目的:借助网络药理学及分子对接方法探讨头花蓼治疗糖尿病的作用机制,为扩大头花蓼用药范围提供依据。方法:通过文献研究及Swiss数据库筛选得到头花蓼活性成分并分析相关作用蛋白靶点,借助OMIM、GeneCards、DRUG BANK、DisGeNet等数据库分析糖尿病基因靶点,并取交集;借助STRING数据库及Cytoscape软件构建PPI网络关系并得到头花蓼干预糖尿病关键靶点;借助metascape数据库进行生物富集分析;使用autodock软件进行核心化合物–蛋白分子对接。结果:得到头花蓼核心化合物为49个,涉及45个蛋白靶点,其中与糖尿病相关核心靶点为ALB、EGFR、SRC、CASP3等;在60组分子对接结果中,Affinity < −3 kcal∙mol−1的对接结果有45个,提示多数化合物–蛋白有较大结合能;KEGG富集分析结果显示关键通路为Lipid and atherosclerosis、Pathways in cancer、IL-17 signaling pathway等。结论:头花蓼干预糖尿病依靠多通路、多靶点协调作用,其主要活性成分为槲皮素、红景天甘、木犀草素、齐墩果酸等,通过ALB、EGFR、SRC、CASP3等蛋白作用与Lipid and atherosclerosis、Pathways in cance、IL-17 signaling pathway等通路发挥治疗糖尿病作用。 Objective: To explore the mechanism of action of Polygonum cuspidatum in the treatment of diabetes mellitus with the help of network pharmacology and molecular docking methods, and to provide a basis for expanding the scope of drug use of Polygonum cuspidatum. Methods: We obtained the active ingredients of Polygonum cuspidatum through literature research and screening of Swiss database and analyzed the related protein targets, analyzed the diabetes gene targets with the help of OMIM, GeneCards, DRUG BANK, DisGeNet and other databases, and took the intersection; constructed PPI network relationships with the help of STRING database and Cytoscape software and obtained the key targets of Polygonum cuspidatum for diabetes intervention were obtained by using STRING database and Cytoscape software; bioenrichment analysis was performed by using metascape database; core compound-protein molecular docking was performed by using autodock software. Results: 49 core compounds were obtained, involving 45 protein targets, including ALB, EGFR, SRC and CASP3, which are related to diabetes. Among the 60 groups of molecular docking results, 45 docking results with Affinity < −3 kcal∙mol−1 suggested that most of the compounds had large binding energy to proteins; the results of KEGG enrichment analysis showed that the key pathways were Lipid and atherosclerosis, Pathways in cancer, IL-17 signaling pathway, etc. Conclusion: Polygonum cuspidatum intervenes in diabetes mellitus relying on multi-pathway and multi-target coordination, and its main active ingredients are quercetin, rhodopsin, lignan, oleanolic acid, etc., which act with Lipid and atherosclerosis, Pathways in cance, IL-17 signaling pathway through ALB, EGFR, SRC, CASP3 and other proteins signaling pathway and other pathways to play a role in the treatment of diabetes.
