Molecular Research Institute, 2495 Old Middlefield Way, Mountain View, CA 94043, USA 1 These two authors contributed equally to the work described
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Abstract |
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Keywords: G-protein coupled receptors/ligand binding site/opioid receptors/transmembrane helices
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Introduction |
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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, 1991; Hargrave, 1991
) connected by intracellular and extracellular loops both exterior to the lipid membrane. GPCRs bind to trimeric proteins called G-proteins, consisting of
, ß and
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
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
subunit from the receptor and from ß and
subunits. The GTP bound
subunit (G
) 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
(Prather et al., 1994
; Piros et al., 1995
), Gi2
(McKenzie and Milligan, 1990
; Tang et al., 1995
; Murthy and Makhlouf, 1996
) and Go
(Prather et al., 1994
; Murthy and Makhlouf, 1996
) G proteins. Activation of G proteins by opioid receptors in turn results in inhibition of voltage dependent calcium channels (McKenzie and Milligan, 1990
; Prather et al., 1994
; Piros et al., 1995
; Tang et al., 1995
; Murthy and Makhlouf, 1996
; Piros et al., 1996
) activation of inwardly rectifying K+ channels (Henry et al., 1995
; Ma et al., 1995
) and inhibition of adenyl cyclase (Arden et al., 1995
; Keith et al., 1996
; Malatynska et al., 1996
; Piros et al., 1996
; Segredo et al., 1997
).
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., 1993; Surratt et al., 1994
; Fukuda et al., 1995a
; Hjorth et al., 1995
; Befort et al., 1996a
,b
; Claude et al., 1996
; Valiquette et al., 1996
). 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., 1994
; Misicka et al., 1994
; Wang et al., 1994
; Xue et al., 1994
; Chen et al., 1995
; Fukuda et al., 1995b
; Hjorth et al., 1995
; Meng et al., 1995
; Minami et al., 1995
; Onogi et al., 1995
; Claude et al., 1996
; Valiquette et al., 1996
; Zhu et al., 1996a
,b
).
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 69 Å (Schertler et al., 1993; Schertler and Hargrave, 1995
; Unger and Schertler, 1995
; Davies et al., 1996
; Unger et al., 1997
). 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, 1996
; Strahs and Weinstein, 1997
; Pogozheva et al., 1998
; Filizola et al., 1999
). 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, 1996
). Another used putative H-bonding residues combined with distance geometry calculations (Pogozheva et al., 1998
). The third (Strahs and Weinstein, 1997
) was based on the techniques of homology modeling refined by a variety of criteria based upon biophysical properties of membrane proteins (Ballesteros and Weinstein, 1995
) and use of the low-resolution electron cryomicroscopy 2D structures of frog and bovine rhodopsin (Schertler et al., 1993
; Schertler and Hargrave, 1995
; Unger and Schertler, 1995
). The fourth (Filizola et al., 1999
), also utilized information deduced from the rhodopsin 2D electron density but coupled to a general procedure developed for construction of GPCRs (Filizola et al., 1998
).
In the work reported here, renewed efforts have been made to construct 3D models of the transmembrane domains of the cloned mouse , µ and
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, 1997
). 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 ligandreceptor 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., 1997) 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 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 ligandreceptor 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 ligandreceptor 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 ligandreceptor 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 ligandreceptor complexes with each other and with the ligand-free 3D receptor model. These results provided support for an activation mechanism of mouse
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.
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Materials and methods |
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The set of sequences related to opioid receptors was retrieved with a BLAST search (Altschul et al., 1990) from the publicly available protein databases Swissprot (Bairoch and Bieckmann, 1994
), translated GenBank (Benson et al., 1998
) and PIR (Barker et al., 1998
). 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., 1994) 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, 1996). 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, 1996
) defined as the difference between the lipophilic (Vlj) and hydrophobic (Vhj) variability indices (i.e. Vj = Vlj Vhj) at the consensus position j of the multiple sequence alignment. Figure 1
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, 1995
).
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The results of the helicity analysis of the TM sections of the opioid receptors shown in Figure 2 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, 1996
) 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 , µ and
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 angles to preferred values determined for these receptors from values in available known transmembrane
-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., 1997). 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.
This consistency provided initial evidence for the reliability of the constructed models.
