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.     

G protein- and agonist-bound serotonin 5-HT2A receptor model activated by steered molecular dynamics simulations

A 5-HT(2A) receptor model was constructed by homology modeling based on the β(2)-adrenergic receptor and the G protein-bound opsin crystal structures. The 5-HT(2A) receptor model was transferred into an active conformation by an agonist ligand and a G(αq) peptide in four subsequent steered molecular dynamics (MD) simulations. The driving force for the transformation was the addition of several known intermolecular and receptor interhelical hydrogen bonds enforcing the necessary helical and rotameric movements. Subsquent MD simulations without constraints confirmed the stability of the activated receptor model as well as revealed new information about stabilizing residues and bonds. The active 5-HT(2A) receptor model was further validated by retrospective ligand screening of more than 9400 compounds, whereof 182 were known ligands. The results show that the model can be used in drug discovery for virtual screening and structure-based ligand design as well as in GPCR activation studies.     

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.     

Biophysical approaches to G protein-coupled receptors: Structure, function and dynamics

G protein-coupled receptors (GPCR) represent a large family of drug targets for which there is no high-resolution structural information. In order to understand the mechanisms of ligand recognition and receptor activation, there is a strong need for novel biophysical methods. In this Perspective we provide an overview of recent experimental approaches used to explore the molecular architecture and dynamics of GPCR and their interactions with ligands and G proteins using biophysical, non-crystallographic, methods.     

Diketopiperazine receptors: a novel class of highly selective receptors for binding small peptides

A novel class of receptors consisting of a rigid diketopiperazine backbone and peptidic side chains has been developed with the use of combinatorial chemistry. These diketopiperazine receptors interact with peptidic substrates with high specificity as shown in combinatorial on-bead assays. The central diketopiperazine moiety can be easily obtained from natural 4-hydroxyproline and serves as a rigidifying template for the peptidic modules which allow for structural as well as functional variations. Screenings of several dye-marked receptor prototypes against an encoded tripeptide library demonstrated not only the high binding specificities of the diketopiperazine receptors towards peptides but also revealed that small structural changes induce significant changes in their binding properties.     

In silico identification of new ligands for GPR17: a promising therapeutic target for neurodegenerative diseases

GPR17, a previously orphan receptor responding to both uracil nucleotides and cysteinyl-leukotrienes, has been proposed as a novel promising target for human neurodegenerative diseases. Here, in order to specifically identify novel potent ligands of GPR17, we first modeled in silico the receptor by using a multiple template approach, in which extracellular loops of the receptor, quite complex to treat, were modeled making reference to the most similar parts of all the class-A GPCRs crystallized so far. A high-throughput virtual screening exploration of GPR17 binding site with more than 130,000 lead-like compounds was then applied, followed by the wet functional and pharmacological validation of the top-scoring chemical structures. This approach revealed successful for the proposed aim, and allowed us to identify five agonists or partial agonists with very diverse chemical structure. None of these compounds could have been expected ‘a priori’ to act on a GPCR, and all of them behaved as much more potent ligands than GPR17 endogenous activators.     

Homology model-based virtual screening for GPCR ligands using docking and target-biased scoring

The current study investigates the combination of two recently reported techniques for the improvement of homology model-based virtual screening for G-protein coupled receptor (GPCR) ligands. First, ligand-supported homology modeling was used to generate receptor models that were in agreement with mutagenesis data and structure-activity relationship information of the ligands. Second, interaction patterns from known ligands to the receptor were applied for scoring and rank ordering compounds from a virtual library using ligand-receptor interaction fingerprint-based similarity (IFS). Our approach was evaluated in retrospective virtual screening experiments for antagonists of the metabotropic glutamate receptor (mGluR) subtype 5. The results of our approach were compared to the results obtained by conventional scoring functions (Dock-Score, PMF-Score, Gold-Score, ChemScore, and FlexX-Score). The IFS lead to significantly higher enrichment rates, relative to the competing scoring functions. Though using a target-biased scoring approach, the results were not biased toward the chemical classes of the reference structures. Our results indicate that the presented approach has the potential to serve as a general setup for successful structure-based GPCR virtual screening.     

