Family resemblances? Ligand binding and activation of family A and B G-protein-coupled receptors

In April 2007, the Biochemical Society held a meeting to compare and contrast ligand binding and activation of Family A and B GPCRs (G-protein-coupled receptors). Being the largest class, Family A GPCRs usually receive the most attention, although a previous Biochemical Society meeting has focused on Family B GPCRs. The aim of the present meeting was to bring researchers of both families together in order to identify commonalities between the two. The present article introduces the proceedings of the meeting, briefly commenting on the focus of each of the following articles. ©The Authors.

A key role for transmembrane prolines in calcitonin receptor-like receptor agonist binding and signalling:implications for family B G-protein-coupled receptors

Calcitonin receptor like-receptor is a family B G-protein coupled receptor (GPCR). It requires receptor activity modifying protein (RAMP) 1 to give a calcitonin gene-related peptide (CGRP) receptor. Little is known of how members of this receptor family function. Proline residues often form important kinks in alpha-helices. Therefore, all proline residues within the transmembrane helices of the receptor (Pro241, Pro244 in helix 4, Pro275 in helix 5, Pro321 and Pro331 in helix 6) were mutated to alanine. Pro241 Pro275, and Pro321 are highly conserved throughout all family B GPCRs. The binding of CGRP and its ability to stimulate cAMP production were investigated in mutant and wild-type receptors after transient transfection into COS-7 cells with RAMP1. The P321A mutation significantly decreased the pEC(50) for CGRP and reduced its affinity but did not change cell-surface expression. Antagonist binding [CGRP(8-37) and 1-piperidinecarboxamide N-[2-[[5amino-1-[[4-(4-pyridinyl)-1-piperazinyl]carbonyl]pentyl]amino]-1-[(3 5-dibromo-4-hydroxyphenyl)methyl]-2-oxoethyl]-4-(1,4-dihydro-2-oxo-3(2H)-quina zolinyl) (BIBN4096BS)] was little altered by the mutation. Adrenomedullin-mediated signaling was disrupted when P321A was coexpressed with RAMP1, RAMP2, or RAMP3. The P331A mutant produced a moderate reduction in CGRP binding and receptor activation. Mutation of the other residues had no effect on receptor function. Thus, Pro321 and Pro331 are required for agonist binding and receptor activation. Modeling suggested that Pro321 induces a bend in helix 6, bringing its C terminus near that of helix 3, as seen in many family A GPCRs. This is abolished in P321A. P321A-I325P predicted to restore this conformation, showed wild-type activation. Modeling can also rationalize the effects of transmembrane proline mutants previously reported for another family B GPCR, the VPAC(1) receptor.

RAMPs and the modulation of G-protein coupled receptors

Receptor activity modifying proteins (RAMPs) are a family of three proteins that can interact with G-protein coupled receptors (GPCRs) to create new receptors and also alter the signalling and trafficing of existing receptors. There are frequently changes in RAMP expression in cardiovascular disease RAMPs represent important drug targets.

Present perspectives on the automated classification of the G-protein coupled receptors (GPCRs) at the protein sequence level

The G-protein coupled receptors--or GPCRs--comprise simultaneously one of the largest and one of the most multi-functional protein families known to modern-day molecular bioscience. From a drug discovery and pharmaceutical industry perspective, the GPCRs constitute one of the most commercially and economically important groups of proteins known. The GPCRs undertake numerous vital metabolic functions and interact with a hugely diverse range of small and large ligands. Many different methodologies have been developed to efficiently and accurately classify the GPCRs. These range from motif-based techniques to machine learning as well as a variety of alignment-free techniques based on the physiochemical properties of sequences. We review here the available methodologies for the classification of GPCRs. Part of this work focuses on how we have tried to build the intrinsically hierarchical nature of sequence relations, implicit within the family, into an adaptive approach to classification. Importantly, we also allude to some of the key innate problems in developing an effective approach to classifying the GPCRs: the lack of sequence similarity between the six classes that comprise the GPCR family and the low sequence similarity to other family members evinced by many newly revealed members of the family.

Secretin family (Class B) G protein-coupled receptors - from molecular to clinical perspectives

Family B G protein-coupled receptors represent an important but under-researched group of receptors. This edition of the British Journal of Pharmacology considers the roles and pharmacology of a number of these receptors. Whilst common themes emerge, it is clear that more work is needed to understand the details of each receptor in order to properly exploit them therapeutically.

