3D modeling, ligand binding and activation studies of the cloned mouse {delta}, µ and {kappa} opioid receptors

Marta Filizola1, Liisa Laakkonen1 and Gilda H. loew2

Molecular Research Institute, 2495 Old Middlefield Way, Mountain View, CA 94043, USA 1 These two authors contributed equally to the work described


    Abstract
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 Abstract
 Introduction
 Materials and methods
 Results and discussion
 References
 
Refined 3D models of the transmembrane domains of the cloned {delta}, µ and {kappa} opioid receptors belonging to the superfamily of G-protein coupled receptors (GPCRs) were constructed from a multiple sequence alignment using the alpha carbon template of rhodopsin recently reported. Other key steps in the procedure were relaxation of the 3D helix bundle by unconstrained energy optimization and assessment of the stability of the structure by performing unconstrained molecular dynamics simulations of the energy optimized structure. The results were stable ligand-free models of the TM domains of the three opioid receptors. The ligand-free {delta} receptor was then used to develop a systematic and reliable procedure to identify and assess putative binding sites that would be suitable for similar investigation of the other two receptors and GPCRs in general. To this end, a non-selective, `universal' antagonist, naltrexone, and agonist, etorphine, were used as probes. These ligands were first docked in all sites of the model {delta} opioid receptor which were sterically accessible and to which the protonated amine of the ligands could be anchored to a complementary proton-accepting residue. Using these criteria, nine ligand–receptor complexes with different binding pockets were identified and refined by energy minimization. The properties of all these possible ligand–substrate complexes were then examined for consistency with known experimental results of mutations in both opioid and other GPCRs. Using this procedure, the lowest energy agonist–receptor and antagonist–receptor complexes consistent with these experimental results were identified. These complexes were then used to probe the mechanism of receptor activation by identifying differences in receptor conformation between the agonist and the antagonist complex during unconstrained dynamics simulation. The results lent support to a possible activation mechanism of the mouse {delta} opioid receptor similar to that recently proposed for several other GPCRs. They also allowed the selection of candidate sites for future mutagenesis experiments.

Keywords: G-protein coupled receptors/ligand binding site/opioid receptors/transmembrane helices


    Introduction
 Top
 Abstract
 Introduction
 Materials and methods
 Results and discussion
 References
 
The multiple in vivo effects of opioids have been well documented and include analgesia, euphoria, sedation, respiratory depression, changes in thermoregulation, inhibition of gastrointestinal (GI) motility, muscle rigidity and the potential for physical dependence and abuse (Jaffe and Martin, 1990; Dickenson, 1991Go; Maldonado et al., 1992Go; Mather and Cousins, 1992Go; Olson et al., 1996Go). Recent evidence for multiple opioid receptors, {delta}, µ and {kappa}, from molecular cloning and pharmacological studies (Evans et al., 1992Go; Kieffer et al., 1992Go; Chen et al., 1993Go; Fukuda et al., 1993Go; Meng et al., 1993Go; Yasuda et al., 1993Go) have suggested a possible explanation for the multiple activities of opiates. However, use of selective ligands for each of the three cloned receptors has not led to a clear separation of potent analgesia and the unwanted side effects such as respiratory depression and physical dependence liability. In order to design more selective analgesics, new insights into the requirements for recognition and activation of each opioid receptor appear to be necessary.

Opioid receptors are members of the large superfamily of G-protein coupled receptors (GPCRs). GPCRs are integral membrane proteins characterized by a common structural motif of seven transmembrane spanning helices (TMH) (Bockaert, 1991Go; Hargrave, 1991Go) connected by intracellular and extracellular loops both exterior to the lipid membrane. GPCRs bind to trimeric proteins called G-proteins, consisting of {alpha}, ß and {gamma} subunits that mediate signal transduction through a GTPase cycle. Specifically, interaction of the ligand with a specific GPCR is thought to produce an alteration in its conformation that causes GDP dissociation from the {alpha} subunit of the G protein, directly bound to the receptor. Subsequently, binding of GTP to the empty guanine nucleotide-binding pocket results in the dissociation of the {alpha} subunit from the receptor and from ß and {gamma} subunits. The GTP bound {alpha} subunit (G{alpha}) can then activate a number of different effector proteins that transduce the signal through second messenger pathways. All three classes of opioid receptors activate Gi1{alpha} (Prather et al., 1994Go; Piros et al., 1995Go), Gi2{alpha} (McKenzie and Milligan, 1990Go; Tang et al., 1995Go; Murthy and Makhlouf, 1996Go) and Go{alpha} (Prather et al., 1994Go; Murthy and Makhlouf, 1996Go) G proteins. Activation of G proteins by opioid receptors in turn results in inhibition of voltage dependent calcium channels (McKenzie and Milligan, 1990Go; Prather et al., 1994Go; Piros et al., 1995Go; Tang et al., 1995Go; Murthy and Makhlouf, 1996Go; Piros et al., 1996Go) activation of inwardly rectifying K+ channels (Henry et al., 1995Go; Ma et al., 1995Go) and inhibition of adenyl cyclase (Arden et al., 1995Go; Keith et al., 1996Go; Malatynska et al., 1996Go; Piros et al., 1996Go; Segredo et al., 1997Go).

The three different opioid receptor types can bind a large number of structurally different ligands, from small rigid alkaloids to larger peptides. Both alkaloids and peptides interact with the transmembrane regions of the receptors, as has been demonstrated by site-directed mutagenesis experiments (Kong et al., 1993Go; Surratt et al., 1994Go; Fukuda et al., 1995aGo; Hjorth et al., 1995Go; Befort et al., 1996aGo,bGo; Claude et al., 1996Go; Valiquette et al., 1996Go). In addition to this common recognition domain, peptidyl ligands can also interact with the extracellular loops, as has been suggested by studies of chimeric receptors (Kong et al., 1994Go; Misicka et al., 1994Go; Wang et al., 1994Go; Xue et al., 1994Go; Chen et al., 1995Go; Fukuda et al., 1995bGo; Hjorth et al., 1995Go; Meng et al., 1995Go; Minami et al., 1995Go; Onogi et al., 1995Go; Claude et al., 1996Go; Valiquette et al., 1996Go; Zhu et al., 1996aGo,bGo).

In order to identify molecular determinants within the common recognition domain (transmembrane region) of the three opioid receptors, which are involved in the conformational changes producing signal transduction, 3D structures of the receptors are necessary. Unfortunately, there is no experimentally known high-resolution 3D structure of any GPCR. Rhodopsin is the only GPCR structurally characterized to date by electron cryomicroscopy studies of 2D crystals with a resolution of 6–9 Å (Schertler et al., 1993Go; Schertler and Hargrave, 1995Go; Unger and Schertler, 1995Go; Davies et al., 1996Go; Unger et al., 1997Go). Because of the lack of experimentally determined 3D structures, diverse computational strategies are now being actively explored to help bridge this gap for many specific GPCRs. Accordingly, 3D models of opioid receptors have been proposed recently (Alkorta and Loew, 1996Go; Strahs and Weinstein, 1997Go; Pogozheva et al., 1998Go; Filizola et al., 1999Go). These models were constructed using different procedures. Specifically, one was based on sequence divergence analysis that did not involve use of a template (Alkorta and Loew, 1996Go). Another used putative H-bonding residues combined with distance geometry calculations (Pogozheva et al., 1998Go). The third (Strahs and Weinstein, 1997Go) was based on the techniques of homology modeling refined by a variety of criteria based upon biophysical properties of membrane proteins (Ballesteros and Weinstein, 1995Go) and use of the low-resolution electron cryomicroscopy 2D structures of frog and bovine rhodopsin (Schertler et al., 1993Go; Schertler and Hargrave, 1995Go; Unger and Schertler, 1995Go). The fourth (Filizola et al., 1999Go), also utilized information deduced from the rhodopsin 2D electron density but coupled to a general procedure developed for construction of GPCRs (Filizola et al., 1998Go).

