Modeling G protein-coupled receptors and their interactions with ligands

Descriptive molecular pharmacology of the δ opioid receptor (DOR): A computational study with structural approach

This work focuses on the δ receptor (DOR), a G protein-coupled receptor (GPCR) belonging to the opioid receptor group. DOR is expressed in numerous tissues, particularly within the nervous system. Our study explores computationally the receptor's interactions with various ligands, including opiates and opioid peptides. It elucidates how these interactions influence the δ receptor response, relevant in a wide range of health and pathological processes. Thus, our investigation aims to explore the significance of DOR as an incoming drug target for pain relief and neurodegenerative diseases and as a source for novel opioid non-narcotic analgesic alternatives. We analyze the receptor's structural properties and interactions using Molecular Dynamics (MD) simulations and Gaussian-accelerated MD across different functional states. To thoroughly assess the primary differences in the structural and conformational ensembles across our different simulated systems, we initiated our study with 1 μs of conventional Molecular Dynamics. The strategy was chosen to encompass the full activation cycle of GPCRs, as activation processes typically occur within this microsecond range. Following the cMD, we extended our study with an additional 100 ns of Gaussian accelerated Molecular Dynamics (GaMD) to enhance the sampling of conformational states. This simulation approach allowed us to capture a comprehensive range of dynamic interactions and conformational changes that are crucial for GPCR activation as influenced by different ligands. Our study includes comparing agonist and antagonist complexes to uncover the collective patterns of their functional states, regarding activation, blocking, and inactivation of DOR, starting from experimental data. In addition, we also explored interactions between agonist and antagonist molecules from opiate and opioid classifications to establish robust structure-activity relationships. These interactions have been systematically quantified using a Quantitative Structure-Activity Relationships (QSAR) model. This research significantly contributes to our understanding of this significant pharmacological target, which is emerging as an attractive subject for drug development.

Identification of Candidate Genes Regulating Drought Tolerance in Pearl Millet

Pearl millet is an important crop of the arid and semi-arid ecologies to sustain food and fodder production. The greater tolerance to drought stress attracts us to examine its cellular and molecular mechanisms via functional genomics approaches to augment the grain yield. Here, we studied the drought response of 48 inbreds representing four different maturity groups at the flowering stage. A set of 74 drought-responsive genes were separated into five major phylogenic groups belonging to eight functional groups, namely ABA signaling, hormone signaling, ion and osmotic homeostasis, TF-mediated regulation, molecular adaptation, signal transduction, physiological adaptation, detoxification, which were comprehensively studied. Among the conserved motifs of the drought-responsive genes, the protein kinases and MYB domain proteins were the most conserved ones. Comparative in-silico analysis of the drought genes across millet crops showed foxtail millet had most orthologs with pearl millet. Of 698 haplotypes identified across millet crops, MyC2 and Myb4 had maximum haplotypes. The protein–protein interaction network identified ABI2, P5CS, CDPK, DREB, MYB, and CYP707A3 as major hub genes. The expression assay showed the presence of common as well as unique drought-responsive genes across maturity groups. Drought tolerant genotypes in respective maturity groups were identified from the expression pattern of genes. Among several gene families, ABA signaling, TFs, and signaling proteins were the prospective contributors to drought tolerance across maturity groups. The functionally validated genes could be used as promising candidates in backcross breeding, genomic selection, and gene-editing schemes in pearl millet and other millet crops to increase the yield in drought-prone arid and semi-arid ecologies.

Covalent cannabinoid receptor ligands - structural insight and selectivity challenges

X-ray crystallography and cryogenic electronic microscopy have provided significant advancement in the knowledge of GPCR structure and have allowed the rational design of GPCR ligands. The class A GPCRs cannabinoid receptor type 1 and type 2 are implicated in many pathophysiological processes and thus rational design of drug and tool compounds is of great interest. Recent structural insight into cannabinoid receptors has already led to a greater understanding of ligand binding sites and receptor residues that likely contribute to ligand selectivity. Herein, classes of heterocyclic covalent cannabinoid receptor ligands are reviewed in light of the recent advances in structural knowledge of cannabinoid receptors, with particular discussion regarding covalent ligand selectivity and rationale design.

