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.     

Structural Insights into Ligand—Receptor Interactions Involved in Biased Agonism of G-Protein Coupled Receptors

G protein-coupled receptors (GPCRs) are versatile signaling proteins that mediate complex cellular responses to hormones and neurotransmitters. Ligand directed signaling is observed when agonists, upon binding to the same receptor, trigger significantly different configuration of intracellular events. The current work reviews the structurally defined ligand – receptor interactions that can be related to specific molecular mechanisms of ligand directed signaling across different receptors belonging to class A of GPCRs. Recent advances in GPCR structural biology allow for mapping receptors’ binding sites with residues particularly important in recognition of ligands’ structural features that are responsible for biased signaling. Various studies show particular role of specific residues lining the extended ligand binding domains, biased agonists may alternatively affect their interhelical interactions and flexibility what can be translated into intracellular loop rearrangements. Studies on opioid and angiotensin receptors indicate importance of residues located deeper within the binding cavity and direct interactions with receptor residues linking the ortosteric ligand binding site with the intracellular transducer binding domain. Collection of results across different receptors may suggest elements of common molecular mechanisms which are responsible for passing alternative signals from biased agonists.     

G Protein-coupled Receptor (GPCR) Reconstitution and Labeling for Solution Nuclear Magnetic Resonance (NMR) Studies of the Structural Basis of Transmembrane Signaling

G protein-coupled receptors (GPCRs) are a large membrane protein family found in higher organisms, including the human body. GPCRs mediate cellular responses to diverse extracellular stimuli and thus control key physiological functions, which makes them important targets for drug design. Signaling by GPCRs is related to the structure and dynamics of these proteins, which are modulated by extrinsic ligands as well as by intracellular binding partners such as G proteins and arrestins. Here, we review some basics of using nuclear magnetic resonance (NMR) spectroscopy in solution for the characterization of GPCR conformations and intermolecular interactions that relate to transmembrane signaling.     

Nanodiscs for INPHARMA NMR Characterization of GPCRs: Ligand Binding to the Human A2A Adenosine Receptor

G-protein-coupled-receptors (GPCRs) are of fundamental importance for signal transduction through cell membranes. This makes them important drug targets, but structure-based drug design (SBDD) is still hampered by the limitations for structure determination of unmodified GPCRs. We show that the interligand NOEs for pharmacophore mapping (INPHARMA) method can provide valuable information on ligand poses inside the binding site of the unmodified human A_2A adenosine receptor reconstituted in nanodiscs. By comparing experimental INPHARMA spectra with back-calculated spectra based on ligand poses obtained from molecular dynamics simulations, a complex structure for A_2AR with the low-affinity ligand 3-pyrrolidin-1-ylquinoxalin-2-amine was determined based on the X-ray structure of ligand ZM-241,358 in complex with a modified A_2AR.     

A virtual screen for diverse ligands: discovery of selective G protein-coupled receptor antagonists

Virtual screening has become a major focus of bioactive small molecule lead identification, and reports of agonists and antagonists discovered via virtual methods are becoming more frequent. G protein-coupled receptors (GPCRs) are the one class of protein targets for which success with this approach has been limited. This is likely due to the paucity of detailed experimental information describing GPCR structure and the intrinsic function-associated structural flexibility of GPCRs which present major challenges in the application of receptor-based virtual screening. Here we describe an in silico methodology that diminishes the effects of structural uncertainty, allowing for more inclusive representation of a potential docking interaction with exogenous ligands. Using this approach, we screened one million compounds from a virtual database, and a diverse subgroup of 100 compounds was selected, leading to experimental identification of five structurally diverse antagonists of the thyrotropin-releasing hormone receptors (TRH-R1 and TRH-R2). The chirality of the most potent chemotype was demonstrated to be important in its binding affinity to TRH receptors; the most potent stereoisomer was noted to have a 13-fold selectivity for TRH-R1 over TRH-R2. A comprehensive mutational analysis of key amino acid residues that form the putative binding pocket of TRH receptors further verified the binding modality of these small molecule antagonists. The described virtual screening approach may prove applicable in the search for novel small molecule agonists and antagonists of other GPCRs.     

SEC–MS as an Approach to Isolate and Directly Identifying Small Molecular GPCR–Ligands from Complex Mixtures Without Labeling

G protein coupled receptors (GPCRs) belong to the most successful targets in drug discovery. However, the development of assays with an appropriately labeled high affinity reporter compound is laborious. In the present study an MS-based binding assay is described using the rat histamine receptor 2 (rH2) as a model GPCR system. Instead of using a purified receptor it is demonstrated that it is possible to use an unpurified receptor to extract active compounds from a solution or small mixture of compounds. By using SEC it is possible to separate the bound ligand from the unbound ligand. The major advantage of this approach is that there is no labeling of ligands required (direct monitoring based on the appropriate m/z values).     