In the work reported here, renewed efforts have been made to construct 3D models of the transmembrane domains of the cloned mouse {delta}, µ and {kappa} opioid receptors. Unfortunately, there are no rigorous internal standards that can be applied to select the most reliable procedures. However, simplifying assumptions made in our previous work have been reconsidered. Recently available experimental data have been used both in the construction of the 3D models and in their assessment. The three ligand-free receptor models have been examined for their stability to unconstrained MD simulations. Only one of the previous studies included this criterion (Strahs and Weinstein, 1997Go). Finally, the uses made here of these models have not yet been reported for the opioid receptors. These include:(i) systematic identification of the most likely ligand binding sites, (ii) assessment of the temporal stability of ligand–receptor complexes and (iii) use of these complexes to probe an activation mechanism.

In the present study, the alpha carbon template of rhodopsin recently deduced from the electron cryomicroscopy map (Baldwin et al., 1997Go) has been used to construct 3D models of the transmembrane domain of the three opioid receptors. The 3D models of the ligand-free opioid receptors were found to be stable to unconstrained MD simulations. These models were further assessed using criteria derived from available experimental information.

The ligand-free {delta} receptor was then used to develop a reliable systematic procedure to identify and assess putative binding sites. To this end, naltrexone, known to be a non-selective, `universal' antagonist of all three receptors, and etorphine, a non-selective, `universal' agonist at all three receptors, were used as probes that would be suitable for similar investigation of the other two receptors and for GPCRs in general. In the procedure developed, the most plausible ligand–receptor complex for each was chosen from among the nine sites identified as the lowest energy complex that was most consistent with the known experimental results. These ligand–receptor complexes were then further assessed and used to probe possible modes of receptor activation. To this end, they were each subjected to unconstrained molecular dynamics simulations. During these simulations, each ligand remained in the binding pocket. The MD-averaged, energy-optimized ligand–receptor complexes were further assessed by comparisons with experimental results. Finally, possible differential effects on receptor conformation of agonist and antagonist binding were examined by comparisons of the two ligand–receptor complexes with each other and with the ligand-free 3D receptor model. These results provided support for an activation mechanism of mouse {delta} opioid receptor similar to that recently proposed for other GPCRs. In addition, specific receptor residues that could affect recognition and activation have been suggested for mutagenesis experiments.


    Materials and methods
 Top
 Abstract
 Introduction
 Materials and methods
 Results and discussion
 References
 
Determination of transmembrane spanning helices

The set of sequences related to opioid receptors was retrieved with a BLAST search (Altschul et al., 1990Go) from the publicly available protein databases Swissprot (Bairoch and Bieckmann, 1994Go), translated GenBank (Benson et al., 1998Go) and PIR (Barker et al., 1998Go). All opioid sequences were used as search sequences and the 250 sequences closest to any of these were selected. All fragments and duplicates were discarded. The search resulted in a set of 329 sequences.

The sequences were aligned using the program ClustalW (Thompson et al., 1994Go) with the slow algorithm, standard penalties for gaps and Blosum30 matrix. Two different multiple sequence alignments were examined, a smaller one consisting of 99 sequences and a larger one that included 326 sequences. The smaller one consisted of 99 sequences obtained by iterative pruning.

For the smaller alignment, the set of sequences was pruned iteratively using the value of 24% identity with the opioid receptors as a cutoff in a multiple alignment. The resulting set of sequences included all the known opioid receptor sequences, somatostatin receptors, a galanin receptor, chemokine receptors, interleukin8 receptors, angiotensin II receptors, bradykinin receptors, p2y purinoceptors, N-formylpeptide receptors and neuropeptide Y receptors in descending order of sequence identity. Because a lot of manual editing of the multiple alignment was required, it was rebuilt stepwise with the profile method in ClustalW, starting from opioid receptors only and adding the above-mentioned families one at a time. This approach pays closest attention to the similarities and differences of the actual target sequences with the remaining ones. The alignment was studied carefully after each addition of new sequences and only a few minimal adjustments were made by hand to correct the positions of gaps. The galanin receptor and two neuropeptide Y receptors were removed from the alignment because they caused additional gaps in the best conserved hydrophobic stretches. Insertions distort the helical faces and thus cause critical differences in the final structures.

After completing the alignment of the 99 sequences, the remaining 227 sequences of the initial set of sequences were then aligned with the 99 sequences, to form a larger alignment of 326 sequences that included many additional families of GPCRs. Both alignments were used to determine the positions of the transmembrane spanning helical segments and the results were compared.

The alignments were analyzed with an in-house program de novo to determine the boundaries of the transmembrane helices. The algorithm used is described in detail elsewhere (Alkorta and Loew, 1996Go). It is based on the requirements that: (i) residues facing the membrane are hydrophobic and variable and (ii) all hydrophilic residues in the helices point into the protein or H-bond to the backbone. These requirements were assessed and validated by determining that they were satisfied in bacteriorhodopsin, a transmembrane helical protein with a known structure, 2brd.pdb and its homologues. Thus, the seven TM spanning helices were determined as contiguous residues with positive values of a single variability index Vj (Alkorta and Loew, 1996Go) defined as the difference between the lipophilic (Vlj) and hydrophobic (Vhj) variability indices (i.e. Vj = VljVhj) at the consensus position j of the multiple sequence alignment. Figure 1Go shows the variability profile obtained using the multiple alignment of 326 sequences. These results clearly indicate seven regions with Vj > 0 that can be identified as TMH regions. Whenever possible, the helix ends were modified to include R, K residues on the lipid facing side of the helices, where they can interact with phospholipid headgroups (Ballesteros and Weinstein, 1995Go).



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Fig. 1. Variability profile of the multiple sequence alignment consisting of 326 sequences. Regions with Vj > 0 have been identified as TMH regions.

 
The exact helix borders defined by the small and the large alignment varied by about one turn. Because of this uncertainty and the even larger variation of up to eight residues in predicted helix borders in the published models of opioid receptors (Strahs and Weinstein, 1997Go; Pogozheva et al., 1998Go), several different possible sets of helix boundaries were analyzed. Helices of about same length were chosen for stability of molecular dynamics simulations of the helix bundle. The periodicity of the variability index within these different possible segments can provide an estimation of their helicity. Accordingly, a Fourier transform power spectrum, P({omega}), was calculated as a function of the angle between two adjacent side chains, {omega}, viewed down each segment (Cornette et al., 1987Go). The tendency for {alpha}-helix formation in the predicted TMH regions was estimated by calculation of the {alpha} helical periodicity index, AP. Specifically, AP corresponds to the integration of P({omega}) over a range of {omega} values between 90 and 120°, normalized by the integration of P({omega}) over the full {omega} space from 0 to 180° (Cornette et al., 1987Go; Komiya et al., 1988Go). All the TMH regions should have AP values close to 2.0 or higher and a maximum value of P({omega}) near 100° to indicate the helical conformation of these regions (Komiya et al., 1988Go). Figure 2Go shows the best AP values calculated for the different possible segments. The results are similar for the small and large alignments and for minor variations of exact helix boundaries. They differ qualitatively among individual helices, a variation that should reflect the actual differences in properties of the helices. The boundaries of each helix that gave the best results were selected as TMH regions and are given in Figure 3Go, in residue numbers corresponding to the mouse {delta} opioid receptor. This numbering is used throughout the text and in all the tables and figures. Also given in this figure is the sequence alignment of this region of the {delta} receptor with the mouse µ and {kappa} opioid receptors.



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Fig. 2. Assessment of helicity of the identified TMH regions by calculation of {alpha}-propensity (AP). AP values >=2.0 indicate {alpha}-helical secondary structures.

 


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Fig. 3. Sequence alignment of the TMH regions of mouse {delta}, µ and {kappa} receptors. Residue numbers refer to the mouse {delta} opioid receptor sequence.

