Quantitative Structural Assessment of Graded Receptor Agonism

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Quantitative structural assessment of graded receptor agonism

Jinsai Shanga, Richard Brusta, Patrick R. Griffina,b, Theodore M. Kameneckab, and Douglas J. Kojetina,b,1

aDepartment of Integrative Structural and Computational Biology, The Scripps Research Institute, Jupiter, FL 33458; and bDepartment of Molecular Medicine, The Scripps Research Institute, Jupiter, FL 33458

Edited by Michael F. Summers, University of Maryland, Baltimore County, Baltimore, MD, and approved September 20, 2019 (received for review June 5, 2019) Ligand–receptor interactions, which are ubiquitous in physiology, cellular transcription factors that recruit chromatin remodeling are described by theoretical models of receptor pharmacology. transcriptional machinery in a ligand-dependent manner to con- Structural evidence for graded efficacy receptor conformations trol gene expression (9). Nuclear receptor agonists, which bind to predicted by receptor theory has been limited but is critical to fully an internal hydrophobic orthosteric pocket within nuclear recep- validate theoretical models. We applied quantitative structure– tor ligand-binding domain (LBD), activate transcription by stabi- function approaches to characterize the effects of structurally sim- lizing structural elements that comprise the activation function-2 ilar and structurally diverse agonists on the conformational ensem- (AF-2) coregulator interaction surface, located adjacent to the γ ble of nuclear receptor peroxisome proliferator-activated receptor ligand-binding pocket, including helix 3, helix 4/5, and a critical γ (PPAR ). For all ligands, agonist functional efficacy is correlated to a regulatory switch element, helix 12. Agonists stabilize an active shift in the conformational ensemble equilibrium from a ground AF-2 surface conformation, which increases the binding affin- state toward an active state, which is detected by NMR spectros- ity for and recruitment of transcriptional coactivator pro- copy but not observed in crystal structures. For the structurally sim- teins that in turn promote chromatin remodeling and increased ilar ligands, ligand potency and affinity are also correlated to transcription. efficacy and conformation, indicating ligand residence times among Although receptor theory as practiced in the membrane re- related analogs may influence receptor conformation and function. ceptor fields is not used in the nuclear receptor field, in principle Our results derived from quantitative graded activity–conformation

correlations provide experimental evidence and a platform with the functional endpoints derived from nuclear receptor functional BIOPHYSICS AND assays can be applied to the same pharmacological models of re- which to extend and test theoretical models of receptor pharmacol- COMPUTATIONAL BIOLOGY ogy to more accurately describe and predict ligand-dependent ceptor function. For example, the CTC model (Fig. 1) describes receptor activity. how a receptor exists as a conformational ensemble that fluctuates between resting (R) or active (R*) states capable of binding to nuclear receptor | ligand binding | receptor theory | NMR spectroscopy | ligand (L) and an effector protein or peptide (P). Ligand affinity is X-ray crystallography described by a combination of L + R ↔ LR and L + R* ↔ LR*. The ligand-bound receptor state (LR/LR*) influences effector – eceptor theory has been used to describe the actions of binding (LRP/LR*P) by changing the receptor effector binding Rpharmacological ligands in various forms for nearly a century affinity constant. Ligand potency is related to ligand affinity but (1, 2). The idea of a receptor has evolved from a conceptual “black box” to one founded in the principles of biophysics and Significance allostery (3). The 2-state model of receptor activation (4), which extended the Black/Leff operational model of pharmacological Pharmacological receptor theory models are used to predict agonism (5) with the Monod–Wyman–Changeux model of pro- and interpret how small-molecule ligands impact the function tein allostery (6) to describe the actions of pharmacological re- of cellular receptors. Theoretical models have been modified ceptor ligands, conceptually represents a minimal theoretical and extended over time to more accurately describe relation- model to describe the action of ligands within the context of a ships between ligand affinity and receptor functional. How- binary ligand–receptor complex. More complex models were ever, structural studies are lacking that describe the influence subsequently developed that accounted for improved under- of receptor conformation, or shape, as predicted by these standing of receptor functions. The extended ternary complex models. Using NMR spectroscopy, we detected how the nuclear (ETC) model describes how the receptor–ligand complex influ- receptor transcription factor peroxisome proliferator-activated ences interaction with an effector protein or a signaling pathway receptor γ (PPARγ) structurally responds to ligand binding. We (7), whereas the cubic ternary complex (CTC) model extends the observed a correlation between ligand affinity, function, and ETC model to account for receptor–effector interactions in the receptor conformation for chemically similar ligands but not absence of ligand (8). These and other theoretical receptor chemical diverse ligands, revealing a potential limitation of models could be further improved with a more comprehensive theoretical receptor models. experimental understanding of how ligands affect receptor struc- Author contributions: J.S. and D.J.K. designed research; J.S., R.B., and D.J.K. performed ture and function. research; P.R.G. and T.M.K. contributed new reagents/analytic tools; J.S. and D.J.K. ana- Applying theoretical receptor models to graded activity dose- lyzed data; and J.S. and D.J.K. wrote the paper. responsive pharmacological data are common in studies of mem- The authors declare no competing interest. – brane receptors, including G-protein coupled receptors (GPCRs), This article is a PNAS Direct Submission. ligand-gated ion channels, and enzyme-linked receptors (2). These Published under the PNAS license. receptors bind extracellular ligands and transduce signals across Data deposition: The crystal structures generated in the current study have been depos- the cell membrane via conformational rearrangement of the in- ited in the Protein Data Bank (PDB), http://www.wwpdb.org/ (ID codes 6DGL, 6DGO, tracellular portion of the membrane receptor to affect various 6DGQ, 6DGR, 6O67, and 6O68). downstream signaling pathways, the activities of which can be 1To whom correspondence may be addressed. Email: [email protected]. measured in cellular assays and applied to pharmacological models This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. of receptor function. Conceptually, the principles of receptor 1073/pnas.1909016116/-/DCSupplemental. theory also apply to nuclear receptors, a superfamily of intra-