Design of Drug Efficacy Guided by Free Energy Simulations of the β2-Adrenoceptor

G-protein-coupled receptors (GPCRs) play important roles in physiological processes and are modulated by drugs that either activate or block signaling. Rational design of the pharmacological efficacy profiles of GPCR ligands could enable the development of more efficient drugs, but is challenging even if high-resolution receptor structures are available. We performed molecular dynamics simulations of the β₂ adrenergic receptor in active and inactive conformations to assess if binding free energy calculations can predict differences in ligand efficacy for closely related compounds. Previously identified ligands were successfully classified into groups with comparable efficacy profiles based on the calculated shift in ligand affinity upon activation. A series of ligands were then predicted and synthesized, leading to the discovery of partial agonists with nanomolar potencies and novel scaffolds. Our results demonstrate that free energy simulations enable design of ligand efficacy and the same approach can be applied to other GPCR drug targets.     

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.     

Differences between G‐Protein‐Stabilized Agonist–GPCR Complexes and their Nanobody‐Stabilized Equivalents

Protein nanobodies have been used successfully as surrogates for unstable G‐proteins in order to crystallize G‐protein‐coupled receptors (GPCRs) in their active states. We used molecular dynamics (MD) simulations, including metadynamics enhanced sampling, to investigate the similarities and differences between GPCR–agonist ternary complexes with the α‐subunits of the appropriate G‐proteins and those with the protein nanobodies (intracellular binding partners, IBPs) used for crystallization. In two of the three receptors considered, the agonist‐binding mode differs significantly between the two alternative ternary complexes. The ternary‐complex model of GPCR activation entails enhancement of ligand binding by bound IBPs: Our results show that IBP‐specific changes can alter the agonist binding modes and thus also the criteria for designing GPCR agonists.     

Defining the nucleotide binding sites of P2Y receptors using rhodopsin-based homology modeling

Ongoing efforts to model P2Y receptors for extracellular nucleotides, i.e., endogenous ADP, ATP, UDP, UTP, and UDP-glucose, were summarized and correlated for the eight known subtypes. The rhodopsin-based homology modeling of the P2Y receptors is supported by a growing body of site-directed mutagenesis data, mainly for P2Y₁ receptors. By comparing molecular models of the P2Y receptors, it was concluded that nucleotide binding could occur in the upper part of the helical bundle, with the ribose moiety accommodated between transmembrane domain (TM) 3 and TM7. The nucleobase was oriented towards TM1, TM2, and TM7, in the direction of the extracellular side of the receptor. The phosphate chain was oriented towards TM6, in the direction of the extracellular loops (ELs), and was coordinated by three critical cationic residues. In particular, in the P2Y₁, P2Y₂, P2Y₄, and P2Y₆ receptors the nucleotide ligands had very similar positions. ADP in the P2Y₁₂ receptor was located deeper inside the receptor in comparison to other subtypes, and the uridine moiety of UDP-glucose in the P2Y₁₄ receptor was located even deeper and shifted toward TM7. In general, these findings are in agreement with the proposed binding site of small molecules to other class A GPCRs.     

Differences between G-Protein-Stabilized Agonist–GPCR Complexes and their Nanobody-Stabilized Equivalents

Protein nanobodies have been used successfully as surrogates for unstable G-proteins in order to crystallize G-protein-coupled receptors (GPCRs) in their active states. We used molecular dynamics (MD) simulations, including metadynamics enhanced sampling, to investigate the similarities and differences between GPCR–agonist ternary complexes with the α-subunits of the appropriate G-proteins and those with the protein nanobodies (intracellular binding partners, IBPs) used for crystallization. In two of the three receptors considered, the agonist-binding mode differs significantly between the two alternative ternary complexes. The ternary-complex model of GPCR activation entails enhancement of ligand binding by bound IBPs: Our results show that IBP-specific changes can alter the agonist binding modes and thus also the criteria for designing GPCR agonists.     

基于结构和药效团特征的人类腺苷受体拮抗剂选择性比较

腺苷受体是重要的治疗靶标,选择性腺苷受体拮抗剂具有广泛的临床应用前景.本文通过同源模建构建了腺苷A₁、A_2B和A₃受体的结构,采用LigandScout 3.12软件分别构建了腺苷受体四种亚型的拮抗剂药效团模型.然后利用Schrödinger程序中的Induced Fit Docking模块完成受体-拮抗剂结合模式的预测,并与药效团结果进行比对.结果发现,由于结合口袋部位的残基在家族间高度保守,模建得到的各个亚型受体的初始结构活性口袋部位极为相似,无法用于亚型选择性拮抗剂的识别.而腺苷受体四种亚型拮抗剂药效团的药效特征与空间排布都不同,并与以前突变实验信息相吻合.研究结果说明,结合口袋部位的优化是模建中的关键步骤,基于配体的药效团模型所包含的一系列药效特征元素如氢键受体、氢键供体、疏水基团、芳环中心,可以很好地表征受体结合部位氢键、疏水空腔的位置及其方向.本文研究结果可以为进一步的优化同源模建结果,寻找新型的人类腺苷受体选择性拮抗剂提供理论依据.     

