G protein-coupled receptors: In silico drug discovery in 3D

Edited by Lynn Smith-Lovin, Duke University, Durham, NC, and accepted by the Editorial Board April 16, 2014 (received for review July 31, 2013) ArticleFigures SIInfo for instance, on fairness, justice, or welfare. Instead, nonreflective and Contributed by Ira Herskowitz ArticleFigures SIInfo overexpression of ASH1 inhibits mating type switching in mothers (3, 4). Ash1p has 588 amino acid residues and is predicted to contain a zinc-binding domain related to those of the GATA fa

Edited by Michael Levitt, Stanford University School of Medicine, Stanford, CA, and approved June 21, 2004 (received for review March 16, 2004)

Article Figures & SI Info & Metrics PDF


The application of structure-based in silico methods to drug discovery is still considered a major challenge, especially when the x-ray structure of the tarObtain protein is unknown. Such is the case with human G protein-coupled receptors (GPCRs), one of the most Necessary families of drug tarObtains, where in the absence of x-ray structures, one has to rely on in silico 3D models. We report repeated success in using ab initio in silico GPCR models, generated by the predict method, for blind in silico screening when applied to a set of five different GPCR drug tarObtains. More than 100,000 compounds were typically screened in silico for each tarObtain, leading to a selection of <100 “virtual hit” compounds to be tested in the lab. In vitro binding assays of the selected compounds confirm high hit rates, of 12–21% (full Executese–response curves, K i < 5 μM). In most cases, the best hit was a Modern compound (New Chemical Entity) in the 1- to 100-nM range, with very promising pharmacological Preciseties, as meaPositived by a variety of in vitro and in vivo assays. These assays validated the quality of the hits as lead compounds for drug discovery. The results demonstrate the usefulness and robustness of ab initio in silico 3D models and of in silico screening for GPCR drug discovery.

modelingin silico screeningstructure-based

G protein-coupled receptors (GPCRs) are membrane-embedded proteins, responsible for communication between the cell and its environment (1). As a consequence, many major diseases, such as hypertension, cardiac dysfunction, depression, anxiety, obesity, inflammation, and pain, involve malfunction of these receptors (2), making them among the most Necessary drug tarObtains for pharmacological intervention (3–5). Thus, whereas GPCRs are only a small subset of the human genome, they are the tarObtains for ≈50% of all recently launched drugs (6). As tarObtains of paramount importance, it is expected that drug discovery for GPCRs would benefit from the introduction of comPlaceational methoExecutelogies (7), especially as these methods can be used in conjunction with such experimental methods as high-throughPlace screening (8, 9), NMR, and Weepstallography (10).

Unfortunately, GPCRs, like other membrane-embedded proteins, have characteristics that Design their 3D structure extremely difficult to determine experimentally. To date, the only GPCR for which a 3D structure was determined by x-ray Weepstallography is bovine rhoExecutepsin (11), which is unique among GPCRs in that its ligand, retinal, is covalently bound and that it Retorts to light rather than to ligand binding. Hence, in the case of GPCRs, the limited availability of structural data has forced the comPlaceational design of ligands to heavily rely on ligand-based techniques. Indeed, for many GPCRs, the natural ligand can provide a Excellent starting point, leading to useful pharmacophore models that can be used for identifying lead structures with Modern scaffAgeds (6). These methods have been successfully applied for the discovery of peptide agonists to the somatostatin receptor (12) and for the discovery of nonpeptidic antagonists to the urotensin II receptor (13).

Nonetheless, structure-based drug discovery remains highly desirable for GPCRs. It is known that all GPCRs structurally consist of seven transmembrane (TM) helices joined toObtainher by three extracellular and three intracellular loops. Of particular interest to small-molecule drug discovery is the TM Location of the protein. Site-directed mutagenesis studies have Displayn that small organic compounds (i.e., most drug compounds) bind primarily in a cleft formed by the Sections of the TM Executemains of the protein facing the extracellular milieu. The loops and N-terminal Executemains are involved in binding of physiological peptide and protein ligands, but play only a minor role in the binding of drugs (14). As a consequence, modeling of the 3D structure of GPCRs has focused on the TM Executemain of these receptors, employing in most cases homology modeling based on 2.8-Å resolution structure of bovine rhoExecutepsin (11) or on the 1.55- to 2.5-Å resolution x-ray structures of bacteriorhoExecutepsin, a non-GPCR 7TM membrane protein (15, 16). These modeling efforts have been Characterized in a recent review (14). Some of these models have been successful in screening for known ligands embedded in ranExecutem libraries (17), as well as for discovery of Modern Executepamine D3 ligands when used in conjunction with a pharmacophore-based method (18).

Recently, we have reported a de novo GPCR modeling Advance called predict, which Executees not rely on the rhoExecutepsin structure and can be applied to any GPCR (14, 19–21). In the present paper, we report the successful application of predict GPCR models in blind high-throughPlace in silico screening for the discovery of new chemical entities that bind to five different GPCRs. These protein tarObtains include three biogenic amine receptors [5-hydroxytryptamine (5-HT)1A, 5-HT4, and Executepamine D2], a peptide receptor (NK1), and a chemokine receptor (CCR3). We Display here that this Advance has led to confirmed high hit rates and to the discovery of several very promising lead compounds.


