Structure-Guided Screening for Functionally Selective D2 Dopamine Receptor Ligands from a Virtual Chemical Library

Rational Design of Drugs Targeting G-Protein-Coupled Receptors: Ligand Search and Screening

G protein-coupled receptors (GPCRs) are transmembrane proteins that participate in many physiological processes and represent major pharmacological targets. Recent advances in structural biology of GPCRs have enabled the development of drugs based on the receptor structure (structure-based drug design, SBDD). SBDD utilizes information about the receptor-ligand complex to search for suitable compounds, thus expanding the chemical space of possible receptor ligands without the need for experimental screening. The review describes the use of structure-based virtual screening (SBVS) for GPCR ligands and approaches for the functional testing of potential drug compounds, as well as discusses recent advances and successful examples in the application of SBDD for the identification of GPCR ligands.

Optical Control of Dopamine D2-like Receptors with Cell-Specific Fast-Relaxing Photoswitches

Dopamine D2-like receptors (D2R, D3R, and D4R) control diverse physiological and behavioral functions and are important targets for the treatment of a variety of neuropsychiatric disorders. Their complex distribution and activation kinetics in the brain make it difficult to target specific receptor populations with sufficient precision. We describe a new toolkit of light-activatable, fast-relaxing, covalently taggable chemical photoswitches that fully activate, partially activate, or block D2-like receptors. This technology combines the spatiotemporal precision of a photoswitchable ligand (P) with cell type and spatial specificity of a genetically encoded membrane anchoring protein (M) to which the P tethers. These tools set the stage for targeting endogenous D2-like receptor signaling with molecular, cellular, and spatiotemporal precision using only one wavelength of light.

Recent advances in dopamine D2 receptor ligands in the treatment of neuropsychiatric disorders

Dopamine is a biologically active amine synthesized in the central and peripheral nervous system. This biogenic monoamine acts by activating five types of dopamine receptors (D_1-5 Rs), which belong to the G protein-coupled receptor family. Antagonists and partial agonists of D₂ Rs are used to treat schizophrenia, Parkinson's disease, depression, and anxiety. The typical pharmacophore with high D₂ R affinity comprises four main areas, namely aromatic moiety, cyclic amine, central linker and aromatic/heteroaromatic lipophilic fragment. From the literature reviewed herein, we can conclude that 4-(2,3-dichlorophenyl), 4-(2-methoxyphenyl)-, 4-(benzo[b]thiophen-4-yl)-1-substituted piperazine, and 4-(6-fluorobenzo[d]isoxazol-3-yl)piperidine moieties are critical for high D₂ R affinity. Four to six atoms chains are optimal for D₂ R affinity with 4-butoxyl as the most pronounced one. The bicyclic aromatic/heteroaromatic systems are most frequently occurring as lipophilic appendages to retain high D₂ R affinity. In this review, we provide a thorough overview of the therapeutic potential of D₂ R modulators in the treatment of the aforementioned disorders. In addition, this review summarizes current knowledge about these diseases, with a focus on the dopaminergic pathway underlying these pathologies. Major attention is paid to the structure, function, and pharmacology of novel D₂ R ligands, which have been developed in the last decade (2010-2021), and belong to the 1,4-disubstituted aromatic cyclic amine group. Due to the abundance of data, allosteric D₂ R ligands and D₂ R modulators from patents are not discussed in this review.

Allosteric Modulators of Dopamine D2 Receptors for Fine-Tuning of Dopaminergic Neurotransmission in CNS Diseases: Overview, Pharmacology, Structural Aspects and Synthesis

Allosteric modulation of G protein-coupled receptors (GPCRs) is nowadays a hot topic in medicinal chemistry. Allosteric modulators, i.e., compounds which bind in a receptor site topologically distinct from orthosteric sites, exhibit a number of advantages. They are more selective, safer and display a ceiling effect which prevents overdosing. Allosteric modulators of dopamine D₂ receptor are potential drugs against a number of psychiatric and neurological diseases, such as schizophrenia and Parkinson's disease. In this review, an insightful summary of current research on D₂ receptor modulators is presented, ranging from their pharmacology and structural aspects of ligand-receptor interactions to their synthesis.