Hierarchical classification of G-protein-coupled receptors with data-driven selection of attributes and classifiers

We address the important bioinformatics problem of predicting protein function from a protein's primary sequence. We consider the functional classification of G-Protein-Coupled Receptors (GPCRs), whose functions are specified in a class hierarchy. We tackle this task using a novel top-down hierarchical classification system where, for each node in the class hierarchy, the predictor attributes to be used in that node and the classifier to be applied to the selected attributes are chosen in a data-driven manner. Compared with a previous hierarchical classification system selecting classifiers only, our new system significantly reduced processing time without significantly sacrificing predictive accuracy.

In silico identification of novel G protein coupled receptors

G-protein coupled receptors (GPCRs) are a superfamily of membrane integral proteins responsible for a large number of physiological functions. Approximately 50% of marketed drugs are targeted toward a GPCR. Despite showing a high degree of structural homology, there is a large variance in sequence within the GPCR superfamily which has lead to difficulties in identifying and classifying potential new GPCR proteins. Here the various computational techniques that can be used to characterize a novel GPCR protein are discussed, including both alignment-based and alignment-free approaches. In addition, the application of homology modeling to building the three-dimensional structures of GPCRs is described.

Similarity between class A and class B G-protein-coupled receptors exemplified through calcitonin gene-related peptide receptor modelling and mutagenesis studies

Modelling class B G-protein-coupled receptors (GPCRs) using class A GPCR structural templates is difficult due to lack of homology. The plant GPCR, GCR1, has homology to both class A and class B GPCRs. We have used this to generate a class A-class B alignment, and by incorporating maximum lagged correlation of entropy and hydrophobicity into a consensus score, we have been able to align receptor transmembrane regions. We have applied this analysis to generate active and inactive homology models of the class B calcitonin gene-related peptide (CGRP) receptor, and have supported it with site-directed mutagenesis data using 122 CGRP receptor residues and 144 published mutagenesis results on other class B GPCRs. The variation of sequence variability with structure, the analysis of polarity violations, the alignment of group-conserved residues and the mutagenesis results at 27 key positions were particularly informative in distinguishing between the proposed and plausible alternative alignments. Furthermore, we have been able to associate the key molecular features of the class B GPCR signalling machinery with their class A counterparts for the first time. These include the [K/R]KLH motif in intracellular loop 1, [I/L]xxxL and KxxK at the intracellular end of TM5 and TM6, the NPXXY/VAVLY motif on TM7 and small group-conserved residues in TM1, TM2, TM3 and TM7. The equivalent of the class A DRY motif is proposed to involve Arg(2.39), His(2.43) and Glu(3.46), which makes a polar lock with T(6.37). These alignments and models provide useful tools for understanding class B GPCR function.

G-protein-coupled receptors:from structural insights to functional mechanisms

The papers resulting from the recent Biochemical Society Focused Meeting 'G-Protein-Coupled Receptors: from Structural Insights to Functional Mechanisms' held in Prato in September 2012 are introduced in the present overview. A number of future goals for GPCR (G-protein-coupled receptor) research are considered, including the need to develop biophysical and computational methods to explore the full range of GPCR conformations and their dynamics, the need to develop methods to take this into account for drug discovery and the importance of relating observations on isolated receptors or receptors expressed in model systems to receptor function in vivo. © 2013 Biochemical Society.

On the hierarchical classification of G protein-coupled receptors

MOTIVATION: G protein-coupled receptors (GPCRs) play an important role in many physiological systems by transducing an extracellular signal into an intracellular response. Over 50% of all marketed drugs are targeted towards a GPCR. There is considerable interest in developing an algorithm that could effectively predict the function of a GPCR from its primary sequence. Such an algorithm is useful not only in identifying novel GPCR sequences but in characterizing the interrelationships between known GPCRs. RESULTS: An alignment-free approach to GPCR classification has been developed using techniques drawn from data mining and proteochemometrics. A dataset of over 8000 sequences was constructed to train the algorithm. This represents one of the largest GPCR datasets currently available. A predictive algorithm was developed based upon the simplest reasonable numerical representation of the protein's physicochemical properties. A selective top-down approach was developed, which used a hierarchical classifier to assign sequences to subdivisions within the GPCR hierarchy. The predictive performance of the algorithm was assessed against several standard data mining classifiers and further validated against Support Vector Machine-based GPCR prediction servers. The selective top-down approach achieves significantly higher accuracy than standard data mining methods in almost all cases.

Integrative bioinformatics for functional genorne annotation:trawling for G protein-coupled receptors

G protein-coupled receptors (GPCR) are amongst the best studied and most functionally diverse types of cell-surface protein. The importance of GPCRs as mediates or cell function and organismal developmental underlies their involvement in key physiological roles and their prominence as targets for pharmacological therapeutics. In this review, we highlight the requirement for integrated protocols which underline the different perspectives offered by different sequence analysis methods. BLAST and FastA offer broad brush strokes. Motif-based search methods add the fine detail. Structural modelling offers another perspective which allows us to elucidate the physicochemical properties that underlie ligand binding. Together, these different views provide a more informative and a more detailed picture of GPCR structure and function. Many GPCRs remain orphan receptors with no identified ligand, yet as computer-driven functional genomics starts to elaborate their functions, a new understanding of their roles in cell and developmental biology will follow.

