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The bitter pill: clinical drugs that activate the human bitter taste receptor TAS2R14 Anat Levit,*,†,1 Stefanie Nowak,§,1 Maximilian Peters,‡,1 Ayana Wiener,*,† Wolfgang Meyerhof,§ Maik Behrens,§ and Masha Y. Niv*,†,2 *Institute of Biochemistry, Food Science, and Nutrition, Robert H. Smith Faculty of Agriculture, Food, and Environment, The Hebrew University, Rehovot, Israel; †Fritz Haber Center for Molecular Dynamics and ‡Department of Medical Neurobiology, Institute of Medical Research Israel–Canada, The Hebrew University, Jerusalem, Israel; and §Department of Molecular Genetics, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany Bitter taste receptors (TAS2Rs) mediate aversive response to toxic food, which is often bitter. These G-protein-coupled receptors are also expressed in extraoral tissues, and emerge as novel targets for therapeutic indications such as asthma and infection. Our goal was to identify ligands of the broadly tuned TAS2R14 among clinical drugs. Molecular properties of known human bitter taste receptor TAS2R14 agonists were incorporated into pharmacophore- and shape-based models and used to computationally predict additional ligands. Predictions were tested by calcium imaging of TAS2R14transfected HEK293 cells. In vitro testing of the virtual screening predictions resulted in 30 – 80% success rates, and 15 clinical drugs were found to activate the TAS2R14. hERG potassium channel, which is predominantly expressed in the heart, emerged as a common off-target of bitter drugs. Despite immense chemical diversity of known TAS2R14 ligands, novel ligands and previously unknown polypharmacology of drugs were unraveled by in vitro screening of computational predictions. This enables rational repurposing of traditional and standard drugs for bitter taste signaling modulation for therapeutic indications.—Levit, A., Nowak, S., Peters, M., Wiener, A., Meyerhof, W., Behrens, M., Niv, M. Y. The bitter pill: clinical drugs that activate the human bitter taste receptor TAS2R14. FASEB J. 28, 1181–1197 (2014). www.fasebj.org

ABSTRACT

Key Words: GPCRs 䡠 cross-reactivity 䡠 drug repositioning 䡠 multispecificity The mammalian sense of taste evaluates the composi-

Abbreviations: 1D, 1-dimensional; 2D, 2-dimensional; 3D, 3-dimensional; ADRB1, ␤1-adrenergic receptor; ALogP, atomic log P; EC50, half-maximal effective concentration; ECFP, extended connectivity fingerprint; FN, false negative; FP, false positive; GPCR, G-protein-coupled receptor; HBA, hydrogen bond acceptor; HBD, hydrogen bond donor; hERG, human ether-á-go-go-related; KATP, ATP-sensitive K⫹; LBP, ligandbased pharmacophore; RB, rotatable bond; TC, Tanimoto coefficient; TN, true negative; TP, true positive; Se, sensitivity, SMILES, simplified molecular input line entry specification; Sp, specificity; TAS2R, bitter taste receptor; MW, molecular weight; VLS, virtual ligand screening 0892-6638/14/0028-1181 © FASEB

tion of ingested food by specialized sensory cells located in taste buds in the oral cavity, capable of detecting one of the basic taste qualities (sweet, sour, salty, umami, and bitter; ref. 1) and possibly fat (2). In particular, bitter taste is detected in humans by 25 members of the bitter taste receptor (TAS2R) subfamily of G protein-coupled receptors (GPCRs). Bitter taste is a sentinel that provides protection against poison consumption. The “arms race” between plants that produce various poisonous secondary metabolites to protect themselves against predators, and predators (including humans) that need to protect themselves against being poisoned, led to the evolution of multiple and diverse poisons that are detected as bitter by the predators. However, the correlation between toxicity and bitterness is complicated. Many bitter compounds (such as caffeine, hop humulones that give beer its bitter taste, the bitter glucosinolates and isothionates in broccoli; ref. 3) are not toxic at concentrations that are typically consumed. In fact, there are health benefits to many bitter bioactive compounds, such as chemoprotection (protection of healthy tissue from the toxic effects of anticancer drugs; ref. 3) and reduced cancer risk (4). However, the bitter taste of many bioactive food ingredients makes them aversive to the consumer in most foods (5). As a result, the food industry routinely removes these compounds from plant foods through selective breeding and a variety of debittering processes. This poses a dilemma for the designers of functional foods, because increasing the content of bitter phytonutrients for health purposes may be wholly incompatible with consumer acceptance (4). Better understanding of molecular recognition of bitter compounds may provide improved tools for rational design of functional foods. Furthermore, 1

These authors contributed equally to this work. Correspondence: The Institute of Biochemistry, Food Science, and Nutrition, Robert H. Smith Faculty of Agriculture, Food, and Environment, The Hebrew University of Jerusalem, P.O. Box 12, Rehovot 76100, Israel. E-mail: [email protected]. doi: 10.1096/fj.13-242594 This article includes supplemental data. Please visit http:// www.fasebj.org to obtain this information. 2

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many clinical drugs have bitter taste (6, 7). These “bitter pills” suffer from low patient compliance due to aversion to their taste. Thus, identifying which bitter taste receptors they activate, and the chemical features involved in molecular receptor-ligand recognition, may provide a rational way for designing drugs with acceptable taste and higher compliance rates. On the other hand, the fact that clinical drugs have bitter taste receptors as off-targets could also be beneficial: the extraoral expression of taste receptors, including TAS2Rs (8 –11) is drawing much attention in recent years. Extraorally expressed TAS2Rs were shown to have important roles in airways bronchodilation, as TAS2Rs were found to be expressed in chemosensory, ciliated epithelial, and smooth muscle cells lining the airways (12). Stimulation of airway smooth muscle cells with various bitter compounds causes potent muscle relaxation, suggesting that inhaled bitter compounds could be used therapeutically for treatment of airway diseases (13). TAS2Rs are also suggested to be involved in response to bacterial infection. The bitter taste receptor TAS2R38 has been shown to regulate the mucosal innate defense of the human upper airway, as it is activated in response to acyl-homoserine lactone quorum-sensing molecules, secreted by the respiratory pathogen Pseudomonas aeruginosa and other gram-negative bacteria (14). Because of these findings, TAS2Rs are suggested as novel targets for several indications, such as asthma (15) and upper respiratory tract infection (14). Growing knowledge on bitter taste receptors and their ligand repertoires may possibly help unravel new uses for existing bitter-tasting drugs such as acetaminophen (analgesic), ofloxacin (antibiotic), and cromoglicic acid (antiasthmatic), to name a few (16). Here, we focus on TAS2R14, which represents an interesting test case as it is one of the most broadly tuned bitter taste receptors (17–20). Furthermore, RTqPCR of heart tissue from failing human hearts showed that TAS2R14 is expressed at comparable levels to the ␤1-adrenergic receptor (ADRB1), and, like other TAS2Rs in the heart, is up-regulated following nutrient deprivation and starvation (21). It was suggested that exogenous toxins might be taken into the circulatory system to target the cardiac-TAS2Rs, or, alternatively, that there are endogenous TAS2R agonists produced within the circulation and/or cardiovascular tissues (21). Thus, it is of particular interest to identify TAS2R14 ligands and develop computational predictors that can be applied to additional pools of metabolites and other potential ligands. Toward this goal, we apply several computational approaches to discovery of novel ligands. Computational prediction of ligands for a receptor of interest can be carried out by either structure-based or ligandbased virtual screening approaches. Such virtual screening tools have been highly successful in drug design (22). For example, subnanomolar antagonists of the ␣1A adrenergic receptor were found by use of structure-based tools, while ligand-based pharmacophore (LBP) models were applied for discovery, for example, of endothelin A receptor and muscarinic M3 receptor antagonists (reviewed in ref. 23). 1182