Allosteric modulation of dopamine D2L receptor in complex with Gi1 and Gi2 proteins: the effect of subtle structural and stereochemical ligand modifications

Background

Allosteric modulation of G protein-coupled receptors (GPCRs) is nowadays one of the hot topics in drug discovery. In particular, allosteric modulators of D₂ receptor have been proposed as potential modern therapeutics to treat schizophrenia and Parkinson’s disease.

Methods

To address some subtle structural and stereochemical aspects of allosteric modulation of D₂ receptor, we performed extensive in silico studies of both enantiomers of two compounds (compound 1 and compound 2), and one of them (compound 2) was synthesized as a racemate in-house and studied in vitro.

Results

Our molecular dynamics simulations confirmed literature reports that the R enantiomer of compound 1 is a positive allosteric modulator of the D_2L receptor, while its S enantiomer is a negative allosteric modulator. Moreover, based on the principal component analysis (PCA), we hypothesized that both enantiomers of compound 2 behave as silent allosteric modulators, in line with our in vitro studies. PCA calculations suggest that the most pronounced modulator-induced receptor rearrangements occur at the transmembrane helix 7 (TM7). In particular, TM7 bending at the conserved P7.50 and G7.42 was observed. The latter resides next to the Y7.43, which is a significant part of the orthosteric binding site. Moreover, the W7.40 conformation seems to be affected by the presence of the positive allosteric modulator.

Conclusions

Our work reveals that allosteric modulation of the D_2L receptor can be affected by subtle ligand modifications. A change in configuration of a chiral carbon and/or minor structural modulator modifications are solely responsible for the functional outcome of the allosteric modulator.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s43440-021-00352-x.

Structure-Based Virtual Screening for Ligands of G Protein-Coupled Receptors: What Can Molecular Docking Do for You?

G protein-coupled receptors (GPCRs) constitute the largest family of membrane proteins in the human genome and are important therapeutic targets. During the last decade, the number of atomic-resolution structures of GPCRs has increased rapidly, providing insights into drug binding at the molecular level. These breakthroughs have created excitement regarding the potential of using structural information in ligand design and initiated a new era of rational drug discovery for GPCRs. The molecular docking method is now widely applied to model the three-dimensional structures of GPCR-ligand complexes and screen for chemical probes in large compound libraries. In this review article, we first summarize the current structural coverage of the GPCR superfamily and the understanding of receptor-ligand interactions at atomic resolution. We then present the general workflow of structure-based virtual screening and strategies to discover GPCR ligands in chemical libraries. We assess the state of the art of this research field by summarizing prospective applications of virtual screening based on experimental structures. Strategies to identify compounds with specific efficacy and selectivity profiles are discussed, illustrating the opportunities and limitations of the molecular docking method. Our overview shows that structure-based virtual screening can discover novel leads and will be essential in pursuing the next generation of GPCR drugs. SIGNIFICANCE STATEMENT: Extraordinary advances in the structural biology of G protein-coupled receptors have revealed the molecular details of ligand recognition by this large family of therapeutic targets, providing novel avenues for rational drug design. Structure-based docking is an efficient computational approach to identify novel chemical probes from large compound libraries, which has the potential to accelerate the development of drug candidates.

Structure-based design of haloperidol analogues as inhibitors of acetyltransferase Eis from Mycobacterium tuberculosis to overcome kanamycin resistance

