Sequence-Derived Three-Dimensional Pharmacophore Models for G-Protein-Coupled Receptors and Their Application in Virtual Screening

Structure-based pharmacophore modeling 2. Developing a novel framework for structure-based pharmacophore model generation and selection

Pharmacophore models are three-dimensional arrangements of molecular features required for biological activity that are used in ligand identification efforts for many biological targets, including G protein-coupled receptors (GPCR). Though GPCR are integral membrane proteins of considerable interest as targets for drug development, many of these receptors lack known ligands or experimentally determined structures necessary for ligand- or structure-based pharmacophore model generation, respectively. Thus, we here present a structure-based pharmacophore modeling approach that uses fragments placed with Multiple Copy Simultaneous Search (MCSS) to generate high-performing pharmacophore models in the context of experimentally determined, as well as modeled GPCR structures. Moreover, we have addressed the oft-neglected topic of pharmacophore model selection via development of a cluster-then-predict machine learning workflow. Herein score-based pharmacophore models were generated in experimentally determined and modeled structures of 13 class A GPCR and resulted in pharmacophore models exhibiting high enrichment factors when used to search a database containing 569 class A GPCR ligands. In addition, classification of pharmacophore models with the best performing cluster-then-predict logistic regression classifier resulted in positive predictive values (PPV) of 0.88 and 0.76 for selecting high enrichment pharmacophore models from among those generated in experimentally determined and modeled structures, respectively.

Application of computational methods for class A GPCR Ligand discovery

G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development due to their role in transmitting cellular signals in a multitude of biological processes. Of the six classes categorizing GPCR (A, B, C, D, E, and F), class A contains the largest number of therapeutically relevant GPCR. Despite their importance as drug targets, many challenges exist for the discovery of novel class A GPCR ligands serving as drug precursors. Though knowledge of the structural and functional characteristics of GPCR has grown significantly over the past 20 years, a large portion of GPCR lack reported, experimentally determined structures. Furthermore, many GPCR have no known endogenous and/or synthetic ligands, limiting further exploration of their biochemical, cellular, and physiological roles. While many successes in GPCR ligand discovery have resulted from experimental high-throughput screening, computational methods have played an increasingly important role in GPCR ligand identification in the past decade. Here we discuss computational techniques applied to GPCR ligand discovery. This review summarizes class A GPCR structure/function and provides an overview of many obstacles currently faced in GPCR ligand discovery. Furthermore, we discuss applications and recent successes of computational techniques used to predict GPCR structure as well as present a summary of ligand- and structure-based methods used to identify potential GPCR ligands. Finally, we discuss computational hit list generation and refinement and provide comprehensive workflows for GPCR ligand identification.

Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development

The use of computer-aided drug design (CADD) for the identification of lead compounds in radiotracer development is steadily increasing. Traditional CADD methods, such as structure-based and ligand-based virtual screening and optimization, have been successfully utilized in many drug discovery programs and are highlighted throughout this review. First, we discuss the use of virtual screening for hit identification at the beginning of drug discovery programs. This is followed by an analysis of how the hits derived from virtual screening can be filtered and culled to highly probable candidates to test in in vitro assays. We then illustrate how CADD can be used to optimize the potency of experimentally validated hit compounds from virtual screening for use in positron emission tomography (PET). Finally, we conclude with a survey of the newest techniques in CADD employing machine learning (ML).

Structure-based pharmacophore modeling 1. Automated random pharmacophore model generation

Pharmacophores are three-dimensional arrangements of molecular features required for biological activity that are often used in virtual screening efforts to prioritize ligands for experimental testing. G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for ligand discovery and drug development. Ligand-based pharmacophore models can be constructed to identify structural commonalities between known bioactive ligands for targets including GPCR. However, structure-based pharmacophores (which only require an experimentally determined or modeled structure for a protein target) have gained more attention to aid in virtual screening efforts as the number of publicly available experimentally determined GPCR structures have increased (140 unique GPCR represented as of October 24, 2022). Thus, the goal of this study was to develop a method of structure-based pharmacophore model generation applicable to ligand discovery for GPCR that have few known ligands. Pharmacophore models were generated within the active sites of 8 class A GPCR crystal structures via automated annotation of 5 randomly selected functional group fragments to sample diverse combinations of pharmacophore features. Each of the 5000 generated pharmacophores was then used to search a database containing active and decoy/inactive compounds for 30 class A GPCR and scored using enrichment factor and goodness-of-hit metrics to assess performance. Application of this method to the set of 8 class A GPCR produced pharmacophore models possessing the theoretical maximum enrichment factor value in both resolved structures (8 of 8 cases) and homology models (7 of 8 cases), indicating that generated pharmacophore models can prove useful in the context of virtual screening.

