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Volume 38 Issue 6
Nov.  2020
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FENG Qun, GUAN Yongxia, HUANG Zhiyan, YE Shili, CHENG Guoliang, YAO Jingchun, ZHANG Guimin. Study on active ingredients of Jingfang Baidu San for preventing COVID-19 based on network pharmacology and molecular docking[J]. Journal of Pharmaceutical Practice and Service, 2020, 38(6): 485-491, 538. doi: 10.12206/j.issn.1006-0111.202005078
Citation: FENG Qun, GUAN Yongxia, HUANG Zhiyan, YE Shili, CHENG Guoliang, YAO Jingchun, ZHANG Guimin. Study on active ingredients of Jingfang Baidu San for preventing COVID-19 based on network pharmacology and molecular docking[J]. Journal of Pharmaceutical Practice and Service, 2020, 38(6): 485-491, 538. doi: 10.12206/j.issn.1006-0111.202005078

Study on active ingredients of Jingfang Baidu San for preventing COVID-19 based on network pharmacology and molecular docking

doi: 10.12206/j.issn.1006-0111.202005078
  • Received Date: 2020-05-26
  • Rev Recd Date: 2020-10-16
  • Publish Date: 2020-11-25
  •   Objective  To investigate the active ingredients of Jingfang Baidu San for the prevention and treatment of COVID-19 by using network pharmacology and molecular docking, and to provide references for clinical applications.  Methods  The chemical constituents and action targets of all medicinal materials in Jingfang Baidu San were retrieved from TCMSP. Uniprot database was used to search the corresponding genes of targets. Cytoscape software was used to construct the network of medicinal materials-compounds-targets for visualization. The target proteins of COVID-19 were searched by disease databases. The intersection of both was considered to be analyzed to establish the protein-protein interaction (PPI) network by STRING database. GO function enrichment analysis and KEGG pathway enrichment analysis were performed through Metascape database to predict its mechanism. The effective strength of core constituents on preventing COVID-19 was calculated by molecular docking method.  Results  A total of 159 effective ingredients and 277 potential targets were obtained in Jingfang Baidu San within the given screening conditions [oral bioavailability (OB) ≥30%; drug-like (DL) ≥ 0.18], including 55 core targets with the intersection of 273 targets of COVID-19. According to the results of GO and KEGG enrichment analysis performed on the core targets, 1376 GO items and 136 KEGG pathways were obtained, involving infectious diseases, cancer, cell progress, immune system, signaling pathways etc. The results of molecular docking indicated strong binding capacity between the core ingredients and SARS-CoV-2 3CL hydrolase or angiotensin-converting enzyme II (ACE2). The hydrogen binding and hydrophobic effect were the main forms of the interaction.  Conclusion  The active ingredients in Jingfang Baidu San can inhibit the binding between SARS-CoV-2 protein and ACE2, thus regulating multiple targets and signal pathways, which plays a role in the prevention and the treatment of COVID-19.
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Study on active ingredients of Jingfang Baidu San for preventing COVID-19 based on network pharmacology and molecular docking

doi: 10.12206/j.issn.1006-0111.202005078

Abstract:   Objective  To investigate the active ingredients of Jingfang Baidu San for the prevention and treatment of COVID-19 by using network pharmacology and molecular docking, and to provide references for clinical applications.  Methods  The chemical constituents and action targets of all medicinal materials in Jingfang Baidu San were retrieved from TCMSP. Uniprot database was used to search the corresponding genes of targets. Cytoscape software was used to construct the network of medicinal materials-compounds-targets for visualization. The target proteins of COVID-19 were searched by disease databases. The intersection of both was considered to be analyzed to establish the protein-protein interaction (PPI) network by STRING database. GO function enrichment analysis and KEGG pathway enrichment analysis were performed through Metascape database to predict its mechanism. The effective strength of core constituents on preventing COVID-19 was calculated by molecular docking method.  Results  A total of 159 effective ingredients and 277 potential targets were obtained in Jingfang Baidu San within the given screening conditions [oral bioavailability (OB) ≥30%; drug-like (DL) ≥ 0.18], including 55 core targets with the intersection of 273 targets of COVID-19. According to the results of GO and KEGG enrichment analysis performed on the core targets, 1376 GO items and 136 KEGG pathways were obtained, involving infectious diseases, cancer, cell progress, immune system, signaling pathways etc. The results of molecular docking indicated strong binding capacity between the core ingredients and SARS-CoV-2 3CL hydrolase or angiotensin-converting enzyme II (ACE2). The hydrogen binding and hydrophobic effect were the main forms of the interaction.  Conclusion  The active ingredients in Jingfang Baidu San can inhibit the binding between SARS-CoV-2 protein and ACE2, thus regulating multiple targets and signal pathways, which plays a role in the prevention and the treatment of COVID-19.