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Structure-based approaches capture the steric and chemical complementarity of the three-dimensional (3D) structure of a target protein and the bound ligand (i.e., the binding mode) and use this knowledge for identifying additional ligands. We have previously compared the binding sites of TAS2R46 (24) and TAS2R10 (25) by use of an iterative combination of in silico homology modeling and in vitro site-directed mutagenesis. Interestingly, we found that the same compound may have different binding modes with different bitter taste receptors, and the same receptor may use different types of interactions for binding different ligands, enabling binding of chemically diverse bitter tastants (24 –26). Thus, a very careful elucidation of the binding modes of individual ligands to the bitter taste receptors is needed in order to enable structure-based design, and is the subject of ongoing research in our laboratories. Ligand-based approaches capture the ligands’ chemical features, without use of knowledge regarding the target protein. Such tools require preliminary knowledge on known biologically active molecules, and search for novel ligands that will have 3D similarity to the known ligand set, in terms of ligand volume (molecular shape; ref.27), or in terms of spatial location of chemical features [hydrogen bond donors (HBDs), hydrogen bond acceptors (HBAs), hydrophobic regions, etc.], typically represented by LBP models. LBP models are simplistic 3D representations of the essential chemical features necessary to exert optimal interactions with the biological target and to trigger its biological response (28). Here, LBP models and shape and electrostatics virtual screening approaches are applied to identify novel ligands of TAS2R14. We are able to capture some recurring features of known TAS2R14 ligands despite their high chemical diversity and use these features to identify additional novel agonists. The computationally predicted compounds are confirmed by in vitro assays, thus, validating the proposed predictors, and they provide novel links between commonly used drugs and the bitter taste receptor TAS2R14. In particular, our results highlight the human ether-á-go-go-related (hERG) potassium channel as a frequent off-target of bitter ligands of TAS2R14 and of other bitter taste receptors.

MATERIALS AND METHODS Known agonist data set and 1-dimensional (1D) descriptors A data set consisting of 43 known active TAS2R14 smallmolecule agonists [potency up to 100 ␮M, true positives (TPs)], and 83 bitter small molecules, shown experimentally not to activate TAS2R14 [true negatives (TNs)], was compiled from the BitterDB 1.0 database (Hebrew University, Rehovoth; http://bitterdb.agri.huji.ac.il/bitterdb/; ref. 18) and from complementary literature searches. The chemical structures of the ligands were obtained as simplified molecular input line entry specification (SMILES) strings from either BitterDB or from the PubChem database (U.S. National Institutes of Health, Bethesda, MD, USA; http://pubchem.ncbi.nlm.nih.gov/; ref.29).

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All ligands were prepared in Discovery Studio Modeling Environment 3.1 (Accelrys Software, San Diego, CA, USA) using the Prepare Ligands module. This preparation step included removal of inconsistencies and salts, generation of all possible tautomers and enantiomers (except where stereochemistry of a bitter ligand is defined), ionization and protonation at a biological pH of 7.5, and setting of standard formal charges. Finally, a conformational set of 50 “best” low-energy conformations was generated for each molecule. All conformers within 20 kcal/mol from the global energy minimum were included in the set. The following 1D molecular descriptors were calculated for each molecule in the data set, using Discovery Studio Modeling Environment 3.1: molecular weight (MW), lipophilicity [as atomic log P (ALogP)], HBD count, HBA count, rotatable bond (RB) count, aromatic ring count, and formal charge. The ranges of molecular properties for the active TAS2R14 ligands in the data set were analyzed and used to derive a 1D molecular properties filter to reduce the searchable chemical space. These ranges are MW 100 – 600 g/mol, ALogP ⫺2 to ⫹5, HBA count 2–7, HBD count 0 –3, aromatic ring count 0 –3, RB count 0 –10, and formal charge 0 –1.

On the basis of this analysis, the 3 top-ranking LBP models were selected for virtual screening purposes. Details of enrichment analysis are given below.

Two-dimensional (2D) fingerprint-based molecular similarity

Performance of the different ligand and shape-based models was evaluated by testing their ability to successfully recapture all known active TAS2R14 agonists in our test set, while rejecting the true TAS2R14-inactive molecules (TNs). Following screening of the test set against each of the models, a confusion matrix was constructed, reporting the instances of false positives (FPs), false negatives (FNs), TPs, TNs identified in the screen (33). These were then used to calculate sensitivity (Se), the percentage of truly active compounds selected by the model [Se⫽TP/(TP⫹FN)], and specificity (Sp), the percentage of truly inactive compounds correctly identified by the model, and, therefore, discarded [Sp⫽TN/(TN⫹FP)]. On the basis of this analysis, we selected 3 LBP models and 3 shape-based models (based on structures of flufenamic acid, 8-prenylnaringenin, and cis-isohumulone) for virtual screening. For the LBPs, FitValue selection thresholds, which allow for Sp ⬎ 95%, were set, and for the shape-based models, selection thresholds with a 95% positive hit rate on the training set were identified.

The intrasimilarity of data set ligands was evaluated by use of 2D extended connectivity fingerprints (ECFPs). Briefly, ECFPs are circular topological fingerprints that use circular atom neighborhoods to represent molecular structures and also include their physical chemical properties (30). Here, we used the variant ECFP4 setting, which considers all neighbor atoms within a 4-bond diameter for feature calculation of each atom. The similarity between each pair of molecules was quantified using the Tanimoto coefficient (TC) distance measure (31), given by TC ⫽ c/(a ⫹ b ⫺ c), where a and b are the number of bits set in the fingerprints of molecules A and B, respectively, and c is the number of bits being set in both of the fingerprints. The similarity between the virtual hits and known smallmolecule TAS2R14 active agonists was also evaluated by the TC. 3D LBP models 3D LBP models were generated using the HipHop algorithm (32), as implemented in Discovery Studio Modeling Environment 3.1. The algorithm derives common features of pharmacophore models using information from a set of compounds active against the target. The following top 5 highly active TAS2R14 ligands were selected as training compounds from the data set described above: flufenamic acid, 8-prenylnaringenin, cis-isohumulone, artemorin, and parthenolide. The remaining molecules in the data set (TPs and TNs) were used as a test set for model testing and validation, as described below. The conformational sets of the 5 molecules were used in pharmacophore model generation, and a total of 50 models were generated, presenting different combinations of chemical features and their 3D locations. An enrichment study was performed to evaluate these pharmacophore models. For this purpose, the test set was screened against the different models using the Ligand Pharmacophore Mapping module (Discovery Studio Modeling Environment 3.1), with all parameters set to default, except for the minimum interference distance, which was set to 1 Å and the maximum omitted features set to 0. A ligand was counted as a hit if ⱖ1 of its enumerated conformers could be aligned to the pharmacophore model. BITTER PILL