An Updated Review of Computer-Aided Drug Design and Its Application to COVID-19

The recent outbreak of the deadly coronavirus disease 19 (COVID-19) pandemic poses serious health concerns around the world. The lack of approved drugs or vaccines continues to be a challenge and further necessitates the discovery of new therapeutic molecules. Computer-aided drug design has helped to expedite the drug discovery and development process by minimizing the cost and time. In this review article, we highlight two important categories of computer-aided drug design (CADD), viz., the ligand-based as well as structured-based drug discovery. Various molecular modeling techniques involved in structure-based drug design are molecular docking and molecular dynamic simulation, whereas ligand-based drug design includes pharmacophore modeling, quantitative structure-activity relationship (QSARs), and artificial intelligence (AI). We have briefly discussed the significance of computer-aided drug design in the context of COVID-19 and how the researchers continue to rely on these computational techniques in the rapid identification of promising drug candidate molecules against various drug targets implicated in the pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The structural elucidation of pharmacological drug targets and the discovery of preclinical drug candidate molecules have accelerated both structure-based as well as ligand-based drug design. This review article will help the clinicians and researchers to exploit the immense potential of computer-aided drug design in designing and identification of drug molecules and thereby helping in the management of fatal disease.

Truly Target-Focused Pharmacophore Modeling: A Novel Tool for Mapping Intermolecular Surfaces

Pharmacophore models are an accurate and minimal tridimensional abstraction of intermolecular interactions between chemical structures, usually derived from a group of molecules or from a ligand-target complex. Only a limited amount of solutions exists to model comprehensive pharmacophores using the information of a particular target structure without knowledge of any binding ligand. In this work, an automated and customable tool for truly target-focused (T²F) pharmacophore modeling is introduced. Key molecular interaction fields of a macromolecular structure are calculated using the AutoGRID energy functions. The most relevant points are selected by a newly developed filtering cascade and clustered to pharmacophore features with a density-based algorithm. Using five different protein classes, the ability of this method to identify essential pharmacophore features was compared to structure-based pharmacophores derived from ligand-target interactions. This method represents an extremely valuable instrument for drug design in a situation of scarce ligand information available, but also in the case of underexplored therapeutic targets, as well as to investigate protein allosteric pockets and protein-protein interactions.

A Structural Framework for GPCR Chemogenomics: What's In a Residue Number?

The recent surge of crystal structures of G protein-coupled receptors (GPCRs), as well as comprehensive collections of sequence, structural, ligand bioactivity, and mutation data, has enabled the development of integrated chemogenomics workflows for this important target family. This chapter will focus on cross-family and cross-class studies of GPCRs that have pinpointed the need for, and the implementation of, a generic numbering scheme for referring to specific structural elements of GPCRs. Sequence- and structure-based numbering schemes for different receptor classes will be introduced and the remaining caveats will be discussed. The use of these numbering schemes has facilitated many chemogenomics studies such as consensus binding site definition, binding site comparison, ligand repurposing (e.g. for orphan receptors), sequence-based pharmacophore generation for homology modeling or virtual screening, and class-wide chemogenomics studies of GPCRs.

Structure and Function of Peptide-Binding G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) are the largest family of cell surface receptors and are important human drug targets. Of the 826 human GPCRs, 118 of them recognize endogenous peptide or protein ligands, and 30 of the 118 are targeted by approved drug molecules, including the very high-profile class B glucagon-like peptide 1 receptor. In this review, we analyze the 21 experimentally determined three-dimensional structures of the known peptide-binding GPCRs in relation to the endogenous peptides and drug molecules that modulate their cell signaling processes. Our integrated analyses reveal that half of the marketed drugs and most of the drugs in clinical trials that interact with peptide GPCRs are small molecules with a wide range of binding modes distinct from those of large peptide ligands. As we continue to collect additional data on these receptors from orthogonal approaches, including nuclear magnetic resonance and electron microscopy, we are beginning to understand how these receptors interact with their ligands at the molecular level and how improving the pharmacology of GPCR signal transduction requires us to study these receptors using multiple biophysical techniques.