FENG Qun, GUAN Yongxia, HUANG Zhiyan, YE Shili, CHENG Guoliang, YAO Jingchun, ZHANG Guimin. Study on active ingredients of Jingfang Baidu San for preventing COVID-19 based on network pharmacology and molecular docking[J]. Journal of Pharmaceutical Practice and Service, 2020, 38(6): 485-491, 538. doi: 10.12206/j.issn.1006-0111.202005078
Citation: FENG Qun, GUAN Yongxia, HUANG Zhiyan, YE Shili, CHENG Guoliang, YAO Jingchun, ZHANG Guimin. Study on active ingredients of Jingfang Baidu San for preventing COVID-19 based on network pharmacology and molecular docking[J]. Journal of Pharmaceutical Practice and Service, 2020, 38(6): 485-491, 538. doi: 10.12206/j.issn.1006-0111.202005078
  • 2019年12月,武汉市出现多例不明原因的病毒性肺炎病例,病例临床表现主要为发热、咳嗽,少数病人腹泻、呕吐、呼吸困难,胸片呈双肺浸润性病灶[1]。2020年2月11日,世界卫生组织将该病命名为新型冠状病毒肺炎(corona virus disease 2019,COVID-19),并称引起该病的病毒为SARS-CoV-2,与成年人相比,儿童更不易感染该病毒,65岁以上老年人更易受感染[2]。目前全球疫情愈演愈烈,国内用了两个多月控制住疫情,中医药做出了巨大贡献,但部分地区输入性病例和无症状感染者不断增加,寻找相应的群体性配方,是当前一项十分紧迫的研究任务。据古文献记载,加上黄煌教授临床经验和近期的个案报道,建议可以采用两首古代相传的治疗时令病的经验成方——荆防败毒散和十神汤,作为群体性预防用方[3]

    荆防败毒散,出自《摄生众妙方》,由荆芥、防风、羌活、独活、柴胡、前胡、川芎、枳壳、茯苓、桔梗、甘草等十一味中药组成,已上市的中成药包括荆防颗粒、荆防合剂。临床研究表明,荆防败毒散能缓解发热、咳嗽、喘息与肺部啰音等作用,调节机体炎症因子和细胞免疫状况,增强机体的免疫功能[4-6]。现代药理学研究证明其具有解热、镇痛和抗炎的作用[7]

    本文通过网络药理学筛选出荆防败毒散作用靶点,并进行聚类分析,预测荆防败毒散中核心活性成分,进而运用分析软件对药材-成分-靶点进行分子对接及信号通路分析,并预测其治疗COVID-19的作用机制,为荆防败毒散用于预防及治疗COVID-19的可能性提供理论参考。

  • 借助中药系统药理分析平台(TCMSP,http://tcmspw.com/tcmsp.php)[8],分别以荆芥、防风、羌活、独活、柴胡、前胡、川芎、枳壳、茯苓、桔梗、甘草为关键词搜索荆防败毒散中的成分。本研究结合口服生物利用度(OB≥30%)和类药性(DL≥0.18),筛选收集到的化学成分,并结合《中国药典》2015年版中药物的含量测定项对已筛选的成分进行补充,最终建立荆防败毒散的成分库。

  • 经OB和DL筛选合格的成分,在TCMSP数据库对其成分靶点进行收录。对未在TCMSP中收录靶点的成分,在PubChem查询成分对应的Canonical SMILES序列,并利用此序列在SwissTarget数据库[9](http://www.swisstargetprediction.ch/)中对该成分的靶点进行预测,收集预测结果中的靶标蛋白基因名称。最后对收集的所有靶点在Unitprot数据库[10](http://www.Unitprot.org/)输入蛋白名称并限定来源为Homo sapiens,获取官方基因名作为荆防败毒散的靶点库。通过Cytoscape 3.6.1软件,构建荆防败毒散药材-成分-靶点网络,分析成分和靶点网络。