Three-dimensional shape-based model Flufenamic acid, 8-prenylnaringenin, cis-isohumulone, artemorin, and parthenolide, the top 5 highly active TAS2R14 ligands used to generate the LBP models, were also used as input to generate 3D shape-based models for virtual screening. Up to 50 molecular shapes were generated with OMEGA 2.4.6 (OpenEye Scientific Software, Santa Fe, NM, USA) and compared to shapes of the reference ligands using ROCS 3.1.2 (OpenEye Scientific). The electrostatics of the top scoring shapes were then compared using EON 2.1.0 (OpenEye Scientific). The BitterDB and DrugBank molecules were manually filtered, removing inorganic salts and molecules with heavy atoms which are not suitable for the OMEGA algorithm. Evaluation of model performance

Virtual screening Compound libraries Virtual screening was performed on the BitterDB and DrugBank databases. The BitterDB 1.0 database (18) contains ⬎550 compounds that were reported in the literature to taste bitter to humans. Around 100 of these bitter compounds have been experimentally linked to their corresponding bitter taste receptors. For virtual screening purposes, the database was filtered to remove those compounds, which are either known TAS2R14 agonists or molecules that have previously been shown experimentally not to activate TAS2R14. This resulted in a library of 442 unassigned bitter compounds. The DrugBank 3.0 database (34) contains 6729 drug entries, including U.S. Food and Drug Administration (FDA)approved small-molecule drugs, FDA-approved biotech (protein/peptide) drugs, nutraceuticals, and experimental drugs. LBP virtual ligand screening (VLS) Both libraries were prepared for ligand-based VLS, as described previously, and a set of up to 255 low-energy confor1183

mations was generated for each molecule using the fast option; all conformations were within 20 kcal/mol from the global energy minimum. The libraries were screened against the chosen pharmacophore models using the Ligand Pharmacophore Mapping module (Discovery Studio Modeling Environment 3.1), as described above. To prioritize the virtual hits, FitValues were extracted to reflect the quality of molecule mapping onto the pharmacophore. Only molecules with FitValues above the selection thresholds determined in the enrichment analysis were retained as virtual hits. To generate a hit list for experimental testing, we first chose molecules that were identified by ⱖ2 of the pharmacophore models, and then filtered those hits using the 1D molecular properties filter derived from analysis of training set molecules. For hits retrieved from the DrugBank library, an additional postprocessing step was added, which included clustering of hits using a custom protocol in Discovery Studio Modeling Environment 3.1, with the FCFP6 fingerprints and maximum distance from cluster center set to 0.7. Cluster centers were selected for experimental testing. Shape-based virtual screening For screening purposes, up to 50 possible conformers of each molecule in the compound libraries were generated using OMEGA 2.4.6 (OpenEye Scientific) and shape, compared to possible conformations of flufenamic acid, cis-isohumulone, and 8-prenylnaringenin using ROCS 3.1.2 (OpenEye Scientific). The top-scoring conformations were electrostatically compared to the reference ligands using EON 2.1.0 (OpenEye Scientific). Only molecules with scores above the selection thresholds were retained as virtual hits.

Analysis of additional proteins, which are targeted by the known TAS2R14 agonists and bitter molecules in general (retrieved from the BitterDB database), was conducted by extracting target information for these molecules (where available) from the Zinc database (35). Ligands that act on nonhuman targets, such as antibiotics, antiparasitics, and antifungals, were excluded from the analysis, as well as ligands that do not have any annotated targets. The extracted targets were classified into 4 categories [based on ChEMBL (European Molecular Biology Laboratory–European Bioinformatics Institute, Hinxton, UK; https://www.ebi.ac.uk/chembl) target classification]: enzyme, ion channel, 7TM membrane receptor, and other, and the relative distribution of the 4 target classes within each set of molecules was calculated. To assess whether the resulting target distributions are unique to bitter molecules, the analysis was repeated on 2 additional sets of molecules: the FDA-approved subset of the DrugBank database and the TCM Database@Taiwan of small molecules from traditional Chinese medicines (China Medical University, Taichung City, Taiwan; http://tcm.cmu.edu. tw/ý; ref. 36). Statistical analysis Data were analyzed by the ␹2 test using the JMP 7.0.1 statistical software package (SAS Institute, Cary, NC, USA). To account for multiple pair-wise comparisons, the Bonferroni correction was applied. Vol. 28

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Compounds Test compounds were purchased from Sigma-Aldrich (St. Louis, MO, USA). Stock solutions were prepared in DMSO or, if possible, in C1 buffer (10 mM glucose, 130 mM NaCl, 5 mM KCl, 10 mM HEPES, and 2 mM CaCl2; pH 7.4). Functional calcium imaging assay HEK 293T cells stably expressing the G-protein chimera G␣16gust44 (37) were cultivated in DMEM supplemented with 10% FBS at 37°C, 5% CO2 in 96-well-plates until reaching ⬃60% confluence. For transient transfection, a cDNA construct consisting of the TAS2R14 coding sequence (accession no. NM 023922) modified with an amino-terminal sst3 tag and a carboxyl terminal HSV tag (38) or the corresponding empty expression vector pcDNA5/FRT as negative (mock-treatment) control (Invitrogen, Carlsbad, CA, USA) was used. Transient transfection was done using Lipofectamine 2000 (Invitrogen), as described previously (e.g., refs. 17, 38). Calcium imaging experiments were performed as described previously (e.g., ref. 17). Briefly, 24 h after transfection, cells were loaded for 1 h with Fluo4-AM in the presence of 2.5 mM probenecid, washed with C1 buffer, and placed in a fluorometric imaging plate reader (FLIPR; Molecular Devices, Sunnyvale, CA, USA). The compounds dissolved in C1 buffer were then automatically applied in 3 consecutive 1:10 dilutions. The highest substance concentrations applicable to the cells were determined in pilot experiments and limited by solubility and/or the receptorindependent occurrence of artifact signals in mock-transfected cells. Determination of half-maximal effective concentration (EC50) and threshold values of receptor activation

Off-target analysis

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Experimental testing of virtual hits

Data were collected from ⱖ2 independent experiments carried out in triplicates. The fluorescence signals from receptortransfected cells receiving the same substance concentration were averaged, and the fluorescence changes of the corresponding mock-transfected cells were subtracted. Signals were normalized to background fluorescence. The lowest substance concentrations effectively activating TAS2R14 transfected cells compared to the corresponding negative (mock-treated) controls were determined by ANOVA followed by a contrast test with an ␣-risk level adjusted by Bonferroni multiple-testing correction using the statistics software package SPSS Statistics20.0.0 (IBM SPSS, Chicago, IL, USA). Plotting of dose-response relations for secobarbital, tributyrin, and triethyl were done using SigmaPlot software (Systat Software Inc., San Jose, CA, USA). Nonlinear regression of the plots to the function f(x) ⫽ (a ⫺ d)/[1 ⫹ (x/EC50)H] ⫹ d, where a is minimum, d is maximum, x is substance concentration, and H is the Hill coefficient, revealed EC50 values. To determine whether ibuprofen, which was predicted to represent an agonist but did not activate TAS2R14, inhibits the receptor’s responses and, hence, can be considered to be a ligand as well, we determined dose-response relations. The receptor-transfected cells received an application of 1 ␮M of the agonist flufenamic acid alone or 1 ␮M flufenamic acid plus different concentrations of ibuprofen. To correct for general impairments of cell viability, the signal obtained after application of flufenamic acid/ibuprofen was corrected by the signals obtained by a subsequent application of 100 nM somatostatin-14 activating endogenous receptors.

