scholarly journals Network Pharmacology-Based Strategy and Molecular Docking to Explore the Potential Mechanism of Jintiange Capsule for Treating Osteoporosis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhao Yang ◽  
Zhen-Zhen Yuan ◽  
Xin-Long Ma
Keyword(s):  
Molecular Docking ◽  
Calcium Phosphate ◽  
Calcium Channel ◽  
Signaling Pathways ◽  
Drug Targets ◽  
Ion Homeostasis ◽  
Bioinformatic Analysis ◽  
Network Pharmacology ◽  
Ageing Population ◽  
Main Ingredient

Background. With the advent of ageing population, osteoporosis (OP) has already become a global challenge. Jintiange capsule is extensively applied to treat OP in China. Although recent studies demonstrate that it generates significant effects on strengthening bone, the exact mechanism of the jintiange capsule for treating OP remains unknown. Purpose. To understand the main ingredients of the jintiange capsule, predict the possible targets and the relevant signal transduction pathways, and explore the mechanism of the jintiange capsule for the treatment of OP. Methods. Main ingredients of the jintiange capsule, drug targets, and potential disease targets for OP were obtained from public databases. Molecular biological processes and signaling pathways were determined via bioinformatic analysis, containing protein-protein interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, the disease-drug-ingredient-targets-pathways networks were constructed using Cytoscape. According to CytoNCA, core targets were acquired. Finally, the present study conducted molecular docking for better testing the abovementioned results. Results. In the current work, we found that 4 main ingredients of the jintiange capsule, 33 drug targets, 4745 potential disease targets for OP, and 12 overlapping targets were identified. PPI network containing 12 nodes and 25 edges proved that there existed a complex relationship. As revealed by GO functional annotation, the intersected targets were mostly associated with BP, CC, and MF. The targets were enriched to 368 items in BP, 27 items in CC, and 42 items in MF. They mainly included calcium ion homeostasis, calcium channel complex, and calcium channel regulator activity. According to KEGG pathway analysis, the intersected targets were mostly associated with Rap 1, cGMP-PKG, Ras, cAMP, calcium pathways, and so on. Based on the analysis with CytoNCA, we acquired 4 core targets, respectively—CALR, SPARC, CALM1, and CALM2. Besides, 2 core targets, CALR and CALM1, were selected for molecular docking experiments. Molecular docking revealed that the main ingredient, calcium phosphate, had good binding with the CALR protein and CALM1 protein. Conclusion. To conclude, the main ingredient of the jintiange capsule, particularly calcium phosphate, may interact with 2 targets, CALR and CALM1, and regulate multiple signaling pathways to treat OP. Additionally, this also benefits us in further understanding the mechanism of the jintiange capsule for treating OP.

scholarly journals Potential Molecular Target Prediction and Docking Verification of Hua-Feng-Dan in Stroke Based on Network Pharmacology

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Ping Yang ◽  
Haifeng He ◽  
Shangfu Xu ◽  
Ping Liu ◽  
Xinyu Bai
Keyword(s):  
Molecular Docking ◽  
Signaling Pathways ◽  
Target Genes ◽  
Target Prediction ◽  
Interaction Network ◽  
Network Pharmacology ◽  
Core Network ◽  
Active Ingredients ◽  
Protein Protein Interaction ◽  
Highly Correlated

