G Protein-coupled Receptors Ligand Binding Research Articles

What Makes GPCRs from Different Families Bind to the Same Ligand?

G protein-coupled receptors (GPCRs) are the largest class of cell-surface receptor proteins with important functions in signal transduction and often serve as therapeutic drug targets. With the rapidly growing public data on three dimensional (3D) structures of GPCRs and GPCR-ligand interactions, computational prediction of GPCR ligand binding becomes a convincing option to high throughput screening and other experimental approaches during the beginning phases of ligand discovery. In this work, we set out to computationally uncover and understand the binding of a single ligand to GPCRs from several different families. Three-dimensional structural comparisons of the GPCRs that bind to the same ligand revealed local 3D structural similarities and often these regions overlap with locations of binding pockets. These pockets were found to be similar (based on backbone geometry and side-chain orientation using APoc), and they correlate positively with electrostatic properties of the pockets. Moreover, the more similar the pockets, the more likely a ligand binding to the pockets will interact with similar residues, have similar conformations, and produce similar binding affinities across the pockets. These findings can be exploited to improve protein function inference, drug repurposing and drug toxicity prediction, and accelerate the development of new drugs.

BCL7B is a potential novel diagnosis and prognosis biomarker for sarcomas using bioinformatics analysis.

BCL7B plays a potential role in the progression of various cancers, while its role in sarcomas is unknown. We aimed to evaluate BCL7B's diagnostic and prognostic value, and potential BCL7B-related mechanisms in sarcomas based on The Cancer Genome Atlas (TCGA) database. We collected patients with sarcoma from TCGA. Wilcoxon rank sum test was used to compare the expression of BCL7B in sarcoma samples with different clinical-pathologic features. Univariate Cox regression analysis and multivariate Cox regression analysis were used to evaluate prognosis factors for sarcoma. Gene set enrichment analysis (GSEA) was conducted to elucidate the significant functions and pathways associated with BCL7B. BCL7B was a potential biomarker for distinguishing normal and tumor tissues with the analysis of ROC curve (AUC = 0.588). Low BCL7B expression was significantly correlated with tumor multifocal (OR = 0.39 for yes vs no), larger residual tumor (OR = 0.40 for R1,R2 vs RO), male gender (OR = 0.48 for male vs female) and White race (OR = 0.29 for White vs Asian, Black or African American). High BCL7B expression was correlated with leiomyosarcoma histological type (OR = 6.08 for leiomyosarcoma vs dedifferentiated liposarcoma, pleomorphic sarcoma). Univariate and multivariate Cox regression analysis showed that low BCL7B expression was independently associated with poor overall survival (P = .008). GSEA showed that GPCR (G protein-coupled receptors) ligand binding, secreted factors, class A1 rhodopsin-like receptors, extracellular matrix organization, core matrisome, Fc epsilon receptor I mediated NF-κB activation, and WNT signaling pathway were differentially enriched in BCL7B low expression phenotype (|NES| > 1, adjusted P value <.05, and FDR value <0.25). BCL7B may play an important role in sarcoma progression and may be a potential biomarker for prognosis and diagnosis in sarcomas.

Detection of genome-edited and endogenously expressed G protein-coupled receptors.

G protein‐coupled receptors (GPCRs) are the largest family of membrane receptors and major targets for FDA‐approved drugs. The ability to quantify GPCR expression and ligand binding characteristics in different cell types and tissues is therefore important for drug discovery. The advent of genome editing along with developments in fluorescent ligand design offers exciting new possibilities to probe GPCRs in their native environment. This review provides an overview of the recent technical advances employed to study the localisation and ligand binding characteristics of genome‐edited and endogenously expressed GPCRs.

Accurate Prediction of GPCR Ligand Binding Affinity with Free Energy Perturbation.

The computational prediction of relative binding free energies is a crucial goal for drug discovery, and G protein-coupled receptors (GPCRs) are arguably the most important drug target class. However, they present increased complexity to model compared to soluble globular proteins. Despite breakthroughs, experimental X-ray crystal and cryo-EM structures are challenging to attain, meaning computational models of the receptor and ligand binding mode are sometimes necessary. This leads to uncertainty in understanding ligand-protein binding induced changes such as, water positioning and displacement, side chain positioning, hydrogen bond networks, and the overall structure of the hydration shell around the ligand and protein. In other words, the very elements that define structure activity relationships (SARs) and are crucial for accurate binding free energy calculations are typically more uncertain for GPCRs. In this work we use free energy perturbation (FEP) to predict the relative binding free energies for ligands of two different GPCRs. We pinpoint the key aspects for success such as the important role of key water molecules, amino acid ionization states, and the benefit of equilibration with specific ligands. Initial calculations following typical FEP setup and execution protocols delivered no correlation with experiment, but we show how results are improved in a logical and systematic way. This approach gave, in the best cases, a coefficient of determination (R2) compared with experiment in the range of 0.6-0.9 and mean unsigned errors compared to experiment of 0.6-0.7 kcal/mol. We anticipate that our findings will be applicable to other difficult-to-model protein ligand data sets and be of wide interest for the community to continue improving FE binding energy predictions.

