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Volume 9 Supplement 1

Asia Pacific Bioinformatics Network (APBioNet) Sixth International Conference on Bioinformatics (InCoB2007)

Preferential binding of allosteric modulators to active and inactive conformational states of metabotropic glutamate receptors

BMC Bioinformatics volume 9, Article number: S16 (2008) Cite this article

Abstract

Metabotropic glutamate receptors (mGluRs) are G protein coupled receptors that play important roles in synaptic plasticity and other neuro-physiological and pathological processes. Allosteric mGluR ligands are particularly promising drug targets because of their modulatory effects – enhancing or suppressing the response of mGluRs to glutamate. The mechanism by which this modulation occurs is not known. Here, we propose the hypothesis that positive and negative modulators will differentially stabilize the active and inactive conformations of the receptors, respectively. To test this hypothesis, we have generated computational models of the transmembrane regions of different mGluR subtypes in two different conformations. The inactive conformation was modeled using the crystal structure of the inactive, dark state of rhodopsin as template and the active conformation was created based on a recent model of the light-activated state of rhodopsin. Ligands for which the nature of their allosteric effects on mGluRs is experimentally known were docked to the modeled mGluR structures using ArgusLab and Autodock softwares. We find that the allosteric ligand binding pockets of mGluRs are overlapping with the retinal binding pocket of rhodopsin, and that ligands have strong preferences for the active and inactive states depending on their modulatory nature. In 8 out of 14 cases (57%), the negative modulators bound the inactive conformations with significant preference using both docking programs, and 6 out of 9 cases (67%), the positive modulators bound the active conformations. Considering results by the individual programs only, even higher correlations were observed: 12/14 (86%) and 8/9 (89%) for ArgusLab and 10/14 (71%) and 7/9 (78%) for AutoDock. These findings strongly support the hypothesis that mGluR allosteric modulation occurs via stabilization of different conformations analogous to those identified in rhodopsin where they are induced by photochemical isomerization of the retinal ligand – despite the extensive differences in sequences between mGluRs and rhodopsin.

Background

Glutamate is the most important excitatory neurotransmitter in the brain. Glutamatergic neurotransmission proceeds primarily via ion gated channels (ionotropic glutamate receptors). In addition, there are metabotropic glutamate receptors (mGluRs), which belong to the G protein coupled receptor (GPCR) family and play modulatory roles in neuronal processes such as anxiety, learning, memory and perception of pain [1]. Because of these roles they form attractive drug targets for treatment of neuronal dysfunction including seizures, epilepsy, Parkinson's disease and night blindness [2–4]. Both types of glutamate receptors share a common extracellular ligand binding architecture, albeit different topology and protein family membership. X-ray crystallographic structures are available for the soluble extracellular domains of mGluRs [5–8] and ionotropic glutamate receptors [9–11]. Because of the common ligand recognized by these extracellular domains, targeting mGluRs specifically without interfering with the ubiquitous glutamatergic neurotransmission therefore requires designing allosteric ligands that bind in the transmembrane domains of mGluRs. Being members of the GPCR superfamily, mGluRs are structurally characterized by seven transmembrane helices that divide each protein into an extracellular, cytoplasmic and transmembrane domain. Based on sequence homology and pharmacological considerations the GPCR family is subdivided into several classes. Class A, the largest subfamily, is the rhodopsin like family. Members of other GPCR classes share very little sequence homology with class A members and some also differ in the length of their N-termini. In particular, the ligand binding domain of class C GPCRs is located in a 500 amino acid long addition to the N-terminus. This is unusual for the GPCR family where the ligand binding domain is typically located in the transmembrane domain, near its interface with the extracellular domain. The prototypical members of class C GPCRs are the mGluRs. In human, there are eight subtypes, divided into three groups based on their pharmacological and signaling properties. Group I mGluRs (subtypes 1 and 5) are primarily localized postsynaptically where they modulate ion channel activity and neuronal excitability. Groups II (subtypes 2 and 3) and III (subtypes 4, 6, 7, and 8) are primarily located presynaptically and regulate the release of neurotransmitters, including glutamate [12].

For Group I mGluRs it was shown by mutagenesis that allosteric modulators bind in the transmembrane domain near the interface between the transmembrane and extracellular domains similar to the ligand binding pockets in class A GPCRs [13, 14]. Allosteric mGluR ligands act as positive or negative modulators of mGluR activity in response to glutamate or glutamate analogs, enhancing or suppressing the responses respectively [15]. Small changes in the chemical structures of ligands can switch their modulatory effects. For example, 4,4'-difluorobenzaldazine (4,4'-DFB) is a negative modulator for mGluR5, while 3,3'-difluorobenzaldazine (3,3'-DFB) is a positive modulator for the same receptor [16]. Being able to predict if a given ligand will have positive or negative modulatory effects on mGluRs would be highly beneficial in the design of future drug candidates targeted at these receptors. With the long-term goal of building such a predictor, we here propose the hypothesis that positive and negative modulators can be distinguished by their higher affinities for the active and inactive conformations of the receptors, respectively. To test this hypothesis, we have generated computational models of the transmembrane regions of different mGluR subtypes in two different conformations. The inactive conformation was modeled using the crystal structure of the inactive, dark state of rhodopsin as template [17] and the active conformation was created based on a recent model of the light-activated state of rhodopsin [18]. We find that in the majority of ligand-receptor pairs, binding energies for positive modulators are more favorable when docked to the active conformation than the inactive conformation and vice versa for negative modulators.

Results

Identification and analysis of the ligand binding pocket

A total of 24 ligands were identified that bind to three mGluR subtypes, the Class I mGluRs mGluR1 and mGluR5, the Class II mGluR, mGluR2 and the Class III mGluRs, mGluR4 and mGluR7. The structures of the ligands are shown in Figure 1. Receptor modulation was reported in human and in rat receptors (for references, see Table 1) and docking was performed with homology models of the mGluR of the respective species. We compared the results from two different docking programs, ArgusLab and AutoDock. Of the rank-ordered list of bound ligand conformations, we have chosen in each case the ligand conformation where the ligand was most buried and had minimum energy in comparison to all other conformations in the same binding pocket. In some cases, ligands were predicted not to bind, but this was only the case for one of the two programs in each case. If a ligand did not dock in AutoDock, it did dock in ArgusLab, and vice versa, so that for all ligands binding could be examined. The unbiased searching of the whole receptor with each of the modulators studied revealed that all of the ligands preferentially bound in a region similar to that of retinal in rhodopsin (Figure 2), in the transmembrane domain including helices 3, 5–7 near the interface with the extracellular domain, especially extracellular loop 2. The binding pocket was similar for all ligands docked to all receptors, and is exemplary described in more detail for mGluR5, below.

Figure 1
figure 1

The structures of the ligands studied. (A) EM-TBPC (B) Ro67-7476 (C) Ro01-6128 (D) Ro67-4853 (E) R214127 (F) triazafluorenone (G) CPCCOEt (H) YM298198 (I) MPEP (J) SIB-1757 (K) SIB-1893 (L) Fenobam (M) MTEP (N) DFB-derivatives. The positions of the fluorine atoms are indicated for DFB-2,2' and DFB-4,4'. DFB-3,3' is shown. (O) PTEB (P) NPS2390 (Q) CPPHA (R) 5MPEP (S) MPEPy (T) PHCCC (U) AMN082. For definition of ligand names, see abbreviations list. Images were created using ArgusLab software [58].

