Structure-guided discovery of food-derived GABA-T inhibitors as hunters for anti-anxiety compounds. Issue 24 (16th November 2022)

• Record Type: Journal Article
• Authors: Liu, Meng-Qi; Wang, Tong; Wang, Qin-Ling; Zhou, Jie; Wang, Bao-Rong; Zhang, Bing; Wang, Kun-Long; Zhu, Hao; Zhang, Ying-Hua
• Is Part Of: Food & function. Volume 13:Issue 24(2022)
• Page Start: 12674
• Page End: 12685
• Publication Date: 2022-11-16
• Subjects: Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; Nutrition -- Periodicals; 664.07
• DOI: 10.1039/d2fo01315k
• Languages: English
• ISSNs: 2042-6496

Abstract

Abstract : A rapid screening method for food-derived GABA-T inhibitors. Abstract : With the acceleration of the pace of life, people may face all kinds of pressure, and anxiety has become a common mental issue that is seriously affecting human life. Safe and effective food-derived compounds may be used as anti-anxiety compounds. In this study, anti-anxiety compounds were collected and curated for database construction. Quantitative structure–activity relationship (QSAR) models were developed using a combination of various machine-learning approaches and chemical descriptors to predict natural compounds in food with anti-anxiety effects. High-throughput molecular docking was used to screen out compounds that could function as anti-anxiety molecules by inhibiting γ-aminobutyrate transaminase (GABA-T) enzyme, and 7 compounds were screened for in vitro activity verification. Pharmacokinetic analysis revealed three compounds (quercetin, lithocholic acid, and ferulic acid) that met Lipinski's Rule of Five and inhibited the GABA-T enzyme to alleviate anxiety in vitro . The established QSAR model combined with molecular docking and molecular dynamics was proved by the synthesis and discovery of novel food-derived anti-anxiety compounds.