Glucocorticoid Receptor Point Mutation V571M Facilitates Coactivator and Ligand Binding by Structural Rearrangement and Stabilization

Peter Carlsson, Konrad F. Koehler and Lennart Nilsson

Department of Structural Biology (P.C., K.F.K.), Karo Bio AB, Novum, and Department of Biosciences at Novum (P.C., L.N.), Karolinska Institutet, S-141 57 Huddinge, Sweden

Address all correspondence and requests for reprints to: Peter Carlsson, Karo Bio AB, Novum, S-141 57 Huddinge, Sweden. E-mail: peter.carlsson{at}karobio.se.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Two-point mutations in the human glucocorticoid receptor have been studied by computer simulations to rationalize experimental data, where mutants comprising the V571M substitution improve both transcriptional activity and affinity for aldosterone despite large distances between the mutated residue and the coactivator-binding surface and ligand-binding pocket, respectively. Our molecular dynamics simulations show that the V571M mutation modifies the coactivator-binding site defined by helices 3, 4, and 12, and that specific structural rearrangement of the coactivator-binding site correlates well with transactivation data. A similar reorganization of the coactivator-binding cleft is observed in crystallographic structures of the estrogen receptor in the presence of coactivator peptide, compared with structures without peptide, indicating that induced fit for coactivator binding is a general phenomenon for nuclear receptors. These results suggest that the V571M substitution facilitates recruitment of coactivator protein by promotion of a conformational substate reducing the energetic penalty for the induced fit of the receptor-coactivator complex. Furthermore, our simulations of V571M mutants showed reduced fluctuations of residues lining the ligand-binding pocket. This indicates that a reduction of the entropic cost for ligand binding may explain the increased affinity of V571M mutants for certain ligands.


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
NUCLEAR RECEPTORS are ligand-activated transcription factors that regulate the expression of target genes in a cell- and promoter-specific manner. Key to nuclear receptor activation by agonist ligands is positioning of the C-terminal {alpha}-helix [helix 12 H12)]) of the ligand-binding domain (LBD) in a conformation that promotes and prevents binding of coactivator and corepressor proteins, respectively (1). The glucocorticoid receptor (GR) and mineralocorticoid receptor (MR) are homologous members of the steroid hormone receptor superfamily. Intriguingly, whereas the GR interacts primarily with glucocorticoid ligands, the MR is activated by both glucocorticoids and mineralocorticoids. Synthetic glucocorticoids are widely used in treatment of inflammatory and immune disorders, such as asthma, and mineralocorticoids are involved in regulation of the electrolytic balance, affecting renal function and blood pressure among others.

Site-directed mutagenesis of the GR has been used as a tool to probe the mechanism of nuclear receptor ligand specificity. By screening a yeast model system for GR mutants with increased sensitivity for the mineralocorticoid aldosterone, Lind et al. (2) have shown that residue 571 in H3 of the human GR is crucial for the discrimination between glucocorticoids and mineralocorticoids. When Val-571 in GR is replaced by the structurally corresponding residue in MR (V571M), the affinity of the GR LBD (Fig. 1Go) for aldosterone exhibits an approximately 3-fold increase as well as the transcriptional activity measured in a reporter cell line (EC50 values). Hence, the GR V571M mutant acts rather like MR than GR wild type (wt). A double-GR mutant (containing both V571M and A573Q) shows an even larger (10-fold) increase in aldosterone activity but only a 3-fold increase in affinity. This is clearly a cooperative effect, because the single mutant A573Q has an aldosterone activity indistinguishable from the wt.



View larger version (31K):
[in this window]
[in a new window]
 
Fig. 1. Schematic Drawing of the GR LBD Where H12 Is in the Agonist Conformation

The GR LBD lacks H2 as present in RAR/RXR (38 ) and ER (1 ). Small molecule ligands bind to the receptor in a buried hydrophobic pocket formed by H3, H5, H7, H11, and H12. The LXXLL motif of coactivator proteins binds to the hydrophobic groove on the protein surface formed by H3, H4, and H12. Whereas most nuclear receptors dimerize through an interface involving H10 and H11, the GR dimerization interface is located near the small ß-sheet between H5 and H6 (4 ). This figure was created with the Molscript software (39 ), and helix nomenclature is adapted from the crystal structure of the progesterone receptor (40 ). ns, Nanosecond.

 
The above results are hard to rationalize in structural terms because homology models (3) as well as the recent crystal structures (4, 5) of hGR show that the closest distance between valine 571 and a bound steroid ligand is more than 8 Å. Beyond this distance, only electrostatic interactions should have a significant direct effect on ligand-receptor affinity, and both valine and the methionine homolog in MR have nonpolar side chains. Lind et al. (2) suggested that the altered ligand affinity is due to a indirect steric effect, with residue 571 acting as a regional organizer and shifting the position of H12, which in turn leads to altered interactions between H12 and the 11,18-hemiacetal group of aldosterone. The A573Q mutation is proposed to affect dexamethasone (DEX) affinity by inducing changes to the hydrogen bond network surrounding the 3-keto group of the steroid.

It is, however, plausible that these mutations also alter the coactivator-binding properties of GR by structural remodeling of the activation function 2 (AF-2) surface, (6) or through an intrinsic stabilization of transcriptionally active forms of the LBD. The latter hypothesis is inspired by the conformational selection model (7, 8, 9), which describes proteins in solution as an ensemble of a very large number of states, and infers a more multifaceted picture of ligand activation than the two-state model (active/inactive or agonist/antagonist conformation) traditionally used in nuclear receptor research. Binding of a generic ligand (ion, detergent, hormone, or another protein) to a receptor protein results in a shift in probabilities for all the states in the ensemble. In the case of an agonist ligand binding to a nuclear hormone receptor, the probability for the transcriptionally active conformation to emerge is increased. Pissios et al. (10) proposed that the overall stability of the LBD in the transcriptionally active form is governed both by ligand binding and cofactor association. They show that either of these events promotes association of a free H1 fragment in several nuclear receptors by stabilizing the LBD as a whole. Holt et al. (11) identified point mutations in H1 of the peroxisome proliferator-activated receptor {gamma} (PPAR{gamma}), which disrupt H1-H8 interactions and alter the response of the receptor in a compound-specific manner. These PPAR{gamma} mutations are similar to GR V571M in that the mutated residues are located far from both the ligand-binding pocket and cofactor-binding surface, but have significant impact on affinity and/or activity.

Two-point mutations of isoleucine 747 in human GR LBD have also been reported. The I747T mutant (12) requires a 100-fold higher concentration of the synthetic glucocorticoid DEX to achieve the same transcriptional activity as wt GR, whereas there is only 2-fold lowering of the DEX affinity. Residue 747 is located in the H11-H12 loop near the ligand-binding pocket, but is not directly involved in ligand-receptor interactions. In their later work, Roux et al. (13) show differential proteolysis patterns for GR wt and GR I747T and conclude that the mutation alters the dynamics of the conformational change involved in ligand binding and transcriptional activation. Vottero et al. (14) have described discovery and characterization of GR I747M in a cortisol-resistant patient. This mutation lowered DEX affinity 2-fold, whereas transcriptional activity was decreased 20- to 30-fold. The authors conclude that this effect is related to defects in protein-protein interactions between GR and various cofactors.

