GPCRDB: information system for G protein-coupled receptors

G protein-coupled receptor (GPCR) pharmacogenomics

The field of pharmacogenetics, the investigation of the influence of one or more sequence variants on drug response phenotypes, is a special case of pharmacogenomics, a discipline that takes a genome-wide approach. Massively parallel, next generation sequencing (NGS), has allowed pharmacogenetics to be subsumed by pharmacogenomics with respect to the identification of variants associated with responders and non-responders, optimal drug response, and adverse drug reactions. A plethora of rare and common naturally-occurring GPCR variants must be considered in the context of signals from across the genome. Many fundamentals of pharmacogenetics were established for G protein-coupled receptor (GPCR) genes because they are primary targets for a large number of therapeutic drugs. Functional studies, demonstrating likely-pathogenic and pathogenic GPCR variants, have been integral to establishing models used for in silico analysis. Variants in GPCR genes include both coding and non-coding single nucleotide variants and insertion or deletions (indels) that affect cell surface expression (trafficking, dimerization, and desensitization/downregulation), ligand binding and G protein coupling, and variants that result in alternate splicing encoding isoforms/variable expression. As the breadth of data on the GPCR genome increases, we may expect an increase in the use of drug labels that note variants that significantly impact the clinical use of GPCR-targeting agents. We discuss the implications of GPCR pharmacogenomic data derived from the genomes available from individuals who have been well-phenotyped for receptor structure and function and receptor-ligand interactions, and the potential benefits to patients of optimized drug selection. Examples discussed include the renin-angiotensin system in SARS-CoV-2 (COVID-19) infection, the probable role of chemokine receptors in the cytokine storm, and potential protease activating receptor (PAR) interventions. Resources dedicated to GPCRs, including publicly available computational tools, are also discussed.

GPCRana: A web server for quantitative analysis of GPCR structures

G protein-coupled receptors (GPCRs) attract tremendous attention from both industrial and academic researchers with currently over 900 released structures. Structural analysis is widely used to understand receptor functionality and pharmacology, but more user-friendly tools are needed. Residue-residue contact score (RRCS) is an atomic distance-based method that allows a quantitative description of GPCR structures. Here, we present GPCRana, a web server that provides a user-friendly interface to analyze GPCR structures. After uploading selected structures, GPCRana immediately generates a comprehensive report covering four aspects: (i) RRCS for all residue pairs incorporated with real-time 3D visualization; (ii) ligand-receptor interactions; (iii) activation pathway analysis; and (iv) RRCS_TMs that indicates the global movements of transmembrane helices. Moreover, conformational changes between two structures can be analyzed. Applying GPCRana on AlphaFold2-predicted models reveals differentiated inter-helical packing forms in a receptor-dependent manner. Our web server offers a fast and precise way to study GPCR structures and is freely available at http://gpcranalysis.com/#/.

PyPLIF HIPPOS-Assisted Prediction of Molecular Determinants of Ligand Binding to Receptors

Identification of molecular determinants of receptor-ligand binding could significantly increase the quality of structure-based virtual screening protocols. In turn, drug design process, especially the fragment-based approaches, could benefit from the knowledge. Retrospective virtual screening campaigns by employing AutoDock Vina followed by protein-ligand interaction fingerprinting (PLIF) identification by using recently published PyPLIF HIPPOS were the main techniques used here. The ligands and decoys datasets from the enhanced version of the database of useful decoys (DUDE) targeting human G protein-coupled receptors (GPCRs) were employed in this research since the mutation data are available and could be used to retrospectively verify the prediction. The results show that the method presented in this article could pinpoint some retrospectively verified molecular determinants. The method is therefore suggested to be employed as a routine in drug design and discovery.

Structural basis of GABAB receptor-Gi protein coupling

G-protein-coupled receptors (GPCRs) have central roles in intercellular communication^1,2. Structural studies have revealed how GPCRs can activate G proteins. However, whether this mechanism is conserved among all classes of GPCR remains unknown. Here we report the structure of the class-C heterodimeric GABA_B receptor, which is activated by the inhibitory transmitter GABA, in its active form complexed with G_i1 protein. We found that a single G protein interacts with the GB2 subunit of the GABA_B receptor at a site that mainly involves intracellular loop 2 on the side of the transmembrane domain. This is in contrast to the G protein binding in a central cavity, as has been observed with other classes of GPCR. This binding mode results from the active form of the transmembrane domain of this GABA_B receptor being different from that of other GPCRs, as it shows no outside movement of transmembrane helix 6. Our work also provides details of the inter- and intra-subunit changes that link agonist binding to G-protein activation in this heterodimeric complex.

G protein-coupled estrogen receptor-1: homology modeling approaches and application in screening new GPER-1 modulators

G protein-coupled receptors (GPCRs) belong to the largest family of protein targets comprising over 800 members in which at least 500 members are the therapeutic targets. Among the GPCRs, G protein-coupled estrogen receptor-1 (GPER-1) has shown to have the ability in estrogen signaling. As GPER-1 plays a critical role in several physiological responses, GPER-1 has been considered as a potential therapeutic target to treat estrogen-based cancers and other non-communicable diseases. However, the progress in the understanding of GPER-1 structure and function is relatively slow due to the availability of a only a few selective GPER-1 modulators. As with many GPCRs, the X-ray crystal structure of GPER-1 is yet to be resolved and thus has led the researchers to search for new GPER-1 modulators using homology models of GPER-1. In this review, we aim to summarize various approaches used in the generation of GPER-1 homology model and their applications that have resulted in new GPER-1 ligands.

The fine art of preparing membrane transport proteins for biomolecular simulations: Concepts and practical considerations

Molecular dynamics (MD) simulations have developed into an invaluable tool in bimolecular research, due to the capability of the method in capturing molecular events and structural transitions that describe the function as well as the physiochemical properties of biomolecular systems. Due to the progressive development of more efficient algorithms, expansion of the available computational resources, as well as the emergence of more advanced methodologies, the scope of computational studies has increased vastly over time. We now have access to a multitude of online databases, software packages, larger molecular systems and novel ligands due to the phenomenon of emerging novel psychoactive substances (NPS). With so many advances in the field, it is understandable that novices will no doubt find it challenging setting up a protein-ligand system even before they run their first MD simulation. These initial steps, such as homology modelling, ligand docking, parameterization, protein preparation and membrane setup have become a fundamental part of the drug discovery pipeline, and many areas of biomolecular sciences benefit from the applications provided by these technologies. However, there still remains no standard on their usage. Therefore, our aim within this review is to provide a clear overview of a variety of concepts and methodologies to consider, providing a workflow for a case study of a membrane transport protein, the full-length human dopamine transporter (hDAT) in complex with different stimulants, where MD simulations have recently been applied successfully.

GPCR-SAS: A web application for statistical analyses on G protein-coupled receptors sequences

G protein-coupled receptors (GPCRs) are one of the largest protein families in mammals. They mediate signal transduction across cell membranes and are important targets for the pharmaceutical industry. The G Protein-Coupled Receptors-Sequence Analysis and Statistics (GPCR-SAS) web application provides a set of tools to perform comparative analysis of sequence positions between receptors, based on a curated structural-informed multiple sequence alignment. The analysis tools include: (i) percentage of occurrence of an amino acid or motif and entropy at a position or range of positions, (ii) covariance of two positions, (iii) correlation between two amino acids in two positions (or two sequence motifs in two ranges of positions), and (iv) snake-plot representation for a specific receptor or for the consensus sequence of a group of selected receptors. The analysis of conservation of residues and motifs across transmembrane (TM) segments may guide the design of more selective ligands or help to rationalize activation mechanisms, among others. As an example, here we analyze the amino acids of the "transmission switch", that initiates receptor activation following ligand binding. The tool is freely accessible at http://lmc.uab.cat/gpcrsas/.

Approaches for Differentiation and Interconverting GPCR Agonists and Antagonists

Predicting the functional preferences of the ligands was always a highly demanding task, much harder that predicting whether a ligand can bind to the receptor. This is because of significant similarities of agonists, antagonists and inverse agonists which are binding usually in the same binding site of the receptor and only small structural changes can push receptor toward a particular activation state. For G protein-coupled receptors, due to a large progress in crystallization techniques and also in receptor thermal stabilization, it was possible to obtain a large number of high-quality structures of complexes of these receptors with agonists and non-agonists. Additionally, the long-time-scale molecular dynamics simulations revealed how the activation processes of GPCRs can take place. Using both theoretical and experimental knowledge it was possible to employ many clever and sophisticated methods which can help to differentiate agonists and non-agonists, so one can interconvert them in search of the optimal drug.

Representation Learning for Class C G Protein-Coupled Receptors Classification

G protein-coupled receptors (GPCRs) are integral cell membrane proteins of relevance for pharmacology. The complete tertiary structure including both extracellular and transmembrane domains has not been determined for any member of class C GPCRs. An alternative way to work on GPCR structural models is the investigation of their functionality through the analysis of their primary structure. For this, sequence representation is a key factor for the GPCRs' classification context, where usually, feature engineering is carried out. In this paper, we propose the use of representation learning to acquire the features that best represent the class C GPCR sequences and at the same time to obtain a model for classification automatically. Deep learning methods in conjunction with amino acid physicochemical property indices are then used for this purpose. Experimental results assessed by the classification accuracy, Matthews' correlation coefficient and the balanced error rate show that using a hydrophobicity index and a restricted Boltzmann machine (RBM) can achieve performance results (accuracy of 92.9%) similar to those reported in the literature. As a second proposal, we combine two or more physicochemical property indices instead of only one as the input for a deep architecture in order to add information from the sequences. Experimental results show that using three hydrophobicity-related index combinations helps to improve the classification performance (accuracy of 94.1%) of an RBM better than those reported in the literature for class C GPCRs without using feature selection methods.

Comparative analysis of membrane protein structure databases

BACKGROUND

Membrane proteins play important roles in cell survival and cell communication, as they function as transporters, receptors, anchors and enzymes. They are also potential targets for drugs that block receptors or inhibit enzymes related to diseases. Although the number of known structures of membrane proteins is still small relative to the size of the proteome as a whole, many new membrane protein structures have been determined recently.

SCOPE OF THE ARTICLE

We compared and analyzed the widely used membrane protein databases, mpstruc, Orientations of Proteins in Membranes (OPM), and PDBTM, as well as the extended dataset of mpstruc based on sequence similarity, the PDB structures whose classification field indicates that they are "membrane proteins" and the proteins with Structural Classification of Proteins (SCOP) class-f domains. We evaluated the relationships between these databases or datasets based on the overlap in their contents and the degree of consistency in the structural, topological, and functional classifications and in the transmembrane domain assignment.

MAJOR CONCLUSIONS

The membrane databases differ from each other in their coverage, and in the criteria that they use for annotation and classification. To ensure the efficient use of these databases, it is important to understand their differences and similarities. The establishment of more detailed and consistent annotations for the sequence, structure, membrane association, and function of membrane proteins is still required.

GENERAL SIGNIFICANCE

Considering the recent growth of experimentally determined structures, a broad survey and cumulative analysis of the sum of knowledge as presented in the membrane protein structure databases can be helpful to elucidate structures and functions of membrane proteins. We also aim to provide a framework for future research and classification of membrane proteins.

Central Hypothyroidism Due to a TRHR Mutation Causing Impaired Ligand Affinity and Transactivation of Gq

CONTEXT

Central congenital hypothyroidism (CCH) is an underdiagnosed disorder characterized by deficient production and bioactivity of thyroid-stimulating hormone (TSH) leading to low thyroid hormone synthesis. Thyrotropin-releasing hormone (TRH) receptor (TRHR) defects are rare recessive disorders usually associated with incidentally identified CCH and short stature in childhood.

OBJECTIVES

Clinical and genetic characterization of a consanguineous family of Roma origin with central hypothyroidism and identification of underlying molecular mechanisms.

DESIGN

All family members were phenotyped with thyroid hormone profiles, pituitary magnetic resonance imaging, TRH tests, and dynamic tests for other pituitary hormones. Candidate TRH, TRHR, TSHB, and IGSF1 genes were screened for mutations. A mutant TRHR was characterized in vitro and by molecular modeling.

RESULTS

A homozygous missense mutation in TRHR (c.392T > C; p.I131T) was identified in an 8-year-old boy with moderate hypothyroidism (TSH: 2.61 mIU/L, Normal: 0.27 to 4.2; free thyroxine: 9.52 pmol/L, Normal: 10.9 to 25.7) who was overweight (body mass index: 20.4 kg/m2, p91) but had normal stature (122 cm; -0.58 standard deviation). His mother, two brothers, and grandmother were heterozygous for the mutation with isolated hyperthyrotropinemia (TSH: 4.3 to 8 mIU/L). The I131T mutation, in TRHR intracellular loop 2, decreases TRH affinity and increases the half-maximal effective concentration for signaling. Modeling of TRHR-Gq complexes predicts that the mutation disrupts the interaction between receptor and a hydrophobic pocket formed by Gq.

CONCLUSIONS

A unique missense TRHR defect identified in a consanguineous family is associated with central hypothyroidism in homozygotes and hyperthyrotropinemia in heterozygotes, suggesting compensatory elevation of TSH with reduced biopotency. The I131T mutation decreases TRH binding and TRHR-Gq coupling and signaling.

