Predicting the Mechanism of Tiannanxing-shengjiang Drug Pair in Treating Pain Using Network Pharmacology and Molecular Docking Technology

机制(生物学) 计算生物学 生物 对接(动物) 药理学 药品 化学 医学 认识论 哲学 护理部
作者
Boning Wang,Yanlei Wang,Peng Mao,Yi Zhang,Yifan Li,Xing Liu,Bifa Fan
出处
期刊:Current Computer - Aided Drug Design [Bentham Science Publishers]
卷期号:20 (5): 463-473
标识
DOI:10.2174/1573409919666230525122447
摘要

Objective: This study aimed to analyze the potential targets and mechanism of the Tiannanxing-shengjiang drug pair in pain treatment using network pharmacology and molecular docking technology. Methods: The active components and target proteins of Tiannanxing-Shengjiang were obtained from the TCMSP database. The pain-related genes were acquired from the DisGeNET database. The common target genes between Tiannanxing-Shengjiang and pain were identified and subjected to the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analyses on the DAVID website. AutoDockTools and molecular dynamics simulation analysis were used to assess the binding of the components with the target proteins. Results: Ten active components were screened out, such as stigmasterol, β-sitosterol, and dihydrocapsaicin. A total of 63 common targets between the drug and pain were identified. GO analysis showed the targets to be mainly associated with biological processes, such as inflammatory response and forward regulation of the EKR1 and EKR2 cascade. KEGG analysis revealed 53 enriched pathways, including pain-related calcium signaling, cholinergic synaptic signaling, and serotonergic pathway. Five compounds and 7 target proteins showed good binding affinities. These data suggest that Tiannanxing-shengjiang may alleviate pain through specific targets and signaling pathways. Conclusion: The active ingredients in Tiannanxing-shengjiang might alleviate pain by regulating genes, such as CNR1, ESR1, MAPK3, CYP3A4, JUN, and HDAC1 through the signaling pathways, including intracellular calcium ion conduction, cholinergic prominent signaling, and cancer signaling pathway.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
哈哈哈完成签到,获得积分10
刚刚
丘比特应助个性友蕊采纳,获得10
刚刚
萌小萌发布了新的文献求助10
刚刚
三三发布了新的文献求助10
1秒前
心落失发布了新的文献求助10
1秒前
彰武完成签到,获得积分10
1秒前
所所应助melonnale采纳,获得10
2秒前
科研小王子完成签到,获得积分10
2秒前
龙潭鑫发布了新的文献求助10
2秒前
3秒前
星辰大海应助苏酒采纳,获得10
3秒前
3秒前
ljyyy完成签到,获得积分10
4秒前
Ava应助背后的元龙采纳,获得10
4秒前
Dalalal发布了新的文献求助10
4秒前
ds发布了新的文献求助10
5秒前
5秒前
5秒前
大模型应助传统的幻波采纳,获得30
5秒前
轩辕沛柔发布了新的文献求助10
5秒前
harry2021完成签到,获得积分10
5秒前
7秒前
脑洞疼应助qhy采纳,获得10
7秒前
7秒前
Orange应助清枫采纳,获得10
7秒前
Xxxxxxx发布了新的文献求助10
7秒前
xiajingsong完成签到,获得积分10
8秒前
OOO发布了新的文献求助10
8秒前
踏实的听白完成签到,获得积分20
9秒前
9秒前
研友_8Y0reZ发布了新的文献求助10
9秒前
9秒前
家若完成签到 ,获得积分10
9秒前
cc发布了新的文献求助20
10秒前
11秒前
刁德一完成签到,获得积分20
11秒前
vungocbinh完成签到,获得积分10
11秒前
Ada发布了新的文献求助10
11秒前
马桶盖盖子完成签到 ,获得积分10
11秒前
ds完成签到,获得积分20
12秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Handbook of pharmaceutical excipients, Ninth edition 5000
Aerospace Standards Index - 2026 ASIN2026 3000
Digital Twins of Advanced Materials Processing 2000
Polymorphism and polytypism in crystals 1000
Signals, Systems, and Signal Processing 610
Discrete-Time Signals and Systems 610
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 纳米技术 有机化学 物理 生物化学 化学工程 计算机科学 复合材料 内科学 催化作用 光电子学 物理化学 电极 冶金 遗传学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 6040936
求助须知:如何正确求助?哪些是违规求助? 7778635
关于积分的说明 16232424
捐赠科研通 5186891
什么是DOI,文献DOI怎么找? 2775644
邀请新用户注册赠送积分活动 1758672
关于科研通互助平台的介绍 1642237