GPCRDB: an information system for G protein-coupled receptors

AI-based identification of therapeutic agents targeting GPCRs: introducing ligand type classifiers and systems biology

Identifying ligands targeting G protein coupled receptors (GPCRs) with novel chemotypes other than the physiological ligands is a challenge for in silico screening campaigns. Here we present an approach that identifies novel chemotype ligands by combining structural data with a random forest agonist/antagonist classifier and a signal-transduction kinetic model. As a test case, we apply this approach to identify novel antagonists of the human adenosine transmembrane receptor type 2A, an attractive target against Parkinson's disease and cancer. The identified antagonists were tested here in a radio ligand binding assay. Among those, we found a promising ligand whose chemotype differs significantly from all so-far reported antagonists, with a binding affinity of 310 ± 23.4 nM. Thus, our protocol emerges as a powerful approach to identify promising ligand candidates with novel chemotypes while preserving antagonistic potential and affinity in the nanomolar range.

G-Protein coupled receptors: structure and function in drug discovery

The G-protein coupled receptors (GPCRs) superfamily comprise similar proteins arranged into families or classes thus making it one of the largest in the mammalian genome. GPCRs take part in many vital physiological functions making them targets for numerous novel drugs. GPCRs share some distinctive features, such as the seven transmembrane domains, they also differ in the number of conserved residues in their transmembrane domain. Here we provide an introductory and accessible review detailing the computational advances in GPCR pharmacology and drug discovery. An overview is provided on family A-C GPCRs; their structural differences, GPCR signalling, allosteric binding and cooperativity. The dielectric constant (relative permittivity) of proteins is also discussed in the context of site-specific environmental effects.

Substitution scoring matrices for proteins - An overview

Sequence analysis is the primary and simplest approach to discover structural, functional and evolutionary details of related proteins. All the alignment based approaches of sequence analysis make use of amino acid substitution matrices, and the accuracy of the results largely depends on the type of scoring matrices used to perform alignment tasks. An amino acid substitution matrix is a 20 × 20 matrix in which the individual elements encapsulate the rates at which each of the 20 amino acid residues in proteins are substituted by other amino acid residues over time. In contrast to most globular/ordered proteins whose amino acids composition is considered as standard, there are several classes of proteins (e.g., transmembrane proteins) in which certain types of amino acid (e.g., hydrophobic residues) are enriched. These compositional differences among various classes of proteins are manifested in their underlying residue substitution frequencies. Therefore, each of the compositionally distinct class of proteins or protein segments should be studied using specific scoring matrices that reflect their distinct residue substitution pattern. In this review, we describe the development and application of various substitution scoring matrices peculiar to proteins with standard and biased compositions. Along with most commonly used standard matrices (PAM, BLOSUM, MD and VTML) that act as default parameters in various homologs search and alignment tools, different substitution scoring matrices specific to compositionally distinct class of proteins are discussed in detail.

Low expression of RGL4 is associated with a poor prognosis and immune infiltration in lung adenocarcinoma patients

Lung adenocarcinoma (LUAD) is a frequently diagnosed histologic subtype with increasing morbidity and mortality. RalGDS-Like 4 (RGL4) has not been reported to be associated with cancer risk, prognosis, immunotherapy or any other treatments. We perform a bioinformatics analysis on data downloaded from the Cancer Genome Atlas (TCGA)-LUAD, and we find that low expression of RGL4 is accompanied by worse outcomes and prognosis in LUAD patients. As a promising predictor, the potential influence and mechanisms of RGL4 on overall survival are worth exploring. Moreover, RGL4 expression is significantly associated with a variety of tumor-infiltrating immune cells (TIICs), particularly memory B cells, CD8⁺T cells and neutrophils. Subsequently, we evaluated the most notable KEGG pathways, including glycolysis gluconeogenesis, the P53 signaling pathway, RNA degradation, and the B cell receptor signaling pathway, among others. Our findings provide evidence that the decreased expression of RGL4 is significantly associated with poor prognosis and immune cell infiltration in patients with LUAD and highlight the use of RGL4 as a novel predictive biomarker for the prognosis of LUAD and other cancers. RGL4 may also be used in combination with immune checkpoints to identify the benefits of immunotherapy. Subjects: Bioinformatics, Genomics, Oncology, Thoracic surgery.

The European Research Network on Signal Transduction (ERNEST): Toward a Multidimensional Holistic Understanding of G Protein-Coupled Receptor Signaling

G protein-coupled receptors (GPCRs) are intensively studied due to their therapeutic potential as drug targets. Members of this large family of transmembrane receptor proteins mediate signal transduction in diverse cell types and play key roles in human physiology and health. In 2013 the research consortium GLISTEN (COST Action CM1207) was founded with the goal of harnessing the substantial growth in knowledge of GPCR structure and dynamics to push forward the development of molecular modulators of GPCR function. The success of GLISTEN, coupled with new findings and paradigm shifts in the field, led in 2019 to the creation of a related consortium called ERNEST (COST Action CA18133). ERNEST broadens focus to entire signaling cascades, based on emerging ideas of how complexity and specificity in signal transduction are not determined by receptor-ligand interactions alone. A holistic approach that unites the diverse data and perspectives of the research community into a single multidimensional map holds great promise for improved drug design and therapeutic targeting.

Structural Modeling and in Silico Screening of Potential Small-Molecule Allosteric Agonists of a Glucagon-like Peptide 1 Receptor

The glucagon-like peptide 1 receptor (GLP-1R) belongs to the pharmaceutically important class B family of G-protein-coupled receptors (GPCRs), and its incretin peptide ligand GLP-1 analogs are adopted drugs for the treatment of type 2 diabetes. Despite remarkable antidiabetic effects, GLP-1 peptide-based drugs are limited by the need of injection. On the other hand, developing nonpeptidic small-molecule drugs targeting GLP-1R remains elusive. Here, we first constructed a three-dimensional structure model of the transmembrane (TM) domain of human GLP-1R using homology modeling and conformational sampling techniques. Next, a potential allosteric binding site on the TM domain was predicted computationally. In silico screening of druglike compounds against this predicted allosteric site has identified nine compounds as potential GLP-1R agonists. The independent agonistic activity of two compounds was subsequently confirmed using a cAMP response element-based luciferase reporting system. One compound was also shown to stimulate insulin secretion through in vitro assay. In addition, this compound synergized with GLP-1 to activate human GLP-1R. These results demonstrated that allosteric regulation potentially exists in GLP-1R and can be exploited for developing small-molecule agonists. The success of this work will help pave the way for small-molecule drug discovery targeting other class B GPCRs through allosteric regulations.

