Computer‐Assisted Screening of Active Compounds in Traditional Chinese Medicine Targeting SNX10 as a Promising Treatment for Inflammatory Bowel Diseases

炎症性肠病 医学 药理学 中医药 炎症性肠病 重症监护医学 内科学 疾病 替代医学 病理
作者
Yongpan An,Bowen Zhang,Yuwei Ye,Xianghong Wang,Chi Zhang,Jianjun Ding,Ke Gao,Yanan Ouyang,Ruixiao Li,Ying Yi,Xiaorong Xue,Guojun Wu
出处
期刊:Basic & Clinical Pharmacology & Toxicology [Wiley]
卷期号:137 (4): e70098-e70098 被引量:1
标识
DOI:10.1111/bcpt.70098
摘要

Inflammatory bowel diseases (IBD) are chronic and recurrent gastrointestinal disorders affecting millions worldwide, imposing significant social and economic burdens. Safe and effective medications for IBD prevention and treatment are urgently needed. SNX10 has emerged as a potential therapeutic target, while traditional Chinese medicine (TCM) active compounds offer unique advantages in drug development due to their inherent safety and therapeutic properties. This study aimed to identify TCM compounds targeting SNX10 using molecular docking, molecular dynamics (MD) and MMGBSA binding free energy calculations. From a pool of 300 + TCM compounds, vitexin-4″-O-glucoside, scutellarin, diosmin and alpha-hederin were identified as promising candidates. Alpha-hederin exhibited the strongest binding affinity (-50.19 kJ/mol) via robust electrostatic and hydrophobic interactions, as revealed by MMGBSA, correlating with its superior efficacy in alleviating DSS-induced IBD in mice. Additionally, surface plasmon resonance (SPR) results showed that alpha-hederin can directly bind to SNX10 with a dissociation constant (Kd) of 3.02 μM. RNA-sequencing results show that alpha-hederin works by reducing inflammation and promoting gut cell proliferation. These findings not only propose novel TCM candidates for IBD management but also reinforce SNX10 as a therapeutic target and provide a scalable screening framework for TCM-based drug discovery.
最长约 10秒,即可获得该文献文件

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
1秒前
2秒前
qianlu完成签到,获得积分10
3秒前
6秒前
6秒前
烽火完成签到,获得积分10
6秒前
qianlu发布了新的文献求助10
8秒前
一蓑烟雨完成签到,获得积分10
9秒前
11秒前
molihuakai的应助被烽火采纳,获得30
11秒前
JamesPei的应助被烽火采纳,获得10
11秒前
12秒前
斯文半烟完成签到,获得积分10
16秒前
lvsehx发布了新的文献求助10
16秒前
17秒前
小小雪完成签到 ,获得积分10
18秒前
乐乐的应助被账号已注销采纳,获得10
19秒前
20秒前
22秒前
清爽的千易完成签到,获得积分10
23秒前
大模型的应助被赵玮佳采纳,获得10
23秒前
邓青坤完成签到 ,获得积分10
23秒前
666完成签到,获得积分10
25秒前
weitao0916完成签到,获得积分10
25秒前
26秒前
老的火龙果的应助被lvsehx采纳,获得10
27秒前
愉快的乾发布了新的文献求助10
28秒前
李希完成签到,获得积分10
29秒前
大个的应助被念汐采纳,获得30
29秒前
30秒前
贪玩的秋柔的应助被干净的琦采纳,获得50
31秒前
Forever完成签到 ,获得积分10
32秒前
33秒前
Tao完成签到,获得积分10
33秒前
34秒前
睡觉晒太阳完成签到,获得积分10
35秒前
35秒前
Li发布了新的文献求助10
36秒前
sagitar的应助被Mr.xu采纳,获得20
38秒前
Docter发布了新的文献求助10
38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
Research Methodology: Best Practices for Rigorous, Credible, and Impactful Research 1000
自動車の空力技術 800
Essentials of Carbohydrate Chemistry and Biochemistry, 4th Edition 800
Organizational Behavior 510
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 计算机科学 化学工程 工程类 有机化学 物理 复合材料 生物化学 内科学 细胞生物学 基因 遗传学 免疫学 冶金 光电子学 癌症研究
热门帖子
关注 科研通微信公众号,转发送积分 7783102
求助须知:如何正确求助?哪些是违规求助? 9322551
关于积分的说明 20390277
捐赠科研通 7371800
什么是DOI,文献DOI怎么找? 3320576
关于科研通互助平台的介绍 2468623
邀请新用户注册赠送积分活动 2336780