Using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, https://tcmsp-e.com/), we screened all active components of Nelumbo nucifera leaves based on criteria of oral bioavailability (OB) ≥ 20% (Veber et al., 2002) and drug-likeness (DL) ≥ 0.18 (Ursu et al., 2011). To analyze the clustering of small molecule similarities, we utilized molecular fingerprints, dimensionality reduction, clustering, and 3D visualization techniques. Specifically, Morgan fingerprints were computed for each compound using RDKit (Landrum, 2013), with a radius of 2 and 2048 bits. SMILES strings were converted to molecular objects for fingerprint generation, excluding invalid ones, and duplicates were removed to ensure uniqueness in the dataset. We applied t-SNE (Van der Maaten and Hinton, 2008) with a perplexity of 30 to reduce the fingerprint data to three dimensions. Hierarchical clustering using Ward’s method was then performed on the t-SNE results (Van der Maaten and Hinton, 2008), setting the number of clusters to three. Each compound was assigned a cluster label, and within each cluster, we calculated a similarity matrix using the Tanimoto coefficient (Bajusz et al., 2015). The molecule with the highest average similarity within each cluster was identified as the representative. A 3D scatter plot was created using Plotly, coloring each compound by cluster assignment and distinctly marking representative molecules, enabling intuitive exploration of small molecule similarities and relationships (He et al., 2022).

In this study, potential target genes related to obesity were initially identified from four authoritative databases: DisGeNET (Piñero et al., 2016), GeneCards (Safran et al., 2010), PharmGKB (Hewett et al., 2002), and UniProt (UniProt Consortium, 2015). By comprehensively comparing these databases and removing duplicates, we screened a total of 2,063 potential target genes for further analysis. To predict possible interactions between the active components of Nelumbo nucifera leaves and these obesity target genes, we utilized databases and tools such as SEA, SuperPred (Gallo et al., 2022), and SwissTargetPrediction (Daina et al., 2019). These platforms enabled us to perform an in-depth prediction of intersection target genes for the active components found in Nelumbo nucifera leaves.

To explore the potential interactions among key target proteins, we constructed a comprehensive Protein-Protein Interaction (PPI) (Athanasios et al., 2017) network using the STRING database (Wagner and Fischer, 1974), a prominent bioinformatics tool. This network was visualized with the latest version of Cytoscape software (Kohl et al., 2011). We conducted a thorough analysis of the network’s topological features using advanced analytical tools within Cytoscape, identifying ten hub genes that play a crucial role in the disease mechanism.

We performed enrichment analyses of Gene Ontology (GO) (Harris et al., 2004) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa and Goto, 2000) pathways using R packages including clusterProfiler (Yu et al., 2012), org. Hs.eg.db (Carlson et al., 2019), and pathview. Gene lists were converted to Entrez IDs and analyzed for enrichment using enrichGO and enrichKEGG with p-value and q-value cutoffs of 0.01 and 0.05, respectively. Results were visualized with dot plots and bar plots, highlighting significant terms and pathways. Additionally, pathview was used to map genes onto KEGG pathways for detailed visualization (Wang et al., 2024).

A receptor protein model based on PPARG (PDB ID: 8WFE) was constructed, encompassing 268 residues. The crystal structure was the latest structure available and was determined using the gold standard X-ray diffraction method, with a relatively high resolution of 2.20 Å. Additionally, it is a human-derived protein, and it is free from the conformational influences of other ligands or bound proteins, thus more closely representing the natural conformation. Therefore, we selected this structure. Homology modeling was performed with MODELER 10.1 to include missing residues and domains. Small molecules, initially in SMILES format, were converted to pdbqt format using Open Babel (O’Boyle et al., 2011). Molecular docking was then carried out using AutoDock Vina 1.2.0 (Eberhardt et al., 2021). The docking box was set to dimensions of 29.25 Å × 34.5 Å × 21.0 Å with center coordinates at x = −7.574, y = 10.801, z = 138.847. The docking site was chosen because it was described as the binding site for PPARG inhibitors and inverse agonists (Irwin et al., 2022; Nolte et al., 1998). Batch docking of 18 small molecules with their respective target proteins was conducted. This methodology enabled a detailed evaluation of potential inhibitors by analyzing their predicted interaction strengths with the target enzyme (Song et al., 2024). We also used the R357A PPARG mutant (PDB ID: 4O8F) and the V290M PPARG mutant (PDB ID: 4OJ4) and conducted molecular docking at the same site with a consistent method.