Identification of Histamine H3 Receptor Ligands Using a New Crystal Structure Fragment-based Method

Virtual screening offers an efficient alternative to high-throughput screening in the identification of pharmacological tools and lead compounds. Virtual screening is typically based on the matching of target structures or ligand pharmacophores to commercial or in-house compound catalogues. This study provides the first proof-of-concept for our recently reported method where pharmacophores are instead constructed based on the inference of residue-ligand fragments from crystal structures. We demonstrate its unique utility for G protein-coupled receptors, which represent the largest families of human membrane proteins and drug targets. We identified five neutral antagonists and one inverse agonist for the histamine H₃ receptor with potencies of 0.7-8.5 μM in a recombinant receptor cell-based inositol phosphate accumulation assay and validated their activity using a radioligand competition binding assay. H₃ receptor antagonism is of large therapeutic value and our ligands could serve as starting points for further lead optimisation. The six ligands exhibit four chemical scaffolds, whereof three have high novelty in comparison to the known H₃ receptor ligands in the ChEMBL database. The complete pharmacophore fragment library is freely available through the GPCR database, GPCRdb, allowing the successful application herein to be repeated for most of the 285 class A GPCR targets. The method could also easily be adapted to other protein families.

Model-Based Discovery of Synthetic Agonists for the Zn2+-Sensing G-Protein-Coupled Receptor 39 (GPR39) Reveals Novel Biological Functions

The G-protein-coupled receptor 39 (GPR39) is a G-protein-coupled receptor activated by Zn²⁺. We used a homology model-based approach to identify small-molecule pharmacological tool compounds for the receptor. The method focused on a putative binding site in GPR39 for synthetic ligands and knowledge of ligand binding to other receptors with similar binding pockets to select iterative series of minilibraries. These libraries were cherry-picked from all commercially available synthetic compounds. A total of only 520 compounds were tested in vitro, making this method broadly applicable for tool compound development. The compounds of the initial library were inactive when tested alone, but lead compounds were identified using Zn²⁺ as an allosteric enhancer. Highly selective, highly potent Zn²⁺-independent GPR39 agonists were found in subsequent minilibraries. These agonists identified GPR39 as a novel regulator of gastric somatostatin secretion.

Pharmacophore-Map-Pick: A Method to Generate Pharmacophore Models for All Human GPCRs

GPCR-based drug discovery is hindered by a lack of effective screening methods for most GPCRs that have neither ligands nor high-quality structures. With the aim to identify lead molecules for these GPCRs, we developed a new method called Pharmacophore-Map-Pick to generate pharmacophore models for all human GPCRs. The model of ADRB2 generated using this method not only predicts the binding mode of ADRB2-ligands correctly but also performs well in virtual screening. Findings also demonstrate that this method is powerful for generating high-quality pharmacophore models. The average enrichment for the pharmacophore models of the 15 targets in different GPCR families reached 15-fold at 0.5 % false-positive rate. Therefore, the pharmacophore models can be applied in virtual screening directly with no requirement for any ligand information or shape constraints. A total of 2386 pharmacophore models for 819 different GPCRs (99 % coverage (819/825)) were generated and are available at http://bsb.kiz.ac.cn/GPCRPMD.

Role of computer-aided drug design in modern drug discovery

Drug discovery utilizes chemical biology and computational drug design approaches for the efficient identification and optimization of lead compounds. Chemical biology is mostly involved in the elucidation of the biological function of a target and the mechanism of action of a chemical modulator. On the other hand, computer-aided drug design makes use of the structural knowledge of either the target (structure-based) or known ligands with bioactivity (ligand-based) to facilitate the determination of promising candidate drugs. Various virtual screening techniques are now being used by both pharmaceutical companies and academic research groups to reduce the cost and time required for the discovery of a potent drug. Despite the rapid advances in these methods, continuous improvements are critical for future drug discovery tools. Advantages presented by structure-based and ligand-based drug design suggest that their complementary use, as well as their integration with experimental routines, has a powerful impact on rational drug design. In this article, we give an overview of the current computational drug design and their application in integrated rational drug development to aid in the progress of drug discovery research.