The available experimental data for the GR mutants V571M, A573Q, and V571M/A573Q and the recently published crystallographic structures of GR provide an excellent starting point for further elucidation of the structural details of glucocorticoid specificity of GR. To this end, we have used molecular dynamics (MD) simulations, a well-established technique for theoretical studies of local structural properties in proteins, to investigate structural changes in the activation function 2 (AF-2) region due to these amino acid substitutions. Our computer simulations demonstrate an induced-fit effect on the binding of coactivator peptides to the transcriptionally active conformation of the GR LBD. This result is consistent with crystallographic data on the estrogen receptor (ER), and, due to the extensive structural homology among nuclear hormone receptors, our conclusions may help improve understanding of how nuclear receptor ligands can control details in transcriptional activation, such as tissue selectivity governed by differential expression of cofactors.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
An amino acid substitution in a protein may influence both structural and dynamic properties of that protein. MD simulation is a technique that models both of these properties. To elucidate the effect of the V571M and A573Q substitutions, we chose to measure interhelical distances and C{alpha} fluctuations, i.e. properties that directly describe the structure and dynamics of the protein, respectively.

Stability of MD Simulations
Stable MD simulation is a prerequisite for reliable results and conclusions. We characterized our simulations (summarized in Table 1Go) by monitoring indirect descriptors such as electrostatic interaction energies and root-mean square deviation (RMSD) from starting structures, as well as direct descriptors such as selected C{alpha}-C{alpha} distances. Electrostatic interactions are expected to require the longest equilibration; hence, this is an appropriate measure to monitor. To focus on the behavior of the solute (protein and ligand complex), we monitored the solute-solute and solute-water terms, while excluding the solvent term (water-water interactions). Our simulations are reasonably stable over the full 10-nsec simulation, as judged from energy and RMSD plots (Fig. 2Go). Importantly, no sudden shifts are observed in these characteristics. The RMSDs from the starting structure, measured over all C{alpha} atoms, level off at approximately 0.2 nm after a couple of nanoseconds and stay at this magnitude for (at least) up to 10 nsec of simulation (Fig. 2Go, C and D). Thus, the canonical tertiary structure of nuclear receptors is well described by our model throughout the course of the simulations.


View this table:
[in this window]
[in a new window]
 
Table 1. Overview of Simulations

 


View larger version (37K):
[in this window]
[in a new window]
 
Fig. 2. Characterization of Protein Simulations

Panels A and B show the time evolution of electrostatic energy (Coulomb term) of protein/ligand-protein/ligand and protein/ligand-water interactions, respectively. Panels A and B are based on wt holo simulations. Panels C and D show the time evolution of the C{alpha} RMSD for the apo and holo forms, respectively, with wt, black; V571M, red; A573Q, green; v1ma31, blue. ns, Nanosecond.

 
The present paper is largely based on the collective pattern of small changes to a large number of C{alpha}-C{alpha} distances. The time series of one example taken from Table 2Go, the single distance between the {alpha}-carbon atoms of residue 572 in H3 and residue 756 in H12, is shown in Fig. 3GoGo. Within the fairly large range of fluctuation these measurements are stable over the simulated time period. From Fig. 3Go it is qualitatively apparent that complexes having the V571M substitution (right-hand panels) exhibit a larger average H3–H12 distance than wt and the A573Q mutant (left-hand panels).


View this table:
[in this window]
[in a new window]
 
Table 2. Most Prominent Structural Changes in holo Receptor Models

 


View larger version (53K):
[in this window]
[in a new window]
 
Fig. 3. Time Dependence and Intrinsic Variation of C{alpha}-C{alpha} Distance between Residues 572 in H3 and 756 in H12

Each plot shows data from three independent simulations differing only in initial velocities (given by random generator seed iseed). The SD used to describe the variation is summarized in the following table.Go ps, Picosecond.

 

View this table:
[in this window]
[in a new window]
 
Table 3F.
 
Mutations Increase H3–H12 Distance
The structural changes induced by the point mutations of residues 571 and 573 are visualized in a distance-activity correlation plot (Fig. 4AGo). This plot shows the correlation between time-averaged pairwise C{alpha}-C{alpha} distances and biological activity for the four simulated holo (ligand-bound) complexes. A single point in this plot corresponds to one linear regression analysis as shown in Fig. 5Go. The distance between the central part of H12 and amino acids near residues 571 and 573 increases with increased biological activity (Fig. 4AGo, blue area). Simultaneously, the distance decreases between H12 and the N-terminal third of H3 with increased activity (Fig. 4AGo, red area). A similar trend is seen for H3 and the C-terminal end of H11. Several other distances between secondary structural elements also show strong correlations with biological activity. H4 is moved closer to the N-terminal part of H12 (Fig. 4AGo) as well as the N-terminal half of H3 (Fig. 4BGo), whereas H5 increases its distance to H12. The mutated residues 571 and 573 increase the distance to the kink between H4 and H5.



View larger version (70K):
[in this window]
[in a new window]
 
Fig. 4. Correlation Plot for C{alpha}-C{alpha} Distances in holo Structures vs. ln(EC50) Covering H12 to H3–H5 Distances (A) and H3 to H4–H5 Distances (B)

Blue areas indicate a negative correlation between C{alpha}-C{alpha} distances and EC50 in the wt and three mutant complexes. Because a high activity is characterized by low EC50, blue areas indicate distances that are increased in complexes with higher activity. Distances between residues 570–577 in H3 and 751–758 in H12 that define part of the coactivator-binding surface are found in the large blue area to the left in panel A, confirming the strong correlation found between H3–H12 distance and activity. This plot is based on 250 snapshots from 0.0–5.0 nsec. Excluding the first 2.5 nsec of all trajectories results in an essentially identical plot (data not shown), indicating that these correlations converge well before 2.5 nsec.

 


View larger version (12K):
[in this window]
[in a new window]
 
Fig. 5. Example of Negative Correlation of H3–H12 Distance (nm) and Biological Activity (ln EC50) for holo Structures

Because a high biological activity is characterized by a low EC50 value, the negative correlation between distance and EC50 indicates that an increased H3–H12 distance leads to higher biological activity. This plot is based upon the C{alpha} pair of residues 572 (in H3) and 756 (H12).

 
To quantify the observed structural rearrangement of the AF-2 function, a number of strongly correlated distances were selected (Table 2Go). The selection was based on two criteria; 1) the difference between wt and double-mutant C{alpha}-C{alpha} distance should be in the top 5% largest differences (of 1891 possible); and 2) the r2 value should be larger than 0.90. This combination of arbitrary yet objective criteria yielded 20 C{alpha}-C{alpha} pairs, which clustered into four distinct groups in three-dimensional space. The maximum change in C{alpha}-C{alpha} distance between the wt and double-mutant structures is 0.10–0.12 nm. This is about twice the standard deviation for the distance between residues 572 and 756 (Fig. 3Go). Group III in Table 2Go comprises negative correlations between distance and activity, which means that the interresidual distance tends to increase with increasing activity, whereas the other three groups describe decreased distances. A structural summary of the major conformational changes described by the four groups is that the modeled mutations of residues 571 and 573 shift the position of H3 in its C-terminal direction, and simultaneously tilt the N-terminal end of H3 toward H11 and H12. This reorientation is consistent with the observed pattern of changed C{alpha}-C{alpha} distances over this series of mutants.