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RESULTS Virtual screening strategy To identify novel ligands of the broadly tuned TAS2R14 bitter taste receptor, we designed a ligand-based virtual screening strategy that incorporates computational tools widely used in state-of-the-art drug discovery processes. These tools include the simple 1D molecular descriptors, 2D fingerprint-based molecular similarity, and the more sophisticated 3D approaches, which consist of database screening using LBP models and the shape-based method ROCS (39, 40). We chose to apply several virtual screening approaches simultaneously, as this has been shown to help balance the differences and shortcomings in the ability of each specific method to search the chemical space (41). Schematic representation of the workflow is shown in Fig. 1. First, an initial data set, comprised of 43 experimentally verified active TAS2R14 agonists with potency of up to 100 ␮M (TPs), and 83 bitter molecules that were shown experimentally not to activate TAS2R14

(TNs) is constructed using information from the BitterDB database (18), which, in turn, is based on the literature (17, 20, 42– 44). The freely available BitterDB 1.0 database is a manually curated database that holds ⬎550 compounds that were reported to taste bitter to humans or to activate human bitter taste receptors in vitro (18). This initial data set includes bitter molecules from natural origins, such as quinine, a compound from the bark of the cinchona tree, and absinthin, a naturally occurring sesquiterpene lactone from Artemisia absinthium (wormwood), as well as synthetic bitter molecules, such as benzoin, benzamide, diphenylthiourea, divinyl sulfoxide, and sodium benzoate. We characterized this data set in terms of ranges of 1D molecular properties and of 2D (connectivity of chemical groups) similarity between the molecules in the set, as detailed below. Next, this data set was split into 2 groups: a training set comprising 5 known agonists with the highest potency toward TAS2R14, which was used for development of 3D LBP models and shape-based queries; and a test set containing both TP and TN molecules, which was used to test the performance of the ligand-based models and shape-based queries, and

Figure 1. Virtual screening strategy. Flow chart outlining the process of development and validation of ligand-based models (I) and screening of compound libraries (II). BITTER PILL

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to choose optimal cutoff values. The best-performing models were employed for virtual screening of the BitterDB and DrugBank databases. The BitterDB database was chosen for screening since it holds ⬎400 bitter compounds that have not yet been assigned to a specific bitter taste receptor target (BitterDB unassigned), but are known to elicit a bitter taste. The DrugBank 3.0 database (34) is a compendium of almost 7000 FDAapproved and exploratory drugs and is often screened for the purpose of drug repurposing (45, 46). In total, ⬎7100 compounds were virtually screened, and 52 of these were selected to be tested experimentally, using the procedures described below. Analyzing the chemical space of known TAS2R14 agonists The 43 known TAS2R14 agonists in the initial set were subjected to 2D intrasimilarity analysis using the ECFP4 fingerprints, and the TC distance measure was employed for similarity ranking. A value of TC ⬍ 0.35 obtained using ECFP4 fingerprints suggests that the molecules are structurally unrelated (47). Figure 2 presents the resulting similarity matrix, based on pair-wise TC values for all the molecules in the set.

The similarity matrix includes three main groups: the vast majority of molecules display a high degree of variability and are structurally unrelated, having pair-wise values of TC ⬍ 0.2; several of the molecules display intermediate structural similarity, e.g., picrotoxinin and picrotin (TC⫽0.64), arborescin and arglabin (TC⫽0.59), and 1-naphthoic acid and 1,8naphthaladehyde acid (TC⫽0.48); and one subgroup of molecules displays a high degree of structural similarity (T C ⫽0.42–1) and is composed o f h u m u l o nes, major constituents of hop (Humulus lupulus L.), which were shown to elicit the typical bitterness of beer (43, 48). On the basis of this analysis, we can conclude that the majority of known TAS2R14 agonists are structurally dissimilar from one another, and thus 2D similarity searches for additional ligands are not likely to be particularly efficient. This is supported by evaluating the ability of 2D fingerprint-based molecular similarity to separate TP from TN molecules in our test set, using 5 of the most potent TAS2R14 agonists as reference ligands in the similarity search. None of the reference ligands was capable of achieving a clear separation between known activators and known nonactivators, and the majority of the initial

Figure 2. Molecular similarity matrix of known TAS2R14 agonists. Matrix was generated based on TC values, which were calculated using ECFP4 fingerprints for 43 known TAS2R14 active agonists (potency up to 100 ␮M). 1186

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set of molecules (both TPs and TNs) had values of TC ⬍ 0.3 to the reference ligands. The only exception is cis-isohumulone, which was capable of identifying the other iso-␣-acids from the humulone family but still could not differentiate between TAS2R14 activators and nonactivators within this chemical family of ligands (data not shown). Thus, 2D similarity was not used in virtual screening, but rather to assess the complementarity or redundancy of hit lists obtained using different 3D virtual screening tools, and the novelty of these hits compared to known TAS2R14 binders. To explore the physicochemical space occupied by TAS2R14 agonists and determine the acceptable range of molecular properties of the potential TAS2R14 activators, commonly used 1D physicochemical descriptors were calculated for each of the molecules in the data set: MW, lipophilicity (as ALogP), HBD count, HBA count, RB count, aromatic ring count, and formal charge. From this analysis, the hitherto encountered ranges of 1D physicochemical properties for potential TAS2R14 binders were determined and are presented as boxplots in Supplemental Fig. S1. For example, TAS2R14 agonists tend to be hydrophobic and to have a low number of aromatic rings. The typical ranges of molecular properties of known TAS2R14 agonists were used during the virtual screening procedure to decrease the searchable chemical space (see Supplemental Figs. S1 and S2). Generation and evaluation of 3D LBP models and shape-based models for virtual screening For 3D model generation, the data set again was split into a training set, namely 5 of the most potent and structurally diverse TAS2R14 agonists, flufenamic acid, 8-prenylnaringenin, cis-isohumulone, artemorin, and parthenolide (Fig. 3), as well as a test set, comprising the remaining TP and TN ligands. Different combinations of the training set agonists were used for LBP model generation and resulted in a total of 50 different pharmacophore models, generated using the HipHop algorithm (28). The performance of these models was evaluated by screening them against our test set and assessing their ability to successfully identify all known active TAS2R14 agonists, while rejecting the true TAS2R14-inactive molecules. From this,

