Structural determinants of non-covalent PPARγ inverse agonism and their therapeutic implications

Domain: chemistry
Published: 2025-12-19
Editorial outcome: minor_revision

Abstract
Peroxisome proliferator-activated receptor gamma (PPARγ) is a validated therapeutic target for type 2 diabetes (T2D), but current FDA-approved agonists are limited by adverse effects. SR10171, a non-covalent partial inverse agonist with modest binding potency, improves insulin sensitivity in mice without bone loss or marrow adiposity. Here, we characterize a series of SR10171 analogs to define structure-function relationships using biochemical assays, hydrogen-deuterium exchange (HDX), and computational modeling. Analogs featuring flipped indole scaffolds with N-alkyl substitutions exhibited 10-to 100-fold enhanced binding to PPARγ while retaining inverse agonist activity. HDX and molecular dynamics simulations revealed that ligand-induced dynamics within ligand-binding pocket and AF2 domain correlate with enhanced receptor binding and differential repression. Lead analogs restored receptor activity in loss-of-function PPARγ variants and improved insulin sensitivity in adipocytes from a diabetic patient. These findings elucidate mechanisms of non-covalent PPARγ modulation establishing a framework for developing safer, next-generation insulin sensitizers for metabolic disease therapy.

Introduction
Insulin resistance is a defining feature of type 2 diabetes (T2D) and contributes to a broad spectrum of metabolic disorders, including metabolic dysfunction-associated steatohepatitis, cardiovascular disease and obesity. The pathogenesis of T2D involves progressive insulin resistance, which initially provokes compensatory hyperinsulinemia, followed by pancreatic β-cell dysfunction and eventual chronic hyperglycemia. This metabolic dysregulation substantially increases the risk of severe comorbidities such as cardiovascular disease, renal failure, osteoporosis, and stroke. Despite the availability of insulin-sensitizing agents, their clinical utility is limited by adverse effects, including increased risk of bone loss and heart failure, and reduced efficacy in patients with coexisting conditions like chronic kidney disease (CKD). Metformin, the first-line treatment for T2D, is often insufficient as monotherapy and contraindicated in patients with renal impairment. Thiazolidinediones (TZDs), such as rosiglitazone (Rosi) or pioglitazone, target the nuclear receptor peroxisome proliferator-activated receptor gamma (PPARγ) as agonists to improve insulin sensitivity, but are associated with plasma volume expansion, weight gain, bone loss and increased cardiovascular risk, and, in rare cases, increased bladder cancer risk. These limitations underscore the need for alternative PPARγ-targeted therapies that enhance insulin sensitivity while minimizing adverse effects.

PPARγ, which forms a heterodimer with retinoid X receptor alpha, regulates gene networks involved in adipogenesis, lipid metabolism, and insulin sensitivity. Dysregulated PPARγ signaling is implicated in insulin resistance, obesity, and T2D progression. PPARγ exists as two isoforms, PPARγ1 and PPARγ2, that differ by 28 N-terminal amino acids; PPARγ1 is broadly expressed, while PPARγ2 is adipocyte-specific. However, both isoforms share a modular architecture comprising an N-terminal activation function-1 domain, a DNA-binding domain, a hinge region, and a C-terminal ligand-binding domain (LBD) containing the activation function-2 (AF2) surface. Ligand binding to the LBD induces conformational change, particularly at the AF2 surface, to allosterically modulate coactivator and corepressor recruitment and transcriptional activity. As such, the LBD is the principal target for therapeutic modulation of PPARγ activity.

PPARγ ligands, like that observed with other nuclear receptors such as RORγ, fall into three categories: agonists that enhance basal receptor activity, antagonists that neutralize basal activity by blocking the effects of endogenous or exogenous ligands, and inverse agonists that suppress basal receptor activity. We previously reported that the insulin sensitizing efficacy of TZDs and the partial agonist MRL24, is correlated with the inhibition of serine 273 phosphorylation (pS273) on PPARγ, a CDK5-mediated post-translational modification that represses genes linked to insulin sensitivity. This finding inspired the development of PPARγ modulators that preserve insulin-sensitizing properties by blocking pS273 while minimizing agonism-associated adverse effects. Among these, non-covalent antagonists and inverse agonists emerged as a promising yet underexplored class, distinct from both agonists and covalent ligands. We previously described SR1664, a non-covalent antagonist with an improved therapeutic index but poor pharmacokinetics. Building on this scaffold, we conducted structure-activity relationship (SAR) studies and developed a series of compounds, including SR11023 (antagonist), SR10171 (partial inverse agonist), and SR10221 (full inverse agonist), each exhibiting improved drug metabolism and pharmacokinetic properties. Despite their structural similarity, and enhanced binding and repression potency, only antagonists and partial inverse agonists demonstrate antidiabetic efficacy. For example, SR10171 improved insulin sensitivity in diet-induced obese mice while preventing weight gain and bone loss, suggesting functional differences between partial and full inverse agonists. The bone-protective effect may partly reflect modest PPARα activation. PPARα is expressed in osteocytes and regulates bone microenvironment and systemic energy metabolism, promoting osteocyte health and contributing to anabolic bone remodeling. When combined with partial PPARγ inverse agonism that enhances insulin sensitization, this dual modulation may underlie the favorable metabolic and skeletal outcomes observed with SR10171. However, SR10171’s modest binding affinity limits its pharmacological potential.

We hypothesized that strategic modifications to the SR10171 scaffold could enhance binding affinity, enabling lower oral doses and improved in vivo efficacy. Yet the molecular mechanisms underlying non-covalent inverse agonism of PPARγ remain unclear. Agonists stabilize the AF2 surface via hydrogen bonding with Helix 12 (H12), promoting co-activator recruitment and an active conformation. Covalent inverse agonists, by contrast, reposition H12 into the ligand-binding pocket (LBP), favoring corepressor binding and a repressive conformation. Non-covalent inverse agonists are proposed to position H12 at the front of H3, disfavoring coactivator binding, and in the presence of corepressors, may reorient to the back of H3, leading to H12 disorder. These mechanisms have largely been inferred from co-crystal structures of PPARγ-LBD bound to SR10221, and the structural basis distinguishing partial from full inverse agonism remains undefined. Notably, SR10171 and SR10221, despite the chemical structural similarity, exhibit divergent pharmacological profiles, underscoring the need for deeper mechanistic insight to guide rational design of next-generation non-covalent inverse agonists.

In this study, we present structure-guided optimization of the SR10171 scaffold aimed at enhancing its binding affinity and therapeutic efficacy. We synthesized and characterized a series of SR10171 analogs featuring an inverted indole nitrogen orientation, a modification that unexpectedly improved receptor engagement. To elucidate the structure-function relationships underlying non-covalent inverse agonism, we employed a multidisciplinary approach combining analog synthesis, in vitro and in cellulo biochemical assays, hydrogen-deuterium exchange mass spectrometry (HDX-MS), computational modeling, molecular dynamics simulations (MDS), and functional evaluation of insulin sensitizing activity in murine and human adipocytes. Together, these findings advance the mechanistic understanding of PPARγ modulation and lay the foundation for developing safer and more effective next-generation insulin sensitizers.

Results
Biochemical analysis of SR10171 analogs
To further investigate structure activity relationships of the SR10171 and SR10221 series of PPARγ inverse agonists, the flipped-indole versions of these leads were synthesized (Fig. 1A). Resulting analogs were tested in a LanthaScreen™ TR-FRET-based competitive binding assay, where Rosi and SR10221 consistently demonstrated approximately threefold lower Kd values compared to SR10171 (Supplementary Fig. 2A, B and Supplementary Table 1). Compounds 1 and 2, the flipped indole analogs of SR10171 and SR10221, respectively, showed approximately 1.5-fold and five-fold improvements in binding affinity relative to their parental compounds, indicating that this scaffold modification enhances ligand binding to PPARγ. To determine whether the flipped indole scaffold retains key functional features of SR10171 (i.e., potent PPARγ inverse agonism while maintaining modest PPARα agonism), these analogs were evaluated in a Gal4-LBD:UAS-luciferase reporter co-transfection assay system. Gal4-VP16 was first included to assess cytotoxicity. As shown in Supplementary Fig. 2C, the flipped SR10171 scaffold is not toxic to cells at concentrations below 10 μΜ. Testing of these compounds on Gal4-PPARγ and Gal4-PPARα reveal that altering the indole nitrogen orientation slightly increasing agonism of PPARγ (Supplementary Fig. 2D) while partially reduced PPARα activation (Supplementary Fig. 2E) in a dose-dependent fashion relative to their parental compounds. As expected, compound-mediated agonism of PPARδ was not observed in the Gal4-PPARδ assay apart from the control compounds GW0742 (a potent PPARδ agonist) and GW7647 (a dual potent PPARα agonist and weak PPARδ agonist), suggesting that the SR10171 scaffold with the flipped indole orientation has minimal effects on PPARδ activity (Supplementary Fig. 2F). These results support the use of this indole configuration to develop third generation non-covalent PPARγ inverse agonists with improved binding affinity to PPARγ.