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), is a deadly bacterial disease. Drug-resistant strains of Mtb make eradication of TB a daunting task. Overexpression of the enhanced intracellular survival (Eis) protein by Mtb confers resistance to the second-line antibiotic kanamycin (KAN). Eis is an acetyltransferase that acetylates KAN, inactivating its antimicrobial function. Development of Eis inhibitors as KAN adjuvant therapeutics is an attractive path to forestall and overcome KAN resistance. We discovered that an antipsychotic drug, haloperidol (HPD, 1), was a potent Eis inhibitor with IC₅₀ = 0.39 ± 0.08 μM. We determined the crystal structure of the Eis-haloperidol (1) complex, which guided synthesis of 34 analogues. The structure-activity relationship study showed that in addition to haloperidol (1), eight analogues, some of which were smaller than 1, potently inhibited Eis (IC₅₀ ≤ 1 μM). Crystal structures of Eis in complexes with three potent analogues and droperidol (DPD), an antiemetic and antipsychotic, were determined. Three compounds partially restored KAN sensitivity of a KAN-resistant Mtb strain K204 overexpressing Eis. The Eis inhibitors generally did not exhibit cytotoxicity against mammalian cells. All tested compounds were modestly metabolically stable in human liver microsomes, exhibiting 30-60% metabolism over the course of the assay. While direct repurposing of haloperidol as an anti-TB agent is unlikely due to its neurotoxicity, this study reveals potential approaches to modifying this chemical scaffold to minimize toxicity and improve metabolic stability, while preserving potent Eis inhibition.

A practical guide to large-scale docking

Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and compound libraries have grown, the ability to screen hundreds of millions, and even billions, of compounds has become feasible for modest-sized computer clusters. This allows the rapid and cost-effective exploration and categorization of vast chemical space into a subset enriched with potential hits for a given target. To accomplish this goal at speed, approximations are used that result in undersampling of possible configurations and inaccurate predictions of absolute binding energies. Accordingly, it is important to establish controls, as are common in other fields, to enhance the likelihood of success in spite of these challenges. Here we outline best practices and control docking calculations that help evaluate docking parameters for a given target prior to undertaking a large-scale prospective screen, with exemplification in one particular target, the melatonin receptor, where following this procedure led to direct docking hits with activities in the subnanomolar range. Additional controls are suggested to ensure specific activity for experimentally validated hit compounds. These guidelines should be useful regardless of the docking software used. Docking software described in the outlined protocol (DOCK3.7) is made freely available for academic research to explore new hits for a range of targets.

Trends in application of advancing computational approaches in GPCR ligand discovery

G protein-coupled receptors (GPCRs) comprise the most important superfamily of protein targets in current ligand discovery and drug development. GPCRs are integral membrane proteins that play key roles in various cellular signaling processes. Therefore, GPCR signaling pathways are closely associated with numerous diseases, including cancer and several neurological, immunological, and hematological disorders. Computer-aided drug design (CADD) can expedite the process of GPCR drug discovery and potentially reduce the actual cost of research and development. Increasing knowledge of biological structures, as well as improvements on computer power and algorithms, have led to unprecedented use of CADD for the discovery of novel GPCR modulators. Similarly, machine learning approaches are now widely applied in various fields of drug target research. This review briefly summarizes the application of rising CADD methodologies, as well as novel machine learning techniques, in GPCR structural studies and bioligand discovery in the past few years. Recent novel computational strategies and feasible workflows are updated, and representative cases addressing challenging issues on olfactory receptors, biased agonism, and drug-induced cardiotoxic effects are highlighted to provide insights into future GPCR drug discovery.

Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?

The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy of in silico models of receptor-ligand complexes that were submitted to a community-wide assessment of GPCR structure prediction (GPCR Dock). Two simulation protocols were used to refine 30 models of the D3 dopamine receptor (D3R) in complex with an antagonist. Close to 60 μs of simulation time was generated and the resulting MD refined models were compared to a D3R crystal structure. In the MD simulations, the receptor models generally drifted further away from the crystal structure conformation. However, MD refinement was able to improve the accuracy of the ligand binding mode. The best refinement protocol improved agreement with the experimentally observed ligand binding mode for a majority of the models. Receptor structures with improved virtual screening performance, which was assessed by molecular docking of ligands and decoys, could also be identified among the MD refined models. Application of weak restraints to the transmembrane helixes in the MD simulations further improved predictions of the ligand binding mode and second extracellular loop. These results provide guidelines for application of MD refinement in prediction of GPCR-ligand complexes and directions for further method development.