The In Silico Drug Discovery Toolbox: Applications in Lead Discovery and Optimization

BACKGROUND

Discovery and development of a new drug is a long lasting and expensive journey that takes around 20 years from starting idea to approval and marketing of new medication. Despite R&D expenditures have been constantly increasing in the last few years, the number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. To cope with this issue, a number of in silico techniques are currently being used for an early stage evaluation/prediction of potential safety issues, allowing to increase the drug-discovery success rate and reduce costs associated with the development of a new drug.

METHODS

In the present review, we will analyse the early steps of the drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan.

RESULTS

A comprehensive list of widely used in silico tools, databases, and public initiatives that can be effectively implemented and used in the drug discovery pipeline has been provided. A few examples of how these tools can be problem-solving and how they may increase the success rate of a drug discovery and development program have been also provided. Finally, selected examples where the application of in silico tools had effectively contributed to the development of marketed drugs or clinical candidates will be given.

CONCLUSION

The in silico toolbox finds great application in every step of early drug discovery: (i) target identification and validation; (ii) hit identification; (iii) hit-to-lead; and (iv) lead optimization. Each of these steps has been described in details, providing a useful overview on the role played by in silico tools in the decision-making process to speed-up the discovery of new drugs.

Genome-wide analysis of the cotton G-coupled receptor proteins (GPCR) and functional analysis of GTOM1, a novel cotton GPCR gene under drought and cold stress

BACKGROUND

The efficient detection and initiation of appropriate response to abiotic stresses are important to plants survival. The plant G-protein coupled receptors (GPCRs) are diverse membranous proteins that are responsible for signal transduction.

RESULTS

In this research work, we identified a novel gene of the GPCR domain, transformed and carried out the functional analysis in Arabidopsis under drought and cold stresses. The transgenic lines exposed to drought and cold stress conditions showed higher germination rate, increased root length and higher fresh biomass accumulation. Besides, the levels of antioxidant enzymes, glutathione (GSH) and ascorbate peroxidase (APX) exhibited continuously increasing trends, with approximately threefold higher than the control, implying that these ROS-scavenging enzymes were responsible for the detoxification of ROS induced by drought and cold stresses. Similarly, the transgenic lines exhibited stable cell membrane stability (CMS), reduced water loss rate in the detached leaves and significant values for the saturated leaves compared to the wild types. Highly stress-responsive miRNAs were found to be targeted by the novel gene and based on GO analysis; the protein encoded by the gene was responsible for maintaining an integral component of membrane. In cotton, the virus-induced gene silencing (VIGS) plants exhibited a higher susceptibility to drought and cold stresses compared to the wild types.

CONCLUSION

The novel GPCR gene enhanced drought and cold stress tolerance in transgenic Arabidopsis plants by promoting root growth and induction of ROS scavenging enzymes. The outcome showed that the gene had a role in enhancing drought and cold stress tolerance, and can be further exploited in breeding for more stress-resilient and tolerant crops.

Modeling and Deorphanization of Orphan GPCRs

Despite tremendous efforts, approximately 120 GPCRs remain orphan. Their physiological functions and their potential roles in diseases are poorly understood. Orphan GPCRs are extremely important because they may provide novel therapeutic targets for unmet medical needs. As a complement to experimental approaches, molecular modeling and virtual screening are efficient techniques to discover synthetic surrogate ligands which can help to elucidate the role of oGPCRs. Constitutively activated mutants and recently published active structures of GPCRs provide stimulating opportunities for building active molecular models for oGPCRs and identifying activators using virtual screening of compound libraries. We describe the molecular modeling and virtual screening process we have applied in the discovery of surrogate ligands, and provide examples for CCKA, a simulated oGPCR, and for two oGPCRs, GPR52 and GPR34.