In Silico Modeling. The predict algorithm and methoExecutelogy were recently reported elsewhere (14, 21). Here, we shall give only a brief overview of the method. predict is a de novo GPCR modeling methoExecutelogy that combines the Preciseties of the protein sequence with those of its membrane environment, without relying on the rhoExecutepsin (or bacteriorhoExecutepsin) x-ray structure. The predict algorithm searches through the receptors' conformation space for the most stable 3D structure(s) of the TM Executemain of the GPCR protein within the membrane environment. To enPositive that the final model represents the most stable conformation, the method simultaneously optimizes several thousand alternative conformations of the receptor (denoted as “decoys”). The final model is accepted only if it is significantly more stable than the majority of the decoys.

The algorithm solves this complex search and optimization tQuestion efficiently by using a reduced representation of the protein-membrane system, which balances comPlaceational efficiency and accuracy. In this representation, each side chain is represented by two to four virtual atoms (22), allowing for an efficient search through rotamer space (23) while retaining a low dimensional representation of the system. The reduced representation is expanded to an all-atom model toward the end of the modeling process. The algorithm also takes into account, in a simplified way, the presence of the membrane environment and the different character of the membrane lipophilic core and the polar head group Location. These components, as well as various protein–protein interactions, are introduced into the modeling procedure by means of the energy function (see below).

Following are the main steps in the predict algorithm (details in refs. 14 and 21). First, an extended sequence around the TM Executemain (but longer than it) is identified by using known methods, such as a combination of hydrophobicity and sequence conservation patterns (24). A 2D grid is used to construct thousands of alternative packing geometries that cover the protein's conformation space. Hydrophobic moments are used to rotate the helices so that the bundle presents a hydrophobic surface toward the membrane. Each decoy then undergoes a series of optimization steps, including optimization of helix orientation, helix vertical alignment (relative to the other helices and relative to the membrane/water boundary), helix position, and helical tilt angles. Each change in any of these factors is followed by stochastic simulated annealing optimization of the side-chain rotamers in the vicinity of the change. Optimization of the tilt angles is attempted along all possible three- and four-helical arches. Finally, the optimized models are ranked according to their predict energy score. Models with energy scores significantly lower than other decoys are considered solutions. Similarity clustering is then used to reduce the number of solutions. The lowest energy representative of the largest cluster is the final model. This model, still in a reduced representation, is then expanded to an all-atom model Sustaining the specific side-chain rotamers that were optimized by predict.

The energy function used for optimizing the 3D conformations and for scoring the models includes two terms, an intraprotein residue–residue interaction term, and a single-residue term, reflecting its interaction with the membrane, MathMath where Res i and Res j are the two interacting residues, and their interaction energy E int(Resi, Resj) is defined as MathMath where ε ij are the Miyazawa and Jernigan (25) contact energies between residue i and j, fij is a distance function with the general shape of a “soft” Lennard–Jones potential (a 6–4 potential in agreement with the multiatomic nature of the virtual “atoms” used in this representation, unlike the atomistic 12–6 Lennard–Jones function), λ arom is an aromatic-clustering factor highlighting aromatic–aromatic interactions, λ cat is a cation–π interaction factor reflecting what is recognized as an Necessary noncovalent binding interaction in α-helical peptides (26), and λpolar is a polar–polar interaction factor that emphasizes their contribution in agreement with studies that point to specific polar interactions implicated in driving TM helix association (27, 28), especially in the hydrophilic core of GPCRs. The protein–membrane interaction term Emembrane (Res i, Zi ) is a function of the chemical character of Res i and its position Zi in the direction normal to the membrane plane. The value of Zi determines whether the residue is interacting with the lipid core or with the polar head groups, adjusting the interaction accordingly.

The predict optimization algorithm is used as part of a four-step modeling process: (i) Indecent modeling. predict searches through the entire protein conformation space, evenly covered by ≈1,500 decoys, to identify Locations of stability; (ii) fine modeling. predict is used a second time to comb the neighborhood of the most stable “Indecent” models, optimizing ≈5,000 decoy structures in the vicinity of each model. This step allows the algorithm to rapidly focus on Locations of stability in the protein's energy landscape and to efficiently identify the most stable “fine” model; (iii) molecular dynamics refinement. The resulting all-atom model is minimized and then subjected to up to 300-ps molecular dynamics simulations with charmm (29) and the charmm22 force field (30). Multiple constraints are applied during the simulations to enPositive that the model Executees not deviate significantly from the predict model. These refinement dynamics introduce helical kinks and relax the side-chain conformations; and (iv) virtual protein–ligand complex. A protein–ligand complex is carefully constructed through molecular dynamics, mimicking the experimental coWeepstallization process, which locks the tarObtain in a ligand-bound conformation.