To expand the SAR, an additional 95 analogs of Compound 1 were synthesized modifying different portions of the molecule as highlighted in Fig. 1A and evaluated (Supplementary Tables 1–3). To assess their structural diversity, molecular fingerprints were generated using RDKit (https://www.rdkit.org), capturing circular substructures up to two bonds from each atom to encode topological features, including atom types, connectivity, and local chemical environment. Principal Component Analysis (PCA) of these fingerprints revealed four distinct compound clusters (Fig. 1B). Each cluster represents a group of analogs with similar molecular environments and potentially shared pharmacological properties, despite subtle differences in substitution patterns (Fig. 1A). All analogs were tested in a microsomal stability assay (human and murine) to exclude compounds with high intrinsic clearance. Functional profiling of using LanthaScreen and Gal4-LBD::UAS-luciferase reporter co-transfection assays was then performed to eliminate low-affinity binders and compounds exhibiting robust PPARγ agonism as described below.

Functional characterization of SR10171 analogs
Twenty-five compounds, including Compound 1 serving as a control compound, were selected for further evaluation (Supplementary Tables 1 and 3). Consistent with results for Compounds 1 and 2, these analogs exhibited lower IC50 values than SR10171 in the LanthaScreen, ranging from 10 to 500-fold, suggesting that the flipped indole orientation greatly enhances ligand binding affinity to PPARγ (Fig. 1C, Supplementary Table 1). Comparing reporter activity in Gal4 assays at 1 μΜ, Compounds 1, 2, 3, and 15 showed a slight but non-significant increase on PPARγ agonism compared to SR10171 (Fig. 1D), whereas SR10171 resulted in ~14% of PPARγ activation compared to that observed with Rosi. Compounds 3 and 4 showed similar PPARα agonism to SR10171 and SR10221 (Fig. 1E), whereas SR10171 led to ~45% activation of PPRAα compared to that observed with GW7647 treatment. As shown in Fig. 1F: Gal4-PPARα panel, the SR10171 analogs differentially enhanced PPARα activity, demonstrating partial PPARα agonism at higher concentrations. While comparing to Rosi (Fig. 1F: Gal4-PPARγ panel), all 25 analogs demonstrated weak or modest PPARγ activity at all concentrations, indicating a lack of PPARγ agonistic activity. As expected, these analogs did not enhance PPARδ activity (Fig. 1F: Gal4-PPARδ panel and Supplementary Fig. 3A). In addition, minimal cytotoxicity was only observed for some analogs at 10 μΜ and above in the Gal4-VP16 assay (Fig. 1F: Gal4-VP16 panel), suggesting that 1 μΜ is a reasonable concentration to evaluate compounds in subsequent in vitro pharmacology experiments. The above results indicate that these analogs target PPARγ with weak agonism of PPARα like that observed with SR10171. Compound 1 was removed after the primary screens due to its higher Kd than Rosi.

The top 24 analogs were tested in the AlphaScreen assay for their ability to alter PPARγ interaction with peptides representative of NR coregulators, including SRC1-2 and NCoR (D2) that are representative of nuclear receptor coactivator 1 and nuclear receptor corepressor 2, respectively. As expected, the PPARγ agonist Rosi enhanced interaction with the coactivator SRC1-2 peptide while decreasing interaction with the corepressor peptide NCoR (D2), while the inverse agonists SR10171, SR10221, and T0070907 had opposing effects to Rosi (Supplementary Fig. 3B). Similarly, all SR10171 analogs decreased SRC1-2 binding and increased NCoR (D2) interaction with PPARγ (Fig. 1F, AS-SRC1 and AS-NCoR) with few exceptions. Prior studies demonstrate that insulin-sensitizing ligands binding to PPARγ reduced CDK5-dependent pS273 within the receptor’s LBD. Using an in vitro phosphorylation assay, analogs reduced phosphorylation of PPARγ-LBD (Fig. 1F (pS273) and Supplementary Fig. 3C). Collectively, these functional characteristics confirmed the SR10171-like properties of these analogs in modulating PPARγ-LBD dynamics are reflected in transcriptional repression, distinct coregulator interactions, and decreased CDK5-mediated phosphorylation.

To confirm analog activity in the context of full-length (FL) PPARγ, compounds were tested in the following two functional assays. First, a peroxisome proliferator response element (PPRE) promoter::reporter assay was used to determine compound inverse agonism efficacy. Second, given the essential role of PPARγ in adipogenesis, a well-established 3T3-L1 preadipocyte cell model was used to evaluate the impact of SR10171 analogs on fat cell differentiation. Analogs were first tested for cytotoxicity in cells at 1 μΜ concentration for 24-h where all analogs were devoid of activity (Supplementary Fig. 4A). The PPRE::reporter assay revealed that all analogs differentially reduced PPARγ transcriptional activity at 1 μΜ (Fig. 2A). When 3T3-L1 cells were differentiated in the presence of 1 μM of each compound, reduced expression of pro-adipogenic genes, including CEBPA (Supplementary Fig. 4B) and FABP4 (Fig. 2B) was observed as well as a reduction in lipid droplet formation (Fig. 2C, D and Supplementary Fig. 4C) with a few exceptions.

Using results from the above assays, hierarchical clustering analysis classified the analogs into three groups based on their pharmacological profile (Fig. 2E). The first group (non-IA, green) clustered with SR10171 and exhibited partial agonism, antagonism, or weak partial inverse agonism on FL-PPARγ. The second group (partial-IA, blue) resulted in higher affinity of PPARγ for the co-repressor peptide (NCoR (D2)) while further repressing FL-PPARγ activity in cell-based assays as compared to SR10171. The third group (full-IA, SR10221-like compounds) blocked both PPARγ-LBD Gal4-UAS and FL-PPARγ PPRE reporter activity, and repressed L1 cell adipogenesis. This clustering analysis reveal how small structural modifications to ligands can distinctly influence the regulatory behavior of PPARγ. To determine if distinct regulation of PPARγ by analogs is driven by binding potency or chemical structure, pairwise correlation analysis was performed across the different assays. This analysis revealed that PPARγ transcription activity significantly correlated to differential preference for coregulator peptide interaction and adipogenesis, but the binding potency (IC50 determined using LanthaScreen) is poorly correlated with functional outcomes (Supplementary Fig. 5). Comparisons of clustering based on structure and functional profiles revealed similar groupings (Fig. 2E), highlighting a clear link between chemical motifs and biochemical properties.

HDX characterization of the conformational dynamics of PPARγ-LBD complexed with inverse agonists
Previous HDX studies of the PPARγ-LBD suggest that two on-exchange timepoints, an early 10 s and late 4 h timepoints, are sufficient to distinguish differential receptor-ligand dynamics. Accordingly, a two-timepoint HDX-MS screen was employed to detect perturbations in rapid and sustained protection from solvent exchange modulated by ligand binding. Heatmap clustering analysis of the screen results revealed that PPARγ-LBD complexes with SR10171 analogs exhibited conformational dynamics similar to those observed with SR10171- and SR10221-bound complexes (Fig. 3A), suggesting broadly conserved receptor-ligand conformations. The 10 s HDX data, representative of highly dynamic solvent exchange events, revealed significant reduction in deuterium uptake was primarily localized to β-sheet 1 (β1), H2b, and H10-12. These regions are situated on the surface of the LBP opposite the dimerization interface. Notably, compounds in the top cluster in the 10 s HDX panel (Fig. 3A), classified as full-IA (purple), showed increased deuterium uptake at H12 (activation helix), suggesting that these full-IAs destabilize H12, promoting an inactive receptor conformation. Conversely, at 4 h, differential protection from deuterium uptake was limited to regions of the receptor that include H2a to β1, H3, and β2–3 to H7. These regions surround the surface of the LBP near the dimerization interface and are likely stabilized by H-bonding and steric shielding by the bound ligand.

In the HDX dataset, individual peptides are proxies for regions within the folded intact receptor. To identify regions of the receptor LBD that exhibit coupled solvent exchange behavior, covariation analysis was conducted. This analysis suggests that ligand-binding induced LBD dynamics are primarily coordinated within regions spanning H2-H3, H5-H7, and H10-H12 (Supplementary Fig. 6). To help elucidate the mechanism by which compounds modulate PPARγ activity, Pearson correlation analysis was performed with the HDX-MS data and the results from the cell-based assays. This analysis identified 12 peptides whose deuterium uptake significantly correlated (p < 0.05) with the repression of PPARγ transcriptional activity, and 11 peptides correlated with FABP4 expression, a direct PPARγ target gene. Mapping the correlation coefficients onto the PPARγ-LBD structure suggests distinct regional trends (Fig. 3B). There was reduced deuterium uptake in regions of the LBD encompassing H3, particularly the region including FRSVE (Residues 315–319), and the β2/β3 strands with extension towards H7 strongly correlated with enhanced repression of PPARγ activity measured in the PPRE::reporter assay (Fig. 3B, top panel; Fig. 3C). Conversely, the increased deuterium uptake within H12 correlated with increased transcriptional repression. These findings were further supported by correlation analysis of expression levels of FABP4 (Fig. 3B, bottom panel; Fig. 3D) and CEBPA (Supplementary Fig. 7) measured during the early stage of adipogenesis. Collectively, these results demonstrate that stabilization of the LBP coupled with increased flexibility in H12 induced by non-covalent inverse agonists, plays a pivotal role in attenuating PPARγ transcriptional activity.