www.pnas.org/cgi/doi/10.1073/pnas.1909016116 PNAS Latest Articles | 1of10 Downloaded by guest on October 1, 2021 approaches can predict ligand functional efficacy and assess R*P LR*P theoretical models of receptor function. Results Graded Potency and Functional Efficacy within a Structurally Related Series of PPARγ Agonists. We assembled a series of 10 thiazolidinedione (TZD) PPARγ agonists (Fig. 2A) that include several US Food and Drug Administration-approved antidiabetic drugs (13). All ligands within series contain the conserved TZD head group connected R* LR* by a linker to a central aromatic moiety and a variable tail group. The linker in all but one of the ligands is a flexible, saturated methylene group that links the TZD head group to a central aromatic moiety; RP LRP CAY10638 contains an unsaturated linker, which restricts the mobility of the TZD head group. The central aromatic moieties mostly comprise phenyl moieties with the exception of naphthalene and benzothiophene moieties in netoglitazone and edaglitazone, respectively. In contrast to these relatively conservative changes near the TZD head group, the series encompasses a variety of tail moieties extended from the central aromatic core. Agonists increase PPARγ-mediated transcription by enhanc- RLRing binding of transcriptional coactivator proteins, such as TRAP220, also known as MED1 or DRIP205 (14). We assessed Fig. 1. The cubic ternary complex (CTC) model. This theoretical model the activities of the TZD series in 2 quantitative biochemical schematic describes the relationship the receptor resting (R) and active (R*) assays. We used a time-resolved fluorescence resonance energy state when complexed to ligand (L), an effector protein or peptide (P), or transfer (TR-FRET) biochemical assay that measures the ligand- both. In the absence of an effector, the apo-state is represented by R and R*, γ and the ligand-bound state by LR and LR*, also known as the 2-state model dependent change in the interaction between the PPAR LBD of receptor activation. and a peptide derived from the TRAP220 coactivator (Fig. 2B) containing an “LXXLL” nuclear receptor interaction motif (15). In the TR-FRET coactivator recruitment assay, differences in describes the concentration of ligand required to elicit the func- the overall TR-FRET ratio assay window span, which we refer tional response (efficacy). Assuming that the ligand-free receptor to as TR-FRET functional efficacy, relates to the relative can interact with the effector, which is the case for nuclear re- degree of TRAP220 peptide recruitment and are indicative ceptor LBDs interacting with coactivator peptides, ligand potency of ligand-dependent differences in the binding affinity of the is described by RP ↔ LRP and R*P ↔ LR*P. TRAP220 coactivator peptide to the PPARγ LBD, which we It has been challenging to structurally observe ligand-bound probed directly using a fluorescence polarization (FP) assay graded receptor activity conformations predicted by receptor (Fig. 2C). theory. Receptor crystal structures derived from X-ray diffraction In the TR-FRET coactivator recruitment assay, the TZD se- D data can bias conformations such that the ground state (R or LR) ries spans nearly 10,000-fold in potency (EC50) (Fig. 2 ) and or fully active state (R* or LR*) is observed but the spectrum of showed decreasing coactivator peptide recruitment efficacy (Fig. graded or partial activity states between R/LR and LR/LR* is not. 2E) as ligand potency decreases (Fig. 2G). Coactivator binding However, solution NMR spectroscopy studies are capable of affinity was also decreased when PPARγ was bound to less po- detecting graded activity conformational states (10–12). Further- tent ligands (Fig. 2F). Consistent with the correlation between more, to our knowledge, a direct quantitative assessment has yet TR-FRET recruitment efficacy and coactivator affinity, there is a to be reported on the relationship between graded ligand potency correlation between TR-FRET efficacy with coactivator affinity H γ with functional efficacy and receptor conformation as predicted by (Fig. 2 ) and the coactivator-bound population of the PPAR I the theoretical models of receptor activation. That is, does ligand LBD in the TR-FRET assay (Fig. 2 ). The coactivator peptide potency correlate with functional efficacy—where ligands with concentration used in the TR-FRET assay has a slight effect on high potency display high functional efficacy, and ligands with low ligand potency (higher peptide concentrations result in slightly potency display low functional efficacy—within a structurally re- more potent responses) and a more notable effect on TR-FRET lated series of ligands or among a structurally diverse set of efficacy (higher peptide concentrations result in higher efficacy) (SI Appendix, Fig. S1). These correlated patterns of decreased ligands? ligand potency and TR-FRET efficacy are consistent with the Here, we present a quantitative receptor activity–conformation allosteric principles of receptor activation theoretical models, analysis using 2 distinct sets of agonists of the nuclear receptor which describe the actions of full and partial, or less efficacious peroxisome proliferator-activated receptor γ (PPARγ). We char- agonists where decreasing or graded potency within a ligand acterized a series of 10 structurally related synthetic PPARγ ag- ∼ series is associated with graded functional efficacy (16, 17). That onists spanning 10,000-fold in affinity using biochemical, is, the more potent ligands show higher functional efficacy (e.g., biophysical, and cellular assays and structural analysis using X-ray higher TR-FRET assay window span), and the less potent li- crystallography and NMR spectroscopy. Ligand potency and gands show lower functional efficacy (e.g., lower TR-FRET assay functional efficacy in this series are correlated to the degree to window span). These trends also manifest in differences in the γ which the ligands shift the conformational ensemble of PPAR kinetic rate constants for binding TRAP220 coactivator peptide, toward an active state, which we detected by NMR but not in where Kon and to a larger degree Koff are correlated to ligand crystal structures, in a manner consistent with relationships potency and functional efficacy from the TR-FRET assay (Fig. 2 predicted by theoretical models of receptor activation. However, J and K and SI Appendix, Fig. S2). using a structurally diverse set of endogenous and synthetic PPARγ ligands, we found that functional efficacy and receptor confor- Correlation of TZD Affinity, Receptor Stability, and Cellular Transcription. mation are correlated independent of ligand potency. Collectively, We determined ligand binding affinities for the TZD series using a our studies indicate that data from quantitative structure–function TR-FRET assay that measures the displacement of a fluorescent

2of10 | www.pnas.org/cgi/doi/10.1073/pnas.1909016116 Shang et al. Downloaded by guest on October 1, 2021 A high potency & efficacy

O O O O O Darglitazone S N N N S N HN HN HN HN Edaglitazone O O HN H S S S N S N S S O O O O CAY10506 O O O O O O O Rosiglitazone Pioglitazone Darglitazone Edaglitazone CAY10506 Rosiglitazone Pioglitazone Troglitazone Netoglitazone O O O O O Ciglitazone F N HN HN HN HN HN S O S S S S Mitoglitazone O O O O O S O O O O O CAY10638 O OH DMSO (panel c) Troglitazone Netoglitazone Ciglitazone Mitoglitazone CAY10638 (MCC-555) (CAY10415, MSDC-0160) low potency & efficacy

B TR-FRET coactivator recruitment assay C FP coactivator binding assay D ligand potency E ligand efficacy F coactivator affinity 4 80 Darglitazone Edaglitazone 3 60 CAY10506 Rosiglitazone Pioglitazone 2 40 Troglitazone Netoglitazone 20 Ciglitazone