Mutations and Ligand Binding Induce Similar Structural Changes
It is a reasonable assumption that the perturbations to the receptor structure induced by amino acid substitutions are similar in character to perturbations induced by structurally different ligands. The apo receptor structure could be seen as complexed with an extreme ligand, i.e. no ligand, and to this end we compared structural changes between the apo and the holo receptor for all four GR models (Table 3Go). Upon ligand binding, the H3–H12 distance (group III) is reduced in the wt and in the A573Q mutant, whereas it is increased in both mutants containing the V571M substitution. The three other groups show the opposite trend in which distances are reduced in the latter mutants.


View this table:
[in this window]
[in a new window]
 
Table 3. Comparison of Selected C{alpha}–C{alpha} Distances (nm) in apo and holo Structures

 
Orientation of Met-571
Visual inspection of snapshots from our V571M simulations revealed that the Met-571 side chain neatly fills up the empty space toward the backbone of residues Leu-596, Gln-597, and Trp-600 in H5 (Fig. 6AGo). The long side chain of methionine also interacts sterically with Met-752, Leu-753, and Ile-756 of H12, thereby increasing the H3–H12 distance. Met-571 had 5.3 kilojoule/mol more favorable van der Waals (vdW) interaction with the rest of the system compared with Val-571 (Fig. 6BGo), as judged from average interaction energies calculated on snapshots (every 20 psec, 0.0–5.0 nsec) from the MD simulations. Because there are no crystallographic structures of the MR publicly available, there are currently no experimental data indicating the preferred side chain rotamer of Met-571. Therefore, two additional simulations of GR V571M in which the {chi}-1 torsion of Met-571 was rotated ± 120° in the starting structure were performed. The Met-571 side chain rapidly (<0.5 nsec) reoriented to the original structure described above. In the longest MD simulation of the original orientation of Met-571, this side chain kept a constant orientation characterized by {chi}-1 near 180° and {chi}-2 near +60° throughout the whole 10-nsec trajectory. This result is consistent with the Dunbrack backbone-dependent rotamer library (15), which shows a relatively high Bayesian probability of 12% for the {chi}-1 = 180°, {chi}-2 = +60° rotamer of methionine in an {alpha}-helical conformation.



View larger version (45K):
[in this window]
[in a new window]
 
Fig. 6. Steric Fit of Mutant Residue Met-571 (A) and wt Residue Val-571 (B)

The side chains of residue 571 are shown as vdW spheres, and the Connolly surface of the neighboring residues is shown in green. The yellowish tubes indicate the tertiary structure of the GR LBD, and displayed helices are labeled according to Fig. 1Go. The cavity present near Val-571 in the wt structure is efficiently filled by the longer Met-571 side chain in the mutant, leading to better vdW packing. Molecular representations in Figs. 7Go, 8Go, 10Go, 11Go, and 13 were created with the PyMOL software (41 ).

 
Orientation of Gln-573
The side chain of Gln-573 interacted with Glu-542 in our simulations of the A573Q and V571M/A573Q mutations. In all six basic holo simulations (Table 1Go) containing the A573Q substitution, it frequently (41–82% of the time) formed an H bond (O–N distance < 0.32 nm) with the backbone carboxylic oxygen of Glu-542, thereby bridging H3 and the unstructured (but not disordered) H1–H3 coil (Fig. 7Go). In one of two additional simulations in which the Gln-573 side chain was initially oriented toward Gln-570 and the ligand-binding pocket, and the side chain of Gln-570 was oriented toward the ligand, the hydrogen bond network anchoring the 3-keto group of aldosterone as described by Lind et al. (2) was recreated and found to be a persistent dynamic structure. However, this hydrogen bond network did not appear spontaneously in any of the seven other simulations in which Gln-573 was present. Instead, the O{epsilon} of Gln-570 frequently (31–48%) formed a hydrogen bond (O–N distance < 0.32 nm) with the backbone NH of Val-544 (cf. Fig. 7Go).



View larger version (36K):
[in this window]
[in a new window]
 
Fig. 7. Hydrogen Bonding between Glu-542 and Mutated Residue Gln-573

The yellowish tubes indicate the tertiary structure of the GR LBD, and displayed helices are labeled according to Fig. 1Go. Note that the side chains of residues 571 and 573 extend in opposite directions.

 
Overall Backbone Fluctuations
Considering the improved steric fit for methionine in position 571 and the H bonding possibility for arginine in position 573, one might expect a general stabilization of the LBD in some or all mutants due to anchoring of H3 and the H1–H3 coil to the core helices. Thus, the root mean square fluctuations (rmsfs) for all {alpha}-carbons were calculated from their average positions (Fig. 8Go). All holo complexes had very similar fluctuation profiles, in which helices had an rmsf of 0.05–0.07 nm, and loop regions fluctuated between 0.10 and 0.40 nm. The lowest fluctuations were observed for the core H5 and H8, the beginning of H3, and the C-terminal end of H11, which defines part of the ligand-binding pocket. Residues in H3 near the mutated positions 571 and 573 had very similar rmsfs in all complexes. For helices at the protein surface, a fine-grained pattern was observed in which residues facing outward had slightly higher fluctuations than neighboring residues facing inward.



View larger version (21K):
[in this window]
[in a new window]
 
Fig. 8. C{alpha} Fluctuations Plotted per Residue for Simulations of wt holo Model

The secondary structure is diagrammed along the horizontal axis. Fluctuations are averaged over three independent simulations. Error bars indicate the maximal to minimal variation per residue within the three simulations. The overall fluctuation pattern is representative for all simulated apo and holo models.

 
Residue-wise differences in fluctuations for the wt and the mutants are generally too small to be observed on the scale used in Fig. 8Go. Too visualize changes in fluctuations caused by the amino acid substitutions in the mutants, we performed a correlation analysis for each residue in which the average fluctuation of the {alpha}-carbon atom in each GR model was correlated to the biological activity. The result is shown in Fig. 9Go where the correlation coefficient is projected onto the canonical tertiary receptor structure in terms of a color scale. We observe a strong negative correlation between ln(EC50) and absolute magnitude of fluctuation for virtually all residues on H3, H4, and H5. This means that an increased biological activity is correlated to a reduction in backbone fluctuation in this region of the protein. The magnitude of the reduction, going from wt to the double mutant, is 10–25% in this region. In the loops preceding H3 and H12 there is also a strong correlation between activity and fluctuations, and the reduction is 40–70% and 10–15%, respectively. A weak opposing correlation was found for H7, in which increased activity correlated to approximately 5–10% increase in fluctuation magnitude. Noteworthy, the fluctuation of residues in H12 seems to be uncorrelated to biological activity.



