scholarly journals Construction of Pharmacological Network and Molecular Docking Analysis of Key Molecular Components of Jianpi Yiqi Formula for Idiopathic Thrombocytopenic Purpura

2021 ◽  
Author(s):  
Qiming Li ◽  
Gang Deng ◽  
Yunlei He ◽  
Jiafeng Yang
Keyword(s):  
Molecular Docking ◽  
Idiopathic Thrombocytopenic Purpura ◽  
Target Genes ◽  
Network Pharmacology ◽  
Active Ingredients ◽  
Critical Pathway ◽  
Thrombocytopenic Purpura ◽  
Active Compounds ◽  
Docking Analysis ◽  
Molecular Docking Analysis

Abstract Purpose: To perform network pharmacological analysis so as to identify and screen the active ingredients of Jianpi Yiqi Formula; find its core target and explore its mechanism in the treatment of idiopathic thrombocytopenic purpura (ITP). Materials and Methods: A network pharmacology approach was used to inquire and screen the active ingredients from the Traditional Chinese Medicine System Pharmacology (TCMSP) database for potential active compounds that are commonly contained in the Jianpi Yiqi formula. The Swiss Target Prediction database was used for the prediction of the active ingredient's target of the action; the genecard database was used to search for target genes associated with ITP and to screen for target genes, in which the drug target was intersected with the disease target. Protein interaction network was constructed using string database software for GO and KEGG analysis to construct the "component/target/pathway" pharmacology network of Jianpi Yiqi granules therapy for ITP. Cytoscape 3.7.2 software was harnessed to visualize and integrate this network. The Virtua Drug web-based pharmacology website ( https://www.dockingserver.com ) was used to validate the regulatory relationship between key active compounds and critical pathway molecular signals by molecular docking. Results: Two key active ingredients, quercetin, and kaempferol, were selected from hundreds of herbal ingredients referenced in online pharmacological studies. Molecular docking analysis revealed that quercetin and kaempferol could stably bind PI3K/AKT and exert inhibitory effects, respectively. It was also speculated that PI3K/AKT/mTOR pathway might be the critical pathway for the pharmacokinetic mechanism of Jianpi Yiqi Granules. Conclusion: The present study suggests the multi-component effect characteristic of the treatment of ITP with Jianpi Yiqi granules, thus providing a theoretical basis for the clinical use of Jianpi Yiqi formula.

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.

Network Pharmacology-based and Molecular Docking Analysis of Resveratrol’s Pharmacological Effects on Type I Endometrial Cancer

2021 ◽  
Vol 21 ◽  
Author(s):  
Zixing Zhong ◽  
Xin Guo ◽  
Yanmei Zheng
Keyword(s):  
Endometrial Cancer ◽  
Molecular Docking ◽  
In Silico ◽  
Target Genes ◽  
Network Pharmacology ◽  
Type I ◽  
Pharmacological Effects ◽  
Molecular Docking Study ◽  
Docking Analysis ◽  
Molecular Docking Analysis

Background: Resveratrol is a natural polyphenol commonly seen in foods. It has demonstrated an inhibitive effect on endometrial cancer, but the molecular action is still not known. Objective: We aimed to use network pharmacology to systematically study the possible mechanisms of resveratrol’s pharmacological effects on type I endometrial cancer. Methods: Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) were used to predict resveratrol’s possible target genes. They were then converted to UniProt gene symbols. Simultaneously, type I endometrial cancer-related target genes were collected from GeneCards. All data were pooled to identify common target genes. The protein-protein interaction (PPI) network was constructed and further analyzed via STRING Online Database. Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were also performed afterward. To visualise resveratrol's overall pharmacological effects on type I endometrial cancer, a network of drug components-target gene-disease (CTD) was constructed. Then, we performed in silico molecular docking study to validate the possible binding conformation between resveratrol and candidate targets. Results: There are 150 target genes of resveratrol retrieved after UniProt conversion; 122 of them shared interaction with type I endometrial cancer. Some important oncogenes and signaling pathways are involved in the process of resveratrol’s pharmacological effects on endometrioid cancer. Molecular docking analysis confirmed that hydrogen bonding and hydrophobic interaction are the main interaction between resveratrol and its targets. Conclusion: We have explored the possible underlying mechanism of resveratrol in antagonising type I endometrial cancer through a network pharmacology-based approach and in-silico verification. However, further experiments are necessary to add to the evidence identifying resveratrol as a promising anti-type I endometrial cancer agent.

