scholarly journals Predicting the Molecular Mechanism of Shenling Baizhu San in Treating Convalescent Patients With COVID-19 Based on Network Pharmacology and Molecular Docking

2021 ◽  
Vol 16 (10) ◽  
pp. 1934578X2110460
Author(s):  
Ying Zhang ◽  
Li Lu ◽  
YiWen Liu ◽  
AiXia Yang ◽  
Yanfang Yang
Keyword(s):  
Molecular Docking ◽  
Protein Interactions ◽  
Enrichment Analysis ◽  
Binding Energies ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Protein Protein Interactions ◽  
Active Ingredients ◽  
Active Components ◽  
Pathway Network

Objective: Shenling Baizhu San (SBS) was selected as the regimen for the treatment of COVID-19 in Guangdong Province. It is mainly used for the convalescent treatment of COVID-19 patients with deficiency of both lung and spleen. In this study, we aimed to explore the mechanism of SBS in the treatment of COVID-19 through network pharmacology combined with molecular docking. Methods: The targets of active components of SBS were collected through Traditional Chinese Medicine Systems Pharmacology (TCMSP) and ETCM databases. Using the Genecards, TTD, OMIM and other databases, the targets of COVID-19 were determined. The next step was to use a string database to build a protein–protein interactions (PPI) network between proteins, and use David database to perform gene ontology (GO) function enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on core targets. Then we used Cytoscape software to construct the active ingredients-core target-signaling pathway network, and finally the active ingredients of SBS were molecularly docked with the core targets to predict the mechanism of SBS in the treatment of COVID-19. Results: A total of 177 active compounds, 43 core targets and 58 signaling pathways were selected. Molecular docking results showed that the binding energies of the top six active components and the targets were all less than −5 kcal/MOL. Conclusion: The potential mechanism of action of SBS in the treatment of COVID-19 may be associated with the regulation of genes co-expressed with IL6, DPP4, PTGS2, PTGS1 and TNF.

scholarly journals Predicting the Molecular Mechanism of Sini Jia Renshen Decoction in Treating Severe COVID-19 Patients Based on Network Pharmacology and Molecular Docking

2021 ◽  
Vol 16 (12) ◽  
pp. 1934578X2110592
Author(s):  
Yi Wen Liu ◽  
Ai Xia Yang ◽  
Li Lu ◽  
Tie Hua Huang
Keyword(s):  
Molecular Docking ◽  
Protein Interactions ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Protein Protein Interactions ◽  
Active Ingredients ◽  
Active Compounds ◽  
Pathway Network ◽  
Function Enrichment Analysis

Objective: To explore the potential mechanism of Sini jia Renshen Decoction (SJRD) in the treatment of COVID-19 based on network pharmacology and molecular docking. Methods: The active compounds and potential therapeutic targets of SJRD were collected through the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP). Then a string database was used to build a protein–protein interactions (PPI) network between proteins, and use the David database to perform gene ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on core targets. Then we used Cytoscape software to construct an active ingredients-core target-signaling pathway network, and finally the active ingredients of SJRD were molecularly docked with the core targets to predict the mechanism of SJRD in the treatment of COVID-19. Results: A total of 136 active compounds, 51 core targets and 93 signaling pathways were selected. Molecular docking results revealed that quercetin, 3,22-dihydroxy-11-oxo-delta(12)-oleanene-27-alpha-methoxycarbonyl-29-oic acid, 18α-hydroxyglycyrrhetic acid, gomisin B and ignavine had considerable binding ability with ADRB2, PRKACA, DPP4, PIK3CG and IL6. Conclusions: This study preliminarily explored the mechanism of multiple components，multiple targets，and multiple pathways of SJRD in the treatment of COVID-19 by network pharmacology．

scholarly journals A Combined Network Pharmacology and Molecular Docking Approach to Investigate Candidate Active Components and Multitarget Mechanisms of Hemerocallis Flowers on Antidepressant Effect

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Tiancheng Ma ◽  
Yu Sun ◽  
Chang Jiang ◽  
Weilin Xiong ◽  
Tingxu Yan ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Protein Interactions ◽  
Kegg Pathway ◽  
Endocrine System ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Antidepressant Effect ◽  
Pathway Enrichment Analysis ◽  
Active Components ◽  
Pathway Network