View larger version (62K):
[in this window]
[in a new window]
 
Fig. 9. Correlation Coefficients Mapped to a Canonical GR Structure with Smoothed Loops and Coils

A correlation between absolute magnitude of C{alpha} fluctuations and biological activity (ln EC50) is calculated for each residue of the GR wt and the three mutants. The color scale is from blue for negative correlation (r = –1) to red for positive correlation (r = +1). H3, H4, and H5 are mainly blue, implying that increased biological activity is correlated to a reduction in C{alpha} fluctuations in this region. The fluctuation of H12 residues is only weakly correlated to activity. Note that the loops preceding H3 and H12 have a strong negative correlation between C{alpha} fluctuations and biological activity.

 
The possibility of double anchoring of H3 and the H1–H3 coil in the double mutant raised the question whether the relative motion of secondary structure elements changed due to the various amino acid substitutions. Covariance matrices were plotted (data not shown) for the wt LBD and the three mutants V571M, A573Q, and V571M/A573Q to determine what effects the mutations had on the overall motions of the four proteins. No consistent patterns, apart from the expected strong correlation between sequentially adjacent residues, were identified by this analysis.

Effect of Ligand Binding on H3 to H1–H3 Coil Distance
Recently, it has been proposed that the distances between H3, the H1–H3 coil, and the ß-sheet are important for the subtype specificity in ER (16). We found clear differences between the four GR models for the change in H3 to H1–H3 coil distance upon ligand binding (Table 4Go). In our wt models, the distance was reduced by 0.03–0.05 nm when ligand was bound, whereas it was increased by approximately 0.10 nm in the V571M mutant. In the A573Q mutant the distance was reduced by 0.03–0.10 nm, and in the double mutant the distance was virtually unaffected by ligand binding. In a majority of our simulations, the H1–H3 coil is connected to the ß-sheet by a double hydrogen bond mediated by Asp-626 in the ß-sheet loop (Fig. 10Go). With few exceptions, these hydrogen bonds have an occupancy over 80% of the simulated time (Table 5Go).


View this table:
[in this window]
[in a new window]
 
Table 4. Changes in H3 – H1–H3 Coil Distances (nm) upon Ligand Binding (holo minus apo)

 


View larger version (33K):
[in this window]
[in a new window]
 
Fig. 10. Double Hydrogen Bond between the ß-Sheet and the H1–H3 Coil

The two ß-strands s1 and s2, and the short H6 are green. Asn-626 is located in the ß-sheet turn and may establish two hydrogen bonds to the backbone NH-groups of Ala-546 and Gly-547 in the H1–H3 coil (red). To facilitate orientation, the N-terminal half of H3 is also included.

 

View this table:
[in this window]
[in a new window]
 
Table 5. H-Bond Distances (nm) between Asp-626 and Ala-546 or Gly-547, and the Occupancy of Each Possible H Bond

 
Effect of Ligand Binding on Backbone Fluctuations
The ligand-binding site in GR is lined by residues 563, 564, and 567 from H3; 601 and 604 from H5; 639, 642, 643, and 646 from H7; 732; 735, 736, and 739 from H11; and residues 753 and 757 from H12. rmsf Values of the {alpha}-carbons of these residues were measured in simulations of the apo structure (no ligand present) and the holo (ligand-complexed) structure (Fig. 11Go). We observed an overall reduction in the magnitude of the fluctuations upon ligand binding for both GR wt and GR A573Q, but less so for GR V571M and GR V571M/A573Q. This pattern qualitatively coincided with experimental data (2) for aldosterone affinity and receptor activity, in that no lowering of fluctuations corresponds to increased affinity/activity and vice versa. With the possible exception of residue 639 in H7, the variation in fluctuation magnitude was too large to achieve statistical significance for the observed changes in fluctuations upon ligand binding. Extension of this comparison of backbone fluctuation between apo and holo complexes did not reveal any other regions in the protein in which a significant change in fluctuations occurs upon ligand binding.



View larger version (24K):
[in this window]
[in a new window]
 
Fig. 11. Heavy-Atom rmsfs of Residues in the Ligand-Binding Pocket

Solid lines refer to holo structures (ligand-bound), dotted lines refer to apo structures (ligand free).

 
Comparison with Crystallographic Structures
Our GR simulation results were compared with crystallographic structures of ER with and without coactivator peptide. ER was chosen because the number of published structures is much greater for ER than for GR. In Table 6Go, H3–H12 distances for seven experimental structures solved without coactivator peptide are compared with 10 structures that are cocrystallized with coactivator peptide. Although ligands and coactivator peptide sequences differ among the 17 structures, these measurements show a statistically significant increase of 0.03 nm in H3–H12 distance upon coactivator binding, in qualitative agreement with our simulation results.


View this table:
[in this window]
[in a new window]
 
Table 6. H3–H12 Distances in Crystallographic ER Structures Solved (A) with and (B) without Cocrystallized Coactivator Peptide Fragments

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
In the present work we have used MD simulations to examine why the GR V571M mutant is functionally more MR-like than wt GR. Consistent with previous results (2) we find that Met-571 indeed acts as a regional organizer. However, from our observations of altered distances and reduced fluctuations we conclude that the origin of the Met-571 effect on activity is 2-fold. First, transcriptional activity is increased due to a small but functionally relevant rearrangement of H3 relative to H4 and H12, which facilitates coactivator binding rather than ligand binding. Second, we propose that the binding of ligand is facilitated by a reduction in rmsfs of residues lining the binding cavity. This would lead to a reduced entropic penalty upon ligand-receptor association.

Distance Correlation Plots
Figure 4Go visualizes the relation between structural differences and variation in biological activity for the four complexes. The strength of this approach is that trends are picked up at two separate levels, which makes it less sensitive to noise. At the local level, i.e. individual points corresponding to specific amino acid pairs, the plot shows the correlation to biological activity for a specific distance (cf. Fig. 5Go). The individual correlation coefficient is determined within a certain error range, which may be rather large due to the small number of data points. However, when presented in concert with a large number of other correlation coefficients, it becomes clear that the global pattern is not random, in which case Fig. 4Go would show white-noise patterns. Instead, large clusters of positive and negative correlation coefficients arise and, most importantly, these clusters can be related to relevant secondary structure changes in the receptor. Thus, these plots show that structural differences in organization of secondary elements correlate with biological activity. Our interpretation of the rearrangements caused by the mutations is made in terms of consequences for ligand and coactivator associations, which are well-established steps in nuclear receptor-mediated signaling. Clusters of correlation coefficients are not homogenously colored in Fig. 4Go, A and B, but often a repetitive pattern of weaker and stronger correlation having a period of three to four amino acids is observed. This shows that although successive amino acids in a helix are physically connected and, by necessity, move in concert with their neighbors, they also retain a great deal of independence in their interaction with other parts of the protein. Loop regions generally display very low correlation to activity, which is expected as these are mobile and have an ill-defined position relative to more stable parts of the protein.

Stability of H12
In all simulations of wt and mutant models, H12 remains in the canonical agonist position (Fig. 1Go) and displays low C{alpha} fluctuations in both the apo and holo structure model. The fluctuation of H12 residues is virtually uncorrelated to biological activity in the four studied complexes (Fig. 9Go). A noticeable difference between GR and some other nuclear receptors, such as PPAR{gamma}, is the presence of a C-terminal coil immediately after H12 in GR. This coil favorably interacts with the C-terminal end of H8 and parts of H10 and effectively immobilizes H12 in the agonist position (4, 17). Thus, the probability of finding a freely moving H12 during a 5–10 nsec MD simulation of GR is very low. This is advantageous in the present study, in which small structural rearrangements within a given conformational substate rather than partial unfolding or conversions between conformational states are investigated. Presumably, a corresponding simulation of an apo-PPAR{gamma} structure would show more mobility and possibly partial unfolding of H12 due to the lack of a post-H12 segment.