In this study, machine learning models were developed to predict the activity of PPARG inhibitors based on different molecular fingerprints. Molecular data for PPARG inhibitors were collected from publicly available databases, including CHEMBL (Gaulton et al., 2012) and PUBCHEM (Kim et al., 2019) and were represented as SMILES (Simplified Molecular Input Line Entry System) strings (Weininger, 1988). There were in total 7899 PPARG inhibitors, and for data balancing, we selected 7,900 inactive molecules. These SMILES strings were then converted into various molecular fingerprints to capture different structural and chemical features. The dataset was divided into training and test sets in an 8/2 ratio using the train_test_split function from Scikit-Learn, ensuring that both sets maintained a representative distribution of the data.

Five types of molecular fingerprints were generated: MACCS Keys (Jow et al., 1990), Morgan (Zhong and Guan, 2023), RDKit (Qiao et al., 2021), Topological Torsion (Nilakantan et al., 1987), and Atom Pairs fingerprints (Awale et al., 2015). MACCS Keys fingerprints were generated using RDKit’s GetMACCSKeysFingerprint function, which provides a 166-bit vector representation of predefined structural keys. Morgan fingerprints, analogous to Extended Connectivity Fingerprints (ECFP), were created using GetMorganFingerprintAsBitVect with a radius of 2 and 1,024 bits to capture circular substructures. RDKit fingerprints, generated with RDKFingerprint, encoded molecular substructures based on paths of bonded atoms. Topological Torsion fingerprints, produced by GetHashedTopologicalTorsionFingerprintAsBitVect, represented topological torsions within the molecular structure. Finally, Atom Pairs fingerprints, created using GetHashedAtomPairFingerprintAsBitVect, captured the relationship between atom pairs in terms of their topological distances.

Two machine learning algorithms, Random Forest (RF) (Belgiu and Drăguţ, 2016) and Extreme Gradient Boosting (XGBoost) (Sheridan et al., 2016), were employed for model development. The Random Forest model, implemented using Scikit-Learn’s RandomForestClassifier, is an ensemble method based on decision trees. Hyperparameters for the RF model, including the number of trees, maximum tree depth, minimum samples per split, minimum samples per leaf, and bootstrap sampling, were optimized using GridSearchCV. XGBoost, implemented with the XGBClassifier from the XGBoost library, is a gradient boosting algorithm. Key hyperparameters such as the number of estimators, maximum tree depth, learning rate, subsample ratio, and column sampling by tree were similarly optimized. Both models were trained using 5-fold cross-validation with KFold to ensure robust evaluation and hyperparameter optimization. The Area Under the Receiver Operating Characteristic Curve (AUC-ROC) was used as the primary scoring metric for model selection.

The performance of the trained models was evaluated on the test set using several metrics. These included AUC-ROC, which provides a comprehensive measure of model performance across all classification thresholds, sensitivity (SE) to assess the true positive rate, specificity (SP) to evaluate the true negative rate, and the Matthews Correlation Coefficient (MCC) to measure the overall quality of binary classifications. These metrics collectively provided a thorough assessment of the models’ ability to predict RANKL inhibitory activity based on different molecular fingerprints.