 
There are two assumptions behind the requirement of helices to have a maximum at about 110° in helical periodicity and for the {alpha} periodicity to have values greater than 2. To satisfy these requirements: (1) the helices must be close to ideal helices, without major face changes; a strong proline kink, for example, will already broaden the periodicity peak or split it into two; (2) the helices have to be amphiphilic, presenting all along their length the same face of the helix to the membrane. Both of these assumptions are idealizations. There is growing evidence from examination of the known integral membrane structures that the latter idealization breaks down. For example, in the cytochrome c oxidase complex (1occ.pdb) many helices are totally surrounded by neighboring helices and have no separation of hydrophobic and hydrophilic faces. There are also X-ray structures that clearly show helices presenting one face at the membrane at one height of the bundle and another face at a different level (1bl8.pdb, 3bcc.pdb). In neither of these cases would the helices show clear periodicities. As a counterexample, bacteriorhodopsin (2brd.pdb) is indeed formed as a bundle of nearly parallel helices, each with a clearly definable hydrophobic and hydrophilic face.

The results of the helicity analysis of the TM sections of the opioid receptors shown in Figure 2Go are not the same for all helices. For example, helices 1, 4, 5 and 7 show good AP properties. The maxima in these plots between the helix borders are greater than 2.0. Helices 2 and 6 show relatively large values of AP, but no clear cut limits and the curve for helix 3 shows the most deviation. The results strongly suggest that the previously used packing model (Alkorta and Loew, 1996Go) with nearly ideal helices about parallel to each other, was an oversimplified assumption. Accordingly, in the modified models reported here a rhodopsin template was used to construct an initial helix bundle for the opioid receptors.

Construction of initial 3D structures of the {delta}, µ and {kappa} opioid receptors

Standard helices were built from the sequences determined to be transmembrane segments. The main features incorporated in these helices were (i) building in the average proline kinks, using values previously reported (Sankararamakrishnan and Vishveshwara, 1990); (ii) capping the helix termini by acetyl and N-methylamide groups; (iii) setting the side chain {chi} angles to preferred values determined for these receptors from values in available known transmembrane {alpha}-helices; and (iv) neutralizing the four charged residues within the first and last turn of each helical segment.

Subsequently, the seven TM spanning helices were packed together by homology modeling using the alpha carbon template of rhodopsin recently proposed (Baldwin et al., 1997Go). Specifically, the individual helices were fitted to this template superimposing the conserved residues in each helix. Forty-three conserved residues were identified between template and target. The superposition of such residues allowed the simultaneous determination of the initial tilt, orientation and relative heights of the helices even though the lengths of the corresponding helices of the rhodopsin template are different.

Validation of the initial receptor models

The first type of criteria used for validation were based on comparisons with known experimental data as described below.

  1. Mutations of residues Y129, W173, W274, H278 and W284 (Lys in µ and Glu in {kappa}) numbered as in mouse {delta} receptor showed a significant effect on ligand affinities (Befort et al., 1996bGo; Valiquette et al., 1996Go; Mausour et al., 1997Go). Consistently all of these residues face inside the helix bundle.
  2. The distances between the conserved D95 in TMH2 and N314 in TMH7 are close enough for favorable interaction, as deduced in the serotonin 2A (5-HT2A) and gonadotropin-releasing hormone (GnRH) receptors and also in other GPCRs (Zhou et al., 1994Go; Sealfon et al., 1995Go).
  3. The residues L102 (Met in {kappa}), V124, I304 and Y308 (Thr in µ and {kappa}) are close to one other, as deduced from the corresponding residues (G90, E113, A292 and K296, respectively) in rhodopsin (Rao et al., 1994Go). Correspondence between these residues was determined from the superimposition of the 43 conserved residues identified between template and target.
  4. The residues K214 and T285 (Ala in µ and {kappa}) are within contact distance from each other, as deduced from the creation of a Zn binding site at these positions (K227 and A298) in the tachykinin NK-1 receptor (Elling et al., 1995Go; Thirstrup et al., 1996Go). Correspondence between the opioid and tachykinin receptor residues was determined from the multiple sequence alignment described in the first paragraph of the Materials and methods section.

This consistency provided initial evidence for the reliability of the constructed models.

Energy optimization and MD simulations of the initial 3D models of the {delta}, µ and {kappa} opioid receptors

The 3D opioid receptor models constructed as described above were subjected to energy minimization using the AMBER 4.0 program (Pearlman et al., 1991Go) and the parm91 parameter set (Weiner et al., 1984Go) for all residues excluding the neutralized charged residues within the first and last turn of each helical segment. Additional parameters required for the neutralized residues were taken from parm94.dat (Cornell et al., 1995Go). Consistent with the parm91 parameterization of the AMBER force field used for all the other residues of the receptors. Atomic partial charges for these neutralized residues were generated by fitting the molecular electrostatic potential computed with a STO-3G basis set using the Gaussian94 suite of programs (Frisch et al., 1995).

The energy minimization procedure was carried out in two steps using a distance-dependent dielectric constant of 4r. First, the models were subjected to 2000 cycles of steepest descent keeping all the backbone atoms restrained by harmonic potentials. The purpose of this constrained optimization was to eliminate possible steric repulsions between atoms of the side chains. Convergence for the r.m.s.d. was set to 0.001 Å for the structures of two successive iterations. In a subsequent step, unconstrained optimization was carried out using 2000 cycles of steepest descent followed by conjugate gradient minimization until convergence (r.m.s.d. between the structures of two consecutive iterations <0.001 Å).

MD simulations of the three free-ligand opioid receptor models were initiated by heating the minimized structures with no constraints on the atoms from 0 to 300 K in 6 ps. After heating, a 100 ps equilibration at a constant temperature of 300 K was performed, fixing all {phi} and {Psi} backbone dihedral angles and H-bonding backbone distances. This was followed by another 100 ps of equilibration, releasing the restraints on the backbone angles and keeping only the H-bonding distances fixed. Finally, an unconstrained MD simulation of 300 ps was carried out. In these simulations, a cutoff of 13 Å was used to compute non-bonded interactions with the nearest neighbor list updated every 10 steps. To examine structural variations during these simulations, an MD average structure was calculated and energy optimized for each 20 ps segment of the production run.

Characterization of ligand–receptor complexes for the {delta} opioid receptor

The ligand-free model chosen for each of the three opioid receptors was the structure obtained by energy optimization of the average of last 20 ps of unconstrained MD. This ligand-free 3D structure of the {delta} opioid receptor was used to characterize receptor complexes with the two selected ligands, the antagonist naltrexone and the agonist etorphine.

Structures of these two ligands were constructed with the PREP module of AMBER 4.0 guided by equivalent structures taken from the Cambridge data bank. Consistently with the parm91 parameterization of the AMBER force field, atomic partial charges for these ligands were generated by fitting the molecular electrostatic potential computed with an STO-3G basis set using Gaussian94. The ligands were energy minimized and subsequently manually docked inside the helix bundle of the ligand-free model of the {delta} opioid receptor.

The criteria used to identify possible ligand binding sites were (i) the presence of complementary partners in appropriate geometric arrangement to interact with the ligand moieties previously determined in our laboratory (Huang et al., 1997Go) to be important for recognition of the {delta} opioid receptor. These included an H-bonding acceptor to interact with the protonated amine group common to all opioid receptor ligands and two hydrophobic groups to complement the ligand hydrophobic groups found to be the other major components of a universal 3D pharmacophore for recognition of the {delta} opioid receptor by peptide and non-peptide agonists and antagonists; (ii) the ability of the binding site to accommodate the ligands sterically. Energy optimizations of the ligand–receptor complexes identified using these criteria were carried out using the same procedure described above for the energy optimization of the ligand-free receptors.

Among the energy-optimized ligand–receptor complexes identified, the lowest energy one with the greatest consistency with experimental results was selected for MD simulations. Specifically, 1 ns of unconstrained MD simulation was carried out for these optimized complexes with naltrexone and with etorphine, with the receptor in a putative active and inactive state. The protocol used for these MD simulations was the same as that followed for MD simulations of the ligand-free receptor models. The four resulting MD average structures calculated in 1 ns were energy optimized following the same procedure as reported above.


