GPCRdb: an information system for G protein-coupled receptors

Diverse activation pathways in class A GPCRs converge near the G-protein-coupling region

Class A G-protein-coupled receptors (GPCRs) are a large family of membrane proteins that mediate a wide variety of physiological functions, including vision, neurotransmission and immune responses. They are the targets of nearly one-third of all prescribed medicinal drugs such as beta blockers and antipsychotics. GPCR activation is facilitated by extracellular ligands and leads to the recruitment of intracellular G proteins. Structural rearrangements of residue contacts in the transmembrane domain serve as 'activation pathways' that connect the ligand-binding pocket to the G-protein-coupling region within the receptor. In order to investigate the similarities in activation pathways across class A GPCRs, we analysed 27 GPCRs from diverse subgroups for which structures of active, inactive or both states were available. Here we show that, despite the diversity in activation pathways between receptors, the pathways converge near the G-protein-coupling region. This convergence is mediated by a highly conserved structural rearrangement of residue contacts between transmembrane helices 3, 6 and 7 that releases G-protein-contacting residues. The convergence of activation pathways may explain how the activation steps initiated by diverse ligands enable GPCRs to bind a common repertoire of G proteins.

Molecular interaction fingerprint approaches for GPCR drug discovery

Protein-ligand interaction fingerprints (IFPs) are binary 1D representations of the 3D structure of protein-ligand complexes encoding the presence or absence of specific interactions between the binding pocket amino acids and the ligand. Various implementations of IFPs have been developed and successfully applied for post-processing molecular docking results for G Protein-Coupled Receptor (GPCR) ligand binding mode prediction and virtual ligand screening. Novel interaction fingerprint methods enable structural chemogenomics and polypharmacology predictions by complementing the increasing amount of GPCR structural data. Machine learning methods are increasingly used to derive relationships between bioactivity data and fingerprint descriptors of chemical and structural information of binding sites, ligands, and protein-ligand interactions. Factors that influence the application of IFPs include structure preparation, binding site definition, fingerprint similarity assessment, and data processing and these factors pose challenges as well possibilities to optimize interaction fingerprint methods for GPCR drug discovery.

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.

Retrieving GPCR data from public databases

Improvements in databases have already impacted GPCR research. The purpose of the review is to give a snapshot of the GPCR data available and provide utility examples. Consequently, this review covers a small set of major databases, including UniProt for proteins, Ensembl for genes, ChEMBL for bioactive chemistry and SureChEMBL for patents. In addition, two portals are outlined, GPCRdb and the IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb) that are based on expert annotation. The former has an emphasis on structures, sequences, point mutations, analysis tools and visualisation. The latter focuses on endogenous GPCR ligands, pharmacological modulation, approved drugs, clinical candidates and tool compounds. Since data growth is accelerating, those embarking on GPCR projects should not only check databases but also recent journal and patent publications.

Similarities and differences in affinity and binding modes of tricyclic pyrimido- and pyrazinoxanthines at human and rat adenosine receptors

A new series of 32 pyrimido- and 5 tetrahydropyrazino[2,1-f]purinediones was obtained and evaluated for their adenosine receptors (ARs) affinities. The 1,3-dibutyl derivative of 9-(4-(2-(dimethylamino)ethoxy)phenyl)-6,7,8,9-tetrahydropyrimido[1,2-f]purine-2,4(1H,3H)-dione was found to be the most potent A1 AR antagonist of the present series, showing selectivity over the other AR subtypes. The structure-activity for the obtained purinediones was established. Docking experiments of the investigated library to homology models of the human and rat A1 and A2A ARs allowed to compare the expected binding modes for selected compounds. The detailed analysis of binding cavities within individual AR subtypes indicated small but significant structural variations that may underlie the observed differences in binding affinities of purinediones at particular subtypes and species.

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.