Integrated computational modeling of ligand-induced PPARγ conformational dynamics
To investigate the binding mechanisms of non-covalent inverse agonists, molecular docking was employed to predict ligand binding poses (Supplementary Table 4), followed by MDS to explore protein-ligand interactions. The resulting complexes were generally stable throughout the simulations according to backbone root mean square deviation (RMSD) (Supplementary Fig. 8A). Notably, analogs bearing bulky hydrophobic substitutions on the indole ring exhibited significantly lower total free energies (approximate −70 kcal/mol) compared to SR10171 (approximate −50 kcal/mol), as shown in Supplementary Fig. 8B. These findings suggest that such substitutions enhance stabilization of ligand-induced inactive conformation of the receptor LBD. To further dissect the energetic landscape of ligand binding, residue-level free energy distributions were visualized using heatmap clustering analysis (Fig. 4A). Notably, the N-terminal half of H3 exhibited lower free energy values, indicating enhanced stabilization upon ligand binding. This effect was particularly pronounced at the FRSVE motif, which also showed strong protection from deuterium uptake and correlated significantly with PPARγ repression (Fig. 3), implicating R316 and S317 as key interaction hotspots within H3. In contrast, the C-terminal half of H3 displayed higher free energy, suggesting increased flexibility or weaker hydrophobic interactions in this region. These receptor-level structural features align with the ligand-induced conformational dynamics observed in Fig. 3B, collectively highlighting the structural basis of PPARγ repression. Additionally, several residues, including the polar S370 and hydrophobic residues C313, I354, L358, and I369, consistently exhibited low total free energy values (Fig. 4A), underscoring their likely roles in stabilizing the ligand-induced inactive conformation of the PPARγ-LBD in response to non-covalent inverse agonists.

The centroid structure of the top cluster from GROMOS clustering analysis using backbone RMSD was extracted as the representative conformation for each ligand-bound PPARγ-LBD complex. The binding pose of SR10171 closely resembled that of SR10221 in the original crystal structure, with the indole core positioned behind H3, forming hydrophobic interactions with R316 (H3), I354 and L358 (H5) (Fig. 4B and Supplementary Fig. 9A). The 4-tert-butyl-phenyl moiety engaged additional hydrophobic contacts with AF2 interface residues V318, V321, and V350, while the dimethyl groups and phenyl ring interacted with C313 and I369. These hydrophobic interactions were complemented by potential hydrogen bonds between the amide group of SR10171 and S317 (2.9 Ǻ), and between the acidic tail and S370. While SR10221 exhibits a representative binding pose with a rotated 4-tert-butyl-phenyl group (Fig. 4C and Supplementary Fig. 9B), its indole core maintains a similar position within the binding site (Supplementary Fig. 8C). Analysis of Compound 2, a flipped-indole analog of SR10221, reveals a binding pose nearly identical to SR10171 (Fig. 4D and Supplementary Fig. 9C). Notably, this flipped orientation of the indole places the nitrogen atom near the guanidinium group of R316, reducing the distance to 6.7 Ǻ in Compound 2 and further to 5.5 Ǻ in Compound 23 with an N-cyclobutyl substituent (Fig. 4E and Supplementary Fig. 9D). This proximity suggests a potential enhancement of the cation-π interaction between the indole nitrogen and R316, which likely reinforces the polar interaction between the ligand amide and S317, thereby stabilizing the LBP. A similar binding mode is observed for Compound 4, despite the absence of the methyl group on the indole (Supplementary Figs. 8D and 9E). Collectively, these results highlight the role of the alkyl substitution at the indole in enhancing hydrophobic contacts with H5, while the position of the indole nitrogen relative to R316 stabilizes the inactive conformation, likely via cation-π or electrostatic interactions.

To evaluate the conformational dynamics of SR10171 analog-bound LBD complexes during simulations, Dynamic Cross-Correlation Matrices (DCCM) analysis was used to representative simulation clusters, followed by PCA to delineate dominant modes of conformational variability across the ligand-bound and apo ensemble. As shown in Fig. 4F, full-IAs form a distinct conformational cluster, separated from the apo LBD with minimal overlap. This suggests that this unique conformational state underlies their ability to fully repress PPARγ. In contrast, the non- and partial-IA compounds exhibit a more dispersed distribution in conformational space, overlapping with both the full-IA cluster and the apo LBD. This indicates greater conformational flexibility, which likely correlates with their neutral antagonism or partial inverse agonism profiles.

Validation of structure-function relationships of inverse agonists
To validate the structural basis for inverse agonism (as proposed in Fig. 3B) and assess the therapeutic potential of inverse agonists, five PPARγ mutants (A261E, S273A/D, F310A, F388L, and Y501A) were generated. These residues were selected based on their known implications in LBD stability, ligand-mediated transcriptional regulation, and association with metabolic diseases, including lipodystrophy (Fig. 5A). Protein expression level of the mutant receptors was confirmed by immunoblotting and was found to be comparable to that of wild-type (WT) PPARγ, with the exception of the A261E mutant receptor, which exhibited modestly reduced expression (Supplementary Fig. 10A, B). These results demonstrate that all mutant receptors were expressed at levels sufficient for functional characterization using PPRE::reporter assays.

Consistent with previous findings that Y501 mediates hydrogen bonding between PPARγ agonists and the LBD, the Y501A mutation impaired both Rosi-induced agonism and inverse agonism by T0070907 (Fig. 5B). Strikingly, this mutation had no effect on the activity of non-covalent inverse agonists, suggesting that these ligands stabilize the LBD through a mechanism independent of the canonical AF2 hydrogen-bonding network involving H12. The loop region following H2a contains S273, a residue whose phosphorylation has been implicated in osteogenesis and adipogenesis. However, neither S273A nor S273D mutations affected the inverse agonist activity of SR10171 analogs (Supplementary Fig. 10C, D), indicating that their binding and functional effects are not structurally dependent on this loop region.

Mutation of F310 to smaller or charged residues has been shown to reduce basal PPARγ activity and alter ligand pharmacology. Consistent with this, the F310A mutation significantly shifted the pharmacological response of non-covalent inverse agonists from inverse agonism to agonism relative to DMSO (Fig. 5C). This further support that non-covalent inverse agonists engage with the central region of H3 and can stabilize the LBD even when hydrophobic interactions at the base of the pocket are disrupted. In addition, the A261E mutation, associated with familial partial lipodystrophy type 3 (FPLD3), is a loss-of-function variant that destabilizes the LBD. Although reduced expression of A261E-PPARγ may contribute to its lower basal activity compared to wild-type (Supplementary Fig. 10A, B), both non- and partial-IAs substantially enhance receptor activity relative to DMSO (Fig. 5D). This indicates that these ligands can restabilize the mutant LBD, even in the absence of proper H2a positioning. Similarly, the F388L mutation, another FPLD3-associated variant that disrupts van der Waals interactions and destabilizes the H11-H12 interface, converted the response of non- and partial-IAs from inverse agonism to agonism (Fig. 5E). This suggests that these ligands can also stabilize the AF2 domain, compensating for structural deficiencies caused by the mutation.

These findings support the proposed SAR, indicating that non-covalent inverse agonists primarily regulate PPARγ activity through stabilization of the LBP, with minimal reliance on interactions with H12 and the H2-3 loop. Moreover, their ability to restore activity in destabilized receptor mutants suggests that LBP stabilization is sufficient to restore function, independent of canonical activation mechanisms.

Evaluation of insulin sensitization by PPARγ inverse agonists
Given that the SR10171 analogs exhibited similar pharmacological profiles to the parent compound, their ability to enhance insulin sensitivity was evaluated. L1-derived adipocytes were used as a model system to assess the compounds’ effects on adipokine expression, adiponectin secretion, and glucose uptake at 1 μM compound concentration both in the absence and presence of TNFα, a well-established inducer of insulin resistance in vitro. Two sequential treatment regimens were tested (Fig. 6A); (1) Compound treatment first for 24-h followed by 6-h exposure to TNFα (Cpds-TNFα), and 2) TNFα treatment first for 6-h followed by 24-h exposure to compound (TNFα-Cpds). Although not statistically significant, hierarchical clustering revealed that non- and partial-IAs grouped with Rosi and SR10171, showing enhanced adipokine expression and glucose uptake in TNFα-treated adipocytes, suggesting potential insulin sensitizing effects. Based on favorable binding affinity and microsomal stability (Supplementary Table 1), three non-IAs and six partial-IAs were selected for further evaluation in both healthy and type 2 diabetic human adipocytes. Two full-IAs were included as negative controls, as they are expected to fully repress PPARγ activity and lack insulin-sensitizing potential.

In healthy human adipocytes, insulin significantly increased glucose uptake compared to the no-insulin control (Fig. 6B), but no additional enhancement was observed by compound treatment relative to DMSO. In contrast, in diabetic adipocytes, SR10171, Rosi and four potent SR10171 analogs (two non-IAs and two partial-IAs) significantly enhanced insulin-stimulated glucose uptake compared to DMSO (Fig. 6C). Additionally, Rosi, non-IAs and partial-IAs significantly increased adiponectin secretion in healthy adipocytes relative to DMSO (Fig. 6D). Although variability within the DMSO control group limited statistical power in diabetic adipocytes, non-IAs still showed a ~10–20% increase in adiponectin secretion, with Compound 13 inducing a significant 30% increase (Fig. 6E). These results confirm that non- and partial-IAs can enhance insulin responsiveness in diabetic adipocytes by promoting both glucose uptake and adipokine expression, such as adiponectin. Notably, full-IAs significantly reduced insulin responsiveness in both healthy and diabetic adipocytes and had minimal effects on adiponectin secretion (Fig. 6B–E), reinforcing the conclusion that complete repression of PPARγ activity does not support insulin sensitization.

