GC-MS metabolomic profiling and PPARγ-targeted in silico approaches for identifying a potential anti-diabetic compound from traditional rice varieties. (PubMed, Front Nutr)
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 (ΔGbind) of -108.21 kcal/mol, compared to the reference antidiabetic drug pioglitazone (PubChem ID: 4829; -8.759 kcal/mol ΔGbind = -96.81 kcal/mol)...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.