1 Polarization (mP) Mitoglitazone

TR-FRET ratio (520/495 nm) 0 CAY10638 0 -10 -9 -8 -7 -6 -5 -4 -7 -6 -5 -4 -10-9-8-7-6-50 123-7 -6 -5 ligand (log M) γ TR-FRET EC50 (log M) TR-FRET window span Kd (log M) DMSO PPAR (log M)

2 2 R2 = 0.9004 R2 = 0.9350 R = 0.3748 R = 0.7097 -7.0 0.6 150000 1.0 G 3 H I J K

, log M) 0.8 d 0.4 100000 ) 2 ) -1 -1 0.6 (s (s

-6.0 BIOPHYSICS AND on off K K 0.4 1 0.2 50000

0.2 COMPUTATIONAL BIOLOGY TR-FRET window

0 -5.0 0.0 0 0.0

-109-7 -8 -6 -5 coactivator affinity (K 0123 0123 -10 -8 -6 -10 -8 -6 calculated TR-FRET occupancy

TR-FRET EC50 (log M) TR-FRET window span TR-FRET window span TR-FRET EC50 (log M) TR-FRET EC50 (log M)

Fig. 2. Quantitative potency and efficacy characterization of a series of thiazolidinedione (TZD) PPARγ agonists. (A) Chemical structures of the TZD ligands.

The conserved TZD head group colored purple. (B) TR-FRET assay to determine ligand potency (EC50 values) and efficacy (TR-FRET window span) for recruitment of TRAP220 coactivator peptide to PPARγ LBD fit to a 3-parameter sigmoidal dose–response equation; data represent the mean ± SEM of experimental replicates (ligands, n = 3; DMSO, n = 36). (C) FP assay to determine TRAP220 coactivator peptide affinity to PPARγ LBD when bound to the ligands fit to a one-site–total binding equation; data represent the mean ± SEM of experimental replicates (n = 3). (D) Ligand potencies from the fitted TR-FRET data; data represent the mean ± SD of 2 independent experiments. (E) Ligand efficacies from the fitted TR-FRET data of the experimental replicate shown in B; data represent the TR-FRET ratio window span ± SD from the fit. (F) TRAP220 peptide affinities from the fitted FP data; data represent the mean ± SD of 2 independent experiments. The affinity for apo-PPARγ LBD is indicated on the x axis by a gray arrow. (G) Plot of ligand efficacy from E vs. ligand potency from D. (H) Plot of TRAP220 peptide affinity from F vs. ligand efficacy from E fit with a linear regression equation. (I) Plot of calculated TRAP220 peptide occupancy in

the TR-FRET assay (no error bars shown) vs. ligand efficacy fit with a linear regression equation. (J and K) Correlation plots of TRAP220 peptide (J) Kon and (K) Koff values determined in the presence of the TZD ligand series using biolayer interferometry (BLI); BLI data represent the mean ± SD of experimental replicates (n = 2). Errors smaller than the displayed data point size representing the mean value that are too small to display are not shown.

tracer ligand (Fig. 3A). The TZD series spans an affinity (Ki)range Crystal Structures Provide Some Insight into TZD Affinity but Not of 5 orders of magnitude (Fig. 3B)andiscorrelatedtobio- Efficacy. To gain insight into the structural basis for the varied chemical potency in the TR-FRET assay (Fig. 3E). Using dif- TZD affinity, potency, and functional efficacy, we compared ferential scanning calorimetry, we determined how the TZD crystal structures of PPARγ LBD bound to each of the TZDs. SI Appendix series affects the thermal stability of the PPARγ LBD under We solved crystal structures of 6 complexes ( , Table γ our experimental conditions (Fig. 3C) and found a linear cor- S1) of PPAR LBD bound to darglitazone (Protein Data Bank relation between ligand binding affinity and receptor stability [PDB]: 6DGL; 1.95-Å resolution) (19), CAY10506 (PDB: (Fig. 3F). We also assessed the TZD series in cell-based tran- 6DGQ; 2.45-Å resolution) (20), troglitazone (PDB: 6DGO; 3.10-Å scriptional luciferase reporter assay that directly reports on the resolution) (21), ciglitazone (PDB: 6O68; 2.78-Å resolution) (22), mitoglitazone (PDB: 6O67; 2.52-Å resolution) (23), and transcriptional activity of the PPARγ LBD (Fig. 3D)andis CAY10638 (PDB: 6DGR; 2.15-Å resolution) (24). We com- highly sensitive to graded PPARγ agonism (18). The efficacy pared our structures to other previously solved PPARγ LBD trends in the cellular transcription profile of the TZD series is crystal structures bound to edaglitazone (PDB: 5UGM; 2.1-Å similar to the efficacy from the quantitative biochemical TR-FRET resolution) (25), rosiglitazone (PDB: 4EMA; 2.55-Å resolution) coactivator recruitment profiles. Due to cellular toxicity of the (26), pioglitazone (PDB: 5Y2O; 1.80-Å resolution) (27), and ligands at higher concentrations, we were unable to determine netoglitazone/MCC-155 (PDB: 3B0Q; 2.10-Å resolution). This cellular potency values for some TZDs due to nonsaturating cellular enabled a complete X-ray crystallographic analysis of the TZD response profiles. However, there is a correlation between the series (Fig. 4A). In all of the structures, PPARγ LBD crystallized maximal luciferase value derived from a fitted of the transcriptional with 2 molecules configured as a homodimer in the asymmetric reporter assay and TR-FRET ligand potency (Fig. 3G) and ligand unit, although in solution PPARγ LBD is monomeric (28). affinity (Fig. 3H). Thus, ligand affinity, receptor stability, and Similar to other PPARγ LBD crystal structures solved in the transcription are correlated to quantitative functional potency absence of coregulator peptide, in chain A the critical switch and efficacy within the TZD series. element for activation of PPARγ transcription, helix 12, adopts

Shang et al. PNAS Latest Articles | 3of10 Downloaded by guest on October 1, 2021 A TR-FRET ligand displacement assay B ligand affinity C receptor stability 0.8 Darglitazone Darglitazone Darglitazone Edaglitazone Edaglitazone Edaglitazone 0.6 CAY10506 CAY10506 CAY10506 Rosiglitazone Rosiglitazone Rosiglitazone Pioglitazone Pioglitazone Pioglitazone 0.4 Troglitazone Troglitazone Troglitazone Netoglitazone Netoglitazone Netoglitazone Ciglitazone Ciglitazone 0.2 Ciglitazone Mitoglitazone Mitoglitazone Mitoglitazone CAY10638 CAY10638 TR-FRET ratio (520/495 nm) CAY10638 DMSO 0.0 DMSO (panel c) -10 -9 -8 -7 -6 -5 -4 -10 -9 -8 -7 -6 -5 40 45 50 55 60

ligand (log M) Ki (log M) Thermal transition midpoint, TS (°C)