目的:借助网络药理学及分子对接方法探讨头花蓼治疗糖尿病的作用机制,为扩大头花蓼用药范围提供依据。方法:通过文献研究及Swiss数据库筛选得到头花蓼活性成分并分析相关作用蛋白靶点,借助OMIM、GeneCards、DRUG BANK、DisGeNet等数据库分析糖尿病基因靶点,并取交集;借助STRING数据库及Cytoscape软件构建PPI网络关系并得到头花蓼干预糖尿病关键靶点;借助metascape数据库进行生物富集分析;使用autodock软件进行核心化合物–蛋白分子对接。结果:得到头花蓼核心化合物为49个,涉及45个蛋白靶点,其中与糖尿病相关核心靶点为ALB、EGFR、SRC、CASP3等;在60组分子对接结果中,Affinity < −3 kcal∙mol−1的对接结果有45个,提示多数化合物–蛋白有较大结合能;KEGG富集分析结果显示关键通路为Lipid and atherosclerosis、Pathways in cancer、IL-17 signaling pathway等。结论:头花蓼干预糖尿病依靠多通路、多靶点协调作用,其主要活性成分为槲皮素、红景天甘、木犀草素、齐墩果酸等,通过ALB、EGFR、SRC、CASP3等蛋白作用与Lipid and atherosclerosis、Pathways in cance、IL-17 signaling pathway等通路发挥治疗糖尿病作用。
网络药理学,分子对接,糖尿病,头花蓼
Zhiliang Fan1, Yiping Yan2, Deqing Miao3, Lailai Li2, Xiang Pu2, Liyan Zhang1, Guizhen Liu1, Jingwen Tang4, Pei Huang5, Yihui Chai2*
1School of Pharmacy, Guizhou University of Traditional Chinese Medicine, Guiyang Guizhou
2School of Basic Medical Sciences, Guizhou University of Traditional Chinese Medicine, Guiyang Guizhou
3School of Information Engineering, Guizhou Food Engineering Vocational College, Guiyang Guizhou
4Guizhou Weimen Pharmaceutical Co., Ltd., Guiyang Guizhou
5Inspection Center of Guizhou Drug Administration, Guiyang Guizhou
Received: Mar. 13th, 2023; accepted: Mar. 21st, 2023; published: May 18th, 2023
Objective: To explore the mechanism of action of Polygonum cuspidatum in the treatment of diabetes mellitus with the help of network pharmacology and molecular docking methods, and to provide a basis for expanding the scope of drug use of Polygonum cuspidatum. Methods: We obtained the active ingredients of Polygonum cuspidatum through literature research and screening of Swiss database and analyzed the related protein targets, analyzed the diabetes gene targets with the help of OMIM, GeneCards, DRUG BANK, DisGeNet and other databases, and took the intersection; constructed PPI network relationships with the help of STRING database and Cytoscape software and obtained the key targets of Polygonum cuspidatum for diabetes intervention were obtained by using STRING database and Cytoscape software; bioenrichment analysis was performed by using metascape database; core compound-protein molecular docking was performed by using autodock software. Results: 49 core compounds were obtained, involving 45 protein targets, including ALB, EGFR, SRC and CASP3, which are related to diabetes. Among the 60 groups of molecular docking results, 45 docking results with Affinity < −3 kcal∙mol−1suggested that most of the compounds had large binding energy to proteins; the results of KEGG enrichment analysis showed that the key pathways were Lipid and atherosclerosis, Pathways in cancer, IL-17 signaling pathway, etc. Conclusion: Polygonum cuspidatum intervenes in diabetes mellitus relying on multi-pathway and multi-target coordination, and its main active ingredients are quercetin, rhodopsin, lignan, oleanolic acid, etc., which act with Lipid and atherosclerosis, Pathways in cance, IL-17 signaling pathway through ALB, EGFR, SRC, CASP3 and other proteins signaling pathway and other pathways to play a role in the treatment of diabetes.