Evidence that the Rhodopsin Kinase (GRK1) N-Terminus and the Transducin Gα C-Terminus Interact with the Same "Hydrophobic Patch" on Rhodopsin TM5

Phosphorylation of G protein-coupled receptors (GPCRs) terminates their ability to couple with and activate G proteins by increasing their affinity for arrestins. Unfortunately, detailed information regarding how GPCRs interact with the kinases responsible for their phosphorylation is still limited. Here, we purified fully functional GPCR kinase 1 (GRK1) using a rapid method and used it to gain insights into how this important kinase interacts with the GPCR rhodopsin. Specifically, we find that GRK1 uses the same site on rhodopsin as the transducin (Gt) Gtα C-terminal tail and the arrestin "finger loop", a cleft formed in the cytoplasmic face of the receptor upon activation. Our studies also show GRK1 requires two conserved residues located in this cleft (L226 and V230) that have been shown to be required for Gt activation due to their direct interactions with hydrophobic residues on the Gα C-terminal tail. Our data and modeling studies are consistent with the idea that all three proteins (Gt, GRK1, and visual arrestin) bind, at least in part, in the same site on rhodopsin and interact with the receptor through a similar hydrophobic contact-driven mechanism.

GPCR-ModSim: A comprehensive web based solution for modeling G-protein coupled receptors

GPCR-ModSim (http://open.gpcr-modsim.org) is a centralized and easy to use service dedicated to the structural modeling of G-protein Coupled Receptors (GPCRs). 3D molecular models can be generated from amino acid sequence by homology-modeling techniques, considering different receptor conformations. GPCR-ModSim includes a membrane insertion and molecular dynamics (MD) equilibration protocol, which can be used to refine the generated model or any GPCR structure uploaded to the server, including if desired non-protein elements such as orthosteric or allosteric ligands, structural waters or ions. We herein revise the main characteristics of GPCR-ModSim and present new functionalities. The templates used for homology modeling have been updated considering the latest structural data, with separate profile structural alignments built for inactive, partially-active and active groups of templates. We have also added the possibility to perform multiple-template homology modeling in a unique and flexible way. Finally, our new MD protocol considers a series of distance restraints derived from a recently identified conserved network of helical contacts, allowing for a smoother refinement of the generated models which is particularly advised when there is low homology to the available templates. GPCR- ModSim has been tested on the GPCR Dock 2013 competition with satisfactory results.

Galaxy7TM: flexible GPCR-ligand docking by structure refinement

G-protein-coupled receptors (GPCRs) play important physiological roles related to signal transduction and form a major group of drug targets. Prediction of GPCR-ligand complex structures has therefore important implications to drug discovery. With previously available servers, it was only possible to first predict GPCR structures by homology modeling and then perform ligand docking on the model structures. However, model structures generated without explicit consideration of specific ligands of interest can be inaccurate because GPCR structures can be affected by ligand binding. The Galaxy7TM server, freely accessible at http://galaxy.seoklab.org/7TM, improves an input GPCR structure by simultaneous ligand docking and flexible structure refinement using GALAXY methods. The server shows better performance in both ligand docking and GPCR structure refinement than commonly used programs AutoDock Vina and Rosetta MPrelax, respectively.

Structural modeling of G-protein coupled receptors: An overview on automatic web-servers

Despite the significant efforts and discoveries during the last few years in G protein-coupled receptor (GPCR) expression and crystallization, the receptors with known structures to date are limited only to a small fraction of human GPCRs. The lack of experimental three-dimensional structures of the receptors represents a strong limitation that hampers a deep understanding of their function. Computational techniques are thus a valid alternative strategy to model three-dimensional structures. Indeed, recent advances in the field, together with extraordinary developments in crystallography, in particular due to its ability to capture GPCRs in different activation states, have led to encouraging results in the generation of accurate models. This, prompted the community of modelers to render their methods publicly available through dedicated databases and web-servers. Here, we present an extensive overview on these services, focusing on their advantages, drawbacks and their role in successful applications. Future challenges in the field of GPCR modeling, such as the predictions of long loop regions and the modeling of receptor activation states are presented as well.