D Cell-based transcription assay 800000

E R2 = 0.9798 F R2 = 0.9618 G R2 = 0.6993 H R2 = 0.6968 600000 800000 800000 55 -6 400000 600000 600000

(°C) 400000 400000

S 50 (log M) i -8 T K

Luciferase activity 200000 200000 200000 max luciferase (efficacy) 45 max luciferase (efficacy) 0 -10 0 0 -10 -9 -8 -7 -6 -5 -4 -10 -8 -6 -10 -8 -6 -10 -8 -6 -10 -8 -6 TR-FRET EC (log M) K (log M) TR-FRET EC (log M) K (log M) ligand (log M) 50 i 50 i

Fig. 3. Affinity of the TZD series and effects on receptor stability and transcription. (A) TR-FRET assay to determine ligand affinity (Ki values) using a fluorescent tracer ligand fit to the Cheng–Prusoff inhibitor constant equation; data represent the mean ± SEM of experimental replicates (n = 3). (B) Ligand

affinities (Ki values) from the fitted TR-FRET data; data represent the mean ± SD of 2 independent experiments. (C) PPARγ LBD thermal transition midpoint receptor stability temperatures from fitted differential scanning calorimetry (DSC) data (n = 1). (D) Cell-based luciferase assay reporting on transcription of the PPARγ LBD fit to a sigmoidal dose–response equation; error bars, data represent the mean ± SEM of experimental replicates (n = 4). (E) Plot of ligand affinity from B vs. ligand potency from the TR-FRET coactivator recruitment assay from Fig. 2D fit with a linear regression equation. (F) Plot of receptor stability from C vs. ligand affinity from B fit with a linear regression equation. (G and H) Plot of transcriptional window of efficacy via maximum luciferase activity from the highest ligand concentration and associated errors from D vs. (G) ligand potency in the TR-FRET coactivator recruitment assay from Fig. 2D and (H) ligand affinity in the TR-FRET ligand displacement assay from B fit with a linear regression equation.

an active conformation. The conformation of helix 12 in chain B not explain the graded functional efficacy of the TZD series. In is atypical and influenced by a crystallization artifact; it binds to principle, the differences in graded efficacy should manifest in the AF-2 surface of an adjacent molecule (chain A′), which likely conformational differences in the structural elements that com- stabilizes the active conformation of helix 12 in chain A. prise the AF-2 coregulator interaction surface. However, there All ligands in the TZD series show clear density in the chain A are no obvious structural changes in the AF-2 surface among the molecule (Fig. 4 B–K and SI Appendix, Figs. S3 and S4). For 8 of crystal structures for this TZD series (Fig. 4A). This is likely due to the 10 ligands (all except ciglitazone and mitoglitazone), the the aforementioned helix 12/AF-2 surface crystal contacts, although TZD head group associates near helix 12, a region of the ligand- another contribution could be that the crystallized conformations binding pocket called the helix 12 subpocket herein, and forms do not fully represent the conformational ensemble of PPARγ in hydrogen bond contacts with the side chains of up to 4 nearby solution. Namely, the crystallized conformations we captured may residues Ser289, His323, His449, and Tyr473. Among these li- represent a low energy minima of a broader energy landscape that gands, there is a trend in the ligand binding poses whereby the are not sensitive to differences caused by ligand binding in X-ray higher affinity, most potent ligands display weaker polar inter- data collected under cryogenic temperatures (36–39). actions with the side chains of Cys285 and/or Gln286 and contain longer tail groups that wrap around helix 3 and form water- NMR Reveals Graded TZD Potency and Efficacy Are Correlated to mediated polar interactions with residues in the β-sheet. In Receptor Active State. Solution NMR spectroscopy is a powerful contrast to these 8 ligands that adopt “canonical” binding modes, approach to study dynamic allosteric properties in proteins (40). ciglitazone and mitoglitazone, 2 of the least potent ligands in the We therefore envisioned that NMR may detect a shift in the series, show alternate binding modes in the chain A active con- PPARγ LBD conformational ensemble toward the fully active formation where their TZD head groups associate near the state (R/LR → graded activity conformations → LR*) for the flexible, solvent accessible Ω-loop and their relatively shorter TZD series in a manner consistent with the theoretical models of extended tail groups insert into the orthosteric pocket alongside receptor activation, which relate the rate of transformation of the β-sheet surface. Ligand binding to this alternate site has been the receptor conformational ensemble from a ground state to the observed in several other structural studies (29–34). The alter- ligand-bound active state (17, 41). That is, ligands with higher nate binding modes of ciglitazone and mitoglitazone may origi- potency and efficacy would shift the conformational ensemble of nate from their lower affinity, and in the case of ciglitazone, a PPARγ more toward an “active” state than ligands with less lack of atoms in its shorter tail group capable of forming water- potency and efficacy. mediated polar interactions with residues in the β-sheet (35). Previous NMR studies have shown that the ligand-binding Although the significance of these alternate TZD binding modes pocket and helix 12 of apo-PPARγ LBD is dynamic and switches is not yet clear, it is possible they represent an initial encounter between 2 or more conformations on the microsecond-to-millisecond complex binding mode before transitioning to the orthosteric timescale, also known as the intermediate-exchange NMR time- ligand-binding pocket. scale, resulting in the appearance of approximately one-half of Whereas the structural contributions to the graded ligand the expected NMR peaks (18, 42). Exchange between these 2 binding affinity within the TZD series may be apparent from the conformations—thought to be one that is similar to a transcrip- crystallized ligand binding modes (Fig. 4 B–K), the structures do tionally active conformation where the AF-2 surface can bind

4of10 | www.pnas.org/cgi/doi/10.1073/pnas.1909016116 Shang et al. Downloaded by guest on October 1, 2021 A B h11 C h11 D h11 AF-2 surface Darglitazone Edaglitazone CAY10506

h12 h12 h12

-sheet h3 β h3 β-sheet h3 β-sheet

coactivatorpeptide

h12 E h11 F h11 G h11 Rosiglitazone Pioglitazone Troglitazone

h12 h12 h12

ligand-binding pocket h3 β-sheet h3 β-sheet h3 β-sheet

H I J K h11 h11 h11 h11 Netoglitazone Ciglitazone Mitoglitazone CAY10638

h12 h12 h12 h12

h3 β h3 β-sheet h3 -sheet β-sheet h3 β-sheet

Fig. 4. X-ray crystallography analysis of the TZD series. (A) Structural overlay of the 10 TZD-bound PPARγ LBD structures (chain A). (B–K) Ligand binding poses

of the TZD series; (B) darglitazone (PDB 6DGL), (C) edaglitazone (PDB 5UGM), (D) CAY10506 (PDB 6DGQ), (E) rosiglitazone (PDB 4EMA), (F) pioglitazone (PDB BIOPHYSICS AND