Objective. Hua-Feng-Dan (HFD) is a Chinese medicine for stroke. This study is to predict and verify potential molecular targets and pathways of HFD against stroke using network pharmacology. Methods. The TCMSP database and TCMID were used to search for the active ingredients of HFD, and GeneCards and DrugBank databases were used to search for stroke-related target genes to construct the “component-target-disease” by Cytoscape 3.7.1, which was further filtered by MCODE to build a core network. The STRING database was used to obtain interrelationships by topology and to construct a protein-protein interaction network. GO and KEGG were carried out through DAVID Bioinformatics. Autodock 4.2 was used for molecular docking. BaseSpace was used to correlate target genes with the GEO database. Results. Based on OB ≥ 30% and DL ≥ 0.18, 42 active ingredients were extracted from HFD, and 107 associated targets were obtained. PPI network and Cytoscape analysis identified 22 key targets. GO analysis suggested 51 cellular biological processes, and KEGG suggested that 60 pathways were related to the antistroke mechanism of HFD, with p53, PI3K-Akt, and apoptosis signaling pathways being most important for HFD effects. Molecular docking verified interactions between the core target (CASP8, CASP9, MDM2, CYCS, RELA, and CCND1) and the active ingredients (beta-sitosterol, luteolin, baicalein, and wogonin). The identified gene targets were highly correlated with the GEO biosets, and the stroke-protection effects of Xuesaitong in the database were verified by identified targets. Conclusion. HFD could regulate the symptoms of stroke through signaling pathways with core targets. This work provided a bioinformatic method to clarify the antistroke mechanism of HFD, and the identified core targets could be valuable to evaluate the antistroke effects of traditional Chinese medicines.

scholarly journals Network Pharmacology Analysis and Experiments Validation of the Inhibitory Effect of JianPi Fu Recipe on Colorectal Cancer LoVo Cells Metastasis and Growth

2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Xinyi Lu ◽  
Xingli Wu ◽  
Lin Jing ◽  
Lingjia Tao ◽  
Yingxuan Zhang ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Drug Targets ◽  
Biological Function ◽  
Network Pharmacology ◽  
Dependent Manner ◽  
Active Compounds ◽  
Dose Dependent ◽  
Potential Drug Targets

Objective. To analyze the active compounds, potential targets, and diseases of JianPi Fu Recipe (JPFR) based on network pharmacology and bioinformatics and verify the potential biological function and mechanism of JPFR in vitro and in vivo. Methods. Network pharmacology databases including TCMSP, TCM-PTD, TCMID, and DrugBank were used to screen the active compounds and potential drug targets of JPFR. Cytoscape 3.7 software was applied to construct the interaction network between active compounds and potential targets. The DAVID online database analysis was performed to investigate the potential effective diseases and involved signaling pathways according to the results of the GO function and KEGG pathways enrichment analysis. To ensure standardization and maintain interbatch reliability of JPFR, High Performance Liquid Chromatography (HPLC) was used to establish a “chemical fingerprint.” For biological function validation, the effect of JPFR on the proliferation and migration of CRC cells in vitro was investigated by CCK-8 and transwell and wound healing assay, and the effect of JPFR on the growth and metastasis of CRC cells in vivo was detected by building a lung metastasis model in nude mice and in vivo imaging. For the potential mechanism validation, the expressions of MALAT1, PTBP-2, and β-catenin in CRC cells and transplanted CRC tumors were detected by real-time PCR, western blot, and immunohistochemical staining analysis. Results. According to the rules of oral bioavailability (OB) > 30% and drug-likeness (DL) > 0.18, 244 effective compounds in JPFR were screened out, as well as the corresponding 132 potential drug targets. By the analysis of DAVID database, all these key targets were associated closely with the cancer diseases such as prostate cancer, colorectal cancer, bladder cancer, small cell lung cancer, pancreatic cancer, and hepatocellular carcinoma. In addition, multiple signaling pathways were closely related to JPFR, including p53, Wnt, PI3K-Akt, IL-17, HIF-1, p38-MAPK, NF-κB, PD-L1 expression and PD-1 checkpoint pathway, VEGF, JAK-STAT, and Hippo. The systematical analysis showed that various active compounds of JPFR were closely connected with Wnt/β-catenin, EGFR, HIF-1, TGFβ/Smads, and IL6-STAT3 signaling pathway, including kaempferol, isorhamnetin, calycosin, quercetin, medicarpin, phaseol, spinasterol, hederagenin, beta-sitosterol, wighteone, luteolin, and isotrifoliol. For in vitro experiments, the migration and growth of human CRC cells were inhibited by the JPFR extract in a dose-dependent way, and the expression of MALAT1, PTBP-2, β-catenin, MMP7, c-Myc, and Cyclin D1 in CRC cells were downregulated by the JPFR extract in a dose-dependent way. For in vivo metastasis experiments, the numbers of lung metastasis were found to be decreased by the JPFR extract in a dose-dependent manner, and the expressions of metastasis-associated genes including MALAT1, PTBP-2, β-catenin, and MMP7 in the lung metastases were downregulated dose dependently by the JPFR extract. For the orthotopic transplanted tumor experiments, the JPFR extract could inhibit the growth of orthotopic transplanted tumors and downregulate the expression of c-Myc and Cyclin D1 in a dose-dependent manner. Moreover, the JPFR extract could prolong the survival time of tumor-bearing mice in a dose-dependent manner. Conclusions. Through effective network pharmacology analysis, we found that JPFR contains many effective compounds which may directly target cancer-associated signaling pathways. The in vitro and in vivo experiments further confirmed that JPFR could inhibit the growth and metastasis of CRC cells by regulating β-catenin signaling-associated genes or proteins.