Methods
Cell culture
The 3T3-L1 (CL-173) and HEK293T (CRL-3216) cell lines were obtained from the American Type Culture Collection (ATCC). Cells were maintained in Dulbecco’s Modified Eagle Medium (DMEM; 10-013-CV, Corning) supplemented with 10% fetal bovine serum (FBS; F0926, Sigma-Aldrich) and MycoZap™ Plus-LC antibiotic (VZA-2012, Lonza). Cultures were incubated at 37°C in a humidified atmosphere containing 5% CO₂. Routine mycoplasma testing was performed every three months using the MycoStrip™ detection kit (rep-mys-100, InvivoGen). Cells were passaged according to ATCC recommendations and maintained under standardized culture conditions. Primary human subcutaneous preadipocytes from healthy (PT5020 (Batch No. 0000629514), from a 60-year-old male with BMI of 30) and type-II-diabetic (PT5022 (Batch No. 0F3815), from a 75-year-old male) donors were obtained from Lonza. Cells were maintained in preadipocyte growth medium-2 (PT-8202, Lonza) supplemented by SingleQuotsTM kit (PT-9502, Lonza) according to the manufacturing instructions.

Protein expression and purification
The pET46-EkLIC-hPPARγ-LBD (PPARγ2 residues 231–505) construct was expressed in the Escherichia coli strain BL21 DE3 (Gold) competent cells (Invitrogen). Cultures were grown in terrific broth supplemented with Carbenicillin (100 µg/L) in a temperature-controlled orbital shaker (Innova) operating at 225 RPM at 37°C. Once the culture reached an optical density (OD610) of 0.6, the temperature of the incubation chamber was lowered to 16°C, and IPTG was added to a final concentration of 1 mM. The cultures were then incubated for 16 h before being harvested by centrifugation at 4°C. Cell pellets were resuspended in ice-cold phosphate-buffered saline containing protease inhibitors (EDTA-free SigmaFast, Sigma-Aldrich) prior to subsequent harvesting and storage at −80°C. Unless otherwise stated, all protein purification steps were carried out at 4°C. Cell pellets were resuspended in resuspension buffer (50 mM Tris pH 8.0, 150 mM Sodium Chloride (NaCl, S9888, Sigma-Aldrich), 10% (v/v) glycerol (BP229-4, ThermoFisher Scientific), 2 mM tris(2-carboxyethyl)phosphine (TCEP, 50-093-0, Hampton Research), and 40 mM imidazole), supplemented with protease inhibitors, DNase, and lysozyme. Cells were lysed using a French press operating at 20,000 psi. The crude cell lysate was clarified by centrifugation (20,000 rpm for 45 min), and the supernatant was loaded onto Ni-NTA resin pre-equilibrated with Ni-NTA buffer A (20 mM Tris pH 8.0, 500 mM NaCl, 2 mM TCEP, and 40 mM imidazole) using an AKTA protein purification system. Ni-NTA resin was washed with 20 column volumes of buffer A, and the protein was eluted using a continuous imidazole gradient with buffer B (20 mM Tris pH 8.0, 500 mM NaCl, 2 mM TCEP, and 500 mM imidazole). The Ni-NTA eluate was concentrated using Amicon Ultra 10 kDa molecular weight cutoff centrifugation tubes and exchanged into storage buffer (50 mM Tris pH 8.0, 500 mM NaCl, 10% (v/v) glycerol, and 2 mM TCEP). The protein product was verified using SDS-PAGE gel followed by Coomassie blue staining and immunoblotting.

LanthaScreen TR-FRET competitive binding assay
The assays were conducted according to the manufacturer’s manual (Life Technologies Corporation, PV4894). Briefly, 10 μL of 4× compound or vehicle control solutions were dispensed into a black 384-well low-volume plate (Greiner Bio-One), followed by the addition of 5 μL of 4× Fluormone solutions. Subsequently, 5 μL of a 4× solution containing His-hPPARγ-LBD and Goat-anti-His antibody was added. The mixture was then incubated at room temperature for five hours before being read on an Infinite M1000 plate reader (Tecan). The IC50 values of each compound were then calculated and used to estimate the dissociation constant (Kd). We applied the Cheng-Prusoff equation, using the formula: Kd = (IC50 × 10⁹)/(1+[L]/KdFluormone), where [L] is the concentration of the fluorescent ligand (5 nM) and KdFluormone is the reported dissociation constant of the tracer (2.8 nM).

AlphaScreen peptide interaction assay
The Amplified Luminescent Proximity Homogeneous Assay (AlphaScreen) assay utilized streptavidin-coated donor beads (Perkin Elmer, 6760002) and anti-His-tag antibody-coated AlphaLisa acceptor beads (Perkin Elmer, AL178M). They demonstrated high sensitivity to the proximity of the N-terminus to the LXXLL NR-box motif and the incorporation of a flexible Ahx linker. All reagents were prepared in assay buffer (25 mM HEPES pH 7.4, 50 mM KCl, 5 mM DTT, 0.01% NP40). In brief, 5 μL of a 400 nM solution of His-PPARγ LBD was dispensed into a black 384-well low-volume plate (Greiner Bio-One), followed by the addition of 5 μL of 4× compound or vehicle control solutions. Subsequently, 5 μL of a 400 nM representative coactivator peptide biotin-Ahx-SRC1-2 (sequence: LTERHKILHRLLQEGSPSD) or a 400 nM representative corepressor peptide biotin-NCoR-D2 (sequence: DPASNLGLEDIIRKALMGSFDDK) was added, followed by another 5 μL of 4× bead cocktail. The bead mixture was incubated at room temperature for one hour before being read on an EnVision plate reader (Perkin Elmer).

In vitro phosphorylation assay
The assay was conducted using the ADP-Glo™ Kinase Assay kit from Promega. Reagents were prepared in kinase buffer (50 mM Tris, 25 mM MgCl2 (M2670, Sigma-Aldrich), 50 mM DTT, pH=7.5). CDK5/p35 (C0745, Sigma-Aldrich) was incubated with either DMSO or the CDK5 inhibitor 20-223 (865317-30-2, MedChemExpress) for 1 h at 4°C. Purified His-hPPARγ-LBD was also incubated with DMSO or the compounds for 1 h at 4°C. Subsequently, the CDK5/p35 solutions were mixed with the PPARγ-LBD solutions and incubated at 4°C for another 2 h. Following this, 10 μM ATP was added, and the phosphorylation reaction was carried out in a thermocycler (30°C for 2 min, 95°C for 30 s, cooldown). ADP-Glo™ Reagent was then added, and the reaction was incubated at room temperature for 40 min. Detection reagent was then added and incubated in the dark at room temperature for an additional 40 min. Luminescence signal was recorded using the Infinite M1000 plate reader (Tecan).

GAL4-LBD/UAS promoter::Luciferase reporter cotransfection assay
HEK293T cells were plated 3.3 × 106 cells per 10-cm plate. After 24 h, cells were transfected with 8 μg total DNA (1:1 ratio of Gal4-PPAR plasmids or Gal4-VP16 plasmids to UAS-reporter plasmid) using polyethylenimine (PEI). After 24 h of incubation, cells were plated in 384-well plate at density of 10,000 cells/well, incubated 3 h, and treated with compounds or DMSO control for 18 h. Luciferase activity was quantified using Britelite Plus (PerkinElmer). Absorbance at 490 nm was measured using Agilent BioTek Synergy Neo2 Hybrid Multi-Mode Microplate Reader.

Hepatic microsomal stability
Microsome stability was evaluated by incubating 1 µM test compound with 1 mg/mL hepatic microsomes in 100 mM potassium phosphate buffer, pH 7.4. The reaction was initiated by adding NADPH (1 mM final concentration). Aliquots were removed at 0, 5, 10, 20, 40, and 60 min and added to acetonitrile (5× v:v) to stop the reaction and precipitate the protein. NADPH dependence of the reaction was evaluated with -NADPH samples. At the end of the assay, the samples were centrifuged through a Millipore Multiscreen Solvinter 0.45-micron low-binding PTFE hydrophilic filter plate and analyzed by LC-MS/MS. Data were log-transformed and represented as half-life and intrinsic clearance.

PPARγ2 transcription reporter activity assay
HEK293T cells were plated 3.3 × 106 cells per 10-cm plate. After 24 h, cells were transfected with 8 μg total DNA (1:1 ratio of PPARγ2 plasmids to PPRE reporter plasmid) using PEI. After incubating for 24 h, cells were plated in 384-well plate at a density of 10,000 cells/well, incubated 3 h, and treated with either DMSO or 1 μΜ compound for 18 h. Luciferase activity was quantified using Britelite Plus (PerkinElmer), followed by recorded using the Infinite M1000 plate reader (Tecan).

Cell viability assay
HEK293T cells were transfected under the same condition used for the reporter assays. After 24 h, cells were plated in 384-well plate at 10,000 cells/well and incubated 3 h. Cells were then treated with either DMSO or 1 μΜ compound for 18 h. Following treatment, MTS reagent (G5430, Promega) was added and incubated for 2 h. Absorbance was measured at 490 nm using the Infinite M1000 plate reader (Tecan).