we constructed a “confusion matrix,” reporting the rate of FPs, FNs, TPs, and TNs identified in the screen (33), and calculated the Se and Sp of each model (data not shown). On the basis of this analysis, 3 pharmacophore models with the highest performance in terms of specificity were selected for virtual screening of the BitterDB and DrugBank databases. The 3D spatial relationship and geometric parameters of these models are presented in Fig. 4. All models have one general hydrophobic feature and another more specific hydrophobic aliphatic feature (see left and center LBPs in Fig. 4), or two aliphatic features (right LBP in Fig. 4), in line with our observations that TAS2R14 ligands tend to be very hydrophobic, as their median ALogP is 3.1 (Supplemental Fig. S1). The models also have either one HBD (left LBP in Fig. 4) or one HBA feature (center and right LBPs in Fig. 4). The performance of these models against our test set is summarized in Table 1. Applying a cutoff on the resulting pharmacophore fitness values (FitValues; a measure of how well a molecule fits the pharmacophore) results in a high specificity of 95.7– 100%. However, the sensitivity is quite low (28.6%), reflecting the fact that the pharmacophores do not detect all potential hits (i.e., TP molecules). The low sensitivity is expected, as it stems from the great chemical diversity among the known TAS2R14 ligands (Fig. 2) and is further illustrated by the fact that no single pharmacophore model is common to all TP molecules in the TAS2R14 known ligands set (n⫽43) or even in the training set (n⫽5). Thus, we do not expect the pharmacophores to recapture all TPs in virtual screening, but high-specificity rates indicate that those compounds that are retrieved by the pharmacophores are likely to activate TAS2R14. To benefit from high specificity, these cutoffs were used in the subsequent screens. To address the issue of low sensitivity, another virtual screening method was applied, namely, the shape-based method ROCS (27). Shape-based queries were generated for the 5 agonists in our training set using ROCS, as described in Materials and Methods (Fig. 5). The generated molecular shapes, electrostatic shapes, or a combination of the two were evaluated for their ability to discriminate TAS2R14-active from TAS2R14-inactive molecules. Three agonists, flufenamic acid (compound 1), cisisohumulone (compound 5) and 8-prenylnaringenin

Figure 3. Two-dimensional molecular structures of TAS2R14 agonists used as training set. Compound 1: flufenamic acid. Compound 2: artemorin. Compound 3: parthenolide. Compound 4: 8-prenylnaringenin. Compound 5: cis-isohumulone. Agonist affinity data (effective concentrations) are indicated. BITTER PILL

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Figure 4. LBP models for virtual screening. The 3 best models are shown with mapping of cis-isohumulone, one of the known TAS2R14 agonists that were used in the training set for model generation. Pharmacophores are represented as tolerance spheres with directional vectors, where applicable. Green spheres represent HBAs; magenta sphere represents HBD; cyan spheres represent hydrophobic features; and blue spheres represent hydrophobic aliphatic features.

(compound 4) were able to discriminate TP from TN molecules in our test set, using a default combination of the molecular and electrostatic shape (combo score). Applying a cutoff on the combo score resulted in specificity rates of ⬎96%, as summarized in Table 2. However, this cutoff resulted in very low sensitivity rates (⬍20%), as was the case for the LBP models. This again reflects the fact that these queries do not detect all potential hits (i.e., TAS2R14 TP molecules) due to the great chemical diversity among TAS2R14 agonists. Shape models based on artemorin (compound 2) and parthenolide (compound 3) were not able to effectively discriminate ligands in the test set and were therefore not used in the subsequent analysis. Virtual screening for novel TAS2R14 binders Choosing optimal cutoffs obtained at this stage for the pharmacophores and shape-based models, we next performed the virtual screen on the BitterDB and DrugBank databases, to identify potential new binders of TAS2R14. Compounds that appear in both BitterDB and DrugBank were counted only once, as appearing in BitterDB. The LBP models and shape-based models were first screened against the BitterDB database. For this procedure, the database was filtered to remove all known TAS2R14 agonists, as well as molecules that were shown experimentally not to activate TAS2R14, resulting in a library containing 442 molecules (BitterDB unassigned). TABLE 1. Sensitivity and specificity of LBP models, before and after application of cutoff values on pharmacophore FitValues Before cutoff LBP

1 2 3

1188

Sensitivity

Specificity

FitValue cutoff

0.486 0.629 0.486

0.609 0.565 0.609

2.89233 2.79573 2.70065

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After cutoff Sensitivity

Specificity

0.286 0.286 0.286

0.957 0.957 1.000

The molecules identified by each of the LBP models were ranked according to their FitValues, and molecules with scores below the cutoffs were discarded (see Supplemental Fig. S2 for details). To reduce the explored chemical space, the remaining molecules were filtered using the 1D physicochemical properties filter derived from analysis of known TAS2R14 TPs (see Supplemental Fig. S1). Up to one violation of the filter was allowed at this stage (similar to Lipinski’s rules, commonly used in drug design, which allow one violation of drug-likeness molecular properties; ref. 49). Because of high chemical diversity of the ligands, we do not expect that all of them would fit a pharmacophore model, which is based on a few agonists. Nevertheless, some are expected to fit such a model, and some may even fit more than one pharmacophore model. Because our goal is to make the most specific predictions, rather than to optimize sensitivity of predictions at the cost of specificity, we prioritized the molecules for experimental testing using a consensus approach, namely, selecting molecules identified by at least two of the LBP models. The final hit list consisted of 18 potential ligands. Six of these identified substances (Supplemental Table S1) were commercially available and were tested experimentally by monitoring ligand-induced calcium influx in human HEK 293T-G␣16gust44 cells transiently transfected with human TAS2R14, as described previously (26). One of the tested molecules, propafenone hydrochloride, presented high artifact signals in all tested concentrations, and, thus, its activity could not be determined. Four of the 5 remaining tested substances were confirmed experimentally to be novel agonists of TAS2R14, with effective concentrations ranging from 10 to 100 ␮M (see Fig. 6 for details), providing an extremely high success rate. Next, the BitterDB unassigned set was screened using the 3 shape-based queries (see Supplemental Table S2 for complete hit list). Molecular and electrostatic shapes for BitterDB molecules were generated and compared to the references shapes using the same

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Figure 5. Shape-based models for virtual screening. Molecular (left) and electrostatic shapes (right) of the reference ligand flufenamic acid (compound 1; top panel); a known TAS2R14 agonist, genistein (middle panel); and a bitter compound with an unknown target, clonixin (compound 10, bottom panel), which was identified through virtual screening as a TAS2R14 agonist.