Objective. The purpose of our research is to systematically explore the multiple mechanisms of Hemerocallis fulva Flowers (HF) on depressive disorder (DD). Methods. The components of HF were searched from the literature. The targets of components were obtained from PharmMapper. After that, Cytoscape software was used to build a component-target network. The targets of DD were collected from DisGeNET, PharmGKB, TTD, and OMIM. Protein-protein interactions (PPIs) among the DD targets were executed to screen the key targets. Afterward, the GO and KEGG pathway enrichment analysis were performed by the KOBAS database. A compound-target-KEGG pathway network was built to analyze the key compounds and targets. Finally, the potential active substances and targets were validated by molecular docking. Results. A total of 55 active compounds in HF, 646 compound-related targets, and 527 DD-related targets were identified from public databases. After treated with PPI, 219 key targets of DD were acquired. The gene enrichment analysis suggested that HF probably benefits DD patients by modulating pathways related to the nervous system, endocrine system, amino acid metabolism, and signal transduction. The network analysis showed the critical components and targets of HF on DD. Results of molecular docking increased the reliability of this study. Conclusions. It predicted and verified the pharmacological and molecular mechanism of HF against DD from a holistic perspective, which will also lay a foundation for further experimental research and rational clinical application of DD.

scholarly journals Investigation of Active Ingredients and Mechanism of Sanao Decoction in the Treatment of Chronic Cough by Network Pharmacology

2020 ◽  
Author(s):  
Mengke Sheng ◽  
Xing Liu ◽  
Qingsong Qu ◽  
Xiaowen Wu ◽  
Yuyao Liao ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Signaling Pathway ◽  
Chronic Cough ◽  
Fluid Shear Stress ◽  
Oxidant Stress ◽  
Network Pharmacology ◽  
Ppi Network ◽  
Active Ingredients ◽  
Active Components ◽  
Pathway Network

Abstract Background: Chronic cough significantly affects human health and quality of life. Studies have shown that Sanao Decoction（SAD）can clinically treat chronic cough. To investigate its mechanisms, we used the method of network pharmacology to conduct research at the molecular level.Methods: The active ingredients and their targets were screened by pharmacokinetics parameters from the traditional Chinese medicine system pharmacology analysis platform (TCMSP). The relevant targets of chronic cough were obtained from two databases: GeneCards and DrugBank. Take the intersection to get potential targets of SAD to treat chronic cough and establish the component-target regulatory network by CytoScape3.7.2 and protein-protein interaction (PPI) network by STRING 1.0. The function of the target gene and related pathways were analyzed by the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) in the Database for Annotation, Visualization, and Integrated Discovery (DAVID). The significant pathways and their relevant targets were obtained and the target-pathway network was established by CytoScape3.7.2. Finally, molecular docking of the core active components and relevant targets was performed.Results: A total of 98 active components, 113 targets were identified. The component-target and target-pathway network of SAD and PPI network were established. Enrichment analysis of DAVID indicated that 2062 terms were in biological processes, 77 in cellular components, 142 in molecular functions and 20 significant pathways. In addition, the molecular docking showed that quercetin and luteolin had a good combination with the corresponding targets.Conclusions: It indicates that the active compounds of SAD, such as quercetin, luteolin, may act on AKT1, MAPK1, RELA, EGFR, BCL2 and regulate PI3K-Akt signaling pathway, AGE-RAGE signaling pathway in diabetic complications and Fluid shear stress and atherosclerosis pathway to exert the effects of anti-inflammatory, anti-airway remodeling, anti-oxidant stress and repair airway damage to treat chronic cough.

scholarly journals Exploration of the mechanism of Ziyin Tongluo Formula on preventing and treating postmenopausal osteoporosis based on the network pharmacology and molecular docking

2020 ◽  
Author(s):  
Rong-Bin Chen ◽  
Ying-Dong Yang ◽  
Kai Sun ◽  
Shan Liu ◽  
Wei Guo ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Postmenopausal Osteoporosis ◽  
Signaling Pathway ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Active Ingredients ◽  
Active Compounds ◽  
Core Proteins ◽  
Key Genes

Abstract Background: Postmenopausal osteoporosis (PMOP) is a global chronic and metabolic bone disease, which poses huge challenges to individuals and society. Ziyin Tongluo Formula (ZYTLF) has been proved effective in the treatment of PMOP. However, the material basis and mechanism of ZYLTF against PMOP have not been thoroughly elucidated.Methods: Online databases were used to identify the active ingredients of ZYTLF and corresponding putative targets. Genes associated with PMOP were mined, and then mapped with the putative targets to obtain overlapping genes. Multiple networks were constructed and analyzed, from which the key genes were selected. The key genes were imported to the DAVID database to performs GO and KEGG pathway enrichment analysis. Finally, AutoDock Tools and other software were used for molecular docking of core compounds and key proteins. Results: Ninety-two active compounds of ZYTLF corresponded to 243 targets, with 129 target genes interacting with PMOP, and 50 key genes were selected. Network analysis showed the top 5 active ingredients including quercetin, kaempferol, luteolin, scutellarein, and formononetin., and the top 50 key genes such as VEGFA, MAPK8, AKT1, TNF, ESR1. Enrichment analysis uncovered two significant types of KEGG pathways in PMOP, hormone-related signaling pathways (estrogen , prolactin, and thyroid hormone signaling pathway) and inflammation-related pathways (TNF, PI3K-Akt, and MAPK signaling pathway). Moreover, molecular docking analysis verified that the main active compounds were tightly bound to the core proteins, further confirming the anti-PMOP effects. Conclusions: Based on network pharmacology and molecular docking technology, this study initially revealed the mechanisms of ZYTLF on PMOP, which involves multiple targets and multiple pathways.

