scholarly journals Network Pharmacology-Based Prediction of Catalpol and Mechanisms against Stroke

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Jinghui Wang ◽  
Meifeng Zhang ◽  
Si Sun ◽  
Guoran Wan ◽  
Dong Wan ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Ppi Network ◽  
Pharmacological Mechanism ◽  
Binding Force ◽  
Strong Binding ◽  
Prevention And Treatment ◽  
Depth Study ◽  
Geo Database

Aim. To apply the network pharmacology method to screen the target of catalpol prevention and treatment of stroke, and explore the pharmacological mechanism of Catalpol prevention and treatment of stroke. Methods. PharmMapper, GeneCards, DAVID, and other databases were used to find key targets. We selected hub protein and catalpol which were screened for molecular docking verification. Based on the results of molecular docking, the ITC was used to determine the binding coefficient between the highest scoring protein and catalpol. The GEO database and ROC curve were used to evaluate the correlation between key targets. Results. 27 key targets were obtained by mapping the predicted catalpol-related targets to the disease. Hub genes (ALB, CASP3, MAPK1 (14), MMP9, ACE, KDR, etc.) were obtained in the key target PPI network. The results of KEGG enrichment analysis showed that its signal pathway was involved in angiogenic remodeling such as VEGF, neurotrophic factors, and inflammation. The results of molecular docking showed that ACE had the highest docking score. Therefore, the ITC was used for the titration of ACE and catalpol. The results showed that catalpol had a strong binding force with ACE. Conclusion. Network pharmacology combined with molecular docking predicts key genes, proteins, and signaling pathways for catalpol in treating stroke. The strong binding force between catalpol and ACE was obtained by using ITC, and the results of molecular docking were verified to lay the foundation for further research on the effect of catalpol on ACE. ROC results showed that the AUC values of the key targets are all >0.5. This article uses network pharmacology to provide a reference for a more in-depth study of catalpol’s mechanism and experimental design.

scholarly journals Molecular Mechanism of Danxiong Tongmai Granules in Treatment of Coronary Heart Disease

2021 ◽  
Author(s):  
Jiahao Ye ◽  
Ruiping Yang ◽  
Zhixi Hu ◽  
Lin Li ◽  
Senjie Zhong ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Molecular Docking ◽  
Enrichment Analysis ◽  
Dynamics Simulation ◽  
Venn Diagram ◽  
Network Pharmacology ◽  
Ppi Network ◽  
Ligand Interaction

Abstract Background: Network pharmacology has been widely adopted for mechanistic studies of Traditional Chinese Medicines (TCM). The present study uses network pharmacology to investigate the main ingredients, targets and pathways of Danxiong Tongmai Granules (DXTMG) in the treatment of coronary heart disease (CHD). We aim to validate our findings using molecular docking and molecular dynamics simulations.Methods: TCM compounds and targets were identified via searches in the BATMAN-TCM database, and the GeneCards database were used to obtain the main target genes involved in CHD, We combined disease targets with the drug targets to identify common targets, and draw a Venn diagram to visualize the results. The "TCM-compound-target" network was plotted using Cytoscape 3.7.2 software and a protein-protein interaction (PPI) network was constructed using the STRING database from which core targets were obtained. Gene ontology (GO) function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed for common drug-disease targets using R Version 4.0.4 (64 bit) software. Molecular docking of core protein-small molecule ligand interaction was modeled using AutoDock Vina software. A simulation of molecular dynamics was conducted for the optimal protein-ligand complex obtained by molecular docking using Amber18 software.Results: 162 potential targets of DXTMG involved in CHD were identified. These included INS, ALB, IL-6 and TNF according to PPI network studies. GO enrichment analysis identified a total of 3365 GO pathways, including 3049 biological process pathways (BP) concerned with the heart and circulatory system;109 cellular component (CC) pathways, including cation channels and membrane rafts and 207 molecular function (MF) pathways related to receptor ligands and activators. KEGG analysis revealed a total of 137 pathways (p<0.05), including those related to AGE-RAGE signaling associated with diabetic complications, fluid shear stress and atherosclerosis. Molecular docking revealed the highest binding energy for Neocryptotanshinone Ii (the key compound of DXTMG) and TNF. Molecular dynamics simulation indicated stable binding for TNF-Neocryptotanshinone Ii with strong hydrophobic interactions mediated predominantly by the hydrophobic residues, Leu279, Val280 and Phe278 plus hydrogen-bonding with Leu279.Conclusion: The present study reveals novel insights into the mechanism of DXTMG in treating CHD. DXTMG can influence oxidative stress、inflammation response and regulating cardiomyocytes, thereby reducing the occurrence and development of CHD.