The 2016 database issue of Nucleic Acids Research and an updated molecular biology database collection

The 2016 Database Issue of Nucleic Acids Research starts with overviews of the resources provided by three major bioinformatics centers, the U.S. National Center for Biotechnology Information (NCBI), the European Bioinformatics Institute (EMBL-EBI) and Swiss Institute for Bioinformatics (SIB). Also included are descriptions of 62 new databases and updates on 95 databases that have been previously featured in NAR plus 17 previously described elsewhere. A number of papers in this issue deal with resources on nucleic acids, including various kinds of non-coding RNAs and their interactions, molecular dynamics simulations of nucleic acid structure, and two databases of super-enhancers. The protein database section features important updates on the EBI's Pfam, PDBe and PRIDE databases, as well as a variety of resources on pathways, metabolomics and metabolic modeling. This issue also includes updates on popular metagenomics resources, such as MG-RAST, EBI Metagenomics, and probeBASE, as well as a newly compiled Human Pan-Microbe Communities database. A significant fraction of the new and updated databases are dedicated to the genetic basis of disease, primarily cancer, and various aspects of drug research, including resources for patented drugs, their side effects, withdrawn drugs, and potential drug targets. A further six papers present updated databases of various antimicrobial and anticancer peptides. The entire Database Issue is freely available online on the Nucleic Acids Research website (http://nar.oxfordjournals.org/). The NAR online Molecular Biology Database Collection, http://www.oxfordjournals.org/nar/database/c/, has been updated with the addition of 88 new resources and removal of 23 obsolete websites, which brought the current listing to 1685 databases.

7TM Domain Structure of Adhesion GPCRs

Schematic presentation of the overall adhesion G Protein-Coupled Receptor (aGPCR) structure and functional domains, covering an extracellular N-terminal fragment (NTF), a membrane-spanning C-terminal fragment (CTF) and a GPCR proteolysis site (GPS). (Left side) aGPCR model constructed based on the seven-transmembrane (7TM) structure (blue) of secretin family glucagon receptor (GCGR) (PDB, 4L6R) [11] and the GPCR autoproteolysis inducing (GAIN) domain (magenta) structure of latrophilin 1 (PDB, 4DLQ) [9]. The β-13 strand residues are depicted in green. (Right side) The experimentally validated full-length secretin family GCGR structure combining structural and experimental information from the GCGR 7TM crystal structure (PDB, 4L6R) (blue), the GCGR extracellular domain (ECD) structure (PDB, 4ERS) (magenta) and the ECD structure of glucagon-like peptide-1 (GLP-1)-bound glucagon-like peptide-1 receptor (GLP-1R) (PDB, 3IOL) (green), complemented by site-directed mutagenesis, electron microscopy (EM), hydrogen-deuterium exchange (HDX) and cross-linking studies [11-13]) Despite the recent breakthroughs in the elucidation of the three-dimensional structures of the seven transmembrane (7TM) domain of the G protein-coupled receptor (GPCR) superfamily, a corresponding structure of a member of the adhesion GPCR (aGPCR) family has not yet been solved. In this chapter, we give an overview of the current knowledge of the 7TM domain of aGPCRs by comparative structure-based sequence similarity analyses between aGPCRs and GPCRs with known crystal structure. Of the GPCR superfamily, only the secretin family shares some sequence similarity with aGPCRs. This chapter will therefore emphasize on the comparison of these two GPCR families. Two 7TM domain structures of secretin family GPCRs are known that provide insight into the structure-function relationships of conserved sequence motifs that play important roles and are also present in most aGPCRs. This suggests that the 7TM domains of aGPCRs and secretin family GPCRs share a similar structural fold and that the conserved residues in both families may be involved in similar intermolecular interaction networks and facilitate similar conformational changes. Comparison of the residues that line the large peptide hormone binding pocket in the 7TM domain of secretin family GPCRs with corresponding residues in aGPCRs indicates that in the latter, the corresponding pocket in the 7TM domain is relatively hydrophobic and may be even larger. Improved knowledge on these conserved sequence motifs will help to understand the interactions of the aGPCR 7TM domain with ligands and gain insight into the activation mechanism of aGPCRs.

Use of network model to explore dynamic and allosteric properties of three GPCR homodimers

We used an elastic network model and protein structure network to study three class A GPCR homodimers.