cutoff rates. This resulted in a set of predicted molecules (see Supplemental Fig. S2). Interestingly, all of these are new predictions, since none were predicted by the pharmacophore-based models. Seven selected hits were purchased and tested experimentally as described above. One of the tested molecules, calcium panthotenate, was discarded, as the calcium addition to the buffer prevented acceptable measurements. Five of the 6 remaining substances were confirmed experimentally to be novel agonists of TAS2R14, with effective concentrations ranging from 0.5 ␮M to 1 mM (Figs. 6 and 7). For the four hits identified in the BitterDB screen, full-dose response relationships were determined (Fig. 8). The extrapolated EC50 values for 3 of our confirmed hits are in the mid- to high-micromolar range, in line with values previously determined for other TAS2R14 agonists (19, 20). Next, the DrugBank database was screened similarly. Screening of the DrugBank database with the 3 LBPs resulted in identification of 242 molecules, which

passed all screening criteria (FitValue cutoff, prediction by ⱖ2 LBPs and 1D physicochemical filter; see Supplemental Fig. S2). Because the resulting hit list was quite large, additional criteria were introduced to narrow the final hit list for testing: molecules were discarded if they were part of our BitterDB training set (for example, quinine and papaverine, which are known TAS2R14 agonists, and secobarbital, which was already identified in the BitterDB unassigned screen) or if they were not commercially available for testing. This resulted in a list of 40 compounds, which were then clustered based on 2D similarity (Supplemental Fig. S2). The final list was composed of 16 representative compounds (Supplemental Table S3). The potential hits were experimentally tested as described above. Three of the tested compounds did not allow for artifact-free functional analyses: loperamide, nebivolol, and calcipotriol hydrate. Four of the 13 remaining compounds were confirmed as novel TAS2R14 agonists, with effective concentrations ranging from 12.3 to 100 ␮M (Figs. 9 and 10). This corresponds to an approximate hit rate of 30%. Screening of the DrugBank database with the 3 shape-based queries resulted in a preliminary hit list containing 357 molecules from diverse pharmacological families. Among the identified ligands that met the cutoff values, a short list based on commercial availability and structural and pharmacological diversity was compiled (Supplemental Table S4 and Supplemental Fig. S2). In addition, diclofenac, although not predicted as a hit by ROCS, was tested because the algorithm identified several other nonsteroidal anti-inflammatory drugs as potential hits. Seven of these 10 experimentally tested compounds were confirmed as agonists of TAS2R14 (Figs. 9 and 10). These compounds have effective concentrations ranging from 0.5 to 100 ␮M. This represents an excellent hit rate of 70%. Overall, the pharmacophore-based and shape-based methods predict different molecules. The only molecule that was computationally identified by both methods, hyperforin, was only a weak partial agonist, which did not allow artifact-free measurements. We tested hyperforin and ibuprofen (an additional prediction which was not confirmed experimentally as an agonist), for potential antagonism of TAS2R14. No significant inhibition of TAS2R14 activity was found when these compounds were tested in the presence of the TAS2R14 agonist flufenamic acid (Supplemental Fig. S3). The similarity of the newly discovered agonists to the known agonists present in our training and test sets was

TABLE 2. Sensitivity and specificity of shape-based models, before and after application of cutoff values on the combined shape and electrostatics score Before cutoff Shape-based model

Flufenamic acid Cis-isohumulone 8-prenylnaringenin

BITTER PILL

After cutoff

Sensitivity

Specificity

Cutoff

Sensitivity

Specificity

0.581 0.302 0.512

0.512 0.890 0.622

1.42 1.47 1.47

0.116 0.163 0.186

0.976 0.988 0.963

1189

Name

A

Structure

Maximal Rank

Tc

Closest known ligand

Effective concentration

Molecules identified using ligand-based pharmacophore models

Ethylhydrocupreine (6)

8

0.617

Quinine

10µM*

Secobarbital Sodium (7)

39

0.161

Adlupulone

10µM*

Tributyrin (8)

8

0.217

Carisoprodol

10µM**

Triethyl citrate (9)

6

0.191

Carisoprodol

100µM*

B

Molecules identified using shape-based models

Clonixin (10)

7

0.327

Flufenamic acid (1)

2µM**

Naringenin (11)

1

0.509

8-prenylnaringenin (4)

10µM**

Quercetin (12)

42

0.466

Genistein

1µM**

3,5-Diiodosalicyclic acid (13)

871

0.285

1-Naphthoic acid

0.5µM**

o-(p-Anisoyl)benzoic acid sodium salt (14)

79

0.378

1-Naphthoic acid

1000µM***

Figure 6. Confirmed active compounds identified by virtual screen of the BitterDB unassigned compounds. A) Molecules identified using LBP models. B) Molecules identified using shape-based models. Maximal rank indicates highest rank of the compound among the 3 LBP models or shape-based queries (before filtering). TC indicates 2D similarity to the most similar known TAS2R14 agonist in the training and test sets, calculated using ECFP4 fingerprints. Effective concentration indicates the lowest tested concentration that has a significant difference between signals of empty-vector-transfected cells and TAS2R14transfected cells. *P ⬍ 0.05, **P ⬍ 0.01, ***P ⬍ 0.001.

evaluated by calculating the TC between each of the validated hits and each of the compounds in these sets using ECFP4 fingerprints. Seven (compounds 7–9 and 15–18 in Figs. 6 and 9) of the 8 active compounds discovered by the LBP models were topologically unrelated to the TAS2R14 agonists in our sets, having TC ⬍ 1190

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0.35. Moreover, their closest known TAS2R14 ligands were not part of our training set of molecules. Thus, these compounds can be considered as novel chemotypes of TAS2R14. In contrast, 8 of the 12 new agonists (compounds 11, 12, 14, 19 –21, 23, and 24 in Figs. 6 and 9) identified by the shape-based method are more

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LEVIT ET AL.

rence with the words “taste” or “bitter” revealed that some of these compounds were reported as bittertasting for humans. For example, diclofenac at 4.0 mM was described as bitter in a taste profile study of 19 drugs (50); bitter taste of niflumic acid (52), mefenamic acid (53) and diclofenac (54) motivated development of taste-masking techniques to improve drug compliance. This information will be updated in the BitterDB database (18). Furthermore, anecdotal patient reports indicate bitter taste of glimepiride, malathion, and miconazole. Malathion is also reported as “pungent” in PubChem; pantoprazole may taste bitter and cause more general taste disturbance (see links in Supplemental Material).