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Figure 2
figure 2

Cartoon representation of the mGluR5 receptor (A) active and (B) inactive models docked with negative modulator MPEP. MPEP is colored in dark blue and is rendered in spheres. MPEP refers to 2-methyl-6-(phenylethynyl)-pyridine. Images were created using Pymol Software [83].

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Table 1 List of predicted binding energies for mGluR subtypes 1, 2, 4, 5 and 7 with different positive and negative modulators shown in Figure 1.
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In order to compare the residues in contact with different ligands, we analyzed the residues predicted to be located within 5 Ã… distance from the docked ligand. The results obtained with ArgusLab are listed in Table 2 and are shown in Figure 3 for mGluR5 in the active and inactive conformations. To demonstrate the similarities and differences between positive and negative modulators, we compared specifically the negative modulator MPEP with the positive modulator 3,3'-DFB. Analysis of the binding pocket residues in the mGluR5 subtype revealed that W784 was in closest proximity to both docked ligands and in both conformations, active and inactive. W784 is highly conserved in all mGluRs and in class A GPCRs in general. This tryptophan corresponds to W265 in rhodopsin. In addition to W784, residues R647, Y658, L743 and F787 were found to be part of the binding pocket regardless of the type of modulator and conformation of the receptor. In addition, C732, V788, M801 and S804 are found frequently in the binding pockets. In contrast, S657, L785, C781 and T734 were found to be unique for the positive modulator 3,3'-DFB (Figure 3C,D; Table 2) and were not found in the binding pocket of the negative modulator MPEP. Conversely, R726 and V805 were unique to the binding pocket of MPEP (Figure 3A,B; Table 2).

Figure 3
figure 3

Amino acid residues within 5 Ã… of the docked ligands MPEP and 3,3'-DFB in mGluR5. The active receptor conformation is shown in A, C and the inactive receptor conformation is shown in B, D. Models were docked with negative modulator MPEP (A, B) and positive modulator 3,3'-DFB (C, D). The ligands are colored in blue for the active models and in red for the inactive models. Images were created using Pymol Software [83].

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Table 2 Residues within 5 Å distance from the MPEP and 3,3'-DFB ligands in active and Inactive models of mGluR5 in comparison to the experimental results published. Residues colored in red – were not predicted in our docking, green – additional residues predicted and black – residues correctly predicted.
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Validation of the ligand binding pocket with experimental data

Site-directed mutagenesis was used previously to identify residues in mGluR5 that are critical for ligand binding [14, 19]. Table 2 summarizes the comparison between these experimentally identified ligand binding pocket residues and those predicted by our docking studies with ArgusLab. The results for AutoDock are not shown because the overlap between the predicted binding pockets and those experimentally determined was significantly less. The previous experimental studies with mGluR5 have shown that residues P654, Y658, L743, T780, W784, F787, Y791 and A809 are crucial for binding of the negative modulator MPEP [14]. Our prediction of MPEP binding to both active and inactive GRM5 models predicted all of the above residues to be within 5 Ã… of the ligand, except A809, T780 and Y791 (colored in red in Table 2). Additionally there are several residues that are predicted to be important for MPEP binding but that have not yet been experimentally verified (colored green in Table 2). For the binding of the positive modulator 3,3'-DFB, it was concluded from site-directed mutagenesis that M801, S657 and T780 are critical for binding and modulatory function [19]. There was also evidence that P654, S657, L743 and N733 may contribute more weakly to 3,3'-DFB binding. All of these residues are predicted to be part of the 3,3'-DFB binding pocket in the inactive model, but S657 and T780 are not present in the active model. In addition, we predict several residues to be part of the binding pockets that have not been investigated previously (shown in green in Table 2). Thus, the comparison of the predicted ligand binding pockets in mGluR5 inactive and active models with the available experimental site directed mutagenesis data strongly validates our models. In addition, we generated testable hypotheses on important residues previously not investigated, and provide evidence that there may be differences in the roles of the amino acids in the binding pocket depending on the conformation of the receptor.

Analysis of binding energies

The above in-depth analysis of the mGluR5 binding pocket suggests that there may be significant differences between the interactions made by negative and positive modulators with mGluRs depending on the conformation state of the receptor. To test if there is a general trend that distinguishes the action of positive and negative modulators on the receptors, we quantified the overall binding energies for the 24 different ligands with known modulatory nature (positive versus negative). Table 1 shows the binding energies of the ligand-protein complexes calculated by ArgusLab and AutoDock and Figure 4 plots the difference between the respective energies for active and inactive conformations. Where the energies for the active and inactive conformations were very similar, the docking was repeated three times to estimate the error on the predictions (indicated in Table 1 and Figure 4). In general, the results obtained with ArgusLab were less variable between repeated runs than those obtained with AutoDock. A total of 9 ligands were experimentally shown to act as positive modulators of specific subtypes, 14 ligands were negative modulators and one ligand was neutral. In general, the positive modulators bound with more favorable energy to the model of the active mGluR conformation based on the rhodopsin ANM model [18], while the negative modulators bound with more favorable energy to the model of the inactive mGluR conformation based on the rhodopsin inactive, dark-state [17]. The neutral ligand, 5MPEP, showed relatively little differences between the energies of the inactive and the active models, but was consistently better docked to the active model using both programs. ArgusLab predicted 12 of the 14 (86%) negative modulators to bind with more favorable energy to the inactive model and 8/9 (89%) positive modulators to the active model, while the numbers for AutoDock were less correlated: 10/14 (71%) and 7/9 (78%). There were also more incidences in which AutoDock was not able to predict binding for the ligands. Five of the predictions for negative modulators and one positive modulator obtained with AutoDock were near or beyond the accuracy limit of AutoDock as judged by the error obtained when multiple independent docking experiments were carried out. In contrast, in the case of ArgusLab only one difference between docking to active and inactive models was within the noise level. We conclude that the relative difference between the binding energies of the docked ligands for the active and inactive models is highly predictive of the nature of the modulator, positive or negative. Positive modulators in most cases appear to strongly prefer the active conformation over the inactive conformation and negative modulators vice versa.

Figure 4
figure 4

Differences in energy between active (ANM based) and inactive (rhodopsin crystal-structure based) models of mGluRs docked with the ligands shown in Figure 1 and listed in Table 1. Green bars indicate positive modulators, red bars negative modulators and the yellow bar represents a neutral ligand. Where values of 2 are shown, the ligand did not dock to the active model, where values of -2 are shown, the ligand did not dock to the inactive model. Error bars indicate standard deviation in three docking experiments each for the respective active and inactive models. If an error bar is placed at a -2 or 2 bar, the error represents the standard deviation of the ligand and model combination where docking was observed. A. Results from docking with Autodock software. B. Results from docking with ArgusLab software.

