribution of GPRC5D indicates a relationship with hard keratins that constitute the major structural proteins of hard epithelial tissues, we investigated the effect of GPRC5D on acid hard keratins. Analyses of cultured cells showed that transient overexpression resulted in suppression of Ha3 and stimulation of Ha4 hair keratin gene expression. The expression was maintained in the hair follicles of whn-deficient (nude) mice, suggesting that this gene is regulated by a signal pathway different from that of hair keratin synthesis. Collectively, these data provide a framework for understanding the molecular mechanisms of GPRC5D function in hard keratinization.

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.

Construction of a sequence motif characteristic of aminergic G protein-coupled receptors

An approach to discover sequence patterns characteristic of ligand classes is described and applied to aminergic G protein-coupled receptors (GPCRs). Putative ligand-binding residue positions were inferred from considering three lines of evidence: conservation in the subfamily absent or underrepresented in the superfamily, any available mutation data, and the physicochemical properties of the ligand. For aminergic GPCRs, the motif is composed of a conserved aspartic acid in the third transmembrane (TM) domain (rhodopsin position 117) and a conserved tryptophan in the seventh TM domain (rhodopsin position 293); the roles of each are readily justified by molecular modeling of ligand-receptor interactions. This minimally defined motif is an appropriate computational tool for identifying additional, potentially novel aminergic GPCRs from a set of experimentally uncharacterized "orphan" GPCRs, complementing existing sequence matching, clustering, and machine-learning techniques. Motif sensitivity stems from the stepwise addition of residues characteristic of an entire class of ligand (and not tailored for any particular biogenic amine). This sensitivity is balanced by careful consideration of residues (evidence drawn from mutation data, correlation of ligand properties to residue properties, and location with respect to the extracellular face), thereby maintaining specificity for the aminergic class. A number of orphan GPCRs assigned to the aminergic class by this motif were later discovered to be a novel subfamily of trace amine GPCRs, as well as the successful classification of the histamine H4 receptor.