Structural-Functional Features of the Thyrotropin Receptor: A Class A G-Protein-Coupled Receptor at Work

The thyroid-stimulating hormone receptor (TSHR) is a member of the glycoprotein hormone receptors, a sub-group of class A G-protein-coupled receptors (GPCRs). TSHR and its endogenous ligand thyrotropin (TSH) are of essential importance for growth and function of the thyroid gland and proper function of the TSH/TSHR system is pivotal for production and release of thyroid hormones. This receptor is also important with respect to pathophysiology, such as autoimmune (including ophthalmopathy) or non-autoimmune thyroid dysfunctions and cancer development. Pharmacological interventions directly targeting the TSHR should provide benefits to disease treatment compared to currently available therapies of dysfunctions associated with the TSHR or the thyroid gland. Upon TSHR activation, the molecular events conveying conformational changes from the extra- to the intracellular side of the cell across the membrane comprise reception, conversion, and amplification of the signal. These steps are highly dependent on structural features of this receptor and its intermolecular interaction partners, e.g., TSH, antibodies, small molecules, G-proteins, or arrestin. For better understanding of signal transduction, pathogenic mechanisms such as autoantibody action and mutational modifications or for developing new pharmacological strategies, it is essential to combine available structural data with functional information to generate homology models of the entire receptor. Although so far these insights are fragmental, in the past few decades essential contributions have been made to investigate in-depth the involved determinants, such as by structure determination via X-ray crystallography. This review summarizes available knowledge (as of December 2016) concerning the TSHR protein structure, associated functional aspects, and based on these insights we suggest several receptor complex models. Moreover, distinct TSHR properties will be highlighted in comparison to other class A GPCRs to understand the molecular activation mechanisms of this receptor comprehensively. Finally, limitations of current knowledge and lack of information are discussed highlighting the need for intensified efforts toward TSHR structure elucidation.

Molecular Modelling Approaches for the Analysis of Histamine Receptors and Their Interaction with Ligands

Several experimental techniques to analyse histamine receptors are available, e.g. pharmacological characterisation of known or new compounds by different types of assays or mutagenesis studies. To obtain insights into the histamine receptors on a molecular and structural level, crystal structures have to be determined and molecular modelling studies have to be performed. It is widely accepted to generate homology models of the receptor of interest based on an appropriate crystal structure as a template and to refine the resulting models by molecular dynamic simulations. A lot of modelling techniques, e.g. docking, QSAR or interaction fingerprint methods, are used to predict binding modes of ligands and pharmacological data, e.g. affinity or even efficacy. However, within the last years, molecular dynamic simulations got more and more important: First of all, molecular dynamic simulations are very helpful to refine the binding mode of a ligand to a histamine receptor, obtained by docking studies. Furthermore, with increasing computational performance it got possible to simulate complete binding pathways of ions or ligands from the aqueous extracellular phase into the allosteric or orthosteric binding pocket of histamine receptors.

Urolinin: The First Linear Peptidic Urotensin-II Receptor Agonist

This study investigated the role of individual U-II amino acid positions and side chain characteristics important for U-IIR activation. A complete permutation library of 209 U-II variants was studied in an activity screen that contained single substitution variants of each position with one of the other 19 proteinogenic amino acids. Receptor activation was measured using a cell-based high-throughput fluorescence calcium mobilization assay. We generated the first complete U-II substitution map for U-II receptor activation, resulting in a detailed view into the structural features required for receptor activation, accompanied by complementary information from receptor modeling and ligand docking studies. On the basis of the systematic SAR study of U-II, we created 33 further short and linear U-II variants from eight to three amino acids in length, including d- and other non-natural amino acids. We identified the first high-potency linear U-II analogues. Urolinin, a linear U-II agonist (nWWK-Tyr(3-NO₂)-Abu), shows low nanomolar potency as well as improved metabolic stability.