Energy optimization and MD simulations of the initial 3D models of the , µ and
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., 1991) and the parm91 parameter set (Weiner et al., 1984
) 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., 1995
). 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 and
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 ligandreceptor complexes for the 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 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 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., 1997) to be important for recognition of the
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
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 ligandreceptor 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 ligandreceptor 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.
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Results and discussion |
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Shown in Figure 4ac 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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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.
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Taken together, then, the diverse criteria used for the assessment of the ligand-free models support their usefulness to characterize explicit ligandreceptor 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 receptor shown in Figure 6
. The chemical structures of naltrexone (antagonist) and etorphine (agonist) are shown in Figure 9
.
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Using these criteria, nine different binding sites were identified for each ligand resulting in nine different initial opioid receptor complexes with each ligand. These 18 initial ligandreceptor complexes were energy optimized. Table VII
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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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 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 ligandreceptor 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., 1993; Scheer et al., 1996
) and computational modeling studies (Oliveira et al., 1994
; Scheer et al., 1996
; Ballesteros et al., 1998
). 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 68 and in Tables IVVI
by the close distances between D145 and R146.
Activation of the GnRH receptor (Ballesteros et al., 1998) and of the
1ß-adrenergic receptor (Scheer et al., 1996
) 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 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 antagonistreceptor 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 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 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 VIII lists all the interactions in the energy-optimized MD average structures of inactive naltrexone and active etorphine complexes. As can be seen, the D145R146 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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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., 1994; Sealfon et al., 1995
) for the serotonin 5-HT2A receptor and gonadotropin-releasing hormone (GnRH). Consistent with these results, Table VIII
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
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., 1993; Luo et al., 1994
; Sealfon et al., 1995
; Scheer et al., 1996
; Gether et al., 1997a
,b
; Javitch et al., 1997
).
In order to investigate this component of activation of the mouse opioid receptor, further analysis of the inactive antagonist and active agonist receptor complexes was performed. As shown in Figure 10
, 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
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., 1996
) and with results previously demonstrated for ß2-adrenergic (Gether et al., 1997a
,b
; Javitch et al., 1997
) and serotonin 5-HT2A (Zhang et al., 1993
) receptors.
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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 IV, 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
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 D128Q105 (TMH3TMH2) 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 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 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 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 12 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 receptorresidue 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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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., 1996).
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 receptor consistent with experimental observation. Energy optimization and 1 ns MD simulations of ligandreceptor 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 12
) 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
opioid receptor. Finally, similarly to the results obtained for the 5HT2A receptor (Sealfon et al., 1995
), single mutations of residues D95 in TMH2 and N314 in TMH7 should diminish and double reciprocal mutation should restore ligand initiated activation of the
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
opioid receptor.
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Acknowledgments |
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Notes |
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References |
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Altschul,S.F., Gish,W., Miller,W., Myers,E.W. and Lipman,D.J. (1990) J. Mol. Biol., 215, 403410.[ISI][Medline]
Arden,J.R., Segredo,V., Wang,Z., Lameh,J. and Sadee,W. (1995) J. Neurochem., 65, 16361645.[ISI][Medline]
Bairoch,A. and Bieckmann,B. (1994) Nucleic Acids Res., 22, 35783580.[Abstract]
Baldwin,J.M., Schertler,F.X.G. and Unger,V.M. (1997) J. Mol Biol., 272, 144164.[ISI][Medline]
Ballesteros,J. and Weinstein,H. (1995) Methods Neurosci., 25, 366428.
Ballesteros,J. et al. (1998) J. Biol. Chem., 273, 1044510453.
Barker,W.C. et al. (1998) Nucleic Acids Res., 26, 2732.
Befort,K., Tabbara,L., Bausch,S., Chavkin,C., Evans,C. and Kieffer,B. (1996a) Mol. Pharmacol., 49, 216223.[Abstract]
Befort,K., Tabbara,L., Kling,D., Maigret,B. and Kieffer,B. (1996b) J. Biol. Chem., 271, 1016110168.
Benson,D.A., Boguski,M.S., Lipman,D.J., Ostell,J. and Ouellette,B.F. (1998) Nucleic Acids Res., 26, 17.
Bockaert,J. (1991) Therapie (Paris), 46, 413420.[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, 1786617870.
Chen,Y., Mestek,A., Liu,J., Hurley,J.A. and Yu,L. (1993) Mol. Pharmacol., 44, 812.[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, 57155719.