scholarly journals Network Pharmacology-Based Study on the Mechanism of Aloe Vera for Treating Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jing Xie ◽  
Jun Wu ◽  
Sihui Yang ◽  
Huaijun Zhou
Keyword(s):  
Molecular Docking ◽  
Drug Targets ◽  
Aloe Vera ◽  
Beta Carotene ◽  
Network Pharmacology ◽  
Potential Mechanism ◽  
Active Ingredients ◽  
Protein Protein Interaction ◽  
Target Network ◽  
Molecule Docking

Background. Aloe vera has long been considered an anticancer herb in different parts of the world. Objective. To explore the potential mechanism of aloe vera in the treatment of cancer using network pharmacology and molecule docking approaches. Methods. The active ingredients and corresponding protein targets of aloe vera were identified from the TCMSP database. Targets related to cancer were obtained from GeneCards and OMIM databases. The anticancer targets of aloe vera were obtained by intersecting the drug targets with the disease targets, and the process was presented in the form of a Venn plot. These targets were uploaded to the String database for protein-protein interaction (PPI) analysis, and the result was visualized by Cytoscape software. Go and KEGG enrichment were used to analyze the biological process of the target proteins. Molecular docking was used to verify the relationship between the active ingredients of aloe vera and predicted targets. Results. By screening and analyzing, 8 active ingredients and 174 anticancer targets of aloe vera were obtained. The active ingredient-anticancer target network constructed by Cytoscape software indicated that quercetin, arachidonic acid, aloe-emodin, and beta-carotene, which have more than 4 gene targets, may play crucial roles. In the PPI network, AKT1, TP53, and VEGFA have the top 3 highest values. The anticancer targets of aloe vera were mainly involved in pathways in cancer, prostate cancer, bladder cancer, pancreatic cancer, and non-small-cell lung cancer and the TNF signaling pathway. The results of molecular docking suggested that the binding ability between TP53 and quercetin was the strongest. Conclusion. This study revealed the active ingredients of aloe vera and the potential mechanism underlying its anticancer effect based on network pharmacology and provided ideas for further research.

scholarly journals Network pharmacology based research into the effect and mechanism of Yinchenhao Decoction against Cholangiocarcinoma

2021 ◽  
Author(s):  
Zhiqiang Chen ◽  
Tong Lin ◽  
Xiaozhong Liao ◽  
Zeyun Li ◽  
Ruiting Lin ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Signaling Pathways ◽  
Signaling Pathway ◽  
Experimental Results ◽  
Network Pharmacology ◽  
Therapeutic Effects ◽  
Active Components ◽  
Active Compounds ◽  
Apoptosis Rate

