Matching cavities in g protein-coupled receptors to infer ligand-binding sites

To understand the activity and cross reactivity of ligands and G protein-coupled receptors, we take stock of relevant existing receptor mutation, sequence, and structural data to develop a statistically robust and transparent scoring system. Our method evaluates the viability of binding of any ligand for any GPCR sequence of amino acids. This enabled us to explore the binding repertoire of both receptors and ligands, relying solely on correlations between carefully identified receptor features and without requiring any chemical information about ligands. This study suggests that sequence similarity at specific binding pockets can predict relative affinity of ligands; enabling recovery of over 80% of known ligands for a withheld receptor and almost 80% of known receptors for a ligand. The method enables qualitative prediction of ligand binding for all nonredundant human G protein-coupled receptors.     

Biophysical approaches to G protein-coupled receptors: Structure, function and dynamics

G protein-coupled receptors (GPCR) represent a large family of drug targets for which there is no high-resolution structural information. In order to understand the mechanisms of ligand recognition and receptor activation, there is a strong need for novel biophysical methods. In this Perspective we provide an overview of recent experimental approaches used to explore the molecular architecture and dynamics of GPCR and their interactions with ligands and G proteins using biophysical, non-crystallographic, methods.     

Comparative modeling: the state of the art and protein drug target structure prediction

The goal of computational protein structure prediction is to provide three-dimensional (3D) structures with resolution comparable to experimental results. Comparative modeling, which predicts the 3D structure of a protein based on its sequence similarity to homologous structures, is the most accurate computational method for structure prediction. In the last two decades, significant progress has been made on comparative modeling methods. Using the large number of protein structures deposited in the Protein Data Bank (~65,000), automatic prediction pipelines are generating a tremendous number of models (~1.9 million) for sequences whose structures have not been experimentally determined. Accurate models are suitable for a wide range of applications, such as prediction of protein binding sites, prediction of the effect of protein mutations, and structure-guided virtual screening. In particular, comparative modeling has enabled structure-based drug design against protein targets with unknown structures. In this review, we describe the theoretical basis of comparative modeling, the available automatic methods and databases, and the algorithms to evaluate the accuracy of predicted structures. Finally, we discuss relevant applications in the prediction of important drug target proteins, focusing on the G protein-coupled receptor (GPCR) and protein kinase families.     

Molecular dynamics simulations and structure-based network analysis reveal structural and functional aspects of G-protein coupled receptor dimer interactions

A significant amount of experimental evidence suggests that G-protein coupled receptors (GPCRs) do not act exclusively as monomers but also form biologically relevant dimers and oligomers. However, the structural determinants, stoichiometry and functional importance of GPCR oligomerization remain topics of intense speculation. In this study we attempted to evaluate the nature and dynamics of GPCR oligomeric interactions. A representative set of GPCR homodimers were studied through Coarse-Grained Molecular Dynamics simulations, combined with interface analysis and concepts from network theory for the construction and analysis of dynamic structural networks. Our results highlight important structural determinants that seem to govern receptor dimer interactions. A conserved dynamic behavior was observed among different GPCRs, including receptors belonging in different GPCR classes. Specific GPCR regions were highlighted as the core of the interfaces. Finally, correlations of motion were observed between parts of the dimer interface and GPCR segments participating in ligand binding and receptor activation, suggesting the existence of mechanisms through which dimer formation may affect GPCR function. The results of this study can be used to drive experiments aimed at exploring GPCR oligomerization, as well as in the study of transmembrane protein–protein interactions in general.     

GPCR‐CA: A cellular automaton image approach for predicting G‐protein–coupled receptor functional classes

Given an uncharacterized protein sequence, how can we identify whether it is a G‐protein–coupled receptor (GPCR) or not? If it is, which functional family class does it belong to? It is important to address these questions because GPCRs are among the most frequent targets of therapeutic drugs and the information thus obtained is very useful for “comparative and evolutionary pharmacology,” a technique often used for drug development. Here, we present a web‐server predictor called “GPCR‐CA,” where “CA” stands for “Cellular Automaton” (Wolfram, S. Nature 1984, 311, 419), meaning that the CA images have been utilized to reveal the pattern features hidden in piles of long and complicated protein sequences. Meanwhile, the gray‐level co‐occurrence matrix factors extracted from the CA images are used to represent the samples of proteins through their pseudo amino acid composition (Chou, K.C. Proteins 2001, 43, 246). GPCR‐CA is a two‐layer predictor: the first layer prediction engine is for identifying a query protein as GPCR on non‐GPCR; if it is a GPCR protein, the process will be automatically continued with the second‐layer prediction engine to further identify its type among the following six functional classes: (a) rhodopsin‐like, (b) secretin‐like, (c) metabotrophic/glutamate/pheromone; (d) fungal pheromone, (e) cAMP receptor, and (f) frizzled/smoothened family. The overall success rates by the predictor for the first and second layers are over 91% and 83%, respectively, that were obtained through rigorous jackknife cross‐validation tests on a new‐constructed stringent benchmark dataset in which none of proteins has ≥40% pairwise sequence identity to any other in a same subset. GPCR‐CA is freely accessible at http://218.65.61.89:8080/bioinfo/GPCR‐CA , by which one can get the desired two‐layer results for a query protein sequence within about 20 seconds. © 2008 Wiley Periodicals, Inc. J Comput Chem 2009     