  • 在CTD、NCBI和GeneCards数据库中,以“COVID-19”、“novel coronavirus pneumonia”等检索词检索,检索时间为2020年7月13日。将检索结果合并、去重,获取新冠肺炎疾病相关基因,并把相关基因编码的蛋白质作为药物治疗的潜在作用靶点。

  • 将荆防败毒散成分的靶点与COVID-19靶点分别导入String数据库,获取荆防败毒散成分靶点和COVID-19靶点的蛋白-蛋白相互作用(PPI)关系,通过Cytoscape软件Merge功能,取两者交集,挖掘关键靶点网络。

  • 为了进一步了解上述筛选出的靶标蛋白基因的功能及在信号通路中的作用,将筛选得到的作用靶点导入Metascape数据库[11](https://metascape.org/),通过输入靶基因名称列表并限定物种为人,进行GO(gene ontology)生物过程(BP,Biological Process)、细胞组成(CC,cellular component)、分子功能(MF,molecular function)富集分析和KEGG(kyoto encyclopedia of genes and genomes)信号通路富集分析,并利用R 4.0.0软件将其结果可视化。

  • 从ZINC数据库[12](http://zinc.docking.org/)下载Degree值前10成分的mol2格式文件,用Autodock Tool软件打开该成分,使其能量最小化并判定成分的Root、选定可扭转的键,保存为*pdbqt格式文件。从PDB数据库[13](https://www.rcsb.org/)下载SARS-CoV-2 3CL水解酶(Mpro,PDB ID: 6LU7)和血管紧张素转化酶II(ACE2,PDB ID: 1R42)的3D结构PDB格式文件[14-15],运用Pymol软件移除靶蛋白中的配体和非蛋白分子(如水分子),再保存为PDB格式文件。随后用Autodock Tool软件打开的PDB文件,加氢、计算电荷并给蛋白添加原子类型(Assign AD4 type),将其保存为*pdbqt格式文件[16]

    运用Autodock Vina将成分和受体对接。结合能小于0说明配体与受体可以自发结合,目前对于活性分子的靶点筛选尚无统一标准,本文根据结合能进行排序,结合能数值的绝对值越大,对接结果较好,该成分可视为荆防败毒散预防COVID-19的潜在活性成分。

  • 从TCMSP数据库中搜索荆防败毒散各味药的成分,并依据OB≥30%及DL≥0.18要求,得到最终选定的结果为187个不同的成分(28个无已知靶点),其中荆芥11个、防风18个、羌活15个、独活9个、柴胡17个、前胡24个、川芎7个、枳壳5个、茯苓15个、桔梗7个、甘草92个。筛选后的荆防败毒散中部分活性成分的基本信息见表1