Tuning chemotactic responses with synthetic multivalent ligands

BACKGROUND: Multivalent ligands have been used previously to investigate the role of ligand valency and receptor clustering in eliciting biological responses. Studies of multivalent ligand function, however, typically have employed divalent ligands or ligands of undefined valency. How cells respond to multivalent ligands of distinct valencies, which can cluster a signaling receptor to different extents, has never been examined. The chemoreceptors, which mediate chemotactic responses in bacteria, are localized, and clustering has been proposed to play a role in their function. Using multivalent ligands directed at the chemoreceptors, we hypothesized that we could exploit ligand valency to control receptor occupation and clustering and, ultimately, the cellular response. RESULTS: To investigate the effects of ligand valency on the bacterial chemotactic response, we generated a series of linear multivalent arrays with distinct valencies by ring-opening metathesis polymerization. We report that these synthetic ligands elicit bacterial chemotaxis in both Escherichia coli and Bacillus subtilis. The chemotactic response depended on the valency of the ligand; the response of the bacteria can be altered by varying chemoattractant ligand valency. Significantly, these differences in chemotactic responses were related to the ability of the multivalent ligands to cluster chemoreceptors at the plasma membrane. CONCLUSIONS: Our results demonstrate that ligand valency can be used to tune the chemotactic responses of bacteria. This mode of regulation may arise from changes in receptor occupation or changes in receptor clustering or both. Our data implicate changes in receptor clustering as one important mechanism for altering cellular responses. Given the diverse events modulated by changes in the spatial proximity of cell surface receptors, our results suggest a general strategy for tuning biological responses.     

A general technique to rank protein-ligand binding affinities and determine allosteric versus direct binding site competition in compound mixtures

To realize the full potential of combinatorial chemistry-based drug discovery, generic and efficient tools must be developed that apply the strengths of diversity-oriented chemical synthesis to the identification and optimization of lead compounds for disease-associated protein targets. We report an affinity selection-mass spectrometry (AS-MS) method for protein-ligand affinity ranking and the classification of ligands by binding site. The method incorporates the following steps: (1) an affinity selection stage, where protein-binding compounds are selected from pools of ligands in the presence of varying concentrations of a competitor ligand, (2) a first chromatography stage to separate unbound ligands from protein-ligand complexes, and (3) a second chromatography stage to dissociate the ligands from the complexes for identification and quantification by MS. The ability of the competitor ligand to displace a target-bound library member, as measured by MS, reveals the binding site classification and affinity ranking of the mixture components. The technique requires no radiolabel incorporation or direct biochemical assay, no modification or immobilization of the compounds or target protein, and all reaction components, including any buffers or cofactors required for protein stability, are free in solution. We demonstrate the method for several compounds of wide structural variety against representatives of the most important protein classes in contemporary drug discovery, including novel ATP-competitive and allosteric inhibitors of the Akt-1 (PKB) and Zap-70 kinases, and previously undisclosed antagonists of the M(2) muscarinic acetylcholine receptor, a G-protein coupled receptor (GPCR). The theoretical basis of the technique is analyzed mathematically, allowing quantitative estimation of binding affinities and, in the case of allosteric interaction, absolute determination of binding cooperativity. The method is readily applicable to high-throughput screening hit triage, combinatorial library-based affinity optimization, and developing structure-activity relationships among multiple ligands to a given receptor.     

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).     

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

Accurate in silico models for the quantitative prediction of the activity of G protein-coupled receptor (GPCR) ligands would greatly facilitate the process of drug discovery and development. Several methodologies have been developed based on the properties of the ligands, the direct study of the receptor-ligand interactions, or a combination of both approaches. Ligand-based three-dimensional quantitative structure-activity relationships (3D-QSAR) techniques, not requiring knowledge of the receptor structure, have been historically the first to be applied to the prediction of the activity of GPCR ligands. They are generally endowed with robustness and good ranking ability; however they are highly dependent on training sets. Structure-based techniques generally do not provide the level of accuracy necessary to yield meaningful rankings when applied to GPCR homology models. However, they are essentially independent from training sets and have a sufficient level of accuracy to allow an effective discrimination between binders and nonbinders, thus qualifying as viable lead discovery tools. The combination of ligand and structure-based methodologies in the form of receptor-based 3D-QSAR and ligand and structure-based consensus models results in robust and accurate quantitative predictions. The contribution of the structure-based component to these combined approaches is expected to become more substantial and effective in the future, as more sophisticated scoring functions are developed and more detailed structural information on GPCRs is gathered.     

