Investigation of alpha-glucosidase inhibition activity of Artabotrys sumatranus leaf extract using metabolomics, machine learning and molecular docking analysis.

Autor:	Rosa D; Department of Pharmacy, Faculty of Pharmacy, Indonesia University, Depok, Indonesia.; Department of Pharmacy, Faculty of Health Science, Pelita Harapan University, Tangerang, Indonesia., Elya B; Department of Pharmacy, Faculty of Pharmacy, Indonesia University, Depok, Indonesia., Hanafi M; Chemistry Research Centre, National Research and Innovation Agency, Science and Technology Research Centre, Serpong, Indonesia., Khatib A; Department of Pharmaceutical Chemistry, Kulliyah of Pharmacy, International Islamic University Malaysia, Kuantan, Malaysia., Budiarto E; Department of Information Technology, Faculty of Engineering and Information Technology, Swiss German University, Tangerang, Indonesia., Nur S; Department of Pharmaceutical Analysis and Medicinal Chemistry, Almarisah Madani University, Makasar, Indonesia., Surya MI; Research Centre for Plant Conservation, Botanic Gardens and Forestry, National Research and Innovation Agency, Bogor, Indonesia.
Jazyk:	angličtina
Zdroj:	PloS one [PLoS One] 2025 Jan 03; Vol. 20 (1), pp. e0313592. Date of Electronic Publication: 2025 Jan 03 (Print Publication: 2025).
DOI:	10.1371/journal.pone.0313592
Abstrakt:	One way to treat diabetes mellitus type II is by using α-glucosidase inhibitor, that will slow down the postprandial glucose intake. Metabolomics analysis of Artabotrys sumatranus leaf extract was used in this research to predict the active compounds as α-glucosidase inhibitors from this extract. Both multivariate statistical analysis and machine learning approaches were used to improve the confidence of the predictions. After performance comparisons with other machine learning methods, random forest was chosen to make predictive model for the activity of the extract samples. Feature importance analysis (using random feature permutation and Shapley score calculation) was used to identify the predicted active compound as the important features that influenced the activity prediction of the extract samples. The combined analysis of multivariate statistical analysis and machine learning predicted 9 active compounds, where 6 of them were identified as mangiferin, neomangiferin, norisocorydine, apigenin-7-O-galactopyranoside, lirioferine, and 15,16-dihydrotanshinone I. The activities of norisocorydine, apigenin-7-O-galactopyranoside, and lirioferine as α-glucosidase inhibitors have not yet reported before. Molecular docking simulation, both to 3A4A (α-glucosidase enzyme from Saccharomyces cerevisiae, usually used in bioassay test) and 3TOP (a part of α-glucosidase enzyme in human gut) showed strong to very strong binding of the identified predicted active compounds to both receptors, with exception of neomangiferin which only showed strong binding to 3TOP receptor. Isolation based on bioassay guided fractionation further verified the metabolomics prediction by succeeding to isolate mangiferin from the extract, which showed strong α-glucosidase activity when subjected to bioassay test. The correlation analysis also showed a possibility of 3 groups in the predicted active compounds, which might be related to the biosynthesis pathway (need further research for verification). Another result from correlation analysis was that in general the α-glucosidase inhibition activity in the extract had strong correlation to antioxidant activity, which was also reflected in the predicted active compounds. Only one predicted compound had very low positive correlation to antioxidant activity. Competing Interests: No authors have competing interests. (Copyright: © 2025 Rosa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze:	MEDLINE
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