Key Role for H11–H12 Loop
The increased biological activity caused by the V571M mutants strongly correlates with a reduction of backbone fluctuations in H3–H5, the pre-H3 loop, and the H11–H12 loop (Fig. 9Go). The reduction of H11–H12 loop fluctuations may be mediated by the close packing of this loop to the N-terminal part of H3 and is particularly interesting in light of reports on mutations in the H11–H12 loop that are known to affect biological activity of GR (12, 14) and other nuclear receptors such as the androgen receptor (18). One possible explanation for the importance of this loop is its role in controlling the equilibrium between distinct LBD states, in particular the biologically active agonist structure vs. nonactive conformations. Even a modest increase in stability of the H11–H12 loop could lead to a stronger tethering of H12 in the agonist position and a substantial shift in the conformational equilibrium.

Fluctuation Analysis Indicates Existence of Allosteric Network
Interestingly, the effects of the V571M substitution on the fluctuations of individual residues can be observed as far away as the Asp-626 residue in the ß-turn, more than 1.5 nm from residue 571. Because the neighboring residues 625 and 628 form parts of the unique dimerization interface of GR (4), it is plausible that this mutation could not only affect ligand and coactivator binding, but also GR dimerization, which in turn could alter transcriptional activity. Our results indicate part of an allosteric network in the GR LBD, as discussed below. This is similar to what has been described by statistical methods for retinoic X receptor (RXR) heterodimers (19).

In H3, fluctuations of hydrophobic residues facing the ligand-binding pocket (Leu-563 and Gly-567) are lowered upon ligand binding (Fig. 11Go). Both models having the V571M substitution exhibit a very similar reduction in fluctuations for these and neighboring residues also in the absence of ligand, possibly due to improved vdW interaction of H3 with stable core helices mediated by Met-571. The uncertainties are too large for statistical significance on individual residues, but the pattern is consistent over many turns of H3. By reducing the intrinsic backbone fluctuations in the apo structure to the level observed for the holo structure, the V571M mutation may lower the entropic cost for ligand binding to the receptor. The solvent-exposed A573Q point mutation itself does not seem to affect the fluctuations of residues in H3.

For the central part of the H1–H3 coil (residues 545–547), the single V571M mutation leads to increased fluctuations in the holo structure and somewhat reduced fluctuations in the apo structure. A possible mechanism behind this observation is that in the apo structure, residues 545–547, in turn, follow the stabilization of H3 through short-range vdW interaction with Thr-562 and Leu-566 (Fig. 12Go). This effect is transmitted to Asp-626 through double H bonds to backbone NH of residues 546 and 547 (Fig. 10Go). In the holo state, H3 is more tightly bound to the ligand and to the core helices, and consequently the contact with residues 545–547 is lost (Table 4Go). These residues have no side chains toward H3 or side chains that point away from H3; thus the contact dominated by vdW interactions may easily be disrupted by a slight increase in distance. The double hydrogen bonds provide a strong link, which makes Asp-626 follow the dynamics of the central region of the H1–H3 coil, and fluctuate more in the holo structure.



View larger version (34K):
[in this window]
[in a new window]
 
Fig. 12. Residues Governing the Interaction between the H1–H3 Coil and H3

Note that most of the interactions are between short hydrophobic side chains or backbone atom. The polar groups of the tyrosine side chains are oriented away from H3.

 
The A573Q mutation causes increased fluctuations over the entire length of the H1–H3 coil (residues 541–548) in the apo structure. Consequently, Asp-626 also fluctuates more due to the double hydrogen bond link (Fig. 10Go). Because the fluctuations of H3 residues are not affected by this mutation, a similar difference between the apo and holo structure as for the V571M mutant is not observable for A573Q. In the double mutant V571M/A573Q, the stabilizing effect of Met-571 on H3 seems to dominate the increased fluctuations of the H1–H3 coil caused by Gln-573, and it seems that the unstructured segment is able to keep in contact with H3, thus fluctuating less.

Extension of Conformational Sampling of Key Residues
A potential drawback of MD simulations is the short time span accessible for simulations. The low nanosecond timescale used in the present study is not long enough to follow slow protein events such as folding or domain motions in proteins but should, in principle, be sufficient for thorough sampling of side chain motions, at least for neutral residues that are not involved in hydrogen bonds. Because there is no crystallographic model of MR available, the position of the Met-571 side chain cannot be related to experimental data. Instead, in addition to simulations listed in Table 1Go, we ran two simulations with alternative initial orientations of the side chain to allow for alternative local minima around the {chi}-1 torsion angle. In these additional simulations, the Met-571 side chain adopted the orientation observed in the longer V571M simulation in less than 0.5 nsec.

Similarly, the dynamic motions of Gln-573 may not be fully sampled in our simulation, despite up to 10-nsec trajectories. The three identical simulations of the A573Q and V571M/A573Q mutants, respectively, show varying degrees (41–82% at 0.32 nm cutoff) of hydrogen bond formation to the backbone of Glu-542. For sufficiently long simulations one would expect the bond formation ratio to converge to a well-defined value. Our results clearly show that very long simulations are required to fully investigate charged amino acids located at the surface of the protein, unless explicit conformations are systematically examined. Still, this may be practical for only a limited number of amino acids.

Influence of the V571M Substitution on Transactivation Efficacy
The position of H12 relative to H3 is altered in the V571M and V571M/A573Q mutants by steric interaction between the bulkier methionine side chain and residues Met-752, Leu-753, and Ile-756 on H12. We have quantified this alteration by calculating the time-averaged distances between {alpha}-carbons on both H3 and H12 (Table 2Go) and observed an increase in many of these distances that correlates well (Fig. 4AGo) with the biological activity reported by Lind et al. (2). We suggest that the structural basis for this correlation is that the modified coactivator-binding site in the mutants facilitates coactivator recruitment, assuming that the binding site must be slightly larger than in our wt model for optimal fit of the LXXLL-containing helix of the coactivator. This assumption is supported by both published data (cited below) and modeling results.

For eight publicly available crystallographic models of ER (1, 20, 21, 22, 23, 24, 25) containing a total of 17 independently refined molecular structures, a 0.03-nm greater H3–H12 distance was observed in the structures that were solved with both ligand and coactivator peptide present compared with structures solved with ligand only (Table 6Go). The differences observed in the Protein Data Bank structures are small, but the relatively large number of available ER structures reveals a statistically significant difference. Possibly, the varying ligands and coactivator sequences used in these experiments obscure an even more pronounced difference between peptide-bound and peptide-free receptor structures. On the other hand, the observation of a significant difference despite the varying molecular composition of the crystallographic complexes strengthens the notion of a general induced fit mechanism of coactivator binding for the nuclear hormone receptor family.

