Dataset Information

Machine Learning and In Vitro Chemical Screening of Potential α-Amylase and α-Glucosidase Inhibitors from Thai Indigenous Plants.

ABSTRACT: Diabetes mellitus is a major predisposing factor for cardiovascular disease and mortality. α-Amylase and α-glucosidase enzymes are the rate-limiting steps for carbohydrate digestion. The inhibition of these two enzymes is clinically used for the treatment of diabetes mellitus. Here, in vitro study and machine learning models were employed for the chemical screening of inhibiting the activity of 31 plant samples on α-amylase and α-glucosidase enzymes. The results showed that the ethanolic twig extract of Pinus kesiya had the highest inhibitory activity against the α-amylase enzyme. The respective ethanolic extract of Croton oblongifolius stem, Parinari anamense twig, and Polyalthia evecta leaf showed high inhibitory activity against the α-glucosidase enzyme. The classification analysis revealed that the α-glucosidase inhibitory activity of Thai indigenous plants was more predictive based on phytochemical constituents, compared with the α-amylase inhibitory activity (1.00 versus 0.97 accuracy score). The correlation loading plot revealed that flavonoids and alkaloids contributed to the α-amylase inhibitory activity, while flavonoids, tannins, and reducing sugars contributed to the α-glucosidase inhibitory activity. In conclusion, the ethanolic extracts of P. kesiya, C. oblongifolius, P. anamense, and P. evecta have the potential for further chemical characterization and the development of anti-diabetic recipes.

SUBMITTER: Srisongkram T

PROVIDER: S-EPMC8781461 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Compounds from Zanthoxylum chalybeum Engl. were previously reported for inhibitory activities of amylase and glucosidase enzymatic action on starch as a preliminary study toward the establishment of a management strategy against postprandial hyperglycemia, however, the inhibitory kinetics and molecular interaction of these compounds were never established. A study was thus designed to establish the inhibitory kinetics and in silico molecular interaction of α-glucosidase and α-amylase with Z. chalybeum metabolites based on Lineweaver-Burk/Dixon plot analyses and using Molecular Operating Environment (MOE) software, respectively. Skimmianine (5), Norchelerythrine (6), 6-Acetonyldihydrochelerythrine (7), and 6-Hydroxy-N-methyldecarine (8) alkaloids showed mixed inhibition against both α-glucosidase and α-amylase with comparable Ki to the reference acarbose (p > 0.05) on amylase but significantly higher activity than acarbose on α-glucosidase. One phenolic 2,3-Epoxy-6,7-methylenedioxyconiferol (10) showed a competitive mode of inhibition both on amylase and glucosidase which were comparable (p > 0.05) to the activity of acarbose. The other compounds analyzed and displayed varied modes of inhibition between noncompetitive and uncompetitive with moderate inhibition constants included chaylbemide A (1), chalybeate B (2) and chalybemide C (3), fagaramide (4), ailanthoidol (9), and sesame (11). The important residues of the proteins α-glucosidase and α-amylase were found to have exceptional binding affinities and significant interactions through molecular docking studies. The binding affinities were observed in the range of -9.4 to -13.8 and -8.0 to -12.6 relative to the acarbose affinities at -17.6 and -20.5 kcal/mol on α-amylase and α-glucosidase residue, respectively. H-bonding, π-H, and ionic interactions were noted on variable amino acid residues on both enzymes. The study thus provides the basic information validating the application of extracts of Z. chalybeum in the management of postprandial hyperglycemia. Additionally, the molecular binding mechanism discovered in this study could be useful for optimizing and designing new molecular analogs as pharmacological agents against diabetes.

Dataset Information

Machine Learning and In Vitro Chemical Screening of Potential α-Amylase and α-Glucosidase Inhibitors from Thai Indigenous Plants.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets