Investigation of α‐glucosidase inhibitory metabolites from Tetracera scandens leaves by GC–MS metabolite profiling and docking studies
Abstract: Stone leaf (Tetracera scandens) is a Southeast Asian medicinal plant that has been traditionally used for the management of diabetes mellitus. The underlying mechanisms of the antidiabetic activity have not been fully explored yet. Hence, this study aime...
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Multidisciplinary Digital Publishing Institute (MDPI)
2020
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iium-787132020-02-17T05:01:22Z http://irep.iium.edu.my/78713/ Investigation of α‐glucosidase inhibitory metabolites from Tetracera scandens leaves by GC–MS metabolite profiling and docking studies Ahmed, Nokhala Siddiqui, Mohammad Jamshed Ahmad Ahmed, Qamar Uddin Ahamad Bustamam, Muhammad Safwan Zakaria, Zainul Amiruddin QD Chemistry Abstract: Stone leaf (Tetracera scandens) is a Southeast Asian medicinal plant that has been traditionally used for the management of diabetes mellitus. The underlying mechanisms of the antidiabetic activity have not been fully explored yet. Hence, this study aimed to evaluate the α‐glucosidase inhibitory potential of the hydromethanolic extracts of T. scandens leaves and to characterize the metabolites responsible for such activity through gas chromatography–mass spectrometry (GC–MS) metabolomics. Crude hydromethanolic extracts of different strengths were prepared and in vitro assayed for αglucosidase inhibition. GC–MS analysis was further carried out and the mass spectral data were correlated to the corresponding α‐glucosidase inhibitory IC50 values via an orthogonal partial least squares (OPLS) model. The 100%, 80%, 60% and 40% methanol extracts displayed potent α‐glucosidase inhibitory potentials. Moreover, the established model identified 16 metabolites to be responsible for the αglucosidase inhibitory activity of T. scandens. The putative α‐glucosidase inhibitory metabolites showed moderate to high affinities (binding energies of −5.9 to −9.8 kcal/mol) upon docking into the active site of Saccharomyces cerevisiae isomaltase. To sum up, an OPLS model was developed as a rapid method to characterize the α‐glucosidase inhibitory metabolites existing in the hydromethanolic extracts of T. scandens leaves based on GC–MS metabolite profiling. Multidisciplinary Digital Publishing Institute (MDPI) 2020-02-12 Article PeerReviewed application/pdf en http://irep.iium.edu.my/78713/1/biomolecules-10-00287.pdf Ahmed, Nokhala and Siddiqui, Mohammad Jamshed Ahmad and Ahmed, Qamar Uddin and Ahamad Bustamam, Muhammad Safwan and Zakaria, Zainul Amiruddin (2020) Investigation of α‐glucosidase inhibitory metabolites from Tetracera scandens leaves by GC–MS metabolite profiling and docking studies. Biomolecules, 10 (2). pp. 1-18. E-ISSN 2218-273X https://www.mdpi.com/2218-273X/10/2/287 doi:10.3390/biom10020287 |
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QD Chemistry Ahmed, Nokhala Siddiqui, Mohammad Jamshed Ahmad Ahmed, Qamar Uddin Ahamad Bustamam, Muhammad Safwan Zakaria, Zainul Amiruddin Investigation of α‐glucosidase inhibitory metabolites from Tetracera scandens leaves by GC–MS metabolite profiling and docking studies |
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Abstract: Stone leaf (Tetracera scandens) is a Southeast Asian medicinal plant that has been traditionally used for the management of diabetes mellitus. The underlying mechanisms of the antidiabetic activity have not been fully explored yet. Hence, this study aimed to evaluate the α‐glucosidase inhibitory potential of the hydromethanolic extracts of T. scandens leaves and to characterize the metabolites responsible for such activity through gas chromatography–mass spectrometry (GC–MS) metabolomics. Crude hydromethanolic extracts of different strengths were prepared and in vitro assayed for αglucosidase inhibition. GC–MS analysis was further carried out and the mass spectral data were correlated to the corresponding α‐glucosidase inhibitory IC50 values via an orthogonal partial least squares (OPLS) model. The 100%, 80%, 60% and 40% methanol extracts displayed potent α‐glucosidase inhibitory potentials. Moreover, the established model identified 16 metabolites to be responsible for the αglucosidase inhibitory activity of T. scandens. The putative α‐glucosidase inhibitory metabolites showed moderate to high affinities (binding energies of −5.9 to −9.8 kcal/mol) upon docking into the active site of Saccharomyces cerevisiae isomaltase. To sum up, an OPLS model was developed as a rapid method to characterize the α‐glucosidase inhibitory metabolites existing in the hydromethanolic extracts of T. scandens leaves based on GC–MS metabolite profiling. |
format |
Article |
author |
Ahmed, Nokhala Siddiqui, Mohammad Jamshed Ahmad Ahmed, Qamar Uddin Ahamad Bustamam, Muhammad Safwan Zakaria, Zainul Amiruddin |
author_facet |
Ahmed, Nokhala Siddiqui, Mohammad Jamshed Ahmad Ahmed, Qamar Uddin Ahamad Bustamam, Muhammad Safwan Zakaria, Zainul Amiruddin |
author_sort |
Ahmed, Nokhala |
title |
Investigation of α‐glucosidase inhibitory metabolites from Tetracera scandens leaves by GC–MS metabolite profiling and docking studies |
title_short |
Investigation of α‐glucosidase inhibitory metabolites from Tetracera scandens leaves by GC–MS metabolite profiling and docking studies |
title_full |
Investigation of α‐glucosidase inhibitory metabolites from Tetracera scandens leaves by GC–MS metabolite profiling and docking studies |
title_fullStr |
Investigation of α‐glucosidase inhibitory metabolites from Tetracera scandens leaves by GC–MS metabolite profiling and docking studies |
title_full_unstemmed |
Investigation of α‐glucosidase inhibitory metabolites from Tetracera scandens leaves by GC–MS metabolite profiling and docking studies |
title_sort |
investigation of α‐glucosidase inhibitory metabolites from tetracera scandens leaves by gc–ms metabolite profiling and docking studies |
publisher |
Multidisciplinary Digital Publishing Institute (MDPI) |
publishDate |
2020 |
url |
http://irep.iium.edu.my/78713/ http://irep.iium.edu.my/78713/ http://irep.iium.edu.my/78713/ http://irep.iium.edu.my/78713/1/biomolecules-10-00287.pdf |
first_indexed |
2023-09-18T21:50:51Z |
last_indexed |
2023-09-18T21:50:51Z |
_version_ |
1777413759301582848 |