Role of Extracellular Loops and Membrane Lipids for Ligand Recognition in the Neuronal Adenosine Receptor Type 2A: An Enhanced Sampling Simulation Study

Human G-protein coupled receptors (GPCRs) are important targets for pharmaceutical intervention against neurological diseases. Here, we use molecular simulation to investigate the key step in ligand recognition governed by the extracellular domains in the neuronal adenosine receptor type 2A (hA_2AR), a target for neuroprotective compounds. The ligand is the high-affinity antagonist (4-(2-(7-amino-2-(furan-2-yl)-[1,2,4]triazolo[1,5-a][1,3,5]triazin-5-ylamino)ethyl)phenol), embedded in a neuronal membrane mimic environment. Free energy calculations, based on well-tempered metadynamics, reproduce the experimentally measured binding affinity. The results are consistent with the available mutagenesis studies. The calculations identify a vestibular binding site, where lipids molecules can actively participate to stabilize ligand binding. Bioinformatic analyses suggest that such vestibular binding site and, in particular, the second extracellular loop, might drive the ligand toward the orthosteric binding pocket, possibly by allosteric modulation. Taken together, these findings point to a fundamental role of the interaction between extracellular loops and membrane lipids for ligands’ molecular recognition and ligand design in hA_2AR.

Engineering cell sensing and responses using a GPCR-coupled CRISPR-Cas system

G-protein-coupled receptors (GPCRs) are the largest and most diverse group of membrane receptors in eukaryotes and detect a wide array of cues in the human body. Here we describe a molecular device that couples CRISPR-dCas9 genome regulation to diverse natural and synthetic extracellular signals via GPCRs. We generate alternative architectures for fusing CRISPR to GPCRs utilizing the previously reported design, Tango, and our design, ChaCha. Mathematical modeling suggests that for the CRISPR ChaCha design, multiple dCas9 molecules can be released across the lifetime of a GPCR. The CRISPR ChaCha is dose-dependent, reversible, and can activate multiple endogenous genes simultaneously in response to extracellular ligands. We adopt the design to diverse GPCRs that sense a broad spectrum of ligands, including synthetic compounds, chemokines, mitogens, fatty acids, and hormones. This toolkit of CRISPR-coupled GPCRs provides a modular platform for rewiring diverse ligand sensing to targeted genome regulation for engineering cellular functions.

Classification of G-protein coupled receptors based on a rich generation of convolutional neural network, N-gram transformation and multiple sequence alignments

Sequence classification is crucial in predicting the function of newly discovered sequences. In recent years, the prediction of the incremental large-scale and diversity of sequences has heavily relied on the involvement of machine-learning algorithms. To improve prediction accuracy, these algorithms must confront the key challenge of extracting valuable features. In this work, we propose a feature-enhanced protein classification approach, considering the rich generation of multiple sequence alignment algorithms, N-gram probabilistic language model and the deep learning technique. The essence behind the proposed method is that if each group of sequences can be represented by one feature sequence, composed of homologous sites, there should be less loss when the sequence is rebuilt, when a more relevant sequence is added to the group. On the basis of this consideration, the prediction becomes whether a query sequence belonging to a group of sequences can be transferred to calculate the probability that the new feature sequence evolves from the original one. The proposed work focuses on the hierarchical classification of G-protein Coupled Receptors (GPCRs), which begins by extracting the feature sequences from the multiple sequence alignment results of the GPCRs sub-subfamilies. The N-gram model is then applied to construct the input vectors. Finally, these vectors are imported into a convolutional neural network to make a prediction. The experimental results elucidate that the proposed method provides significant performance improvements. The classification error rate of the proposed method is reduced by at least 4.67% (family level I) and 5.75% (family Level II), in comparison with the current state-of-the-art methods. The implementation program of the proposed work is freely available at: https://github.com/alanFchina/CNN .

In silico design of novel probes for the atypical opioid receptor MRGPRX2

The primate-exclusive MRGPRX2 G protein-coupled receptor (GPCR) has been suggested to modulate pain and itch. Despite putative peptide and small molecule MRGPRX2 agonists, selective nanomolar potency probes have not yet been reported. To identify a MRGPRX2 probe, we first screened 5,695 small molecules and found many opioid compounds activated MRGPRX2, including (−)- and (+)-morphine, hydrocodone, sinomenine, dextromethorphan and the prodynorphin-derived peptides, dynorphin A, dynorphin B, and α- and β-neoendorphin. We used these to select for mutagenesis-validated homology models and docked almost 4 million small molecules. From this docking, we predicted ZINC-3573, which represents a potent MRGPRX2-selective agonist, showing little activity against 315 other GPCRs and 97 representative kinases, and an essentially inactive enantiomer. ZINC-3573 activates endogenous MRGPRX2 in a human mast cell line inducing degranulation and calcium release. MRGPRX2 is a unique atypical opioid-like receptor important for modulating mast cell degranulation, which can now be specifically modulated with ZINC-3573.