    Results and discussion
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 Abstract
 Introduction
 Materials and methods
 Results and discussion
 References
 
Generation and assessment of ligand-free {delta}, µ and {kappa} opioid receptor models

Shown in Figure 4a–cGo are the total and kinetic energies during 500 ps of MD simulation of the three opioid receptors constructed according to the procedure described in the Materials and methods section. It can be seen from these figures that all three receptors were energetically stable during the last 300 ps of unconstrained MD simulation.



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Fig. 4. Total and kinetic energies during 500 ps of MD simulation of the (a) {delta}, (b) µ and (c) {kappa} opioid receptors.

 
To examine structural variations during these simulations, a number of different reference structures were chosen. For each one, the root mean square deviation (r.m.s.d.) of the C{alpha} atoms between the reference structure and each snapshot of the 300 ps unconstrained MD simulation was calculated and compared. Shown in Figure 5a, b and cGo for {delta}, µ and {kappa} opioid receptors,respectively, are the comparisons that led to the smallest r.m.s.d. values for the C{alpha} atoms during the 300 ps production run. These values were obtained for all three models by using as a reference structure the last 20 ps segment (280–300 ps) of the 300 ps unconstrained MD.



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Fig. 5. C{alpha} atom r.m.s.d. values calculated using as a reference the last 20 ps segment and of the 300 ps unconstrained MD for (a) {delta}, (b) µ and (c) {kappa} opioid receptors, respectively.

 
As shown in Figure 5a, b and cGo for the {delta}, µ and {kappa} receptors, respectively, the r.m.s.d. from the reference structure decreases significantly during the unconstrained MD simulations. Thus although the last recorded r.m.s.d. value is in the range 2–3 Å., these decreasing and converging values of the r.m.s.d. with respect to the structure used as a reference is indicative of the structural stability of the ligand-free 3D models. This structural stability was further demonstrated by the results of subsequent extensive 1 ns MD simulations of ligand–receptor complexes built by using the reference structure of the ligand-free receptor model. The energetic and structural stability of these models obtained during the entire 1 ns of unconstrained MD simulations can be taken as additional support for the structural stability of the ligand free 3D models chosen as representative of the final helix bundle shapes. These three models are shown in Figures 6, 7 and 8GoGoGo for the {delta}, µ and {kappa} receptors, respectively.



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Fig. 6. Two views of the 3D model of the transmembrane domain of the ligand-free {delta} opioid receptor, defined as the energy-optimized average structure of the last 20 ps (280–300 ps) fragment of the unconstrained MD simulation. The interactions between R146 and D145 of TMH3 and between D95 of TMH2 and N314 of TMH7 are shown in the vertical orientation of the ligand-free receptor model.

 


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Fig. 7. Two views of the 3D model of the transmembrane domain of the ligand-free µ opioid receptor, defined as the energy-optimized average structure of the last 20 ps (280–300 ps) fragment of the unconstrained MD simulation. The interactions between R146 and D145 of TMH3 and between D95 of TMH2 and N314 of TMH7 are shown in the vertical orientation of the ligand-free receptor model.

 


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Fig. 8. Two views of the 3D model of the transmembrane domain of the ligand-free {kappa} opioid receptor, defined as the energy-optimized average structure of the last 20 ps (280–300 ps) fragment of the unconstrained MD simulation. The interactions between R146 and D145 of TMH3 and between D95 of TMH2 and N314 of TMH7 are shown in the vertical orientation of the ligand-free receptor model.

 
Further validation of the ligand-free models of the{delta}, µ and {kappa} receptors

In addition to structural stability during unconstrained MD simulations, further validation of these ligand-free models was provided by comparisons of calculated properties before and after the MD simulations with each other and with those derived from experimental results.

  1. The energy-optimized average structures of the last 20 ps of unconstrained MD retained similarity to the alpha carbon template of rhodopsin (Baldwin et al., 1997Go) used for the construction of the initial opioid structures. Differences in the helix lengths did not permit a direct determination of the r.m.s.d. of target and template structures. However, visual inspection of a manual superposition showed that many features of the rhodopsin helix bundle were maintained in the energy-optimized average structures of the last 20 ps of the three ligand-free opioid models. The main differences could be traced to the formation of helix kinks induced by prolines in TMH2, TMH4 (excluding the {kappa} receptor), TMH5, TMH6 and TMH7 in the opioid receptor models.
  2. The disposition of the polar and ionizable residues in each helix was preserved during MD simulations and is consistent with known experimental and computational results. Tables I, II and III GoGoGoindicate whether the polar/ionizable residue side chains in each helix are directed inside or outside the helix bundle for the {delta}, µ and {kappa} receptors, respectively. To see the effect of MD simulations on this disposition, these tables show results for each model of the three ligand-free opioid receptors in the energy-optimized structure (before MD) and in the energy-optimized MD average of the last 20 ps (280–300 ps) of unconstrained MD simulations. The numbering of the residues is referred to the mouse {delta} opioid receptor. This numbering is used throughout the text and in all the tables and figures. As seen in these tables:
    1. Residues Y129, W173, W274, H278 and W284 (Lys in µ and Glu in {kappa}) which were demonstrated to be important by mutation studies (Befort et al., 1996bGo; Valiquette et al., 1996Go; Mausour et al., 1997Go) remain directed inside the bundle.

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      Table I. Directions of polar/ionizable residue side chains in the initial energy-optimized (before MD) and energy-optimized unconstrained MD average of the last 20 ps segment in mouse opioid {delta} receptor
       

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      Table II. Directions of polar/ionizable residue side chains in the initial energy-optimized (before MD) and energy-optimized unconstrained MD average of the last 20 ps segment in mouse opioid µ receptor
       

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      Table III. Directions of polar/ionizable residue side chains in the initial energy-optimized (before MD) and energy-optimized unconstrained MD average of the last 20 ps segment in mouse opioid {kappa} receptor
       
    2. Residues D95 in TMH2 and N314 in TMH7 are both inside and, in addition, are close enough for the favorable interaction, deduced in the serotonin 2A (5-HT2A) and gonadotropin-releasing hormone (GnRH) receptors as well as in other GPCRs (Zhou et al., 1994Go; Sealfon et al., 1995Go).
    3. The three residues in TMH3 that form the conserved DRY motif of all rhodopsin-like GPCRs (D145, R146 and Y147) are all directed inside the helix bundle in all three models of the ligand-free opioid receptors. In addition, the D and R side chains of the DRY motif form a strong salt bridge in all three opioid models, as shown in Tables IV, V and VI GoGoGofor the {delta}, µ and {kappa} receptors, respectively. The interaction found between D and R of the DRY motif in all three ligand-free wild-type receptor models is consistent with previous experimental (Min et al., 1993Go; Scheer et al., 1996Go) and computational studies (Oliveira et al., 1994Go; Scheer et al., 1996Go; Ballesteros et al., 1998Go), which suggests the presence of a strong salt bridge in the inactive wild-type form of GnRH and {alpha}1ß-adrenergic receptors.

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      Table IV. Inter-helical interactions in the energy-optimized unconstrained MD average of last 20 ps segment in mouse opioid {delta} receptor
       

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      Table V. Inter-helical interactions in the energy-optimized unconstrained MD average of last 20 ps segment in mouse opioid µ receptor
       

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      Table VI. Inter-helical interactions in the energy-optimized unconstrained MD average of last 20 ps segment in mouse opioid {kappa} receptor
       

  3. Most of the polar residues found to be pointing inside the helix bundle listed in Tables I–IIIGoGoGo have H-bonding partners, as shown in Tables IV, V and VI GoGoGofor the {delta}, µ and {kappa} receptors, respectively. Specifically, these tables list the inter- and intra-helical interaction found for the polar/ionizable residues, which are pointing inside the helix bundle of each opioid receptor. We see from these tables that most of these inward-facing residues are indeed involved in robust H-bonding interaction. The exceptions were all threonine and serine residues except for one tyrosine. Specifically, these were: T134 (TMH3), T140 (TMH3), Y233 (TMH5) and T285 (TMH6) in the {delta} receptor; S135 (TMH3) and T141 (TMH3) in the µ-receptor; and T99 (TMH2) and T275 (TMH6) in the {kappa}-receptor. While both serine and threonine have hydroxyl groups, these residues can also interact with hydrophobic groups and are found on lipid-facing sides of membrane helices.