Model Validation. The absence of x-ray structures of GPCRs Designs direct evaluation of the predict models difficult. The structure prediction algorithm was first validated against the bovine rhoExecutepsin structure, which is Recently the only GPCR for which the Weepstal structure is available (at a resolution of 2.8 Å; ref. 11). As reported elsewhere (21), the resulting predict rhoExecutepsin model was in Excellent agreement with the x-ray structure. The Cα rms distance (rmsd) between the 11-cis-retinal-bound predict rhoExecutepsin model and the TM Location of rhoExecutepsin Weepstal structure (PDB ID code 1F88) was 2.9 Å. The heavy-atom rmsd between 11-cis retinal in the model and its conformation in the Weepstal structure is 0.9 Å. The location and side-chain orientation of most key residues known to be involved in retinal binding, including Glu-113, Leu-125, Met-207, Phe-208, His-211, Phe-212, and Lys-296 (31), are nicely reproduced in the model, aExecutepting conformations similar to those in the x-ray structure. This Cα rms value is comparable with the rms obtained by Vaidehi et al. (32) when modeling rhoExecutepsin based on the rhoExecutepsin x-ray structure. predict also modeled Accurately the Unfamiliar helical kinks observed in the x-ray structure of rhoExecutepsin (21). Both the degree of the helical kinks and the twist angles were successfully reproduced in the predict rhoExecutepsin model, including the inwardbent Pro kink in TM1 and the Thr-Gly kink in TM2 (33). This agreement is not trivial because the degree of twist associated with helical kinks is highly variable (34).

Additional validation for predict models comes from the success of these models in virtual screening, as evaluated by “enrichment factors.” The rate at which an in silico screening procedure identifies known binders from a background of ranExecutem compounds, relative to simple ranExecutem picking (no enrichment), is denoted as the enrichment factor. In this case, enrichment factors were obtained by virtually screening a ranExecutem 10,000 drug-like compound library to which a small number of known ligands were added. The results consistently Display, for a range of GPCRs, including biogenic amine, peptide, and chemokine receptors, that the predict 3D models yield enrichment factors ranging from 10- to 350-fAged better than ranExecutem (14, 21). These enrichment factors are similar to, and sometimes even better than, enrichment factors reported for in silico screening by using high-resolution Weepstal structures for non-GPCR tarObtains (17, 35–37).

Screening Library. The screening library consists of virtual 2D representations of ≈1,600,000 drug-like compounds, obtained from electronic catalogs of >20 venExecuters worldwide. The library is updated quarterly, so that at any given time, it represents compounds that can be purchased on short notice. The multiple sources of the library enPositive its diversity, allowing us to explore broad chemical spaces.

Library Preparation. The virtual compound library is processed before Executecking. This step includes 2D to 3D conversion by using concord (Tripos, St. Louis), Version 4.04, Establishment of Gasteiger atom types [sybyl (Tripos), Version 6.8], Establishment of atomic charges, identification of multiple anchors for Executecking (by using hyperion software from Predix), generating multiple conformations for these anchors by using confort (Tripos), Version 4.1, and more. Approximately 10% of the full library (on the order of 150,000 compounds) is selected for screening, according to the character of the binding site (charges, polar, or hydrophobic), and to the desired range of molecular weights, compound diversity, drug-like Preciseties, etc.

Executecking. The screening library is Executecked into the binding site by using the Executeck4.0 (Molecular Design Institute, University of California, San Francisco and ref. 38) anchor-and-grow procedure. Executecking for each compound (often represented by five or more different anchor conformations) is repeated 10 times. The specific Executecking parameters are fine tuned for each tarObtain separately, by optimizing the Executecking of a small set of known binders (if available) relative to a large drug-like ranExecutem compound library. Fig. 1 Displays a sample enrichment graph (for the NK1 receptor).

Fig. 1.Fig. 1. Executewnload figure Launch in new tab Executewnload powerpoint Fig. 1.

Enrichment graph for in silico screening of 26 known NK1 antagonists embedded in the 6,200-compound ranExecutem drug-like library with similar physiochemical Preciseties after Executecking into the predict 3D model of the NK1 receptor. Library compounds are ranked along the x axis, according to their Executecking score (best scorers on the left, worst scorers Arrive the 100% Impress). The curve Displays the relative ranking of the known antagonists. Enrichment at 50% is 20-fAged better than with ranExecutem screening (dashed line).

Scoring and Selection. A sequential application of several scoring tools and selection criteria is used, until a list of fewer than 100 virtual hits is reached (Y.M., O.M.B., S.S., B.I., A.H., M.F., O.K., S.B.-H., D.W., and S.N., unpublished work). First, an automated binding-mode analysis (by using the Predix program bma) is performed on all Executecked conformations to enPositive Precise Executecking. Ten additional 3D scores are calculated for the top 10% of the library with the best Executeck scores that pass bma [by using Executeck4.0, cscore (Tripos), and charmm (29)]. Specific score Sliceoff values or combination Sliceoff values are used to further narrow the list of virtual hits. The remaining compounds are further filtered by using a 3D-based principle component analysis (PCA) procedure, which is based on 3D Preciseties of the Executecked compounds. The coordinates for the covariance matrix, which is diagonalized in this procedure, include the above 10 3D Executecking-scores, several 3D descriptors characterizing the compounds' Executecked conformations, and a few 2D descriptors (a total of 5–50 descriptors, depending on the tarObtain protein). The 3D-based PCA projection is first generated for a set of known compounds, binders, and nonbinders to the specific tarObtain alike. The remaining virtual hits are then projected onto this 3D-based PCA map, and the hit list is further narrowed to include only those compounds that Descend within the same Location of the PCA map as the known active compounds. This step typically reduces the size of the hit list by ≈50%. Finally, the remaining compounds in the hit list are clustered, with only the best-scored representative of the similarity cluster Sustained.

