Discussing the Mechanism of Dahuang Huanglian Xiexin Decoction in the Treatment of Type 2 Diabetes Mellitus via Network Pharmacology and Molecular Docking

2021 ◽  
Author(s):  
Xi Cen ◽  
Yan Wang ◽  
LeiLei Zhang ◽  
XiaoXiao Xue ◽  
Yan Wang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Molecular Docking ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Overlapping Genes ◽  
Potential Mechanism ◽  
Active Components ◽  
The Core ◽  
Core Genes

Abstract BackgroundType 2 diabetes mellitus (T2DM) is regarded as Pi Dan disease in traditional Chinese medicine (TCM). Dahuang Huanglian Xiexin Decoction (DHXD), a classical TCM formula, has been used for treating Pi Dan disease in clinic, its pharmacological mechanism has not been elucidated. MethodsThis study used network pharmacological analysis and molecular docking approach to explore the mechanism of DHXD on T2DM. Firstly, the compounds in DHXD were obtained from TCMSP and TCMID databases, the potential targets were determined based on TCMSP and UniProt databases. Next, Genecards, Digenet and UniProt databases were used to identify the targets of T2DM. Then, the protein-protein interaction (PPI) network was established with overlapping genes of T2DM and compounds, and the core targets in the network were identified and analyzed. Then, the David database was used for GO and KEGG enrichment analysis. Finally, the target genes were selected and the molecular docking was completed by Autodock software to observe the binding level of active components with target genes.ResultsA total of 397 related components and 128 overlapping genes were identified. After enrichment analysis, it was found that HIF-1, TNF, IL-17 and other signaling pathways, as well as DNA transcription, gene expression, apoptosis and other cellular biological processes had the strongest correlation with the treatment of T2DM by DHXD, and most of them occurred in the extracellular space, plasma membrane and other places, which were related to enzyme binding and protein binding. In addition, 42 core genes of DHXD, such as VEGFA, TP53 and MAPK1, were considered as potential therapeutic targets, indicating the potential mechanism of DHXD on T2DM. Finally, the results of molecular docking showed that HIF-1 pathway had strong correlation with the target genes INSR and GLUT4, quercetin and berberine had the strongest binding power with them respectively.ConclusionThis study summarized the main components of DHXD in the treatment of T2DM, identified the core genes and pathways, and systematically analyzed the interaction of related targets, trying to lay the foundation for clarifying the potential mechanism of DHXD on T2DM, so as to carry out further research in the future.

scholarly journals Integrating Network Pharmacology with Molecular Docking to Unravel the Active Compounds and Potential Mechanism of Simiao Pill Treating Rheumatoid Arthritis

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Mengshi Tang ◽  
Xi Xie ◽  
Pengji Yi ◽  
Jin Kang ◽  
Jiafen Liao ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Molecular Docking ◽  
Signaling Pathway ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Binding Activity ◽  
New Combination ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Potential Mechanism

Objective. To explore the main components and unravel the potential mechanism of simiao pill (SM) on rheumatoid arthritis (RA) based on network pharmacological analysis and molecular docking. Methods. Related compounds were obtained from TCMSP and BATMAN-TCM database. Oral bioavailability and drug-likeness were then screened by using absorption, distribution, metabolism, and excretion (ADME) criteria. Additionally, target genes related to RA were acquired from GeneCards and OMIM database. Correlations about SM-RA, compounds-targets, and pathways-targets-compounds were visualized through Cytoscape 3.7.1. The protein-protein interaction (PPI) network was constructed by STRING. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed via R packages. Molecular docking analysis was constructed by the Molecular Operating Environment (MOE). Results. A total of 72 potential compounds and 77 associated targets of SM were identified. The compounds-targets network analysis indicated that the 6 compounds, including quercetin, kaempferol, baicalein, wogonin, beta-sitosterol, and eugenol, were linked to ≥10 target genes, and the 10 target genes (PTGS1, ESR1, AR, PGR, CHRM3, PPARG, CHRM2, BCL2, CASP3, and RELA) were core target genes in the network. Enrichment analysis indicated that PI3K-Akt, TNF, and IL-17 signaling pathway may be a critical signaling pathway in the network pharmacology. Molecular docking showed that quercetin, kaempferol, baicalein, and wogonin have good binding activity with IL6, VEGFA, EGFR, and NFKBIA targets. Conclusion. The integrative investigation based on bioinformatics/network topology strategy may elaborate on the multicomponent synergy mechanisms of SM against RA and provide the way out to develop new combination medicines for RA.