We conducted molecular dynamics (MD) simulations (Hollingsworth and Dror, 2018) on two key molecules, Cycloartenol and Sitogluside, using the PMEMD module of Amber 22 (Case et al., 2022) with CUDA acceleration. Each system underwent 100 ns MD simulations. Hydrogen bonds were constrained using the SHAKE algorithm (Elber et al., 2011), and electrostatic interactions were managed with the Particle Mesh Ewald (PME) method (He et al., 2024), with an 8 Å cutoff. Following initial system construction, atomic clashes were resolved through 500 steps of steepest descent and conjugate gradient minimization. Systems were then heated from 0 K to 300 K over 50 ps, followed by density equilibration and constant pressure operations at 300 K for 500 ps in the NPT ensemble. Once the systems stabilized, three 100 ns MD simulations were run, with data recorded every 1 fs using a 2 fs time step and a Langevin thermostat with a 1 ps collision frequency. Data storage occurred every 2 ps, resulting in 2,000 frames for analysis. Trajectory analysis was performed using the CPPTRAJ (Roe et al., 2013) module of Amber 22, assessing RMSD, RMSF, radius of gyration (Rg), and solvent-accessible surface area (SASA). K-means clustering within CPPTRAJ produced 10 representative structures. The binding free energy differences of protein-ligand complexes were estimated using MM-PBSA (Genheden and Ryde, 2015) calculations from 500 snapshots of the final trajectory. This method reduces errors related to covalent energy and is frequently used alongside MM-GBSA to predict binding free energies in a continuum solvent model.

The murine embryonic fibroblast cell line, 3T3-L1, was procured from the National Infrastructure of Cell Line Resource (China Infrastructure of Cell Line Resource). Dulbecco’s Modified Eagle’s Medium (DMEM) was purchased from Gibco. Sitogluside was obtained from TargetMol. The triglyceride (TG) assay kit was sourced from Applygen Technologies. Fetal calf serum (FCS) was supplied by Lablead. The modified Oil Red O staining kit was procured from Beyotime. Isobutylmethylxanthine (IBMX) was acquired from Sigma-Aldrich, Dexamethasone from MedChemExpress, and Insulin from Lablead.

Cell Culture and Treatment: 3T3-L1 preadipocytes were maintained in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% fetal calf serum, at 37°C in a 5% CO2 atmosphere. Differentiation was induced when cells reached confluence, using a differentiation medium containing 1 µM Dexamethasone, 0.5 mM IBMX, and 10 μg/mL Insulin with 10% FCS for 48 h. Thereafter, the medium was replaced with DMEM containing only 10 μg/mL Insulin and the cells were cultured for an additional 5–7 days, with the medium refreshed every 2 days.

Sitogluside Treatment: Post-differentiation, 3T3-L1 adipocytes were treated with Sitogluside at concentrations of 0 μM, 5 μM, and 10 µM. The treatments were administered in DMEM throughout the experiment to evaluate their effects on lipid accumulation and metabolic activity.

Lipid Accumulation Assessment (Oil Red O Staining): Cells were washed with PBS and fixed in 4% paraformaldehyde for 10 min at room temperature. Fixed cells were stained with Oil Red O for 40 min to visualize lipid droplets. The dye was subsequently eluted with isopropanol and quantified by measuring the absorbance at 490 nm.

Triglyceride Quantification: Triglycerides were extracted using a lysis buffer and quantified using a triglyceride quantification kit according to the manufacturer’s instructions. Absorbance was measured at 570 nm and concentrations were calculated against a standard curve.

Optical Density Measurements: The optical density of the dye extracted from the stained cells was measured at 490 nm using a spectrophotometer, to quantify the lipid content indicative of adipogenesis under various treatment conditions.

All experiments were conducted in triplicate. Data are expressed as mean ± standard deviation (SD). Differences between treated and control groups were analyzed using one-way ANOVA followed by Tukey’s post hoc test, where a p-value of less than 0.05 was considered statistically significant.

As shown in Figure 2 and Table 1, 18 active molecules in Nelumbo nucifera leaves were clustered, resulting in three categories of molecules. The first category is represented by Sitogluside, the second category by Kaempferol, and the third category by Nuciferine.