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Discussion

So far, the only available three-dimensional structure of any GPCR is that of rhodopsin [17]. Previous approaches to docking of ligands to GPCRs have therefore used mostly receptor models based on the rhodopsin structure [13, 14, 20–43]. The advantages and disadvantages of this approach have been discussed [44, 45] and it was shown that in some cases alternative approaches such as the Membstruk modeling approach provides more useful models than those based directly on homology to rhodopsin [46–55]. In particular, it was observed recently that there is a difference in the structures obtained after short molecular dynamics simulations depending on whether receptor agonists or antagonists were docked [56, 57]. These observations indicate that the conformation of the receptor will be important for the stability and nature of a receptor-ligand complex. To our knowledge however there has been no previous attempt to predicting different conformations of the receptors first, and then docking ligands to these conformations. We describe here the docking of ligands to two different conformations of mGluR receptors, active and inactive, using two docking programs, ArgusLab [58] and AutoDock [59].

AutoDock is a stochastic Grid-based approach that uses the genetic algorithm to sample different populations of ligand conformations in their binding to the receptor. Each bound conformation is energetically evaluated by a series of energy minimization steps, in which unsuccessful docking results are discarded. While the genetic algorithm is a widely used and reliable algorithm, it has known limitations [60], among the most significant is the possibility of the optimization of the ligand conformations getting trapped in local minima [61]. This is also confirmed by our observation that individual runs may give different results (Figure 4 and Table 1). ArgusLab therefore provides both algorithms, the stochastic search, analogous to the genetic algorithm provided by AutoDock, as well as an exhaustive search method based on identification of complementary shapes of the ligand and the receptor, referred to as "ShapeDock" or "ArgusDock". When using ArgusLab with the ArgusDock algorithm, 12 of the 14 (86%) negative modulators were predicted to bind with more favorable energy to the inactive model. Due to the high reproducibility between different runs, the error margins are small and in all cases but one the errors were significantly smaller than the differences observed between docking to active and inactive models. Similarly, 8/9 (89%) positive modulators bound significantly more favorably to the active model with ArgusLab. The results obtained for AutoDock were less correlated, as expected: 10/14 (71%) and 7/9 (78%) bound the predicted conformation more favorably. We consider the AutoDock results less reliable than those obtained with ArgusLab for several reasons. In six of the predictions, where the differences between docking to active and inactive models were small, docking energy differences were close to or smaller than the noise level. There were also more incidences in which AutoDock was not able to predict binding for the ligands at all. Finally, the ligand binding pockets predicted by AutoDock showed less agreement with the experiments than ArgusLab. However, one should keep in mind that some ligand/binding site types are problematic because the shape does not match if the starting ligand conformation is not correct and/or if the scoring function is not appropriate. This could be the case in the prediction of AMN082, where both algorithms incorrectly predicted this ligand to be a negative modulator, because the ligand did not dock at all to the inactive conformation. Such difficulties may also give rise to larger errors when docking one ligand as compared to another. For example, the docking energies of ligand YM298198 were associated with a larger error when using both algorithms. If we were to equally weigh predictions made by ArgusLab and AutoDock, we would still have agreement between the two methods and strong preferences (i.e. strong differences in both AutoDock and ArgusLab) in 8 out of 14 cases (57%), where the negative modulators bound the inactive conformations with significant preference using both docking programs, and 6 out of 9 cases (67%), supporting the hypothesis.

In addition to comparing predicted ligand binding energies, we also investigated the details of the interactions between the ligand binding pockets and the ligands for mGluR5. Of the experimentally known ligand binding residues of MPEP, our prediction identified all residues, except A809, T780 and Y791 (colored in red in Table 2). These three residues are more than 6.2 Ã… away from any atom within the ligand, and are not simply missed due to a too short cut-off distance in the definition of ligand binding residues. Furthermore, A809 and T780 are facing the outside of the helical bundle, and it is possible that the experimental effects reported might have been secondary effects. In the case of 3,3'-DFB all known ligand binding residues are also predicted. For both ligands on the other hand, we predict a number of residues to be important for ligand binding that have not been tested experimentally. Finally, we show that while there are several residues in the ligand binding pocket that are shared between active and inactive conformations, there are also residues that bind ligand only in one conformation, and that are specific for positive versus negative modulators. Thus, our models provide useful, experimentally testable hypotheses.

The dependence of the properties of ligand binding on receptor conformation has important functional implications. Recently, it was shown for rhodopsin that the dark-state inactive structure already contains the information needed to form the light-activated structure [18]. This supports the notion that all-trans retinal in rhodopsin stabilizes a conformation that is already partially accessible to the 11-cis retinal bound dark, inactive state of the receptor. Translated to other GPCRs, this suggests that receptors may partially form activated conformations and that agonists could stabilize such conformations, while inverse agonists would destabilize such conformations. In mGluRs, the situation is slightly different from other GPCRs because their ligand binding domain is located in an extracellular domain added to the conserved GPCR seven-transmembrane helical scaffold. In this case, ligands also bind to the transmembrane/extracellular domain interface but here they act as modulators for ligand binding in the extracellular domain. However, it was shown that in the absence of the extracellular domain, positive and negative modulators act as agonists and inverse agonists, respectively. Thus, the findings reported here for mGluRs are likely to have functional implications for the GPCR family in general, implying that agonists and antagonists are likely to prefer the active and inactive conformation of GPCRs, respectively.

Conclusion

Here we proposed the idea that allosteric ligands can be docked to inactive and active conformational models of mGluRs. We found that the relative difference in binding energy between the two conformations is highly predictive of whether the ligand is a positive or a negative modulator. A positive modulator will bind more favorably to the active conformation, while the negative modulator will bind more favorably to the inactive conformation. Furthermore, we identified similarities and differences in the interactions made between ligand and receptor depending on the nature of the modulator and the conformation of the receptor. The findings are likely to have general utility in predicting functional classification of ligands, such as classification as agonists or antagonists.

Methods

Alignment

An alignment of the seven-transmembrane helices of rat and human mGluR1, mGluR2, mGluR4, mGluR5 and mGluR7 with respect to the transmembrane helices of bovine rhodopsin (Protein Data Bank code 1f88[17]) was generated using ClustalW [62]. The alignment was manually validated by comparison with the alignment proposed in previous molecular modeling studies of mGluRs [13, 14, 63]. Sequences were obtained from SWISS-PROT: mGluR1 (P23385 (rat), Q13255 (human)), mGluR2 (Q14416), mGluR4 (Q14833), mGluR5 (P31424 (rat), P41594 (human)) and mGluR7 (Q14831). The sequence of bovine rhodopsin was read directly from the rhodopsin crystal structure [17].

Structure prediction using homology modeling

Using the generated sequence alignment, three-dimensional models of the different mGluR subtypes were built by homology modeling using the MODELLER software [64, 65]. The crystal structure of dark, inactive bovine rhodopsin with pdb id 1f88[17] and the ANM generated model of the activated state of rhodopsin [18] were used as the structural templates for generating the inactive and active models of mGluRs, respectively. All models were evaluated using PROCHECK [66], MOLPROBITY [67] and WHAT-IF [68].