scholarly journals Identifying Active Compounds and Targets of Fritillariae thunbergii against Influenza-Associated Inflammation by Network Pharmacology Analysis and Molecular Docking

Molecules ◽  
2020 ◽  
Vol 25 (17) ◽  
pp. 3853 ◽  
Author(s):  
Minjee Kim ◽  
Ki Hoon Park ◽  
Young Bong Kim
Keyword(s):  
Molecular Docking ◽  
Binding Affinity ◽  
Target Genes ◽  
Inflammatory Diseases ◽  
Network Pharmacology ◽  
Pharmacokinetic Parameters ◽  
Active Compounds ◽  
Biological Targets ◽  
Underlying Mechanisms ◽  
Cox 1

Complications due to influenza are often associated with inflammation with excessive release of cytokines. The bulbs of Fritillariae thunbergii (FT) have been traditionally used to control airway inflammatory diseases, such as bronchitis and pneumonia. To elucidate active compounds, the targets, and underlying mechanisms of FT for the treatment of influenza-induced inflammation, systems biology was employed. Active compounds of FT were identified through the TCMSP database according to oral bioavailability (OB) and drug-likeness (DL) criteria. Other pharmacokinetic parameters, Caco-2 permeability (Caco-2), and drug half-life (HL) were also identified. Biological targets of FT were retrieved from DrugBank and STITCH databases, and target genes associated with influenza, lung, and spleen inflammation were collected from DisGeNET and NCBI databases. Compound-disease-target (C-D-T) networks were constructed and merged using Cytoscape. Target genes retrieved from the C-D-T network were further analyzed with GO enrichment and KEGG pathway analysis. In our network, GO and KEGG results yielded two compounds (beta-sitosterol (BS) and pelargonidin (PG)), targets (PTGS1 (COX-1) and PTGS2 (COX-2)), and pathways (nitric oxide, TNF) were involved in the inhibitory effects of FT on influenza-associated inflammation. We retrieved the binding affinity of each ligand-target, and found that PG and COX-1 showed the strongest binding affinity among four binding results using a molecular docking method. We identified the potential compounds and targets of FT against influenza and suggest that FT is an immunomodulatory therapy for influenza-associated inflammation.

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 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 Exploring the Active Compounds of Traditional Mongolian Medicine Baolier Capsule (BLEC) in Patients with CAD Based on Network Pharmacology Analysis and Molecular Docking Method

2021 ◽  
Author(s):  
Mengqiu Wei ◽  
Jun Liu ◽  
Jun Lai ◽  
Meifang Leng ◽  
Zebing Ye ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Topological Analysis ◽  
Network Pharmacology ◽  
Pharmacological Effects ◽  
Ppi Network ◽  
Active Ingredients ◽  
Active Compounds ◽  
Docking Method ◽  
Traditional Mongolian Medicine ◽  
Molecular Docking Method

Abstract Baolier Capsule (BLEC) is a Traditional Mongolian Medicine comprising of fifteen herbs. In China, this medicine has been used to treat CAD for many years. However, the molecular mechanism of BLEC in the treatment of CAD is not yet fully understood. Hence, this study aims to illustrate the synergistic mechanism of BLEC in the treatment of CAD by using network pharmacology method and molecular docking. Searching and screening the active ingredients of different herbs in BLEC and target genes related to CAD in multiple databases. Subsequently, STRING and Cytoscape were used to analyze and construct the PPI network. In addition, clustering and topological analysis are used to analyze the PPI network. Then, using R project for GO and KEGG enrichment analysis. Finally, AutoDock was used to verify the binding ability between the active ingredient and the key target through molecular docking. There are 144 active components and 80 CAD-related targets that are identified in BLEC in the treatment of CAD. What is more, 8 core genes (AKT1, EGFR, FOS, etc.) were obtained by clustering and topological analysis. Further, GO and KEGG analysis showed that fluid shear stress and atherosclerosis is the key pathways for RWW to treat CAD. These results were validated by molecular docking method. Our research firstly revealed the basic pharmacological effects and relevant mechanisms of the BLEC in the treatment of CAD. The prediction results might facilitate the development of BLEC or its active compounds as alternative therapy for CAD. Our research first revealed the basic pharmacological effects and related mechanisms of BLEC in the treatment of CAD. The predicted results provide some theoretical support for BLEC or its important active ingredients to treat CAD.

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