5Y2O), (G) troglitazone (PDB 6DGO), (H) netoglitazone (PDB 3B0Q), (I) ciglitazone (PDB 6O68), (J) mitoglitazone (PDB 6O67), and (K) CAY10638 (PDB 6DGR). COMPUTATIONAL BIOLOGY The cartoon structures in A are colored to their corresponding ligand colors in the binding pose views in B–K.

coactivator and another that is similar to a transcriptionally re- Appendix,Fig.S5), indicative of microsecond-to-millisecond pressive conformation where the AF-2 can bind corepressor—is timescale dynamics, that persist from the apo-form for residues the likely origin of the NMR peak broadening in the apo-state. within the ligand entry/exit site, ligand-binding pocket, and struc- NMR peaks missing in the apo-form, which include residues tural elements comprising the AF-2 coregulator interaction sur- within ligand-binding pocket and the AF-2 coregulator in- face (Fig. 5C). The structural regions with persistent microsecond- teraction surface (helix 3, helix 4/5, and helix 12), are stabilized to-millisecond timescale dynamics when bound to the less upon binding potent full agonists that robustly activate PPARγ potent TZDs comprise similar structural regions with persistent but remain absent, or persist, upon binding potent non-TZD microsecond-to-millisecond timescale dynamics when bound partial agonists that weakly activate PPARγ (18, 42–44). Ago- to potent non-TZD partial agonists. In the case of the less potent nist binding selects for a transcriptionally active conformation, so TZD ligands, the persistent microsecond-to-millisecond time- there could be at least 2 potential origins of the NMR peak scale dynamics likely originates from relatively fast ligand off- broadening when bound to partial agonists: broadening due to exchange compared to the more potent TZDs (46). However, for protein dynamics in the ligand-bound state, and “competition” the potent non-TZD partial agonists, the persistent microsecond- between the less potent agonists binding to the active conforma- to-millisecond timescale dynamicslikelyoriginatesfroma tion and the repressive conformation. In either case, microsecond- different mechanism not related to ligand off-exchange; these to-millisecond timescale dynamics that persist in the AF-2 surface ligands were developed to retain high affinity but lack a TZD- in the apo-form and ligand-bound forms cause less favorable in- comparable head group capable of forming hydrogen bond con- teraction with coactivators (45). tacts to residues within the pocket and on helix 12 that stabilize the We performed differential NMR analysis comparing 2D [1H, AF-2 surface. Thus, when bound to PPARγ at high affinity, the 15N]-transverse relaxation optimized spectroscopy (TROSY)– non-TZD partial agonists enable the dysfunctional microsecond- heteronuclear single quantum coherence (HSQC) NMR spectra to-millisecond timescale dynamics present in the apo-form to of 15N-labeled PPARγ LBD bound to each ligand within the TZD persist. series to the apo-form under the same ligand vehicle DMSO condition. In contrast to these NMR line broadening changes, we ob- Ligands within the series contain different extended side-chain served colinear NMR chemical shift perturbations for residues moieties, including various aromatic groups that interact with the distal from the ligand-binding pocket but proximal to the AF-2 β-sheet region and wrap around helix 3 resulting in ring current surface (Fig. 5B and SI Appendix, Fig. S6), which includes resi- effects that impart unpredictable nonlinear NMR peak shifting dues that form a surface connected by helix 1, helix 4/5, helix 8, for residues within this region of the pocket. NMR peak shifting helix 8–9 loop, and helix 9 (Fig. 5C). In most cases, one NMR is also observed for residues near the ligand entry/exit site, likely peak is visible for each ligand-bound state; however, in select due in part to slight differences in the relative binding mode of cases for a few ligands with intermediate or weak potency, a the ligands. Despite these general binding mode-induced NMR combination shifting, line broadening, and 2 receptor populations peak shifting, 2 general NMR observable conformational trends are observed. The coincident shifting and broadening suggest are apparent for the TZD series. conversion on the fast (peak shifting) to intermediate (peak We found that the less potent, and therefore less efficacious, broadening) NMR timescale. The 2 receptor populations ob- ligands within the series that caused partial or graded functional served for residues in the helix 8–9 loop (G399 and L400) indicate efficacy resulted in NMR peak line broadening (Fig. 5A and SI there is exchange between 2 ligand-bound receptor populations

Shang et al. PNAS Latest Articles | 5of10 Downloaded by guest on October 1, 2021 L311 V307 A helix 12 B proximal to AF-2 surface C

L468 1D projection V307 L311 F403 h9 h1 L401

124.0 107.4 128.8 L400 G399

107.8 AF-2 h4/5 h8 125.0 129.2 surface 11.10 10.90 11.10 10.90 8.30 8.20 8.70 8.60

Y473 1D projection G399 L400 coactivatorpeptide 120.0 114.0 K474 Y473 108.0 L468 120.5 114.4 h12 h2a 108.5 h11 8.45 8.35 8.50 8.30 8.50 8.40 6.90 6.80 h6/7 K474 1D projection L401 V403 β h3 -sheet 128.2 122.8

123.0 123.0 entrance to 128.6 ligand-binding pocket colinear 123.5 h2b

N (p.p.m.) peak shifting

15 7.50 7.40 7.50 7.30 11.65 11.55 8.40 8.35 peak line 1H (p.p.m.) broadening

D V307 L311 G399 L400 L401 V403 R2 = 0.7731 R2 = 0.8482 R2 = 0.6270 R2 = 0.8347 R2 = 0.6284 R2 = 0.5065 0.05 0.05 0.12 0.08 0.04 0.04 0.04 0.04 0.06 0.03 0.03 0.08 0.03 0.03 0.04 0.02 0.02 0.02 0.02 0.04 Darglitazone 0.01 0.01 0.02 0.01 0.01 Edaglitazone 0.00 0.00 0.00 0.00 0.00 0.00 CAY10506 NMR CSP -10 -8 -6 -10 -8 -6 -10 -8 -6 -10 -8 -6 -10 -8 -6 -10 -8 -6 Rosiglitazone Pioglitazone TR-FRET EC50 (log M) Troglitazone Netoglitazone R2 = 0.6289 R2 = 0.7907 R2 = 0.8956 R2 = 0.7539 R2 = 0.6605 R2 = 0.7849 E 0.05 0.05 0.12 0.08 0.04 0.04 Ciglitazone Mitoglitazone 0.04 0.04 0.06 0.03 0.03 0.08 CAY10638 0.03 0.03 0.04 0.02 0.02 DMSO 0.02 0.02 0.04 0.01 0.01 0.02 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00