Property-Unmatched Decoys in Docking Benchmarks

Enrichment of ligands versus property-matched decoys is widely used to test and optimize docking library screens. However, the unconstrained optimization of enrichment alone can mislead, leading to false confidence in prospective performance. This can arise by over-optimizing for enrichment against property-matched decoys, without considering the full spectrum of molecules to be found in a true large library screen. Adding decoys representing charge extrema helps mitigate over-optimizing for electrostatic interactions. Adding decoys that represent the overall characteristics of the library to be docked allows one to sample molecules not represented by ligands and property-matched decoys but that one will encounter in a prospective screen. An optimized version of the DUD-E set (DUDE-Z), as well as Extrema and sets representing broad features of the library (Goldilocks), is developed here. We also explore the variability that one can encounter in enrichment calculations and how that can temper one's confidence in small enrichment differences. The new tools and new decoy sets are freely available at http://tldr.docking.org and http://dudez.docking.org.

G protein-coupled receptors: structure- and function-based drug discovery

As one of the most successful therapeutic target families, G protein-coupled receptors (GPCRs) have experienced a transformation from random ligand screening to knowledge-driven drug design. We are eye-witnessing tremendous progresses made recently in the understanding of their structure-function relationships that facilitated drug development at an unprecedented pace. This article intends to provide a comprehensive overview of this important field to a broader readership that shares some common interests in drug discovery.

Novel Positive Allosteric Modulators of µ Opioid Receptor-Insight from In Silico and In Vivo Studies

Opioids are the drugs of choice in severe pain management. Unfortunately, their use involves serious, potentially lethal side effects. Therefore, efforts in opioid drug design turn toward safer and more effective mechanisms, including allosteric modulation. In this study, molecular dynamics simulations in silico and ‘writhing’ tests in vivo were used to characterize potential allosteric mechanism of two previously reported compounds. The results suggest that investigated compounds bind to μ opioid receptor in an allosteric site, augmenting action of morphine at subeffective doses, and exerting antinociceptive effect alone at higher doses. Detailed analysis of in silico calculations suggests that first of the compounds behaves more like allosteric agonist, while the second compound acts mainly as a positive allosteric modulator.

Controlling receptor function from the extracellular vestibule of G-protein coupled receptors

Receptor function is traditionally controlled from the orthosteric binding site of G-protein coupled receptors. Here, we show that the functional activity and signalling of human dopamine D2 and D3 receptor ligands can be fine-tuned from the extracellular secondary binding pocket (SBP) located far from the signalling interface suggesting optimization of the SBP binding part of bitopic ligands might be a useful strategy to develop GPCR ligands with designed functional and signalling profile.

Substrate Specific Inhibitor Designed against the Immunomodulator GMF-beta Reversed the Experimental Autoimmune Encephalomyelitis

The concept of substrate inhibition to prevent its phosphorylation has potential in drug discovery and is envisioned to treat the autoimmune disorder multiple sclerosis (MS). Glia maturation factor-β (GMF-β) Ser83 phosphorylation by protein kinase A (PKA) is pivotal in the activation of GMF-β-p38MAPK-NFκB biochemical pathway towards proinflammatory response induction in experimental autoimmune encephalomyelitis (EAE). Using structure-based drug design, we identified the small molecule inhibitor 1-H-indazole-4yl methanol (GMFBI.1) that specifically blocked Ser83 phosphorylation site on GMF-β substrate. Using in vitro and in vivo techniques, molecular mechanism of action of GMFBI.1’s direct interaction with GMF-β substrate and prevention of its Ser83 phosphorylation was established. GMFBI.1 down regulated p38MAPK phosphorylation and NFκB expression essential for proinflammatory response. Further, GMFBI.1 administration at peak of EAE reversed clinical symptoms, immunopathology, proinflammatory cytokine response and up regulated the anti-inflammatory cytokines. Present strategy of substrate inhibition against the key immunomodulatory target has immense therapeutic potential in MS.

Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity

Rational drug design for G protein-coupled receptors (GPCRs) is limited by the small number of available atomic resolution structures. We assessed the use of homology modeling to predict the structures of two therapeutically relevant GPCRs and strategies to improve the performance of virtual screening against modeled binding sites. Homology models of the D2 dopamine (D2R) and serotonin 5-HT2A receptors (5-HT2AR) were generated based on crystal structures of 16 different GPCRs. Comparison of the homology models to D2R and 5-HT2AR crystal structures showed that accurate predictions could be obtained, but not necessarily using the most closely related template. Assessment of virtual screening performance was based on molecular docking of ligands and decoys. The results demonstrated that several templates and multiple models based on each of these must be evaluated to identify the optimal binding site structure. Models based on aminergic GPCRs showed substantial ligand enrichment and there was a trend toward improved virtual screening performance with increasing binding site accuracy. The best models even yielded ligand enrichment comparable to or better than that of the D2R and 5-HT2AR crystal structures. Methods to consider binding site plasticity were explored to further improve predictions. Molecular docking to ensembles of structures did not outperform the best individual binding site models, but could increase the diversity of hits from virtual screens and be advantageous for GPCR targets with few known ligands. Molecular dynamics refinement resulted in moderate improvements of structural accuracy and the virtual screening performance of snapshots was either comparable to or worse than that of the raw homology models. These results provide guidelines for successful application of structure-based ligand discovery using GPCR homology models.

Haloperidol bound D2 dopamine receptor structure inspired the discovery of subtype selective ligands

The D₂ dopamine receptor (DRD2) is one of the most well-established therapeutic targets for neuropsychiatric and endocrine disorders. Most clinically approved and investigational drugs that target this receptor are known to be subfamily-selective for all three D₂-like receptors, rather than subtype-selective for only DRD2. Here, we report the crystal structure of DRD2 bound to the most commonly used antipsychotic drug, haloperidol. The structures suggest an extended binding pocket for DRD2 that distinguishes it from other D₂-like subtypes. A detailed analysis of the structures illuminates key structural determinants essential for DRD2 activation and subtype selectivity. A structure-based and mechanism-driven screening combined with a lead optimization approach yield DRD2 highly selective agonists, which could be used as chemical probes for studying the physiological and pathological functions of DRD2 as well as promising therapeutic leads devoid of promiscuity.

The D₂ dopamine receptor (DRD2) is one of the most well-established therapeutic targets for neuropsychiatric and endocrine disorders. Here, the authors report the crystal structure of the antipsychotic drug haloperidol bound to DRD2 via an extended binding pocket that distinguishes it from other D₂-like subtypes.

In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery

Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two decades to become a successful key technology in the pharmaceutical industry for early stage drug discovery and development. The FBDD strategy consists of screening low molecular weight compounds against macromolecular targets (usually proteins) of clinical relevance. These small molecular fragments can bind at one or more sites on the target and act as starting points for the development of lead compounds. In developing the fragments attractive features that can translate into compounds with favorable physical, pharmacokinetics and toxicity (ADMET-absorption, distribution, metabolism, excretion, and toxicity) properties can be integrated. Structure-enabled fragment screening campaigns use a combination of screening by a range of biophysical techniques, such as differential scanning fluorimetry, surface plasmon resonance, and thermophoresis, followed by structural characterization of fragment binding using NMR or X-ray crystallography. Structural characterization is also used in subsequent analysis for growing fragments of selected screening hits. The latest iteration of the FBDD workflow employs a high-throughput methodology of massively parallel screening by X-ray crystallography of individually soaked fragments. In this review we will outline the FBDD strategies and explore a variety of in silico approaches to support the follow-up fragment-to-lead optimization of either: growing, linking, and merging. These fragment expansion strategies include hot spot analysis, druggability prediction, SAR (structure-activity relationships) by catalog methods, application of machine learning/deep learning models for virtual screening and several de novo design methods for proposing synthesizable new compounds. Finally, we will highlight recent case studies in fragment-based drug discovery where in silico methods have successfully contributed to the development of lead compounds.