A subcellular map of the human proteome

Resolving the spatial distribution of the human proteome at a subcellular level can greatly increase our understanding of human biology and disease. Here we present a comprehensive image-based map of subcellular protein distribution, the Cell Atlas, built by integrating transcriptomics and antibody-based immunofluorescence microscopy with validation by mass spectrometry. Mapping the in situ localization of 12,003 human proteins at a single-cell level to 30 subcellular structures enabled the definition of the proteomes of 13 major organelles. Exploration of the proteomes revealed single-cell variations in abundance or spatial distribution and localization of about half of the proteins to multiple compartments. This subcellular map can be used to refine existing protein-protein interaction networks and provides an important resource to deconvolute the highly complex architecture of the human cell.

Cysteinyl Leukotrienes Pathway Genes, Atopic Asthma and Drug Response: From Population Isolates to Large Genome-Wide Association Studies

Genetic variants associated with asthma pathogenesis and altered response to drug therapy are discussed. Many studies implicate polymorphisms in genes encoding the enzymes responsible for leukotriene synthesis and intracellular signaling through activation of seven transmembrane domain receptors, such as the cysteinyl leukotriene 1 (CYSLTR1) and 2 (CYSLTR2) receptors. The leukotrienes are polyunsaturated lipoxygenated eicosatetraenoic acids that exhibit a wide range of pharmacological and physiological actions. Of the three enzymes involved in the formation of the leukotrienes, arachidonate 5 lipoxygenase 5 (ALOX5), leukotriene C4 synthase (LTC4S), and leukotriene hydrolase (LTA4H) are all polymorphic. These polymorphisms often result in variable production of the CysLTs (LTC4, LTD4, and LTE4) and LTB4. Variable number tandem repeat sequences located in the Sp1-binding motif within the promotor region of the ALOX5 gene are associated with leukotriene burden and bronchoconstriction independent of asthma risk. A 444A > C SNP polymorphism in the LTC4S gene, encoding an enzyme required for the formation of a glutathione adduct at the C-6 position of the arachidonic acid backbone, is associated with severe asthma and altered response to the CYSLTR1 receptor antagonist zafirlukast. Genetic variability in the CysLT pathway may contribute additively or synergistically to altered drug responses. The 601 A > G variant of the CYSLTR2 gene, encoding the Met201Val CYSLTR2 receptor variant, is associated with atopic asthma in the general European population, where it is present at a frequency of ∼2.6%. The variant was originally found in the founder population of Tristan da Cunha, a remote island in the South Atlantic, in which the prevalence of atopy is approximately 45% and the prevalence of asthma is 36%. In vitro work showed that the atopy-associated Met201Val variant was inactivating with respect to ligand binding, Ca²⁺ flux and inositol phosphate generation. In addition, the CYSLTR1 gene, located at Xq13-21.1, has been associated with atopic asthma. The activating Gly300Ser CYSLTR1 variant is discussed. In addition to genetic loci, risk for asthma may be influenced by environmental factors such as smoking. The contribution of CysLT pathway gene sequence variants to atopic asthma is discussed in the context of other genes and environmental influences known to influence asthma.

Signal transmission through the CXC chemokine receptor 4 (CXCR4) transmembrane helices

The atomic-level mechanisms by which G protein-coupled receptors (GPCRs) transmit extracellular ligand binding events through their transmembrane helices to activate intracellular G proteins remain unclear. Using a comprehensive library of mutations covering all 352 residues of the GPCR CXC chemokine receptor 4 (CXCR4), we identified 41 amino acids that are required for signaling induced by the chemokine ligand CXCL12 (stromal cell-derived factor 1). CXCR4 variants with each of these mutations do not signal properly but remain folded, based on receptor surface trafficking, reactivity to conformationally sensitive monoclonal antibodies, and ligand binding. When visualized on the structure of CXCR4, the majority of these residues form a continuous intramolecular signaling chain through the transmembrane helices; this chain connects chemokine binding residues on the extracellular side of CXCR4 to G protein-coupling residues on its intracellular side. Integrated into a cohesive model of signal transmission, these CXCR4 residues cluster into five functional groups that mediate (i) chemokine engagement, (ii) signal initiation, (iii) signal propagation, (iv) microswitch activation, and (v) G protein coupling. Propagation of the signal passes through a "hydrophobic bridge" on helix VI that coordinates with nearly every known GPCR signaling motif. Our results agree with known conserved mechanisms of GPCR activation and significantly expand on understanding the structural principles of CXCR4 signaling.

Integrating structural and mutagenesis data to elucidate GPCR ligand binding

G protein-coupled receptors (GPCRs) represent the largest family of human membrane proteins, as well as drug targets. A recent boom in GPCR structural biology has provided detailed images of receptor ligand binding sites and interactions on the molecular level. An ever-increasing number of ligands is reported that exhibit activity through multiple receptors, binding in allosteric sites, and bias towards different intracellular signalling pathways. Furthermore, a wealth of single point mutants has accumulated in literature and public databases. Integrating these structural and mutagenesis data will help elucidate new GPCR ligand binding sites, and ultimately design drugs with tailored pharmacological activity.

Signal transduction pathways activated by insulin-like peptide 5 at the relaxin family peptide RXFP4 receptor

BACKGROUND AND PURPOSE

Insulin-like peptide 5 (INSL5) is a two-chain, three-disulfide-bonded peptide of the insulin/relaxin superfamily, uniquely expressed in enteroendocrine L-cells of the colon. It is the cognate ligand of relaxin family peptide RXFP4 receptor that is mainly expressed in the colorectum and enteric nervous system. This study identifies new signalling pathways activated by INSL5 acting on RXFP4 receptors.

EXPERIMENTAL APPROACH

INSL5/RXFP4 receptor signalling was investigated using AlphaScreen® proximity assays. Recruitment of Gα_i/o proteins by RXFP4 receptors was determined by rescue of Pertussis toxin (PTX)-inhibited cAMP and ERK1/2 responses following transient transfection of PTX-insensitive Gα_i/o C351I mutants. Cell proliferation was studied with bromodeoxyuridine. RXFP4 receptor interactions with β-arrestins, GPCR kinase 2 (GRK2), KRas and Rab5a was assessed with real-time BRET. Gene expression was investigated using real-time quantitative PCR. Insulin release was measured using HTRF and intracellular Ca²⁺ flux monitored in a Flexstation® using Fluo-4-AM.

KEY RESULTS

INSL5 inhibited forskolin-stimulated cAMP accumulation and increased phosphorylation of ERK1/2, p38MAPK, Akt Ser⁴⁷³ , Akt Thr³⁰⁸ and S6 ribosomal protein. cAMP and ERK1/2 responses were abolished by PTX and rescued by mGα_oA , mGα_oB and mGα_i2 and to a lesser extent mGα_i1 and mGα_i3 . RXFP4 receptors interacted with GRK2 and β-arrestins, moved towards Rab5a and away from KRas, indicating internalisation following receptor activation. INSL5 inhibited glucose-stimulated insulin secretion and Ca²⁺ mobilisation in MIN6 insulinoma cells and forskolin-stimulated cAMP accumulation in NCI-H716 enteroendocrine cells.