Adipocyte differentiation
After 4 days of reaching confluence, 3T3-L1 cells underwent induction by replacing the media with differentiation media containing 1 μM dexamethasone, 0.5 mM IBMX, and 850 nM insulin. After 48 h, media was replaced with DMEM containing 10% FBS plus insulin. Then, the medium was replaced every 2 days until the assay date. Human pre-adipocytes were differentiated based on the manufacturing instructions. On day 7 post differentiation, cells were replaced with DMEM (1 g/mL glucose) supplemented with 0.2% BSA in the absence or presence of compounds for 24 h.

Oil red staining
Following the medium removal, cells were rinsed twice with PBS. Subsequently, the cells were fixed in 10% formalin at room temperature for 1 h, followed by two washes with water. The cells were then rinsed twice with 60% isopropanol and incubated in 60% isopropanol for 5 min. Freshly prepared oil red solution was added to the wells and incubated at room temperature for 20 min. Cells were then washed twice with 70% ethanol, followed by two washes with water. The samples were then immersed in water for microscope imaging using the GE Healthcare IN Cell Analyzer 6000 High-Content Imaging System. After imaging, the samples were allowed to air dry overnight. The following day, 100% isopropanol was added to the samples and incubated at room temperature for 10 min. Absorbance at 490 nm was measured using Agilent BioTek Synergy Neo2 Hybrid Multi-Mode Microplate Reader.

Quantitative RT-PCR
Total RNA was extracted from 3T3-L1 cells using RNeasy Plus Micro Kit (Qiagen), and the RNA was reverse transcribed using the ABI reverse transcription kit (Applied Biosystems/Thermo Fisher Scientific, Waltham, MA). Quantitative PCR was performed with a 7900HT Fast Real-Time PCR System (Applied Biosystems) using SYBR green (Roche). A list of primers used for these studies is shown in Supplementary Table 5.

Immunoblotting
Proteins were extracted from transiently transfected HEK293T cell pellets using RIPA lysis and extraction buffer (Thermo Fisher Scientific), supplemented with Halt™ Protease and Phosphatase Inhibitor Cocktail (100X, Thermo Fisher Scientific). Protein concentrations were determined using the BCA Protein Assay Kit (Thermo Fisher Scientific) before equal amounts of samples heated at 95°C for 5 min with 6× SDS-PAGE loading buffer with 40 mM DTT. SDS-PAGE was performed with 15 µL of cell lysate. Following transfer (BioRad Trans-turbo) and blocking the membrane using PBS containing 3% BSA, membranes were immunoblotted using primary antibodies (anti-PPARγ (rabbit polyclonal, D69, #2430, Cell Signaling Technology, 1:1000) and anti-β-actin (rabbit monoclonal, 13E5, #4970, Cell Signaling Technology, 1:2000)) in PBS plus 0.2% Tween 20 overnight at 4°C. Membranes were washed 3 times with PBS plus 0.1% Tween 20, treated with secondary antibodies (Li-COR, anti-rabbit 926-32211, anti-mouse 926-68070, 1:10000) in PBS plus 0.2% Tween 20 at room temperature for 1 h and imaged (LI-COR Odyssey). Fluorescence signal was quantified using ImageJ software.

Glucose uptake assay
Glucose uptake was measured using the Glucose Uptake-Glo™ Assay kit (Promega) following the manufacturer’s instructions. Briefly, cells were washed and equilibrated in Krebs-Ringer phosphate (KRP) buffer (130 mM NaCl, 5 mM KCl, 1.3 mM CaCl₂, 1.3 mM MgSO₄, and 10 mM sodium phosphate buffer, pH 7.4) for 20 min at 37°C. Cells were then stimulated either with 0 or with 100 nM insulin for 10 min. 2-deoxy-D-glucose (2DG) was added and incubated for an additional 10 min at room temperature to allow uptake. After several additions of buffer, luminescence was measured according to the assay protocol using the Infinite M1000 microplate reader (Tecan). All samples were processed in parallel, and data were normalized to control conditions where indicated. For primary human adipocytes, cells were equilibrated in KRP buffer supplemented with 0.2% BSA, followed by 0 or 100 nM insulin treatment for 1 h before 2DG was added.

Adiponectin ELISA
Cell culture media were collected and centrifuged at 500 × g for 10 min at 4°C to remove cellular debris. The resulting supernatants were stored at −80°C until analysis. Secreted Adiponectin concentrations from human primary adipocytes and 3T3-L1-derived adipocytes were measured using the Adiponectin/Acrp30 Quantikine ELISA kits specific for human (DRP300) and mouse (MRP300) samples (Bio-Techne Corporation) according to the manufacturer’s instructions. Absorbance was read at 450 nm with a 570 nm reference wavelength using an Infinite M1000 microplate reader (Tecan).

Hydrogen-deuterium exchange mass spectrometry (HDX-MS)
Differential HDX-MS experiments were conducted as previously described with a few modifications. Differential HDX-MS experiments that reported here are deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD066095. Experimental details are summarized in Supplementary Table 6.

Peptide identification. Protein samples were subjected to inline pepsin digestion, and the resulting peptides were analyzed by tandem MS/MS using a high-resolution Orbitrap mass spectrometer (QExactive, Thermo Fisher Scientific, San Jose, CA). Following digestion, peptides were desalted on a C8 trap column and separated using a 70-min linear gradient from 5% to 40% solvent B (solvent A: 0.3% formic acid in water; solvent B: 0.3% formic acid in 95% acetonitrile). MS data were acquired in data-dependent acquisition mode with a one-second duty cycle, during which the five most abundant precursor ions were selected for higher-energy collisional dissociation MS/MS analysis. After fragmentation, each precursor ion was excluded from reselection for 16 s. The resulting MS/MS spectra were searched using Mascot (Matrix Science) with the following parameters: precursor ion mass tolerance ±10 ppm, fragment ion mass tolerance ±0.6 Da, variable modification of methionine oxidation, non-specific digestion allowing up to four missed cleavages. Peptide identifications were filtered to retain only those with a Mascot score > 20 and false discovery rate < 1%. Manual inspection of MS/MS spectra was performed to validate peptide assignments. Duplicate charge states were retained for each peptide, and decoy database searches were used to eliminate ambiguous identifications. Only unambiguous peptides were included in the final peptide set for HDX analysis.

HDX-MS analysis. Experiments were carried out on a fully automated system (CTC HTS PAL, LEAP Technologies, Carrboro, NC; housed inside a 4°C cabinet) as previously described with the following modifications: For differential HDX, PPARγ-LBD-ligand complexes were formed by incubating PPARγ-LBD (10 µM) with compounds (50 µM) for 1 h at 4°C. Next, 5 μL of sample was diluted into 20 μL D2O buffer (50 mM Tris pH 8.0, 500 mM NaCl, and 2 mM TCEP) and incubated for three timepoints (0, 10, and 14400 s (4 h)) at 4°C for continuous labeling. The deuterium exchange was then quenched by mixing with 25 L of cold (4°C) 3 M urea (51456, Sigma-Aldrich) and 1% trifluoroacetic acid (TFA, A116, Fisher Chemical). Quenched samples were immediately injected into the HDX platform. Upon injection, samples were passed through a 2 mm × 2 cm immobilized pepsin column at 50 μL min−1 (0.1% (v/v) TFA) and the digested peptides were captured on a 2 mm × 1 cm C8 trap column (Hypersil Gold, Thermo Fisher Scientific) and desalted. Peptides were eluted across a 2.1 mm × 5 cm C18 column (Hypersil Gold, Thermo Fisher Scientific) with a linear gradient of 4–40% (v/v) acetonitrile (CH₃CN) and 0.3% (v/v) formic acid, over 5 min. The eluted peptides were then ionized directly using electrospray ionization, coupled to a QExactive (Thermo Fisher Scientific). The intensity weighted mean m/z centroid value of each peptide envelope was calculated and subsequently converted into a percentage of deuterium incorporation. This is accomplished determining the observed averages of the undeuterated and fully deuterated spectra and using the conventional formula described elsewhere.

Data rendering. The HDX data from all overlapping peptides were consolidated to individual amino acid values using a residue averaging approach. Briefly, for each residue, the deuterium incorporation values and peptide lengths from all overlapping peptides were assembled. A weighting function was applied in which shorter peptides were weighted more heavily, and longer peptides were weighted less. Each of the weighted deuterium incorporation values were then averaged to produce a single value for each amino acid. The initial two residues of each peptide, as well as prolines, were omitted from the calculations. HDX analysis was performed in technical triplicate on two independent preparations (n=2) of each purified protein/complex. Statistical significance for the differential HDX data is determined by t-test for each timepoint and is integrated into the HDX Workbench software. Heatmap and correlation analysis were performed using in-house R scripts.

Molecular docking and ligand preparation
Molecular docking was performed using AutoDock Vina. Initially, the compound structures in mol format were converted to energy-minimized 3D conformations and saved as mol2 files using the Maestro viewer (Schrodinger, academic license). The receptor was prepared based on the SR10221-bound PPARγ-LBD (PDB: 8B8X), covering residues 201–473 (residues 229–501 in PPARγ isoform 2) with the corepressor peptide removed. The receptor model was prepared for molecular docking with extensive energy minimization and refinement using ICM-Pro (Molsoft, LLC). AutoDock Tools (version 1.5.7) was then used to prepare the ligand and receptor.pdbqt files (with torsions assigned and hydrogens added to the receptor). Docking calculations were carried out using Autodock Vina (version 1.2.5) with the search grid centered on the LBP and an exhaustiveness parameter of 100; the top 20 predicted binding modes saved per ligand. Promising binding poses were selected based on the criteria: (1) docking poses that exhibited consistent alignment of the core indole moiety and preserved the overall binding geometry relative to the SR10221-bound crystal structure were prioritized; (2) predicted binding affinity lesser than −9.25 kcal/mol, which is the value associated with SR10221’s optimal docking pose, although a few exceptions with predicted affinities around −8.5 kcal/mol were retained based on favorable spatial orientations. The selected.pdbqt docking poses were then converted back to.mol2 format with bonding information restored using an in-house Python script. Hydrogen atoms were subsequently added using PyMOL (3.1.6.1, Schrödinger), and the finalized ligand structures were submitted to Swissparam to generate corresponding.pdb and.itp files for subsequent MDS.