Application of GPCR Structures for Modelling of Free Fatty Acid Receptors

Five G protein-coupled receptors (GPCRs) have been identified to be activated by free fatty acids (FFA). Among them, FFA1 (GPR40) and FFA4 (GPR120) bind long-chain fatty acids, FFA2 (GPR43) and FFA3 (GPR41) bind short-chain fatty acids and GPR84 binds medium-chain fatty acids. Free fatty acid receptors have now emerged as potential targets for the treatment of diabetes, obesity and immune diseases. The recent progress in crystallography of GPCRs has now enabled the elucidation of the structure of FFA1 and provided reliable templates for homology modelling of other FFA receptors. Analysis of the crystal structure and improved homology models, along with mutagenesis data and structure activity, highlighted an unusual arginine charge-pairing interaction in FFA1-3 for receptor modulation, distinct structural features for ligand binding to FFA1 and FFA4 and an arginine of the second extracellular loop as a possible anchoring point for FFA at GPR84. Structural data will be helpful for searching novel small-molecule modulators at the FFA receptors.

Prediction of the substrate for nonribosomal peptide synthetase (NRPS) adenylation domains by virtual screening

Nonribosomal peptide synthetases (NRPSs) synthesize a diverse array of bioactive small peptides, many of which are used in medicine. There is considerable interest in predicting NRPS substrate specificity in order to facilitate investigation of the many "cryptic" NRPS genes that have not been linked to any known product. However, the current sequence similarity-based methods are unable to produce reliable predictions when there is a lack of prior specificity data, which is a particular problem for fungal NRPSs. We conducted virtual screening on the specificity-determining domain of NRPSs, the adenylation domain, and found that virtual screening using experimentally determined structures results in good enrichment of the cognate substrate. Our results indicate that the conformation of the adenylation domain and in particular the conformation of a key conserved aromatic residue is important in determining the success of the virtual screening. When homology models of NRPS adenylation domains of known specificity, rather than experimentally determined structures, were built and used for virtual screening, good enrichment of the cognate substrate was also achieved in many cases. However, the accuracy of the models was key to the reliability of the predictions and there was a large variation in the results when different models of the same domain were used. This virtual screening approach is promising and is able to produce enrichment of the cognate substrates in many cases, but improvements in building and assessing homology models are required before the approach can be reliably applied to these models.

Ins and outs of GPCR signaling in primary cilia

Primary cilia are specialized microtubule-based signaling organelles that convey extracellular signals into a cellular response in most vertebrate cell types. The physiological significance of primary cilia is underscored by the fact that defects in assembly or function of these organelles lead to a range of severe diseases and developmental disorders. In most cell types of the human body, signaling by primary cilia involves different G protein-coupled receptors (GPCRs), which transmit specific signals to the cell through G proteins to regulate diverse cellular and physiological events. Here, we provide an overview of GPCR signaling in primary cilia, with main focus on the rhodopsin-like (class A) and the smoothened/frizzled (class F) GPCRs. We describe how such receptors dynamically traffic into and out of the ciliary compartment and how they interact with other classes of ciliary GPCRs, such as class B receptors, to control ciliary function and various physiological and behavioral processes. Finally, we discuss future avenues for developing GPCR-targeted drug strategies for the treatment of ciliopathies.

Enhancing the Reliability of GPCR Models by Accounting for Flexibility of Their Pro-Containing Helices: the Case of the Human mAChR1 Receptor

To better investigate the GPCR structures, we have recently proposed to explore their flexibility by simulating the bending of their Pro-containing TM helices so generating a set of models (the so-called chimeras) which exhaustively combine the two conformations (bent and straight) of these helices. The primary objective of the study is to investigate whether such an approach can be exploited to enhance the reliability of the GPCR models generated by distant templates. The study was focused on the human mAChR1 receptor for which a presumably reliable model was generated using the congener mAChR3 as the template along with a second less reliable model based on the distant β2-AR template. The second model was then utilized to produce the chimeras by combining the conformations of its Pro-containing helices (i.e., TM4, TM5, TM6 and TM7 with 16 modeled chimeras). The reliability of such chimeras was assessed by virtual screening campaigns as evaluated using a novel skewness metric where they surpassed the predictive power of the more reliable mAChR1 model. Finally, the virtual screening campaigns emphasize the opportunity of synergistically combining the scores of more chimeras using a specially developed tool which generates highly predictive consensus functions by maximizing the corresponding enrichment factors.

