虚拟筛选
变构调节
体外
分子动力学
计算生物学
化学
生物
生物化学
计算化学
受体
作者
Peiran Meng,Wenjun Mu,Daobo Ding,Huan Chen,Zhaohui Li,Hongwei Hou,Qingyuan Hu
出处
期刊:Journal of computational biophysics and chemistry
[World Scientific]
日期:2024-05-23
卷期号:23 (07): 925-937
标识
DOI:10.1142/s2737416524500200
摘要
The [Formula: see text]7 nicotinic acetylcholine receptor (nAChR) is a neurotransmitter receptor and a promising therapeutic target for neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease. In this study, we used deep learning-based classification modeling, molecular docking, and intracellular calcium signal detection experiments to identify positive allosteric modulators (PAMs) of the [Formula: see text]7 nAChR from natural tobacco products. First, we used a classification prediction model based on the direct message-passing neural network (D-MPNN) and molecular docking to predict potential [Formula: see text]7 PAMs. Subsequently, absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions were performed to determine its pharmacological properties and minimal toxic side effects, leading to the selection of three natural compounds for in vitro activity validation. Calcium signaling assay experiments revealed that diosmetin can enhance nicotine-induced calcium ion signaling. 100 ns molecular dynamics (MD) simulations indicated that diosmetin can form a dense and stable complex with [Formula: see text]7 nAChR. This study provides valuable insights for the design of more effective and selective ligands for the [Formula: see text]7 nAChR.
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