scholarly journals Network Pharmacology-Based Prediction of the Active Ingredients and Potential Targets of Chinese Herbal Danyu Gukang Pills for Application to Osteonecrosis of the Femoral Head Disease

2021 ◽  
Author(s):  
Yongchang Guo ◽  
Dapeng Zhang ◽  
Yuju Cao ◽  
Xiaoyan Feng ◽  
Caihong Shen ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Femoral Head ◽  
Signaling Pathway ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
In Vitro Experiments ◽  
Active Components

Abstract Ethnopharmacological relevanceOsteonecrosis of the femoral head (ONFH) is still a challenge for orthopedists worldwide, which may lead to disability in patients without effective treatment. A newly developed formula of Chinese medicine, Danyu Gukang Pills (DGP), was recognized to be effective for ONFH. Nevertheless, its molecular mechanisms remain to be clarified. MethodsNetwork pharmacology was adopted to detect the mechanism of DGP on ONFH. The compounds of DGP were collected from the online databases, and active components were selected based on their OB and DL index. The potential proteins of DGP were acquired from TCMSP database, while the potential genes of ONFH were obtained from Gene Cards and Pubmed Gene databases. The function of Gene and potential pathways were researched by GO and KEGG pathway enrichment analysis. The compounds-targets and targets-pathways network were constructed in an R and Cytosacpe software. The mechanism was further investigated via molecular docking. Finally, in-vitro experiments were validated in the BMSCs. ResultsA total of 2305 compounds in DGP were gained, among which, 370 were selected as active components for which conforming to criteria. Combined the network analysis, molecular docking and in-vitro experiments, the results firstly demonstrated that the treatment effect of DGP on ONFH may be closely related to HIF-1α, VEGFA and HIF-1 signaling pathway. ConclusionThe current study firstly researched the molecular mechanism of DGP on ONFH based on network pharmacology. The results indicated that DGP may exert the effect on ONFH targeting on HIF-1α and VEGFA via HIF-1 signaling pathway.

Systematic evaluation of the mechanisms of Mulberry leaf (Morus alba Linne) acting on diabetes based on network pharmacology and molecular docking

2020 ◽  
Vol 23 ◽  
Author(s):  
Qiguo Wu ◽  
Yeqing Hu
Keyword(s):  
Molecular Docking ◽  
Signaling Pathway ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Active Compounds ◽  
Pathway Network ◽  
Pathway Enrichment ◽  
Mulberry Leaves ◽  
Mulberry Leaf

Background: Diabetes mellitus is one of the most common endocrine metabolic disorder diseases. The application of herbal medicine to control glucose levels and improve insulin action might be a useful approach in the treatment of diabetes. Mulberry leaves (ML) has been reported to exert important activities of anti-diabetic. Objective: In this work, we aimed to explore the multi-targets and multi-pathways regulatory molecular mechanism of Mulberry leaves (ML, Morus alba Linne) acting on diabetes. Methods: Identification of active compounds of Mulberry leaves using Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. Bioactive components were screened by FAF-Drugs4 website (Free ADME-Tox Filtering Tool). The targets of bioactive components were predicted from SwissTargetPrediction website, and the diabetes related targets were screened from GeneCards database. The common targets of ML and diabetes are used for Gene Ontology (GO) and pathway enrichment analysis. The visualization networks were constructed by Cytoscape 3.7.1 software. The construction of biological networks were performed to analyze the mechanisms as follows: (1) Compound-Target network; (2) Common target-Compound network; (3) Common targets protein interaction network; (4) Compound-Diabetes protein-protein interactions (PPI) network; (5) Target-Pathway network; (6) Compound-Target-Pathway network. At last, the prediction results of network pharmacology were verified by molecular docking method. Results: 17 active components were obtained by TCMSP database and FAF-Drugs4 website. 51 potential targets (11 common targets and 40 associated indirect targets) were obtained and used to build the PPI network by String database. Furthermore, the potential targets were used to GO and pathway enrichment analysis. 8 key active compounds (quercetin, Iristectorigenin A, 4-Prenylresveratrol, Moracin H, Moracin C, Isoramanone, Moracin E and Moracin D) and 8 key targets (AKT1, IGF1R, EIF2AK3, PPARG, AGTR1, PPARA, PTPN1 and PIK3R1) were obtained to play major roles in Mulberry leaf acting on diabetes. And the signal pathways involved in the mechanisms mainly include AMPK signaling pathway, PI3K-Akt signaling pathway, mTOR signaling pathway, insulin signaling pathway and insulin resistance. The molecular docking results show that the 8 key active compounds have good affinity with the key target of AKT1, and the 5 key targets (IGF1R, EIF2AK3, PPARG, PPARA and PTPN1) have better affinity than AKT1 with the key compound of quercetin. Conclusion: Based on network pharmacology and molecular docking of this work provided an important systematic and visualized basis for further understanding the synergy mechanism of ML acting on diabetes.