Activation of corticotropin-releasing factor 1 receptor: insights from molecular dynamics simulations

G-protein-coupled receptors (GPCRs) constitute the largest family of membrane-bound proteins involved in translation of extracellular signals into intracellular responses. They regulate diverse physiological and pathophysiological processes, and hence, they are prime drug targets for therapeutic intervention. In spite of the recent advancements in membrane protein crystallography, limited information is available on the molecular signatures of activation of GPCRs. Although few studies have been reported for class A GPCRs, the activation mechanism of class B GPCRs remains unexplored. Corticotropin-releasing factor 1 receptor (CRF1R), a class B GPCR, is associated with various disease conditions including stress, anxiety, and irritable bowel syndrome. Here, we report the activation of CRF1R using accelerated molecular dynamics simulations of the apo receptor. The breakage of His155(2.50)-Glu209(3.50) and Glu209(3.50)-Thr316(6.42) interactions is found to be crucial in transition of the receptor to its active conformation. Compared to the inactive crystal structure, major structural rearrangements occurred in the intracellular region of the transmembrane (TM) domain upon activation: TM3 twisted away from TM2, and an opening of the G-protein binding site occurred as a result of the outward movements of TM5 and TM6 from the helical bundle. Further, an inward tilt of TM7 toward the helical core is observed at the extracellular side, in agreement with recent findings (Coin et al. Cell 2013, 155, 1258-1269), where it is proposed that this movement helps in establishing favorable interactions with peptide agonist. Moreover, different allosteric pathways in the inactive and active states are identified using the correlations in torsion angle space. The inactive state is found to be less dynamic as compared to the putative active state of the receptor. Results from the current study could present a model for class B GPCRs activation and aid in the design of CRF1R modulators against brain and metabolic disorders.

Development of small molecule non-peptide formyl peptide receptor (FPR) ligands and molecular modeling of their recognition

Formyl peptide receptors (FPRs) are G protein-coupled receptors (GPCRs) expressed on a variety of cell types. These receptors play an important role in the regulation of inflammatory reactions and sensing cellular damage. They have also been implicated in the pathogenesis of various diseases, including neurodegenerative diseases, cataract formation, and atherogenesis. Thus, FPR ligands, both agonists and antagonists, may represent novel therapeutics for modulating host defense and innate immunity. A variety of molecules have been identified as receptor subtype-selective and mixed FPR agonists with potential therapeutic value during last decade. This review describes our efforts along with recent advances in the identification, optimization, biological evaluation, and structure-activity relationship (SAR) analysis of small molecule non-peptide FPR agonists and antagonists, including chiral molecules. Questions regarding the interaction at the molecular level of benzimidazoles, pyrazolones, pyridazin-3(2H)-ones, N-phenylureas and other derivatives with FPR1 and FPR2 are discussed. Application of computational models for virtual screening and design of FPR ligands is also considered.

Docking and Virtual Screening Strategies for GPCR Drug Discovery

Progress in structure determination of G protein-coupled receptors (GPCRs) has made it possible to apply structure-based drug design (SBDD) methods to this pharmaceutically important target class. The quality of GPCR structures available for SBDD projects fall on a spectrum ranging from high resolution crystal structures (<2 Å), where all water molecules in the binding pocket are resolved, to lower resolution (>3 Å) where some protein residues are not resolved, and finally to homology models that are built using distantly related templates. Each GPCR project involves a distinct set of opportunities and challenges, and requires different approaches to model the interaction between the receptor and the ligands. In this review we will discuss docking and virtual screening to GPCRs, and highlight several refinement and post-processing steps that can be used to improve the accuracy of these calculations. Several examples are discussed that illustrate specific steps that can be taken to improve upon the docking and virtual screening accuracy. While GPCRs are a unique target class, many of the methods and strategies outlined in this review are general and therefore applicable to other protein families.