Docking with ArgusDock

We assembled from literature a list of ligands for which their effect (positive or negative modulation) is known (listed in Table 1). All ligands were docked to inactive and active models of the respective mGluR subtypes using ArgusLab software, version 4.0 [58]. Ligand pdb files were generated using JME Molecular Editor software [69]. Hydrogen atoms were added to the ligand coordinate file prior to docking using ArgusLab. The docking between each receptor subtype and ligand was performed using the "Dock a ligand" option. All the residues of the receptor were defined to be part of the binding site i.e, cubic boxes measuring 151 × 123 × 145 points for the inactive model and 111 × 151 × 151 for the active model were built to include the entire protein in each case, allowing no bias towards the binding pocket. A spacing of 0.4 Å between the grid points was used. Docking simulations were performed by selecting "ArgusDock" as the docking engine. "Dock" was chosen as the calculation type, "flexible" for the ligand and the AScore was used as the scoring function. The AScore function, with the parameters read from the AScore.prm file was used to calculate the binding energies of the resulting docked structures. This file contains the coefficients for each term in the scoring function. Structures were visualized and the best docked structure was chosen based on lowest energy and minimal solvent accessibility of the ligand, as follows. First, the top 150 unique poses were retrieved. Typically, 20% of these conformations were ligands docked to the surface of the protein, highly water accessible. These conformations were discarded manually. In at least ~2/3 of the remaining structures, the ligand was bound in a pocket analogous to the retinal binding pocket. Those ligands that were only partially buried in the protein interior, with parts of the ligand facing the outside of the helical bundle, were also discarded. Only ligands with maximal burial in the protein interior were retained. This typically included a list of 50 structures. These structures were rank-ordered by minimum energy and the structure with the lowest energy was chosen as the predicted receptor-bound conformation of the ligand.

Docking with AutoDock

All ligands shown in Figure 1 were docked to inactive and active models of the various mGluR subtypes, using the Lamarckian Genetic algorithm (LGA) provided by the AutoDock program, version 3.0 [59]. Solvation parameters were added to the protein coordinate file with the "Addsol" option in AutoDock, and the ligand torsions were defined using the "Ligand torsions" menu option of AutoDock. The grid maps representing the protein were calculated using the "AutoGrid" option. A cubic box was built around the protein with 126 × 126 × 126 points; a spacing of 0.403 Å between the grid points was used. The protein was centered on the geometric center prior to docking. Docking simulations were carried out with an initial population of 300 individuals, and a maximum number of 50,000,000 energy evaluations. Apart from this a maximum number of 27,000 generations, a translation step of 2 Å, a quarternion step of 50° and a torsion step of 50° were used as the docking parameters for obtaining the final docked structures. Resulting orientations that have less than or equal to 0.5 Å root mean square deviation were clustered. In addition to returning the docked structure, AutoDock also calculates an affinity constant for each ligand-receptor configuration. The best ligand-receptor structure from the docked structures was chosen based on lowest energy and minimal solvent accessibility of the ligand, analogous to the procedure described above for ArgusLab, with the difference being that only the top 10 most favorably bound ligand structures were analyzed.

Abbreviations

mGluR :

metabotropic glutamate receptor

GPCR :

G protein coupled receptor

CPCCOEt :

cyclopropan[b]chromen-1a-carboxylate

DFB :

difluorobenzaldazine

fenobam :

N-(3-chlorophenyl)-N'-(4,5-dihydrol-1-methyl-4-oxo-1-H-imidazole-2-yl)-urea

MPEP :

2-methyl-6-((3-methoxyphenyl)ethynyl)-pyridine

R214127 :

1-(3,4-dihydro-2H-pyrano[2,3-b]quinolin-7-yl)-2-phenyl-1-ethanone

Ro01-6128 :

diphenylacetyl-carbamic acid ethyl ester

Ro67-4853 :

(9H-xanthene-9-carbonyl)-carbamic acid butyl ester

Ro67-7476 :

(S)-2-(4-fluoro-phenyl)-1-(toluene-4-sulphonyl)-pyrrolidine

SIB1757 :

6-methyl-2-(phenylazo)-3-pyrindol

SIB1893 :

([phenylazo]-3-pyrindole)-2-methyl-6-(2-phenylethenyl)pyridine

MTEP :

3-[(2-methyl-1,3-thiazol-4-yl)ethynyl]pyridine

YM298198 :

(6-([(2-methoxyethyl)amino]methyl)-N-methyl-N-neopentylthiaolo[3,2-a]benzoimidazole-2-carboxamide

EM-TBPC – 1-ethyl-2-methyl-6-oxo-4-(1:

2,4,5-tetrahydro-benzo[d]azepin-3-yl)-1,6-dihydro-pyrimidine-5-carbonitrile

PTBE :

(1-(2-hydroxy-3-propyl-4,4-[4-(2H-tetrazol-5-yl)phenoxy]butoxyphenyl)ethanone)

NPS2390 :

2-quinoxaline-carboxamide-N-adamantan-1-yl

CPPHA :

N-(4-chloro-2-[(1,3-dioxo-1,3-dihydro-2H-isoindol-2-yl)methyl]phenyl)-2-hydroxybenzamide

5MPEP :

5-methyl-6-(phenylethynyl)-pyridine

MPEPy :

3-Methoxy-5-pyridin-2-ylethynylpyridine

PHCCC :

N-phenyl-7-(hydroxylimino)cyclopropa[b]chromen-1a-carboxamide

AMN082 :

N,N'-Dibenzhydrylethane-1,2-diamine dihydrochloride.

References

  1. Swanson CJ, Bures M, Johnson MP, Linden AM, Monn JA, Schoepp DD: Metabotropic glutamate receptors as novel targets for anxiety and stress disorders. Nat Rev Drug Discov 2005, 4(2):131–44. 10.1038/nrd1630

    Article  CAS  PubMed  Google Scholar 

  2. Kew JN: Positive and negative allosteric modulation of metabotropic glutamate receptors: emerging therapeutic potential. Pharmacol Ther 2004, 104(3):233–44. 10.1016/j.pharmthera.2004.08.010

    Article  CAS  PubMed  Google Scholar 

  3. Dryja TP, McGee TL, Berson EL, Fishman GA, Sandberg MA, Alexander KR, Derlacki DJ, Rajagopalan AS: Night blindness and abnormal cone electroretinogram ON responses in patients with mutations in the GRM6 gene encoding mGluR6. Proc Natl Acad Sci USA 2005, 102(13):4884–9. 10.1073/pnas.0501233102

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  4. Parmentier ML, Prézeau L, Bockaert J, Pin JP: A model for the functioning of family 3 GPCRs. Trends Pharmacol Sci 2002, 23(6):268–74. 10.1016/S0165-6147(02)02016-3

    Article  CAS  PubMed  Google Scholar 

  5. Kunishima N, Shimada Y, Tsuji Y, Sato T, Yamamoto M, Kumasaka T, Nakanishi S, Jingami H, Morikawa K: Structural basis of glutamate recognition by a dimeric metabotropic glutamate receptor. Nature 2000, 407(6807):971–7. 10.1038/35039564

