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May 9, 2026Frontiers in Nutrition0 citationsOpen Access

GC–MS metabolomic profiling and PPARγ-targeted in silico approaches for identifying a potential anti-diabetic compound from traditional rice varieties

SNSaranya NallusamyTamil Nadu Agricultural University KVK. VanithaTamil Nadu Agricultural University MBM. Riswana BegamTamil Nadu Agricultural University

Key Points

To investigate the anti-diabetic potential of selected traditional rice varieties and identify bioactive compounds.
Gas chromatography-mass spectrometry (GC-MS) profiling of traditional rice varieties to identify compounds.
Network and pathway enrichment analysis followed by ADMET prediction and virtual screening against PPARγ.
Molecular dynamics simulation and structural analysis of the PPARγ-oleic acid complex.
Identified oleic acid as the most promising compound with a docking score of -9.451 kcal/mol.
Binding free energy for oleic acid was -108.21 kcal/mol, compared to pioglitazone’s -96.81 kcal/mol.
Molecular dynamics simulation showed stable binding of the PPARγ-oleic acid complex with favorable structural parameters.

Abstract

Traditional rice varieties are known to exhibit anti-oxidant, anti-inflammatory, anti-diabetic, anti-cancer, and cardioprotective properties due to their nutritional profile and diverse phytochemical composition. The present study aimed to investigate the anti-diabetic potential of selected traditional rice varieties, including Annamazhagi, Karuppu kavuni, Karunkuruvai, Mappillai Samba, Milagu Samba, and Poongar. Metabolic profiling using gas chromatography–mass spectrometry (GC–MS) identified 561 compounds across the selected rice varieties. The top 20 compounds from each variety were selected based on retention time and relative abundance and pooled into a non-redundant compound dataset. These compounds were further subjected to network and pathway enrichment analysis, followed by ADMET prediction and virtual screening against peroxisome proliferator-activated receptor gamma (PPARγ; PDB ID: 3G9E), a key regulator of glucose homeostasis and insulin sensitivity. Molecular docking using PyRx, followed by refined docking with the Schrödinger Glide XP module and binding free energy analysis using Schrödinger Prime MM-GBSA, identified oleic acid (PubChem ID: 445639) as the most promising compound, with a docking score of −9.451 kcal/mol and binding free energy (ΔG bind ) of −108.21 kcal/mol, compared to the reference antidiabetic drug pioglitazone (PubChem ID: 4829; −8.759 kcal/mol ΔG bind = −96.81 kcal/mol). To further evaluate ligand–receptor stability, a 200 ns molecular dynamics simulation was performed using GROMACS with the CHARMM36 force field. Structural analyses, including root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA), hydrogen bond interactions, principal component analysis (PCA), free energy landscape (FEL), and Molecular Mechanics/Poisson–Boltzmann Surface Area (MM-PBSA) binding energy calculations analysis, indicated stable binding behavior of the PPARγ-oleic acid complex throughout the simulation. These findings suggest that oleic acid may act as a potential natural modulator of PPARγ, highlighting the therapeutic potential of traditional rice varieties as sources of antidiabetic bioactive compounds.

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