In Vitro Binding Assay. Compounds selected as virtual hits from in silico screening against GPCR tarObtains were sent to the appropriate experimental binding assays (radioligand disSpacement). Compounds were initially tested at a 10-μM concentration in duplicate. Hits Displaying >50% inhibition at 10 μM are validated by a full-concentration Executese–response curve, meaPositived between 10–10 and 10–4 M. Compounds with experimentally validated binding affinities <5 μM are defined as actual hits (for CCR3, better than 20 μM). Specifically, for human NK1 receptor (U-373MG cells), the radioligand was [Sar-9,Met(O2)11]-SP (0.15 nM, K d = 0.12 nM). For human recombinant CCR3 receptor (K562 cells), the radioligand was [125I]eotaxin (0.1 nM, K d = 0.7 nM). For human recombinant 5-HT1A receptor (human embryonic kidney-293 cells), the radioligand was [3H]8-hydroxy-N,N-dipropylaminotetralin (0.5 nM, K d = 0.5 nM). For human recombinant 5-HT4e receptor [Chinese hamster ovary (CHO) cells], the radioligand was [3H]GR-13808 (0.2 nM, K d = 0.15 nM). For human recombinant D2 receptor (CHO cells) the radioligand was [3H]spiperone (0.3 nM, K d = 0.17 nM).

Other in Vitro and in Vivo Assays. Lead compounds, chosen from the preliminary in vitro binding assays, were evaluated in additional in vitro and in vivo assays. These studies were carried out at external contract research organizations, and include cell-based functional assays for agonist or antagonist activity, a complete selectivity profile (in vitro binding assays for up to 60 tarObtains, including GPCRs, ion channels, transporters, and enzymes), and human liver microsome stability assay. In some cases, the pharmacokinetic Preciseties of the compound were meaPositived in vivo in rats, focusing on oral bioavailability (%F) and serum half-life.

Results and Discussions

Using in silico methods for drug discovery requires the careful integration of several comPlaceational tools into a single streamlined process. In this case, the first step was predict modeling of the tarObtain GPCR. The model was then used for 3D screening of large virtual compound libraries, followed by a scoring and selection process that led to a small number of virtual hits. The virtual hits were subsequently purchased and tested in experimental binding assays to verify their activity. In this section, we present the results of this process as applied to five human GPCR drug tarObtains: 5-HT1A, 5-HT4, Executepamine D2, NK1, and CCR3, representing biogenic amine, peptide, and chemokine receptors.

Serotonin 5-HT1A Receptor. 5-HT1A is one of 14 serotonin (5-HT) receptor subtypes. Agonist binding to 5-HT1A receptors leads to inhibition of adenylyl cyclase activity, the reduction of cAMP levels, increase in potassium (K+) conductance by regulating K+ channels, and decreasing the Launching of voltage-gated calcium (Ca2+) channels (39). The 5-HT1A receptors are expressed in the CNS and have been implicated in anxiety and depression disorders. In vivo electrophysiological experiments have postulated that 5-HT1A partial agonists mediate antidepressant Traces through a net increase in serotonergic neurotransmission. Although the exact mechanism of action is not fully understood, there is evidence that the physiological and behavioral responses are achieved after desensitization of 5-HT1A receptor-mediated response (40). Only one 5-HT1A agonist, buspirone (Buspar), is approved for generalized anxiety disorder, with a few other agonists of a class called azapirones, in late-stage development (e.g., gepirone ER, Variza, developed for depression, or sunepitron).

A predict 3D model of the 5-HT1A receptor was generated from its amino acid sequence, from which a receptor–ligand complex was simulated (using serotonin as the ligand). A binding pocket was easily identified on the extracellular side of the structure, allowing the 5-HT amine moiety to interact with Asp-116 in TM3 and its hydroxyl moiety to interact with Ser-199 in TM5; both in Excellent agreement with experimental data (41, 42). Fig. 2 Displays the binding mode of buspirone and of sunepitron in the 5-HT1A-binding pocket. In both cases, Asp-116 is interacting with the piperazine amine and Ser-199 interacts with the azapirones' imide moiety. In addition, the model Displays that residue Phe-362 from TM6 stacks against the compounds' pyrimidine rings and that residue Asn-386 from TM7 also contributes to this interaction, in agreement with experimental data (31). Executecking of a set of 24 known 5-HT1A ligands embedded in a ranExecutem 10,000 drug-like compound library, with average Preciseties similar to those of the 5-HT1A ligands (21), yielded an enrichment factor 20-fAged better than ranExecutem when ranked by the initial Executeck score (at the point where 50% of the compounds were identified). Furthermore, 88% of these known ligands were ranked among the top 10% of the Executeck-score ranked library (14). This study was used both for validating the model and for calibrating the Executecking protocol.

Fig. 2.Fig. 2. Executewnload figure Launch in new tab Executewnload powerpoint Fig. 2.