    Article  CAS  PubMed  Google Scholar 

  6. Tsuchiya D, Kunishima N, Kamiya N, Jingami H, Morikawa K: Structural views of the ligand-binding cores of a metabotropic glutamate receptor complexed with an antagonist and both glutamate and Gd3+. Proc Natl Acad Sci USA 2002, 99(5):2660–5. 10.1073/pnas.052708599

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  7. Hampson DR, Huang XP, Pekhletski R, Peltekova V, Hornby G, Thomsen C, Thogersen H: Probing the ligand-binding domain of the mGluR4 subtype of metabotropic glutamate receptor. J Biol Chem 1999, 274(47):33488–95. 10.1074/jbc.274.47.33488

    Article  CAS  PubMed  Google Scholar 

  8. Muto T, Tsuchiya D, Morikawa K, Jingami H: Structures of the extracellular regions of the group II/III metabotropic glutamate receptors. Proc Natl Acad Sci USA 2007, 104(10):3759–64. 10.1073/pnas.0611577104

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  9. Armstrong N, Sun Y, Chen GQ, Gouaux E: Structure of a glutamate-receptor ligand-binding core in complex with kainate. Nature 1998, 395(6705):913–7. 10.1038/27692

    Article  CAS  PubMed  Google Scholar 

  10. Armstrong N, Gouaux E: Mechanisms for activation and antagonism of an AMPA-sensitive glutamate receptor: crystal structures of the GluR2 ligand binding core 118. Neuron 2000, 28(1):165–81. 10.1016/S0896-6273(00)00094-5

    Article  CAS  PubMed  Google Scholar 

  11. Mayer ML: Glutamate receptors at atomic resolution. Nature 2006, 440(7083):456–62. 10.1038/nature04709

    Article  CAS  PubMed  Google Scholar 

  12. Conn PJ, Pin JP: Pharmacology and functions of metabotropic glutamate receptors. Annu Rev Pharmacol Toxicol 1997, 37: 205–37. 10.1146/annurev.pharmtox.37.1.205

    Article  CAS  PubMed  Google Scholar 

  13. Malherbe P, Kratochwil N, Knoflach F, Zenner MT, Kew JN, Kratzeisen C, Maerki HP, Adam G, Mutel V: Mutational analysis and molecular modeling of the allosteric binding site of a novel, selective, noncompetitive antagonist of the metabotropic glutamate 1 receptor. J Biol Chem 2003, 278(10):8340–7. 10.1074/jbc.M211759200

    Article  CAS  PubMed  Google Scholar 

  14. Malherbe P, Kratochwil N, Zenner MT, Piussi J, Diener C, Kratzeisen C, Fischer C, Porter RH: Mutational analysis and molecular modeling of the binding pocket of the metabotropic glutamate 5 receptor negative modulator 2-methyl-6-(phenylethynyl)-pyridine. Mol Pharmacol 2003, 64(4):823–32. 10.1124/mol.64.4.823

    Article  CAS  PubMed  Google Scholar 

  15. Goudet C, Gaven F, Kniazeff J, Vol C, Liu J, Cohen-Gonsaud M, Acher F, Prézeau L, Pin LP: Heptahelical domain of metabotropic glutamate receptor 5 behaves like rhodopsin-like receptors. Proc Natl Acad Sci USA 2004, 101(1):378–83. 10.1073/pnas.0304699101

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  16. O'Brien JA, Lemaire W, Chen TB, Chang RS, Jacobson MA, Ha SN, Lindsley CW, Schaffhauser HJ, Sur C, Pettibone DJ, Conn PJ, Williams DL Jr: A family of highly selective allosteric modulators of the metabotropic glutamate receptor subtype 5. Mol Pharmacol 2003, 64(3):731–40. 10.1124/mol.64.3.731

    Article  PubMed  Google Scholar 

  17. Palczewski K, Kumasaka T, Hori T, Behnke CA, Motoshima H, Fox BA, Le Trong I, Teller DC, Okada T, Stenkamp RE, Yamamoto M, Miyano M: Crystal structure of rhodopsin: A G protein-coupled receptor. Science 2000, 289(5480):739–45. 10.1126/science.289.5480.739

    Article  CAS  PubMed  Google Scholar 

  18. Isin B, Rader AJ, Dhiman HK, Klein-Seetharaman J, Bahar I: Predisposition of the dark state of rhodopsin to functional changes in structure. Proteins 2006, 65(4):970–83. 10.1002/prot.21158

    Article  CAS  PubMed  Google Scholar 

  19. Muhlemann A, Ward NA, Kratochwil N, Diener C, Fischer C, Stucki A, Jaeschke G, Malherbe P, Porter RH: Determination of key amino acids implicated in the actions of allosteric modulation by 3,3'-difluorobenzaldazine on rat mGlu5 receptors. Eur J Pharmacol 2006, 529(1–3):95–104. 10.1016/j.ejphar.2005.11.008

    Article  PubMed  Google Scholar 

  20. Man O, Gilad Y, Lancet D: Prediction of the odorant binding site of olfactory receptor proteins by human-mouse comparisons. Protein Sci 2004, 13(1):240–54. 10.1110/ps.03296404

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  21. Miedlich SU, Gama L, Seuwen K, Wolf RM, Breitwiesser GE: Homology modeling of the transmembrane domain of the human calcium sensing receptor and localization of an allosteric binding site. J Biol Chem 2004, 279(8):7254–63. 10.1074/jbc.M307191200

    Article  CAS  PubMed  Google Scholar 

  22. Kim S, Chamberlain AK, Bowie JU: A simple method for modeling transmembrane helix oligomers. J Mol Biol 2003, 329(4):831–40. 10.1016/S0022-2836(03)00521-7

    Article  CAS  PubMed  Google Scholar 

  23. Petrel C, Kessler A, Maslah F, Dauban P, Dodd RH, Rognan D, Ruat M: Modeling and mutagenesis of the binding site of Calhex 231, a novel negative allosteric modulator of the extracellular Ca(2+)-sensing receptor. J Biol Chem 2003, 278(49):49487–94. 10.1074/jbc.M308010200

    Article  CAS  PubMed  Google Scholar 

  24. Ruan KH, Wu J, So SP, Jenkins LA: Evidence of the residues involved in ligand recognition in the second extracellular loop of the prostacyclin receptor characterized by high resolution 2D NMR techniques. Arch Biochem Biophys 2003, 418(1):25–33. 10.1016/S0003-9861(03)00401-6

    Article  CAS  PubMed  Google Scholar 

  25. Axe FU, Bembenek SD, Szalma S: Three-dimenstional models of histamine H3 receptor antagonist complexes and their pharamcophore. J Mol Graph Model 2006, 24(6):456–464. 10.1073/pnas.2136795100

    Article  CAS  PubMed  Google Scholar 

  26. Berkhout TA, Blaney FE, Bridges AM, Cooper DG, Forbes IT, Gribble AD, Groot PH, Hardy A, Ife RJ, Kaur R, Moores KE, Shillito H, Wiletts J, Witherington J: CCR2: characterization of the antagonist binding site from a combined receptor modeling/mutagenesis approach. J Med Chem 2003, 46(19):4070–86. 10.1021/jm030862l