Methods for generating and applying pharmacophore models as virtual screening filters and for bioactivity profiling

Biological effects of small molecules in an organism result from favorable interactions between the molecules and their target proteins. These interactions depend on chemical functionalities, bonds, and their 3D-orientations towards each other. These 3D-arrangements of chemical functionalities that make a small molecule active towards its target can be described by pharmacophore models. In these models, chemical functionalities are represented as so-called features. Commonly, they are obtained either from a set of active compounds or directly from the observed protein-ligand interactions as present in X-ray crystal structures, NMR structures, or docking poses. In this review, we explain the basics of pharmacophore modeling including dataset generation, 3D-representations and conformational analysis of small molecules, pharmacophore model construction, model validation, and its benefits to virtual screening and other applications.

The apelin receptor inhibits the angiotensin II type 1 receptor via allosteric trans-inhibition

BACKGROUND AND PURPOSE

The apelin receptor (APJ) is often co-expressed with the angiotensin II type-1 receptor (AT1) and acts as an endogenous counter-regulator. Apelin antagonizes Ang II signalling, but the precise molecular mechanism has not been elucidated. Understanding this interaction may lead to new therapies for the treatment of cardiovascular disease.

EXPERIMENTAL APPROACH

The physical interaction of APJ and AT1 receptors was detected by co-immunoprecipitation and bioluminescence resonance energy transfer (BRET). Functional and pharmacological interactions were measured by G-protein-dependent signalling and recruitment of β-arrestin. Allosterism and cooperativity between APJ and AT1 were measured by radioligand binding assays.

KEY RESULTS

Apelin, but not Ang II, induced APJ : AT1 heterodimerization forced AT1 into a low-affinity state, reducing Ang II binding. Likewise, apelin mediated a concentration-dependent depression in the maximal production of inositol phosphate (IP(1) ) and β-arrestin recruitment to AT1 in response to Ang II. The signal depression approached a limit, the magnitude of which was governed by the cooperativity indicative of a negative allosteric interaction. Fitting the data to an operational model of allosterism revealed that apelin-mediated heterodimerization significantly reduces Ang II signalling efficacy. These effects were not observed in the absence of apelin.

CONCLUSIONS AND IMPLICATIONS

Apelin-dependent heterodimerization between APJ and AT1 causes negative allosteric regulation of AT1 function. As AT1 is significant in the pathogenesis of cardiovascular disease, these findings suggest that impaired apelin and APJ function may be a common underlying aetiology.

LINKED ARTICLE

This article is commented on by Goupil et al., pp. 1101-1103 of this issue. To view this commentary visit http://dx.doi.org/10.1111/bph.12040.

A structural chemogenomics analysis of aminergic GPCRs: lessons for histamine receptor ligand design

BACKGROUND AND PURPOSE

Chemogenomics focuses on the discovery of new connections between chemical and biological space leading to the discovery of new protein targets and biologically active molecules. G-protein coupled receptors (GPCRs) are a particularly interesting protein family for chemogenomics studies because there is an overwhelming amount of ligand binding affinity data available. The increasing number of aminergic GPCR crystal structures now for the first time allows the integration of chemogenomics studies with high-resolution structural analyses of GPCR-ligand complexes.

EXPERIMENTAL APPROACH

In this study, we have combined ligand affinity data, receptor mutagenesis studies, and amino acid sequence analyses to high-resolution structural analyses of (hist)aminergic GPCR-ligand interactions. This integrated structural chemogenomics analysis is used to more accurately describe the molecular and structural determinants of ligand affinity and selectivity in different key binding regions of the crystallized aminergic GPCRs, and histamine receptors in particular.

KEY RESULTS

Our investigations highlight interesting correlations and differences between ligand similarity and ligand binding site similarity of different aminergic receptors. Apparent discrepancies can be explained by combining detailed analysis of crystallized or predicted protein-ligand binding modes, receptor mutation studies, and ligand structure-selectivity relationships that identify local differences in essential pharmacophore features in the ligand binding sites of different receptors.