Influence of the V571M Substitution on Ligand Affinity
Examination of backbone fluctuations of residues lining the ligand-binding pocket shows that ligand binding stabilizes the protein structure, as indicated by reduced backbone fluctuations. This is consistent with literature reports (11, 26) and is intuitively plausible because a ligand offers the protein a more rigid body to interact with than the few water molecules present in the ligand-binding pocket of our apo structure model. The lowering of backbone fluctuations upon ligand-receptor association implies an entropic penalty for this process, and because the V571M substitution is able to prestabilize the binding pocket in the apo form, the entropic penalty is lower in this mutant compared with wt. Assuming that the remote V571M substitution does not directly affect the enthalpy of ligand binding, the free energy gain in ligand binding is increased; thus the mutated protein has a higher affinity for aldosterone than the wt receptor.

Consequently, our results suggest that the GR V571M mutation increases the sensitivity not only to the mineralocorticoid aldosterone but also to other ligands, e.g. the glucocorticoid triamcinolone acetonide (TA). This may seem contradictory to the conclusion made by Lind et al. (2) based on a statistical test for differences between the wt and various mutants with respect to biological activity. There was no statistically significant difference in EC50 for TA between GR wt and V571M at the 95% confidence limit, probably due to the large standard deviations, but the trend in the measured values is strikingly similar to the corresponding EC50 for aldosterone. Thus, we would conclude that the V571M mutation does affect the sensitivity of TA, but to a lesser extent than it affects aldosterone. Slightly different sensitivity to various ligands should be expected due to their distinct structures, and this secondary effect on binding enthalpy could very well make the difference between significance and nonsignificance in a statistical test.

Our simulations support the assumption that the large distance between residue 571 and the ligand-binding pocket makes it unlikely for this mutation to cause major ligand-specific effects. Instead, we suggest the primary effect of GR V571M to be steric interaction with H4 and H12, leading to a modified coactivator-binding site, and the secondary effect to be a general stabilization of the apo-LBD structure, which could facilitate binding of many different (agonist) ligands and coactivators. A third possible effect of the V571M mutation is that the shift or reorientation of H12 alters the direct hydrophobic interaction between Leu-753 in H12 and the ligand, thereby decreasing the ligand affinity. In our simulations of V571M and V571M/A573Q, Leu-753 actually reaches for Met-571 rather than the ligand. However, Leu-573 contributes to only 1–2% of the total attractive vdW interaction energy between ligand and receptor (data not shown); therefore, this secondary effect of the V571M substitution on ligand affinity should be negligible.

Structural Interpretation of the Effect of Other GR Mutations
Lind et al. (2) have found five additional mutants, all containing the V571M substitution, with increased aldosterone sensitivity compared with wt. The A574V substitution appears frequently (eight of 22 clones), and the structural model of GR suggests that the larger valine side chain may fill up empty space toward the backbone of Trp-600, and side chains of Leu-603 and Met-604, and analogously to the V571M substitution, lower the fluctuations in this region of the protein by improved vdW interactions between H3 and H4. However, Val-574 cannot contact residues in H5 and H12; thus it cannot directly alter the shape of the coactivator-binding site. The function of the similarly abundant A573E should be analogous to that of A573Q, considering the polar character of both glutamic acid and glutamine presenting similar H-bonding capabilities. Residues 567 and 569 are in contact with the ligand-binding pocket, and the effect of mutants on these positions should therefore be simpler to explain in terms of altered ligand binding properties of the protein.

General Implications for Drug Design
The central dogma of nuclear receptor pharmacology is that binding of an agonist to a nuclear receptor shifts the conformational equilibrium of H12 toward a conformation that favors coactivator and disfavors corepressor binding, which in turn results in an up-regulation of gene expression (27). In the case of ER, this conformational shift involves a rotation of 130 ° and a 10 Å translation of H12 (1). Similar large-scale conformational shifts of H12 (a first order perturbation to the LBD structure) have been observed in the case of GR, liver X receptor, retinoic acid receptor, RXR, and thyroid hormone receptor. The present work suggests an induced fit mechanism for binding of coactivators to nuclear receptors. Mutations in GR can cause small shifts in the position of H12 relative to H3, H4, and H5 when bound to an agonist ligand (a second-order perturbation), which alters the energetic costs of the induced fit of coactivators binding to GR. The binding of different agonist ligands could similarly cause small shifts in the position of H12 and thereby modulate the affinity of coactivators for nuclear receptors, resulting in ligands that span a spectrum ranging from partial to full to superagonism. This notion is supported by our observations of a small, but statistically significant, shift in the position of H12 between the apo- and holo-GR structures in the absence of coactivator peptide because the apo state represents binding to an extreme ligand (i.e. no ligand). Furthermore, if different coactivators induce somewhat different conformations in the coactivator-binding cleft of their cognate nuclear receptor, then different ligands can differentially affect the relative affinities of different coactivator proteins.

The discovery of nuclear receptor partial agonists (or more properly mixed agonists/antagonists) has great potential for the development of improved therapeutics with reduced side effects. A classic example is the ER ligand raloxifene (28), which displays agonistic properties in bone (prevents osteoporosis) and liver (lowers cholesterol levels) but is an antagonist in breast and uterus (no increased and perhaps decreased risk of developing cancer in these tissues). A mechanistic explanation for these phenomena is that, depending on which specific coregulators are present in a particular cell type and the concentration ratio of coactivators to corepressors, a ligand may display agonistic properties in some tissues but be an antagonist in others (29).

To date, the design of mixed agonist/antagonists for nuclear receptors has been primarily based on shifting the large-scale conformational equilibrium of H12. This work suggests that an additional degree of selective receptor modulation may be obtained by a second-order perturbation of the AF-2 region.

Concluding Remarks
In the present work we have used MD simulations to examine why the GR V571M mutant is more sensitive to the mineralocorticoid aldosterone than GR wt. We have observed that the larger side chain of Met-571 shifts the relative positions of H3, H4, and H12, creating a modified binding cleft for the coactivator peptide. By means of improved steric interactions, the V571M substitution also anchors H3 to the rigid core of the protein, thus reducing the fluctuations of residues involved in ligand binding. We have also observed an increase in H3–H12 distance upon coactivator binding both in our MD simulations of GR and in published crystallographic models of ER. We conclude that the rearrangement of AF-2 helices facilitates coactivator binding by lowering the energetic cost for induced fit in the receptor-coactivator complex and thus explains the increased activity of the mutant receptor. We consider this effect to be largely ligand independent, i.e. not specific to aldosterone, although it is possible that different ligands could modulate the effect of the mutation in different ways. The reduced fluctuations of H3 may improve ligand binding affinity through a lowered entropic cost for binding. Based on the coactivator docking to GR and ER, we propose that the coactivator pocket remodeling may be a general phenomenon for nuclear receptor-coactivator interaction.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Structural Models
A summary of the eight primary structure models used in the present work is given in Table 1Go. The wt model of the GR LBD was based on the crystallographic model by Kauppi et al. (5) (PDB entry 1P93). The {alpha}-carbon positions of the missing residues Glu-705, Gly-706, and Asn-707 in the H8–H9 loop of 1P93 were interactively modeled in Sybyl 6.9 (Tripos, Inc., St. Louis, MO). Missing side chain atoms were automatically added in an extended conformation, and hydrogen atoms were added (30) by the CHARMM (31) program. The coactivator peptide and the DEX ligand were not included. A TIP3 (32) water droplet of radius 36.0 Å was centered on the center of mass of the {alpha}-carbon atoms in the protein model, and all water molecules closer to the protein than 2.5 Å were removed. Thus, water molecules were retained in some internal cavities, although no explicit crystallographic waters are present in the 1P93 model. The size of the water droplet is sufficient to cover the protein surface with at least two layers of water molecules, which should yield a stable and reliable simulation (33, 34). For the holo models, an aldosterone residue template based on parameters (35) for the closest matching CHARMM atom types was superimposed on the DEX coordinates in the 1P93 crystallographic model before solvation. Mutant models were created from the wt model by keeping side chain atoms present in both wt and mutant residues and automatically rebuilding missing side chain atoms in an extended conformation. Each model was briefly minimized (20 steps steepest descent) before dynamics.