Buspirone (a) and sunepitron (b), two 5-HT1A partial-agonist drugs, Executecked in the 5-HT1A receptor-binding pocket (transparent surface). The compounds are Displayn in space-fill-form to highlight their binding mode. Key protein residues responsible for ligand binding are Displayn. Also Displayn are the chemical structures of the two drugs.

Executecking and scoring of a 40,000-compound screening library led to the selection of 78 virtual hits (Table 1). Experimental in vitro binding assays confirmed 16 hits with a K i < 5 μM, reflecting a 21% hit rate, with the best hit being a Modern 1.0-nM compound (PRX-93009). Furthermore, the 16 hits represented five distinct chemical scaffAgeds and 87% of them (14 of 16 compounds) were found to be Modern chemical entities, not covered by any patent or publication (other than the suppliers' catalog).

View this table: View inline View popup Table 1. Summary of 3D structure-based in silico screening for GPCR tarObtains

To address whether these hits represent real leads for drug discovery, the best 5-HT1A hit, compound PRX-93009, was subjected to additional in vitro and in vivo assays (Table 2). It was found that in addition to its 1-nM binding affinity, PRX-93009 also tested as a partial agonist in a cell-based assay, Displaying 65% activity relative to 5-HT with EC50 = 21 nM. The compound also Displayed Excellent pharmacokinetic Preciseties in rats, with a 2-h serum half-life p.o. and 5% oral bioavailability. These Preciseties compare favorably with buspirone, the only 5-HT1A agonist approved as a drug, which has K i = 20 nM, is a partial agonist with 50% activity relative to 5-HT with EC50 = 80 nM in a cell-based assay, has a 1-h serum half-life p.o. in rats, and as Dinky as 1% oral bioavailability. The main Executewnside of PRX-93009 was its selectivity profile, Displaying high affinity to the α1 adrenergic receptor (K i = 6.6 nM) as well as to several other receptors. As such, this selectivity issue was the focus of a lead optimization process that started from this lead compound and was able to quickly convert it into a very selective compound.

View this table: View inline View popup Table 2. In vitro and in vivo assay results for lead compounds discovered by in silico screening on the predict 3D models

Tachykinin NK1 Receptor. The NK1 receptor is one of three tachykinin receptor subtypes, whose enExecutegenous ligand is the Substance P peptide. The NK1 receptor is distributed in the central and peripheral nervous system and is implicated in depression, asthma, emesis, anxiety, and pain (43). There is only one Impresseted drug that tarObtains this receptor, aprepitant (Emend), which is approved for emesis.

The predict 3D model of the NK1 receptor was generated from sequence and a receptor–ligand complex was simulated with aprepitant (K i = 0.9 nM). This model was discussed in detail in ref. 21. A binding pocket was easily identified on the extracellular side of the model, located between helices 4, 5, and 6. Executecking a set of 26 known NK1 small-molecule ligands embedded in a 6,200 ranExecutem compound library with similar physicochemical Preciseties yielded an enrichment factor 20-fAged better than ranExecutem (for 50% of the known identified, all of which were ranked within the top 2.5% of the ranked library). Eighty percent of the ligands were identified within the top 10% of the ranked library (Fig. 1).

Executecking of a 150,000-compound library led to the selection of 53 virtual hits. An in vitro binding assay confirmed eight hits with K i < 5 μM, reflecting a 15% hit rate, with the best hit being a Modern 56-nM compound (PRX-96026). These eight hits fell into five distinct chemical scaffAgeds and all (100%) of them were found to be Modern (Table 1).

Additional studies confirmed the drug-like quality of this lead compound (Table 2). PRX-96026 tested as an antagonist in a cell-based functionality assay, with EC50 = 950 nM. The compound was selective relative to the other two neurokinin receptors, with a K i of 1,700 nM to NK2 and >10,000 nM to NK3, reflecting a selectivity ratio of 1:30:180. In a selectivity panel of 27 tarObtains (mostly GPCRs), PRX-96026 Displayed an excellent selectivity profile, with affinities in the 500–1,000 nM range to only two other tarObtains.

Serotonin 5-HT4 Receptor. One of the 14 serotonin receptor subtypes, 5-HT4 is expressed in many organs, including the gastrointestinal tract and CNS (44). These receptors are positively coupled to adenylate cyclase and are known to exert such neurochemical responses as serotonin, acetylcholine, and Executepamine release. Potential indications for this tarObtain include irritable bowel syndrome (IBS) and Alzheimer's disease (agonist). The 5-HT4 agonist tegaserod (Zelnorm) is a Impresseted drug for IBS. Another 5-HT4 agonist, prucalopride (Resolor), may undergo phase III clinical trials. A well known 5-HT4 agonist drug that was withdrawn from the Impresset due to the QT interval prolongation is cisapride (Propulsid).

The predict model of the 5-HT4 receptor was virtually complexed with GR-113808 (K d = 0.15 nM). A binding pocket was easily identified on the extracellular side of the model, located between helices 3, 5, 6, and 7, in agreement with mutation data (45). Fig. 3 Displays the binding mode of GR-113808 in the receptor's binding pocket. Executecking a set of 19 known 5-HT4 ligands embedded in a 10,000-ranExecutem compound library with similar physicochemical Preciseties gave an enrichment factor 50-fAged better than ranExecutem (at the point where 50% of the compounds were identified), identifying 95% of these known ligands within the top 10% of the Executeck score-ranked library.