CONCLUSIONS AND IMPLICATIONS

Knowledge of signalling pathways activated by INSL5 at RXFP4 receptors is essential for understanding the biological roles of this novel gut hormone.

LINKED ARTICLES

This article is part of a themed section on Recent Progress in the Understanding of Relaxin Family Peptides and their Receptors. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v174.10/issuetoc.

Structural Mechanisms of Voltage Sensing in G Protein-Coupled Receptors

G-protein-coupled receptors (GPCRs) form the largest superfamily of membrane proteins and one-third of all drug targets in humans. A number of recent studies have reported evidence for substantial voltage regulation of GPCRs. However, the structural basis of GPCR voltage sensing has remained enigmatic. Here, we present atomistic simulations on the δ-opioid and M2 muscarinic receptors, which suggest a structural and mechanistic explanation for the observed voltage-induced functional effects. The simulations reveal that the position of an internal Na(+) ion, recently detected to bind to a highly conserved aqueous pocket in receptor crystal structures, strongly responds to voltage changes. The movements give rise to gating charges in excellent agreement with previous experimental recordings. Furthermore, free energy calculations show that these rearrangements of Na(+) can be induced by physiological membrane voltages. Due to its role in receptor function and signal bias, the repositioning of Na(+) has important general implications for signal transduction in GPCRs.

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.

The role of the dopamine D1 receptor in social cognition: studies using a novel genetic rat model

Social cognition is an endophenotype that is impaired in schizophrenia and several other (comorbid) psychiatric disorders. One of the modulators of social cognition is dopamine, but its role is not clear. The effects of dopamine are mediated through dopamine receptors, including the dopamine D1 receptor (Drd1). Because current Drd1 receptor agonists are not Drd1 selective, pharmacological tools are not sufficient to delineate the role of the Drd1. Here, we describe a novel rat model with a genetic mutation in Drd1 in which we measured basic behavioural phenotypes and social cognition. The I116S mutation was predicted to render the receptor less stable. In line with this computational prediction, this Drd1 mutation led to a decreased transmembrane insertion of Drd1, whereas Drd1 expression, as measured by Drd1 mRNA levels, remained unaffected. Owing to decreased transmembrane Drd1 insertion, the mutant rats displayed normal basic motoric and neurological parameters, as well as locomotor activity and anxiety-like behaviour. However, measures of social cognition like social interaction, scent marking, pup ultrasonic vocalizations and sociability, were strongly reduced in the mutant rats. This profile of the Drd1 mutant rat offers the field of neuroscience a novel genetic rat model to study a series of psychiatric disorders including schizophrenia, autism, depression, bipolar disorder and drug addiction.

A novel thromboxane receptor antagonist, nstpbp5185, inhibits platelet aggregation and thrombus formation in animal models

A novel benzimidazole derivative, nstpbp5185, was discovered through in vitro and in vivo evaluations for antiplatelet activity. Thromaboxane receptor (TP) is important in vascular physiology, haemostasis and pathophysiological thrombosis. Nstpbp5185 concentration-dependently inhibited human platelet aggregation caused by collagen, arachidonic acid and U46619. Nstpbp5185 caused a right-shift of the concentration-response curve of U46619 and competitively inhibited the binding of 3H-SQ-29548 to TP receptor expressed on HEK-293 cells, with an IC50 of 0.1 µM, indicating that nstpbp5185 is a TP antagonist. In murine thrombosis models, nstpbp5185 significantly prolonged the latent period in triggering platelet plug formation in mesenteric and FeCl3-induced thrombi formation, and increased the survival rate in pulmonary embolism model with less bleeding than aspirin. This study suggests nstpbp5185, an orally selective anti-thrombotic agent, acting through blockade of TXA2 receptor, may be efficacious for prevention or treatment of pathologic thrombosis.

Molecular Evolution and Functional Divergence of Trace Amine-Associated Receptors

Trace amine-associated receptors (TAARs) are a member of the G-protein-coupled receptor superfamily and are known to be expressed in olfactory sensory neurons. A limited number of molecular evolutionary studies have been done for TAARs so far. To elucidate how lineage-specific evolution contributed to their functional divergence, we examined 30 metazoan genomes. In total, 493 TAAR gene candidates (including 84 pseudogenes) were identified from 26 vertebrate genomes. TAARs were not identified from non-vertebrate genomes. An ancestral-type TAAR-like gene appeared to have emerged in lamprey. We found four therian-specific TAAR subfamilies (one eutherian-specific and three metatherian-specific) in addition to previously known nine subfamilies. Many species-specific TAAR gene duplications and losses contributed to a large variation of TAAR gene numbers among mammals, ranging from 0 in dolphin to 26 in flying fox. TAARs are classified into two groups based on binding preferences for primary or tertiary amines as well as their sequence similarities. Primary amine-detecting TAARs (TAAR1-4) have emerged earlier, generally have single-copy orthologs (very few duplication or loss), and have evolved under strong functional constraints. In contrast, tertiary amine-detecting TAARs (TAAR5-9) have emerged more recently and the majority of them experienced higher rates of gene duplications. Protein members that belong to the tertiary amine-detecting TAAR group also showed the patterns of positive selection especially in the area surrounding the ligand-binding pocket, which could have affected ligand-binding activities and specificities. Expansions of the tertiary amine-detecting TAAR gene family may have played important roles in terrestrial adaptations of therian mammals. Molecular evolution of the TAAR gene family appears to be governed by a complex, species-specific, interplay between environmental and evolutionary factors.

Mimicking of Arginine by Functionalized N(ω)-Carbamoylated Arginine As a New Broadly Applicable Approach to Labeled Bioactive Peptides: High Affinity Angiotensin, Neuropeptide Y, Neuropeptide FF, and Neurotensin Receptor Ligands As Examples

Derivatization of biologically active peptides by conjugation with fluorophores or radionuclide-bearing moieties is an effective and commonly used approach to prepare molecular tools and diagnostic agents. Whereas lysine, cysteine, and N-terminal amino acids have been mostly used for peptide conjugation, we describe a new, widely applicable approach to peptide conjugation based on the nonclassical bioisosteric replacement of the guanidine group in arginine by a functionalized carbamoylguanidine moiety. Four arginine-containing peptide receptor ligands (angiotensin II, neurotensin(8-13), an analogue of the C-terminal pentapeptide of neuropeptide Y, and a neuropeptide FF analogue) were subject of this proof-of-concept study. The N(ω)-carbamoylated arginines, bearing spacers with a terminal amino group, were incorporated into the peptides by standard Fmoc solid phase peptide synthesis. The synthesized chemically stable peptide derivatives showed high receptor affinities with Ki values in the low nanomolar range, even when bulky fluorophores had been attached. Two new tritiated tracers for angiotensin and neurotensin receptors are described.