Combinatorial Synthesis and Multidimensional NMR Spectroscopy: An Approach to Understanding Protein–Ligand Interactions

Combinatorial chemistry is a laboratory emulation of natural recombination and selection processes. Strategies in this developing discipline involve the generation of diverse, molecular libraries through combinatorial synthesis and the selection of compounds that possess a desired property. Such approaches can facilitate the identification of ligands that bind to biological receptors, promoting our chemical understanding of cellular processes. This article illustrates that the coupling of combinatorial synthesis, multidimensional NMR spectroscopy, and biochemical methods has enhanced our understanding of a protein receptor used commonly in signal transduction, the Src Homology 3 (SH3) domain. This novel approach to studying molecular recognition has revealed a set of rules that govern SH3–ligand interactions, allowing models of receptor–ligand complexes to be constructed with only a knowledge of the polypeptide sequences. Combining combinatorial synthesis with structural methods provides a powerful new approach to understanding how proteins bind their ligands in general.     

GPCR structure-based virtual screening approach for CB2 antagonist search

The potential for therapeutic specificity in regulating diseases has made cannabinoid (CB) receptors one of the most important G-protein-coupled receptor (GPCR) targets in search for new drugs. Considering the lack of related 3D experimental structures, we have established a structure-based virtual screening protocol to search for CB2 bioactive antagonists based on the 3D CB2 homology structure model. However, the existing homology-predicted 3D models often deviate from the native structure and therefore may incorrectly bias the in silico design. To overcome this problem, we have developed a 3D testing database query algorithm to examine the constructed 3D CB2 receptor structure model as well as the predicted binding pocket. In the present study, an antagonist-bound CB2 receptor complex model was initially generated using flexible docking simulation and then further optimized by molecular dynamic and mechanical (MD/MM) calculations. The refined 3D structural model of the CB2-ligand complex was then inspected by exploring the interactions between the receptor and ligands in order to predict the potential CB2 binding pocket for its antagonist. The ligand-receptor complex model and the predicted antagonist binding pockets were further processed and validated by FlexX-Pharm docking against a testing compound database that contains known antagonists. Furthermore, a consensus scoring (CScore) function algorithm was established to rank the binding interaction modes of a ligand on the CB2 receptor. Our results indicated that the known antagonists seeded in the testing database can be distinguished from a significant amount of randomly chosen molecules. Our studies demonstrated that the established GPCR structure-based virtual screening approach provided a new strategy with a high potential for in silico identifying novel CB2 antagonist leads based on the homology-generated 3D CB2 structure model.     

Matching cavities in g protein-coupled receptors to infer ligand-binding sites

To understand the activity and cross reactivity of ligands and G protein-coupled receptors, we take stock of relevant existing receptor mutation, sequence, and structural data to develop a statistically robust and transparent scoring system. Our method evaluates the viability of binding of any ligand for any GPCR sequence of amino acids. This enabled us to explore the binding repertoire of both receptors and ligands, relying solely on correlations between carefully identified receptor features and without requiring any chemical information about ligands. This study suggests that sequence similarity at specific binding pockets can predict relative affinity of ligands; enabling recovery of over 80% of known ligands for a withheld receptor and almost 80% of known receptors for a ligand. The method enables qualitative prediction of ligand binding for all nonredundant human G protein-coupled receptors.     