CONCLUSIONS AND IMPLICATIONS

We have performed structural chemogenomics studies that identify links between (hist)aminergic receptor ligands and their binding sites and binding modes. This knowledge can be used to identify structure-selectivity relationships that increase our understanding of ligand binding to (hist)aminergic receptors and hence can be used in future GPCR ligand discovery and design.

New strategy for receptor-based pharmacophore query construction: a case study for 5-HT₇ receptor ligands

In this paper, a new approach for generating receptor-based 3D pharmacophore models for rapid in silico virtual screening is presented. The method combines information from docking poses of known ligands of different structures and further ligand-receptor complexes analyses using structural interaction fingerprints (SIFts). Next, the best linear combination of three-, four-, and five-feature pharmacophores in terms of selected performance parameter (i.e., recall, F-score, and MCC) is constructed. The resultant queries showed significantly better VS performance and new scaffold recognition when compared with the known ligand- and receptor-based pharmacophore models. The approach was developed and validated on 5-HT₇ receptor homology models created on available crystal structure templates. The efficiency of the obtained linear combinations exhibited only a minor dependence on the template selection.

Are GPCRs Still a Source of New Targets?

G-protein–coupled receptors (GPCRs) still offer enormous scope for new therapeutic targets. Currently marketed agents are dominated by those with activity at aminergic receptors and yet they account for only ~10% of the family. Progress up until now with other subfamilies, notably orphans, Family A/peptide, Family A/lipid, Family B, Family C, and Family F, has been, at best, patchy. This may be attributable to the heterogeneous nature of GPCRs, their endogenous ligands, and consequently their binding sites. Our appreciation of receptor similarity has arguably been too simplistic, and screening collections have not necessarily been well suited to identifying leads in new areas. Despite the relative shortage of high-quality tool molecules in a number of cases, there is an emerging, and increasingly substantial, body of evidence associating many as yet “undrugged” receptors with a very wide range of diseases. Significant advances in our understanding of receptor pharmacology and technical advances in screening, protein X-ray crystallography, and ligand design methods are paving the way for new successes in the area. Exploitation of allosteric mechanisms; alternative signaling pathways such as G12/13, Gβγ, and β-arrestin; the discovery of “biased” ligands; and the emergence of GPCR-protein complexes as potential drug targets offer scope for new and much improved drugs.

Target based virtual screening by docking into automatically generated GPCR models

Target based virtual screening (VS) combined with high-throughput measurements is an extremely useful tool to identify small molecule hits for proteins and in particular for G-protein coupled receptors (GPCRs). However, this is a quite difficult process for GPCRs due to the paucity of 3D structural information on these receptors. Therefore, the only possibility for target based VS is to build a structural model of the GPCR to be used for docking. However, GPCR model building is a very time consuming process, if the model should be able to explain all experimental findings and this investment is not always justified, if the model is only used for VS. Thus, a fully automated workflow is presented here, where a large number of GPCR models is built, and the best model is identified to be used for docking. The workflow leads to moderate enrichments with a very low effort. The inputs required are the sequence of the targeted GPCR, a reference ligand with experimental information and a database of small molecules to be used for docking. Manual intervention is recommended at various points, but it is strictly speaking not necessary.

Chemogenomics of allosteric binding sites in GPCRs

Chemogenomic techniques connect the chemical and biological domains to establish ligand and target relationships not evident from the individual disciplines. Chemogenomics has been applied in lead generation, target classification, focused library design as well as selectivity and polypharmacology profiling. This review describes recent developments structured into ligand-, target- and combined chemogenomic techniques and applications to allosteric GPCR ligands. It also outlines relative strengths and limitations of these techniques and the impact of the increasing crystallographic data.

Signatures of the ATP-binding pocket as a basis for structural classification of the serine/threonine protein kinases of gram-positive bacteria

Eukaryotic-like serine/threonine protein kinases (ESTPKs) are widely spread throughout the bacterial genomes. These enzymes can be potential targets of new antibacterial drugs useful for the treatment of socially important diseases such as tuberculosis. In this study, ESTPKs of pathogenic, probiotic, and antibiotic-producing Gram-positive bacteria were classified according to the physicochemical properties of amino acid residues in the ATP-binding site of the enzyme. Nine residues were identified that line the surface of the adenine-binding pocket, and ESTPKs were classified based on these signatures. Twenty groups were discovered, five of them containing >10 representatives. The two most abundant groups contained >150 protein kinases that belong to the various branches of the phylogenetic tree, whereas certain groups are genus- or even species-specific. Homology modeling of the typical representatives of each group revealed that the classification is reliable, and the differences between the protein kinase ATP-binding pockets predicted based on their signatures are apparent in their structure. The classification is expected to be useful for the selection of targets for new anti-infective drugs.