Molecular dynamics simulations
The MDS for each ligand were conducted with GROMACS (version 2023.2) using an in-house automated pipeline. The Charmm36 force field and TIP3 water model were used to construct the topology of the protein-ligand complex. Subsequently, a dodecahedral box was generated to center the complex, ensuring a minimum distance of 1.0 nm between the protein and the box edges. Following this, the protein was solvated with a TIP3 water model, and ions were introduced to neutralize charge of the system. Energy minimization was then reiterated using steepest descent methods for a maximum step of 50000 or for a maximum force reached < 1000.0 kJ/mol/nm. The energy-minimized system was sequentially equilibrated under 1-ns NVT and NPT restrained simulations to uphold a constant temperature (310 K) and pressure (1 bar) using a Lincs hydrogen bond algorithm, Particle Mesh Ewald long-range electrostatics, Berendsen thermostat coupling, and Parrinello-Rahman pressure coupling (NPT only). Triplicate trajectories for each ligand were generated from the last frame and two other dumps extracted from the final 10% of dumps during the NPT equilibration phase. Finally, restraints were released during each 100-ns production simulation, employing a timestep of 2 fs with frames stored every 5 ps. GROMOS clustering analysis was performed using a dynamic cutoff starting at 0.15 nm, with the goal of meeting the following criteria: (1) the top cluster contains at least 30% of the total frames, and (2) the total number of clusters is at least five. Except for RMSD calculation, the first 20 ns of each simulation were excluded from the downstream analysis to minimize the noise from initial structural rearrangements and ensure equilibrium sampling. Post-simulation analyses and visualization were carried out using a combination of MDTraj (version 1.10.0), NumPy (version 2.2.6), Pandas (version 2.23), Scikit-learn (version 1.6.1), Matplotlib (version 3.10.3), and Seaborn (version 0.13.2), along with in-house Python scripts. Dynamic Cross-Correlation analysis was conducted based on Cartesian displacements of Cα atoms. Molecular mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) analysis was performed using the gmx_MMPBSA tool (version 1.6.4) to evaluate key residues within 0.6 nm of the ligands during simulations. The MD simulation checklist is included as Supplementary Table 7.