    成分名称MOL IDMWOB (%)DL药味归属
    β谷甾醇MOL000358414.7936.910.75荆芥、防风、羌活、前胡、独活、枳壳
    谷甾醇MOL000359414.7936.910.75荆芥、防风、羌活、前胡、川芎、甘草
    槲皮素MOL000098302.2546.430.28荆芥、柴胡、前胡、甘草
    异欧前胡素MOL001942270.3045.460.23防风、前胡、羌活、独活
    欧前胡素MOL001941270.3034.550.22防风、前胡、羌活、独活
    紫花前胡苷MOL004792408.4457.120.69羌活、独活、前胡
    柚皮素MOL004328272.2759.290.21枳壳、甘草
    异鼠李素MOL000354316.2849.600.31柴胡、甘草
    豆甾醇MOL000449412.7743.830.76荆芥、柴胡
    亚油酸乙酯MOL001494308.5642.000.19防风、川芎
    山奈酚MOL000422286.2541.880.24柴胡、甘草
    紫花前胡素MOL013077328.3939.270.38防风、前胡
    木犀草素MOL000006286.2536.160.25荆芥、桔梗
    甘草酚MOL002311366.3990.780.67甘草
    宽叶甘松酸MOL013098328.3987.480.37前胡
    Divaricate acidMOL011737320.3287.000.32防风
    甘草吡喃
    香豆素
    MOL004904384.4180.360.65甘草
    shinpterocarpinMOL004891322.3880.300.73甘草
    芒柄花黄素MOL000392268.2869.670.21甘草
    xambioonaMOL005018388.4954.850.87甘草
    丹参酮IIAMOL007154294.3749.890.40前胡
    异甘草酚MOL004948366.3944.700.84甘草
    去氢齿孔酸MOL000300453.7544.170.83茯苓
    7-甲氧基-2-甲基异黄酮MOL003896266.3142.560.20甘草
    美迪紫檀素-3-O-葡萄糖苷MOL004924432.4640.990.95甘草
    过氧化麦角
    甾醇
    MOL000283430.7440.360.81茯苓
    去氢茯苓酸MOL000276526.8335.110.81茯苓
    茯苓酸MOL000289528.8533.630.81茯苓
    kanzonol FMOL004988420.5432.470.89甘草
    汉黄芩素MOL000173284.2830.680.23防风
  • 利用Cytoscape软件进行“荆防败毒散药材-成分-靶点”网络的构建,网络共包括447个节点(11种药材节点、159个成分节点、277个靶点节点)和2718条边,如图1所示,其中形状“△”代表药材,“〇”代表成分,“◇”代表基因,每条边则表示药材中所含成分及成分与靶点相互作用关系。性状的大小代表Degree值的大小。按照Degree值,排名前10位的成分分别是槲皮素、山奈酚、木樨草素、汉黄芩素、β-谷甾醇、7-甲氧基-2甲基异黄酮、丹参酮IIA、柚皮素、芒柄花黄素、异鼠李素。

  • 在数据库中检索并筛选得到COVID-19相关的273个靶点,将273个疾病靶点和277个荆防败毒散活性成分的作用靶点导入String数据库,得到靶点PPI关系,利用Cytoscape软件将两者进行Merge取交集处理,得到包含55个靶点和766条边的Hub网络,见图2。按照Degree值从高到低,Hub网络中排名前10位的靶点分别为MAPK3、TNF、IL6、CASP3、TP53、MAPK8、MAPK1、IL10、CCL2、MAPK14。

  • 通过Metascape数据库进行的GO功能富集分析得到GO条目1376个(P<0.01),其中BP条目1304个,包括细胞因子和凋亡信号、刺激反应、多生物过程、免疫过程、细胞代谢、生物进程调控等;CC条目19个,包括细胞膜、细胞器膜、基质、转录因子等;MF条目53个,包括酶活性和酶结合、细胞因子活性和结合能力、转录因子结合、蛋白特异性结合等各类别分析中排名前20位的条目,见图3

    KEGG通路富集分析筛选得到136条(P<0.01)通路,涉及与寄生虫、真菌、病毒感染引起的疾病通路有22条(如朊病毒、甲型流感、人类嗜T淋巴细胞病毒I型感染、丙肝、肺结核、疟疾、百日咳等)、癌症相关的通路17条(如非小细胞肺癌、小细胞肺癌、黑色素瘤、癌症中碳代谢、转录失调等)、细胞进程、免疫系统进程、信号通路等。选Count值较大的前20条通路进行可视化,结果见图4

  • 将荆防败毒散中排序前10的核心成分分别与Mpro、ACE2受体进行分子对接。一般认为配体与受体结合的构象稳定时能量越低,发生的作用可能性越大,结合能≤–5.0 kJ/mol作为筛选标准,结合能≤–20.93 kJ/mol时则视为成分与靶点有较好的活性,结合能≤–29.336 kJ/mol时则结合活性强烈[17]。分子对接结果显示,筛选出的荆防败毒散核心成分与Mpro结合能远小于–20.93 kJ/mol,与ACE2受体结合能远小于–29.336 kJ/mol(见表2)。选择结合能均小于–29.336 kJ/mol的β-谷甾醇、丹参酮IIA、芒柄花黄素,对其与Mpro、ACE2的结合形式进行分析,丹参酮IIA可与Mpro的110位谷氨酰胺(GLN)和ACE2的158位络氨酸(TYR)形成氢键(键长20 nm和22 nm);芒柄花黄素可与Mpro的131位精氨酸(ARG)和287位亮氨酸(LEU)分别形成氢键(键长27 nm和19 nm),与ACE2的615位天冬氨酸(ASP)形成氢键(键长22 nm)。氢键、疏水作用可能是荆防败毒散成分与两个受体主要的结合形式,结果见图5。分子对接结果表明荆防败毒散中活性成分与Mpro、ACE2结合活性较强,与后者的结合能力优于前者。