    Article  CAS  PubMed  Google Scholar 

  27. Anzini M, Canullo L, Braile C, Cappelli A, Gallelli A, Vomero S, Menziani MC, De Benedetti PG, Rizzo M, Collina S, Azzolina O, Sbacchi M, Ghelardini C, Galeotti N: Synthesis, biological evaluation, and receptor docking simulations of 2-[(acylamino)ethyl]-1,4-benzodiazepines as kappa-opioid receptor agonists endowed with antinociceptive and antiamnesic activity. J Med Chem 2003, 46(18):3853–64. 10.1021/jm0307640

    Article  CAS  PubMed  Google Scholar 

  28. Hulme EC, Lu ZL, Bee MS: Scanning mutagenesis studies of the M1 muscarinic acetylcholine receptor. Receptors Channels 2003, 9(4):215–28. 10.1080/10606820308261

    Article  CAS  PubMed  Google Scholar 

  29. Schulz A, Schoneberg T: The structural evolution of a P2Y-like G-protein-coupled receptor. J Biol Chem 2003, 278(37):35531–41. 10.1074/jbc.M303346200

    Article  CAS  PubMed  Google Scholar 

  30. Becker OM, Shacham S, Marantz Y, Noiman S: Modeling the 3D structure of GPCRs: advances and application to drug discovery. Curr Opin Drug Discov Devel 2003, 6(3):353–61.

    CAS  PubMed  Google Scholar 

  31. Mehler EL, Periole X, Hassan SA, Weinstein H: Key issues in the computational simulation of GPCR function: representation of loop domains. J Comput Aided Mol Des 2002, 16(11):841–53. 10.1023/A:1023845015343

    Article  CAS  PubMed  Google Scholar 

  32. Johren K, Holtje HD: A model of the human M2 muscarinic acetylcholine receptor. J Comput Aided Mol Des 2002, 16(11):795–801. 10.1023/A:1023880611709

    Article  PubMed  Google Scholar 

  33. Shim JY, Welsh WJ, Howlett AC: Homology model of the CB1 cannabinoid receptor: Sites critical for nonclassical cannabinoid agonist interaction. Biopolymers 2003, 71(2):169–89. 10.1002/bip.10424

    Article  CAS  PubMed  Google Scholar 

  34. Bhave G, Nadin BM, Brasier DJ, Glauner KS, Shah RD, Heinemann SF, Karim F, Gereau RW 4th: Membrane topology of a metabotropic glutamate receptor. J Biol Chem 2003, 278(32):30294–301. 10.1074/jbc.M303258200

    Article  CAS  PubMed  Google Scholar 

  35. Chambers JJ, Nichols DE: A homology-based model of the human 5-HT2A receptor derived from an in silico activated G-protein coupled receptor. J Comput Aided Mol Des 2002, 16(7):511–20. 10.1023/A:1021275430021

    Article  CAS  PubMed  Google Scholar 

  36. Stenkamp RE, Filipek S, Driessen CA, Teller DC, Palczewski K: Crystal structure of rhodopsin: a template for cone visual pigments and other G protein-coupled receptors. Biochim Biophys Acta 2002, 1565(2):168–82. 10.1016/S0005-2736(02)00567-9

    Article  CAS  PubMed  Google Scholar 

  37. Deraet M, Rihakova L, Boucard A, Pèrodin J, Sauvé S, Mathieu AP, Guillemette G, Leduc R, Lavigne P, Escher E: Angiotensin II is bound to both receptors AT1 and AT2, parallel to the transmembrane domains and in an extended form. Can J Physiol Pharmacol 2002, 80(5):418–25. 10.1139/y02-060

    Article  CAS  PubMed  Google Scholar 

  38. Lopez-Rodriguez ML, Murcia M, Benhamu B, Olivella M, Campillo M, Pardo L: Computational model of the complex between GR113808 and the 5-HT4 receptor guided by site-directed mutagenesis and the crystal structure of rhodopsin. J Comput Aided Mol Des 2001, 15(11):1025–33. 10.1023/A:1014895611874

    Article  CAS  PubMed  Google Scholar 

  39. Gao ZG, Chen A, Barak D, Kim SK, Müller CE, Jacobson KA: Identification by site-directed mutagenesis of residues involved in ligand recognition and activation of the human A3 adenosine receptor. J Biol Chem 2002, 277(21):19056–63. 10.1074/jbc.M110960200

    Article  CAS  PubMed  Google Scholar 

  40. Zhang Y, Sham YY, Rajamani R, Gao J, Portoghese PS: Homology modeling and molecular dynamics simulations of the mu opoid receptor in a membrane-aqueous system. Chembiochem 2005, 6(5):853–859. 10.1208/ps020102

    Article  CAS  PubMed  Google Scholar 

  41. Greasley PJ, Fanelli F, Scheer A, Abuin L, Nenniger-Tosato M, DeBenedetti PG, Cotecchia S: Mutational and computational analysis of the alpha(1b)-adrenergic receptor. Involvement of basic and hydrophobic residues in receptor activation and G protein coupling. J Biol Chem 2001, 276(49):46485–94. 10.1074/jbc.M105791200

    Article  CAS  PubMed  Google Scholar 

  42. Nikiforovich GV, Galaktionov S, Balodis J, Marshall GR: Novel approach to computer modeling of seven-helical transmembrane proteins: current progress in the test case of bacteriorhodopsin. Acta Biochim Pol 2001, 48(1):53–64.

    CAS  PubMed  Google Scholar 

  43. Nikiforovich GV, Marshall GR: 3D model for TM region of the AT-1 receptor in complex with angiotensin II independently validated by site-directed mutagenesis data. Biochem Biophys Res Commun 2001, 286(5):1204–11. 10.1006/bbrc.2001.5526

    Article  CAS  PubMed  Google Scholar 

  44. Filipek S, Teller DC, Palczewski K, Stenkamp R: The crystallographic model of rhodopsin and its use in studies of other G protein-coupled receptors. Annu Rev Biophys Biomol Struct 2003, 32: 375–97. 10.1146/annurev.biophys.32.110601.142520

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  45. Archer E, Maigret B, Escrieut C, Pradayrol L, Fourmy D: Rhodopsin crystal: new template yielding realistic models of G-protein-coupled receptors? Trends Pharmacol Sci 2003, 24(1):36–40. 10.1016/S0165-6147(02)00009-3

    Article  CAS  PubMed  Google Scholar 

  46. Peng JY, Vaidehi N, Hall SE, Goddard WA 3rd: The predicted 3D structures of the human M1 muscarinic acetylcholine receptor with agonist or antagonist bound. Chem Med Chem 2006, 1(8):878–90.