Multi-Functional Diarylurea Small Molecule Inhibitors of TRPV1 with Therapeutic Potential for Neuroinflammation

Transient receptor potential vanilloid type 1 (TRPV1), a heat-sensitive calcium channel protein, contributes to inflammation as well as to acute and persistent pain. Since TRPV1 occupies a central position in pathways of neuronal inflammatory signaling, it represents a highly attractive potential therapeutic target for neuroinflammation. In the present work, we have in silico identified a series of diarylurea analogues for hTRPV1, of which 11 compounds showed activity in the nanomolar to micromolar range as validated by in vitro biological assays. Then, we utilized molecular docking to explore the detailed interactions between TRPV1 and the compounds to understand the contributions of the different substituent groups. Tyr511, Leu518, Leu547, Thr550, Asn551, Arg557, and Leu670 were important for the recognition of the small molecules by TRPV1. A hydrophobic group in R2 or a polar/hydrophilic group in R1 contributed significantly to the activities of the antagonists at TRPV1. In addition, the subtle different binding pose of meta-chloro in place of para-fluoro in the R2 group converted antagonism into partial agonism, as was predicted by our short-term molecular dynamics (MD) simulation and validated by bioassay. Importantly, compound 15, one of our best TRPV1 inhibitors, also showed potential binding affinity (1.39 μM) at cannabinoid receptor 2 (CB2), which is another attractive target for immuno-inflammation diseases. Furthermore, compound 1 and its diarylurea analogues were predicted to target the C-X-C chemokine receptor 2 (CXCR2), although bioassay validation of CXCR2 with these compounds still needs to be performed. This prediction from the modeling is of interest, since CXCR2 is also a potential therapeutic target for chronic inflammatory diseases. Our findings provide novel strategies to develop a small molecule inhibitor to simultaneously target two or more inflammation-related proteins for the treatment of a wide range of inflammatory disorders including neuroinflammation and neurodegenerative diseases with potential synergistic effect.

GPCRdb: the G protein-coupled receptor database - an introduction

GPCRs make up the largest family of human membrane proteins and of drug targets. Recent advances in GPCR pharmacology and crystallography have shed new light on signal transduction, allosteric modulation and biased signalling, translating into new mechanisms and principles for drug design. The GPCR database, GPCRdb, has served the community for over 20 years and has recently been extended to include a more multidisciplinary audience. This review is intended to introduce new users to the services in GPCRdb, which meets three overall purposes: firstly, to provide reference data in an integrated, annotated and structured fashion, with a focus on sequences, structures, single‐point mutations and ligand interactions. Secondly, to equip the community with a suite of web tools for swift analysis of structures, sequence similarities, receptor relationships, and ligand target profiles. Thirdly, to facilitate dissemination through interactive diagrams of, for example, receptor residue topologies, phylogenetic relationships and crystal structure statistics. Herein, these services are described for the first time; visitors and guides are provided with good practices for their utilization. Finally, we describe complementary databases cross‐referenced by GPCRdb and web servers with corresponding functionality.

G-Protein-Coupled Receptor MrgD Is a Receptor for Angiotensin-(1-7) Involving Adenylyl Cyclase, cAMP, and Phosphokinase A

Angiotensin (Ang)-(1-7) has cardiovascular protective effects and is the opponent of the often detrimental Ang II within the renin-angiotensin system. Although it is well accepted that the G-protein-coupled receptor Mas is a receptor for the heptapeptide, the lack in knowing initial signaling molecules stimulated by Ang-(1-7) prevented definitive characterization of ligand/receptor pharmacology as well as identification of further hypothesized receptors for the heptapeptide. The study aimed to identify a second messenger stimulated by Ang-(1-7) allowing confirmation as well as discovery of the heptapeptide's receptors. Ang-(1-7) elevates cAMP concentration in primary cells, such as endothelial or mesangial cells. Using cAMP as readout in receptor-transfected human embryonic kidney (HEK293) cells, we provided pharmacological proof that Mas is a functional receptor for Ang-(1-7). Moreover, we identified the G-protein-coupled receptor MrgD as a second receptor for Ang-(1-7). Consequently, the heptapeptide failed to increase cAMP concentration in primary mesangial cells with genetic deficiency in both Mas and MrgD Mice deficient in MrgD showed an impaired hemodynamic response after Ang-(1-7) administration. Furthermore, we excluded the Ang II type 2 receptor as a receptor for the heptapeptide but discovered that the Ang II type 2 blocker PD123319 can also block Mas and MrgD receptors. Our results lead to an expansion and partial revision of the renin-angiotensin system, by identifying a second receptor for Ang-(1-7), by excluding Ang II type 2 as a receptor for the heptapeptide, and by enforcing the revisit of such publications which concluded Ang II type 2 function by only using PD123319.

Reassessing ecdysteroidogenic cells from the cell membrane receptors' perspective

Ecdysteroids secreted by the prothoracic gland (PG) cells of insects control the developmental timing of their immature life stages. These cells have been historically considered as carrying out a single function in insects, namely the biochemical conversion of cholesterol to ecdysteroids and their secretion. A growing body of evidence shows that PG cells receive multiple cues during insect development so we tested the hypothesis that they carry out more than just one function in insects. We characterised the molecular nature and developmental profiles of cell membrane receptors in PG cells of Bombyx mori during the final larval stage and determined what receptors decode nutritional, developmental and physiological signals. Through iterative approaches we identified a complex repertoire of cell membrane receptors that are expressed in intricate patterns and activate previously unidentified signal transduction cascades in PG cells. The expression patterns of some of these receptors explain precisely the mechanisms that are known to control ecdysteroidogenesis. However, the presence of receptors for the notch, hedgehog and wingless signalling pathways and the expression of innate immunity-related receptors such as phagocytosis receptors, receptors for microbial ligands and Toll-like receptors call for a re-evaluation of the role these cells play in insects.

The IUPHAR/BPS Guide to PHARMACOLOGY in 2016: towards curated quantitative interactions between 1300 protein targets and 6000 ligands

The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb, http://www.guidetopharmacology.org) provides expert-curated molecular interactions between successful and potential drugs and their targets in the human genome. Developed by the International Union of Basic and Clinical Pharmacology (IUPHAR) and the British Pharmacological Society (BPS), this resource, and its earlier incarnation as IUPHAR-DB, is described in our 2014 publication. This update incorporates changes over the intervening seven database releases. The unique model of content capture is based on established and new target class subcommittees collaborating with in-house curators. Most information comes from journal articles, but we now also index kinase cross-screening panels. Targets are specified by UniProtKB IDs. Small molecules are defined by PubChem Compound Identifiers (CIDs); ligand capture also includes peptides and clinical antibodies. We have extended the capture of ligands and targets linked via published quantitative binding data (e.g. Ki, IC50 or Kd). The resulting pharmacological relationship network now defines a data-supported druggable genome encompassing 7% of human proteins. The database also provides an expanded substrate for the biennially published compendium, the Concise Guide to PHARMACOLOGY. This article covers content increase, entity analysis, revised curation strategies, new website features and expanded download options.