Cornell,W.D. et al. (1995) J. Am. Chem. Soc., 117, 51795197.[ISI]
Cornette,J.L., Cease,K.B., Margalit,H., Spouge,J.L., Berzofsky,J.A. and DeLisi,C. (1987) J. Mol. Biol., 195, 659685.[ISI][Medline]
Davies,A., Schertler,G.F.X., Gowen,B.E. and Saibil,H.R. (1996) J. Struct. Biol., 117, 3644.[ISI][Medline]
Dickenson,A.H., (1991) Br. Med. Bull., 47, 690702.[Abstract]
Elling,C., Nielsen,S. and Schwartz,T. (1995) Nature, 374, 7477.[ISI][Medline]
Evans,C.K., Keith,D.E., Morrison,H., Magendzo,K. and Edwards,R.H. (1992) Science, 258, 19521955.[ISI][Medline]
Farrens,D.L., Altenbach,C., Yang,K., Hubbell,W.L. and Khorana,G. (1996) Science, 274, 768770.
Filizola,M., Perez,J.J. and Carteni-Farina,M. (1998) J. Comput.-Aided Mol. Des., 12, 111118.
Filizola,M., Carteni-Farina,M. and Perez,J.J. (1999) J. Comput.-Aided Mol. Des., 13, 397407.
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, 1127811285.[ISI][Medline]
Fukuda,K., Kato,S., Mori,K., Nishi,M. and Takeshima,H. (1993) FEBS Lett., 327, 311314.[ISI][Medline]
Fukuda,K., Terasako,K., Kato,S. and Mori,K. (1995a) FEBS Lett., 373, 177181.[ISI][Medline]
Fukuda,K., Kato,S. and Mori,K. (1995b) J. Biol. Chem., 270, 67026709.
Gether,U., Ballesteros,J.A., Seifert,R., Sanders-Bush,E., Weinstein,H. and Kobilka,B.K. (1997a) J. Biol. Chem., 272, 25872590.
Gether,U., Lin,S., Ghanouni,P., Ballesteros,J.A., Weinstein,H. and Kobilka, B.K. (1997b) EMBO J., 16, 67376747.
Hargrave,P.A. (1991) Curr. Opin. Struct. Biol., 1, 575581.
Henry,D.J., Grandy,D.K., Lester,H.A., Davidson,N. and Chavkin,C. (1995) Mol. Pharmacol., 47, 551557.[Abstract]
Hjorth,S.A., Thirstrup,K., Grandy,D.K. and Schwartz,T.W. (1995) Mol. Pharmacol., 47, 10891094.[Abstract]
Huang,P., Kim,S. and Loew,G. (1997) J. Comput.-Aided Mol. Des., 11, 2128.
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. 485573.
Javitch,J.A., Fu,D., Liapakis,G. and Chen,J. (1997) J. Biol. Chem., 272, 1854618549.
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, 1902119024.
Kieffer,B.L., Befort,K., Gaveriaux-Guff,C. and Hirth,C.G. (1992) Proc. Natl Acad. Sci. USA, 89, 1204812052.[Abstract]
Komiya,H., Yeates,T.O., Rees,D.C., Allen,J.P. and Feher,G. (1988) Proc. Natl Acad. Sci. USA, 85, 90129016.[Abstract]
Kong,H., Raynor,K., Yasuda,K., Moe,S.T., Portoghese,P.S., Bell,G.I. and Reisine,T. (1993) J. Biol. Chem., 268, 2305523058.
Kong,H., Raynor,K., Yano,H., Takeda,J., Bell,G.I. and Reisine,T. (1994) Proc. Natl Acad. Sci USA, 91, 80428046.[Abstract]
Luo,X., Zhang,D. and Weinstein,H. (1994) Protein Engng, 7, 14411448.[Abstract]
Ma,G.H., Miller,R.J., Kuznetsov,A. and Philipson,L.H. (1995) Mol. Pharmacol., 47, 10351040.[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, 10831089.[Abstract]
Maldonado,R., Negus,S. and Koob,G.F. (1992) Neuropharmacology 31, 12311241.[ISI][Medline]
Mather,L.E. and Cousins,M.J. (1992) Med. J. Aust., 156, 796802.[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, 391398.[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, 99549958.[Abstract]
Meng,F., Hoversten,M.T., Thompson,R.C., Taylor,L., Watson,S.J. and Akil,H. (1995) J. Biol. Chem, 270, 1273012736.