1.3

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∆F/F

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Off-target analysis of TAS2R14 agonists

Figure 7. Newly discovered agonists of TAS2R14, identified by virtual screening of the BitterDB database. Substances were tested in 3 concentrations. Highest concentrations [0.2 mM clonixin, 0.05 mM 3,5-diiodosalicylic acid, 0.01 mM naringenin, 1 mM o-(p-anisoyl)benzoic acid, 0.001 mM quercetin; solid bars, high] were chosen based on solubility and/or highest artifact-free concentration. Medium (hatched bars, medium) and low (open bars, low) concentrations represent 1:10 dilution steps. Flufenamic acid (10 ␮M, right bar) served as reference.

similar to known agonists (TC⬎0.4), and especially to flufenamic acid (compound 1), which was used for shape-based query generation. Thus, the shape-based models provide excellent coverage of the proximal chemical space of known TAS2R14 ligands, while the 3D “simplicity” of the LBP models allows exploration of novel scaffolds and chemotypes that are topologically dissimilar to the known agonists. A targeted literature search in which the name of each of the identified drugs was queried for cooccur-

As one of the goals of this work was to examine the possibility of repurposing known drugs to act via TAS2R14, we examined which (additional) targets are most common for the TAS2R14 agonists in our test set. Ligand-target pairs were compiled on the basis of the ligands’ annotated targets in the Zinc database (35). The resulting human targets were classified into one of 4 major target categories, namely, enzyme, ion channel, 7TM membrane receptor, and other, and the distribution of the different target categories among the compounds in the data set was calculated (Fig. 10). Most TAS2R14 agonists in the initial data set also target various enzymes (38%), ion channels (26%), and, to a slightly lesser extent, GPCRs (21%; Fig. 11A). To analyze whether this finding is unique to the TAS2R14 agonists, or a more general phenomenon associated with bitter molecules, we expanded this analysis to bitter molecules in general [i.e., all molecules in BitterDB having additional (non-bitter taste receptor) targets]. We used two additional sources of compounds: the FDA-approved subset of the DrugBank database and the TCM Database@Taiwan, comprising small molecules from traditional Chinese medicines (36), many of which are known to have therapeutic potential. Our observations for bitter molecules, in general, were in line with the findings for the smaller subset of TAS2R14 agonists (Fig. 11B): the majority of these compounds also target enzymes (40%), ion channels (28.5%), and GPCRs

Figure 8. Representative dose-response curves of novel TAS2R14 agonists identified by virtual screening of the BitterDB database. A) Tributyrin. B) Triethyl citrate. C) Secobarbital. D) Ethylhydrocupreine. HEK 293T-G␣16gust44 transiently transfected with a human TAS2R14 construct were challenged with different compound concentrations. Changes in fluorescence after agonist stimulation (⌬F/F; y axis) were monitored and plotted together with corresponding compound concentration (logarithmically scaled x axis; micromolar or millimolar). Plots obtained for cells transfected with TAS2R14 construct (solid lines, diamonds), and empty vector (mock-treated control, broken lines, circles) are compared within each graph, and calculated extrapolated EC50 values (means⫾se) are indicated if possible. n.d., not determined. BITTER PILL

1191

Name

A

Figure 9. Confirmed TAS2R14 agonists identified by virtual screen of the DrugBank database. A) Molecules identified using LBP models. B) Molecules identified using shape-based models. Maximal rank indicates highest rank of the compound among the 3 LBP models or shape-based queries (before filtering). TC indicates 2D similarity to the most similar known TAS2R14 agonist in the training and test sets, calculated using ECFP4 fingerprints. Effective concentration indicates the lowest tested concentration that has a significant difference between signals of empty-vector-transfected cells and TAS2R14transfected cells. †Diclofenac (compound 25) was not predicted as a hit by ROCS, but was tested as a representative nonsteroidal anti-inflammatory drug compound. *P ⬍ 0.05, **P ⬍ 0.01, ***P ⬍ 0.001.

Structure

Maximal Rank

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Closest known ligand

Effective concentration

Molecules identified using ligand-based pharmacophore models

Glimepiride (15)

511

0.112

Arborescin

12.3µM**

Miconazole (16)

605

0.166

Chloropheniramine

33µM**

Malathion (17)

116

0.160

Carisoprodol

50µM**

Pantoprazole (18)

124

0.242

Papaverine

100µM***

B

Molecules identified using shape-based models

Salsalate (19)

99

0.388

1-Naphthoic acid

100µM***

Kaempherol (20)

122

0.560

Genistein

0.5µM*

Hesperetin (21)

138

0.453

Isoxanthohumol

10µM*

Pemirolast (22)

697

0.085

Artemorin (2)

5µM***

Niflumic acid (23)

1

0.644

Flufenamic acid (1)

5µM**

Mefenamic acid (24)

25

0.477

Flufenamic acid (1)

3µM*

0.288

Flufenamic acid (1)

25µM***

Diclofenac (25)†

1192

Tc

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1.3

high medium

1.1

low

∆F/F

0.9 0.7 0.5 0.3

BITTER PILL

id ac

am

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al

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(17%). In contrast, ⬎60% of targets for the TCM Database@Taiwan set are enzymes (Fig. 11D), while 44% of targets of DrugBank molecules are enzymes, ⬃26% are GPCRs and ⬃20% are ion channels (Fig. 11C). The differences in target distribution between known TAS2R14 agonists and each of the 3 other molecule sets tested here was evaluated using Pearson’s ␹2 test. The difference in target class distribution between known TAS2R14 agonists and TCM Database@Taiwan molecules was found to be statistically significant (P⬍0.0001 after applying Bonferroni correction for multiple comparisons). Interestingly, a high percentage (⬃38%) of the TAS2R14 agonists, and of bitter molecules in general (23%), inhibits the hERG potassium channel (Fig. 11E), as opposed to ⬍3% of the TCM molecules (P⬍0.0001). hERG potassium channels have a critical role in cardiac action potential repolarization. hERG is a promiscuous target that can bind structurally diverse small molecules, inhibition by which leads to acquired long QT syndrome (aLQTS)—prolongation of the action potential and potentiation of occurrence of lethal arrhythmias (55). In fact, many drugs have been removed from the market or terminated during clinical development due to association with aLQTS (56). Thus, screening against hERG off-target is now commonly incorporated in the drug discovery process, and, consequently, only ⬃15% of DrugBank molecules target hERG. The percentage of hERG inhibitors among TAS2R14 agonists is significantly higher than among TCM compounds (P⬍0.0001, using Pearson’s ␹2 test and applying the Bonferroni correction for multiple comparisons). Thus, small molecules from traditional Chinese medicines are a potential resource for nonhERG-targeting compounds, which can be used for drug discovery, targeting the bitter taste receptors family. One hundred twenty compounds were predicted using the LBPs and molecular filters described above, and 1620 compounds were found above cutoff using the shapebased queries. We can, therefore, expect that 30 to 80% of the Chinese medicine hits to activate TAS2R14.

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pe es H

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-0.1

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0.1

Figure 10. Newly discovered agonists of TAS2R14, identified by virtual screening of the DrugBank database. Substances were tested in 3 concentrations. Highest concentrations (0.25 mM diclofenac, 0.123 mM glimepiride, 0.01 mM hesperetin, 0.005 mM kaempherol, 0.5 mM malathion, 0.03 mM mefenamic acid, 0.033 mM miconazole, 0.05 mM niflumic acid, 0.1 mM pantoprazole, 0.005 mM pemirolast, and 0.1 mM salsalate; solid bars, high) were chosen based on solubility and/or highest artifact-free concentration. Medium (hatched bars, medium) and low (open bars, low) represent 1:10 dilution steps. Flufenamic acid (10 ␮M, right bar) served as reference.