Abstract Background: Cholangiocarcinoma refers to an epithelial cell malignancy with poor prognosis. Yinchenhao decoction (YCHD) showed positive effects on cancers, and associations between YCHD and cholangiocarcinoma remain unclear. This study aimed to screen out the effective active components of Yinchenhao decoction (YCHD) using network pharmacology, estimate their potential targets, screen out the pathways, as well as delve into the potential mechanisms on treating cholangiocarcinoma. Methods: By the traditional Chinese medicine system pharmacology database and analysis platform (TCMSP) as well as literature review, the major active components and their corresponding targets were estimated and screened out. Using the software Cytoscape 3.6.0, a visual network was established using the active components of YCHD and the targets of cholangiocarcinoma. Based on STRING online database, the protein interaction network of vital targets was built and analyzed. With the Database for Annotation, Visualization, and Integrated Discovery (DAVID) server, the gene ontology (GO) biological processes and the Kyoto encyclopedia of genes and genomes (KEGG) signaling pathways of the targets enrichment were performed. The AutoDock Vina was used to perform molecular docking and calculate the binding affinity. The PyMOL software was utilized to visualize the docking results of active compounds and protein targets. In vivo experiment, the IC50 values and apoptosis rate in PI-A cells were detected using CCK-8 kit and Cell Cycle Detection Kit. The predicted targets were verified by the real-time PCR and western blot methods. Results: 32 effective active components with anti-tumor effects of YCHD were sifted in total, covering 209 targets, 96 of which were associated with cancer. Quercetin, kaempferol, beta-sitosterol, isorhamnetin, and stigmasterol were identified as the vital active compounds, and AKT1, IL6, MAPK1, TP53 as well as VEGFA were considered as the major targets. The molecular docking revealed that these active compounds and targets showed good binding interactions. These 96 putative targets exerted therapeutic effects on cancer by regulating signaling pathways (e.g., hepatitis B, the MAPK signaling pathway, the PI3K-Akt signaling pathway, and MicroRNAs in cancer). Our in vivo experimental results confirmed that YCHD showed therapeutic effects on cholangiocarcinoma by decreasing IC50 values, down-regulating apoptosis rate of cholangiocarcinoma cells, and lowering protein expressions. Conclusion:As predicted by network pharmacology strategy and validated by the experimental results, YCHD exerts anti-tumor effectsthrough multiple components, targets, and pathways, thereby providing novel ideas and clues for the development of preparations and the treatment of cholangiocarcinoma.

scholarly journals Mechanisms of indigo naturalis on treating ulcerative colitis explored by GEO gene chips combined with network pharmacology and molecular docking

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sizhen Gu ◽  
Yan Xue ◽  
Yang Gao ◽  
Shuyang Shen ◽  
Yuli Zhang ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Molecular Docking ◽  
Signaling Pathways ◽  
Interaction Network ◽  
Oxygen Metabolism ◽  
Network Pharmacology ◽  
Signal Pathways ◽  
Active Components ◽  
Gene Chips ◽  
Indigo Naturalis

Abstract Oral administration of indigo naturalis (IN) can induce remission in ulcerative colitis (UC); however, the underlying mechanism remains unknown. The main active components and targets of IN were obtained by searching three traditional Chinese medicine network databases such as TCMSP and five Targets fishing databases such as PharmMapper. UC disease targets were obtained from three disease databases such as DrugBank,combined with four GEO gene chips. IN-UC targets were identified by matching the two. A protein–protein interaction network was constructed, and the core targets were screened according to the topological structure. GO and KEGG enrichment analysis and bioGPS localization were performed,and an Herbs-Components-Targets network, a Compound Targets-Organs location network, and a Core Targets-Signal Pathways network were established. Molecular docking technology was used to verify the main compounds-targets. Ten core active components and 184 compound targets of IN-UC, of which 43 were core targets, were enriched and analyzed by bioGPS, GO, and KEGG. The therapeutic effect of IN on UC may involve activation of systemic immunity, which is involved in the regulation of nuclear transcription, protein phosphorylation, cytokine activity, reactive oxygen metabolism, epithelial cell proliferation, and cell apoptosis through Th17 cell differentiation, the Jak-STAT and IL-17 signaling pathways, toll-like and NOD-like receptors, and other cellular and innate immune signaling pathways. The molecular mechanism underlying the effect of IN on inducing UC remission was predicted using a network pharmacology method, thereby providing a theoretical basis for further study of the effective components and mechanism of IN in the treatment of UC.