Investigating the Mechanism of Action of Frankincense against Drug-Induced Liver Injury Using Network Pharmacology and Molecular Docking

2021 ◽  
Vol 18 ◽  
Author(s):  
Yu-cheng Liao ◽  
Jing-wen Wang ◽  
Qian Yang ◽  
Wen-jun Wanga ◽  
Chao Zhao ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Liver Injury ◽  
Mechanism Of Action ◽  
Target Genes ◽  
Network Pharmacology ◽  
Active Components ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Pathway Enrichment ◽  
Core Genes

Background: Frankincense has been used as a traditional medicine in many countries. It is an important herb with multiple targets and therapeutic effects including liver protection. However, its mechanism of action in drug-induced liver injury (DILI) remains unknown. Objective: The purpose of this work was to elucidate the active components, core genes, and molecular mechanism of frankincense in DILI through network pharmacology and molecular docking approaches. Method: The active components of frankincense and its target genes were obtained from the BATMAN-TCM database, and the DILI target genes were obtained from the GeneCards and DrugBank databases. Cytoscape was used to create the compound-shared gene target network. STRING and DAVID software were used to analyze key targets and pathway enrichment. Autodock Vina software was used for molecular docking. Results: Network analysis identified 16 compounds in frankincense and 103 target genes that are highly related to DILI. The core genes in the protein-protein interaction network are INS, IL6, TP53, TNF, SRC, PTGS2, IL1B, CAT, IL10, and IGF1. Furthermore, GO and KEGG pathway enrichment analyses indicated that the effect of frankincense on DILI is related to positive regulation of transcription from RNA polymerase II promoter and inflammatory response. Core pathways such as the HIF-1, TNF, FoxO, PI3K-Akt, and the sphingolipid signaling pathway are closely related to DILI. Conclusion: This study revealed the chemical constituents and pharmacological effects of frankincense and unveiled potential DILI healing targets. This study could provide insights for further development of drugs that specifically target DILI.

scholarly journals Study on the Potential Active Components and Molecular Mechanism of Xiao Huoluo Pills in the Treatment of Cartilage Degeneration of Knee Osteoarthritis based on Bioinformatics Analysis and Molecular Docking Technology

2021 ◽  
Author(s):  
Weijian Chen ◽  
Tianye Lin ◽  
Qi He ◽  
Peng Yang ◽  
Gangyu Zhang ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Knee Osteoarthritis ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Cartilage Degeneration ◽  
Target Protein ◽  
Active Components ◽  
Binding Ability ◽  
R Language ◽  
The Core