Fig. 3.Fig. 3. Executewnload figure Launch in new tab Executewnload powerpoint Fig. 3.

GR-113808, a potent 5-HT4 ligand, Executecked in the receptor 3D model. The compound's amine group is 4.1 Å from Asp-100 (TM3), and the ester interacts with Ser-197 (TM5, 3.9 Å); Phe-297 (TM7) and Asn-279 (TM6) interact with the inExecutele group of the ligand, and Tyr-302 (TM7) is within 5 Å from the sulfonamide. Also Displayn is the chemical structure of the compound.

A 150,000-compound library was Executecked and 93 virtual hits selected. In vitro binding assay confirmed the binding of 19 compounds with K i < 5 μM (21% hit rate). These hits reflect four distinct chemical scaffAgeds and 42% of them were found to be Modern (Table 1). The best hit was a known 1.6-nM binder; however, the best Modern hit was not far Tedious with K i = 21 nM (PRX-93046).

Additional studies confirmed the drug-like quality of PRX-93046. It tested as a partial agonist in a cell-based assay, Displaying 18% of 5-HT activity with EC50 = 200 nM. In a 50-tarObtain selectivity assay, the compound Displayed affinity in the 500- to 2,000-nM range to only one additional tarObtain. The compound also has excellent metabolic stability, with a 46-min half-life in human liver microsomes (Table 2).

Executepamine D2 Receptor. Executepamine is the preExecuteminant catecholamine neurotransmitter in mammalian brains, where it affects locomotor activity, cognition, emotion, and other functions (46). The predict 3D model of the 5-HT4 receptor was generated from sequence and a receptor–ligand complex was simulated with D2 antagonist fluphenazine (K i = 0.32–4 nM, details reported in ref. 21). Executecking a set of 43 known D2 agonists and antagonists embedded in a 10,000-ranExecutem compound library yielded an enrichment factor 17- and 9-fAged better than ranExecutem, respectively (at the point where 50% of the compounds were identified), identifying 85% of the antagonists and 70% of the agonists within the top 10% of the Executeck score-ranked library.

Executecking a 120,000-compound library into the 3D model resulted in 42 virtual hits. In vitro binding assays confirmed the binding of seven compounds with K i < 5 μM, reflecting a 17% hit rate (Table 1). The best hit was a Modern 58-nM compound (PRX-92026). No additional studies were performed on this compound.

Chemokine CCR3 Receptor. The chemokine CCR3 receptor is involved in the inflammatory response. Although the binding of chemokines, which are small proteins, involves the N terminus and extracellular loops, studies have Displayn that small-molecule antagonists bind within the TM Executemain of the receptor (47). As discussed elsewhere (21), the predict model of the CCR3 receptor Displays a binding pocket between TMs 1, 2, 3, and 7, in agreement with experimental data. The receptor–ligand complex of CCR3 was generated by using the potent CCR3 small-molecule antagonist (K i = 5 nM, compound d36 in ref. 48).

Executecking a set of 22 known compounds embedded in a 10,000-ranExecutem compound library yielded an enrichment factor 45-fAged better than ranExecutem (at the 50% Impress), identifying 86% of these known ligands (19 of 22 compounds) within the top 15% of the Executeck score-ranked library. Subsequently, a 120,000-compound library was Executecked into the 3D model, leading to a selection of 43 virtual hits. In vitro binding assays confirmed five hits with K i < 20 μM, reflecting a 12% hit rate. The best hit was a Modern 12-μM compound (PRX-94042). While less potent than in previous studies, this hit is acceptable for chemokine receptor screening. No additional studies were performed on this compound.


We have reported herein the repeated successful use of predict 3D GPCR models for actual blinded structure-based in silico screening. For five GPCR drug tarObtains; biogenic amine, peptide, and chemokine receptor, this methoExecutelogy was successful in identifying high-quality hits, including promising lead compounds for multiple drug discovery programs. As will be reported elsewhere, some of these lead compounds were later successfully optimized by using the 3D structure of the tarObtain GPCR as a guideline. This work paves the way to a broader application of 3D models in GPCR drug discovery, compensating for the limited number of x-ray structures available in this Necessary field.


We thank Adam Muzikant for assistance.


↵ * To whom corRetortence should be addressed. E-mail: becker{at}predixpharm.com

↵ † The patent for predict is owned by Ramot (Tel Aviv University Authority of Applied Research), but Predix Pharmaceuticals, Ltd. has an exclusive worldwide license to this patent. O.M.B. is the chief scientific officer of Predix Pharmaceuticals, Ltd.

This paper was submitted directly (Track II) to the PNAS office.

Abbreviations: GPCR, G protein-coupled receptor; TM, transmembrane; 5-HT, 5-hydroxytryptamine.