Pharmacophore-Map-Pick: A Method to Generate Pharmacophore Models for All Human GPCRs

GPCR-based drug discovery is hindered by a lack of effective screening methods for most GPCRs that have neither ligands nor high-quality structures. With the aim to identify lead molecules for these GPCRs, we developed a new method called Pharmacophore-Map-Pick to generate pharmacophore models for all human GPCRs. The model of ADRB2 generated using this method not only predicts the binding mode of ADRB2-ligands correctly but also performs well in virtual screening. Findings also demonstrate that this method is powerful for generating high-quality pharmacophore models. The average enrichment for the pharmacophore models of the 15 targets in different GPCR families reached 15-fold at 0.5 % false-positive rate. Therefore, the pharmacophore models can be applied in virtual screening directly with no requirement for any ligand information or shape constraints. A total of 2386 pharmacophore models for 819 different GPCRs (99 % coverage (819/825)) were generated and are available at http://bsb.kiz.ac.cn/GPCRPMD.

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.

Insights from the genome of Ophiocordyceps polyrhachis-furcata to pathogenicity and host specificity in insect fungi

Background

Ophiocordyceps unilateralis is an outstanding insect fungus for its biology to manipulate host ants’ behavior and for its extreme host-specificity. Through the sequencing and annotation of Ophiocordyceps polyrhachis-furcata, a species in the O. unilateralis species complex specific to the ant Polyrhachis furcata, comparative analyses on genes involved in pathogenicity and virulence between this fungus and other fungi were undertaken in order to gain insights into its biology and the emergence of host specificity.

Results

O. polyrhachis-furcata possesses various genes implicated in pathogenicity and virulence common with other fungi. Overall, this fungus possesses protein-coding genes similar to those found on other insect fungi with available genomic resources (Beauveria bassiana, Metarhizium robertsii (formerly classified as M. anisopliae s.l.), Metarhizium acridum, Cordyceps militaris, Ophiocordyceps sinensis). Comparative analyses in regard of the host ranges of insect fungi showed a tendency toward contractions of various gene families for narrow host-range species, including cuticle-degrading genes (proteases, carbohydrate esterases) and some families of pathogen-host interaction (PHI) genes. For many families of genes, O. polyrhachis-furcata had the least number of genes found; some genes commonly found in other insect fungi are even absent (e.g. Class 1 hydrophobin). However, there are expansions of genes involved in 1) the production of bacterial-like toxins in O. polyrhachis-furcata, compared with other entomopathogenic fungi, and 2) retrotransposable elements.

Conclusions

The gain and loss of gene families helps us understand how fungal pathogenicity in insect hosts evolved. The loss of various genes involved throughout the pathogenesis for O. unilateralis would result in a reduced capacity to exploit larger ranges of hosts and therefore in the different level of host specificity, while the expansions of other gene families suggest an adaptation to particular environments with unexpected strategies like oral toxicity, through the production of bacterial-like toxins, or sophisticated mechanisms underlying pathogenicity through retrotransposons.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2101-4) contains supplementary material, which is available to authorized users.

Label noise in subtype discrimination of class C G protein-coupled receptors: A systematic approach to the analysis of classification errors

BACKGROUND

The characterization of proteins in families and subfamilies, at different levels, entails the definition and use of class labels. When the adscription of a protein to a family is uncertain, or even wrong, this becomes an instance of what has come to be known as a label noise problem. Label noise has a potentially negative effect on any quantitative analysis of proteins that depends on label information. This study investigates class C of G protein-coupled receptors, which are cell membrane proteins of relevance both to biology in general and pharmacology in particular. Their supervised classification into different known subtypes, based on primary sequence data, is hampered by label noise. The latter may stem from a combination of expert knowledge limitations and the lack of a clear correspondence between labels that mostly reflect GPCR functionality and the different representations of the protein primary sequences.

RESULTS

In this study, we describe a systematic approach, using Support Vector Machine classifiers, to the analysis of G protein-coupled receptor misclassifications. As a proof of concept, this approach is used to assist the discovery of labeling quality problems in a curated, publicly accessible database of this type of proteins. We also investigate the extent to which physico-chemical transformations of the protein sequences reflect G protein-coupled receptor subtype labeling. The candidate mislabeled cases detected with this approach are externally validated with phylogenetic trees and against further trusted sources such as the National Center for Biotechnology Information, Universal Protein Resource, European Bioinformatics Institute and Ensembl Genome Browser information repositories.

CONCLUSIONS

In quantitative classification problems, class labels are often by default assumed to be correct. Label noise, though, is bound to be a pervasive problem in bioinformatics, where labels may be obtained indirectly through complex, many-step similarity modelling processes. In the case of G protein-coupled receptors, methods capable of singling out and characterizing those sequences with consistent misclassification behaviour are required to minimize this problem. A systematic, Support Vector Machine-based method has been proposed in this study for such purpose. The proposed method enables a filtering approach to the label noise problem and might become a support tool for database curators in proteomics.

Principal component analysis of binding energies for single-point mutants of hT2R16 bound to an agonist correlate with experimental mutant cell response

Directed evolution is a technique that enables the identification of mutants of a particular protein that carry a desired property by successive rounds of random mutagenesis, screening, and selection. This technique has many applications, including the development of G protein-coupled receptor-based biosensors and designer drugs for personalized medicine. Although effective, directed evolution is not without challenges and can greatly benefit from the development of computational techniques to predict the functional outcome of single-point amino acid substitutions. In this article, we describe a molecular dynamics-based approach to predict the effects of single amino acid substitutions on agonist binding (salicin) to a human bitter taste receptor (hT2R16). An experimentally determined functional map of single-point amino acid substitutions was used to validate the whole-protein molecular dynamics-based predictive functions. Molecular docking was used to construct a wild-type agonist-receptor complex, providing a starting structure for single-point substitution simulations. The effects of each single amino acid substitution in the functional response of the receptor to its agonist were estimated using three binding energy schemes with increasing inclusion of solvation effects. We show that molecular docking combined with molecular mechanics simulations of single-point mutants of the agonist-receptor complex accurately predicts the functional outcome of single amino acid substitutions in a human bitter taste receptor.

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.

Construction and Deciphering of Human Phosphorylation-Mediated Signaling Transduction Networks

Protein phosphorylation is the most abundant reversible covalent modification. Human protein kinases participate in almost all biological pathways, and approximately half of the kinases are associated with disease. PhoSigNet was designed to store and display human phosphorylation-mediated signal transduction networks, with additional information related to cancer. It contains 11 976 experimentally validated directed edges and 216 871 phosphorylation sites. Moreover, 3491 differentially expressed proteins in human cancer from dbDEPC, 18 907 human cancer variation sites from CanProVar, and 388 hyperphosphorylation sites from PhosphoSitePlus were collected as annotation information. Compared with other phosphorylation-related databases, PhoSigNet not only takes the kinase-substrate regulatory relationship pairs into account, but also extends regulatory relationships up- and downstream (e.g., from ligand to receptor, from G protein to kinase, and from transcription factor to targets). Furthermore, PhoSigNet allows the user to investigate the impact of phosphorylation modifications on cancer. By using one set of in-house time series phosphoproteomics data, the reconstruction of a conditional and dynamic phosphorylation-mediated signaling network was exemplified. We expect PhoSigNet to be a useful database and analysis platform benefiting both proteomics and cancer studies.