Comparing Class A GPCRs to bitter taste receptors: Structural motifs, ligand interactions and agonist-to-antagonist ratios

G protein-coupled receptors (GPCRs) are seven transmembrane (TM) proteins that play a key role in human physiology. The GPCR superfamily comprises about 800 members, classified into several classes, with rhodopsin-like Class A being the largest and most studied thus far. A huge component of the human repertoire consists of the chemosensory GPCRs, including ∼400 odorant receptors, 25 bitter taste receptors (TAS2Rs), which are thought to guard the organism from consuming poisons, and sweet and umami TAS1R heteromers, which indicate the nutritive value of food. The location of the binding site of TAS2Rs is similar to that of Class A GPCRs. However, most of the known bitter ligands are agonists, with only a few antagonists documented thus far. The agonist-to-antagonist ratios of Class A GPCRs vary, but in general are much lower than for TAS2Rs. For a set of well-studied GPCRs, a gradual change in agonists-to-antagonists ratios is observed when comparing low (10 μM)- and high (10 nM)-affinity ligand sets from ChEMBL and the DrugBank set of drugs. This shift reflects pharmaceutical bias toward the therapeutically desirable pharmacology for each of these GPCRs, while the 10 μM sets possibly represent the native tendency of the receptors toward either agonists or antagonists. Analyzing ligand-GPCR interactions in 56 X-ray structures representative of currently available structural data, we find that the N-terminus, TM1 and TM2 are more involved in binding of antagonists than of agonists. On the other hand, ECL2 tends to be more involved in binding of agonists. This is of interest, since TAS2Rs harbor variations on the typical Class A sequence motifs, including the absence of the ECL2-TM3 disulfide bridge. This suggests an alternative mode of regulation of conformational states for TAS2Rs, with potentially less stabilized inactive state. The comparison of TAS2Rs and Class A GPCRs structural features and the pharmacology of the their ligands highlights the intricacies of GPCR architecture and provides a framework for rational design of new ligands.

GPCRdb: an information system for G protein-coupled receptors

Recent developments in G protein-coupled receptor (GPCR) structural biology and pharmacology have greatly enhanced our knowledge of receptor structure-function relations, and have helped improve the scientific foundation for drug design studies. The GPCR database, GPCRdb, serves a dual role in disseminating and enabling new scientific developments by providing reference data, analysis tools and interactive diagrams. This paper highlights new features in the fifth major GPCRdb release: (i) GPCR crystal structure browsing, superposition and display of ligand interactions; (ii) direct deposition by users of point mutations and their effects on ligand binding; (iii) refined snake and helix box residue diagram looks; and (iii) phylogenetic trees with receptor classification colour schemes. Under the hood, the entire GPCRdb front- and back-ends have been re-coded within one infrastructure, ensuring a smooth browsing experience and development. GPCRdb is available at http://www.gpcrdb.org/ and it's open source code at https://bitbucket.org/gpcr/protwis.

Apelin attenuates the osteoblastic differentiation of aortic valve interstitial cells via the ERK and PI3-K/Akt pathways

Aortic valve calcification (AVC), which used to be recognized as a passive and irreversible process, is now widely accepted as an active and regulated process characterized by osteoblastic differentiation of aortic valve interstitial cells (AVICs). Apelin, the endogenous ligand for G-protein-coupled receptor APJ, was found to have protective cardiovascular effects in several studies. However, the effects and mechanisms of apelin on osteoblastic differentiation of AVICs have not been elucidated. Using a pro-calcific medium, we devised a method to produce calcific human AVICs. These cells were used to study the relationship between apelin and the osteoblastic calcification of AVICs and the involved signaling pathways. Alkaline phosphatase (ALP) activity/expression and runt-related transcription factor 2 (Runx2) expression were examined as hallmark proteins in this research. The involved signaling pathways were studied using the extracellular signal-regulated kinase (ERK) inhibitor, PD98059, and the phosphatidylinositol 3-kinase (PI3-K) inhibitor, LY294002. The results indicate that apelin attenuates the expression and activity of ALP, the expression of Runx2, and the formation of mineralized nodules. This protective effect was dependent on the dose of apelin, reaching the maximum at 100 pM, and was connected to activity of ERK and Akt (a downstream effector of PI3-K). The activation of ERK and PI3-K initiated the effects of apelin on ALP activity/expression and Runx2, but PD98059 and LY294002 abolished the effect. These results demonstrate that apelin attenuates the osteoblastic differentiation of AVICs via the ERK and PI3-K/Akt pathway.

Applying random forest and subtractive fuzzy c-means clustering techniques for the development of a novel G protein-coupled receptor discrimination method using pseudo amino acid compositions

G protein-coupled receptors (GPCRs) constitute the largest superfamily of integral membrane proteins (IMPs) and they tremendously contribute in the flow of information into cells. In this study, the random forest (RF) and the subtractive fuzzy c-means clustering (SBC) methods have been used to determine the importance of input variables and discriminate GPCRs from non-GPCRs using twenty amino acid and fifty pseudo amino acid compositions derived from GPCR sequences. The studied dataset was retrieved from the UniProt/SWISSPROT database and consists of 1000 GPCR and 1000 non-GPCR reviewed sequences. The top ranked RF-SBC-based model discriminates GPCRs and non-GPCRs successfully with the accuracy, sensitivity, specificity and Matthew's coefficient correlation (MCC) rates of 99.15%, 99.60%, 98.70% and 0.983%, respectively. These rates were obtained from averaged values of 5-fold cross validation using only twenty four out of fifty pseudo amino acid composition features. The results show that the proposed RF-SBC-based model outperforms other existing algorithms in terms of the evaluated performance criteria. The webserver for the proposed algorithm is available at http://brcinfo.shinyapps.io/GPCRIden.