scholarly journals Identifying Synergistic Mechanisms of Multiple Ingredients in Shuangbai Tablets against Proteinuria by Virtual Screening and a Network Pharmacology Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Xiaoqin Ma ◽  
Meixiang Yu ◽  
Chenxia Hao ◽  
Wanhua Yang
Keyword(s):  
Molecular Docking ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Active Ingredients ◽  
Human Phenotype ◽  
Herbal Mixture ◽  
Relationship Of

Shuangbai Tablets (SBT), a traditional herbal mixture, has shown substantial clinical efficacy. However, a systematic mechanism of its active ingredients and pharmacological mechanisms of action against proteinuria continues being lacking. A network pharmacology approach was effectual in discovering the relationship of multiple ingredients and targets of the herbal mixture. This study aimed to identify key targets, major active ingredients, and pathways of SBT against proteinuria by network pharmacology approach combined with thin layer chromatography (TLC). Human phenotype (HP) disease analysis, gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and molecular docking were used in this study. To this end, a total of 48 candidate targets of 118 active ingredients of SBT were identified. Network analysis showed PTGS2, ESR1, and NOS2 to be the three key targets, and beta-sitosterol, quercetin, and berberine were the three major active ingredients; among them one of the major active ingredients, quercetin, was discriminated by TLC. These results of the functional enrichment analysis indicated that the most relevant disease including these 48 candidate proteins is proteinuria, SBT treated proteinuria by sympathetically regulating multiple biological pathways, such as the HIF-1, RAS, AGE-RAGE, and VEGF signaling pathways. Additionally, molecular docking validation suggested that major active ingredients of SBT were capable of binding to HIF-1A and VEGFA of the main pathways. Consequently, key targets, major active ingredients, and pathways based on data analysis of SBT against proteinuria were systematically identified confirming its utility and providing a new drug against proteinuria.

scholarly journals Network Pharmacology and Molecular Docking Analysis on Molecular Targets and Mechanisms of Buyang Huanwu Decoction in the Treatment of Ischemic Stroke

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Qiang Gao ◽  
Danfeng Tian ◽  
Zhenyun Han ◽  
Jingfeng Lin ◽  
Ze Chang ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Molecular Docking ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Ppi Network ◽  
Active Components ◽  
Nf Kappa B ◽  
Buyang Huanwu Decoction ◽  
Pathway Network ◽  
Target Network

Background and Objective. With the exact clinical efficacy, Buyang Huanwu decoction (BHD) is a classical prescription for the treatment of ischemic stroke (IS). Here, we aimed to investigate the pharmacological mechanisms of BHD in treating IS using systems biology approaches. Methods. The bioactive components and potential targets of BHD were screened by TCMSP, BATMAN-TCM, ETCM, and SymMap databases. Besides, compounds that failed to find the targets from the above databases were predicted through STITCH, SwissTargetPrediction, and SEA. Moreover, six databases were searched to mine targets of IS. The intersection targets were obtained and analyzed by GO and KEGG enrichment. Furthermore, BHD-IS PPI network, compound-target network, and herb-target-pathway network were constructed by Cytoscape 3.6.0. Finally, AutoDock was used for molecular docking verification. Results. A total of 235 putative targets were obtained from 59 active compounds in BHD. Among them, 62 targets were related to IS. PPI network showed that the top ten key targets were IL6, TNF, VEGFA, AKT1, etc. The enrichment analysis demonstrated candidate BHD targets were more frequently involved in TNF, PI3K-Akt, and NF-kappa B signaling pathway. Network topology analysis showed that Radix Astragali was the main herb in BHD, and the key components were quercetin, beta-sitosterol, kaempferol, stigmasterol, etc. The results of molecular docking showed the active components in BHD had a good binding ability with the key targets. Conclusions. Our study demonstrated that BHD exerted the effect of treating IS by regulating multitargets and multichannels with multicomponents through the method of network pharmacology and molecular docking.

