Construction of an MLR-QSAR Model Based on Dietary Flavonoids and Screening of Natural α-Glucosidase Inhibitors

Ting Yang; Zichen Yang; Fei Pan; Yijia Jia; Shengbao Cai; Liang Zhao; Lei Zhao; Ou Wang; Chengtao Wang

doi:10.3390/foods11244046

Construction of an MLR-QSAR Model Based on Dietary Flavonoids and Screening of Natural α-Glucosidase Inhibitors

Foods. 2022 Dec 14;11(24):4046. doi: 10.3390/foods11244046.

Authors

Ting Yang¹, Zichen Yang¹, Fei Pan², Yijia Jia³, Shengbao Cai³, Liang Zhao¹, Lei Zhao¹, Ou Wang⁴, Chengtao Wang¹

Affiliations

¹ Beijing Engineering and Technology Research Center of Food Additives, Beijing Technology and Business University, Beijing 100048, China.
² Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing 100093, China.
³ Yunnan Institute of Food Safety, Kunming University of Science and Technology, Kunming 650500, China.
⁴ National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China.

Abstract

Postprandial hyperglycemia can be reduced by inhibiting α-glucosidase activity. Common α-glucosidase inhibitors such as acarbose may have various side effects. Therefore, it is important to find natural products that are non-toxic and have high α-glucosidase-inhibitory activity. In the present study, a comprehensive computational analysis of 27 dietary flavonoid compounds with α-glucosidase-inhibitory activity was performed. These included flavonoids, flavanones, isoflavonoids, dihydrochalcone, flavan-3-ols, and anthocyanins. Firstly, molecular fingerprint similarity clustering analysis was performed on the target molecules. Secondly, multiple linear regression quantitative structure-activity relationship (MLR-QSAR) models of dietary flavonoids (2D descriptors and 3D descriptors optimized), with R² of 0.927 and 0.934, respectively, were constructed using genetic algorithms. Finally, the MolNatSim tool based on the COCONUT database was used to match the similarity of each flavonoid in this study, and to sequentially perform molecular enrichment, similarity screening, and QSAR prediction. After screening, five kinds of natural product molecule (2-(3,5-dihydroxyphenyl)-5,7-dihydroxy-4H-chromen-4-one, norartocarpetin, 2-(2,5-dihydroxyphenyl)-5,7-dihydroxy-4H-chromen-4-one, 2-(3,4-dihydroxyphenyl)-5-hydroxy-4H-chromen-4-one, and morelosin) were finally obtained. Their IC_50pre values were 8.977, 31.949, 78.566, 87.87, and 94.136 µM, respectively. Pharmacokinetic predictions evaluated the properties of the new natural products, such as bioavailability and toxicity. Molecular docking analysis revealed the interaction of candidate novel natural flavonoid compounds with the amino acid residues of α-glucosidase. Molecular dynamics (MD) simulations and molecular mechanics/generalized Born surface area (MMGBSA) further validated the stability of these novel natural compounds bound to α-glucosidase. The present findings may provide new directions in the search for novel natural α-glucosidase inhibitors.

Keywords: MLR-QSAR; dietary flavonoids; molecular dynamics simulation; molecular fingerprint similarity; α-glucosidase.

Abstract

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