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PMID: 41683141 已发表 · epublish 英语

Antidepressant Mechanisms of L-Theanine in Tea Based on Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulations.

Foods (Basel, Switzerland) ·第 15 卷 ·第 3 期 ·2026-02-04

Shi Y, Yang Y, Cheng X, Huang C, Huang Y, Lu L, Wang S, Zheng Y, Wang F, Zhang B, Zheng S

摘要

L-theanine is a bioactive non-protein amino acid predominantly derived from tea plants (Camellia sinensis), widely recognized for its potential benefits in mood regulation and psychological health. Despite its promising neuropsychological profile, the specific molecular targets and mechanisms underlying its antidepressant activity remain incompletely understood. In the present study, an integrated network pharmacology strategy, combined with molecular docking and molecular dynamics (MD) simulations, was employed to systematically elucidate the potential antidepressant mechanisms of L-theanine. By intersecting predicted drug targets with depression-related genes, 40 potential targets were identified. Protein-protein interaction (PPI) network analysis subsequently pinpointed five hub targets: PRKACA, GRIA2, GRIN1, GRIA1, and HTR1A. Functional enrichment analyses (KEGG and GO) indicated that these targets are primarily implicated in critical pathological processes of depression, including neurotransmitter regulation, glutamatergic synaptic transmission, stress response signaling, and neurotrophin-related pathways. Molecular docking revealed favorable binding affinities between L-theanine and the key targets. Furthermore, MD simulations and binding free energy calculations corroborated the structural stability and thermodynamic favorability of these protein-ligand complexes. Overall, this study provides hypothesis-generating insights into the antidepressant mechanisms of L-theanine from a multi-target perspective, offering a theoretical foundation to guide future experimental validation in depression research.

关键词
binding free energy hub targets protein–protein interaction (PPI) synaptic transmission target prediction
文献信息
期刊
Foods (Basel, Switzerland)
期刊简称
Foods
ISSN
2304-8158
发表日期
2026-02-04
语言
英语
国家/地区
Switzerland
NLM ID
101670569
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