NMR CSP 0123 0123 0123 0123 0123 0123 TR-FRET window span

Fig. 5. NMR-detected changes in the PPARγ LBD conformational ensemble correlate with graded ligand potency and efficacy. (A and B) Snapshots of [1H,15N]-TROSY-HSQC NMR spectra of 15N-labeled PPARγ LBD that show NMR changes as a function of graded potency and efficacy, including residues (A)in helix 12 that show NMR peak line broadening, along with 1D projections; or (B) proximal to the AF-2 coactivator interaction surface that show colinear shifting. (C) Analysis of all well-dispersed NMR peaks with reasonable peak separation that could be faithfully analyzed shows widespread correlations throughout the PPARγ LBD that group into 2 surfaces sensitive either to NMR peak line broadening or colinear shifting. The dark orange and dark purple spheres correspond to residues shown in A and B; the light orange and light purple spheres correspond to other residues that also show line broadening (SI Appendix, Fig. S5) or colinear shifting (SI Appendix, Fig. S6) in the TZD series. (D and E) Plot of NMR chemical shift perturbation for each liganded state shown in B relative to the apo-protein state vs. (D) ligand potency from Fig. 2D and (E) ligand efficacy in the TR-FRET coactivator recruitment assay from Fig. 2E fit with a linear regression equation.

(SI Appendix,Fig.S7). One potential contribution to this phe- docking approaches, the (S)-pioglitazone is also predicted to nomenon is the racemic nature of the TZD head group, which display higher affinity than (R)-pioglitazone (35). Thus, if the isomerize between (R)- and (S)- conformers, each of which have overall racemic affinity ratio, (S)/(R)- for intermediate-affinity different affinities for binding PPARγ. A previous study used TZDs becomes closer to 1, the difference in affinity between chromatographic methods to separate rosiglitazone enantiomers isomers becomes less significant allowing both to bind. In this for use in a ligand displacement assay (47). (S)-Rosiglitazone scenario, the different TZD head group isomers will interact displayed higher affinity than (R)-rosiglitazone (30 nM vs. 2 μM, differently with the receptor, which could in principle cause the respectively), although after separation the ligands spontane- peak doubling observed that correspond to 2 receptor pop- ously racemize with a half-life of ∼3 h, which precludes a de- ulations. A second potential contribution is that possible isom- tailed NMR analysis of the individual isomers. Using molecular erization of a proline residue (P398) nearby G399 and L400 may

6of10 | www.pnas.org/cgi/doi/10.1073/pnas.1909016116 Shang et al. Downloaded by guest on October 1, 2021 occur in a ligand-specific manner and contribute to the 2 pop- TZD series also showed colinear shifting in this structurally di- ulations. Notwithstanding these limitations, we found that the verse ligand set (Fig. 6A), indicating a correlation between ligand degree to which the NMR peaks shift colinearly from a ground efficacy and receptor conformation. Compared to the TZD series state conformation (apo-form) toward an “active” conformation NMR data, the majority of natural/endogenous PPARγ ligands (i.e., toward the NMR peaks corresponding to the most effica- show shifted NMR peak positions indicating they are partial cious TZDs) is correlated to ligand potency (Fig. 5D) and efficacy agonists, which is consistent with the idea that PPARγ displays (Fig. 5E) in the TR-FRET coactivator recruitment assay. basal transcriptional activity and can be further activated by There are a few limitations to our quantitative NMR chemical synthetic agonists (25). To assess this more directly, we per- shift analysis. For the 2 residues that display 2 receptor pop- formed our TR-FRET coactivator recruitment assay using a ulations in certain ligand-bound states (G399 and L400), we used single ligand concentration (5 μM) to assess ligand efficacy (Fig. most abundant receptor population (i.e., NMR chemical shift 6B). In contrast to the NMR experiments where the receptor values of the peak with the highest intensity) in the analysis, concentration is relatively high (200 μM) and ligands added rationalizing that this conformation may dominate in functional stoichiometrically result in ∼100% bound occupancy, in the TR- assays. In these cases, the less abundant population also falls FRET assay the receptor concentration is low (4 nM) and li- along the colinear shifting trajectory, indicating this conforma- gands titrated increase binding of the peptide (efficacy) in pro- tion may be more (e.g., CAY10638) or less (e.g., rosiglitazone, portion to their respective binding affinities. Because high troglitazone, pioglitazone, netoglitazone) functionally active than concentrations of excess ligand can result in compound pre- the most abundant conformation (SI Appendix, Fig. S7). Another cipitation or aggregation, we limited this analysis to PPARγ li- limitation is that for the bound occupancy of the lower affinity gands with affinities better than ∼1to2μM(SI Appendix, Fig. ligands will be lower (∼97 to 98%) than the higher affinity ligands S10) that would show appreciable complex formation under the (>99%) under the conditions we performed NMR, which could in TR-FRET assay conditions. For this ligand subset, we observed a principle affect the NMR peak positions and cause extra NMR correlation between TR-FRET coactivator recruitment efficacy signal broadening induced by the exchange between free and and colinear NMR peak shifting (Fig. 6C). However, for these bound states. However, no significant spectral changes are ob- ligands, there is a poor correlation between TR-FRET ligand served for CAY10638 between 1 and 4 molar equivalents (SI affinity and efficacy (Fig. 6D). For example, MRL24 is a partial Appendix,Fig.S8), suggesting that any effects caused by a 1 to agonist with a low TR-FRET efficacy value similar to GQ-16; 3% ligand-free population are likely minor. This analysis also however, MRL20 is much more potent than GQ-16 and is also assumes that the exchange rate (Kex) between the 2 states is far more potent than the superagonist GW1929, which has the BIOPHYSICS AND