Mammalian olfactory receptors: molecular mechanisms of odorant detection, 3D-modeling, and structure-activity relationships

This chapter describes the main characteristics of olfactory receptor (OR) genes of vertebrates, including generation of this large multigenic family and pseudogenization. OR genes are compared in relation to evolution and among species. OR gene structure and selection of a given gene for expression in an olfactory sensory neuron (OSN) are tackled. The specificities of OR proteins, their expression, and their function are presented. The expression of OR proteins in locations other than the nasal cavity is regulated by different mechanisms, and ORs display various additional functions. A conventional olfactory signal transduction cascade is observed in OSNs, but individual ORs can also mediate different signaling pathways, through the involvement of other molecular partners and depending on the odorant ligand encountered. ORs are engaged in constitutive dimers. Ligand binding induces conformational changes in the ORs that regulate their level of activity depending on odorant dose. When present, odorant binding proteins induce an allosteric modulation of OR activity. Since no 3D structure of an OR has been yet resolved, modeling has to be performed using the closest G-protein-coupled receptor 3D structures available, to facilitate virtual ligand screening using the models. The study of odorant binding modes and affinities may infer best-bet OR ligands, to be subsequently checked experimentally. The relationship between spatial and steric features of odorants and their activity in terms of perceived odor quality are also fields of research that development of computing tools may enhance.

What can we learn from molecular dynamics simulations for GPCR drug design?

Recent years have seen a tremendous progress in the elucidation of experimental structural information for G-protein coupled receptors (GPCRs). Although for the vast majority of pharmaceutically relevant GPCRs structural information is still accessible only by homology models the steadily increasing amount of structural information fosters the application of structure-based drug design tools for this important class of drug targets. In this article we focus on the application of molecular dynamics (MD) simulations in GPCR drug discovery programs. Typical application scenarios of MD simulations and their scope and limitations will be described on the basis of two selected case studies, namely the binding of small molecule antagonists to the human CC chemokine receptor 3 (CCR3) and a detailed investigation of the interplay between receptor dynamics and solvation for the binding of small molecules to the human muscarinic acetylcholine receptor 3 (hM3R).

Computational studies to predict or explain G protein coupled receptor polypharmacology

Since G protein-coupled receptors (GPCRs) belong to a very large superfamily of evolutionarily related receptors (>800 members in humans), and due to the rapid progress on their structural biology, they are ideal candidates for polypharmacology studies. Broad screening and bioinformatics/chemoinformatics have been applied to understanding off-target effects of GPCR ligands. It is now feasible to approach the question of GPCR polypharmacology using molecular modeling and the available X-ray GPCR structures. As an example, large and sterically constrained adenosine derivatives (potent adenosine receptor ligands with low conformational freedom and multiple extended substituents) were screened for binding at diverse receptors. Unanticipated off-target interactions, including at biogenic amine receptors, were then modeled using a structure-based approach to provide a consistent understanding of recognition. A conserved Asp in TM3 changed its role from counterion for biogenic amines to characteristic H-bonding to adenosine. The same systematic approach could potentially be applied to many GPCRs or other receptors using other sets of congeneric ligands.

Structure-based drug design for G protein-coupled receptors

Our understanding of the structural biology of G protein-coupled receptors has undergone a transformation over the past 5 years. New protein-ligand complexes are described almost monthly in high profile journals. Appreciation of how small molecules and natural ligands bind to their receptors has the potential to impact enormously how medicinal chemists approach this major class of receptor targets. An outline of the key topics in this field and some recent examples of structure- and fragment-based drug design are described. A table is presented with example views of each G protein-coupled receptor for which there is a published X-ray structure, including interactions with small molecule antagonists, partial and full agonists. The possible implications of these new data for drug design are discussed.