Keywords:Network Pharmacology, Molecular Docking, Diabetes, Polygonum cephalicum
Copyright © 2023 by author(s) and beplay安卓登录
This work is licensed under the Creative Commons Attribution International License (CC BY 4.0).
http://creativecommons.org/licenses/by/4.0/
糖尿病是一种以多饮、多食、多尿、消瘦为主要临床表现、血糖代谢紊乱为病理特征的代谢障碍性疾病 [
热淋清颗粒是由传统苗药头花蓼为原料的单方制剂,本文通过对头花蓼相关化学成分实验归纳总结,借助chemdraw软件绘制各化合物结构并导入SwissADME数据库,以胃肠吸率“高”,五项类药性评价中至少获得2个“是”为原则,若有研究已证实与糖尿病相关性较高的化合物,则仍保留入选头花蓼干预糖尿病潜在化合物。将所得化合物逐一导入PharmMapper数据库得到各化合物核心作用靶点,以NF ≥ 0.9为标准筛选关键靶点,并结合相关文献研究,最终获得头花蓼核心基因靶点。
以“diabetes mellitus”、“glycuresis”、“diabetes”等为检索关键词,检索GeneCards数据库、DRUGBANK数据库、OMIM数据库、TTD数据库获取糖尿病核心基因靶点信息。在这些数据库中,DRUGBANK数据库以临床实验结果构建,GeneCards、OMIM、Therapeutic Target Database数据库基于文献研究构建,因此选用多个数据库进行糖尿病基因靶点获取有益于得到更加系统的靶点数据。
将“2.2.”项所得靶点信息导入STRING分析平台,以物种为“智人”,置信度为0.4为标准,分析建立PPI网络拓扑关系,借助CytoScape 3.7.2软件进一步优化结果,获得头花蓼干预糖尿病关键基因靶点信息。
将“2.3.”项结果导入Metascape平台进行核心基因靶点的生物信息数据富集分析,其中包括GOBP (生物过程) GOMF (分子功能) GOCC (细胞成分)及KEGG通路。将筛选结果构建富集条形分析图及通路气泡图。
整理“2.4.”项分析结果及头花蓼–糖尿病交集基因数据并导入Cytoscape3.7.2软件建立:头花蓼成分–靶点–通路网络拓扑图,根据介度、连接度等分析得到该网络中的核心靶点及药物干预疾病的核心成分。
通过对2.3.项结果分析得到该网络中度值排名最高的6个靶点蛋白,并结合RCSB平台下载该蛋白pdb格式,借助Openbabel软件将头花蓼活性前10化合物转化为pdb格式。借助pymol及Autodock软件对蛋白及化合物进行预处理并进行分子对接,以结合能(Affinity)作为判断二者结合活性的参考。
通过文献研究共收集到100个头花蓼所含化合物,其中主要以黄酮、木质素及挥发油类为主。利用SwissADME平台共筛选出49个化合物,利用PharmMapper平台进行预测并去除所有重复基因后共计得到114个预测靶点。
利用Genecards平台共收集到糖尿病相关靶点17,703个,选取Score ≥ 3.88的结果结合TTD、DRUGBANK、OMIM数据库进行补充,共获得1386个核心疾病靶点。将头花蓼核心靶点与糖尿病核心靶点取交集,VENN分析得到45个核心作用靶点,见图1。
图1. 药物–疾病靶点VENN图
图2. 头花蓼–糖尿病靶点PPI网络
将“3.2.”项所得45个交集靶点导入String11.5平台做PPI网络分析,见图2。该网络共44个节点数,225条边,根据度值推测ALB、EGFR、SRC、CASP3等为核心靶点。
利用Metascape平台对交集基因进行生物信息富集分析,GO分析结果包括GO BP 441条,GO CC 35条,GO MF 63条;KEGG富集分析得到103条信号通路;取GO及KEGG分析结果排名前20建立生物信息富集分析图,见图3。
图3. 头花蓼主要成分潜在靶点的富集分析(注:A:GO-BP分析;B:GO-CC分析;C:GO-MF分析;D:KEGG分析)
使用CytoScape3.7.2软件建立“头花蓼成分–靶点–通路”网络拓扑关系图,并分析得到头花蓼干预糖尿病靶点网络信息数据、核心化学成分及作用靶点,见图4。此网络包含117个节点553条边,其中红色节点表示头花蓼潜在作用靶点,橙色节点表示潜在信号通路,绿色节点表示活性化学成分,连线代表不同节点间的作用关系,图中节点面积越大、颜色越深表明其对糖尿病的影响越大。
通过对网络分析发现,该网络平均度值为6.698,平均介度为0.014,平均紧密的为0.317,且头花蓼每种活性成分对应多个靶点,每个靶点连接多种成分,体现了头花蓼内含的多种成分可能通过多靶点干预糖尿病。其中THL27 (槲皮素)度值为39,介度为0.151,紧密度为0.423,推测THL27可能是头花蓼干预糖尿病的核心化学成分(表1)。MAPK10、CA2、MAPK14、MAPK1、MAPK8靶点基因度值排名靠前(表2)。故预测MAPK通路为头花蓼干预糖尿病的核心通路。
将头花蓼中排名前十名的化合物与ALB、SRC、EGFR、ESR1、CASP3、MAPK1 6个核心蛋白进行分子对接,从而得到60组化合物–蛋白对接结果。其中Affinity < −3 kcal∙mol−1的对接结果有45个,Affinity < −10 kcal∙mol−1的有5个,其中对接得分最高的为SRC-THL37,分值为−16.03 kcal∙mol−1。本文对接结果为进一步进行头花蓼相关实验提供理论支撑,对接结果见图5,核心对接模式见图6。
化合物 | Degree | Betweenness Centrality | Closeness Centrality |
---|---|---|---|
槲皮素(Quercetin) | 39 | 0.15055658 | 0.4225 |
十六烷酸(Palmitate) | 33 | 0.21272711 | 0.43112245 |
酒渣碱(Flazin) | 25 | 0.06029352 | 0.39671362 |
红景天苷(Salidroside) | 23 | 0.08697401 | 0.36266094 |