larger than the chemical shift difference in all cases, and that the highest TR-FRET efficacy value. Thus, for the structurally di- COMPUTATIONAL BIOLOGY chemical shift values of the 2 exchanging states are not largely verse ligand set, these data show that the receptor conformation affected by the bound ligands. shifts from a ground state conformation (apo-form) toward an One interesting observation is that the NMR chemical shift active conformation in a manner that is correlated to ligand ef- values are proportional to the log EC50 values of the ligands as ficacy but independent of ligand affinity. opposed to the (nonlog) EC50 values. A similar relationship was noted in a study of the ligand dissociation mechanism of dihydrofolate Discussion reductase where a series of inhibitors displayed a log-linear relation- The objective of receptor theory is to understand and predict the ship between an NMR measured rate parameter with Ki and Koff relationship between ligand potency and functional efficacy. (46). In our study, the log-linear relationship could indicate there Theoretical receptor models, which are used to extract quanti- is a conformational exchange process associated with reaching tative information from functional activity studies on GPCRs, the ligand-bound active state that is not directly measured in ion channels, and enzyme-linked receptors, have evolved over our experiments, such as a competition between ligand bind- the years to account for new findings in membrane receptor ing to a transcriptionally active state with a transcriptionally function: the realization that GPCRs can affect multiple signal- repressive state. ing pathways and therefore have multiple distinct active states, the discovery of orthosteric vs. allosteric ligand binding sites, Ligand Efficacy Is Correlated to Receptor Active State within a Larger among others. Computer simulations are heavily used in the Set of Structurally Diverse Ligands. We wondered whether there development and assessment of theoretical receptor models by may be a correlation between ligand efficacy and receptor con- varying mathematical parameters to determine relationships formation within a different set of structurally diverse ligands. between ligand potency, functional efficacy, and other model We assessed 17 natural/endogenous and synthetic PPARγ li- system parameters. However, the manner in which the ligand gands not previously reported to span a range of graded tran- affects receptor conformation in these theoretical studies is a scriptional activation efficacy (SI Appendix, Fig. S9). Unlike the conceptual “black box”—there is no knowledge or description of TZD series, which share a common head group that interacts how ligand binding pose or chemical modifications influence li- with the helix 12 subpocket, this diverse ligand set contains dif- gand binding affinity, receptor conformation, and receptor ferent types of head groups and chemotypes, some capable of functional activity. To gain insight into this relationship, here we hydrogen bonding to the helix 12 subpocket and others lacking studied the influence of chemical modifications within a struc- such moieties that function primarily as partial agonists. Given turally related ligand series (TZDs) and a structural distinct li- the diversity of this ligand set, some of which were optimized for gand set using quantitative experimental approaches that enable high-affinity binding with low transcriptional efficacy by in- assessment of ligand potency, graded functional efficacy or ac- corporating different types of head groups, we initiated these tivity, and receptor conformation. studies understanding that ligand potency will not be correlated To our knowledge, theoretical receptor models have not been to efficacy as we observed in the more structurally conserved utilized or considered in studies of nuclear receptor transcription TZD series. factors, but our work here shows they are relevant. We used We compared 2D [1H,15N]-TROSY-HSQC NMR spectra of several quantitative functional assays that report on various 15N-labeled PPARγ LBD bound to each ligand within the struc- ligand-responsive biophysical and functional endpoints related to turally diverse set. Due to the diversity of chemotypes present in nuclear receptor function including direct physical readouts on this ligand set, there are larger differences in the NMR spectra the PPARγ LBD (ligand displacement and thermal stability as- compared to the TZD series. However, several residues with says) as well as coactivator affinity for and recruitment to the well-resolved NMR peaks that showed colinear shifting in the PPARγ LBD (TR-FRET and FP assays), the latter of which has

Shang et al. PNAS Latest Articles | 7of10 Downloaded by guest on October 1, 2021 A L311 G399 L400 B TR-FRET coactivator 120.2 recruitment assay GW1929 128.8 MRL20 108.0 GW1929 MRL20 5-oxoETE DHA SR1663 129.0 SR1663 Dodecanoic acid 120.6 108.4 MRL24 Oleic acid GQ-16 GQ-16 N (p.p.m.)

15 8.66 8.62 8.55 8.45 6.85 6.80 Linoleic Acid MRL24 nTZDpa 1H (p.p.m.) Arachidonic acid 01234 Decanoic acid Docosahexaenoic acid TR-FRET value (ligand efficacy) C L311 G399 L400 L401 Nonanoic acid R2 = 0.7857 R2 = 0.8549 R2 = 0.5767 R2 = 0.6009 R2 = 0.2841 Palmitoleic acid 0.04 0.12 0.08 0.04 D -5 BVT.13 Linoleic acid 0.03 0.06 0.03 -7 0.08 15-oxoETE 0.02 0.04 0.02 nTZDpa (log M) 0.04 i -9 0.01 0.02 0.01 K DMSO -11 0.00 0.00 0.00 0.00 NMR CSP 01234 01234 01234 01234 01234 TR-FRET value TR-FRET value

Fig. 6. NMR-detected correlations PPARγ LBD active state and efficacy of a structurally diverse ligand set. (A) Snapshots of [1H,15N]-TROSY-HSQC NMR spectra of 15N-labeled PPARγ LBD that show colinear NMR peak shifting for residues proximal to the AF-2 coactivator interaction surface. (B) TR-FRET assay to determine ligand efficacy for recruitment of TRAP220 coactivator peptide to PPARγ LBD at a single concentration of ligand (5 μM) fit to a sigmoidal dose– response equation; data represent the mean ± SD of experimental replicates (n = 4). In the legend, ligands with a filled circle correspond to ligands with

affinities (Ki values) better than ∼1to2μM (from SI Appendix, Fig. S10) that were included in the TR-FRET assay. (C) Plot of NMR chemical shift perturbation for each liganded state relative to the apo-protein state (n = 1) vs. ligand efficacy in the TR-FRET coactivator recruitment assay from B fit with a linear regression equation. (D) Binding affinity of ligands included in the TR-FRET and NMR cross-correlation analysis (SI Appendix,Fig.S10; n = 1) show poor correlation to TR-FRET efficacy. Ligands ordered and colored in the legend by the approximate degree to which the ligands shift the NMR peak in A relative to DMSO.