Abstract Background: Knee osteoarthritis is a common joint degenerative disease that severely affects the quality of life. Modern pharmacological studies have confirmed that Xiao Huoluo Pills (XHLP) has anti-inflammatory, anti-oxidant, and analgesic effects. At present, it has been used to treat degenerative diseases such as osteoarthritis and hyperosteogeny. However, XHLP's specific effective ingredients and mechanism of action against osteoarthritis have not been explored. Therefore, bioinformatics technology and molecular docking technology are employed in this study to explore the molecular basis and mechanism of XHLP in the treatment of knee osteoarthritis. Methods: Public databases (TCMSP, Batman-TCM, HERB, DrugBank, and UniProt) are used to find the effective active components and corresponding target proteins of XHLP (screening conditions: OB>30%, DL≥0.18). Differentially expressed genes related to cartilage lesions of knee osteoarthritis are obtained based on the GEO database (screening conditions: adjust P Value<0.01, |log2 FC|≥1.0). The Venn package in R language and the BisoGenet plug-in in Cytoscape are adopted to predict the potential molecules of XHLP in the treatment of knee osteoarthritis. The XHLP-active component-target interaction network and the XHLP-knee osteoarthritis-target protein core network are constructed using Cytoscape software. Besides, GO/KEGG enrichment analysis on core genes is performed using the Bioconductor package and clusterProfiler package in the R language to explain the biological functions and signal pathways of the core proteins. Finally, molecular docking is performed through software such as Vina, LeDock, Discovery Studio 2016, PyMOL, AutoDockTools 1.5.6, so as to verify the binding ability between the active components of the drug and the core target protein. Results: XHLP has been screened out of 71 potentially effective active compounds for the treatment of OA, mainly including quercetin, Stigmasterol, beta-sitosterol, Izoteolin, and ellagic acid. Knee osteoarthritis cartilage lesion sequencing data (GSE114007) was screened out of 1672 differentially expressed genes, including 913 up-regulated genes and 759 down-regulated genes, displayed as heat maps and volcano maps. Besides, 33 core target proteins are calculated by Venn data package in R and BisoGenet plug-in in Cytoscape. The enrichment analysis on these target genes revealed that the core target genes are mainly involved in biological processes such as response to oxygen levels, mechanical stimulus, vitamin, drug, and regulation of smooth muscle cell proliferation. These core target genes are involved in signaling pathways related to cartilage degeneration of knee osteoarthritis such as TNF signaling pathway and PI3K-Akt signaling pathway. Finally, the molecular docking verification demonstrates that some active components of the drug have good molecular docking and binding ability with the core target protein, further confirming that XHLP has the effect of inhibiting cartilage degeneration in knee osteoarthritis.Conclusions: In this study, based on the research foundation of bioinformatics and molecular docking technology, the active components and core target molecules of XHLP for the treatment of cartilage degeneration of knee osteoarthritis are screened out, and the potential mechanism of XHLP inhibiting cartilage degeneration of knee osteoarthritis is deeply explored. The results provide theoretical basis and new treatment plan for XHLP in the treatment of knee osteoarthritis.

Study on the Anti-Tumor Mechanism and Target of Triptolide based on Network Pharmacology and Molecular Docking

2021 ◽  
Vol 16 ◽  
Author(s):  
Tao Zou ◽  
Yuanqiong Huang ◽  
Yifan Hu ◽  
Mingyu Wu ◽  
Yueshui Zhao ◽  
...  
Keyword(s):  
Target Genes ◽  
Inflammatory Responses ◽  
Tumor Development ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Therapeutic Effects ◽  
Specific Target ◽  
Pharmacological Mechanism ◽  
The Core ◽  
Core Genes

Background: According to the special physiological and pharmacological activities of natural compounds, many drugs with special therapeutic effects have been developed. The triptolide (TP) is a kind of natural anti-tumor drug with a world patent, but its target and mechanism are yet not known. Objective: The study aims to explore and predict the target and mechanism of TP on non-small cell lung cancer (NSCLC), pancreatic cancer (PC) and colorectal cancer (CC) through network pharmacology technology. Methods: We screened the core targets of TP with NSCLC, PC and CC, respectively, and carried out network analysis, enrichment analysis and ligand-receptor docking to clarify its potential pharmacological mechanism. Results: By screening the core genes between TP with NSCLC, PC and CC, respectively, it was found that PTGS2 was the common target gene in the three cancers. NSCLC, CCL2, IL6, HMOX1 and COL1A1 are the specific target genes, while MMP2, JUN, and CXCL8 are the specific target genes in PC. In CC, the specific target genes includeERBB2, VEGFA, STAT1 andMAPK8. In enrichment analysis, it was found that the NF- κB, toll-like receptors and IL-17 signaling pathway were mainly involved in TP for these cancers. The binding energy of TP to the core target is less than that of cyclophosphamide. Conclusions: This study preliminarily revealed that TP may prevent and treat cancers\ through multiple targets and pathways. The possible mechanisms of TP include regulating immune and inflammatory responses, promoting apoptosis and inhibiting tumor development. It shows that TP may have a potential in treating kinds of tumors.