From the protein's perspective: the benefits and challenges of protein structure-based pharmacophore modeling

Protein structure-based pharmacophore (SBP) models derive the molecular features a ligand must contain to be biologically active by conversion of protein properties to reciprocal ligand space. SBPs improve molecular understanding of ligand–protein interactions and can be used as valuable tools for hit and lead optimization, compound library design, and target hopping.

A decade of the human genome sequence--how does the medicinal chemist benefit?

Many have claimed that the sequencing of the human genome has failed to deliver the promised new era of drug discovery and development. Here, we argue that in fact, the availability of the human genome sequence and the genomics technologies that resulted from those research efforts have had a major impact on drug discovery. Medicinal chemists are actively using the data gleaned from structural genomics projects over the past decade to design more selective and more effective drug candidates. For example, large superfamilies of related enzymes, such as the kinome, proteome, proteasome, transportome, identified because of the sequencing of the human genome represent a huge number of potential drug targets. Ten years on, we're able to design multitarget drugs where the selectivity for a certain subgroup of receptors can lead to increased efficacy rather than the side effects traditionally associated with "off-targets". New trends and discoveries in biomedical research are notoriously slow to show their value, and this is also true for genomics technologies. However, the examples we've selected show that these are firmly set in the drug-discovery process, and without the human genome sequence, a number of current clinical candidates and promising drug leads would not have been possible.

Crystal structure-based virtual screening for fragment-like ligands of the human histamine H(1) receptor

The recent crystal structure determinations of druggable class A G protein-coupled receptors (GPCRs) have opened up excellent opportunities in structure-based ligand discovery for this pharmaceutically important protein family. We have developed and validated a customized structure-based virtual fragment screening protocol against the recently determined human histamine H(1) receptor (H(1)R) crystal structure. The method combines molecular docking simulations with a protein-ligand interaction fingerprint (IFP) scoring method. The optimized in silico screening approach was successfully applied to identify a chemically diverse set of novel fragment-like (≤22 heavy atoms) H(1)R ligands with an exceptionally high hit rate of 73%. Of the 26 tested fragments, 19 compounds had affinities ranging from 10 μM to 6 nM. The current study shows the potential of in silico screening against GPCR crystal structures to explore novel, fragment-like GPCR ligand space.

Snooker: a structure-based pharmacophore generation tool applied to class A GPCRs

G-protein coupled receptors (GPCRs) are important drug targets for various diseases and of major interest to pharmaceutical companies. The function of individual members of this protein family can be modulated by the binding of small molecules at the extracellular side of the structurally conserved transmembrane (TM) domain. Here, we present Snooker, a structure-based approach to generate pharmacophore hypotheses for compounds binding to this extracellular side of the TM domain. Snooker does not require knowledge of ligands, is therefore suitable for apo-proteins, and can be applied to all receptors of the GPCR protein family. The method comprises the construction of a homology model of the TM domains and prioritization of residues on the probability of being ligand binding. Subsequently, protein properties are converted to ligand space, and pharmacophore features are generated at positions where protein ligand interactions are likely. Using this semiautomated knowledge-driven bioinformatics approach we have created pharmacophore hypotheses for 15 different GPCRs from several different subfamilies. For the beta-2-adrenergic receptor we show that ligand poses predicted by Snooker pharmacophore hypotheses reproduce literature supported binding modes for ∼75% of compounds fulfilling pharmacophore constraints. All 15 pharmacophore hypotheses represent interactions with essential residues for ligand binding as observed in mutagenesis experiments and compound selections based on these hypotheses are shown to be target specific. For 8 out of 15 targets enrichment factors above 10-fold are observed in the top 0.5% ranked compounds in a virtual screen. Additionally, prospectively predicted ligand binding poses in the human dopamine D3 receptor based on Snooker pharmacophores were ranked among the best models in the community wide GPCR dock 2010.