Using multiple databases, we predicted targets and intersected the results with collected obesity disease targets. As shown in Figure 3, Sitogluside intersected with 13 genes, Kaempferol with 48 genes, and Nuciferine with 39 genes. Using the Matthews Correlation Coefficient (MCC) algorithm from the cytoHubba toolkit, we identified the crucial nodes within the Nelumbo nucifera leaf-obesity interactome. The MCC scores, indicating the strength of connectivity, were visually represented with varying color intensities, where a deeper hue indicated higher relevance to obesity pathogenicity. We then cataloged the top 10 targets for each active small molecule, revealing key players such as PPARG, as shown in Figure 4.

Figures 5–7 collectively present the results of GO and KEGG pathway enrichment analyses for genes related to Nelumbo nucifera leaf small molecules and obesity disease. The GO enrichment analyses (A of each figure) identify key biological processes, cellular components, and molecular functions. In the first figure, significant biological processes include cellular response to chemical stress and regulation of inflammatory response, with cellular components such as membrane rafts and neuronal cell bodies, and molecular functions focusing on protein tyrosine kinase and serine hydrolase activities. The KEGG pathway analysis highlights pathways like endocrine resistance, EGFR tyrosine kinase inhibitor resistance, and several cancer-related pathways including prostate, breast, and gastric cancer. In the second figure, enriched biological processes include organic hydroxy compound transport and vascular processes in the circulatory system, with cellular components like synaptic and presynaptic membranes and molecular functions emphasizing G protein-coupled receptor activity and neurotransmitter binding. Key KEGG pathways include neuroactive ligand-receptor interaction, serotonergic synapse, and chemical carcinogenesis, as well as addiction pathways such as cocaine and amphetamine addiction. The third figure’s GO analysis emphasizes processes like organic hydroxy compound transport and stress responses, with cellular components including membrane microdomains and neuronal cell bodies, and molecular functions like neurotransmitter receptor activity. KEGG pathway analysis reveals significant pathways such as lipid and atherosclerosis, various signaling pathways (e.g., MAPK and PI3K-Akt), and cancer-related pathways. Collectively, these analyses suggest that the interaction between Nelumbo nucifera leaf small molecules and obesity involves complex networks affecting inflammation, cellular signaling, and metabolic processes, with broad implications for cancer, neurological disorders, and metabolic diseases.

Due to the inclusion of PPARG as a key target for the three categories of molecules and considering the importance of PPARG in treating obesity, we performed molecular docking to screen the affinity of active small molecules in Nelumbo nucifera leaves for PPARG. The docking energy results are shown in Table 2. The ChemDraw (.cdx) structures of all compounds listed in Table 2 have been uploaded in the supplementary materials. Molecular docking results revealed key interactions between Nelumbo nucifera leaf bioactive compounds and PPARG (Table 2). Cycloartenol showed the highest binding affinity (−9.5 kcal/mol), engaging residues such as HIS351 and LYS502 through hydrophobic interactions. Sitogluside (−8.5 kcal/mol) formed hydrogen bonds with HIS351 and HIS477, and had multiple hydrophobic interactions. Isorhamnetin (−7.9 kcal/mol) formed a hydrogen bond with SER317, while kaempferol (−7.8 kcal/mol) formed one with LEU496. Both compounds also exhibited significant hydrophobic interactions. Other notable compounds, such as anonaine and remirin, showed binding affinities of −7.8 kcal/mol with distinct hydrogen and hydrophobic bonds. TThese findings highlight the potential of cycloartenol and sitogluside as promising PPARG ligands, contributing to the anti-obesity effects of Nelumbo nucifera leaf bioactive compounds.