scholarly journals Application of Network Pharmacology to Elucidate The Potential Mechanism of Finger Citron Against Obesity

2020 ◽  
Author(s):  
Zhen Wu ◽  
Yujiao Yang ◽  
Yao Wei ◽  
Xu-guang Hu
Keyword(s):  
Molecular Docking ◽  
Signaling Pathways ◽  
Target Genes ◽  
Chinese Herbs ◽  
Network Pharmacology ◽  
Potential Mechanism ◽  
Active Components ◽  
Docking Analysis ◽  
Treatment Of Obesity ◽  
Molecular Docking Analysis

Abstract Background: Finger citron (FS) is one of many traditional Chinese herbs that have been used to treat obesity. However, the active components and potential targets of FS in improving obesity remain unclear. Methods：The aim of this study was to elucidate the pharmacological effects and mechanisms of FS on obesity using network pharmacology analysis. We used network pharmacology to determine the active components, potential targets and mechanisms in the treatment of obesity.Results：We identified 25 active ingredients of FS such as diosmetin, hesperidin and sitosterol-alpha1 with important biological effect. A total of 258 key targets were screened, containing TNF，NOS2, MAPK8 which were found to be enriched in 27 signaling pathways, such as apoptosis, TNF, PPAR and AKT1, and Insulin resistance signaling pathways. Moreover, molecular docking analysis showed that the main ingredients were tightly bound to the core targets, further confirming the effects of weight loss. Conclusion: Based on network pharmacology and molecular docking analysis, our study provides insights into the potential mechanism of FS in ameliorating obesity after screening for associated key target genes and signaling pathways. These findings further provide a theoretical basis for further pharmacological research into the potential mechanism of FS in treating obesity.

scholarly journals Study on the potential active components and molecular mechanism of Xiao Huoluo Pills in the treatment of cartilage degeneration of knee osteoarthritis based on bioinformatics analysis and molecular docking technology

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Weijian Chen ◽  
Tianye Lin ◽  
Qi He ◽  
Peng Yang ◽  
Gangyu Zhang ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Knee Osteoarthritis ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Cartilage Degeneration ◽  
Target Protein ◽  
Active Components ◽  
Binding Ability ◽  
R Language ◽  
The Core

Abstract Background Knee osteoarthritis is a common joint degenerative disease. Xiao Huoluo Pills (XHLP) has been used to treat degenerative diseases such as osteoarthritis and hyperosteogeny. However, XHLP’s specific effective ingredients and mechanism of action against osteoarthritis have not been explored. Therefore, bioinformatics technology and molecular docking technology are employed in this study to explore the molecular basis and mechanism of XHLP in the treatment of knee osteoarthritis. Methods Public databases (TCMSP, Batman-TCM, HERB, DrugBank, and UniProt) are used to find the effective active components and corresponding target proteins of XHLP (screening conditions: OB > 30%, DL ≥ 0.18). Differentially expressed genes related to cartilage lesions of knee osteoarthritis are obtained based on the GEO database (screening conditions: adjust P value < 0.01, |log2 FC|≥1.0). The Venn package in R language and the BisoGenet plug-in in Cytoscape are adopted to predict the potential molecules of XHLP in the treatment of knee osteoarthritis. The XHLP-active component-target interaction network and the XHLP-knee osteoarthritis-target protein core network are constructed using Cytoscape software. Besides, GO/KEGG enrichment analysis on core genes is performed using the Bioconductor package and clusterProfiler package in the R language to explain the biological functions and signal pathways of the core proteins. Finally, molecular docking is performed through software such as Vina, LeDock, Discovery Studio 2016, PyMOL, AutoDockTools 1.5.6, so as to verify the binding ability between the active components of the drug and the core target protein. Results XHLP has been screened out of 71 potentially effective active compounds for the treatment of OA, mainly including quercetin, Stigmasterol, beta-sitosterol, Izoteolin, and ellagic acid. Knee osteoarthritis cartilage lesion sequencing data (GSE114007) was screened out of 1672 differentially expressed genes, including 913 upregulated genes and 759 downregulated genes, displayed as heat maps and volcano maps. Besides, 33 core target proteins are calculated by Venn data package in R and BisoGenet plug-in in Cytoscape. The enrichment analysis on these target genes revealed that the core target genes are mainly involved in biological processes such as response to oxygen levels, mechanical stimulus, vitamin, drug, and regulation of smooth muscle cell proliferation. These core target genes are involved in signaling pathways related to cartilage degeneration of knee osteoarthritis such as TNF signaling pathway and PI3K-Akt signaling pathway. Finally, the molecular docking verification demonstrates that some active components of the drug have good molecular docking and binding ability with the core target protein, further confirming that XHLP has the effect of inhibiting cartilage degeneration in knee osteoarthritis. Conclusions In this study, based on the research foundation of bioinformatics and molecular docking technology, the active components and core target molecules of XHLP for the treatment of cartilage degeneration of knee osteoarthritis are screened out, and the potential mechanism of XHLP inhibiting cartilage degeneration of knee osteoarthritis is deeply explored. The results provide theoretical basis and new treatment plan for XHLP in the treatment of knee osteoarthritis.