Key challenges for the creation and maintenance of specialist protein resources

As the volume of data relating to proteins increases, researchers rely more and more on the analysis of published data, thus increasing the importance of good access to these data that vary from the supplemental material of individual articles, all the way to major reference databases with professional staff and long-term funding. Specialist protein resources fill an important middle ground, providing interactive web interfaces to their databases for a focused topic or family of proteins, using specialized approaches that are not feasible in the major reference databases. Many are labors of love, run by a single lab with little or no dedicated funding and there are many challenges to building and maintaining them. This perspective arose from a meeting of several specialist protein resources and major reference databases held at the Wellcome Trust Genome Campus (Cambridge, UK) on August 11 and 12, 2014. During this meeting some common key challenges involved in creating and maintaining such resources were discussed, along with various approaches to address them. In laying out these challenges, we aim to inform users about how these issues impact our resources and illustrate ways in which our working together could enhance their accuracy, currency, and overall value.

Snooker structure-based pharmacophore model explains differences in agonist and blocker binding to bitter receptor hTAS2R39

The human bitter taste receptor hTAS2R39 can be activated by many dietary (iso)flavonoids. Furthermore, hTAS2R39 activity can be blocked by 6-methoxyflavanones, 4'-fluoro-6-methoxyflavanone in particular. A structure-based pharmacophore model of the hTAS2R39 binding pocket was built using Snooker software, which has been used successfully before for drug design of GPCRs of the rhodopsin subfamily. For the validation of the model, two sets of compounds, both of which contained actives and inactives, were used: (i) an (iso)flavonoid-dedicated set, and (ii) a more generic, structurally diverse set. Agonists were characterized by their linear binding geometry and the fact that they bound deeply in the hTAS2R39 pocket, mapping the hydrogen donor feature based on T5.45 and N3.36, analogues of which have been proposed to play a key role in activation of GPCRs. Blockers lack hydrogen-bond donors enabling contact to the receptor. Furthermore, they had a crooked geometry, which could sterically hinder movement of the TM domains upon receptor activation. Our results reveal characteristics of hTAS2R39 agonist and bitter blocker binding, which might facilitate the development of blockers suitable to counter the bitterness of dietary hTAS2R39 agonists in food applications.

A multi-fold string kernel for sequence classification

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

Expanding the horizons of G protein-coupled receptor structure-based ligand discovery and optimization using homology models

We show the key role of structural homology models in GPCR structure-based lead discovery and optimization, highlighting methodological aspects, recent progress and future directions.

Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects

Proteochemometric (PCM) modelling is a computational method to model the bioactivity of multiple ligands against multiple related protein targets simultaneously.

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.

Molecular insights into the dynamics of pharmacogenetically important N-terminal variants of the human β2-adrenergic receptor

The human β₂-adrenergic receptor (β₂AR), a member of the G-protein coupled receptor (GPCR) family, is expressed in bronchial smooth muscle cells. Upon activation by agonists, β₂AR causes bronchodilation and relief in asthma patients. The N-terminal polymorphism of β₂AR at the 16^th position, Arg16Gly, has warranted a lot of attention since it is linked to variations in response to albuterol (agonist) treatment. Although the β₂AR is one of the well-studied GPCRs, the N-terminus which harbors this mutation, is absent in all available experimental structures. The goal of this work was to study the molecular level differences between the N-terminal variants using structural modeling and atomistic molecular dynamics simulations. Our simulations reveal that the N-terminal region of the Arg variant shows greater dynamics than the Gly variant, leading to differential placement. Further, the position and dynamics of the N-terminal region, further, affects the ligand binding-site accessibility. Interestingly, long-range effects are also seen at the ligand binding site, which is marginally larger in the Gly as compared to the Arg variant resulting in the preferential docking of albuterol to the Gly variant. This study thus reveals key differences between the variants providing a molecular framework towards understanding the variable drug response in asthma patients.

The human β₂-adrenergic receptor (β₂AR) is an important member of the GPCR family and a mutation at the 16^th position, Arg16Gly, is commonly found in the population. This variation in asthma patients is linked to differential (good/bad) response to the drug albuterol, an agonist of the β₂AR. To date, the coordinates of the N-terminal residues harboring the 16^th position mutation have not been resolved. In our study we sought to glean insights into the dynamics of the variants that could address the differential response to albuterol. We used knowledge from class A GPCRs to build the N-terminal region of β₂AR variants in conjunction with the available structure of the inactive receptor. This was followed by atomistic simulations in triplicate totaling to a sampling of 6 µs. We observe that the N-terminal region of the Arg variant is more dynamic than the Gly variant. Amongst the various differences between the variants, we observe long-range effects at the binding site leading to preferential docking of albuterol to the Gly variant. Our work is a first step to unravel the molecular mechanism linking the Arg16Gly variation to the differential response to albuterol in asthma patients.

Lysophospholipid receptors in drug discovery

Lysophospholipids (LPs), including lysophosphatidic acid (LPA), sphingosine 1-phospate (S1P), lysophosphatidylinositol (LPI), and lysophosphatidylserine (LysoPS), are bioactive lipids that transduce signals through their specific cell-surface G protein-coupled receptors, LPA1-6, S1P1-5, LPI1, and LysoPS1-3, respectively. These LPs and their receptors have been implicated in both physiological and pathophysiological processes such as autoimmune diseases, neurodegenerative diseases, fibrosis, pain, cancer, inflammation, metabolic syndrome, bone formation, fertility, organismal development, and other effects on most organ systems. Advances in the LP receptor field have enabled the development of novel small molecules targeting LP receptors for several diseases. Most notably, fingolimod (FTY720, Gilenya, Novartis), an S1P receptor modulator, became the first FDA-approved medicine as an orally bioavailable drug for treating relapsing forms of multiple sclerosis. This success is currently being followed by multiple, mechanistically related compounds targeting S1P receptor subtypes, which are in various stages of clinical development. In addition, an LPA1 antagonist, BMS-986020 (Bristol-Myers Squibb), is in Phase 2 clinical development for treating idiopathic pulmonary fibrosis, as a distinct compound, SAR100842 (Sanofi) for the treatment of systemic sclerosis and related fibrotic diseases. This review summarizes the current state of drug discovery in the LP receptor field.