DISCUSSION Mapping of bitter taste signals into regions in the brain revealed a single bitter taste hot spot in the gustatory cortex, which is shared by structurally diverse bitter tastants (57). Clearly, there are differences among bitter taste receptor subtypes in terms of receptive range (17, 18) and tissue distribution (13, 21). Thus, pharmacological profiling of individual TAS2Rs and expansion of known repertoires of their known ligands will help develop biochemical probes for unraveling the interplay and particular roles of individual bitter taste receptors in both oral and extraoral tissues. Highthroughput screening strategies have been applied in recent years for successful discovery of additional TAS2R ligands and their associated receptors (17, 58) and some computer-aided classification (18, 44, 59, 60), design (61), and virtual screening studies (62) have been carried out to date. We used 1D properties, 2D chemical connectivity, and 3D models to characterize TAS2R14 agonists and computationally predict additional ones. Our analysis of molecular properties of TAS2R14 agonists indicates that they tend to have a lower number of aromatic rings compared to the more “flat” ligands that do not activate TAS2R14. Some of the ligands are hydrophobic (as typically expected of bitter compounds; ref. 63), while others are negatively charged. Previous studies on bitter molecules and their associated receptors (including the hop-derived compounds) have shown that even when 2D structural similarity among bitter compounds does exist, it is not always enough for activation of a common bitter taste receptor (17). The “similar compound pairs-dissimilar target activity” phenomenon is a general one, and it is often assessed using activity cliffs, defined as pairs of structurally similar active compounds that have large differences in potency (64). Therefore, in our work, 2D similarity was used to analyze the chemical novelty of hits rather than for virtual screening. The 3D shapebased virtual screening was very efficient and returned molecules with relatively high 2D similarity to the query agonists. The pharmacophore-based models have lower 1193

A

B

TAS2R14 agonists

BitterDB

15%

14.46% 38%

40.00%

21%

17.03%

26%

C

28.51%

D

DrugBank 10.13%

TCM 13.19%

44.01%

26.30%

62.31% 10.90%

13.59%

19.56%

E

Enzyme

40

Ion channel

7TM receptor

Other

*

35

Figure 11. Target class distribution of TAS2R14 agonists and control data sets. Summary of target class distributions for ligand-target pairs in 5 different data sets, based on the ligand annotated targets in the Zinc database. A) Known TAS2R14 agonists. B) BitterDB molecules. C) DrugBank FDA-approved subset. D) TCM Database@Taiwan molecules. E) Percentage of molecules targeting the hERG potassium channel out of total number of compounds with established targets in the different data sets. *P ⬍ 0.0125 after Bonferroni correction for multiple comparisons.

% of total compounds

30

25

20

15

10

5

0

TAS2R14 agonists

BitterDB

DrugBank

TCM

sensitivity but tend to identify agonists with novel scaffolds. The current study is the first to focus on approved drugs as the potential source of bitter ligands. Polypharmacology suggests that existing drugs may be repurposed for additional indications due to their effects on other targets (i.e., off-target effects; refs. 45, 65, 66), as has been successfully accomplished in several cases (67). Clark et al. (16) hypothesized that the off-target concept may be applied to bitter taste receptors and that some side effects of known drugs may be 1194

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explained by their action on bitter taste receptors. We chose to screen the DrugBank database since many drugs are known to have a bitter taste when delivered orally; moreover, some of the experimentally confirmed hits from the BitterDB screen are drugs (compounds 11 and 12 in the current study are FDAapproved experimental drugs). Applying the screens to FDA-approved drugs has resulted in identifying TAS2R14 agonists among drugs with known targets, including glimepiride and salsalate. Glimepiride is a second-generation sulfonylurea, which stimulates pan-

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LEVIT ET AL.

creatic ␤ cells to release insulin and is used in the management of type 2 diabetes mellitus. Glimepiride, and other sulfonylureas, exert their insulin-releasing effect mainly by blocking ATP-sensitive K⫹ (KATP) channels on the surface of pancreatic ␤ cells, which induces membrane depolarization, allowing an influx of calcium in the cell. This, in turn, induces insulin release into the bloodstream (68). However, a number of studies have shown that several members of the sulfonylurea class have insulin-releasing effects independent of ATP-sensitive potassium channels (69). Recent revelations in taste research indicate that the bitter, sweet, and umami receptors, as well as their G protein ␣-gustducin are distributed throughout the stomach, intestine, and pancreas, where they may aid the digestive process by influencing appetite and regulating insulin production (8, 70). It is tempting to speculate that glimepiride’s insulin-secreting action is not only a consequence of its interaction with KATP channels, but also of its association with TAS2R14. This suggestion clearly warrants further experimental testing. Many bitter tastants are known to be toxic to humans and other species. We have found that almost 40% of known TAS2R14 agonists, and ⬎20% of bitter tastants in general, also target hERG. This may indicate that these molecules exert their toxicity via inhibition of hERG and other ion channels. Because hERG is predominantly expressed in the heart, as well as the high level of TAS2R14 expression in this organ (21), the interplay of activation of TAS2R14 and inhibition of hERG in heart tissue by the same ligand merits further study. hERG is also found in neurons, neuroendocrine glands, and smooth muscle (55). Interestingly, expression of hERG has also been reported in taste buds, where it has been found to be expressed only in young taste receptor cells and suggested to be involved in the regulation of taste receptor cell membrane potential by repolarization (71). Occurrence of hERG-targeting molecules is lower for approved drugs, which is not surprising, since assaying against hERG to reduce potential cardiotoxicity has become a standard part of drug development (72). The low occurrence of hERGtargeting molecules among Chinese medicine compounds make this set particularly attractive. In summary, our in silico and in vitro work extends our understanding of the chemical properties responsible for activation of the broadly tuned bitter taste receptor TAS2R14. We have shown that physicochemical properties incorporated into 3D pharmacophore models are successful in finding previously unknown ligands with novel scaffolds. Shape- and electrostaticsbased models were even more specific, giving high hit rates and retrieving different compounds from those identified by the pharmacophore-based models. These hits were typically more similar to the compound on which the shape query was based. Our VLS strategy, which combined both pharmacophore- and shapebased screens, allowed a wider coverage of the chemical space occupied by TAS2R14 ligands. In recent years, it has become clear that in addition to guarding the organism against consumption of bitter foods, which are often toxic, bitter taste receptors also BITTER PILL

play important roles in the respiratory (11, 73, 74), gastrointestinal (10), and reproductive systems (75, 76). Therefore, they are likely to have additional agonists, which may not represent imminent food constituents. Furthermore, bitter taste receptors in the airways have become new targets for asthma treatment (74, 77); they are also strong candidates for mediating crosskingdom communication, as they respond to bacterial quorum-sensing molecules (78, 79). A recent hypothesis (16) suggested that “any drug with a bitter taste could have unintended actions in the body through stimulation of extraoral type 2 taste receptors (T2Rs).” New ligands of bitter taste receptors among clinical, experimental, and traditional drugs identified in the current work open up new venues for studying drugs’ side effects and for repurposing drugs for new therapeutic indications. This work was supported by a Deutsche Forschungsgemeinschaft grant (ME 1024/8-1) to W.M., M.B., M.Y.N. and an Israel Science Foundation grant to M.Y.N. The authors thank Dr. Elite Levin for helpful discussions and OpenEye Scientific Software for free academic license of their software.

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