scholarly journals Network of “drug-target-SARS-CoV-2 Related Genes” Through Integrated Analysis of Pharmacology and Geo Database

2020 ◽  
Author(s):  
Jin ping Hou ◽  
Yong heng Wang ◽  
Yu meng Chen ◽  
Yi hao Chen ◽  
Xiao Zhu ◽  
...  
Keyword(s):  
Gene Therapy ◽  
Molecular Docking ◽  
Folic Acid ◽  
Drug Target ◽  
Drug Targets ◽  
Interaction Mechanism ◽  
Network Pharmacology ◽  
Integrated Analysis ◽  
Hub Genes ◽  
Geo Database

Abstract BackgroundCoronavirus Disease 2019 (COVID-19) respiratory disease rapidly caused a global pandemic and social and economic disruption. The combination of Traditional Chinese medicine (TCM) and Conventional Western medicine (CWM) is more effective for COVID-19 treatment. Moreover, TCM and CWM are important data source for developing new drug targets and promote strategies treat SARS-CoV-2 infections. However, many studies have analyzed the therapeutic mechanism of CWM or TCM alone for COVID-19, it is still unclear the interaction mechanism between TCM and CWM on COVID-19.MethodsThis paper integrates network pharmacology and GEO database to mine and identify COVID-19 molecular therapeutic targets, providing potential targets and new ideas for COVID-19 gene therapy and new drug development. It includes: 1) using TCMSP, TTD, PubChem and CTD databases to analyze drug interactions and associated phenotypes for SARS-CoV-2, to correlate drug and disease interaction mechanisms to screen key drug targets; 2) using GEO database to correlate differential genes and drug targets to screen potential antiviral gene therapy targets, to construct regulatory network and key points of SARS-CoV-2 therapeutic drugs; 3) using computer simulation of molecular docking to screen virus-related proteins for new drugs. ResultsIntegrated analysis of network pharmacology discovered that baicalein, estrone and quercetin are the pivotal active ingredients in TCM and CWM. Combining drug target genes in pharmacology database and virus induced genes in GEO database, the result showed the core hub genes related to COVID-19: STAT1, IL1B, IL6, IL8, PTGS2 and NFKBIA, and these genes were significantly downregulated in A549 and NHBE cells by SARS-CoV-2 infection. Moreover, chemical interaction and molecular docking analysis of hub genes showed that folic acid might as be potential therapeutic drug for COVID-19 treatment, and SARS-CoV-2 nucleocapsid phosphoprotein was a potential drug target. The network of “drug-target-SARS-CoV-2 related genes” provide noval potential compounds and targets for further studies of SARS-CoV-2.ConclusionsIntegrated analysis of network pharmacology and big data mining provided noval potential compounds and targets for further studies of SARS-CoV-2. Our research implied folic acid and SARS-CoV-2 N as therapeutic target in TCM and CWM. Our research also suggests that targeting SARS-CoV-2 N protein is likely to be a common mechanism of TCM and CWM. On the one hand, the identification of pivotal genes provides a target for COVID-19 molecular therapy, on the other hand, it provides ideas for the analysis of interaction mechanism between virus and host.

scholarly journals Research on the Treatment of Colorectal Cancer Based on Network Pharmacology and Molecular Docking

2021 ◽  
Author(s):  
Shu Xinyao ◽  
Zhang Yunwu ◽  
Wang Yujia ◽  
Wu Jing ◽  
Chen Xingyu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Molecular Docking ◽  
Drug Targets ◽  
Cell Metabolism ◽  
Enrichment Analysis ◽  
Endocrine Tumor ◽  
Network Pharmacology ◽  
Active Components ◽  
Sophora Japonica ◽  
Core Components