Statistical treatments and analysis
No statistical analysis was done to estimate samples sizes. All heatmap clustering analysis were clustered based on the Ward’s method. For biochemical and cell-based assays, we defined technical replicates as assay replicates and biological replicates as separate preparations of the protein or cells. Unless specified otherwise, all biochemical assays were performed with at least triplicate technical replicates and at least two biological replicates. Results were normalized as fold change over DMSO-treated cells, statistical analysis was performed using one-way analysis of variance (ANOVA) corrected by Tukey’s multiple comparison and plotted using GraphPad Prism 10 software. Outlier detection was performed using an interquartile range (IQR)-based method adapted from Tukey’s fences. For each data point in small datasets (n≤5), the first (Q1) and third (Q3) quartiles were calculated, excluding the point under the test to prevent self-influence. Data points were classified as outliers if they fell below Q1 − 1.5×IQR or above Q3 + 1.5×IQR, where IQR = Q3 − Q1. To perform the correlation and covariation analysis, R version 4.4.1 was used to analyze a collection of HDX-Workbench outputs from 26 modulator differential experiments. The results of this analysis are shown in Fig. 3. After extracting perturbation values (Δ%D) for 67 peptides at two timepoints for 26 compounds, a two tailed t test was applied to determine likelihood that the values were significantly different from 0, followed by applying a Benjamini–Hochberg multiple test correction to control the false positive rate. Using the set of significant peptides, a Pearson correlation analysis comparing perturbation values to functional assay activity values was conducted using the psych (version 2.5.6) package. Again, the multiple test correction method of Benjamini–Hochberg was applied and the resulting adjusted p values and R2 and representative correlation plots were plotted in R. PerformanceAnalytics (version 2.0.8) and pheatmap (version 1.0.13) packages were used to visualize covariation within the HDX-MS dataset. The correlogram was generated using the corrplot (version 0.95) package in a way that only showed correlation coefficients whose corresponding covariation adjusted p values were less than 0.01.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Kuo et al. report the structural optimization and extensive profiling of non-covalent inverse PPARg agonists. Selected\ncompounds and their activity profiles are used to draw a convincing structural model of inverse PPARg agonism from HDX\nexperiments and molecular modeling. The novel inverse agonist scaffold retained a stabilizing effect on disease-associated\nmutants - in contrast to the agonist rosiglitazone and previous inverse agonists - which may be of therapeutic interest.\nAdditionally, characterization of selected compounds for insulin-sensitizing and related effects in adipocytes from mice,\nhealthy donors and diabetic patients interestingly revealed that non-inverse agonists and partial inverse agonists mediated\nsimilar insulin sensitizing effects as Rosiglitazone while only full inverse agonists lacked this effect. Overall, this is another\ninteresting and quite complete study by the Griffin group on PPARg and its modulation by innovative ligands. The paper will\nbe of broad interest and reads well apart from the missing chemical structures that compromise the traceability of the study.\nThe following points should be addressed before publication.\n- The study relies on the different in vitro profiles of diverse compounds from a common chemotype to establish models of\ninverse PPARg agonism. The manuscript hence refers to the SAR of the compound series but almost no chemical structure\nis shown leaving a black-box feeling. The study is not fully comprehensible and reproducible without the chemical\nstructures. For example, the authors refer to major activity differences between structurally similar molecules, but this cannot\nbe evaluated by the reader as the chemical structures are not presented. Chemical structures of the compounds should be\nadded at least to Table S2 to aid SAR analysis, and the chemical structures of the not extensively tested compounds (95\nthird generation analogues according to Figure 1A) should be shown together with their in vitro activity data (as mentioned\nvery briefly in line 424) in another Supplementary Table. Additionally, analytical characterization is only presented for\ncompounds 1 and 2 but lacking for all other entities.\n- Related to the above, an SAR scheme summarizing the structural features of the compounds driving partial agonism,\npartial inverse agonism and full inverse agonism (as mentioned in line 483) would be very informative (in line 548, the\nauthors refer to “the SAR outlined in Fig.3B” which does not show SAR).\n- The authors’ rationale to maintain moderate PPARalpha agonism is not clear. If the compounds are considered as tools to\nstudy the potential of inverse PPARgamma agonism in the context of T2DM and related conditions, maximum selectivity\nwould appear more desirable.\n- A few compounds (e.g., 19, 22) showing robust inverse agonism in the cellular settings (PPRE, CEBPa, FABP) seem to\nhave no impact of coregulator binding (Fig. 2E) as briefly mentioned in lines 457/458. This disconnect is also addressed in\nthe discussion section and I agree with the authors’ hypothesis. However, the study tested only two of many known PPARg\ninteractors (SRC1 and NCoR), and studying other coactivators and corepressors might offer additional insights. Moreover, it\nhas been shown that cooperativity between ligand and coregulator binding is a relevant mechanistic factor in nuclear\nreceptor agonist activity. Could this also explain large activity differences of structurally similar (supposedly - structures are\nnot available) molecules?\n- 3T3-L1 cell differentiation is a widely used in vitro model of adipogenesis. However, this cell line was derived from an\nembryonal mouse while all other tests were performed with human PPAR. The authors should comment on species\ndifferences.\n- Why are compounds 13, 17 and 19 not shown in Figure 6A? Especially data for 19 would be of interest as this compound\nrepresents an example with disconnect between cellular effects and impact on coregulator binding.\n- Methods of the toxicity assay (mentioned in line 469 and Fig. S3A) are missing. Earlier in the manuscript, a Gal4-VP16\nbased reporter gene assay is described as preliminary cytotoxicity test. This is not an optimal setting as this assay will\nmainly reveal unspecific effects on transcription.\n- Why was no (constitutive) control gene used in the reporter gene assays?\n- The parameters (score, RMSD) of redocking the crystallized ligand should be added to the docking methods for\ncomparison.\n- It would be helpful to present IC50 and Kd in Table S2 with the same units (i.e., nM).\n- A blank should be added between all values and units.", "reviewer_2": "I am both a medicinal chemist and a structural biologist. This comment comes from that perspective.\nThis manuscript relates to the development of new ligands targeting PPARγ, which is associated with insulin resistance\nimprovement effects. Numerous papers target PPARγ, and many related to medicinal chemistry continue to be published\ntoday. One reason is that the ligand-binding cavity of PPARγ is huge, offering high freedom in drug design. I felt this\nmanuscript significantly differs from others and is superior. The reasons are twofold: its conscious consideration of the\nconcept of inverse agonists and its genuine commitment to developing insulin resistance-improving drug.\nI believe this manuscript represents the top of the numerous medicinal chemistry studies targeting PPARγ. If published in\nthis journal, I expect it will accelerate pharmaceutical development using these derivatives. Therefore, I recommend\npublication of the manuscript in this journal after minor revisions.\nThe introduction is written sincerely, without any particular exaggeration.\nRegarding the results, the authors conducted structure-activity relationship studies on numerous derivatives from multiple\nperspectives. Unlike enzymes, nuclear receptors involve various factors such as agonism, cofactors, and gene transcription\nproducts. The authors addressed all necessary points. Furthermore, they multidimensionally expanded the assay results in\nconjunction with structural biology, providing crucial hints for drug design.\nMinor points:\n1. Are there inverse agonists for other nuclear receptors? It would be better to mention this.\n2. While the paper discusses prospects for cancer treatment with this inverse agonist, are there no particular prospects for\nosteoporosis treatment?\n3. If the authors' derivative is used as oral administration in combination with the PPARα agonist pemafibrate (parmodia),\nwould pemafibrate likely prevail over competition?\n4. Personally, I don't think emphasizing “non-covalent” is necessary. Compared to previous covalent approaches, the design\nflexibility is vastly higher, so I believe the uniqueness is sufficiently established.", "reviewer_3": "This study uses a variety of methods, notably HDX-MS, to conduct a mid-throughput investigation of molecular mechanism\nof action for a series of PPARg inverse agonist SR10171 analogues. The study is exceedingly thorough, with orthogonal\nmeasurements/simulations from the molecular to cellular level. The manuscript incorporates a large amount of data, with\nstatistical analyses that seem appropriate.\n- The main 'result' is a detailed picture of how these analogues induce their physiological effects on the molecular level. It is\neffectively an HDX-enabled SAR analysis, which, while not entirely new, it still very rare, and potentially *very* useful. For\nthe analytically inclined, there is more, for instance in the extent of agreement between measurement (HDX) and modeling of\nconformational dynamics.\n- The work identifies specific structural and dynamic shifts related to inverse agonism in PPARg, which is a protein of\nsubstantial interest due to it's link to diabetes.\n- The paper does an excellent job of supporting it's conclusions with data. There is no 'overinterpretation' of the data, and\nfunctional predictions based on molecular level evidence are tested at the cellular level.\n- The data is voluminous and analyzed statistically to provide insights. The data are high quality, the statistics are\nappropriate and properly interpreted.\n- The methodology is sound.\n- The methods could be reproduced from the information provided.\nOverall, this is a thorough and interesting study that is ready for publication as-is, in my view."}, "author_response": "Line by Line response to review\nWe appreciate the constructive feedback from all three reviewers, which has helped us\nsignificantly improve the clarity and impact of the manuscript. As detailed in our point-by-point\nresponse, we have addressed all comments thoroughly. In several instances, we have\nincorporated new experimental details and updated supplementary tables and figures for clarity.\nReviewer #1 (Remarks to the Author):\nReviewer Comment: The study relies on the different in vitro profiles of diverse compounds from\na common chemotype to establish models of inverse PPARγ agonism. The manuscript hence\nrefers to the SAR of the compound series but almost no chemical structure is shown leaving a\nblack-box feeling. The study is not fully comprehensible and reproducible without the chemical\nstructures. For example, the authors refer to major activity differences between structurally similar\nmolecules, but this cannot be evaluated by the reader as the chemical structures are not\npresented. Chemical structures of the compounds should be added at least to Table S2 to aid\nSAR analysis, and the chemical structures of the not extensively tested compounds (95 third\ngeneration analogues according to Figure 1A) should be shown together with their in vitro activity\ndata (as mentioned very briefly in line 424) in another Supplementary Table. Additionally,\nanalytical characterization is only presented for compounds 1 and 2 but lacking for all other\nentities.\nAuthor response: We thank the reviewer for this valuable suggestion to enhance clarity and\nreproducibility. In response, we have added Table S4, which presents the chemical structures of\nall compounds, with high-priority compounds listed first for ease of reference. The synthesis\nprocedures for the parental compounds are included in the Supplementary Methods, and\nadditional synthetic details are available in International Patent Publication WO 2024/151519 A2.\nTo support SAR interpretation, we have also included Table S3, which provides in vitro activity\ndata (IC values from Gal4 activity assays, LanthaScreen binding assays) and microsomal\nstability data for selected compounds that demonstrated improved PPAR activity relative to\n₅₀\nparental analogs. These additions strengthen the manuscript’s utility for the field.\nReviewer Comment: Related to the above, an SAR scheme summarizing the structural features\nof the compounds driving partial agonism, partial inverse agonism and full inverse agonism (as\nmentioned in line 483) would be very informative (in line 548, the authors refer to “the SAR\noutlined in Fig.3B” which does not show SAR).\nAuthor Response:We thank the reviewer for this helpful suggestion and for noting the ambiguity\nin our terminology. Our intention was to highlight the concordance between HDX and MD\nsimulations in capturing ligand-induced repressive conformational dynamics. To clarify, we have\nrevised the sentence in line 548 to read:\n“These receptor-level structural features align with the ligand-induced conformational dynamics\nobserved in Fig. 3B, collectively highlighting the structural basis of PPARγ repression.”\nReviewer Comment: The authors’ rationale to maintain moderate PPAR alpha agonism is not\nclear. If the compounds are considered as tools to study the potential of inverse PPAR gamma\nagonism in the context of T2DM and related conditions, maximum selectivity would appear more\ndesirable.\nAuthor Response:Thank you for raising this important point. We agree that PPARγ selectivity is\ncritical when designing tool compounds for studying inverse agonism in T2DM. However, our data\nindicate that complete repression of PPARγ activity does not enhance glucose uptake in diabetic\nadipocytes (Fig. 6), suggesting that partial inverse agonism, and thus residual PPARγ activity\n(partial repression only) may be necessary for insulin sensitization. For this reason, we prioritized\ncompounds with partial inverse agonism rather than full antagonism.