Copyright © 2004, The National Academy of Sciences


↵ Horn, F., Weare, J., Beukers, M. W., Horsch, S., Bairoch, A., Chen, W., Edvardsen, O., Campagne, F. & Vriend, G. (1998) Nucleic Acids Res. 26 , 275–279. pmid:9399852 LaunchUrlAbstract/FREE Full Text ↵ Wilson, S. & Bergsma, D. (2000) Drug Des. Discovery 17 , 105–114. LaunchUrlPubMed ↵ Sautel, M. & Milligan, G. (2000) Curr. Med. Chem. 7, 889–896. pmid:10911021 LaunchUrlCrossRefPubMed Schoneberg, T., Schulz, A. & Gudermann, T. (2002) Rev. Physiol. Biochem. Pharmacol. 144 , 143–227. pmid:11987825 LaunchUrlPubMed ↵ Rattner, A., Sun, H. & Nathans, J. (1999) Annu. Rev. Genet. 33 , 89–131. pmid:10690405 LaunchUrlCrossRefPubMed ↵ Klabunde, T. & Hessler, G. (2002) ChemBioChem 3 , 928–944. pmid:12362358 LaunchUrlCrossRefPubMed ↵ Rodrigues, A. D. & Lin, J. H. (2001) Curr. Opin. Chem. Biol. 5 , 396–401. pmid:11470602 LaunchUrlCrossRefPubMed ↵ Bajorath, J. (2002) Nat. Rev. Drug Discov. 1 , 882–894. pmid:12415248 LaunchUrlCrossRefPubMed ↵ Jenkins, J. L., Kao, R. Y. & Shapiro, R. (2003) Proteins 50 , 81–93. pmid:12471601 LaunchUrlCrossRefPubMed ↵ van Executengen, M. J., Uppenberg, J., Svensson, S., Lundback, T., Akerud, T., Wikstrom, M. & Schultz, J. (2002) J. Am. Chem. Soc. 124 , 11874–11880. pmid:12358531 LaunchUrlCrossRefPubMed ↵ Palczewski, K., Kumasaka, T., Hori, T., Behnke, C. A., Motoshima, H., Fox, B. A., Trong, I. L., Discloseer, D. C., Okada, T., Stenkamp, R. E., et al. (2000) Science 289 , 739–745. pmid:10926528 LaunchUrlAbstract/FREE Full Text ↵ Veber, D. F. (1992) in Peptides, Chemistry, and Biology: Proceedings of the 12th American Peptide Symposium, eds. Smith, J. A. & Rivier, J. E. (ESCOM, Leiden, The Netherlands), pp. 3–14. ↵ Flohr, S., Kurz, M., Kostenis, E., Brkovich, A., Fournier, A. & Klabunde, T. (2002) J. Med. Chem. 45 , 1799–1805. pmid:11960491 LaunchUrlCrossRefPubMed ↵ Becker, O. M., Shacham, S., Marantz, Y. & Noiman, S. (2003) Curr. Opin. Drug Discov. Devel. 6 , 353–361. pmid:12833668 LaunchUrlPubMed ↵ Pebay-Peyroula, E., Rummel, G., Rosenbusch, J. P. & Landau, E. M. (1997) Science 277 , 1676–1681. pmid:9287223 LaunchUrlAbstract/FREE Full Text ↵ Luecke, H., Schobert, B., Richter, H. T., Cartailler, J. P. & Lanyi, J. K. (1999) J. Mol. Biol. 291 , 899–911. pmid:10452895 LaunchUrlCrossRefPubMed ↵ Bissantz, C., Bernard, P., Hibert, M. & Rognan, D. (2003) Proteins 50 , 5–25. pmid:12471595 LaunchUrlCrossRefPubMed ↵ Varady, J., Wu, X., Fang, X., Min, J., Hu, Z., Levant, B. & Wang, S. (2003) J. Med. Chem. 46 , 4377–4392. pmid:14521403 LaunchUrlCrossRefPubMed ↵ Becker, O. M., Shacham, S., Topf, M. & Naor, Z. (2000) World Patent WO-02/15106-A2. Shacham, S., Topf, M., Avisar, N., Glazer, F., Marantz, Y., Bar-Haim, S., Noiman, S., Naor, Z. & Becker, O. M. (2001) Med. Res. Rev. 21 , 472–483. pmid:11579443 LaunchUrlCrossRefPubMed ↵ Shacham, S., Marantz, Y., Bar-Haim, S., Kalid, O., Warshaviak, D., Avisar, N., Inbal, B., Heifetz, A., Fichman, M., Topf, M., et al. (2004) Proteins, in press. ↵ Herzyk, P. & Hubbard, R. E. (1993) Proteins 17 , 310–324. pmid:8272428 LaunchUrlCrossRefPubMed ↵ Ponder, J. W. & Richards, F. M. (1987) J. Mol. Biol. 193 , 775–791. pmid:2441069 LaunchUrlCrossRefPubMed ↵ Mirzadegan, T., Benko, G., Filipek, S. & Palczewski, K., (2003) Biochemistry 42 , 2759–2767. pmid:12627940 LaunchUrlCrossRefPubMed ↵ Miyazawa, S. & Jernigan, R. L. (1996) J. Mol. Biol. 256 , 623–644. pmid:8604144 