Abstract Background: To predict the mechanism of Huaijiao Diyu Decoction in the treatment of colorectal cancer. Methods: The active components and related targets of 7 kinds of Chinese medicinal materials were collected from the database of Chinese medicinal materials. CRC related targets are obtained from the Genes card database. cross targets of disease and drug targets were input into the STRING database to construct protein-protein interaction networks.GO enrichment analysis and KEGG enrichment analysis using clusterProfiler packets in the language, Finally, the interaction between core components and core targets is discussed by molecular docking.Result: TCMSP database prompts, There are 34 active compounds mapping 114 targets in Sophora japonica decoction; G enecards database prompts, A total of 858 targets are closely related to CRC, The two data sets map each other to obtain 114 intersection targets. Core components of Sophora flavescens decoction in treating CRC may be quercetin, kaempferol, luteolin, The core therapeutic targets may be PTGS2、PTGS1、HSP90AA1 and AR, CRC related pathways involve multiple molecular functions such as cell proliferation, apoptosis, cell signal transduction, metabolism, endocrine, tumor immunity, transcription and cell metabolism. Molecular docking results show that the binding ability of PTGS1、PTGS2、AR to core components is strong, The Vina value (binding energy) of the interaction between PTGS1 protein and core components is the best, -7.9kcal/mol . Conclusion: This study demonstrates the mechanism of multi-component, multi-target and multi-pathway action in the treatment of CRC, Can provide the idea for clarifying the specific mechanism of Huaijiao Diyu decoction in the treatment of CRC in the future.

scholarly journals Network Pharmacology-Based Strategy Reveals the Effects of Hedysarum multijugum Maxim.-Radix Salviae Compound on Oxidative Capacity and Cardiomyocyte Apoptosis in Rats with Diabetic Cardiomyopathy

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Shiying Zhang ◽  
Zhiying Yuan ◽  
Huaying Wu ◽  
Weiqing Li ◽  
Liang Li ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Signaling Pathway ◽  
Diabetic Cardiomyopathy ◽  
Ion Homeostasis ◽  
Oxidative Capacity ◽  
Cardiomyocyte Apoptosis ◽  
Estrogen Signaling ◽  
Network Pharmacology ◽  
Biological Processes ◽  
Active Components

Objective. To explore the effects of the Hedysarum multijugum Maxim.-Radix Salviae compound (Huangqi-Danshen Compound (HDC)) on oxidative capacity and cardiomyocyte apoptosis in rats with diabetic cardiomyopathy by a network pharmacology-based strategy. Methods. Traditional Chinese Medicine (TCM)@Taiwan, TCM Systems Pharmacology Database and Analysis Platform (TCMSP), TCM Integrated Database (TCMID), and High-Performance Liquid Chromatography (HPLC) technology were used to obtain and screen HDC’s active components, and the PharmMapper database was used to predict HDC human target protein targets. The DCM genes were collected from the GeneCards and OMIM databases, and the network was constructed and analyzed by Cytoscape 3.7.1 and the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Finally, HDC was used to intervene in diabetic cardiomyopathy (DCM) model rats, and important biological processes and signaling pathways were verified using techniques such as immunohistochemistry. Results. A total of 176 of HDC’s active components and 442 potential targets were obtained. The results of network analysis show that HDC can regulate DCM-related biological processes (such as negative regulation of the apoptotic process, response to hypoxia, the steroid hormone-mediated signaling pathway, cellular iron ion homeostasis, and positive regulation of phosphatidylinositol 3-kinase signaling) and signaling pathways (such as the HIF-1 signaling pathway, the estrogen signaling pathway, insulin resistance, the PPAR signaling pathway, the VEGF signaling pathway, and the PI3K-Akt signaling pathway). Animal experiments show that HDC can reduce fasting plasma glucose (FPG), HbA1c, and malondialdehyde (MDA) and increase superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) ( P < 0.05 ). The results of immunohistochemistry showed that HDC can regulate the protein expression of apoptosis-related signaling pathways in DCM rats ( P < 0.05 ). Conclusion. It was initially revealed that HDC improves DCM through its antiapoptotic and anti-inflammatory effects. HDC may play a therapeutic role by improving cardiomyocyte apoptosis in DCM rats.

scholarly journals Network Pharmacology Integrated Molecular Docking Reveals the Anti-COVID-19 Mechanism of Yinma Jiedu Granules