Direct interaction between an allosteric agonist pepducin and the chemokine receptor CXCR4

Cell surface heptahelical G protein-coupled receptors (GPCRs) mediate critical cellular signaling pathways and are important pharmaceutical drug targets. (1) In addition to traditional small-molecule approaches, lipopeptide-based GPCR-derived pepducins have emerged as a new class of pharmaceutical agents. (2, 3) To better understand how pepducins interact with targeted receptors, we developed a cell-based photo-cross-linking approach to study the interaction between the pepducin agonist ATI-2341 and its target receptor, chemokine C-X-C-type receptor 4 (CXCR4). A pepducin analogue, ATI-2766, formed a specific UV-light-dependent cross-link to CXCR4 and to mutants with truncations of the N-terminus, the known chemokine docking site. These results demonstrate that CXCR4 is the direct binding target of ATI-2341 and suggest a new mechanism for allosteric modulation of GPCR activity. Adaptation and application of our findings should prove useful in further understanding pepducin modulation of GPCRs as well as enable new experimental approaches to better understand GPCR signal transduction.     

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

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

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

Accurate in silico models for the quantitative prediction of the activity of G protein-coupled receptor (GPCR) ligands would greatly facilitate the process of drug discovery and development. Several methodologies have been developed based on the properties of the ligands, the direct study of the receptor-ligand interactions, or a combination of both approaches. Ligand-based three-dimensional quantitative structure-activity relationships (3D-QSAR) techniques, not requiring knowledge of the receptor structure, have been historically the first to be applied to the prediction of the activity of GPCR ligands. They are generally endowed with robustness and good ranking ability; however they are highly dependent on training sets. Structure-based techniques generally do not provide the level of accuracy necessary to yield meaningful rankings when applied to GPCR homology models. However, they are essentially independent from training sets and have a sufficient level of accuracy to allow an effective discrimination between binders and nonbinders, thus qualifying as viable lead discovery tools. The combination of ligand and structure-based methodologies in the form of receptor-based 3D-QSAR and ligand and structure-based consensus models results in robust and accurate quantitative predictions. The contribution of the structure-based component to these combined approaches is expected to become more substantial and effective in the future, as more sophisticated scoring functions are developed and more detailed structural information on GPCRs is gathered.     

Proteome-wide classification and identification of mammalian-type GPCRs by binary topology pattern

G protein-coupled receptors (GPCRs), a large eukaryotic protein family, have proved difficult to comprehensively detect and functionally identify by homology searches and domain detection, because they are highly divergent and their sequences share strikingly little similarity. Transmembrane (TM) topology pattern analysis has been used to classify TM proteins, and such patterns are conserved within GPCRs of similar function. Here, we developed a stepwise binary topology pattern (BTP) method for GPCR classification and identification and used it to identify and classify mammalian-type GPCRs in the genomes of 10 different eukaryotic species. A binary topology pattern was obtained for each functional class or group by assigning binary loop threshold lengths of "0" (short loop) or "1" (long loop). The GPCR-classification ability of the BTP method had quite high accuracies for classifying GPCR functions at the class level (Classes A, B, C, Frizzled/Smoothened, Non-GPCR, based on the GPCRDB classification scheme), with many classes being classified with 100% accuracy. Sufficiently high accuracies were also maintained at the functional group level, 0.945 over 15 functional groups. Proteome-wide mammalian-type GPCR searches in 10 eukaryotic genomes (H. sapiens, M. musculus, F. rubripes, C. intestinalis, A. thaliana, D. melanogaster, A. gambiae, C. elegans, P. falciparum, S. cerevisiae) using the BTP method showed much higher classification/identification in non-mammalian genomes than typical BLAST searches, in which a higher number of sequences were classified as Non-GPCR. This stepwise BTP method should prove useful for the identification and functional classification of GPCRs from the genomes of a wide range of species.     

Using silico methods predicting ligands for orphan GPCRs

The G-protein coupled receptor (GPCR) superfamily is one of the most important drug target classes for the pharmaceutical industry. The completion of the human genome project has revealed that there are more than 300 potential GPCR targets of interest. The identification of their natural ligands can gain significant insights into regulatory mechanisms of cellular signaling networks and provide unprecedented opportunities for drug discovery. Much effort has been directed towards the GPCR ligand discovery study by both academic institutions and pharmaceutical industries. However, the endogenous ligands still remain unknown for about 150 GPCRs in the human genome. It is necessary to develop new strategies to predict candidate ligands for these so-called orphan receptors. Computational techniques are playing an increasingly important role in finding and validating novel ligands for orphan GPCRs (oGPCRs). In this paper, we focus on recent development in applying bioinformatics approaches for the discovery of GPCR ligands. In addition, some of the data resources for ligand identification are also provided.     

GPCR–drug interactions prediction using random forest with drug-association-matrix-based post-processing procedure

G-protein-coupled receptors (GPCRs) are important targets of modern medicinal drugs. The accurate identification of interactions between GPCRs and drugs is of significant importance for both protein function annotations and drug discovery. In this paper, a new sequence-based predictor called TargetGDrug is designed and implemented for predicting GPCR–drug interactions. In TargetGDrug, the evolutionary feature of GPCR sequence and the wavelet-based molecular fingerprint feature of drug are integrated to form the combined feature of a GPCR–drug pair; then, the combined feature is fed to a trained random forest (RF) classifier to perform initial prediction; finally, a novel drug-association-matrix-based post-processing procedure is applied to reduce potential false positive or false negative of the initial prediction. Experimental results on benchmark datasets demonstrate the efficacy of the proposed method, and an improvement of 15% in the Matthews correlation coefficient (MCC) was observed over independent validation tests when compared with the most recently released sequence-based GPCR–drug interactions predictor. The implemented webserver, together with the datasets used in this study, is freely available for academic use at http://csbio.njust.edu.cn/bioinf/TargetGDrug.     

Evaluation of pharmaceutical activities of G-protein coupled receptor targeted pharmaceuticals in Chinese wastewater effluent

The occurrence of biologically active pharmaceuticals in aquatic environments raised the potential risks to aquatic species. Among these marketed biological active pharmaceuticals, it has been estimated that 40% of them target G-protein-coupled receptors (GPCRs). We have illustrated pharmaceutical activities of GPCR targeted pharmaceuticals in English and Japanese wastewater by the in vitro transforming growth factor-α (TGFα) shedding assay. However, as the most important producer and consumer of pharmaceuticals, the occurrence of GPCR targeted pharmaceuticals in China had remained unclear. In this study, we investigated the pharmaceutical activities of GPCR targeted pharmaceuticals in secondary effluents of Chinese wastewater treatment plants. We discovered antagonistic activities against angiotensin (AT1) receptor at up to 7.2 × 102 ng-valsartan-equivalent quantity/L in Chinese wastewater for the first time as well as agonistic activities against dopamine (D2) receptor. Furthermore, in parallel with the assay, we determined concentrations of GPCR targeted pharmaceuticals in target wastewater by liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS). Through the comparison of predicted antagonistic activities calculated by concentrations and potency values from the assay, we found that the measured antagonistic activities against AT1 receptor from the assay were higher than the predicted AT1 activities from valsartan, irbesartan, and losartan, indicating the potential existence of other unknown AT1 antagonists in wastewater.     

Solid-state NMR evidence for a protonation switch in the binding pocket of the H1 receptor upon binding of the agonist histamine

G protein coupled receptors (GPCRs) represent a major superfamily of transmembrane receptor proteins that are crucial in cellular signaling and are major pharmacological targets. While the activity of GPCRs can be modulated by agonist binding, the mechanisms that link agonist binding to G protein coupling are poorly understood. Here we present a method to accurately examine the activity of ligands in their bound state, even at low affinity, by solid-state NMR dipolar correlation spectroscopy and confront this method with the human H1 receptor. The analysis reveals two different charge states of the bound agonist, dicationic with a charged imidazole ring and monocationic with a neutral imidazole ring, with the same overall conformation. The combination of charge difference and pronounced heterogeneity agrees with converging evidence that the active and inactive states of the GPCR represent a dynamic equilibrium of substates and that proton transfer between agonist and protein side chains can shift this equilibrium by stabilizing the active receptor population relative to the inactive one. In fact, the data suggest a global functional analogy between H1 receptor activation and the meta I/meta II charge/discharge equilibrium in rhodopsin (GPCR). This corroborates current ideas on unifying principles in GPCR structure and function.     

Snooker: a structure-based pharmacophore generation tool applied to class A GPCRs

G-protein coupled receptors (GPCRs) are important drug targets for various diseases and of major interest to pharmaceutical companies. The function of individual members of this protein family can be modulated by the binding of small molecules at the extracellular side of the structurally conserved transmembrane (TM) domain. Here, we present Snooker, a structure-based approach to generate pharmacophore hypotheses for compounds binding to this extracellular side of the TM domain. Snooker does not require knowledge of ligands, is therefore suitable for apo-proteins, and can be applied to all receptors of the GPCR protein family. The method comprises the construction of a homology model of the TM domains and prioritization of residues on the probability of being ligand binding. Subsequently, protein properties are converted to ligand space, and pharmacophore features are generated at positions where protein ligand interactions are likely. Using this semiautomated knowledge-driven bioinformatics approach we have created pharmacophore hypotheses for 15 different GPCRs from several different subfamilies. For the beta-2-adrenergic receptor we show that ligand poses predicted by Snooker pharmacophore hypotheses reproduce literature supported binding modes for ～75% of compounds fulfilling pharmacophore constraints. All 15 pharmacophore hypotheses represent interactions with essential residues for ligand binding as observed in mutagenesis experiments and compound selections based on these hypotheses are shown to be target specific. For 8 out of 15 targets enrichment factors above 10-fold are observed in the top 0.5% ranked compounds in a virtual screen. Additionally, prospectively predicted ligand binding poses in the human dopamine D3 receptor based on Snooker pharmacophores were ranked among the best models in the community wide GPCR dock 2010.     

Discovery of novel regulatory peptides by reverse pharmacology: spotlight on chemerin and the RF-amide peptides metastin and QRFP

Reverse pharmacology is a screening technology that matches G protein-coupled receptors (GPCRs) with unknown cognate ligands in cell-based screening assays by detection of agonist-induced signaling pathways. One decade spent pursuing orphan GPCR screening by this technique assigned over 30 ligand/receptor pairs and revealed previously known or novel undescribed ligands, mostly of a peptidic nature. In this review, we describe the discovery, characterization of the structural composition, biological function, physiological role and therapeutic potential of three recently identified peptidic ligands. These are metastin, QRFP in a context of five RF-amide genes described in humans and the chemoattractant, chemerin. Metastin was initially characterized as a metastasis inhibitor. Investigations using ligand/receptor pairing revealed that metastin was involved in a variety of physiological processes, including endocrine function during pregnancy and gonad development. The novel RF-amide QRFP is implicated in food intake and aldosterone release from the adrenal cortex in the rat. Chemerin, first described as TIG2, is upregulated in tazarotene-treated psoriatic skin. By GPCR screening, bioactive chemerin was isolated from ovarial carcinoma fluid as well as hemofiltrate. Characterization as a chemoattractant for immature dendritic cells and analysis of the expression profile of metastin and its receptor suggested a physiological role of chemerin as a mediator of the immune response, inflammatory processes and bone development.     

Methods for Studying Endocytotic Pathways of Herpesvirus Encoded G Protein-Coupled Receptors

Endocytosis is a fundamental process involved in trafficking of various extracellular and transmembrane molecules from the cell surface to its interior. This enables cells to communicate and respond to external environments, maintain cellular homeostasis, and transduce signals. G protein-coupled receptors (GPCRs) constitute a family of receptors with seven transmembrane alpha-helical domains (7TM receptors) expressed at the cell surface, where they regulate physiological and pathological cellular processes. Several herpesviruses encode receptors (vGPCRs) which benefits the virus by avoiding host immune surveillance, supporting viral dissemination, and thereby establishing widespread and lifelong infection, processes where receptor signaling and/or endocytosis seem central. vGPCRs are rising as potential drug targets as exemplified by the cytomegalovirus-encoded receptor US28, where its constitutive internalization has been exploited for selective drug delivery in virus infected cells. Therefore, studying GPCR trafficking is of great importance. This review provides an overview of the current knowledge of endocytic and cell localization properties of vGPCRs and methodological approaches used for studying receptor internalization. Using such novel approaches, we show constitutive internalization of the BILF1 receptor from human and porcine γ-1 herpesviruses and present motifs from the eukaryotic linear motif (ELM) resources with importance for vGPCR endocytosis.     

Genetically encoded photocrosslinkers as molecular probes to study G-protein-coupled receptors (GPCRs)

The genetic code was expanded with orthogonal pairs to introduce photoactivatable amino acids into G-protein-coupled receptors (GPCRs) in a noninvasive manner. In this way the receptor surface could be mapped by searching for specific ligand interaction sites and the complex dynamics could be studied. This method is also useful for probing the structure of GPCR complexes in living cells.