The two molecules with the highest affinity, Sitogluside (Figure 8A) and Cycloartenol (Figure 8B), both belong to the first category of molecules, with binding affinities of −8.5 kcal/mol and −9.5 kcal/mol, respectively. The docking conformations and binding sites are illustrated in Figure 8. We conducted molecular dynamics simulations on the complexes of these two molecules with the PPARG protein, as well as on the apo protein. The interaction diagram in Figure 8A highlights various residues involved in interactions: van der Waals interactions with residues such as TYR348 and LEU496; conventional hydrogen bonds with residues like HIS351 and HIS477; and alkyl interactions with residues such as VAL478 and LYS502. Similarly, the interaction diagram in Figure 8B details van der Waals interactions with residues like CYS313 and PHE310, and alkyl interactions with residues such as VAL321 and LEU497. These interactions are crucial for understanding the binding affinity and specificity of the ligands to the PPARG protein.

PPARG contains a well-defined hydrophobic binding pocket that plays a critical role in ligand recognition and stabilization. This pocket is composed of key hydrophobic residues, including LEU, VAL, and ILE, which facilitate van der Waals interactions and stabilize the binding of non-polar regions of ligands. Additionally, hydrogen bonds contribute significantly to binding specificity, with residues such as GLN487 and LYS488 forming stable hydrogen bonds with ligand functional groups. Aromatic interactions, including π-alkyl and π-π stacking with residues like TYR473 and HIS477, further enhance ligand binding by providing additional stability through π-electron interactions. These findings suggest that an effective PPARG inhibitor may ideally engage multiple interaction types, including hydrophobic, hydrogen bond, and π-interactions, to ensure both specificity and binding strength.

Target prediction and reverse docking alone are insufficient for achieving biological significance, as it is challenging to distinguish between activity and inhibitory activity. To address this, we developed machine learning models for predicting PPARG inhibitors. The development of machine learning models for PPARG prediction included the use of Random Forest (RF) and Extreme Gradient Boosting (XGB) algorithms, with five molecular fingerprints (MACCS, Morgan, RDKit, Topological Torsion, AtomPairsFP) as inputs, resulting in 10 models in total. The performance of each model, evaluated using metrics such as sensitivity (SE), specificity (SP), accuracy (ACC), Matthews correlation coefficient (MCC), precision (P), F1 score (F1), balanced accuracy (BA), and area under the ROC curve (AUC), is summarized in Figure 9.

The XGB models generally performed well, with Morgan-XGBoost achieving the highest AUC (0.9661) and precision (0.9775), indicating its robustness in identifying true positive PPARG inhibitors while minimizing false positives. The confusion matrices for the XGB models (Figure 10) further demonstrate this, with Morgan-XGBoost showing a very high true negative rate (97.89%) and a reasonably high true positive rate (86.14%), suggesting that this model can reliably predict PPARG inhibitors with high specificity and good sensitivity. Other XGB models, such as AtomPairsFP-XGBoost and TopologicalTorsion-XGBoost, also showed strong performance in both specificity (91.58% and 94.74%, respectively) and sensitivity (87.13% and 83.17%, respectively). The MACCS-XGBoost model exhibited a slightly lower sensitivity (81.19%) but maintained a high specificity (96.84%), reflecting its effectiveness in filtering out false positives while being slightly more conservative in identifying true positives. The Morgan-XGBoost model, with its high precision and balanced accuracy, is particularly suited for distinguishing between active and inactive PPARG inhibitors. Using the best-performing model, Morgan-XGBoost, Cycloartenol and Sitogluside were both identified as potential PPARG inhibitors.

Several mutations, including R357A and V290M in PPARG, render it unresponsive to drugs. To mitigate this impact, we used the R357A PPARG mutant (PDB ID: 4O8F) and the V290M PPARG mutant (PDB ID: 4OJ4) and conducted molecular docking at the same site using a consistent method. The results are provided in the supplementary material. Sitogluside showed an affinity of −7.5 kcal/mol with R357A and −8.5 kcal/mol with V290M, while Cycloartenol had an affinity of −8.0 kcal/mol with R357A and −8.6 kcal/mol with V290M. It can be observed that both Sitogluside and Cycloartenol maintained high affinity in unresponsive mutants (Bharti et al., 2021).