Rational design of agonists for bitter taste receptor TAS2R14: from modeling to bench and back

Human bitter taste receptors (TAS2Rs) are a subfamily of 25 G protein-coupled receptors that mediate bitter taste perception. TAS2R14 is the most broadly tuned bitter taste receptor, recognizing a range of chemically diverse agonists with micromolar-range potency. The receptor is expressed in several extra-oral tissues and is suggested to have physiological roles related to innate immune responses, male fertility, and cancer. Higher potency ligands are needed to investigate TAS2R14 function and to modulate it for future clinical applications. Here, a structure-based modeling approach is described for the design of TAS2R14 agonists beginning from flufenamic acid, an approved non-steroidal anti-inflammatory analgesic that activates TAS2R14 at sub-micromolar concentrations. Structure-based molecular modeling was integrated with experimental data to design new TAS2R14 agonists. Subsequent chemical synthesis and in vitro profiling resulted in new TAS2R14 agonists with improved potency compared to the lead. The integrated approach provides a validated and refined structural model of ligand-TAS2R14 interactions and a general framework for structure-based discovery in the absence of closely related experimental structures.

2016 Philip S. Portoghese Medicinal Chemistry Lectureship: Designing Bivalent or Bitopic Molecules for G-Protein Coupled Receptors. The Whole Is Greater Than the Sum of Its Parts

The genesis of designing bivalent or bitopic molecules that engender unique pharmacological properties began with Portoghese's work directed toward opioid receptors, in the early 1980s. This strategy has evolved as an attractive way to engineer highly selective compounds for targeted G-protein coupled receptors (GPCRs) with optimized efficacies and/or signaling bias. The emergence of X-ray crystal structures of many GPCRs and the identification of both orthosteric and allosteric binding sites have provided further guidance to ligand drug design that includes a primary pharmacophore (PP), a secondary pharmacophore (SP), and a linker between them. It is critical to note the synergistic relationship among all three of these components as they contribute to the overall interaction of these molecules with their receptor proteins and that strategically designed combinations have and will continue to provide the GPCR molecular tools of the future.

The compromise of virtual screening and its impact on drug discovery

Introduction: Docking and structure-based virtual screening (VS) have been standard approaches in structure-based design for over two decades. However, our understanding of the limitations, potential, and strength of these techniques has enhanced, raising expectations. Areas covered: Based on a survey of reports in the past five years, we assess whether VS: (1) predicts binding poses in agreement with crystallographic data (when available); (2) is a superior screening tool, as often claimed; (3) is successful in identifying chemical scaffolds that can be starting points for subsequent lead optimization cycles. Data shows that knowledge of the target and its chemotypes in postprocessing lead to viable hits in early drug discovery endeavors. Expert opinion: VS is capable of accurate placements in the pocket for the most part, but does not consistently score screening collections accurately. What matters is capitalization on available resources to get closer to a viable lead or optimizable series. Integration of approaches, subjective hit selection guided by knowledge of the receptor or endogenous ligand, libraries driven by experimental guides, validation studies to identify the best docking/scoring that reproduces experimental findings, constraints regarding receptor-ligand interactions, thoroughly designed methodologies, and predefined cutoff scoring criteria strengthen VS's position in pharmaceutical research.

D2 Dopamine Receptor G Protein-Biased Partial Agonists Based on Cariprazine

Functionally selective G protein-coupled receptor ligands are valuable tools for deciphering the roles of downstream signaling pathways that potentially contribute to therapeutic effects versus side effects. Recently, we discovered both G_i/o-biased and β-arrestin2-biased D₂ receptor agonists based on the Food and Drug Administration (FDA)-approved drug aripiprazole. In this work, based on another FDA-approved drug, cariprazine, we conducted a structure-functional selectivity relationship study and discovered compound 38 (MS1768) as a potent partial agonist that selectively activates the G_i/o pathway over β-arrestin2. Unlike the dual D₂R/D₃R partial agonist cariprazine, compound 38 showed selective agonist activity for D₂R over D₃R. In fact, compound 38 exhibited potent antagonism of dopamine-stimulated β-arrestin2 recruitment. In our docking studies, compound 38 directly interacts with S193^5.42 on TM5 but has no interactions with extracellular loop 2, which appears to be in contrast to the binding poses of D₂R β-arrestin2-biased ligands. In in vivo studies, compound 38 showed high D₂R receptor occupancy in mice and effectively inhibited phencyclidine-induced hyperlocomotion.