The influence of alignment-free sequence representations on the semi-supervised classification of class C G protein-coupled receptors: semi-supervised classification of class C GPCRs

G protein-coupled receptors (GPCRs) are integral cell membrane proteins of relevance for pharmacology. The tertiary structure of the transmembrane domain, a gate to the study of protein functionality, is unknown for almost all members of class C GPCRs, which are the target of the current study. As a result, their investigation must often rely on alignments of their amino acid sequences. Sequence alignment entails the risk of missing relevant information. Various approaches have attempted to circumvent this risk through alignment-free transformations of the sequences on the basis of different amino acid physicochemical properties. In this paper, we use several of these alignment-free methods, as well as a basic amino acid composition representation, to transform the available sequences. Novel semi-supervised statistical machine learning methods are then used to discriminate the different class C GPCRs types from the transformed data. This approach is relevant due to the existence of orphan proteins to which type labels should be assigned in a process of deorphanization or reverse pharmacology. The reported experiments show that the proposed techniques provide accurate classification even in settings of extreme class-label scarcity and that fair accuracy can be achieved even with very simple transformation strategies that ignore the sequence ordering.

A series of PDB-related databanks for everyday needs

We present a series of databanks (http://swift.cmbi.ru.nl/gv/facilities/) that hold information that is computationally derived from Protein Data Bank (PDB) entries and that might augment macromolecular structure studies. These derived databanks run parallel to the PDB, i.e. they have one entry per PDB entry. Several of the well-established databanks such as HSSP, PDBREPORT and PDB_REDO have been updated and/or improved. The software that creates the DSSP databank, for example, has been rewritten to better cope with π-helices. A large number of databanks have been added to aid computational structural biology; some examples are lists of residues that make crystal contacts, lists of contacting residues using a series of contact definitions or lists of residue accessibilities. PDB files are not the optimal presentation of the underlying data for many studies. We therefore made a series of databanks that hold PDB files in an easier to use or more consistent representation. The BDB databank holds X-ray PDB files with consistently represented B-factors. We also added several visualization tools to aid the users of our databanks.

Sequence and conformational preferences at termini of α-helices in membrane proteins: role of the helix environment

α-Helices are amongst the most common secondary structural elements seen in membrane proteins and are packed in the form of helix bundles. These α-helices encounter varying external environments (hydrophobic, hydrophilic) that may influence the sequence preferences at their N and C-termini. The role of the external environment in stabilization of the helix termini in membrane proteins is still unknown. Here we analyze α-helices in a high-resolution dataset of integral α-helical membrane proteins and establish that their sequence and conformational preferences differ from those in globular proteins. We specifically examine these preferences at the N and C-termini in helices initiating/terminating inside the membrane core as well as in linkers connecting these transmembrane helices. We find that the sequence preferences and structural motifs at capping (Ncap and Ccap) and near-helical (N' and C') positions are influenced by a combination of features including the membrane environment and the innate helix initiation and termination property of residues forming structural motifs. We also find that a large number of helix termini which do not form any particular capping motif are stabilized by formation of hydrogen bonds and hydrophobic interactions contributed from the neighboring helices in the membrane protein. We further validate the sequence preferences obtained from our analysis with data from an ultradeep sequencing study that identifies evolutionarily conserved amino acids in the rat neurotensin receptor. The results from our analysis provide insights for the secondary structure prediction, modeling and design of membrane proteins.

Structural and energetic effects of A2A adenosine receptor mutations on agonist and antagonist binding

To predict structural and energetic effects of point mutations on ligand binding is of considerable interest in biochemistry and pharmacology. This is not only useful in connection with site-directed mutagenesis experiments, but could also allow interpretation and prediction of individual responses to drug treatment. For G-protein coupled receptors systematic mutagenesis has provided the major part of functional data as structural information until recently has been very limited. For the pharmacologically important A(2A) adenosine receptor, extensive site-directed mutagenesis data on agonist and antagonist binding is available and crystal structures of both types of complexes have been determined. Here, we employ a computational strategy, based on molecular dynamics free energy simulations, to rationalize and interpret available alanine-scanning experiments for both agonist and antagonist binding to this receptor. These computer simulations show excellent agreement with the experimental data and, most importantly, reveal the molecular details behind the observed effects which are often not immediately evident from the crystal structures. The work further provides a distinct validation of the computational strategy used to assess effects of point-mutations on ligand binding. It also highlights the importance of considering not only protein-ligand interactions but also those mediated by solvent water molecules, in ligand design projects.

A structural chemogenomics analysis of aminergic GPCRs: lessons for histamine receptor ligand design

BACKGROUND AND PURPOSE

Chemogenomics focuses on the discovery of new connections between chemical and biological space leading to the discovery of new protein targets and biologically active molecules. G-protein coupled receptors (GPCRs) are a particularly interesting protein family for chemogenomics studies because there is an overwhelming amount of ligand binding affinity data available. The increasing number of aminergic GPCR crystal structures now for the first time allows the integration of chemogenomics studies with high-resolution structural analyses of GPCR-ligand complexes.

EXPERIMENTAL APPROACH

In this study, we have combined ligand affinity data, receptor mutagenesis studies, and amino acid sequence analyses to high-resolution structural analyses of (hist)aminergic GPCR-ligand interactions. This integrated structural chemogenomics analysis is used to more accurately describe the molecular and structural determinants of ligand affinity and selectivity in different key binding regions of the crystallized aminergic GPCRs, and histamine receptors in particular.

KEY RESULTS

Our investigations highlight interesting correlations and differences between ligand similarity and ligand binding site similarity of different aminergic receptors. Apparent discrepancies can be explained by combining detailed analysis of crystallized or predicted protein-ligand binding modes, receptor mutation studies, and ligand structure-selectivity relationships that identify local differences in essential pharmacophore features in the ligand binding sites of different receptors.

CONCLUSIONS AND IMPLICATIONS

We have performed structural chemogenomics studies that identify links between (hist)aminergic receptor ligands and their binding sites and binding modes. This knowledge can be used to identify structure-selectivity relationships that increase our understanding of ligand binding to (hist)aminergic receptors and hence can be used in future GPCR ligand discovery and design.

GPCRsort-responding to the next generation sequencing data challenge: prediction of G protein-coupled receptor classes using only structural region lengths

Next generation sequencing (NGS) and the attendant data deluge are increasingly impacting molecular life sciences research. Chief among the challenges and opportunities is to enhance our ability to classify molecular target data into meaningful and cohesive systematic nomenclature. In this vein, the G protein-coupled receptors (GPCRs) are the largest and most divergent receptor family that plays a crucial role in a host of pathophysiological pathways. For the pharmaceutical industry, GPCRs are a major drug target and it is estimated that 60%-70% of all medicines in development today target GPCRs. Hence, they require an efficient and rapid classification to group the members according to their functions. In addition to NGS and the Big Data challenge we currently face, an emerging number of orphan GPCRs further demand for novel, rapid, and accurate classification of the receptors since the current classification tools are inadequate and slow. This study presents the development of a new classification tool for GPCRs using the structural features derived from their primary sequences: GPCRsort. Comparison experiments with the current known GPCR classification techniques showed that GPCRsort is able to rapidly (in the order of minutes) classify uncharacterized GPCRs with 97.3% accuracy, whereas the best available technique's accuracy is 90.7%. GPCRsort is available in the public domain for postgenomics life scientists engaged in GPCR research with NGS: http://bioserver.ceng.metu.edu.tr/GPCRSort .

