Molecular Docking Analysis of Bioactive Constituents Identified by GC-MS from Coprinus comatus as α-Amylase and α-Glucosidase Inhibitors
Keywords:
Molecular docking, Coprinus comatus, α-amylase inhibition, α-glucosidase inhibition, StepharinosineAbstract
Diabetes mellitus remains a major global health concern characterized by chronic hyperglycemia and progressive metabolic dysfunction. One therapeutic strategy for controlling postprandial blood glucose involves inhibiting carbohydrate-hydrolyzing enzymes, particularly α-amylase and α-glucosidase. Natural products have gained increasing attention as potential enzyme inhibitors with improved safety profiles. Coprinus comatus, an edible mushroom, contains diverse bioactive constituents that may contribute to antidiabetic activity. This study aimed to evaluate the inhibitory potential of compounds identified by GC–MS from Coprinus comatus against α-amylase and α-glucosidase through molecular docking analysis. Seven compounds were selected: Methoxypromazine, Stepharinosine, Thioperoxydicarbonic diamide, Methylzinc propoxide, 2,4-Pentadienoic acid, 2-Hexenedioic acid, and Benzenesulfonamide, 2,4-dichloro-3-methyl-N-(3-pyridyl). Docking simulations were performed using the crystal structures of α-amylase (PDB ID: 1HNY) and α-glucosidase (PDB ID: 2ZE0). Acarbose was used as a reference inhibitor. Binding affinity values were calculated to assess ligand–protein interactions. The results demonstrated that acarbose exhibited binding affinities of −6.9 kcal/mol toward 1HNY and −7.0 kcal/mol toward 2ZE0. Among the identified compounds, Stepharinosine showed the strongest interaction with both targets, with binding affinities of −7.5 kcal/mol for α-amylase and −8.8 kcal/mol for α-glucosidase, exceeding the reference drug. Other compounds displayed moderate to weak binding energies. These findings suggest that Stepharinosine is a promising dual inhibitor of α-amylase and α-glucosidase and may contribute to the antidiabetic potential of C. comatus. Further in vitro and in vivo studies are required to validate these computational predictions.
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