Taken together, then, the diverse criteria used for the assessment of the ligand-free models support their usefulness to characterize explicit ligand–receptor complexes and to probe possible mechanisms of activation.

Identification and selection of ligand binding sites

The identification of ligand binding sites in 3D structures of proteins is a challenging task even for globular proteins for which there are known structures. It is, therefore, important to devise systematic procedures for selecting candidate ligand binding sites that will be useful for other model GPCRs. To explore such procedures, two ligands common to all three opioid receptors, the antagonist naltrexone and the agonist etorphine, were chosen for docking in the 3D model of the ligand-free {delta} receptor shown in Figure 6Go. The chemical structures of naltrexone (antagonist) and etorphine (agonist) are shown in Figure 9Go.



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Fig. 9. Chemical structures of naltrexone (antagonist) and etorphine (agonist).

 
The first criterion used to identify candidate ligand binding sites was the presence of an H-bonding acceptor as a partner for the H-bonding donor moiety (NH+) common to all opioid ligands including the two selected ligands, naltrexone and etorphine. Using this criterion, all polar/ionizable residue side chains directed inside the helix bundle of the ligand-free {delta} opioid receptor (Table IGo) were systematically examined as possible anchoring partners. The second criterion used was the presence of hydrophobic groups to complement the ligand hydrophobic groups found to be the other major components of a universal 3D pharmacophore for recognition of the {delta} opioid receptor by peptide and non-peptide agonists and antagonists (Huang et al., 1997Go). The third criterion used was that the ligand be embedded in the helix bundle with no significant exposure to the lipid membrane.

Using these criteria, nine different binding sites were identified for each ligand resulting in nine different initial {delta} opioid receptor complexes with each ligand. These 18 initial ligand–receptor complexes were energy optimized. Table VIIGo gives the residues comprising the nine candidate binding sites for each ligand. These are defined as all residues within 5 Å of any atom of the ligand. As can be seen, even in corresponding sites with each ligand anchored by the same receptor residue, they do not have identical binding pockets.


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Table VII. Mouse {delta} receptor residues within 5 Å radii from the ligand
 
The features of the nine binding sites listed in Table VIIGo were examined to select among them the most favorable site for further study. Among the features of each binding site examined and their usefulness in discriminating among sites were (i) preservation of the H-bond between the ligand donor moiety and the acceptor anchoring residue in the optimized complexes. Use of this criterion led to elimination of binding sites 2, 5, 6 and 8 in the naltrexone complexes and 4, 5, 6 and 7 in the etorphine complexes. (ii) D95 in TMH2 should not be directly interacting with the ligand because it is already involved in two hydrogen bonds with N67 of TMH1 and N314 of TMH7, respectively. Use of this criterion eliminated binding sites 1, 2 and 9 for both naltrexone and etorphine complexes and binding site 8 for the etorphine complex. (iii) The binding sites should include residues of TMH6 and TMH7 that recent experimental studies on dopamine D2 (Fu et al., 1996Go; Javitch et al., 1998Go) and ß2-adrenergic (Javitch et al., 1997Go) receptors have demonstrated to be ligand accessible. Specifically, the corresponding residues in the {delta} opioid receptor in TMH 6 are V266, F270, C273, W274, P276, I277, H278, F280, V281, I282 and T285. In TMH 7 they are A299, L300, H301, L302, C303, I304, A305, Y308, N310, N314, P315 and Y318 in TMH7. These residues are in bold type in Table VIIGo. The absence of these conserved residues in TMH 7 involved in interaction with the ligand led to elimination of binding site 7 of naltrexone.

Taken together, these three criteria led to two favorable sites for naltrexone, 3 and 4 and only one of these, 3, for etorphine. Because naltrexone is known to be a competitive antagonist of etorphine, site 3 common to both ligands was selected and used for further MD simulations.

Probing the mechanism of receptor activation

In this study, three components of receptor activation deduced from prior experimental and computational studies of other GPCRs were investigated for the {delta} opioid receptor. These were: (1) changes in the conserved DRY motif (TMH 3); (2) preservation of the D95 (TMH2)–N314 (TMH7) interaction; and (3) specific conformational changes in the active form of the receptor different from the inactive form. In these studies, the optimized ligand–receptor complex (3) with the greatest consistency with experimental results and a common binding pocket for both agonist and antagonist was used.

The first hypothesis investigated was that the arginine residue in the highly conserved DRY motif of TMH3 plays a central role in receptor activation of rhodopsin-like GPCRs. Support for its role has been presented by a number of investigators by both mutagenesis (Min et al., 1993Go; Scheer et al., 1996Go) and computational modeling studies (Oliveira et al., 1994Go; Scheer et al., 1996Go; Ballesteros et al., 1998Go). Specifically, these studies suggest that the D and R side chains of the DRY motif form a strong salt bridge in the inactive wild-type receptor.

Consistent with these results, the three ligand-free wild-type opioid receptor models developed here have a salt bridge between R146 and D145. This interaction is clearly shown in the vertical orientation of the three receptors in Figures 6–8GoGoGo and in Tables IV–VIGoGoGo by the close distances between D145 and R146.

Activation of the GnRH receptor (Ballesteros et al., 1998Go) and of the {alpha}1ß-adrenergic receptor (Scheer et al., 1996Go) appears to be associated with breaking the salt bridge between D and R in TMH3. Mutations of these residues that disrupt this interaction result in an active form of the ligand-free receptor, i.e. a constitutively active form, as does protonation of the arginine by analogy with rhodopsin. These studies also indicate that with the salt bridge broken in the active form, the arginine still remains in the binding pocket, as a consequence of finding other favorable interactions and perhaps also the presence of a steric barrier to moving away.

To investigate further this hypothesis for the {delta} opioid receptor studies reported here, it was assumed that there are two forms of the receptor, an inactive form in which the DR salt bridge is maintained and an active form in which it is broken. The validity of this hypothesis was assessed by determining the extent to which the agonist-bound receptor favors the active form while the antagonist-bound receptor favors the inactive form. The procedures used and results obtained are described below.

The optimized agonist– and antagonist–receptor complexes (3) with the greatest consistency with experimental results were prepared in the putative active and inactive forms and used for further MD simulations. Specifically, in the receptor inactive state, the salt bridge involving residues D145 and R146 of the wild-type receptor was maintained in the initial structure. In contrast, in the initial structure used for the active form of the receptor, the salt bridge between residues D145 and R146 was broken. This was accomplished by rotation of the Arg side chain in a way that it could form a new H-bond with the Y147 side chain of the DRY in TMH3. These four initial optimized complexes were subjected to an unconstrained MD simulation of 1 ns.

The four complexes were all energetically stable during these MD simulations. Energy optimization was then performed for the structure averaged over the entire simulation period of 1 ns for each of the complexes. The r.m.s.d. of the C{alpha} atoms between the putative active and inactive form of the complexes was 2.4 Å for naltrexone and 1.7 Å for etorphine.

The analysis revealed a striking difference between the simulations of the putative active and inactive agonist- and antagonist-bound receptors. Specifically, the hydrogen bond between residues R146 and D145 of TMH3 is maintained during the dynamics for the simulated inactive states of the {delta} receptor complex. In contrast, during the MD simulations of the candidate active forms of the receptor, this salt bridge reforms for the antagonist complex, but remains broken for the agonist complex.