Protein modeling: what happened to the "protein structure gap"?

Computational modeling of three-dimensional macromolecular structures and complexes from their sequence has been a long-standing vision in structural biology. Over the last 2 decades, a paradigm shift has occurred: starting from a large "structure knowledge gap" between the huge number of protein sequences and small number of known structures, today, some form of structural information, either experimental or template-based models, is available for the majority of amino acids encoded by common model organism genomes. With the scientific focus of interest moving toward larger macromolecular complexes and dynamic networks of interactions, the integration of computational modeling methods with low-resolution experimental techniques allows the study of large and complex molecular machines. One of the open challenges for computational modeling and prediction techniques is to convey the underlying assumptions, as well as the expected accuracy and structural variability of a specific model, which is crucial to understanding its limitations.

G protein-coupled receptors: computer-aided ligand discovery and computational structural analyses in the 2010s

G protein-coupled receptors, or GPCRs, are a large superfamily of proteins found on the plasma membrane of cells. They are involved in most physiological and pathophysiological functions and constitute the target of the majority of marketed drugs. Although these receptors have been historically elusive to attempts of structural determination, GPCR crystallography is now in full blossom, opening the way to structure-based drug discovery and enabling homology modeling. This thematic issue of the journal In Silico Pharmacology, which illustrates how the expanding body of structural knowledge is fostering complex computational analyses of the structure-function relationships of the receptors and their interactions with their ligands, stems from the 31st Camerino-Cyprus-Noordwijkerhout Symposium held in Italy, in May 2013, at the University of Camerino. Specifically, it originates from a session of the symposium entitled “Structure-Based Discovery of Ligands of G Protein-Coupled Receptors: Finally a Reality”, and features a mix of research articles and reviews on the application of computational modeling to the analysis of the structure of GPCRs and the interactions of the receptors with their ligands.

Graph analysis of β2 adrenergic receptor structures: a "social network" of GPCR residues

Purpose

G protein-coupled receptors (GPCRs) are a superfamily of membrane proteins of vast pharmaceutical interest. Here, we describe a graph theory-based analysis of the structure of the β₂ adrenergic receptor (β₂ AR), a prototypical GPCR. In particular, we illustrate the network of direct and indirect interactions that link each amino acid residue to any other residue of the receptor.

Methods

Networks of interconnected amino acid residues in proteins are analogous to social networks of interconnected people. Hence, they can be studied through the same analysis tools typically employed to analyze social networks – or networks in general – to reveal patterns of connectivity, influential members, and dynamicity. We focused on the analysis of closeness-centrality, which is a measure of the overall connectivity distance of the member of a network to all other members.

Results

The residues endowed with the highest closeness-centrality are located in the middle of the seven transmembrane domains (TMs). In particular, they are mostly located in the middle of TM2, TM3, TM6 or TM7, while fewer of them are located in the middle of TM1, TM4 or TM5. At the cytosolic end of TM6, the centrality detected for the active structure is markedly lower than that detected for the corresponding residues in the inactive structures. Moreover, several residues acquire centrality when the structures are analyzed in the presence of ligands. Strikingly, there is little overlap between the residues that acquire centrality in the presence of the ligand in the blocker-bound structures and the agonist-bound structures.

Conclusions

Our results reflect the fact that the receptor resembles a bow tie, with a rather tight knot of closely interconnected residues and two ends that fan out in two opposite directions: one toward the extracellular space, which hosts the ligand binding cavity, and one toward the cytosol, which hosts the G protein binding cavity. Moreover, they underscore how interaction network is by the conformational rearrangements concomitant with the activation of the receptor and by the presence of agonists or blockers.

Electronic supplementary material

The online version of this article (doi:10.1186/2193-9616-1-16) contains supplementary material, which is available to authorized users.