Min,K.C., Zvyaga,T.A., Cypess,A.M. and Sakmar,T.P. (1993) J. Biol. Chem., 268, 94009404.
Minami,M., Onogi,T., Nakagawa,T., Katao,Y., Aoki,Y., Katsumata,S. and Satoh,M. (1995) FEBS Lett., 364, 2327.[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, 8084.[ISI][Medline]
Murthy,K.S. and Makhlouf,G.M. (1996) Mol. Pharmacol., 50, 870877.[Abstract]
Oliveira,L., Paiva,A.C.M., Sander,C., Vriend,G. (1994) Trends Biochem. Sci., 15, 170172.
Olson,G.A., Olson,R.D. and Kastin,A.J. (1996) Peptides, 17, 14211466.[ISI][Medline]
Onogi,T., Minami,M., Katao,Y., Nakagawa,T., Aoki,Y., Toya,T., Katsumata,S. and Satoh,M. (1995) FEBS Lett., 357, 9397.[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, 10411049.[Abstract]
Piros,E.T., Prather,P.L., Law,P.Y., Evans,C.J. and Hales,T.G. (1996) Mol. Pharmacol., 50, 947956.[Abstract]
Pogozheva,I.D., Lomize,A.L., Mosberg,H.I. (1998) Biophys. J., 75, 612634.
Prather,P.L., Loh,H.H. and Law,P.Y. (1994) Mol. Pharmacol., 45, 9971003.[Abstract]
Rao,V., Cohen,G. and Oprian,D. (1994) Nature, 367, 639641.[ISI][Medline]
Sankararamankrishnan,R. and Vishveshwara,S. (1990) Biopolymers, 30, 287298.[ISI][Medline]
Scheer,A., Fanelli,F., Costa,T., DeBenedetti,P.G. and Cotecchia,S. (1996) EMBO J., 15, 35663578.[Abstract]
Schertler,G.F.X. and Hargrave,P.A. (1995) Proc. Natl Acad. Sci USA, 92, 1157811582.[Abstract]
Schertler,G.F.X., Villa,C., Henderson,R. (1993) Nature, 362, 770772.[ISI][Medline]
Sealfon,S.C., Chi,L., Ebersole,B., Rodic,V., Zhang,D., Ballesteros,J. and Weinstein,H. (1995) J. Biol. Chem., 270, 1668316688.
Segredo,V., Burford,N.T., Lameh,J. and Sadee,W. (1997) J. Neurochem., 68, 23952404.[ISI][Medline]
Strahs,D. and Weinstein,H. (1997) Protein Engng, 10, 10191038.[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, 2054820553.
Tang,T., Kiang,J.G., Cote,T.E. and Cox,B.M. (1995) Mol. Pharmacol., 48, 189193.[Abstract]
Thirstrup,K., Elling,C.E., Hjorth,S.A. and Schwartz,T.W. (1996) J. Biol. Chem., 271, 78757878.
Thompson,J.D., Higgins,D. and Gibson,T. (1994) Nucleic Acids Res., 22, 46734680.[Abstract]
Unger,V.M. and Schertler,G.F.X. (1995) Biophys. J., 68, 17761786.[Abstract]
Unger,V.M., Hargrave,P.A., Baldwin,J.M. and Schertler,G.F.X. (1997) Nature, 389, 203206.[ISI][Medline]
Valiquette,M., Vu,H.K., Yue,S.Y., Wahlestedt,C. and Walker,P. (1996) J. Biol. Chem., 271, 1878918796.
Wang,J.B., Johnson,P.S., Wu,J.M., Wang,W.F. and Uhl,G.R. (1994) J. Biol. Chem., 269, 2596625969.
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, 3019530199.
Yasuda,K., Rainor,K., Kong,H., Breder,C., Takeda,J., Reisine,T. and Bell,G.I. (1993) Proc. Natl Acad. Sci. USA, 90, 67366740.[Abstract]
Zhang,D. and Weinstein,H. (1993) J. Med. Chem., 36, 934938.[ISI][Medline]
Zhou,W., Flanagan,C., Ballesteros,J., Konivicka,K., Davidson,J., Weinstein,H., Millar,R. and Sealfon,S. (1994) Mol. Pharmacol., 45, 165170.[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, 198202.[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, 14301434.
Received January 22, 1999; revised July 28, 1999; accepted August 5, 1999.