A Complete Assessment of Dopamine Receptor- Ligand Interactions through Computational Methods

Background: Selectively targeting dopamine receptors (DRs) has been a persistent challenge in the last years for the development of new treatments to combat the large variety of diseases involving these receptors. Although, several drugs have been successfully brought to market, the subtype-specific binding mode on a molecular basis has not been fully elucidated. Methods: Homology modeling and molecular dynamics were applied to construct robust conformational models of all dopamine receptor subtypes (D₁-like and D₂-like). Fifteen structurally diverse ligands were docked. Contacts at the binding pocket were fully described in order to reveal new structural findings responsible for selective binding to DR subtypes. Results: Residues of the aromatic microdomain were shown to be responsible for the majority of ligand interactions established to all DRs. Hydrophobic contacts involved a huge network of conserved and non-conserved residues between three transmembrane domains (TMs), TM2-TM3-TM7. Hydrogen bonds were mostly mediated by the serine microdomain. TM1 and TM2 residues were main contributors for the coupling of large ligands. Some amino acid groups form electrostatic interactions of particular importance for D₁R-like selective ligands binding. Conclusions: This in silico approach was successful in showing known receptor-ligand interactions as well as in determining unique combinations of interactions, which will support mutagenesis studies to improve the design of subtype-specific ligands.

Strategies for the discovery of biased GPCR ligands

G-protein-coupled receptors (GPCRs) represent important drug targets with complex pharmacological characteristics. Biased signaling represents one important dimension, describing ligand-dependent shifts of naturally imprinted signaling profiles. Because biased GPCR modulators provide potential therapeutic benefits including higher efficiencies and reduced adverse effects, the identification of such ligands as drug candidates is highly desirable. This review aims to provide an overview of the challenges and strategies in the discovery of biased ligands. We show different approaches for biased ligand discovery in the example of G-protein-biased opioid analgesics and discuss possibilities to design biased ligands by targeting extracellular receptor regions.

Making Sense of Pharmacology: Inverse Agonism and Functional Selectivity

Constitutive receptor activity/inverse agonism and functional selectivity/biased agonism are 2 concepts in contemporary pharmacology that have major implications for the use of drugs in medicine and research as well as for the processes of new drug development. Traditional receptor theory postulated that receptors in a population are quiescent unless activated by a ligand. Within this framework ligands could act as agonists with various degrees of intrinsic efficacy, or as antagonists with zero intrinsic efficacy. We now know that receptors can be active without an activating ligand and thus display "constitutive" activity. As a result, a new class of ligand was discovered that can reduce the constitutive activity of a receptor. These ligands produce the opposite effect of an agonist and are called inverse agonists. The second topic discussed is functional selectivity, also commonly referred to as biased agonism. Traditional receptor theory also posited that intrinsic efficacy is a single drug property independent of the system in which the drug acts. However, we now know that a drug, acting at a single receptor subtype, can have multiple intrinsic efficacies that differ depending on which of the multiple responses coupled to a receptor is measured. Thus, a drug can be simultaneously an agonist, an antagonist, and an inverse agonist acting at the same receptor. This means that drugs have an additional level of selectivity (signaling selectivity or "functional selectivity") beyond the traditional receptor selectivity. Both inverse agonism and functional selectivity need to be considered when drugs are used as medicines or as research tools.