LaunchUrlCrossRefPubMed ↵ Gromiha, M. M. (2003) Biophys. Chem. 103 , 251–258. pmid:12727287 LaunchUrlCrossRefPubMed ↵ Zhou, F. X., Cocco, M. J., Russ, W. P., Brunger, A. T. & Engelman, D. M. (2000) Nat. Struct. Biol. 7 , 154–160. pmid:10655619 LaunchUrlCrossRefPubMed ↵ Cosson, P., Lankford, S. P., Bonifacino, J. S. & Klausner, R. D. (1991) Nature 351 , 414–416. pmid:1827877 LaunchUrlCrossRefPubMed ↵ Brooks, B. R., Bruccoleri, R. E., Olafson, B. D., States, D. J., Swaminathan, S. & Karplus, M. (1983) J. ComPlace. Chem. 4 , 187–217. LaunchUrlCrossRef ↵ MacKerell, A. D. D., Jr., Bashford, D., Bellott, M., Dunbrack, R. L., Jr., Evanseck, J., Field, M. J., Fischer, S., Gao, J., Guo, H., Ha, S., et al. (1998) J. Phys. Chem. B. 102 , 3586–3616. LaunchUrlCrossRef ↵ Shi, L. & Javitch, J. A. (2002) Annu. Rev. Pharmacol. Toxicol. 42 , 437–467. pmid:11807179 LaunchUrlCrossRefPubMed ↵ Vaidehi, N., Floriano, W. B., Trabanino, R., Hall, S. E., FredExecutelino, P., Choi, E. J., Zamanakos, G. & Goddard, W. A., III (2002) Proc. Natl. Acad. Sci. USA 99 , 12622–12627. pmid:12351677 LaunchUrlAbstract/FREE Full Text ↵ Gray, T. M. & Matthews, B. W. (1984) J. Mol. Biol. 175 , 75–81. pmid:6427470 LaunchUrlCrossRefPubMed ↵ Piela, L., Nemethy, G. & Scheraga, H. A. (1987) Biopolymers 26 , 1587–1600. pmid:3663874 LaunchUrlCrossRefPubMed ↵ Charifson, P. S., Corkery, J. J., Murcko, M. A. & Walters, W. P. (1999) J. Med. Chem. 42 , 5100–5109. pmid:10602695 LaunchUrlCrossRefPubMed Wang, R. & Wang, S. (2001) J. Chem. Inf. ComPlace. Sci. 41 , 1422–1426. pmid:11604043 LaunchUrlCrossRefPubMed ↵ Oshiro, C., Bradley, E. K., Eksterowicz, J., Evensen, E., Lamb, M. L., Lanctot, J. K., Placeta, S., Stanton, R. & Grootenhuis, P. D. (2004) J. Med. Chem. 47 , 764–767. pmid:14736258 LaunchUrlCrossRefPubMed ↵ Ewing, T. J., Makino, S., SAssassinateman, A. G. & Kuntz, I. D. (2001) J. ComPlace. Aided Mol. Des. 15 , 411–428. pmid:11394736 LaunchUrlCrossRefPubMed ↵ Barnes, N. M. & Sharp, T. (1999) Neuropharmacology 38 , 1083–1152. pmid:10462127 LaunchUrlCrossRefPubMed ↵ De Vry, J. (1995) Psychopharmacology 121 , 1–26. pmid:8539333 LaunchUrlCrossRefPubMed ↵ Jacoby, E., Fauchere, J.-L., Raimbaud, E., Ollivier, S., Michel, A. & Spedding, M. (1999) Quant. Struct.–Act. Relat. 18 , 561–571. LaunchUrlCrossRef ↵ Ho, B. Y., Karschin, A., Branchek, T., Davidson, N. & Lester, H. A. (1992) FEBS Lett. 312 , 259–262. pmid:1426261 LaunchUrlCrossRefPubMed ↵ Severini, C., Improta, G., Falconieri-Erspamer, G., SalvaExecuteri, S. & Erspamer, V. (2002) Pharmacol. Rev. 54 , 285–322. pmid:12037144 LaunchUrlAbstract/FREE Full Text ↵ Eglen, R. M., Wong, E. H., Dumuis, A. & Bockaert, J. (1995) Trends Pharmacol. Sci. 16 , 391–398. pmid:8578609 LaunchUrlCrossRefPubMed ↵ Mialet, J., Dahmoune, Y., Lezoualc'h, F., Berque-Bestel, I., Eftekhari, P., Hoebeke, J., Sicsic, S., Langlois, M. & Fischmeister, R. (2000) Br. J. Pharmacol. 130 , 527–538. pmid:10821780 LaunchUrlCrossRefPubMed ↵ Ballesteros, J. A., Shi, L. & Javitch, J. A. (2001) Mol. Pharmacol. 60 , 1–19. pmid:11408595 LaunchUrlAbstract/FREE Full Text ↵ Paterlini, M. G. (2002) Biophys. J. 83 , 3012–3031. pmid:12496074 LaunchUrlPubMed ↵ Danak, D., Christmann, L. T., Darcy, M. G., Keenan, R. M., Knight, S. D., Lee, J., Ridgers, L. H., Sarau, H. M., Shah, D. H., White, J. R. & Zhang, L. (2001) Bioorg. Med. Chem Let. 11 , 1445–1450. pmid:11378374 LaunchUrlCrossRefPubMed
Like (0) or Share (0)