Simulation Details
Langevin MD with spherical boundary conditions (36) applied to the oxygen atoms of all water molecules were run at 300K using the program CHARMM (31), a time step of 2 fsec, and a Langevin buffer of 2 Å. SHAKE (37) was used on all covalent bonds to hydrogen atoms. Nonbonded energies and forces were smoothly shifted to zero at a cutoff distance of 12 Å, with the nonbonded neighbor pair list generated to 14 Å. Nonbonded interactions were updated heuristically (on average every 10–11 steps), and coordinates were saved every 500 steps (1 psec). For all eight models, three independent 5.0-nsec simulations with different initial velocity assignments were performed, and for each model one of these three simulations was extended to 10.0 nsec (Table 1Go).

Statistical Details
From each of the twelve 5-nsec simulations of holo structures (Table 1Go), snapshots were taken every 20 psec for distance measurements. Thus, 3 times 250 observations of each C{alpha}-C{alpha} distance were made for each receptor model. The resulting 750 distance observations were pooled for calculation of mean [({sum}x)/n; where x is a distance observation and n is the number of observations] and standard deviation ([(n{sum}x2 – ({sum}x)2)/(n(n – 1))]1/2). All snapshots (20, 40, ..., 5000 psec) were used because the system was considered equilibrated within 20 psec, as judged from electrostatic energy, C{alpha} RMSD (Fig. 2Go), and individual C{alpha}-C{alpha} distance measurements (Fig. 3Go). For C{alpha}-C{alpha} pairs describing distances between H3, H4, H5, and H12 (i.e. the AF-2 region), the average distances from the four holo models (wt, V571M, A573Q, and V1MA3Q) were correlated to the biological activities for the corresponding receptors by a linear regression equation (cf. Fig. 5Go). A t test as implemented in SigmaStat for Windows Version 2.03 (SPSS Inc., Chicago, IL) was used to assign significance of difference in average H3–H12 distance in crystallographic structures with and without coactivator peptide (Table 6Go).


    ACKNOWLEDGMENTS
 
We thank Jevgeni Starikow, Jan Norberg, Jianxin Duan, Katarina Lindberg, Sofia Burendahl, Boel Nyström, Ana Caballero-Herrera, and our anonymous referees for valuable comments on the manuscript.


    FOOTNOTES
 
This work was supported by the Swedish Research Council, the Foundation for Knowledge and Competence Development (KK-stiftelsen), and Karo Bio AB.

First Published Online March 17, 2005

Abbreviations: AF-2, Activation function 2; DEX, dexamethasone; ER, estrogen receptor; GR, glucocorticoid receptor; H12, helix 12; LBD, ligand-binding domain; MD, molecular dynamics; MR, mineralocorticoid receptor; PPAR{gamma}, peroxisome proliferator-activated receptor {gamma}; RMSD, root mean square deviation; rmsf, root mean square fluctuation; RXR, retinoic X receptor; TA, triamcinolone acetonide; vdW, van der Waals; wt, wild type.

Received for publication May 18, 2004. Accepted for publication March 8, 2005.