scholarly journals Exploring the Antitumor Mechanisms of Zingiberis Rhizoma Combined with Coptidis Rhizoma Using a Network Pharmacology Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Meng Wang ◽  
Youke Qi ◽  
Yongning Sun
Keyword(s):  
Gene Expression ◽  
Molecular Docking ◽  
Protein Interactions ◽  
Disease Free Survival ◽  
Network Pharmacology ◽  
Protein Protein Interactions ◽  
Active Components ◽  
Antitumor Effects ◽  
Coptidis Rhizoma ◽  
Underlying Mechanisms

Background. Although the combination of Zingiberis rhizoma (ZR) and Coptidis rhizoma (CR) is a classic traditional Chinese medicine-based herbal pair used for its antitumor effect, the material basis and underlying mechanisms are unclear. Here, a network pharmacology approach was used to elucidate the antitumor mechanisms of ZR-CR. Materials and Methods. To predict the targets of ZR-CR in treating tumors, we constructed protein–protein interactions and hub component-target networks and performed pathway and process enrichment and molecular docking analysis. We used a surface plasmon resonance (SPR) assay to validate the predicted component-target affinities. Hub gene expression and survival analysis in patients with tumors were used to predict the clinical significance. Results. The active components of ZR-CR—shogaol, daucosterol, ginkgetin, berberine, quercetin, chlorogenic acid, and vanillic acid—exhibited antitumor activities via the MAPK, PI3K-AKT, TNF, FOXO, HIF-1, and VEGF signaling pathways. Molecular docking and SPR analyses suggested direct binding of berberine with AKT1 and TP53; quercetin with EGFR and VEGF165; and ginkgetin, isoginkgetin, and daucosterol with VEGF165 with weak affinities. Gene expression levels of the hub targets of ZR-CR were associated with overall survival and disease-free survival in patients with various tumor types. Conclusions. The antitumor components of the ZR-CR herbal pair and the mechanisms underlying their antitumor effects were identified. These antitumor components deserve to be explored further in experimental and clinical studies.

scholarly journals Predicting the levels of orcinol glucoside during the treatment of osteoporosis by network pharmacology and molecular docking

2020 ◽  
Author(s):  
xia liu ◽  
Mingchun Huang ◽  
Chen Yang ◽  
Qin Wang ◽  
Mei Zhang
Keyword(s):  
Molecular Docking ◽  
Topological Analysis ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Active Components ◽  
Active Constituent ◽  
Treatment Of Osteoporosis ◽  
Similarity Ensemble Approach ◽  
The Common

Abstract Introduction: As a traditional Chinese medicine (TCM), Curculigo orchioides Gaertn. (Xianmao) has been widely used to treat bone-related diseases. However, the active components of this TCM, and the specific mechanisms by which it exerts effect, have yet to be elucidated. To identify potential targets for orcinol glucoside (OG), an active constituent of C. orchioides, during the treatment of osteoporosis (OP) by adopting a network pharmacology approach. Methods: First, we mined the Similarity ensemble approach (SEA), SwissTargetPrediction, DisGeNET, and Genecards databases were mined for data related to the prediction of OG- and OP-related targets. Next, we identified the common targets for OG and OP, and then used STRING software to create a protein-protein interaction (PPI) network. Then, we used topological analysis to identify which of the common targets were most significant. Then, we used the common significant targets and g:profiler to perform gene ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes ( KEGG) pathway enrichment analysis. Finally, we used molecular docking to predict the targets of OG that were most relevant to the treatment of OP and investigated the potential pharmacological mechanisms that might be involved. Results: In total, 130 potential targets of OG, and 4582 targets relevant to OP, were subjected to network analysis. There were 73 common targets; these identified the principal pathways linked to OP. In addition, topological analysis identified 14 key targets. Most of the predicted targets played crucial roles in the PI3K-AKT signaling pathway. Molecular docking identified ten core targets (VEGFA, IL6, EGFR, MAPK1, HRAS, CCND1, FGF2, IL2, MCL1 and CDK4), thus indicating that OG may promote osteoblast proliferation and differentiation by accelerating progression of the cell cycle.Conclusions: This research provides a theoretical base for identifying the specific potential mechanisms of OG in treatment of OP.

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