    Article  CAS  PubMed  Google Scholar 

  47. Vaidehi N, Schlyer S, Trabanino RJ, Floriano WB, Abrol R, Sharma S, Kochanny M, Koovakat S, Dunning L, Liang M, Fox JM, de Mendonça FL, Pease JE, Goddard WA 3rd, Horuk R: Predictions of CCR1 chemokine receptor structure and BX 471 antagonist binding followed by experimental validation. J Biol Chem 2006, 281(37):27613–20. 10.1074/jbc.M601389200

    Article  CAS  PubMed  Google Scholar 

  48. Floriano WB, Hall S, Vaidehi N, Kim U, Drayna D, Goddard WA 3rd: Modeling the human PTC bitter-taste receptor interactions with bitter tastants. J Mol Model 2006, 12(6):931–41. 10.1007/s00894-006-0102-6

    Article  CAS  PubMed  Google Scholar 

  49. Spijker P, Vaidehi N, Freddolino PL, Hilbers PA, Goddard WA 3rd: Dynamic behavior of fully solvated beta2-adrenergic receptor, embedded in the membrane with bound agonist or antagonist. Proc Natl Acad Sci USA 2006, 103(13):4882–7. 10.1073/pnas.0511329103

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  50. Hummel P, Vaidehi N, Floriano WB, Hall SE, Goddard WA 3rd: Test of the Binding Threshold Hypothesis for olfactory receptors: explanation of the differential binding of ketones to the mouse and human orthologs of olfactory receptor 912–93. Protein Sci 2005, 14(3):703–10. 10.1110/ps.041119705

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  51. Hall SE, Floriano WB, Vaidehi N, Goddard WA 3rd: Predicted 3-D structures for mouse I7 and rat I7 olfactory receptors and comparison of predicted odor recognition profiles with experiment. Chem Senses 2004, 29(7):595–616. 10.1093/chemse/bjh063

    Article  CAS  PubMed  Google Scholar 

  52. Floriano WB, Vaidehi N, Goddard WA 3rd: Making sense of olfaction through predictions of the 3-D structure and function of olfactory receptors. Chem Senses 2004, 29(4):269–90. 10.1093/chemse/bjh030

    Article  CAS  PubMed  Google Scholar 

  53. Trabanino RJ, Hall SE, Vaidehi N, Floriano WB, Kam VW, Goddard WA 3rd: First principles predictions of the structure and function of g-protein-coupled receptors: validation for bovine rhodopsin. Biophys J 2004, 86(4):1904–21.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  54. Freddolino PL, Kalani MY, Vaidehi N, Floriano WB, Hall SE, Trabanino RJ, Kam VW, Goddard WA 3rd: Predicted 3D structure for the human beta 2 adrenergic receptor and its binding site for agonists and antagonists. Proc Natl Acad Sci USA 2004, 101(9):2736–41. 10.1073/pnas.0308751101

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  55. Vaidehi N, Floriano WB, Trabanino R, Hall SE, Freddolino P, Choi EJ, Zamanakos G, Goddard WA 3rd: Prediction of structure and function of G protein-coupled receptors. Proc Natl Acad Sci USA 2002, 99(20):12622–7. 10.1073/pnas.122357199

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  56. Kinsella GK, Rozas I, Watson GW: Comparative molecular dynamics simulations of uncomplexed, 'agonist-bound' and 'antagonist-bound' alpha1A adrenoceptor models. Biochem Biophys Res Commun 2005, 333(3):737–41. 10.1016/j.bbrc.2005.05.159

    Article  CAS  PubMed  Google Scholar 

  57. Kinsella GK, Rozas I, Watson GW: Computational study of antagonist/alpha1A adrenoceptor complexes--observations of conformational variations on the formation of ligand/receptor complexes. J Med Chem 2006, 49(2):501–10. 10.1021/jm0503751

    Article  CAS  PubMed  Google Scholar 

  58. Thompson MA: ArgusLab 4.0.1. ArgusLab 4.0.1 Planaria Software LLC, Seattle, WA; [http://www.arguslab.com]

  59. Goodsell DS, Morris GM, Olson AJ: Automated docking of flexible ligands: applications of AutoDock. J Mol Recognit 1996, 9(1):1–5. 10.1002/(SICI)1099-1352(199601)9:1<1::AID-JMR241>3.0.CO;2-6

    Article  CAS  PubMed  Google Scholar 

  60. Morris GA, Goddsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ: Automated docking using a lamarckian genetic algorithm and an empirical binding free energy function. J Comp Chem 1998, 19():1639–1662. 10.1002/(SICI)1096-987X(19981115)19:14<1639::AID-JCC10>3.0.CO;2-B

    Article  CAS  Google Scholar 

  61. Park H, Lee J, Lee S: Critical assessment of the automated AutoDock as a new docking tool for virtual screening. Proteins 2006, 65():549–554. 10.1016/j.jmb.2006.02.008

    Article  CAS  PubMed  Google Scholar 

  62. Thompson JD, Higgins DG, Gibson TJ: CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 1994, 22(22):4673–80. 10.1093/nar/22.22.4673

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  63. Malherbe P, Kew JN, Richards JG, Knoflach F, Kratzeisen C, Zenner MT, Faull RL, Kemp JA, Mutel V: Identification and characterization of a novel splice variant of the metabotropic glutamate receptor 5 gene in human hippocampus and cerebellum. Brain Res Mol Brain Res 2002, 109(1–2):168–78. 10.1016/S0169-328X(02)00557-0

    Article  CAS  PubMed  Google Scholar 

  64. Sali A, Potterton L, Yuan F, van Vlijmen H, Karplus M: Evaluation of comparative protein modeling by MODELLER. Proteins 1995, 23(3):318–26. 10.1002/prot.340230306

    Article  CAS  PubMed  Google Scholar 

  65. Marti-Renom MA, Stuart AC, Fiser A, Sánchez R, Melo F, Sali A: Comparative protein structure modeling of genes and genomes. Annu Rev Biophys Biomol Struct 2000, 29: 291–325. 10.1146/annurev.biophys.29.1.291

    Article  CAS  PubMed  Google Scholar 

  66. Laskowski RA, MacArthur MW, Moss DS, Thornton JM: PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Cryst 1993, 26: 283–291. 10.1107/S0021889892009944

    Article  CAS  Google Scholar 

  67. Davis IW, Murray LW, Richardson JS, Richardson DC: MOLPROBITY: structure validation and all-atom contact analysis for nucleic acids and their complexes. Nucleic Acids Res 2004, (32 Web Server):W615–9. 10.1093/nar/gkh398

  68. Vriend G: WHAT IF: a molecular modeling and drug design program. J Mol Graph 1990, 8(1):52–6, 29. 10.1016/0263-7855(90)80070-V

    Article  CAS  PubMed  Google Scholar 

  69. ChemDraw Software[http://www.cambridgesoft.com/software/ChemDraw/]

  70. Knoflach F, Mutel V, Jolidon S, Kew JN, Malherbe P, Vieria E, Wichmann J, Kemp JA: Positive allosteric modulators of metabotropic glutamate 1 receptor: characterization, mechanism of action, and binding site. Proc Natl Acad Sci USA 2001, 98(23):13402–7. 10.1073/pnas.231358298

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  71. Lavreysen H, Janssen C, Bischoff F, Langlois X, Leysen JE, Lesage AS: [3H]R214127: a novel high-affinity radioligand for the mGlu1 receptor reveals a common binding site shared by multiple allosteric antagonists. Mol Pharmacol 2003, 63(5):1082–93. 10.1124/mol.63.5.1082