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 

  1. Brzozowski AM, Pike ACW, Dauter Z, Hubbard RE, Bonn T, Engstrom O, Ohman L, Greene GL, Gustafsson JA, Carlquist M 1997 Molecular basis of agonism and antagonism in the oestrogen receptor. Nature 389:753–758[CrossRef][Medline]
  2. Lind U, Greenidge P, Gustafsson JA, Wright APH, Carlstedt-Duke J 1999 Valine 571 functions as a regional organizer in programming the glucocorticoid receptor for differential binding of glucocorticoids and mineralocorticoids. J Biol Chem 274:18515–18523[Abstract/Free Full Text]
  3. Kunz S, Sandoval R, Carlsson P, Carlstedt-Duke J, Bloom JW, Miesfeld RL 2003 Identification of a novel glucocorticoid receptor mutation in budesonide-resistant human bronchial epithelial cells. Mol Endocrinol 17:2566–2582[Abstract/Free Full Text]
  4. Bledsoe RK, Montana VG, Stanley TB, Delves CJ, Apolito CJ, McKee DD, Consler TG, Parks DJ, Stewart EL, Willson TM, Lambert MH, Moore JT, Pearce KH, Xu HE 2002 Crystal structure of the glucocorticoid receptor ligand binding domain reveals a novel mode of receptor dimerization and coactivator recognition. Cell 110:93–105[CrossRef][Medline]
  5. Kauppi B, Jakob C, Farnegardh M, Yang J, Ahola H, Alarcon M, Calles K, Engstrom O, Harlan J, Muchmore S, Ramqvist AK, Thorell S, Ohman L, Greer J, Gustafsson JA, Carlstedt-Duke J, Carlquist M 2003 The three-dimensional structures of antagonistic and agonistic forms of the glucocorticoid receptor ligand-binding domain-RU-486 induces a transconformation that leads to active antagonism. J Biol Chem 278:22748–22754[Abstract/Free Full Text]
  6. Warnmark A, Treuter E, Wright APH, Gustafsson JA 2003 Activation functions 1 and 2 of nuclear receptors: molecular strategies for transcriptional activation. Mol Endocrinol 17:1901–1909[Abstract/Free Full Text]
  7. Freire E 1998 Statistical thermodynamic linkage between conformational and binding equilibria. Adv Protein Chem 51:255–279[Medline]
  8. Teague SJ 2003 Implications of protein flexibility for drug discovery. Nat Rev Drug Discov 2:527–541[CrossRef][Medline]
  9. Kumar S, Ma BY, Tsai CJ, Sinha N, Nussinov R 2000 Folding and binding cascades: dynamic landscapes and population shifts. Protein Sci 9:10–19[Abstract]
  10. Pissios P, Tzameli I, Moore DD 2001 New insights into receptor ligand binding domains from a novel assembly assay. J Steroid Biochem Mol Biol 76:3–7[CrossRef][Medline]
  11. Holt JA, Consiler TG, Williams SP, Ayscue AH, Leesnitzer LM, Wisely GB, Billin AN 2003 Helix 1/8 interactions influence the activity of nuclear receptor ligand-binding domains. Mol Endocrinol 17:1704–1714[Abstract/Free Full Text]
  12. Roux S, Terouanne B, Balaguer P, JausonsLoffreda N, Pons M, Chambon P, Gronemeyer H, Nicolas JC 1996 Mutation of isoleucine 747 by a threonine alters the ligand responsiveness of the human glucocorticoid receptor. Mol Endocrinol 10:1214–1226[Abstract]
  13. Roux S, Terouanne B, Couette B, Rafestin-Oblin ME, Nicolas JC 1999 Conformational change in the human glucocorticoid receptor induced by ligand binding is altered by mutation of isoleucine 747 by a threonine. J Biol Chem 274:10059–10065[Abstract/Free Full Text]
  14. Vottero A, Kino T, Combe H, Lecomte P, Chrousos GP 2002 A novel, C-terminal dominant negative mutation of the GR causes familial glucocorticoid resistance through abnormal interactions with p160 steroid receptor coactivators. J Clin Endocrinol Metab 87:2658–2667[Abstract/Free Full Text]
  15. Dunbrack RL, Cohen FE 1997 Bayesian statistical analysis of protein side-chain rotamer preferences. Protein Sci 6:1661–1681[Abstract/Free Full Text]
  16. Nettles KW, Sun J, Radek JT, Sheng S, Rodriguez AL, Katzenellenbogen JA, Katzenellenbogen BS, Greene GL 2004 Allosteric control of ligand selectivity between estrogen receptors a and ß: implications for other nuclear receptors. Mol Cell 13:317–327[CrossRef][Medline]
  17. Zhang S, Liang X, Danielsen M 1996 Role of the C terminus of the glucocorticoid receptor in hormone binding and agonist/antagonist discrimination. Mol Endocrinol 10:24–34[Abstract]
  18. Langley E, Kemppainen JA, Wilson EM 1998 Intermolecular NH2-/carboxyl-terminal interactions in androgen receptor dimerization revealed by mutations that cause androgen insensitivity. J Biol Chem 273:92–101[Abstract/Free Full Text]
  19. Shulman AI, Larson C, Mangelsdorf DJ, Ranganathan R 2004 Structural determinants of allosteric ligand activation in RXR heterodimers. Cell 116:417–429[CrossRef][Medline]
  20. Shiau AK, Barstad D, Loria PM, Cheng L, Kushner PJ, Agard DA, Greene GL 1998 The structural basis of estrogen receptor/coactivator recognition and the antagonism of this interaction by tamoxifen. Cell 95:927–937[CrossRef][Medline]
  21. Shiau AK, Barstad D, Radek JT, Meyers MJ, Nettles KW, Katzenellenbogen BS, Katzenellenbogen JA, Agard DA, Greene GL 2002 Structural characterization of a subtype-selective ligand reveals a novel mode of estrogen receptor antagonism. Nat Struct Biol 9:359–364[Medline]
  22. Warnmark A, Treuter E, Gustafsson JA, Hubbard RE, Brzozowski AM, Pike ACW 2002 Interaction of transcriptional intermediary factor 2 nuclear receptor box peptides with the coactivator binding site of estrogen receptor {alpha}. J Biol Chem 277:21862–21868[Abstract/Free Full Text]
  23. Leduc AM, Trent JO, Wittliff JL, Bramlett KS, Briggs SL, Chirgadze NY, Wang Y, Burris TP, Spatola AF 2003 Helix-stabilized cyclic peptides as selective inhibitors of steroid receptor-coactivator interactions. Proc Natl Acad Sci USA 100:11273–11278[Abstract/Free Full Text]
  24. Gangloff M, Ruff M, Eiler S, Duclaud S, Wurtz JM, Moras D 2001 Crystal structure of a mutant hER {alpha} ligand-binding domain reveals key structural features for the mechanism of partial agonism. J Biol Chem 276:15059–15065[Abstract/Free Full Text]
  25. Eiler S, Gangloff M, Duclaud S, Moras D, Ruff M 2001 Overexpression, purification, and crystal structure of native ER[alpha] LBD. Protein Expr Purif 22:165–173[CrossRef][Medline]
  26. Carlson KE, Choi I, Gee A, Katzenellenbogen BS, Katzenellenbogen JA 1997 Altered ligand binding properties and enhanced stability of a constitutively active estrogen receptor: evidence that an open pocket conformation is required for ligand interaction. Biochemistry 36:14897–14905[CrossRef][Medline]
  27. Bourguet W, Germain P, Gronemeyer H 2000 Nuclear receptor ligand-binding domains three-dimensional structures, molecular interactions and pharmacological implications. Trends Pharmacol Sci 21:381–388[CrossRef][Medline]
  28. Draper MW 2003 The role of selective estrogen receptor modulators (SERMs) in postmenopausal health. Ann NY Acad Sci 997:373–377[Abstract/Free Full Text]
  29. Shang YF, Brown M 2002 Molecular determinants for the tissue specificity of SERMs. Science 295:2465–2468[Abstract/Free Full Text]
  30. Brunger AT, Karplus M 1988 Polar hydrogen positions in proteins: empirical energy placement and neutron-diffraction comparison. Proteins 4:148–156[Medline]
  31. Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S, Karplus M 1983 CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J Comput Chem 4:187–217[CrossRef]
  32. Jorgensen WL, Chandrasekhar J, Madura J, Impey RW, Klein ML 1983 Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926–935[CrossRef]
  33. Sen S, Nilsson L 1999 Structure, interaction, dynamics and solvent effects on the DNA-EcoRI complex in aqueous solution from molecular dynamics simulation. Biophys J 77:1782–1800[Abstract/Free Full Text]
  34. Steinbach PJ, Brooks BR 1993 Protein hydration elucidated by molecular-dynamics simulation. Proc Natl Acad Sci USA 90:9135–9139[Abstract/Free Full Text]
  35. MacKerell Jr AD, Bashford D, Bellott M, Dunbrack Jr RL, Evanseck JD, Field MJ, Fischer S, Gao J, Guo H, Ha S, Joseph-McCarthy D, Kuchnir L, Kuczera K, Lau FTK, Mattos C, Michnick S, Ngo T, Nguyen DT, Prodhom B, Reiher III WE, Roux B, Schlenkrich M, Smith JC, Stote R, Straub J, Watanabe M, Wiorkiewicz-Kuczera J, Yin D, Karplus M 1998 All-atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B 102:3586–3616[CrossRef]
  36. Brooks III C, Karplus M 1983 Deformable stochastic boundaries in molecular dynamics. J Chem Phys 79:6312–6325[CrossRef]
  37. Ryckaert J-P, Ciccotti G, Berendsen HJC 1977 Numerical integration of the Cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J Comp Physiol 23:327–341
  38. Renaud JP, Rochel N, Ruff M, Vivat V, Chambon P, Gronemeyer H, Moras D 1995 Crystal-structure of the RAR-{gamma} ligand-binding domain bound to all-trans-retinoic acid. Nature 378:681–689[CrossRef][Medline]
  39. Kraulis PJ 1991 Molscript - a program to produce both detailed and schematic plots of protein structures. J Appl Crystallogr 24:946–950[CrossRef]
  40. Williams SP, Sigler PB 1998 Atomic structure of progesterone complexed with its receptor. Nature 393:392–396[CrossRef][Medline]
  41. DeLano WL The PyMOL molecular graphics system. San Carlos, CA: DeLano Scientific LLC