Identification of alpha-glucosidase enzyme inhibitors from phytochemicals via integrated deep learning, molecular docking, molecular dynamics simulation, and MMPBSA analysis

Abstract

Alpha-glucosidase is a crucial enzyme involved in carbohydrate metabolism, and inhibiting its activity holds promise for treating Type 2 Diabetes Mellitus (T2DM). This study aimed to identify potential drugs that can selectively target alpha-glucosidase's essential enzyme, Maltase-glucoamylase, to discover potent antidiabetic compounds. A phytochemical library containing 2000 compounds from various plants was subjected to screening using a deep learning approach, molecular docking, ADMET, and DruliTo against the Maltase-glucoamylase enzyme. The compounds exhibiting strong binding affinity with the enzyme were further analyzed through Molecular Dynamics Simulation (MDS). MDS analysis, including (RMSD) Root Mean Square Deviation, Root Mean Square Fluctuation (RMSF), Radius of Gyration, Hydrogen-bond, and PCA, indicates that Carpaine, Pseudocarpaine, Pollinastanol, and Annosquamosin C have favorable interactions with alpha-glucosidase. Moreover, analysis of free binding energy substantiates the stability of these interactions, as evidenced by the binding energies of α-Glucosidase-Reference (-4.6 kJ/mol), α-Glucosidase-Pollinastaol (-4.7 kJ/mol), α-Glucosidase-Annosquamosoin C (-5.6 kJ/mol), α-Glucosidase-Pseudocarpaine (-8.0 kJ/mol), and α-Glucosidase-Carpaine (-10.2 kJ/mol). Based on these results, we conclude that these phytochemicals possess significant potential for inhibiting the alpha-glucosidase enzyme. The findings of this study suggest that these phytochemicals may exhibit promising inhibitory activity against α-Glucosidase and should be further investigated in vitro and in vivo experiments as potential natural resources for developing effective antidiabetic drugs.