\nRegarding PPARα, we retained moderate agonism because prior studies in rodents administered\nSR10171 chronically show that PPARα activation supports bone health and mitigates adverse\nskeletal effects associated with PPARγ-targeted therapies1. This dual modulation appears to\nunderline the favorable metabolic and skeletal outcomes observed with SR10171. We have\nclarified this rationale in the introduction and added supporting references.\n“The bone-protective effect may partly reflect modest PPARα activation. PPARα is expressed in\nosteocytes and regulates bone microenvironment and systemic energy metabolism, promoting\nosteocyte health and contributing to anabolic bone remodeling. When combined with partial\nPPARγ inverse agonism that enhances insulin sensitization, this dual modulation may underlie\nthe favorable metabolic and skeletal outcomes observed with SR10171.”\nReviewer Comment: A few compounds (e.g., 19, 22) showing robust inverse agonism in the\ncellular settings (PPRE, CEBPa, FABP) seem to have no impact of coregulator binding (Fig. 2E)\nas briefly mentioned in lines 457/458. This disconnect is also addressed in the discussion section\nand I agree with the authors’ hypothesis. However, the study tested only two of many known\nPPARγ interactors (SRC1 and NCoR), and studying other coactivators and corepressors might\noffer additional insights. Moreover, it has been shown that cooperativity between ligand and\ncoregulator binding is a relevant mechanistic factor in nuclear receptor agonist activity. Could this\nalso explain large activity differences of structurally similar (supposedly - structures are not\navailable) molecules?\nAuthor Response: We appreciate this insightful comment and agree that a broader co-regulator\nscreen could further illuminate ligand–coregulator cooperativity. To address this, we initially\nevaluated a peptide library (~30 sequences) representing diverse NR and CoRNR motifs. During\nassay optimization, only the two peptides (the ones included in our manuscript; SRC1 and NCoR)\nprovided robust signal-to-noise suitable for compound profiling. These peptides revealed distinct\nligand-induced conformational changes, supporting our mechanistic interpretation.\nWe acknowledge that cooperativity between ligand and coregulator binding may contribute to the\nobserved disconnect and could underlie activity differences among structurally similar\ncompounds. While our current dataset is limited to SRC1 and NCoR, future work will expand this\nanalysis to additional coactivators and corepressors to test this hypothesis more\ncomprehensively.\nReviewer Comment: 3T3-L1 cell differentiation is a widely used in vitro model of adipogenesis.\nHowever, this cell line was derived from an embryonal mouse while all other tests were performed\nwith human PPAR. The authors should comment on species differences.\nAuthor Response:We thank the reviewer for highlighting species differences. While 3T3-L1 cells\nare murine, they remain a widely accepted model for adipogenesis and PPARγ biology. Murine\nand human PPARγ share >90% sequence identity, particularly within the DNA-binding and ligand-\nbinding domains, which govern receptor activation and ligand interaction (NCBI RefSeq: Human\nGene ID 5468; Mouse Gene ID 19016). This high conservation supports similar pharmacological\nresponses across species.\nTo address potential species-specific effects, we complemented murine studies with assays using\nhuman PPARγ constructs and validated insulin-sensitizing activity in human primary adipocytes.\nThe pharmacological profiles were consistent across both models, reinforcing translational\nrelevance. These results are presented in the manuscript.\nReviewer Comment: Why are compounds 13, 17 and 19 not shown in Figure 6A? Especially\ndata for 19 would be of interest as this compound represents an example with disconnect between\ncellular effects and impact on coregulator binding.\nAuthor Response: We thank the reviewer for noting this point. Compound 13 was excluded due\nto poor microsomal stability (see Table S2). Regarding compounds 17 and 19, we selected\ncompounds 21 and 22 for Figure 6A because they exhibit comparable or greater potency and\nfeature larger N-alkyl substitutions on the indole scaffold, which provided clearer SAR trends.\nWhile compound 19 is mechanistically interesting due to the observed disconnect between\ncellular activity and coregulator binding, it was not prioritized to be included in Figure 6A to\nmaintain focus on compounds with optimal pharmacological and stability profiles. Chemical\nstructures for all compounds, including 17 and 19, are now provided in Table S4 for reference.\nReviewer Comment: Methods of the toxicity assay (mentioned in line 469 and Fig. S3A) are\nmissing. Earlier in the manuscript, a Gal4-VP16 based reporter gene assay is described as\npreliminary cytotoxicity test. This is not an optimal setting as this assay will mainly reveal\nunspecific effects on transcription.\nAuthor Response: We thank the reviewer for noting the missing details of the toxicity assay in\nFig. S3A. While the figure legend indicated that cell viability was measured by MTS assay, the full\nprotocol was not included in the manuscript. We have now added these details to the Materials\nand Methods section (see below).\nWe also acknowledge the reviewer’s point that the Gal4-VP16 reporter assay may not be optimal\nfor assessing cytotoxicity. For this reason, we used an independent MTS assay for Fig. S3A to\nprovide a more direct measure of cell viability.\n“Cell Viability Assay\nHEK293T cells were transfected under the same condition used for the reporter assays. After 24\nhours, cells were plated in 384-well plate at 10,000 cells/well and incubated 3 hours. Cells were\nthen treated with either DMSO or 1 μM compound for 18 hours. Following treatment, MTS reagent\n(G5430, Promega) was added and incubated for 2 hours. Absorbance was measured at 490 nm\nusing the Infinite M1000 plate reader (Tecan).”\nReviewer Comment: Why was no (constitutive) control gene used in the reporter gene assays?\nAuthor Response: We thank the reviewer for this important point. Our reporter system included\nonly PPRE-driven luciferase and did not incorporate a constitutive control gene. To minimize\nvariability, we used pooled transfection in HEK293T cells followed by splitting into 384-well plates\nfor compound treatment the next day. All reporter data were normalized to the DMSO control\nwithin each experiment to account for transfection efficiency differences across batches.\nAdditionally, we increased the number of biological replicates to ensure reproducibility. While a\nconstitutive control could provide an additional normalization layer, our approach, combined with\nreplicate design and internal DMSO normalization, effectively controlled variability and supported\nreliable interpretation of compound-dependent effects.\nReviewer Comment: The parameters (score, RMSD) of redocking the crystallized ligand should\nbe added to the docking methods for comparison.\nAuthor Response: We thank the reviewer for this helpful suggestion. The parameters for the\nredocked crystallized ligand (SR10221) have now been explicitly added to the docking methods\nsection for clarity. The docking score is reported in Fig. S5, while RMSD and the final\nrepresentative pose are shown in Fig. S7A and Fig. 4C, respectively. To further improve clarity,\nwe have updated the legend for Table S5 as follows:\n“Table S5. Docking ranks and predicted binding affinities for optimal PPARγ inverse\nagonists binding poses based on AutoDock Vina calculations.\na The binding affinity of the redocked crystallized ligand (SR10221) is highlighted in bold, whereas\nbinding affinities weaker than -9.25 kcal/mol are indicated in italics.”\nReviewer Comment: It would be helpful to present IC50 and Kd in Table S2 with the same units\n(i.e., nM).\nAuthor Response: We agree and we have unified the units into nM in Table S2. Thank you.\nReviewer Comment: A blank should be added between all values and units.\nAuthor Response: We apologize for this oversight. We have added a blank space between all\nvalues and units.\nReviewer #2 (Remarks to the Author):\nReviewer Comment: Are there inverse agonists for other nuclear receptors? It would be better\nto mention this.\nAuthor Response: We thank the reviewer for this helpful suggestion. Inverse agonists have\nindeed been identified for other nuclear receptors, including RORγ, Rev-Erbα, and LXRs. These\nligands suppress basal receptor activity and have been explored for therapeutic applications in\nautoimmune diseases, circadian rhythm regulation, and lipid metabolism. We have modified the\nintroduction to include these examples and added appropriate references to provide context.\nReviewer Comment: While the paper discusses prospects for cancer treatment with this inverse\nagonist, are there no particular prospects for osteoporosis treatment?\nAuthor Response:We thank the reviewer for highlighting this important point. We agree that the\npotential for osteoporosis treatment warrants discussion. Prior studies, including our work with\nSR10171, indicate that partial inverse agonism of PPARγ can promote osteogenesis while\navoiding bone loss, likely through selective suppression of pS273 phosphorylation and modest\nPPARα activation1. Recent evidence also shows that PPARα expressed in osteocytes regulates\nbone microenvironment and systemic energy metabolism2, suggesting a dual role for PPAR\nmodulation in skeletal and metabolic health. To address this, we have expanded the Discussion\nto highlight the therapeutic relevance of non-covalent inverse agonists for bone health and\nosteoporosis and added supporting references. The revised text reads:\n“In parallel, modest activation of PPARα may offer additional therapeutic benefits by supporting\nosteocyte function and systemic energy metabolism, which are often compromised in cancer-\nassociated cachexia and bone metastases. Thus, the dual action of our compounds expands their\ntherapeutic scope beyond insulin sensitization, positioning non-covalent inverse agonists as\nrational candidates for targeting both metabolic and oncogenic PPARγ pathways, while potentially\npreserving skeletal health.”\nReviewer Comment: If the authors' derivative is used as oral administration in combination with\nthe PPARα agonist pemafibrate (parmodia), would pemafibrate likely prevail over competition?\nAuthor Response: We thank the reviewer for this insightful question. We have IC data from\nLanthaScreen binding assays for PPARα-LBD for a few compounds, which are in the micromolar\n₅₀\nrange, significantly weaker than their affinity for PPARγ-LBD. In contrast, the calculated Kd for the\npemafibrate–PPARα-LBD complex is 0.13 μM3, indicating substantially higher potency at PPARα.\nBased on these values, pemafibrate would likely dominate PPARα engagement in a combined\noral administration scenario.\nReviewer Comment: Personally, I don't think emphasizing “non-covalent” is necessary.\nCompared to previous covalent approaches, the design flexibility is vastly higher, so I believe the\nuniqueness is sufficiently established.\nAuthor Response: We thank the reviewer for this thoughtful comment. We agree that design\nflexibility is a key advantage of our approach and that uniqueness is well established. Our initial\nemphasis on “non-covalent” was intended to distinguish these ligands from prior covalent inverse\nagonists, which rely on irreversible binding and may raise safety concerns. To address this, we\nhave reduced repetitive use of “non-covalent” and shifted the focus toward the mechanistic and\ntherapeutic implications of ligand flexibility and reversibility in the revised manuscript.\nReviewer #3 (Remarks to the Author):\nReviewer Comment: This study uses a variety of methods, notably HDX-MS, to conduct a mid-\nthroughput investigation of molecular mechanism of action for a series of PPARg inverse agonist\nSR10171 analogues. The study is exceedingly thorough, with orthogonal\nmeasurements/simulations from the molecular to cellular level. The manuscript incorporates a\nlarge amount of data, with statistical analyses that seem appropriate. The main 'result' is a detailed\npicture of how these analogues induce their physiological effects on the molecular level. It is\neffectively an HDX-enabled SAR analysis, which, while not entirely new, it still very rare, and\npotentially *very* useful. For the analytically inclined, there is more, for instance in the extent of\nagreement between measurement (HDX) and modeling of conformational dynamics.\nAuthor Response: We thank the reviewer for their positive and encouraging comments.\nReviewer Comment on data presentation and quality: The work identifies specific structural\nand dynamic shifts related to inverse agonism in PPARg, which is a protein of substantial interest\ndue to it's link to diabetes. The paper does an excellent job of supporting it's conclusions with\ndata. There is no overinterpretation' of the data, and functional predictions based on molecular\nlevel evidence are tested at the cellular level. The data are high quality, the statistics are\nappropriate and properly interpreted. The methodology is sound. The methods could be reproduced from the information provided.\nOverall, this is a thorough and interesting study that is ready for publication as-is, in my view.\nAuthor response: We thank the reviewer for their positive comments on the presentation of\nresults and the data quality.\nReferences in support of the Line-by-Line response:\nFormation and Bone Resorption. EBioMedicine10, 174-184 (2016).\nhttps://doi.org/10.1016/j.ebiom.2016.06.040\nmaintenance of bone and systemic energy metabolism. Front Endocrinol\n(Lausanne)14, 1145467 (2023). https://doi.org/10.3389/fendo.2023.1145467\nModulator, Pemafibrate, through Combinational Approaches of X-ray\nCrystallography, Thermodynamic Analysis, and First-Principle Calculations.\nInternational Journal of Molecular Sciences21 (2020). https://doi.org/ARTN 361\n10.3390/ijms21010361"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have convincingly addressed my comments.", "reviewer_2": "I believe the authors have addressed my comments and those of other reviewers, particularly the crucial points, very well.\nTherefore, I recommend acceptance."}, "author_response": ""}]

Decision letter