GLASS: a comprehensive database for experimentally validated GPCR-ligand associations

MOTIVATION

G protein-coupled receptors (GPCRs) are probably the most attractive drug target membrane proteins, which constitute nearly half of drug targets in the contemporary drug discovery industry. While the majority of drug discovery studies employ existing GPCR and ligand interactions to identify new compounds, there remains a shortage of specific databases with precisely annotated GPCR-ligand associations.

RESULTS

We have developed a new database, GLASS, which aims to provide a comprehensive, manually curated resource for experimentally validated GPCR-ligand associations. A new text-mining algorithm was proposed to collect GPCR-ligand interactions from the biomedical literature, which is then crosschecked with five primary pharmacological datasets, to enhance the coverage and accuracy of GPCR-ligand association data identifications. A special architecture has been designed to allow users for making homologous ligand search with flexible bioactivity parameters. The current database contains ∼500 000 unique entries, of which the vast majority stems from ligand associations with rhodopsin- and secretin-like receptors. The GLASS database should find its most useful application in various in silico GPCR screening and functional annotation studies.

AVAILABILITY AND IMPLEMENTATION

The website of GLASS database is freely available at http://zhanglab.ccmb.med.umich.edu/GLASS/.

CONTACT

zhng@umich.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Generic GPCR residue numbers - aligning topology maps while minding the gaps

Generic residue numbers facilitate comparisons of, for example, mutational effects, ligand interactions, and structural motifs. The numbering scheme by Ballesteros and Weinstein for residues within the class A GPCRs (G protein-coupled receptors) has more than 1100 citations, and the recent crystal structures for classes B, C, and F now call for a community consensus in residue numbering within and across these classes. Furthermore, the structural era has uncovered helix bulges and constrictions that offset the generic residue numbers. The use of generic residue numbers depends on convenient access by pharmacologists, chemists, and structural biologists. We review the generic residue numbering schemes for each GPCR class, as well as a complementary structure-based scheme, and provide illustrative examples and GPCR database (GPCRDB) web tools to number any receptor sequence or structure.

Evolution of gonadotropin-inhibitory hormone receptor and its ligand

Gonadotropin-inhibitory hormone (GnIH) is a neuropeptide inhibitor of gonadotropin secretion, which was first identified in the Japanese quail hypothalamus. GnIH peptides share a C-terminal LPXRFamide (X=L or Q) motif in most vertebrates. The receptor for GnIH (GnIHR) is the seven-transmembrane G protein-coupled receptor 147 (GPR147) that inhibits cAMP production. GPR147 is also named neuropeptide FF (NPFF) receptor 1 (NPFFR1), because it also binds NPFF that has a C-terminal PQRFamide motif. To understand the evolutionary history of the GnIH system in the animal kingdom, we searched for receptors structurally similar to GnIHR in the genome of six mammals (human, mouse, rat, cattle, cat, and rabbit), five birds (pigeon, chicken, turkey, budgerigar, and zebra finch), one reptile (green anole), one amphibian (Western clawed flog), six fishes (zebrafish, Nile tilapia, Fugu, coelacanth, spotted gar, and lamprey), one hemichordate (acorn worm), one echinoderm (purple sea urchin), one mollusk (California sea hare), seven insects (pea aphid, African malaria mosquito, honey bee, buff-tailed bumblebee, fruit fly, jewel wasp, and red flour beetle), one cnidarian (hydra), and constructed phylogenetic trees by neighbor joining (NJ) and maximum likelihood (ML) methods. A multiple sequence alignment of the receptors showed highly conserved seven-transmembrane domains as well as disulfide bridge sites between the first and second extracellular loops, including the receptor of hydra. Both NJ and ML analyses grouped the receptors of vertebrates into NPFFR1 and NPFFR2 (GPR74), and the receptors of insects into the receptor for SIFamide peptides that share a C-terminal YRKPPFNGSIFamide motif. Although human, quail and zebrafish GnIHR (NPFFR1) were most structurally similar to SIFamide receptor of fruit fly in the Famide peptide (FMRFamide, neuropeptide F, short neuropeptide F, drosulfakinin, myosuppressin, SIFamide) receptor families, the amino acid sequences and the peptide coding regions of GnIH precursors were most similar to FMRFamide precursor of fruit fly in the precursors of Famide peptide families. Chromosome synteny analysis of the precursor genes of human, quail and zebrafish GnIH and fruit fly Famide peptides further identified conserved synteny in vertebrate GnIH and fruit fly FMRFa precursor genes as well as other Famide peptide precursor genes. These results suggest that GnIH and its receptor pair and SIFamide and its receptor pair may have diverged and co-evolved independently in vertebrates and insects, respectively, from their ancestral Famide peptide and its receptor pair, during diversification and evolution of deuterostomian and protostomian species.

Chaperoning G protein-coupled receptors: from cell biology to therapeutics

G protein-coupled receptors (GPCRs) are membrane proteins that traverse the plasma membrane seven times (hence, are also called 7TM receptors). The polytopic structure of GPCRs makes the folding of GPCRs difficult and complex. Indeed, many wild-type GPCRs are not folded optimally, and defects in folding are the most common cause of genetic diseases due to GPCR mutations. Both general and receptor-specific molecular chaperones aid the folding of GPCRs. Chemical chaperones have been shown to be able to correct the misfolding in mutant GPCRs, proving to be important tools for studying the structure-function relationship of GPCRs. However, their potential therapeutic value is very limited. Pharmacological chaperones (pharmacoperones) are potentially important novel therapeutics for treating genetic diseases caused by mutations in GPCR genes that resulted in misfolded mutant proteins. Pharmacoperones also increase cell surface expression of wild-type GPCRs; therefore, they could be used to treat diseases that do not harbor mutations in GPCRs. Recent studies have shown that indeed pharmacoperones work in both experimental animals and patients. High-throughput assays have been developed to identify new pharmacoperones that could be used as therapeutics for a number of endocrine and other genetic diseases.