Table VIIIGo lists all the interactions in the energy-optimized MD average structures of inactive naltrexone and active etorphine complexes. As can be seen, the D145–R146 salt bridge in TMH3 is present only in the inactive antagonist complex. In this complex, both the R146 and Y147 side chains rotate together resulting in the restoration of the interaction between R146 and D145 while also maintaining R146 interaction with Y147. In striking contrast, in the agonist bound form, the DR salt bridge in TMH3 remains broken during the 1 ns MD simulation. This residue, however, remains inside the helix bundle.


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Table VIII. Inter-helical interactions in the energy optimized unconstrained MD average structures of {delta}-naltrexone and {delta}-etorphine complexes
 
These results are consistent with those found for other GPCRs. They provide further support for the importance of the DRY motif in receptor activation and provide the first evidence for the participation of this motif in opioid receptor activation.

The second hypothesis investigated was that a hydrogen-bonding network involving residues D95 in TMH2 and N314 in TMH7 is required for receptor activation by agonists. This hypothesis has been validated by previous computational and experimental studies (Zhou et al., 1994Go; Sealfon et al., 1995Go) for the serotonin 5-HT2A receptor and gonadotropin-releasing hormone (GnRH). Consistent with these results, Table VIIIGo shows that the H-bond between the residues D95 in TMH2 and N314 in TMH7 is maintained in the active etorphine complex, but not in the inactive naltrexone complex. The importance of the H-bond between residues D95 in TMH2 and N314 in TMH7 for activation found here for the {delta} opioid receptor provides further support for this component of activation and extends its relevance to the opioid receptors.

The third hypothesis investigated is that agonist binding to GPCRs promotes a conformational change different from antagonist binding that leads to the formation of the active receptor state and consequent signal transduction. This hypothesis has been proposed and recently corroborated by computational and experimental studies (Zhang et al., 1993Go; Luo et al., 1994Go; Sealfon et al., 1995Go; Scheer et al., 1996Go; Gether et al., 1997aGo,bGo; Javitch et al., 1997Go).

In order to investigate this component of activation of the mouse {delta} opioid receptor, further analysis of the inactive antagonist and active agonist receptor complexes was performed. As shown in Figure 10Go, the values of the r.m.s.d. calculated for each helix compared with the ligand-free state differs for the two complexes. Specifically, the agonist, etorphine, produces a larger effect on helices TMH1, TMH3 and TMH6 whereas the antagonist, naltrexone, induces larger changes in TMH4. These results suggest that binding of agonists and antagonists induces different types of conformational changes in the {delta} opioid receptor. The finding here of the importance of changes in TMH3 and TMH6 for activation is consistent with deductions made from experimental spin labeling studies of light activation of rhodopsin (Farrens et al., 1996Go) and with results previously demonstrated for ß2-adrenergic (Gether et al., 1997aGo,bGo; Javitch et al., 1997Go) and serotonin 5-HT2A (Zhang et al., 1993Go) receptors.



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Fig. 10. R.m.s.d. values of each helix in the {delta}-naltrexone complex and {delta}-etorphine energy complex using the optimized MD average structures of each and comparing with the corresponding helix in the ligand-free model of the {delta} receptor.

 
Proposed sites for mutagenesis that could affect activation of the {delta} opioid receptor

By examination of the differences in the inactive ligand-free, the inactive antagonist-bound and the active agonist-bound complexes obtained, six sites are proposed for future mutagenesis studies.

Specifically, Y77 (TMH1) and Y318 (TMH7) are recommended for mutations that could enhance ligand activation or even lead to constitutively active mutant receptors. The reason for their selection, as shown in Tables VIII and IVGoGo, is that they are each involved in an inter-helical interaction present in both the inactive ligand-free and antagonist-bound form but absent in the active agonist-bound form. Specifically, Y77 (TMH1) interacts with R146, whereas Y318 interacts with D145 in the inactive but not in the active forms. This inter-helical interaction with two partners in the conserved DRY motif appears to stabilize an inactive form of {delta} receptor. Thus, mutations of these residues that disrupt these interactions could destabilize the inactive form and lead to either constitutively active mutants or enhancement of the activation of known agonists.

In contrast to the predicted effect of mutation of Y77 and Y318, mutation of residue Q105 in TMH2 is predicted to diminish agonist-initiated activation. This prediction is based on the finding in these studies that the inter-helical interaction D128–Q105 (TMH3–TMH2) is preferentially disrupted in the antagonist-bound complex but maintained in the active agonist-bound complex.

In the active agonist-bound form, in which the DR salt bridge in TMH3 is broken, R146 forms a new interaction with M236 in TM4. These results suggest that mutation of the M236 residue should diminish agonist-initiated activation of the {delta} opioid receptor.

The H-bond between the residues D95 in TMH2 and N314 in TMH7 is maintained in the active etorphine complex, but not in the inactive naltrexone complexes. The importance of the H-bond between residues D95 in TMH2 and N314 in TMH7 for activation found here for the {delta} opioid receptor is similar to the results found for other GPCRs. Thus, as in the previous studies, mutations at either of these sites that interfere with this interaction should decrease agonist-initiated activation.

Proposed sites for mutagenesis that could affect ligand recognition of the {delta} opioid receptor

There are significant differences in the nature and composition of the binding site in the energy-optimized MD average structures of the inactive naltrexone and active etorphine complexes. Figures 11 and 12GoGo show the binding sites of these two complexes with the side chains of the receptor residues directly involved in the interaction with the ligands explicitly indicated. As shown, ligands that bind in the same binding pocket can nevertheless have both common and distinct receptor–residue interactions. Such explicit differences can account for the frequent observation of differential effects of the same mutation on different ligand binding.



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Fig. 11. Binding site of the {delta}-naltrexone complex with the side chains of the residues interacting with the ligand shown in color.

 


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Fig. 12. Binding site of the {delta}-etorphine complex with the side chains of the residues interacting with the ligand shown in color.

 
Among the residues common to both sites, mutations of residues D128, Y129 and W284 have already been shown (Befort et al., 1996bGo; Valiquette et al., 1996Go; Mausour et al., 1997Go) to affect ligand affinities significantly. Mutations of other common residues such as L125, T285 and F218 are suggested as potentially affecting both agonist and antagonist recognition.

Among the residues involved in the interaction with the agonist that are not present in the interaction with the antagonist, two promising sites for mutagenesis studies are L300 and I304 in TM7, since the corresponding residues in the dopamine D2 receptor, namely Y408 and T412, have been demonstrated to be ligand accessible (Fu et al., 1996Go).

The residues involved in the interaction with the antagonist that are not important for interactions with the agonist are all close to each other in TMH5. These are L219, F222 and V223. Mutation of residues in one or the other of these sites could have a more variable effect on ligand binding.

In summary, 3D models of the mouse opioid receptors have been constructed. They were equilibrated and found to be structurally stable during 300 ps of unconstrained MD simulations. The energy-optimized MD average of the last 20 ps of MD simulations was used as a 3D model for the ligand-free opioid receptors. These models were further validated by consistency with known properties of the receptors. Methods for systematic identification of ligand binding sites were developed and used to identify favorable binding sites in the {delta} receptor consistent with experimental observation. Energy optimization and 1 ns MD simulations of ligand–receptor complexes were used to probe plausible activation mechanisms and to characterize a putative inactive antagonist and active agonist complex. Identification of the residues in the binding site of the antagonist naltrexone and the agonist etorphine (Figures 11 and 12GoGo) was made that can serve as a guide for mutagenesis studies that could affect ligand recognition. Comparison of inter-helical interactions in the inactive antagonist bound state and the active agonist-bound state suggested residues that could be modulators of activation. Specifically, site-specific mutations of residues Y77 in TMH1 and Y318 in TMH7 could disrupt H-bonds that help keep the receptor in an inactive state and could hence lead to constitutively active receptors. By contrast, mutation of M236 in TMH4 and Q105 in TMH2 is predicted to diminish agonist initiated activation of the {delta} opioid receptor. Finally, similarly to the results obtained for the 5HT2A receptor (Sealfon et al., 1995Go), single mutations of residues D95 in TMH2 and N314 in TMH7 should diminish and double reciprocal mutation should restore ligand initiated activation of the {delta} opioid receptor. Mutations at these sites would thus be particularly useful in providing additional insights into the validity of the model, the ligand binding pocket and the proposed activation mechanism of the {delta} opioid receptor.