scholarly journals Uncovering the Pharmacological of Xiaochaihu Decoction in the Treatment of Pancreatitis Based on the Network Pharmacology

2020 ◽  
Author(s):  
Lianghui Zhan ◽  
Jinbao Pu ◽  
Yijuan Hu ◽  
Pan Xu ◽  
Weiqing Liang ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Pancreatic Neoplasms ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Mapk Signaling ◽  
Network Pharmacology ◽  
Potential Mechanism ◽  
Ppi Network ◽  
Kegg Analysis ◽  
Key Genes

Abstract BackgroundXiaochaihu Decoction (XD) was a traditional prescription, has been demonstrated the pharmacodynamic on pancreatitis. But the underline mechanism remained to be explored. Therefore, this study was aimed to combined network pharmacology method and molecular docking technology to demonstrate the potential mechanism of XD treated with pancreatitis.MethodsFirstly, compounds of seven herbs containing XD were collected from TCMSP Database and the putative targets of Pancreatitis were obtained from OMIM, TTD, Genecards Database. Then PPI network was constructed according to the matching results between XD potential targets and pancreatic neoplasms targets. Furthermore, enrichment analysis on GO and KEGG by DAVID utilized bioinformatics resources. Finally, Molecular Docking was performed to simulate the interaction between the active compound of XD and putative targets.ResultsA total of 196 active ingredients and 91 putative targets were selected out. The PPI interaction network analysis demonstrated that Quercetin was the candidate agents and MAPK3, IL-6 and TP53 were the potential targets for the XD treatment of pancreatitis. The KEGG analysis revealed that pathways in cancers, TNF signaling way, MAPK signaling way might play an important role in pancreatitis therapy. And Molecular Docking results showed that Quercetin combined well with MAPK3, IL-6 and TP53.ConclusionThis study illustrated that Quercetin containing in XD might played an important role in pancreatitis therapy by acting the key genes of MPAK3, IL-6 and TP53. And it also provided a strategy to elucidate the mechanisms of Traditional Chinese Medicine (TCM) at the level of network pharmacology.

scholarly journals Network Pharmacology and Molecular Docking Combined to Analyze the Molecular and Pharmacological Mechanism of Pinellia ternata in the Treatment of Hypertension

2021 ◽  
Vol 43 (1) ◽  
pp. 65-78
Author(s):  
Zhaowei Zhai ◽  
Xinru Tao ◽  
Mohammad Murtaza Alami ◽  
Shaohua Shu ◽  
Xuekui Wang
Keyword(s):  
Molecular Docking ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Western Medicine ◽  
Network Pharmacology ◽  
Ppi Network ◽  
Active Components ◽  
Pharmacological Mechanism ◽  
Effective Components ◽  
Treatment Of Hypertension