scholarly journals Study on the Potential Active Components and Molecular Mechanism of Xiao Huoluo Pills in the Treatment of Cartilage Degeneration of Knee Osteoarthritis Based on Bioinformatics Analysis and Molecular Docking Technology

2021 ◽  
Author(s):  
Weijian Chen ◽  
Tianye Lin ◽  
Qi He ◽  
Peng Yang ◽  
Gangyu Zhang ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Knee Osteoarthritis ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Cartilage Degeneration ◽  
Target Protein ◽  
Active Components ◽  
Binding Ability ◽  
R Language ◽  
The Core

Abstract Background: Knee osteoarthritis is a common joint degenerative disease that severely affects the quality of life. Modern pharmacological studies have confirmed that Xiao Huoluo Pills (XHLP) has anti-inflammatory, anti-oxidant, and analgesic effects. At present, it has been used to treat degenerative diseases such as osteoarthritis and hyperosteogeny. However, XHLP's specific effective ingredients and mechanism of action against osteoarthritis have not been explored. Therefore, bioinformatics technology and molecular docking technology are employed in this study to explore the molecular basis and mechanism of XHLP in the treatment of knee osteoarthritis. Methods: Public databases (TCMSP, Batman-TCM, HERB, DrugBank, and UniProt) are used to find the effective active components and corresponding target proteins of XHLP (screening conditions: OB>30%, DL≥0.18). Differentially expressed genes related to cartilage lesions of knee osteoarthritis are obtained based on the GEO database (screening conditions: adjust P Value<0.01, |log2 FC|≥1.0). The Venn package in R language and the BisoGenet plug-in in Cytoscape are adopted to predict the potential molecules of XHLP in the treatment of knee osteoarthritis. The XHLP-active component-target interaction network and the XHLP-knee osteoarthritis-target protein core network are constructed using Cytoscape software. Besides, GO/KEGG enrichment analysis on core genes is performed using the Bioconductor package and clusterProfiler package in the R language to explain the biological functions and signal pathways of the core proteins. Finally, molecular docking is performed through software such as Vina, LeDock, Discovery Studio 2016, PyMOL, AutoDockTools 1.5.6, so as to verify the binding ability between the active components of the drug and the core target protein. Results: XHLP has been screened out of 71 potentially effective active compounds for the treatment of OA, mainly including quercetin, Stigmasterol, beta-sitosterol, Izoteolin, and ellagic acid. Knee osteoarthritis cartilage lesion sequencing data (GSE114007) was screened out of 1672 differentially expressed genes, including 913 up-regulated genes and 759 down-regulated genes, displayed as heat maps and volcano maps. Besides, 33 core target proteins are calculated by Venn data package in R and BisoGenet plug-in in Cytoscape. The enrichment analysis on these target genes revealed that the core target genes are mainly involved in biological processes such as response to oxygen levels, mechanical stimulus, vitamin, drug, and regulation of smooth muscle cell proliferation. These core target genes are involved in signaling pathways related to cartilage degeneration of knee osteoarthritis such as TNF signaling pathway and PI3K-Akt signaling pathway. Finally, the molecular docking verification demonstrates that some active components of the drug have good molecular docking and binding ability with the core target protein, further confirming that XHLP has the effect of inhibiting cartilage degeneration in knee osteoarthritis.Conclusions: In this study, based on the research foundation of bioinformatics and molecular docking technology, the active components and core target molecules of XHLP for the treatment of cartilage degeneration of knee osteoarthritis are screened out, and the potential mechanism of XHLP inhibiting cartilage degeneration of knee osteoarthritis is deeply explored. The results provide theoretical basis and new treatment plan for XHLP in the treatment of knee osteoarthritis.