    Acknowledgments
 
The support of NIDA grant DA02622 is gratefully acknowledged. The use of the facilities at the Pittsburgh Supercomputing Center, the National Partnership for Advanced Computational Infrastructure and the National Center for Supercomputing Applications is also greatly appreciated.


    Notes
 
2 To whom correspondence should be addressed. E-mail: loew{at}montara.molres.org Back


    References
 Top
 Abstract
 Introduction
 Materials and methods
 Results and discussion
 References
 
Alkorta,I. and Loew,G.H. (1996) Protein Engng, 9, 573–583.[Abstract]

Altschul,S.F., Gish,W., Miller,W., Myers,E.W. and Lipman,D.J. (1990) J. Mol. Biol., 215, 403–410.[ISI][Medline]

Arden,J.R., Segredo,V., Wang,Z., Lameh,J. and Sadee,W. (1995) J. Neurochem., 65, 1636–1645.[ISI][Medline]

Bairoch,A. and Bieckmann,B. (1994) Nucleic Acids Res., 22, 3578–3580.[Abstract]

Baldwin,J.M., Schertler,F.X.G. and Unger,V.M. (1997) J. Mol Biol., 272, 144–164.[ISI][Medline]

Ballesteros,J. and Weinstein,H. (1995) Methods Neurosci., 25, 366–428.

Ballesteros,J. et al. (1998) J. Biol. Chem., 273, 10445–10453.[Abstract/Free Full Text]

Barker,W.C. et al. (1998) Nucleic Acids Res., 26, 27–32.[Abstract/Free Full Text]

Befort,K., Tabbara,L., Bausch,S., Chavkin,C., Evans,C. and Kieffer,B. (1996a) Mol. Pharmacol., 49, 216–223.[Abstract]

Befort,K., Tabbara,L., Kling,D., Maigret,B. and Kieffer,B. (1996b) J. Biol. Chem., 271, 10161–10168.[Abstract/Free Full Text]

Benson,D.A., Boguski,M.S., Lipman,D.J., Ostell,J. and Ouellette,B.F. (1998) Nucleic Acids Res., 26, 1–7.[Abstract/Free Full Text]

Bockaert,J. (1991) Therapie (Paris), 46, 413–420.[Medline]

Chen,C., Xue,J.C., Zhue,J., Chen,Y.W., Kunapuli,S., de Riel,J.K., Yu,L. and Liu Chen,L.Y. (1995) J. Biol. Chem., 270, 17866–17870.[Abstract/Free Full Text]

Chen,Y., Mestek,A., Liu,J., Hurley,J.A. and Yu,L. (1993) Mol. Pharmacol., 44, 8–12.[Abstract]

Claude,P.A., Wotta,D.R., Zhang,X.H., Prather,P.L., McGinn,T.M., Erickson,L.J., Loh,H.H. and Law,P.Y. (1996) Proc. Natl Acad. Sci. USA, 93, 5715–5719.[Abstract/Free Full Text]

Cornell,W.D. et al. (1995) J. Am. Chem. Soc., 117, 5179–5197.[ISI]

Cornette,J.L., Cease,K.B., Margalit,H., Spouge,J.L., Berzofsky,J.A. and DeLisi,C. (1987) J. Mol. Biol., 195, 659–685.[ISI][Medline]

Davies,A., Schertler,G.F.X., Gowen,B.E. and Saibil,H.R. (1996) J. Struct. Biol., 117, 36–44.[ISI][Medline]

Dickenson,A.H., (1991) Br. Med. Bull., 47, 690–702.[Abstract]

Elling,C., Nielsen,S. and Schwartz,T. (1995) Nature, 374, 74–77.[ISI][Medline]

Evans,C.K., Keith,D.E., Morrison,H., Magendzo,K. and Edwards,R.H. (1992) Science, 258, 1952–1955.[ISI][Medline]

Farrens,D.L., Altenbach,C., Yang,K., Hubbell,W.L. and Khorana,G. (1996) Science, 274, 768–770.[Abstract/Free Full Text]

Filizola,M., Perez,J.J. and Carteni-Farina,M. (1998) J. Comput.-Aided Mol. Des., 12, 111–118.

Filizola,M., Carteni-Farina,M. and Perez,J.J. (1999) J. Comput.-Aided Mol. Des., 13, 397–407.

Frisch,M.J. et al. (1995) Gaussian94, Revision B.2. Gaussian, Pittsburgh, PA.

Fu,D., Ballesteros,J.A., Weinstein,H., Chen,J. and Javitch,J.A. (1996) Biochemistry, 35, 11278–11285.[ISI][Medline]

Fukuda,K., Kato,S., Mori,K., Nishi,M. and Takeshima,H. (1993) FEBS Lett., 327, 311–314.[ISI][Medline]

Fukuda,K., Terasako,K., Kato,S. and Mori,K. (1995a) FEBS Lett., 373, 177–181.[ISI][Medline]

Fukuda,K., Kato,S. and Mori,K. (1995b) J. Biol. Chem., 270, 6702–6709.[Abstract/Free Full Text]

Gether,U., Ballesteros,J.A., Seifert,R., Sanders-Bush,E., Weinstein,H. and Kobilka,B.K. (1997a) J. Biol. Chem., 272, 2587–2590.[Abstract/Free Full Text]

Gether,U., Lin,S., Ghanouni,P., Ballesteros,J.A., Weinstein,H. and Kobilka, B.K. (1997b) EMBO J., 16, 6737–6747.[Abstract/Free Full Text]

Hargrave,P.A. (1991) Curr. Opin. Struct. Biol., 1, 575–581.

Henry,D.J., Grandy,D.K., Lester,H.A., Davidson,N. and Chavkin,C. (1995) Mol. Pharmacol., 47, 551–557.[Abstract]

Hjorth,S.A., Thirstrup,K., Grandy,D.K. and Schwartz,T.W. (1995) Mol. Pharmacol., 47, 1089–1094.[Abstract]

Huang,P., Kim,S. and Loew,G. (1997) J. Comput.-Aided Mol. Des., 11, 21–28.

Jaffe,J.M. and Martin,W.R. (1990) In Gilman,A., Rall,J., Nies,M. and Taylor,P. (eds.), The Pharmacologic Basis of Therapeutics. Pergamon Press, New York, pp. 485–573.

Javitch,J.A., Fu,D., Liapakis,G. and Chen,J. (1997) J. Biol. Chem., 272, 18546–18549.[Abstract/Free Full Text]

Javitch,J.A., Ballesteros,J.A., Weinstein,H. and Chen,J. (1998) Biochemistry, 37, 998-1006.[ISI][Medline]

Keith,D.E., Murray,S.R., Zaki,P.A., Chu,P.C., Lissin,D.V., Kang,L., Evans,C.J. and Zastrow,M.V. (1996) J. Biol. Chem., 271, 19021–19024.[Abstract/Free Full Text]

Kieffer,B.L., Befort,K., Gaveriaux-Guff,C. and Hirth,C.G. (1992) Proc. Natl Acad. Sci. USA, 89, 12048–12052.[Abstract]

Komiya,H., Yeates,T.O., Rees,D.C., Allen,J.P. and Feher,G. (1988) Proc. Natl Acad. Sci. USA, 85, 9012–9016.[Abstract]

Kong,H., Raynor,K., Yasuda,K., Moe,S.T., Portoghese,P.S., Bell,G.I. and Reisine,T. (1993) J. Biol. Chem., 268, 23055–23058.[Abstract/Free Full Text]

Kong,H., Raynor,K., Yano,H., Takeda,J., Bell,G.I. and Reisine,T. (1994) Proc. Natl Acad. Sci USA, 91, 8042–8046.[Abstract]