Allosteric modulation of G protein‐coupled receptors by amiloride and its derivatives. Perspectives for drug discovery?

The function of G protein‐coupled receptors (GPCRs) can be modulated by compounds that bind to other sites than the endogenous orthosteric binding site, so‐called allosteric sites. Structure elucidation of a number of GPCRs has revealed the presence of a sodium ion bound in a conserved allosteric site. The small molecule amiloride and analogs thereof have been proposed to bind in this same sodium ion site. Hence, this review seeks to summarize and reflect on the current knowledge of allosteric effects by amiloride and its analogs on GPCRs. Amiloride is known to modulate adenosine, adrenergic, dopamine, chemokine, muscarinic, serotonin, gonadotropin‐releasing hormone, GABAB, and taste receptors. Amiloride analogs with lipophilic substituents tend to be more potent modulators than amiloride itself. Adenosine, α‐adrenergic and dopamine receptors are most strongly modulated by amiloride analogs. In addition, for a few GPCRs, more than one binding site for amiloride has been postulated. Interestingly, the nature of the allosteric effect of amiloride and derivatives varies considerably between GPCRs, with both negative and positive allosteric modulation occurring. Since the sodium ion binding site is strongly conserved among class A GPCRs it is to be expected that amiloride also binds to class A GPCRs not evaluated yet. Investigating this typical amiloride‐GPCR interaction further may yield general insight in the allosteric mechanisms of GPCR ligand binding and function, and possibly provide new opportunities for drug discovery.

A Metadynamics-Based Protocol for the Determination of GPCR-Ligand Binding Modes.

G protein-coupled receptors (GPCRs) are a main drug target and therefore a hot topic in pharmaceutical research. One important prerequisite to understand how a certain ligand affects a GPCR is precise knowledge about its binding mode and the specific underlying interactions. If no crystal structure of the respective complex is available, computational methods can be used to deduce the binding site. One of them are metadynamics simulations which have the advantage of an enhanced sampling compared to conventional molecular dynamics simulations. However, the enhanced sampling of higher-energy states hampers identification of the preferred binding mode. Here, we present a novel protocol based on clustering of multiple walker metadynamics simulations which allows identifying the preferential binding mode from such conformational ensembles. We tested this strategy for three different model systems namely the histamine H1 receptor in combination with its physiological ligand histamine, as well as the adrenoceptor with its agonist adrenaline and its antagonist alprenolol. For all three systems, the proposed protocol was able to reproduce the correct binding mode known from the literature suggesting that the approach can more generally be applied to the prediction of GPCR ligand binding in future.

Dynamic States of the Ligand-Free Class A G Protein-Coupled Receptor Extracellular Side.

G protein-coupled receptors (GPCRs) make up the largest family of drug targets. The second extracellular loop (ECL2) and extracellular end of the third transmembrane helix (TM3) are basic structural elements of the GPCR ligand binding site. Currently, the disulfide bond between the two conserved cysteines in the ECL2 and TM3 is considered to be a basic GPCR structural feature. This disulfide bond has a significant effect on receptor dynamics and ligand binding. Here, molecular dynamics simulations and experimental results show that the two cysteines are distant from one another in the highest-population conformational state of ligand-free class A GPCRs and do not form a disulfide bond, indicating that the dynamics of the GPCR extracellular side are different from our conventional understanding. These surprising dynamics should have important effects on the drug binding process. On the basis of the two distinct ligand-free states, we suggest two kinetic processes for binding of ligands to GPCRs. These results challenge our commonly held beliefs regarding both GPCR structural features and ligand binding.

Recent Advances in Structure-Based Drug Design Targeting Class A G Protein-Coupled Receptors Utilizing Crystal Structures and Computational Simulations.