New strategies in drug discovery for GPCRs: high throughput detection of cellular ERK phosphorylation

G-protein coupled receptors (GPCRs) are a large family of receptors for a wide range of stimulants, including hormones, neurotransmitters, and taste and olfactory chemicals. Due to their broad involvement in cellular responses, GPCRs affect many important body functions both in health and disease. Compared to other receptor families, the GPCRs have been a rich source of extracellularly-acting pharmaceuticals, due largely to the fact that many GPCR ligands are small molecules when compared with ligands for other receptors, such as the tyrosine kinase receptor family. This has allowed the development of small molecule modulators of receptor function that act on specific GPCRs, such as those involved in cardiovascular regulation. However, at several levels, current screening technologies of drug development for GPCRs are lacking. Firstly, responses from many GPCRs, such as the Gi-coupled GPCRs, are not easily measured in large screening programs by current techniques. Secondly, there are few options for detecting agonists of orphan GPCRs. Thirdly, it is now clear that the signaling from GPCRs is more complex than once thought, and the measurement of Ca(2+) and cAMP can account for only a fraction of the biological information emanating from an activated GPCR. Studies of the discrete and sometimes separable activation of the Ras/Raf/Mek/ERK cascade by many GPCRs is likely to offer development of new agonists and antagonists, contribute to new pharmacologies from receptors, and raise the potential for novel drug candidates in this important area of biology. Downstream activation of the ERK pathway, with or without transactivation of growth factor receptors, has not been measurable by high throughput methodologies. This article presents recent advances and associated applications for screening of GPCRs and other receptor species through the rapid measurement of protein phosphorylation events, such as ERK phosphorylation, as new readouts for drug discovery.     

Virtual screening for ligands of the insect molting hormone receptor

Insect growth is regulated by the orchestrated event of ecdysteroids and their receptor proteins. Agonists/antagonists of ecdysteroid receptor are predicted to disrupt normal growth, providing good candidates of new insecticides. A database of over 2 million compounds was subjected to a shape-based virtual screening cascade to identify novel nonsteroidal hits similar to the known EcR ligand ponasterone A. Testing revealed micromolar hits against two strains of insect cells. Docking experiments against EcR were used to support the predicted binding mode of these ligands based on their overlay to ponasterone A.     

High content screening for G protein-coupled receptors using cell-based protein translocation assays

G protein-coupled receptors (GPCRs) have been one of the most productive classes of drug targets for several decades, and new technologies for GPCR-based discovery promise to keep this field active for years to come. While molecular screens for GPCR receptor agonist- and antagonist-based drugs will continue to be valuable discovery tools, the most exciting developments in the field involve cell-based assays for GPCR function. Some cell-based discovery strategies, such as the use of beta-arrestin as a surrogate marker for GPCR function, have already been reduced to practice, and have been used as valuable discovery tools for several years. The application of high content cell-based screening to GPCR discovery has opened up additional possibilities, such as direct tracking of GPCRs, G proteins and other signaling pathway components using intracellular translocation assays. These assays provide the capability to probe GPCR function at the cellular level with better resolution than has previously been possible, and offer practical strategies for more definitive selectivity evaluation and counter-screening in the early stages of drug discovery. The potential of cell-based translocation assays for GPCR discovery is described, and proof-of-concept data from a pilot screen with a CXCR4 assay are presented. This chemokine receptor is a highly relevant drug target which plays an important role in the pathogenesis of inflammatory disease and also has been shown to be a co-receptor for entry of HIV into cells as well as to play a role in metastasis of certain cancer cells.     

Real time differentiation of G-protein coupled receptor (GPCR) agonist and antagonist by two photon fluorescence laser microscopy

Receptor-based signaling mechanisms are the primary source of cellular regulation. The superfamily of G protein-coupled receptors (GPCR) is the largest and most ubiquitous of the receptor-mediated processes. Desensitization of G-protein-coupled receptors is a fundamental mechanism regulating the cellular response to agonists. We have recently studied the agonist and antagonist of the human melanocortin receptors (hMC1, hMC3, hMC4, and hMC5 receptors), the human delta opioid receptor, and the human gluacagon receptor with the help of synthetic fluorescent labeled ligands and fluorescent protein-labeled beta-arrestin-receptors that shed new insight on cellular signaling and rapid screening of drugs in real time. It was demonstrated that stimulation of these receptors by the cognate agonist triggers the rapid internalization of ligand-receptor complexes, while the interaction of the receptor with antagonists does not follow this pathway. Furthermore, receptor internalization is dependent upon beta-arrestin, which has been shown to be responsible for the rapid desensitization of cAMP-signaling processes.