HMM-ModE: implementation, benchmarking and validation with HMMER3

BACKGROUND

HMM-ModE is a computational method that generates family specific profile HMMs using negative training sequences. The method optimizes the discrimination threshold using 10 fold cross validation and modifies the emission probabilities of profiles to reduce common fold based signals shared with other sub-families. The protocol depends on the program HMMER for HMM profile building and sequence database searching. The recent release of HMMER3 has improved database search speed by several orders of magnitude, allowing for the large scale deployment of the method in sequence annotation projects. We have rewritten our existing scripts both at the level of parsing the HMM profiles and modifying emission probabilities to upgrade HMM-ModE using HMMER3 that takes advantage of its probabilistic inference with high computational speed. The method is benchmarked and tested on GPCR dataset as an accurate and fast method for functional annotation.

RESULTS

The implementation of this method, which now works with HMMER3, is benchmarked with the earlier version of HMMER, to show that the effect of local-local alignments is marked only in the case of profiles containing a large number of discontinuous match states. The method is tested on a gold standard set of families and we have reported a significant reduction in the number of false positive hits over the default HMM profiles. When implemented on GPCR sequences, the results showed an improvement in the accuracy of classification compared with other methods used to classify the familyat different levels of their classification hierarchy.

CONCLUSIONS

The present findings show that the new version of HMM-ModE is a highly specific method used to differentiate between fold (superfamily) and function (family) specific signals, which helps in the functional annotation of protein sequences. The use of modified profile HMMs of GPCR sequences provides a simple yet highly specific method for classification of the family, being able to predict the sub-family specific sequences with high accuracy even though sequences share common physicochemical characteristics between sub-families.

Identification of G protein-coupled receptors in Schistosoma haematobium and S. mansoni by comparative genomics

BACKGROUND

Schistosomiasis is a parasitic disease affecting ~200 million people worldwide. Schistosoma haematobium and S. mansoni are two relatively closely related schistosomes (blood flukes), and the causative agents of urogenital and hepatointestinal schistosomiasis, respectively. The availability of genomic, transcriptomic and proteomic data sets for these two schistosomes now provides unprecedented opportunities to explore their biology, host interactions and schistosomiasis at the molecular level. A particularly important group of molecules involved in a range of biological and developmental processes in schistosomes and other parasites are the G protein-coupled receptors (GPCRs). Although GPCRs have been studied in schistosomes, there has been no detailed comparison of these receptors between closely related species. Here, using a genomic-bioinformatic approach, we identified and characterised key GPCRs in S. haematobium and S. mansoni (two closely related species of schistosome).

METHODS

Using a Hidden Markov Model (HMM) and Support Vector Machine (SVM)-based pipeline, we classified and sub-classified GPCRs of S. haematobium and S. mansoni, combined with phylogenetic and transcription analyses.

RESULTS

We identified and classified classes A, B, C and F as well as an unclassified group of GPCRs encoded in the genomes of S. haematobium and S. mansoni. In addition, we characterised ligand-specific subclasses (i.e. amine, peptide, opsin and orphan) within class A (rhodopsin-like).

CONCLUSIONS

Most GPCRs shared a high degree of similarity and conservation, except for members of a particular clade (designated SmGPR), which appear to have diverged between S. haematobium and S. mansoni and might explain, to some extent, some of the underlying biological differences between these two schistosomes. The present set of annotated GPCRs provides a basis for future functional genomic studies of cellular GPCR-mediated signal transduction and a resource for future drug discovery efforts in schistosomes.

Alpha-bulges in G protein-coupled receptors

Agonist binding is related to a series of motions in G protein-coupled receptors (GPCRs) that result in the separation of transmembrane helices III and VI at their cytosolic ends and subsequent G protein binding. A large number of smaller motions also seem to be associated with activation. Most helices in GPCRs are highly irregular and often contain kinks, with extensive literature already available about the role of prolines in kink formation and the precise function of these kinks. GPCR transmembrane helices also contain many α-bulges. In this article we aim to draw attention to the role of these α-bulges in ligand and G-protein binding, as well as their role in several aspects of the mobility associated with GPCR activation. This mobility includes regularization and translation of helix III in the extracellular direction, a rotation of the entire helix VI, an inward movement of the helices near the extracellular side, and a concerted motion of the cytosolic ends of the helices that makes their orientation appear more circular and that opens up space for the G protein to bind. In several cases, α-bulges either appear or disappear as part of the activation process.

A yeast screening method to decipher the interaction between the adenosine A2B receptor and the C-terminus of different G protein α-subunits

The expression of human G protein-coupled receptors (GPCRs) in Saccharomyces cerevisiae containing chimeric yeast/mammalian Gα subunits provides a useful tool for the study of GPCR activation. In this study, we used a one-GPCR-one-G protein yeast screening method in combination with molecular modeling and mutagenesis studies to decipher the interaction between GPCRs and the C-terminus of different α-subunits of G proteins. We chose the human adenosine A2B receptor (hA2BR) as a paradigm, a typical class A GPCR that shows promiscuous behavior in G protein coupling in this yeast system. The wild-type hA2BR and five mutant receptors were expressed in 8 yeast strains with different humanized G proteins, covering the four major classes: Gαi, Gαs, Gαq, and Gα12. Our experiments showed that a tyrosine residue (Y) at the C-terminus of the Gα subunit plays an important role in controlling the activation of GPCRs. Receptor residues R103(3.50) and I107(3.54) are vital too in G protein-coupling and the activation of the hA2BR, whereas L213(IL3) is more important in G protein inactivation. Substitution of S235(6.36) to alanine provided the most divergent G protein-coupling profile. Finally, L236(6.37) substitution decreased receptor activation in all G protein pathways, although to a different extent. In conclusion, our findings shed light on the selectivity of receptor/G protein coupling, which may help in further understanding GPCR signaling.

Mining rare associations between biological ontologies

The constantly increasing volume and complexity of available biological data requires new methods for their management and analysis. An important challenge is the integration of information from different sources in order to discover possible hidden relations between already known data. In this paper we introduce a data mining approach which relates biological ontologies by mining cross and intra-ontology pairwise generalized association rules. Its advantage is sensitivity to rare associations, for these are important for biologists. We propose a new class of interestingness measures designed for hierarchically organized rules. These measures allow one to select the most important rules and to take into account rare cases. They favor rules with an actual interestingness value that exceeds the expected value. The latter is calculated taking into account the parent rule. We demonstrate this approach by applying it to the analysis of data from Gene Ontology and GPCR databases. Our objective is to discover interesting relations between two different ontologies or parts of a single ontology. The association rules that are thus discovered can provide the user with new knowledge about underlying biological processes or help improve annotation consistency. The obtained results show that produced rules represent meaningful and quite reliable associations.