The use of G-protein coupled receptor models in lead optimization

With the emerging new crystal structures of G-protein coupled receptors (GPCRs), the number of reported in silico receptor models vastly increases every year. The use of these models in lead optimization (LO) is investigated here. Although there are many studies where GPCR models are used to identify new chemotypes by virtual screening, the classical application in LO is rarely reported. The reason for this may be that the quality of a model, which is appropriate for atomistic modeling, must be very high, and the biology of GPCR ligand-dependent signaling is still not fully understood. However, the few reported studies show that GPCR models can be used efficiently in LO for various problems, such as affinity optimization or tuning of physicochemical parameters.

Conference report: trekking through receptor chemistry

The series of Camerino Symposia present the most recent knowledge and discoveries in the growing field of drug-receptor interactions and the design and mechanisms of drug action. Lead discovery, G protein-coupled receptors (GPCRs) and polypharmacology were the general subjects covered in this Tripartite European Meeting in Medicinal Chemistry chaired by Mario Giannella (University of Camerino, Italy). Specifically, the meeting focused on the selection criteria and application of computational methods and stem cell technology in/to lead discovery, as well as providing an update on targeting GPCRs. In addition, the importance of polypharmacology in drug discovery for many GPCRs was widely highlighted. Approximately 110 participants in an international audience composed principally of chemists, biochemists and pharmacologists spent the 5 days of the conference in the eye-catching medieval setting of the University of Camerino and enjoyed both the scientific and cultural programs.

Understanding functional residues of the cannabinoid CB1

The brain cannabinoid (CB(1)) receptor that mediates numerous physiological processes in response to marijuana and other psychoactive compounds is a G protein coupled receptor (GPCR) and shares common structural features with many rhodopsin class GPCRs. For the rational development of therapeutic agents targeting the CB(1) receptor, understanding of the ligand-specific CB(1) receptor interactions responsible for unique G protein signals is crucial. For a more than a decade, a combination of mutagenesis and computational modeling approaches has been successfully employed to study the ligand-specific CB(1) receptor interactions. In this review, after a brief discussion about recent advances in understanding of some structural and functional features of GPCRs commonly applicable to the CB(1) receptor, the CB(1) receptor functional residues reported from mutational studies are divided into three different types, ligand binding (B), receptor stabilization (S) and receptor activation (A) residues, to delineate the nature of the binding pockets of anandamide, CP55940, WIN55212-2 and SR141716A and to describe the molecular events of the ligand-specific CB(1) receptor activation from ligand binding to G protein signaling. Taken these CB(1) receptor functional residues, some of which are unique to the CB(1) receptor, together with the biophysical knowledge accumulated for the GPCR active state, it is possible to propose the early stages of the CB(1) receptor activation process that not only provide some insights into understanding molecular mechanisms of receptor activation but also are applicable for identifying new therapeutic agents by applying the validated structure-based approaches, such as virtual high throughput screening (HTS) and fragment-based approach (FBA).

The use of GPCR structures in drug design

Structure-based drug discovery is routinely applied to soluble targets such as proteases and kinases. It is only recently that multiple high-resolution X-ray structures of G protein-coupled receptors (GPCRs) have become available. Here we review the technology developments that have led to the recent plethora of GPCR structures. These include developments in protein expression and purification as well as techniques to stabilize receptors and crystallize them. We discuss the findings derived from the new structures with regard to understanding GPCR function and pharmacology. Finally, we examine the utility of structure-based drug discovery approaches including homology modeling, virtual screening, and fragment screening for GPCRs in the context of what has been learnt from other target classes.

Studying the catechol binding cavity in comparative models of human dopamine D2 receptor

Obtaining more structural information of human dopamine D(2) receptor may help in the design of better therapeutic agents against diseases such as Parkinson. In this study attempts have been made to develop a functional model for the catechol binding site of the human dopamine D(2) receptor, with two primary models being postulated based on the presence of a disulfide bridge in the second extracellular loop. The models have been subjected to subsequent molecular dynamics simulation and receptor based virtual screening of catechol structures. During steady state of the simulations, representative models with the reduced disulfide bridge were more capable of discriminating between active and inactive catechol structures. It is postulated that similar conformational changes of the second extracellular loop observed in 5-HT4 and β-adrenergic receptors, might also take place in the human D(2) receptor during its interaction with agonist ligands.