3-甲氧基槲皮素(3-O-Methylquercetin) | 23 | 0.0423299 | 0.38940092 |
短叶苏木酚酸乙酯(Ethyl brevifolincarboxylate) | 21 | 0.04826872 | 0.38761468 |
木犀草素(Luteolin) | 20 | 0.03989139 | 0.36899563 |
齐墩果酸(Oleanic acid) | 20 | 0.06245947 | 0.38063063 |
儿茶素(catechin) | 15 | 0.02942223 | 0.34489796 |
没食子酸乙酯(Ethyl gallate) | 15 | 0.01733204 | 0.37061404 |
表1. 头花蓼主要活性成分网络节点特征参数
name | Degree | Betweenness | name |
---|---|---|---|
MAPK10 | 26 | 0.05465465 | 0.3912037 |
CA2 | 25 | 0.16553936 | 0.45187166 |
MAPK14 | 23 | 0.06085452 | 0.40238095 |
MAPK1 | 23 | 0.06476933 | 0.39858491 |
MAPK8 | 21 | 0.03149689 | 0.37723214 |
GSTP1 | 17 | 0.04476366 | 0.40238095 |
EGFR | 17 | 0.04472257 | 0.36580087 |
ESR1 | 16 | 0.02321246 | 0.35654008 |
BCHE | 16 | 0.04419829 | 0.40238095 |
AR | 15 | 0.02812022 | 0.38940092 |
表2. 头花蓼主要活性成分靶点网络节点特征参数
图4. 头花蓼干预糖尿病化成分–靶点–通路图
图5. 分子对接结果
图6. 头花蓼部分核心化合物分子对接模式
2型糖尿病是由机体胰岛素抵抗造成的体内葡萄糖代谢障碍 [
中医中糖尿病属于“消渴”的范畴,首载于《素问·奇病论篇》。唐代《外台秘要》云“渴而饮水不能多,小便数,阴痿弱,但腿肿 [
头花蓼为蓼科植物头花蓼的干燥全草或地上部分,是贵州省的特色苗药之一 [
本研究利用网络药理学方法,构建成分–靶点–信号通路网络,通过分析相关网络要素(节点、度值等),探讨了头花蓼治疗糖尿病主要物质基础。
通过网络药理学方法,发现头花蓼中槲皮素、红景天甘,木犀草素,齐墩果酸为潜在治疗糖尿病的有效成分。铁死亡可导致胰腺β细胞(PBC)丢失和功能障碍,研究结果表明,槲皮素除有抗炎、抗氧化作用外 [
网络拓扑分析结果显示,头花蓼活性成分49个,对应114个蛋白靶点,其中与糖尿病相关的共同靶点45个。通过构建PPI网络发现,ALB、EGFR、SRC、CASP3等的度值较大,可能是头花蓼最关键的治疗糖尿病靶点。Caspase-3是一种凋亡蛋白酶,研究发现,糖尿病患者血浆中Caspase-3增加 [
KEGG信号通路分析发现头花蓼主要是通过Lipid and atherosclerosis (脂质和动脉粥样硬化),Pathways in cancer,IL-17 signaling pathway (IL-17信号通路)等通路抵抗糖尿病。高胰岛素血症及血脂异常是糖尿病患癌的重要因素 [
本研究借助网络药理学及分子对接技术,利用数据库进行靶点预测、蛋白互作关系、生物富集分析,初步探明头花蓼作用与糖尿病的多通路、多靶点协调作用。头花蓼可能通过槲皮素、红景天甘,木犀草素,齐墩果酸等主要活性成分,作用于ALB、EGFR、SRC、CASP3等蛋白靶点,参与Akt/mTOR、PI3K/Akt、IL-17信号通路等经典通路发挥治疗糖尿病作用。本研究采用网络药理学及分子对接方法初步探讨了头花蓼干预糖尿病的作用机制,为相关新药开发提供理论依据,但本研究仍存在一定的局限性,后续研究团队将在此基础上开展动物体内实验,进一步探明头花蓼干预糖尿病的作用机制,为中医药防治糖尿病提供新思路。
基金项目
贵阳市科技计划项目筑科合同[
范志梁,晏一平,苗得庆,李来来,蒲 翔,张丽艳,刘贵珍,唐靖雯,黄 蓓,柴艺汇. 基于网络药理学及分子对接技术探讨头花蓼治疗糖尿病的预防作用Mechanism of Cephalophyllum cephalus Intervention in Diabetes Based on Network Pharmacology and Molecular Docking Technology[J]. 药物化学, 2023, 11(02): 81-92. https://doi.org/10.12677/HJMCe.2023.112011
https://doi.org/10.2147/DDDT.S341354
https://doi.org/10.2337/dc13-S067
https://doi.org/10.4093/dmj.2021.0077
https://doi.org/10.1007/s00394-018-1713-2
https://doi.org/10.3390/nu12102954
https://doi.org/10.1007/s40291-019-00402-4
https://doi.org/10.1016/j.phymed.2018.11.034
https://doi.org/10.3390/nu14030623
https://doi.org/10.1007/s10495-008-0254-1
https://doi.org/10.1159/000447907
https://doi.org/10.3390/ijms19113634
https://doi.org/10.3390/biom12040542
https://doi.org/10.1530/JME-13-0152
https://doi.org/10.1038/s41574-020-0329-9
https://doi.org/10.1016/j.ctrv.2018.08.004
https://doi.org/10.3390/ijms21051835
https://doi.org/10.1016/j.chom.2017.06.014
https://doi.org/10.1159/000351896