G protein-coupled receptors (GPCRs) represent the largest and most physiologically important integral membrane protein family, and these receptors respond to a wide variety of physiological and environmental stimuli. GPCRs are among the most critical therapeutic targets for numerous human diseases, and approximately one-third of the currently marketed drugs target this receptor family. The recent breakthroughs in GPCR structural biology have significantly contributed to our understanding of GPCR function, ligand binding, and pharmacological action as well as to the design of new drugs. This perspective highlights the latest advances in GPCR structures with a focus on the receptor-ligand interactions of each receptor family in class A nonrhodopsin GPCRs as well as the structural features for their activation, biased signaling, and allosteric mechanisms. The current state-of-the-art methodologies of structure-based drug design (SBDD) approaches in the GPCR research field are also discussed.

Successful prediction of the intra- and extracellular loops of four G-protein-coupled receptors

We present results of the restoration of all crystallographically available intra- and extracellular loops of four G-protein-coupled receptors (GPCRs): bovine rhodopsin (bRh), the turkey β-1 adrenergic receptor (β1Ar), and the human β-2 adrenergic (β2Ar) and A2A adenosine (A2Ar) receptors. We use our Protein Local Optimization Program (PLOP), which samples conformational space from first principles to build sets of loop candidates and then discriminates between them using our physics-based, all-atom energy function with implicit solvent. We also discuss a new kind of explicit membrane calculation developed for GPCR loops that interact, either in the native structure or in low-energy false-positive structures, with the membrane, and thus exist in a multiphase environment not previously incorporated in PLOP. Our results demonstrate a significant advance over previous work reported in the literature, and of particular note we are able to accurately restore the extremely long second extracellular loop (ECL2), which is also key for GPCR ligand binding. In the case of β2Ar, accurate ECL2 restoration required seeding a small helix into the loop in the appropriate region, based on alignment with the β1Ar ECL2 loop, and then running loop reconstruction simulations with and without the seeded helix present; simulations containing the helix attain significantly lower total energies than those without the helix, and have rmsds close to the native structure. For β1Ar, the same protocol was used, except the alignment was done to β2Ar. These results represent an encouraging start for the more difficult problem of accurate loop refinement for GPCR homology modeling.

G-protein-coupled receptor-focused drug discovery using a target class platform approach

In recent years, several large pharmaceutical companies have taken a novel approach to drug discovery biology and chemistry in that they channel their efforts with respect to particular target classes, such as G-protein-coupled receptors (GPCRs), toward dedicated, specialized teams. Benefits of such an organizational structure are the prospects of establishing several target-family-specific experimental techniques and skill sets, thereby enabling a comprehensive functional profiling of drug candidates in different pharmacological respects. In this context, the recently increased number of reports on GPCR ligand-biased signaling has further spurred the efforts in the pharmaceutical industry toward broader biological characterization of the test compounds, for example employing high-content screening to analyze different GPCR ligand-induced signaling pathways. The knowledge of the disease-relevant functional properties of the small molecule GPCR ligands enables target-specific chemical optimization and GPCR-subclass-directed library design. In the case of GPCRs, where little--although at present slowly expanding--structural information on the targets is available, the modeling of GPCR structures crucially depends on biological validation (typically supported by site-directed mutagenesis of the GPCR ligand binding site). In this review, we aim to recapitulate efforts in the pharmaceutical industry to address GPCR-directed drug discovery in a target-class-directed platform approach: establishing GPCR-specific biological assay panels and creating computational chemistry methods for finding and optimizing small molecules modulating the activity of GPCRs.

Classification of G-protein coupled receptors by alignment-independent extraction of principal chemical properties of primary amino acid sequences.

We have developed an alignment-independent method for classification of G-protein coupled receptors (GPCRs) according to the principal chemical properties of their amino acid sequences. The method relies on a multivariate approach where the primary amino acid sequences are translated into vectors based on the principal physicochemical properties of the amino acids and transformation of the data into a uniform matrix by applying a modified autocross-covariance transform. The application of principal component analysis to a data set of 929 class A GPCRs showed a clear separation of the major classes of GPCRs. The application of partial least squares projection to latent structures created a highly valid model (cross-validated correlation coefficient, Q(2) = 0.895) that gave unambiguous classification of the GPCRs in the training set according to their ligand binding class. The model was further validated by external prediction of 535 novel GPCRs not included in the training set. Of the latter, only 14 sequences, confined in rapidly expanding GPCR classes, were mispredicted. Moreover, 90 orphan GPCRs out of 165 were tentatively identified to GPCR ligand binding class. The alignment-independent method could be used to assess the importance of the principal chemical properties of every single amino acid in the protein sequences for their contributions in explaining GPCR family membership. It was then revealed that all amino acids in the unaligned sequences contributed to the classifications, albeit to varying extent; the most important amino acids being those that could also be determined to be conserved by using traditional alignment-based methods.