Improving VEGFR-2 docking-based screening by pharmacophore postfiltering and similarity search postprocessing

Conventional docking-based virtual screening (VS) of chemical databases is based on the ranking of compounds according to the values retrieved by a scoring function (typically, the binding affinity estimation). However, using the most suitable scoring function for each kind of receptor pocket is not always an effective process to rank compounds, and sometimes neither to distinguish between correct binding modes from incorrect ones. To improve actives from decoys selection, here we propose a three-step VS protocol, which includes the conventional docking step, a pharmacophore postfilter step, and a similarity search postprocess. This VS protocol is retrospectively applied to VEGFR-2 (Kdr-kinase) inhibitors. The resulting docking poses calculated using the Alpha HB scoring function implemented in MOE are postfiltered according to defined pharmacophore interactions (structure based). The selected poses are again ranked according to their molecular similarity (MACCS fingerprint) to the cognate ligand. Results show that both the overall and early VS performance improve the application of this protocol.     

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.     

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.     

Pharmacophore modeling and parallel screening for PPAR ligands

We describe the generation and validation of pharmacophore models for PPARs, as well as a large scale validation of the parallel screening approach by screening PPAR ligands against a large database of structure-based models. A large test set of 357 PPAR ligands was screened against 48 PPAR models to determine the best models for agonists of PPAR-alpha, PPAR-delta, and PPAR-gamma. Afterwards, a parallel screen was performed using the 357 PPAR ligands and 47 structure-based models for PPARs, which were integrated into a 1537 models comprising in-house pharmacophore database, to assess the enrichment of PPAR ligands within the PPAR hypotheses. For these purposes, we categorized the 1537 database models into 181 protein targets and developed a score that ranks the retrieved targets for each ligand. Thus, we tried to find out if the concept of parallel screening is able to predict the correct pharmacological target for a set of compounds. The PPAR target was ranked first more often than any other target. This confirms the ability of parallel screening to forecast the pharmacological active target for a set of compounds.     

Prediction of the 3D structure and dynamics of human DP G-protein coupled receptor bound to an agonist and an antagonist

Prostanoids play important physiological roles in the cardiovascular and immune systems and in pain sensation in peripheral systems through their interactions with eight G-protein coupled receptors. These receptors are important drug targets, but development of subtype specific agonists and antagonists has been hampered by the lack of 3D structures for these receptors. We report here the 3D structure for the human DP G-protein coupled receptor (GPCR) predicted by the MembStruk computational method. To validate this structure, we use the HierDock computational method to predict the binding mode for the endogenous agonist (PGD2) to DP. Based on our structure, we predicted the binding of different antagonists and optimized them. We find that PGD2 binds vertically to DP in the TM1237 region with the alpha chain toward the extracellular (EC) region and the omega chain toward the middle of the membrane. This structure explains the selectivity of the DP receptor and the residues involved in the predicted binding site correlate very well with available mutation experiments on DP, IP, TP, FP, and EP subtypes. We report molecular dynamics of DP in explicit lipid and water and find that the binding of the PGD2 agonist leads to correlated rotations of helices of TM3 and TM7, whereas binding of antagonist leads to no such rotations. Thus, these motions may be related to the mechanism of activation.     

Label-free cell-based assays for GPCR screening

G protein-coupled receptors (GPCRs) have been proven to be the largest family of druggable targets in the human genome. Given the importance of GPCRs as drug targets and the de-orphanization of novel targets, GPCRs are likely to remain the frequent targets of many drug discovery programs. With recent advances in instrumentation and understanding of cellular mechanisms for the signals measured, biosensor-centered label-free cell assay technologies become a very active area for GPCR screening. This article reviews the principles and potential of current label-free cell assay technologies in GPCR drug discovery.     

INPHARMA-Based Determination of Ligand Binding Modes at α1-Adrenergic Receptors Explains the Molecular Basis of Subtype Selectivity

The structural poses of ligands that bind weakly to protein receptors are challenging to define. In this work we have studied ligand interactions with the adrenoreceptor (AR) subtypes, α_1A-AR and α_1B-AR, which belong to the G protein-coupled receptor (GPCR) superfamily, by employing the solution-based ligand-observed NMR method interligand NOEs for pharmacophore mapping (INPHARMA). A lack of receptor crystal structures and of subtype-selective drugs has hindered the definition of the physiological roles of each subtype and limited drug development. We determined the binding pose of the weakly binding α_1A-AR-selective agonist A-61603 relative to an endogenous agonist, epinephrine, at both α_1A-AR and α_1B-AR. The NMR experimental data were quantitatively compared, by using SpINPHARMA, to the back-calculated spectra based on ligand poses obtained from all-atom molecular dynamics simulations. The results helped mechanistically explain the selectivity of (R)-A-61603 towards α_1A-AR, thus demonstrating an approach for targeting subtype selectivity in ARs.