    Article  CAS  PubMed  Google Scholar 

  72. Zheng GZ, Bhatia P, Daanen J, Kolasa T, Patel M, Latshaw S, El Kouhen OF, Chang R, Uchic ME, Miller L, Nakane M, Lehto SG, Honore MP, Moreland RB, Brioni JD, Stewart AO: Structure-activity relationship of triazafluorenone derivatives as potent and selective mGluR1 antagonists. J Med Chem 2005, 48(23):7374–88. 10.1021/jm0504407

    Article  CAS  PubMed  Google Scholar 

  73. Kohara A, Toya T, Tamura S, Watabiki T, Nagakura Y, Shitaka Y, Hayashibe S, Kawabata S, Okada M: Radioligand binding properties and pharmacological characterization of 6-amino-N-cyclohexyl-N,3-dimethylthiazolo[3,2-a]benzimidazole-2-carboxamide (YM-298198), a high-affinity, selective, and noncompetitive antagonist of metabotropic glutamate receptor type 1. J Pharmacol Exp Ther 2005, 315(1):163–9. 10.1124/jpet.105.087171

    Article  CAS  PubMed  Google Scholar 

  74. Gasparini F, Andres H, Flor PJ, Heinrich M, Inderbitzin W, Lingenhohl K, Müller H, Munk VC, Omilusik K, Stierlin C, Stoehr N, Vranesic I, Kuhn R: [(3)H]-M-MPEP, a Potent, Subtype-Selective Radioligand for the Metabotropic Glutamate Receptor Subtype 5. Bioorg Med Chem Lett 2002, 12(3):407–9. 10.1016/S0960-894X(01)00767-3

    Article  CAS  PubMed  Google Scholar 

  75. Gasparini F, Kuhn R, Pin JP: Allosteric modulators of group I metabotropic glutamate receptors: novel subtype-selective ligands and therapeutic perspectives. Curr Opin Pharmacol 2002, 2(1):43–9. 10.1016/S1471-4892(01)00119-9

    Article  CAS  PubMed  Google Scholar 

  76. O'Brien JA, Lemaire W, Wittmann M, Jacobson MA, Ha SN, Wisnoski DD, Lindsley CW, Schaffhauser HJ, Rowe B, Sur C, Duggan ME, Pettibone DJ, Conn PJ, Williams DL Jr: A novel selective allosteric modulator potentiates the activity of native metabotropic glutamate receptor subtype 5 in rat forebrain. J Pharmacol Exp Ther 2004, 309(2):568–77. 10.1124/jpet.103.061747

    Article  PubMed  Google Scholar 

  77. Porter RH, Jaeschke G, Spooren W, Ballard TM, Büttelmann B, Kolczewski S, Peters JU, Prinssen E, Wichmann J, Vieria E, Mühlemann A, Gatti S, Mutel V, Malherbe P: Fenobam: a clinically validated nonbenzodiazepine anxiolytic is a potent, selective, and noncompetitive mGlu5 receptor antagonist with inverse agonist activity. J Pharmacol Exp Ther 2005, 315(2):711–21. 10.1124/jpet.105.089839

    Article  CAS  PubMed  Google Scholar 

  78. Anderson JJ, Rao SP, Rowe B, Giracello DR, Holtz G, Chapman DF, Tehrani L, Bradbury MJ, Cosford ND, Varney MA: [3H]Methoxymethyl-3-[(2-methyl-1,3-thiazol-4-yl)ethynyl]pyridine binding to metabotropic glutamate receptor subtype 5 in rodent brain: in vitro and in vivo characterization. J Pharmacol Exp Ther 2002, 303(3):1044–51. 10.1124/jpet.102.040618

    Article  CAS  PubMed  Google Scholar 

  79. Rodriguez AL, Nong Y, Sekaran NK, Alagille D, Tamagnan GD, Conn PJ: A close structural analog of 2-methyl-6-(phenylethynyl)-pyridine acts as a neutral allosteric site ligand on metabotropic glutamate receptor subtype 5 and blocks the effects of multiple allosteric modulators. Mol Pharmacol 2005, 68(6):1793–802.

    CAS  PubMed  Google Scholar 

  80. Pinkerton AB, Vernier JM, Schaffhauser H, Rowe BA, Campbell UC, Rodriguez DE, Lorrain DS, Baccei CS, Daggett LP, Bristow LJ: Phenyl-tetrazolyl acetophenones: discovery of positive allosteric potentiatiors for the metabotropic glutamate 2 receptor. J Med Chem 2004, 47(18):4595–9. 10.1021/jm040088h

    Article  CAS  PubMed  Google Scholar 

  81. Maj M, Bruno V, Dragic Z, Yamamoto R, Battaglia G, Inderbitzin W, Stoehr N, Stein T, Gasparini F, Vranesic I, Kuhn R, Nicoletti F, Flor PJ: (-)-PHCCC, a positive allosteric modulator of mGluR4: characterization, mechanism of action, and neuroprotection. Neuropharmacology 2003, 45(7):895–906. 10.1016/S0028-3908(03)00271-5

    Article  CAS  PubMed  Google Scholar 

  82. Mitsukawa K, Yamamoto R, Ofner S, Nozulak J, Pescott O, Lukic S, Stoehr N, Mombereau C, Kuhn R, McAllister KH, van der Putten H, Cryan JF, Flor PJ: A selective metabotropic glutamate receptor 7 agonist: activation of receptor signaling via an allosteric site modulates stress parameters in vivo. Proc Natl Acad Sci USA 2005, 102(51):18712–7. 10.1073/pnas.0508063102

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  83. Pymol software[http://www.pymol.org]

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Acknowledgements

This work was in part supported by the Sofya Kovalevskaya Prize of the Humboldt Foundation, Germany/Zukunftsinvestitionsprogramm der Bundesregierung Deutschland, National Science Foundation grants EIA0225636 and CAREER CC044917, and National Institutes of Health Grant NLM108730, USA.

This article has been published as part of BMC Bioinformatics Volume 9 Supplement 1, 2008: Asia Pacific Bioinformatics Network (APBioNet) Sixth International Conference on Bioinformatics (InCoB2007). The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/9?issue=S1.

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  1. Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15260, USA

    Naveena Yanamala, Kalyan C Tirupula & Judith Klein-Seetharaman

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  1. Naveena Yanamala
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  2. Kalyan C Tirupula
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  3. Judith Klein-Seetharaman
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Correspondence to Judith Klein-Seetharaman.

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NY carried out all the modeling and docking studies, analyzed and interpreted the data and wrote the manuscript. KT collected the list of ligands and their effects on mGluRs and participated in the analysis of the docked structures. JKS designed the hypothesis and computational experiments to test it, participated in analysis and interpretation of data and the writing of the manuscript.

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The authors declare that they have no competing interests.

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Yanamala, N., Tirupula, K.C. & Klein-Seetharaman, J. Preferential binding of allosteric modulators to active and inactive conformational states of metabotropic glutamate receptors. BMC Bioinformatics 9 (Suppl 1), S16 (2008). https://doi.org/10.1186/1471-2105-9-S1-S16

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