The Beta-Arrestin-Biased Dopamine D2 Receptor Ligand, UNC9994, Is a Partial Agonist at G-Protein-Mediated Potassium Channel Activation

BACKGROUND

Previous evidence suggests that UNC9994 is a beta-arrestin2-selective agonist at the dopamine D2 receptor, lacking ability both to activate and antagonize G protein-dependent signaling. However, this has only been reported by one laboratory using a single assay.

METHODS

We used G protein-coupled inward rectifier potassium channel activation in Xenopus oocytes to investigate UNC9994-induced modulation of G protein-dependent signaling at dopamine D2 receptor and dopamine D3 receptor.

RESULTS

At dopamine D2 receptor, UNC9994 induced G protein-coupled inward rectifier potassium channel currents that were 15% of the maximal response to dopamine, with an EC50 of 185 nM. At dopamine D3 receptor, the ligand elicited 89% of the maximal dopamine response with an EC50 of 62 nM. Pertussis toxin abolished G protein-coupled inward rectifier potassium channel activation. Furthermore, UNC9994 antagonized dopamine-induced G protein-coupled inward rectifier potassium channel activation at dopamine D2 receptor.

CONCLUSIONS

UNC9994 modulates G protein-coupled inward rectifier potassium channel channel activation via pertussis toxin-sensitive G proteins at dopamine D2 receptor and dopamine D3 receptor. These findings may have implications for the interpretation of data obtained with this ligand.

Dopamine D2 Receptor Supersensitivity as a Spectrum of Neurotoxicity and Status in Psychiatric Disorders

Abnormality of dopamine D₂ receptor (D₂R) function, often observed as D₂R supersensitivity (D₂RSS), is a commonality of schizophrenia and related psychiatric disorders in humans. Moreover, virtually all psychotherapeutic agents for schizophrenia target D₂R in brain. Permanent D₂RSS as a feature of a new animal model of schizophrenia was first reported in 1991, and then behaviorally and biochemically characterized over the next 15-20 years. In this model of schizophrenia characterized by production of D₂RSS in ontogeny, there are demonstrated alterations of signaling processes, as well as functional links between the biologic template of the animal model and ability of pharmacotherapeutics to modulate or reverse biologic and behavioral modalities toward normality. Another such animal model, featuring knockout of trace amine-associated receptor 1 (TAAR1), demonstrates D₂RSS with an increase in the proportion of D₂R in the high-affinity state. Currently, TAAR1 agonists are being explored as a therapeutic option for schizophrenia. There is likewise an overlay of D₂RSS with substance use disorder. The aspect of adenosine A_2A-D₂ heteroreceptor complexes in substance use disorder is highlighted, and the association of adenosine A_2A receptor antagonists in discriminative and rewarding effects of psychostimulants is outlined. In summary, these new animal models of schizophrenia have face, construct, and predictive validity, and distinct advantages over earlier models. While the review summarizes elements of D₂RSS in schizophrenia per se, and its interplay with substance use disorder, a major focus is on presumed new molecular targets attending D₂RSS in schizophrenia and related clinical entities.

Fragment-Based Discovery and Optimization of Enzyme Inhibitors by Docking of Commercial Chemical Space

Fragment-based lead discovery has emerged as a leading drug development strategy for novel therapeutic targets. Although fragment-based drug discovery benefits immensely from access to atomic-resolution information, structure-based virtual screening has rarely been used to drive fragment discovery and optimization. Here, molecular docking of 0.3 million fragments to a crystal structure of cancer target MTH1 was performed. Twenty-two predicted fragment ligands, for which analogs could be acquired commercially, were experimentally evaluated. Five fragments inhibited MTH1 with IC₅₀ values ranging from 6 to 79 μM. Structure-based optimization guided by predicted binding modes and analogs from commercial chemical libraries yielded nanomolar inhibitors. Subsequently solved crystal structures confirmed binding modes predicted by docking for three scaffolds. Structure-guided exploration of commercial chemical space using molecular docking gives access to fragment libraries that are several orders of magnitude larger than those screened experimentally and can enable efficient optimization of hits to potent leads.