Molecular dynamics simulations were performed for the three systems (Sitogluside, Cycloartenol and Apo). The solvent-accessible surface area (SASA) of a protein can be used to analyze its hydrophobicity and the degree of surface exposure (Wang et al., 2023). Higher SASA values indicate greater hydrophobicity, while lower values indicate less. As shown in Figure 11A, the SASA of Sitogluside decreases after 40 ns, falling below that of the apo protein (Apo) and Cycloartenol, indicating that the conformation of the protein changes after binding with this inhibitor, resulting in increased hydrophobicity and a more closed surface. Figure 11B shows that the SASA distribution center of Sitogluside is at 140 nm², while those of Apo and Cycloartenol are at 145 nm², which is consistent with the trend observed in the line chart.

The root mean square deviation (RMSD) of the backbone carbon atoms relative to their initial positions is an indicator of the stability of the simulated system and reveals the deviation of the complex from its initial conformation, indicating conformational changes. Analysis of Figure 11C shows that the RMSD of Sitogluside is higher than that of the apo protein (Apo) and Cycloartenol after 20 ns, indicating greater conformational changes. As shown in Figure 11D, the RMSD distribution center of Sitogluside is at 1.7 Å, also higher than that of Apo and Cycloartenol, which aligns with the trend observed in the line chart.

The radius of gyration (Rg) reveals the compactness of the complex. Analysis of Figure 11E, F shows that the Rg of Sitogluside is smaller than that of Cycloartenol, indicating a more compact conformation after binding. However, the Rg of Sitogluside is slightly larger than that of Apo, because Apo is an apo protein without the supporting effect of a ligand.

The root mean square fluctuation (RMSF) of protein amino acids is used to analyze the extent of fluctuation of individual amino acids during the simulation process, revealing the flexibility changes of residues (Figure 12). The overall lower RMSF of Sitogluside indicates better stability, suggesting that Sitogluside induces a stable conformational change in PPARG. Therefore, despite having a higher RMSD value, Sitogluside maintains a relatively low RMSF.

We performed a protein secondary structure (DSSP) analysis and found no significant changes (Figure 13). This reveals that the influence of Sitogluside on PPARG is not closely related to secondary structure changes. Instead of affecting the conformation of alpha-helices, Sitogluside impacts the overall compactness of the protein by modulating the flexibility of loops.

After studying the conformational changes, we then focused on the dynamic characteristics of the protein. The analysis of specific motion patterns, as shown by PCA (Figures 14A–C), indicates that the first two principal components of the apo group and the Cycloartenol group account for only 27.1% and 28.8% of the motion modes, with PC1 accounting for only 16.7% and 16.2%, respectively. In contrast, the first two principal components of the Sitogluside group account for 40.5%, with PC1 alone accounting for 32.9%. This reveals that the binding of Sitogluside leads to significant differences in the protein’s motion patterns.

To investigate the changes in the internal motion patterns of the protein, we calculated the dynamic cross-correlation matrix (DCCM) for each residue in the three systems (Figures 14D–F). Blue indicates positive correlated motion between related residues, while pink indicates negatively correlated motion between related residues. The diagonal correlations are relatively high because they represent the correlation of a residue with its own motion. Compared to the Apo group, the blue and pink colors in the Cycloartenol group are slightly deeper, indicating a slight increase in internal interconnectivity and a similar overall matrix shape, suggesting that its dynamic characteristics have not significantly changed.

In contrast, the Sitogluside group shows a substantial increase in internal correlation. The 1–80 region exhibits enhanced positive correlation within itself, the 80–175 region shows increased negative correlation with the 1–80 region, the 80–175 region also has increased negative correlation with the 175–200 region, and the 230–270 region shows increased positive correlation within itself. Previously uncorrelated white regions have become interrelated, visibly changing the matrix shape. Loosely packed proteins generally have lower internal motion interconnectivity, indicating that the binding of Sitogluside increases the protein’s compactness by affecting flexible regions, thereby enhancing internal stability and interconnectivity.