scholarly journals Network Pharmacology Integrated Molecular Docking Reveals the Anti-COVID-19 and SARS Mechanism of Fufang Banlangen Keli

2021 ◽  
Vol 16 (1) ◽  
pp. 1934578X2098842
Author(s):  
Li Cheng ◽  
Fei Wang ◽  
Shun Bo Zhang ◽  
Qiu Yun You
Keyword(s):  
Molecular Docking ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Docking Studies ◽  
Functional Enrichment ◽  
Network Pharmacology ◽  
Active Components

Purpose Fufang Banlangen Keli (FBK) has been recommended for its clinical treatment of Coronavirus disease 2019 (COVID-19) and severe acute respiratory syndrome (SARS), but the mechanism of action is unclear. So, using network pharmacology and molecular docking, we studied the active components and mechanism of FBK in the treatment of COVID-19 and SARS. Methods The Encyclopedia of Traditional Chinese Medicine and Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform were used to screen the active components by oral bioactivity and drug likeness. Then, PharmMapper and SwissTargetPrediction databases were used to screen potential target genes of active components; the related target genes of COVID-19 and SARS were obtained from the GeneCards database. The intersection of the active components and disease-related targets was performed by the Venny2.1.0 database. The DAVID6.8 database and KOBAS3.0 database were used to get gene ontology (GO) function enrichment and Kyoto Encyclopedia of Genes and Genomes pathway annotation of gene targets. The “components-targets-pathways (C-T-P)” network of FBK was conducted by Cytoscape3.6.1 software. The top active components, angiotensin-converting enzyme 2 (ACE2) and SARS-CoV-2 3 Cl, were imported into AutoDock and PyMOL for molecular docking. Results From the FBK, a total of 28 active components and 73 gene targets were screened through network pharmacology. Twenty pathways were analyzed, including pathways in cancer, nod-like receptor signaling pathway, and pancreatic cancer, etc. ( P < 0.05). A total of 337 items were obtained by GO functional enrichment analysis ( P < 0.05), including 257 items for biological process, 38 items for cell composition, and 42 items for molecular function. Furthermore, molecular docking studies were performed to study potential binding between the key gene targets and selected active components. Conclusion Based on network pharmacology and molecular docking technology, qingdainone, (2Z)-2-(2-oxoindolin-3-ylidene) indolin-3-one, sinensetin, and acacetin in FBK were verified to bind to ACE2 and SARS-COV-2 3 Cl, so as to treat COVID-19 and SARS.

scholarly journals Mechanism of Compound Houttuynia Mixture as an Anti-COVID-19 Drug Based on Network Pharmacology and Molecular Docking

2021 ◽  
Vol 16 (5) ◽  
pp. 1934578X2110167
Author(s):  
Xing-Pan Wu ◽  
Tian-Shun Wang ◽  
Zi-Xin Yuan ◽  
Yan-Fang Yang ◽  
He-Zhen Wu
Keyword(s):  
Molecular Docking ◽  
Target Prediction ◽  
Interaction Network ◽  
Scientific Basis ◽  
Network Pharmacology ◽  
Chemical Components ◽  
Active Components ◽  
Discovery Studio ◽  
The Core ◽  
Pathway Annotation