a direct relation to cellular transcription (luciferase assay). As Furthermore, within this diverse ligand set, there are many other predicted by receptor theory, we observed correlations between chemical variations in the tail groups that distinctly contribute to the various functional activity readouts for PPARγ agonists within affinity. Although these diverse ligands contradicted the potency– the structurally related TZD series. To relate the graded activities efficacy correlation predicted by receptor theory models, our of the TZD series to receptor conformation, we performed NMR analysis revealed a correlation between efficacy and graded structural analysis of ligand-bound PPARγ LBD complexes using activity conformation state for all ligands, independent of chemical X-ray crystallography and NMR spectroscopy. We did not observe structure. any apparent graded activity conformations in crystal structures of Related to our work here on nuclear receptors, NMR studies PPARγ LBD bound to the TZD series. However, NMR detected a have been used to define how ligand binding influences the graded shift in the conformational ensemble of the PPARγ LBD conformational ensemble of GPCRs in ways not detectable in toward an active conformation correlated to the degree of ligand ligand-bound crystal structures. 19F NMR studies of the GPCRs potency and efficacy, and therefore also correlated to the other func- β2-adrenergic receptor (β2AR) and adenosine A2 receptor (A2AR) tional activity readouts (ligand affinity, receptor stability, coactivator revealed that pharmacologically distinct classes of ligands or affinity). These NMR-observed structural findings are consistent nanobodies differentially stabilize the receptor conformational with underpinnings of allostery in receptor theory models of re- ensemble into G protein inactive and active states (48–54). We ceptor activation. We were unable to directly measure the binding similarly used 19F NMR to show that pharmacologically distinct kinetics of the TZD series due to apparent fast binding kinetics in PPARγ ligands differentially affect the conformation of the AF-2 surface plasmon resonance experiments, suggesting a 2-state helix 12, and that the conformations we observed are predictive of binding mechanism. However, assuming that all of the TZDs ligand efficacy (55). Furthermore, other work has revealed the display similar association rate constants, the notion that all of the existence of ligand-bound GPCR conformational states that exist functional efficacy and conformational data correlate to ligand in the continuum between the inactive/ground state and fully active affinity indicates that ligand residence times (i.e., ligand disso- – ciation kinetic rate constants) may be involved in the functional conformations previously captured in crystal structures (56 65). Among these, most related to our study here is an NMR analysis activities of the TZD series. β In contrast to our results with the structurally related TZD of backbone amide groups of valine residues in 1-adrenergic β agonist series, we found that the predicted relationship between receptor ( 1AR) bound to structurally diverse agonists and an- ligand potency and efficacy breaks down for structurally diverse tagonists (62), which observed a heterogenous conformational β endogenous and synthetic PPARγ agonists. On the structural response to the ligands. Within the structurally diverse 1AR ligand level, this can be rationalized because different ligand scaffolds set, there did not seem to be any relationship between ligand affinity or small chemical modifications result in different chemical and functional efficacy. The conformation of several residues within bonding patterns between ligand and receptor that can influence the extracellular ligand binding pocket were differentially correlated affinity distinct from functional efficacy. The TZD series have a to ligand affinity and ligand chemical composition, whereas the conserved head group capable of hydrogen bonding to residues conformation of a different group of residues on the intracellular within the helix 12 subpocket, whereas the varied length of tail side of transmembrane helix 5 (TM5) correlated to G-protein groups that contribute to ligand affinity are all structurally distal ligand efficacy independent of ligand affinity. This is similar to from the AF-2 surface. In contrast, the structurally distinct li- our finding here that structurally distinct PPARγ agonists stabilize gands possess different types of head groups, some capable of an active conformation to a degree correlated to their functional robustly stabilizing the AF-2 surface and others that are not. efficacy independent of ligand potency.

8of10 | www.pnas.org/cgi/doi/10.1073/pnas.1909016116 Shang et al. Downloaded by guest on October 1, 2021 Our findings raise a question as to whether theoretical re- ammonium chloride, as a tobacco etch virus-cleavable N-terminal His-tagged ceptor models can be updated to better predict the relationship (6×-His) fusion protein using a pET46 Ek/LIC vector (Novagen) and purified between ligand potency with functional efficacy and receptor using Ni-NTA affinity chromatography and gel filtration chromatography as conformation. This would not be a trivial task as it would require previously described (18). knowledge of ligand binding poses and a precise understanding of how small chemical modifications affect ligand affinity and Biochemical Experiments. TR-FRET, FP, biolayer interferometry, and differ- stabilization of functional surfaces. However, it is difficult if not ential scanning calorimetry experiments were performed as detailed in possible to predict how small chemical changes in ligand com- SI Appendix. position affect potency and efficacy (66, 67). Structural differences in how different ligand-binding proteins bind synthetic ligands Cellular Transactivation. The luciferase reporter assay was performed in HEK293T cells (ATCC CRL03216) as detailed in SI Appendix. could also differentially influence potency–efficacy relationships. In nuclear receptors, residues within the ligand-binding pocket that Crystallization and Structure Determination. Crystal structures of PPARγ bound contact the ligand are connected to nearby structural elements in to ciglitazone, troglitazone, mitoglitazone, CAY10506, and CAY10638 were the AF-2 coregulator binding surface. In contrast, the extracellular determined from data collected at Advanced Light Source Beamline 5.0.2 at ligand-binding pocket of GPCRs is structurally distant from the Berkeley Center for Structural Biology (Lawrence Berkeley National Laboratory) intracellular effector protein binding surface. It is therefore possible or an in-house MicroMax007 HF X-ray generator equipped with the mar345 that small chemical differences in nuclear receptor ligands, which detector as detailed in SI Appendix. could impact structural elements that are closer to functional surfaces, could impact affinity–function relationships differently NMR Spectroscopy. The 2D [1H,15N]-TROSY HSQC NMR data of 15N-labeled than membrane receptors. Nonetheless, our studies here on the PPARγ LBD were acquired at 298 K on a Bruker 700-MHz NMR instrument PPARγ nuclear receptor as well as the aforementioned studies equipped with a QCI cryoprobe and analyzed NMR chemical shift assign- on GPCRs show that NMR analysis can predict ligand efficacy ments previously reported for ligand-bound PPARγ (18) as detailed in SI and provide structural insight into the influence of ligands on Appendix. activity-related receptor conformational ensembles. Data Availability. Crystal structures generated in the current study have been Methods deposited in the PDB under accession codes 6DGL, 6DGO, 6DGQ, 6DGR, 6O67, Materials and Reagents. Ligands and peptides were purchased or synthesized and 6O68. NMR chemical shift assignments used in our analysis were obtained

from commercial vendors, or synthesized in-house. See SI Appendix for from the Biological Magnetic Resonance Data Bank (BMRB), http://www.bmrb. BIOPHYSICS AND full details. wisc.edu/ (ID codes 17975, 17976, and 17977). COMPUTATIONAL BIOLOGY

Protein Expression and Purification. Human PPARγ LBD (residues 203 to 477, ACKNOWLEDGMENTS. We thank Sarah Mosure and Paola Munoz-Tello for isoform 1 numbering) was expressed in Escherichia coli BL21(DE3) cells using helpful discussions. This work was supported in part by National Institutes of autoinduction ZY media, or M9 minimal media supplemented with 15N-labeled Health Grants R01DK101871 (to D.J.K.) and F32DK108442 (to R.B.).

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