Modulation of bitter taste perception by a small molecule hTAS2R antagonist

Human bitter taste is mediated by the hTAS2R family of G protein-coupled receptors. The discovery of the hTAS2Rs enables the potential to develop specific bitter receptor antagonists that could be beneficial as chemical probes to examine the role of bitter receptor function in gustatory and nongustatory tissues. In addition, they could have widespread utility in food and beverages fortified with vitamins, antioxidants, and other nutraceuticals, because many of these have unwanted bitter aftertastes. We employed a high-throughput screening approach to discover a novel bitter receptor antagonist (GIV3727) that inhibits activation of hTAS2R31 (formerly hTAS2R44) by saccharin and acesulfame K, two common artificial sweeteners. Pharmacological analyses revealed that GIV3727 likely acts as an orthosteric, insurmountable antagonist of hTAS2R31. Surprisingly, we also found that this compound could inhibit five additional hTAS2Rs, including the closely related receptor hTAS2R43. Molecular modeling and site-directed mutagenesis studies suggest that two residues in helix 7 are important for antagonist activity in hTAS2R31 and hTAS2R43. In human sensory trials, GIV3727 significantly reduced the bitterness associated with the two sulfonamide sweeteners, indicating that hTAS2R antagonists are active in vivo. Our results demonstrate that small molecule bitter receptor antagonists can effectively reduce the bitter taste qualities of foods, beverages, and pharmaceuticals.

Using ligand-based virtual screening to allosterically stabilize the activated state of a GPCR

G-protein coupled receptors play an essential role in many biological processes. Despite an increase in the number of solved X-ray crystal structures of G-protein coupled receptors, capturing a G-protein coupled receptor in its activated state for structural analysis has proven to be difficult. An unexplored paradigm is stabilization of one or more conformational states of a G-protein coupled receptor via binding a small molecule to the intracellular loops. A short tetrazole peptidomimetic based on the photoactivated state of rhodopsin-bound structure of Gt(alpha)(340-350) was previously designed and shown to stabilize the photoactivated state of rhodopsin, the G-protein coupled receptor involved in vision. A pharmacophore model derived from the designed tetrazole tetrapeptide was used for ligand-based virtual screening to enhance the possible discovery of novel scaffolds. Maybridge Hitfinder and National Cancer Institute diversity libraries were screened for compounds containing the pharmacophore. Forty-seven compounds resulted from virtually screening the Maybridge library, whereas no hits resulted with the National Cancer Institute library. Three of the 47 Maybridge compounds were found to stabilize the MII state. As these compounds did not inhibit binding of transducin to photoactivated state of rhodopsin, they were assumed to be allosteric ligands. These compounds are potentially useful for crystallographic studies where complexes with these compounds might capture rhodopsin in its activated conformational state.

9-Aminomethyl-9,10-dihydroanthracene (AMDA) analogs as structural probes for steric tolerance in 5-HT2A and H1 receptor binding sites

Synthesis, radioligand binding and molecular modeling studies of several 9-aminomethyl-9,10-dihydroanthracene (AMDA) analogs were carried out to determine the extent of the steric tolerance associated with expansion of the tricyclic ring system and amine substitution at 5-HT(2A) and H(1) receptors. A mixture of (7,12-dihydrotetraphene-12-yl)methanamine and (6,11-dihydrotetracene-11-yl)methanamine in a 75-25% ratio was found to have an apparent K(i) of 10nM at the 5-HT(2A) receptor. A substantial binding affinity for (7,12-dihydrotetraphene-3-methoxy-12-yl)methanamine at the 5-HT(2A) receptor (K(i)=21 nM) was also observed. Interestingly, this compound was found to have 100-fold selectivity for 5-HT(2A) over the H(1) receptor (K(i)=2500 nM). N-Phenylalkyl-AMDA derivatives, in which the length of the alkyl chain varied from methylene to n-butylene, were found to have only weak affinity for both 5-HT(2A) and H(1) receptors (K(i)=223 to 964 nM). Our results show that large rigid annulated AMDA analogs can be sterically accommodated within the proposed 5-HT(2A) binding site.