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.

Recent progress in the development of antagonists to the chemokine receptors CCR3 and CCR4

INTRODUCTION

The chemokine receptors CCR3 and CCR4 have been shown to be important therapeutic targets for the treatment of a variety of diseases. Although only two chemokine receptor inhibitors have been approved so far, there are numerous compounds that are in various stages of development.

AREAS COVERED

In this review article, the authors provide an update on the progress made in the identification of antagonists against the chemokine receptors CCR3 and CCR4 from 2009 to the present. The rationale of writing this review article is to cover the most important approaches to identifying antagonists to these two receptors, which could prove to be useful therapeutics in treating proinflammatory diseases.

EXPERT OPINION

Pharmaceutical companies have expended a considerable amount of money and effort to identify potent inhibitors of CCR3 and CCR4 for the treatment of asthma and atopic diseases. Although a variety of compounds have been described and several have progressed into the clinic, none have so far made it as approved drugs. There are, however, novel approaches such as mogamulizumab, a monoclonal antibody to CCR4 currently is in clinical trials for cancer and ASM8, an antisense nucleotide to CCR3, which is in Phase II clinical trials for asthma that might still prove to be successful new therapeutics.

Genetics of taste receptors

Taste receptors function as one of the interfaces between internal and external milieus. Taste receptors for sweet and umami (T1R [taste receptor, type 1]), bitter (T2R [taste receptor, type 2]), and salty (ENaC [epithelial sodium channel]) have been discovered in the recent years, but transduction mechanisms of sour taste and ENaC-independent salt taste are still poorly understood. In addition to these five main taste qualities, the taste system detects such noncanonical "tastes" as water, fat, and complex carbohydrates, but their reception mechanisms require further research. Variations in taste receptor genes between and within vertebrate species contribute to individual and species differences in taste-related behaviors. These variations are shaped by evolutionary forces and reflect species adaptations to their chemical environments and feeding ecology. Principles of drug discovery can be applied to taste receptors as targets in order to develop novel taste compounds to satisfy demand in better artificial sweeteners, enhancers of sugar and sodium taste, and blockers of bitterness of food ingredients and oral medications.

GPCRDB: an information system for G protein-coupled receptors

For the past 20 years, the GPCRDB (G protein-coupled receptors database; http://www.gpcr.org/7tm/) has been a ‘one-stop shop’ for G protein-coupled receptor (GPCR)-related data. The GPCRDB contains experimental data on sequences, ligand-binding constants, mutations and oligomers, as well as many different types of computationally derived data, such as multiple sequence alignments and homology models. The GPCRDB also provides visualization and analysis tools, plus a number of query systems. In the latest GPCRDB release, all multiple sequence alignments, and >65 000 homology models, have been significantly improved, thanks to a recent flurry of GPCR X-ray structure data. Tools were introduced to browse X-ray structures, compare binding sites, profile similar receptors and generate amino acid conservation statistics. Snake plots and helix box diagrams can now be custom coloured (e.g. by chemical properties or mutation data) and saved as figures. A series of sequence alignment visualization tools has been added, and sequence alignments can now be created for subsets of sequences and sequence positions, and alignment statistics can be produced for any of these subsets.

Using ensemble SVM to identify human GPCRs N-linked glycosylation sites based on the general form of Chou's PseAAC

As the most frequent drug target, G-protein coupled receptors (GPCRs) are a large family of seven transmembrane receptors that sense molecules outside the cell and activate inside signal transduction pathways. Glycosylation is one of the most complex post-translational modifications (PTMs) of proteins in eukaryotic cells. It plays important roles in a variety of cellular functions, including protein folding, protein trafficking and localization, cell-cell interactions and epitope recognition. Therefore, investigating the exact position of glycosylation site in GPCR sequence can provide useful clues for drug design and other biotechnology applications. Experimental identification of glycosylation sites is expensive and laborious. Hence, there is a significant interest in the development of computational methods for reliable prediction of glycosylation sites from amino acid sequences. In this article, we presented an effective method to recognize the sites of human GPCRs by combining amino acid hydrophobicity with ensemble support vector machine. The prediction accuracy, sensitivity, specificity, Matthews correlation coefficient and area under the curve values were 94.4, 89.7, 98.9%, 0.895 and 0.989, respectively. The establishment of such a fast and accurate prediction method will speed up the pace of identifying proper GPCRs functional sites to facilitate drug discovery.

GPR18 in microglia: implications for the CNS and endocannabinoid system signalling

A review of what is presently known about the G protein coupled receptor GPR18 in terms of its expression and distribution, pharmacology and potential implications for central nervous system and endocannabinoid system signalling. LINKED ARTICLES This article is part of a themed section on Cannabinoids. To view the other articles in this section visit http://dx.doi.org/10.1111/bph.2012.167.issue-8.

Kernel Generative Topographic Mapping of Protein Sequences

The world of pharmacology is becoming increasingly dependent on the advances in the fields of genomics and proteomics. The –omics sciences bring about the challenge of how to deal with the large amounts of complex data they generate from an intelligent data analysis perspective. In this chapter, the authors focus on the analysis of a specific type of proteins, the G protein-coupled receptors, which are the target for over 15% of current drugs. They describe a kernel method of the manifold learning family for the analysis of protein amino acid symbolic sequences. This method sheds light on the structure of protein subfamilies, while providing an intuitive visualization of such structure.

Identifying subset errors in multiple sequence alignments

Multiple sequence alignment (MSA) accuracy is important, but there is no widely accepted method of judging the accuracy that different alignment algorithms give. We present a simple approach to detecting two types of error, namely block shifts and the misplacement of residues within a gap. Given a MSA, subsets of very similar sequences are generated through the use of a redundancy filter, typically using a 70-90% sequence identity cut-off. Subsets thus produced are typically small and degenerate, and errors can be easily detected even by manual examination. The errors, albeit minor, are inevitably associated with gaps in the alignment, and so the procedure is particularly relevant to homology modelling of protein loop regions. The usefulness of the approach is illustrated in the context of the universal but little known [K/R]KLH motif that occurs in intracellular loop 1 of G protein coupled receptors (GPCR); other issues relevant to GPCR modelling are also discussed.