2021 ◽  
Vol 16 (2) ◽  
pp. 1934578X2199171
Author(s):  
ZiXin Yuan ◽  
Can Zeng ◽  
Bing Yu ◽  
Ying Zhang ◽  
TianShun Wang ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Signaling Pathways ◽  
Hypoxia Inducible Factor ◽  
Growth Factor Receptor ◽  
Interaction Network ◽  
Mitogen Activated Protein Kinase ◽  
Network Pharmacology ◽  
Chemical Components ◽  
Active Components ◽  
Discovery Studio

To investigate the mechanism of action of components of Yinma Jiedu granules in the treatment of coronavirus disease 2019 (COVID-19) using network pharmacology and molecular docking. The main chemical components of Yinma Jiedu granules were collected in the literature and Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform database. Using the SwissTargetPrediction database, the targets of the active component were identified and further correlated to the targets of COVID-19 through the GeneCards database. The overlapping targets of Yinma Jiedu granules components and COVID-19 were identified as the research target. Using the Database for Annotation, Visualization and Integrated Discovery database to carry out the target gene function Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway annotation and Cytoscape 3.6.1 software was used to construct a “component-target-pathway” network. The protein-protein interaction network was built using Search Tool for the Retrieval of Interacting Genes/Proteins database. Using Discovery Studio 2016 Client software to study the virtual docking of key protein and active components. One hundred active components were screened from the Yinma Jiedu Granules that involved 67 targets, including mitogen-activated protein kinase 3 (MAPK3), epidermal growth factor receptor, tumor necrosis factor, tumor protein 53, and MAPK1. These targets affected 109 signaling pathways including hypoxia-inducible factor-1, apoptosis, and Toll-like receptor signaling pathways. Molecular docking results showed that the screened active components have a strong binding ability to the key targets. In this study, through network pharmacology and molecular docking, we justified the multicomponent, multitarget, and multipathways of Yinma Jiedu Granules in the treatment of COVID-19.

scholarly journals Research on active compounds regarding SGMD for the treatment of COVID-19 based on network pharmacology and molecular docking

2020 ◽  
Author(s):  
Ma Donglai ◽  
Yuxin Jia ◽  
Mingdong Si ◽  
Huigai Sun ◽  
Huiru Du ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Drug Targets ◽  
Function Analysis ◽  
Chemical Constituents ◽  
Binding Energies ◽  
Network Pharmacology ◽  
Toxic Substances ◽  
The Core ◽  
Effective Components ◽  
Core Components

Abstract Background: Retrieve Curative effect of Six Gentlemen Modified Decoction (SGMD) in treating with coronavirus disease ( COVID-19 ) by network pharmacology and verify its authenticity by molecular docking. Methods: The chemical constituents, effective components, and action targets were screened using TCMSP. COVID-19 related targets were retrieved by the GeneCards and NCBI databases, and drug targets and disease targets were mapped by Venny to obtain potential targets for treatment. The regulatory network of traditional Chinese medicine (TCM) compounds was established with Cytoscape to obtain the key components, and the PPI network and its network topology were established with the Bisogenet and CytoNCA plug-ins to obtain the core targets. Bioconductor was used for GO function analysis and KEGG pathway analysis to obtain the relevant functions and pathways. Results: 173 effective components, 253 targets, and 348 targets related to COVID-19 were obtained after screening, 50 cross targets were shown, and the key components of the top 15 are flavonoids such as quercetin, luteolin, kaempferol, naringenin, licochalcone A, etc. The top 28 core targets include TP53, EGFR, SRC, AR, ABL1, and others. Biological processes such as the responses to metal ions, molecules of bacterial origin, lipopolysaccharide, toxic substances, and oxidative stress were involved. The main pathway involved the AGE−RAGE signaling pathway in diabetic complications as well as the TNF and IL-17 signaling pathways. The average binding energies of the first three core components connected with 6LU7 and 1R42 were -4.16 kJ/mol and -4.12 kJ/mol, respectively.Conclusion: The core compounds of SGMD can spontaneously combine with SARS-CoV-2 3CL hydrolase and ACE2 to treat COVID-19.

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