分子动力学
抗抑郁药
化学
计算生物学
对接(动物)
结合亲和力
药效团
药物发现
药理学
药品
药物开发
神经科学
神经递质
生物
网络动力学
分子模型
机制(生物学)
生物化学
谷氨酸的
分子结合
作者
Yutao Shi,Yuan Yang,Xi Cheng,Canyang Huang,Yan Huang,Li Lu,Shuyan Wang,yucheng Zheng,Feiquan Wang,Bo Zhang,Shulin Zheng
出处
期刊:Foods
[Multidisciplinary Digital Publishing Institute]
日期:2026-02-04
卷期号:15 (3): 555-555
被引量:1
标识
DOI:10.3390/foods15030555
摘要
), 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.
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