    成分CAS号化学式结合能(kJ/mol)
    MproACE2
    槲皮素117-39-5C15H10O7−27.21−34.33
    山奈酚520-18-3C15H10O6−27.21−32.66
    木樨草素491-70-3C15H10O6−28.89−34.33
    汉黄芩素10-29-7C16H12O5−27.21−33.91
    7-甲氧基-2-甲基异黄酮19725-44-1C17H14O3−25.96−32.24
    β-谷甾醇83-46-5C29H50O−31.40−36.84
    丹参酮IIA568-72-9C19H18O3−30.14−36.43
    异鼠李素480-19-3C16H12O7−27.21−33.49
    芒柄花黄素485-72-3C16H12O4−29.73−30.14
    柚皮素153-18-4C15H12O5−28.47−33.49
  • 突如其来的疫情给人类带来巨大挑战,人类必须努力了解疾病特点,尽快寻找到控制措施[18]。荆防败毒散,由人参败毒散去人参加荆芥、防风而成。以荆芥、防风,羌活、独活发汗解表,开泄皮毛,使风寒之邪随汗而解,为通治一身风寒湿邪的常用组合。柴胡、枳壳、桔梗调畅气机,川芎行血合营,羌活、茯苓化痰渗湿,三组合用,意在解表祛邪与疏通气血津液。甘草调和药性,祛风散寒之力较强,宜于外感风寒湿邪较重者。荆防败毒散治退热效果极佳,用于流行性感冒见效快[19-20]。新冠肺炎疫情属于寒湿疫。因此,基于辨证论治的原则,荆防败毒散可作为群体性预防用药的选择,并对初期轻症(寒湿证)的新冠肺炎有一定治疗效果。

    根据KEGG分析,得到136条通路,包括感染性疾病通路、癌症通路、细胞进程通路、免疫系统通路、信号通路等。KEGG前20条通路中,频率最高的靶点为RELA、MAPK1、MAPK3、TNF、IL6。RELA在调节对感染的免疫应答中起关键作用,而且其磷酸化调节作用可抑制肿瘤的发生[21]。丝裂原活化蛋白激酶(MAPK)是信号从细胞表面传导到细胞核内部的重要传递者。TNF在抗肿瘤、抗感染、免疫、炎症等多种生理病理过程中发挥着关键的作用[22]。结果表明荆防败毒散呈现出中药多成分-多靶点-多途径协同作用的特点,通过对上述靶点的作用,调节感染类疾病通路、免疫损伤性、炎症通路,起到防治COVID-19的作用。

    ACE2是SARS-CoV和SARS-CoV-2的宿主细胞受体,SARS-CoV-2 通过表达的S-蛋白与人体ACE2结合,导致病毒入侵而致病,这可作为治疗COVID-19的突破口[23-24]。Mpro是单正链RNA病毒前体多聚蛋白水解酶核心部分,将宿主细胞内的病毒RNA翻译成蛋白以产生子代毒,在RNA复制、逆转录过程中具有重要的作用[25-26],抑制Mpro活性将能阻止病毒的感染和复制。通过分子对接,这10个成分与SARS-CoV-2 3CL水解酶的结合能远小于−20.93 kJ/mol,与ACE2受体的结合能远小于−29.336 kJ/mol,与二者结合最好的成分均为β-谷甾醇和丹参酮IIA,结合形式包括氢键、疏水作用。此外,槲皮素、山柰酚、异鼠李素也具有较强的结合能力[27-28]。表明荆防败毒散核心成分与COVID-19相关蛋白有较强的结合能力。

    基于上述研究,荆防败毒散对肺部疾病有一定的保护治疗作用,能提高机体免疫力,对COVID-19具有潜在的防治作用,可作为群体性预防用药以及发病初期的治疗。鉴于网络药理学和分子对接的局限性,荆防败毒散防治COVID-19的效果有待临床进一步验证。

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