Finally, we analyzed the binding energy contributions using the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) method. The binding energy are shown in Table 3, and the energy contribution graph is illustrated as Figure 15.

The results of the MM-PBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) are presented in Table 2. This method evaluates the binding free energy between the ligand and the target enzyme, providing a quantitative measure of the interaction strength. Sitogluside exhibited a binding free energy of −36.49 ± 3.69 kcal/mol, indicating a stronger binding affinity than Cycloartenol which showed a binding free energy of −33.41 ± 2.34 kcal/mol.

In summary, we identified 18 bioactive compounds from Nelumbo nucifera leaves with potential anti-obesity effects, focusing on their interactions with the PPARG protein. Clustering analysis classified these compounds into three distinct groups, with sitogluside, kaempferol, and nuciferine selected as representative molecules for further evaluation.

The molecular docking results revealed significant interactions between these compounds and PPARG, with sitogluside and cycloartenol displaying the highest binding affinities (−9.5 kcal/mol and −8.5 kcal/mol, respectively). Sitogluside demonstrated multiple hydrogen bonds with key residues such as HIS351 and HIS477, as well as extensive hydrophobic interactions, indicating a strong inhibitory potential. Cycloartenol exhibited similar binding strength, primarily through hydrophobic interactions involving residues such as LEU481 and VAL350.

Machine learning models, including Random Forest (RF) and Extreme Gradient Boosting (XGB), were trained to predict PPARG inhibitors based on molecular fingerprints. The XGB model using Morgan fingerprints demonstrated the best performance, achieving an AUC-ROC of 0.9661, with high precision and specificity. Sitogluside and cycloartenol were both predicted as likely PPARG inhibitors by the XGB model, validating our docking findings.

Molecular dynamics (MD) simulations were conducted for the PPARG complexes with sitogluside, cycloartenol, and the apo protein. The SASA, RMSD, and Rg analyses indicated that sitogluside binding led to a more compact and stable PPARG structure. The root mean square fluctuation (RMSF) analysis revealed that sitogluside reduced flexibility in key regions, further supporting its potential as a stabilizing ligand for PPARG. Principal component analysis (PCA) and dynamic cross-correlation matrix (DCCM) analysis highlighted significant shifts in PPARG’s motion patterns upon sitogluside binding, suggesting an allosteric effect.

Overall, the results indicate that sitogluside, followed closely by cycloartenol, exhibits strong binding and stabilizing interactions with PPARG, providing a molecular basis for their potential anti-obesity effects. These findings suggest that Nelumbo nucifera bioactive compounds could serve as promising candidates for natural PPARG modulators in obesity treatment.

Treatment with Sitogluside significantly reduced lipid accumulation in 3T3-L1 cells in a dose-dependent manner (Figure 16A–C). The untreated cells showed a high percentage of lipid accumulation (∼43%), which decreased to ∼22% with 5 µM and further to ∼15% with 10 µM Sitogluside (Figure 16D).

Consistent with the reduction in visible lipid accumulation, the optical density measurements at 490 nm decreased with increasing Sitogluside concentrations. Cells treated with 0 µM Sitogluside exhibited an OD of approximately 0.175, which reduced to 0.125 with 5 µM and further to 0.085 with 10 µM (Figure 16E).

A similar trend was observed in the triglyceride concentrations measured in the cells. The control group displayed high triglyceride levels (approximately 500 μmol/L), which significantly dropped to about 250 μmol/L with 5 µM Sitogluside and to around 50 μmol/L with 10 µM Sitogluside treatment (Figure 16F).

These results demonstrate the efficacy of Sitogluside in modulating lipid metabolism in 3T3-L1 cells, suggesting its potential as a therapeutic agent in the treatment of obesity.