Structure-based drug design using GPCR homology modeling: toward the discovery of novel selective CysLT2 antagonists

3D structure of CysLT2 receptor was constructed by using homology modeling and molecular simulations. The binding pocket of CysLT2 receptor and the proposition of the interaction mode between CysLT2 and HAMI3379 were identified. A series of dicarboxylated chalcones was then virtually evaluated through molecular docking studies. A total of six compounds 13a-f with preferable scores was further synthesized and tested for CysLT2 antagonistic activities by determination of the cytosolic free Ca(2+) levels in HEK293 cells. Compounds 13e and 13f exhibited potent and selective CysLT2 antagonistic activities with IC50 values being 7.5 and 0.25 μM, respectively.

The pepper MLO gene, CaMLO2, is involved in the susceptibility cell-death response and bacterial and oomycete proliferation

Loss-of-function alleles of the mildew resistance locus O (MLO) gene provide broad-spectrum powdery mildew disease resistance. Here, we identified a pepper (Capsicum annuum) MLO gene (CaMLO2) that is transcriptionally induced by Xanthomonas campestris pv. vesicatoria (Xcv) infection. Topology and subcellular localization analyses reveal that CaMLO2 is a plasma membrane-anchored and amphiphilic Ca²⁺-dependent calmodulin-binding protein. CaMLO2 expression is up-regulated by Xcv and salicylic acid, as well as abiotic stresses. Silencing of CaMLO2 in pepper plants confers enhanced resistance against virulent Xcv, but not against avirulent Xcv. This resistance is accompanied by a compromised susceptibility cell-death response and reduced bacterial growth, as well as an accelerated reactive oxygen species burst. Virulent Xcv infection drastically induces expression of the salicylic acid-dependent defense marker gene CaPR1 in CaMLO2-silenced leaves. CaMLO2 over-expression in Arabidopsis enhances susceptibility to Pseudomonas syringae pv. tomato and Hyaloperonospora arabidopsidis. Leaves of plants over-expressing CaMLO2 exhibit a susceptibility cell-death response and high bacterial growth during virulent Pst DC3000 infection. These are accompanied by enhanced electrolyte leakage but compromised induction of some defense response genes and the reactive oxygen species. Together, our results suggest that CaMLO2 is involved in the susceptibility cell-death response and bacterial and oomycete proliferation in pepper and Arabidopsis.

A minimal ligand binding pocket within a network of correlated mutations identified by multiple sequence and structural analysis of G protein coupled receptors

Background

G protein coupled receptors (GPCRs) are seven helical transmembrane proteins that function as signal transducers. They bind ligands in their extracellular and transmembrane regions and activate cognate G proteins at their intracellular surface at the other side of the membrane. The relay of allosteric communication between the ligand binding site and the distant G protein binding site is poorly understood. In this study, GREMLIN [1], a recently developed method that identifies networks of co-evolving residues from multiple sequence alignments, was used to identify those that may be involved in communicating the activation signal across the membrane. The GREMLIN-predicted long-range interactions between amino acids were analyzed with respect to the seven GPCR structures that have been crystallized at the time this study was undertaken.

Results

GREMLIN significantly enriches the edges containing residues that are part of the ligand binding pocket, when compared to a control distribution of edges drawn from a random graph. An analysis of these edges reveals a minimal GPCR binding pocket containing four residues (T118^3.33, M207^5.42, Y268^6.51 and A292^7.39). Additionally, of the ten residues predicted to have the most long-range interactions (A117^3.32, A272^6.55, E113^3.28, H211^5.46, S186^EC2, A292^7.39, E122^3.37, G90^2.57, G114^3.29 and M207^5.42), nine are part of the ligand binding pocket.

Conclusions

We demonstrate the use of GREMLIN to reveal a network of statistically correlated and functionally important residues in class A GPCRs. GREMLIN identified that ligand binding pocket residues are extensively correlated with distal residues. An analysis of the GREMLIN edges across multiple structures suggests that there may be a minimal binding pocket common to the seven known GPCRs. Further, the activation of rhodopsin involves these long-range interactions between extracellular and intracellular domain residues mediated by the retinal domain.

Life beyond kinases: structure-based discovery of sorafenib as nanomolar antagonist of 5-HT receptors

Of great interest in recent years has been computationally predicting the novel polypharmacology of drug molecules. Here, we applied an "induced-fit" protocol to improve the homology models of 5-HT(2A) receptor, and we assessed the quality of these models in retrospective virtual screening. Subsequently, we computationally screened the FDA approved drug molecules against the best induced-fit 5-HT(2A) models and chose six top scoring hits for experimental assays. Surprisingly, one well-known kinase inhibitor, sorafenib, has shown unexpected promiscuous 5-HTRs binding affinities, K(i) = 1959, 56, and 417 nM against 5-HT(2A), 5-HT(2B), and 5-HT(2C), respectively. Our preliminary SAR exploration supports the predicted binding mode and further suggests sorafenib to be a novel lead compound for 5HTR ligand discovery. Although it has been well-known that sorafenib produces anticancer effects through targeting multiple kinases, carefully designed experimental studies are desirable to fully understand whether its "off-target" 5-HTR binding activities contribute to its therapeutic efficacy or otherwise undesirable side effects.