Luo,X., Zhang,D. and Weinstein,H. (1994) Protein Engng, 7, 1441–1448.[Abstract]

Ma,G.H., Miller,R.J., Kuznetsov,A. and Philipson,L.H. (1995) Mol. Pharmacol., 47, 1035–1040.[Abstract]

Malatynska,E., Wang,Y., Knapp,R.J., Waite,S., Calderon,S., Rice,K., Hruby,V.J., Yamamura,H.I., Roeske,W.R. (1996) J. Pharmacol. Exp. Ther., 278, 1083–1089.[Abstract]

Maldonado,R., Negus,S. and Koob,G.F. (1992) Neuropharmacology 31, 1231–1241.[ISI][Medline]

Mather,L.E. and Cousins,M.J. (1992) Med. J. Aust., 156, 796–802.[ISI][Medline]

Mausour,A., Taylor,L.P., Fine,J.L., Thompson,R.C., Horersten,M.T., Mosberg,H.I., Watson,S.J. and Akil,H., (1997) J. Neurochem., 68, 344.[ISI][Medline]

McKenzie,F.R. and Milligan (1990) Biochem. J., 267, 391–398.[ISI][Medline]

Meng,F., Xie,G.X., Thompson,R.C., Mansour,A., Goldstein,A., Watson,S.J. and Akil,H. (1993) Proc. Natl Acad. Sci. USA, 90, 9954–9958.[Abstract]

Meng,F., Hoversten,M.T., Thompson,R.C., Taylor,L., Watson,S.J. and Akil,H. (1995) J. Biol. Chem, 270, 12730–12736.[Abstract/Free Full Text]

Min,K.C., Zvyaga,T.A., Cypess,A.M. and Sakmar,T.P. (1993) J. Biol. Chem., 268, 9400–9404.[Abstract/Free Full Text]

Minami,M., Onogi,T., Nakagawa,T., Katao,Y., Aoki,Y., Katsumata,S. and Satoh,M. (1995) FEBS Lett., 364, 23–27.[ISI][Medline]

Misicka,A., Lipkowski,A.W., Horvarth,R., Davis,P., Porreca,F., Yamamura,H.I. and Hruby,V.J. (1994) Int. J. Pept. Protein Res., 44, 80–84.[ISI][Medline]

Murthy,K.S. and Makhlouf,G.M. (1996) Mol. Pharmacol., 50, 870–877.[Abstract]

Oliveira,L., Paiva,A.C.M., Sander,C., Vriend,G. (1994) Trends Biochem. Sci., 15, 170–172.

Olson,G.A., Olson,R.D. and Kastin,A.J. (1996) Peptides, 17, 1421–1466.[ISI][Medline]

Onogi,T., Minami,M., Katao,Y., Nakagawa,T., Aoki,Y., Toya,T., Katsumata,S. and Satoh,M. (1995) FEBS Lett., 357, 93–97.[ISI][Medline]

Pearlman,D.A., Case,D.A., Caldwell,J.C, Seibel,G.L., Singh,U.C., Weiner,P. and Kollman,P.A. (1991) AMBER 4.0. University of California, San Francisco.

Piros,E.T., Prather,P.L., Loh,H.H., Law,P.Y., Evans,C.J. and Hales,T.G. (1995) Mol. Pharmacol., 47, 1041–1049.[Abstract]

Piros,E.T., Prather,P.L., Law,P.Y., Evans,C.J. and Hales,T.G. (1996) Mol. Pharmacol., 50, 947–956.[Abstract]

Pogozheva,I.D., Lomize,A.L., Mosberg,H.I. (1998) Biophys. J., 75, 612–634.[Abstract/Free Full Text]

Prather,P.L., Loh,H.H. and Law,P.Y. (1994) Mol. Pharmacol., 45, 997–1003.[Abstract]

Rao,V., Cohen,G. and Oprian,D. (1994) Nature, 367, 639–641.[ISI][Medline]

Sankararamankrishnan,R. and Vishveshwara,S. (1990) Biopolymers, 30, 287–298.[ISI][Medline]

Scheer,A., Fanelli,F., Costa,T., DeBenedetti,P.G. and Cotecchia,S. (1996) EMBO J., 15, 3566–3578.[Abstract]

Schertler,G.F.X. and Hargrave,P.A. (1995) Proc. Natl Acad. Sci USA, 92, 11578–11582.[Abstract]

Schertler,G.F.X., Villa,C., Henderson,R. (1993) Nature, 362, 770–772.[ISI][Medline]

Sealfon,S.C., Chi,L., Ebersole,B., Rodic,V., Zhang,D., Ballesteros,J. and Weinstein,H. (1995) J. Biol. Chem., 270, 16683–16688.[Abstract/Free Full Text]

Segredo,V., Burford,N.T., Lameh,J. and Sadee,W. (1997) J. Neurochem., 68, 2395–2404.[ISI][Medline]

Strahs,D. and Weinstein,H. (1997) Protein Engng, 10, 1019–1038.[Abstract]

Surratt,C.K., Johnson,P.S., Moriwaki,A., Seidleck,B.K., Blaschak,C.J., Wang,J.B. and Uhl,G.R. (1994) J. Biol. Chem., 269, 20548–20553.[Abstract/Free Full Text]

Tang,T., Kiang,J.G., Cote,T.E. and Cox,B.M. (1995) Mol. Pharmacol., 48, 189–193.[Abstract]

Thirstrup,K., Elling,C.E., Hjorth,S.A. and Schwartz,T.W. (1996) J. Biol. Chem., 271, 7875–7878.[Abstract/Free Full Text]

Thompson,J.D., Higgins,D. and Gibson,T. (1994) Nucleic Acids Res., 22, 4673–4680.[Abstract]

Unger,V.M. and Schertler,G.F.X. (1995) Biophys. J., 68, 1776–1786.[Abstract]

Unger,V.M., Hargrave,P.A., Baldwin,J.M. and Schertler,G.F.X. (1997) Nature, 389, 203–206.[ISI][Medline]

Valiquette,M., Vu,H.K., Yue,S.Y., Wahlestedt,C. and Walker,P. (1996) J. Biol. Chem., 271, 18789–18796.[Abstract/Free Full Text]

Wang,J.B., Johnson,P.S., Wu,J.M., Wang,W.F. and Uhl,G.R. (1994) J. Biol. Chem., 269, 25966–25969.[Abstract/Free Full Text]

Weiner,S.J., Kollman,P.A., Case,D.A., Singh,U.C., Ghio,C., Alagona,G., Profeta,S. and Weiner,P. (1984) J. Am. Chem. Soc., 106, 765.[ISI]

Xue,J.C., Chen,C., Zhu,J., Kunapuli,S., De Riel,J.K., Yu,L. and Liu Chen,L.Y. (1994) J. Biol. Chem., 269, 30195–30199.[Abstract/Free Full Text]

Yasuda,K., Rainor,K., Kong,H., Breder,C., Takeda,J., Reisine,T. and Bell,G.I. (1993) Proc. Natl Acad. Sci. USA, 90, 6736–6740.[Abstract]

Zhang,D. and Weinstein,H. (1993) J. Med. Chem., 36, 934–938.[ISI][Medline]

Zhou,W., Flanagan,C., Ballesteros,J., Konivicka,K., Davidson,J., Weinstein,H., Millar,R. and Sealfon,S. (1994) Mol. Pharmacol., 45, 165–170.[Abstract]

Zhu,J., Xue,J.C., Law,P.Y., Claude,P.A., Luo,L.Y., Yin,J., Chen,C. and Liu Chen,L.Y. (1996a) FEBS Lett., 384, 198–202.[ISI][Medline]

Zhu,J., Yin,J., Law,P.Y., Claude,P.A., Rice,K.C., Evans,C.J., Chen,C., Yu,L. and Liu Chen,L.Y. (1996b) J. Biol. Chem., 271, 1430–1434.[Abstract/Free Full Text]

Received January 22, 1999; revised July 28, 1999; accepted August 5, 1999.