Objective To explore the anti-COVID-19 active components and mechanism of Compound Houttuynia mixture by using network pharmacology and molecular docking. Methods First, the main chemical components of Compound Houttuynia mixture were obtained by using the TCMSP database and referring to relevant chemical composition literature. The components were screened for OB ≥30% and DL ≥0.18 as the threshold values. Then Swiss Target Prediction database was used to predict the target of the active components and map the targets of COVID-19 obtained through GeneCards database to obtain the gene pool of the potential target of COVID-19 resistance of the active components of Compound Houttuynia mixture. Next, DAVID database was used for GO enrichment and KEGG pathway annotation of targets function. Cytoscape 3.8.0 software was used to construct a “components-targets-pathways” network. Then String database was used to construct a “protein-protein interaction” network. Finally, the core targets, SARS-COV-2 3 Cl, ACE2 and the core active components of Compound Houttuyna Mixture were imported into the Discovery Studio 2016 Client database for molecular docking verification. Results Eighty-two active compounds, including Xylostosidine, Arctiin, ZINC12153652 and ZINC338038, were screened from Compound Houttuyniae mixture. The key targets involved 128 targets, including MAPK1, MAPK3, MAPK8, MAPK14, TP53, TNF, and IL6. The HIF-1 signaling, VEGF signaling, TNF signaling and another 127 signaling pathways associated with COVID-19 were affected ( P < 0.05). From the results of molecular docking, the binding ability between the selected active components and the core targets was strong. Conclusion Through the combination of network pharmacology and molecular docking technology, this study revealed that the therapeutic effect of Compound Houttuynia mixture on COVID-19 was realized through multiple components, multiple targets and multiple pathways, which provided a certain scientific basis of the clinical application of Compound Houttuynia mixture.

scholarly journals Studies on the Mechanism of Gegen Qinlian Decoction in Treating Diabetes Mellitus Based on Network Pharmacology

2021 ◽  
Vol 16 (1) ◽  
pp. 1934578X2098213
Author(s):  
Xiaodong Deng ◽  
Yuhua Liang ◽  
Jianmei Hu ◽  
Yuhui Yang
Keyword(s):  
Diabetes Mellitus ◽  
Molecular Docking ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Network Pharmacology ◽  
Hub Genes ◽  
Topological Parameters

Diabetes mellitus (DM) is a chronic disease that is very common and seriously threatens patient health. Gegen Qinlian decoction (GQD) has long been applied clinically, but its mechanism in pharmacology has not been extensively and systematically studied. A GQD protein interaction network and diabetes protein interaction network were constructed based on the methods of system biology. Functional module analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and Gene Ontology (GO) enrichment analysis were carried out on the 2 networks. The hub nodes were filtered by comparative analysis. The topological parameters, interactions, and biological functions of the 2 networks were analyzed in multiple ways. By applying GEO-based external datasets to verify the results of our analysis that the Gene Set Enrichment Analysis (GSEA) displayed metabolic pathways in which hub genes played roles in regulating different expression states. Molecular docking is used to verify the effective components that can be combined with hub nodes. By comparing the 2 networks, 24 hub targets were filtered. There were 7 complex relationships between the networks. The results showed 4 topological parameters of the 24 selected hub targets that were much higher than the median values, suggesting that these hub targets show specific involvement in the network. The hub genes were verified in the GEO database, and these genes were closely related to the biological processes involved in glucose metabolism. Molecular docking results showed that 5,7,2', 6'-tetrahydroxyflavone, magnograndiolide, gancaonin I, isoglycyrol, gancaonin A, worenine, and glyzaglabrin produced the strongest binding effect with 10 hub nodes. This compound–target mode of interaction may be the main mechanism of action of GQD. This study reflected the synergistic characteristics of multiple targets and multiple pathways of traditional Chinese medicine and discussed the mechanism of GQD in the treatment of DM at the molecular pharmacological level.

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