Improvement of the Prediction Power of the CoMFA and CoMSIA Models on Histamine H3 Antagonists by Different Variable Selection Methods

The aim of this study is to enhance the predictivity power of CoMFA and CoMSIA models by means of different variable selection algorithms. The genetic algorithm (GA), successive projection algorithm (SPA), stepwise multiple linear regression (SW-MLR), and the enhanced replacement method (ERM) were used and tested as variable selection algorithms. Then, the selected variables were used to generate a simple and predictive model by the multilinear regression algorithm. A set of 74 histamine H₃ antagonists were split into 40 compounds as a training set, and 17 compounds as a test set, by the Kennard-Stone algorithm. Before splitting the data, 17 compounds were randomly selected from the pool of the whole data set as an evaluation set without any supervision, pretreatment, or visual inspection. Among applied variable selection algorithms, ERM had noticeable improvement on the statistical parameters. The r² values of training, test, and evaluation sets for the ERM-MLR model using CoMFA fields were 0.9560, 0.8630, and 0.8460 and using the CoMSIA fields were 0.9800, 0.8521, and 0.9080, respectively. In this study, the principles of organization for economic cooperation and development (OECD) for regulatory acceptability of QSARs are considered.

Using core hydrophobicity to identify phosphorylation sites of human G protein-coupled receptors

As the most frequent drug target, G protein-coupled receptors (GPCRs) are a large family of seven trans-membrane receptors that sense molecules outside the cell and activate inside signal transduction pathways. The activity and lifetime of activated receptors are regulated by receptor phosphorylation. Therefore, investigating the exact positions of phosphorylation sites in GPCRs sequence could provide useful clues for drug design and other biotechnology applications. Experimental identification of phosphorylation sites is expensive and laborious. Hence, there is significant interest in the development of computational methods for reliable prediction of phosphorylation sites from amino acid sequences. In this article, we presented a simple and effective method to recognize phosphorylation sites of human GPCRs by combining amino acid hydrophobicity and support vector machine. The prediction accuracy, sensitivity, specificity, Matthews correlation coefficient and area under the curve values for phosphoserine, phosphothreonine, and phosphotyrosine were 0.964, 0.790, 0.999, 0.866, 0.941; 0.954, 0.800, 0.985, 0.828, 0.958; and 0.976, 0.820, 0.993, 0.861, 0.959, respectively. The establishment of such a fast and accurate prediction method will speed up the pace of identifying proper GPCRs sites to facilitate drug discovery.

Drug design for ever, from hype to hope

In its first 25 years JCAMD has been disseminating a large number of techniques aimed at finding better medicines faster. These include genetic algorithms, COMFA, QSAR, structure based techniques, homology modelling, high throughput screening, combichem, and dozens more that were a hype in their time and that now are just a useful addition to the drug-designers toolbox. Despite massive efforts throughout academic and industrial drug design research departments, the number of FDA-approved new molecular entities per year stagnates, and the pharmaceutical industry is reorganising accordingly. The recent spate of industrial consolidations and the concomitant move towards outsourcing of research activities requires better integration of all activities along the chain from bench to bedside. The next 25 years will undoubtedly show a series of translational science activities that are aimed at a better communication between all parties involved, from quantum chemistry to bedside and from academia to industry. This will above all include understanding the underlying biological problem and optimal use of all available data.

Gonadotropin-inhibitory hormone action in the brain and pituitary

Gonadotropin-inhibitory hormone (GnIH) was first identified in the Japanese quail as a hypothalamic neuropeptide inhibitor of gonadotropin secretion. Subsequent studies have shown that GnIH is present in the brains of birds including songbirds, and mammals including humans. The identified avian and mammalian GnIH peptides universally possess an LPXRFamide (X = L or Q) motif at their C-termini. Mammalian GnIH peptides are also designated as RFamide-related peptides from their structures. The receptor for GnIH is the G protein-coupled receptor 147 (GPR147), which is thought to be coupled to G(αi) protein. Cell bodies of GnIH neurons are located in the paraventricular nucleus (PVN) in birds and the dorsomedial hypothalamic area (DMH) in mammals. GnIH neurons in the PVN or DMH project to the median eminence to control anterior pituitary function. GPR147 is expressed in the gonadotropes and GnIH suppresses synthesis and release of gonadotropins. It was further shown in immortalized mouse gonadotrope cell line (LβT2 cells) that GnIH inhibits gonadotropin-releasing hormone (GnRH) induced gonadotropin subunit gene transcriptions by inhibiting adenylate cyclase/cAMP/PKA-dependent ERK pathway. GnIH neurons also project to GnRH neurons in the preoptic area, and GnRH neurons express GPR147 in birds and mammals. Accordingly, GnIH may inhibit gonadotropin synthesis and release by decreasing the activity of GnRH neurons as well as directly acting on the gonadotropes. GnIH also inhibits reproductive behavior possibly by acting within the brain. GnIH expression is regulated by a nocturnal hormone melatonin and stress in birds and mammals. Accordingly, GnIH may play a role in translating environmental information to inhibit reproductive physiology and behavior of birds and mammals. Finally, GnIH has therapeutic potential in the treatment of reproductive cycle and hormone-dependent diseases, such as precocious puberty, endometriosis, uterine fibroids, and prostatic and breast cancers.

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.

ChEMBL: a large-scale bioactivity database for drug discovery

ChEMBL is an Open Data database containing binding, functional and ADMET information for a large number of drug-like bioactive compounds. These data are manually abstracted from the primary published literature on a regular basis, then further curated and standardized to maximize their quality and utility across a wide range of chemical biology and drug-discovery research problems. Currently, the database contains 5.4 million bioactivity measurements for more than 1 million compounds and 5200 protein targets. Access is available through a web-based interface, data downloads and web services at: https://www.ebi.ac.uk/chembldb.

Integrating GPCR-specific information with full text articles

Background

With the continued growth in the volume both of experimental G protein-coupled receptor (GPCR) data and of the related peer-reviewed literature, the ability of GPCR researchers to keep up-to-date is becoming increasingly curtailed.

Results

We present work that integrates the biological data and annotations in the GPCR information system (GPCRDB) with next-generation methods for intelligently exploring, visualising and interacting with the scientific articles used to disseminate them. This solution automatically retrieves relevant information from GPCRDB and displays it both within and as an adjunct to an article.

Conclusions

This approach allows researchers to extract more knowledge more swiftly from literature. Importantly, it allows reinterpretation of data in articles published before GPCR structure data became widely available, thereby rescuing these valuable data from long-dormant sources.