Structure-guided discovery of food-derived GABA-T inhibitors as hunters for anti-anxiety compounds

Meng Qi Liu, Tong Wang, Qin Ling Wang, Jie Zhou, Bao Rong Wang, Bing Zhang, Kun Long Wang, Hao Zhu, Ying Hua Zhang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)12674-12685
Number of pages12
JournalFood and Function
Issue number24
StatePublished - Nov 3 2022

All Science Journal Classification (ASJC) codes

  • Food Science


Dive into the research topics of 'Structure-guided discovery of food-derived GABA-T inhibitors as hunters for anti-anxiety compounds'. Together they form a unique fingerprint.

Cite this