G-protein-coupled receptor solubilization and purification for biophysical analysis and functional studies, in the total absence of detergent

G-protein coupled receptors (GPCRs) constitute the largest class of membrane proteins and are a major drug target. A serious obstacle to studying GPCR structure/function characteristics is the requirement to extract the receptors from their native environment in the plasma membrane, coupled with the inherent instability of GPCRs in the detergents required for their solubilization. In the present study, we report the first solubilization and purification of a functional GPCR [human adenosine A2A receptor (A2AR)], in the total absence of detergent at any stage, by exploiting spontaneous encapsulation by styrene maleic acid (SMA) co-polymer direct from the membrane into a nanoscale SMA lipid particle (SMALP). Furthermore, the A2AR-SMALP, generated from yeast (Pichia pastoris) or mammalian cells, exhibited increased thermostability (∼5°C) compared with detergent [DDM (n-dodecyl-β-D-maltopyranoside)]-solubilized A2AR controls. The A2AR-SMALP was also stable when stored for prolonged periods at 4°C and was resistant to multiple freeze-thaw cycles, in marked contrast with the detergent-solubilized receptor. These properties establish the potential for using GPCR-SMALP in receptor-based drug discovery assays. Moreover, in contrast with nanodiscs stabilized by scaffold proteins, the non-proteinaceous nature of the SMA polymer allowed unobscured biophysical characterization of the embedded receptor. Consequently, CD spectroscopy was used to relate changes in secondary structure to loss of ligand binding ([³H]ZM241385) capability. SMALP-solubilization of GPCRs, retaining the annular lipid environment, will enable a wide range of therapeutic targets to be prepared in native-like state to aid drug discovery and understanding of GPCR molecular mechanisms.

Structural basis for receptor activity-modifying protein-dependent selective peptide recognition by a G protein-coupled receptor

Association of receptor activity-modifying proteins (RAMP1-3) with the G protein-coupled receptor (GPCR) calcitonin receptor-like receptor (CLR) enables selective recognition of the peptides calcitonin gene-related peptide (CGRP) and adrenomedullin (AM) that have diverse functions in the cardiovascular and lymphatic systems. How peptides selectively bind GPCR:RAMP complexes is unknown. We report crystal structures of CGRP analog-bound CLR:RAMP1 and AM-bound CLR:RAMP2 extracellular domain heterodimers at 2.5 and 1.8 Å resolutions, respectively. The peptides similarly occupy a shared binding site on CLR with conformations characterized by a β-turn structure near their C termini rather than the α-helical structure common to peptides that bind related GPCRs. The RAMPs augment the binding site with distinct contacts to the variable C-terminal peptide residues and elicit subtly different CLR conformations. The structures and accompanying pharmacology data reveal how a class of accessory membrane proteins modulate ligand binding of a GPCR and may inform drug development targeting CLR:RAMP complexes.

IUPHAR-DB:the IUPHAR database of G protein-coupled receptors and ion channels

The IUPHAR database (IUPHAR-DB) integrates peer-reviewed pharmacological, chemical, genetic, functional and anatomical information on the 354 nonsensory G protein-coupled receptors (GPCRs), 71 ligand-gated ion channel subunits and 141 voltage-gated-like ion channel subunits encoded by the human, rat and mouse genomes. These genes represent the targets of approximately one-third of currently approved drugs and are a major focus of drug discovery and development programs in the pharmaceutical industry. IUPHAR-DB provides a comprehensive description of the genes and their functions, with information on protein structure and interactions, ligands, expression patterns, signaling mechanisms, functional assays and biologically important receptor variants (e.g. single nucleotide polymorphisms and splice variants). In addition, the phenotypes resulting from altered gene expression (e.g. in genetically altered animals or in human genetic disorders) are described. The content of the database is peer reviewed by members of the International Union of Basic and Clinical Pharmacology Committee on Receptor Nomenclature and Drug Classification (NC-IUPHAR); the data are provided through manual curation of the primary literature by a network of over 60 subcommittees of NC-IUPHAR. Links to other bioinformatics resources, such as NCBI, Uniprot, HGNC and the rat and mouse genome databases are provided. IUPHAR-DB is freely available at http://www.iuphar-db.org. © 2008 The Author(s).