Hypertension is a cardiovascular disease that causes great harm to health and life, affecting the function of important organs and accompanied by a variety of secondary diseases, which need to be treated with drugs for a long time. P. ternata alone or combination with western medicine has played an important role in traditional Chinese medicine. Although P. ternata is used clinically to treat hypertension, its functional molecular mechanism and pharmacological mechanism have not been elucidated. Therefore, in this study, the potentially effective components, and targets of P. ternata in the treatment of hypertension were screened by the method of network pharmacology, and the mechanism of P. ternata in the treatment of hypertension was analyzed by constructing a component-target relationship network, PPI interaction network, targets’ function analysis, and molecular docking. In the study, 12 potentially effective components and 88 targets were screened, and 3 potential protein modules were found and analyzed after constructing a PPI network using targets. In addition, 10 targets were selected as core targets of the PPI network. After that, the targets were analyzed by Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Finally, the molecular docking method is used to study the interaction between the targets and the active components. The above evidence shows that the mechanism of P. ternata in the treatment of hypertension is complicated, as it acts in many ways, mainly by affecting nerve signal transmission, cell proliferation, and apoptosis, calcium channels, and so on. The binding between targets and active components mainly depends on Pi bonds and hydrogen bonds. Using the method of network pharmacology and molecular docking to analyze the mechanism of P. ternata in the treatment of hypertension will help to provide a better scientific basis for the combined use of traditional Chinese medicine and western medicine, and will better help to improve the quality of P. ternata and point out its direction.

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 The Mechanism of Aidi Injection on Breast Cancer Based on Network Pharmacologyanalysis

2020 ◽  
Author(s):  
Jing Su ◽  
Kedi Liu ◽  
Xingru Tao ◽  
Fei Li ◽  
Shi Zhao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Signal Pathways ◽  
Bioactive Components ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Pharmacological Mechanism ◽  
Target Network ◽  
Aidi Injection

Abstract Background: Aidi injection (ADI)is a Chinese patent medicine with anti-cancer effect, which has been used to treat breast cancer (BC) in China for many years, but its potential pharmacological mechanism is still indeterminacy. In this resaearch, network pharmacology, a systematic and comprehensive approach, was used to reveal ADI's potential pharmacological mechanism in treating BC for the first time.Methods: Databases were used to collect targets related to the bioactive components of ADI and BC. the relevant networks were established by the string database, and were screened potential bioactive components and core targets. Eventually, core targets and pathway enrichment were analyzed by DAVID database.Results: As the results showed, we collected 99 bioactive ingredients,345 ADI-related targets after deduplication and 368 BC-related targets. Of these, 108 common targets were overlapped. We then performed an enrichment analysis on the common target network and the protein-protein interaction (PPI) network.Conclusion: The results showed that ADI may inhibit breast cancer through seven important signal pathways involved in the "regulation of vascular endothelial function", "inflammatory response" and "apoptosis” biological processes. Through further clustering and enrichment analysis of the PPI network of ADI’s bioactive component targets and BC-related targets, we found that cancer, ErbB, MAPK, TLR, chemokine, p53 and cell cycle signaling pathway, mainly contributed to the effects of ADI in treating BC. In conclusion, this study reveals the possible mechanism of ADI in treating BC, and provides a new direction for drug development for ADI in treating BC.

scholarly journals Based on network pharmacology and molecular docking to explore the Traditional Chinese Medicine anti-influenza mechanisms:Chinese protocol for diagnosis and treatment of influenza.

2020 ◽  
Author(s):  
yifei Chen
Keyword(s):  
Molecular Docking ◽  
Influenza A ◽  
Influenza Infection ◽  
Diagnosis And Treatment ◽  
Network Pharmacology ◽  
Ppi Network ◽  
Active Components ◽  
Pharmacological Mechanism ◽  
Severe Influenza ◽  
Target Network

Background Explore the possible mechanism of anti-influenza virus, based on the study of the active components-drug-target network, Protein-Protein Interaction (PPI) network and molecular docking verification, we explored the potential action mechanism of TCM in Chinese protocol for diagnosis and treatment of influenza 2019. Methods Screening the active components and potential targets of 12 drugs in the scheme by using TCMSP database, and Obtaining the target of influenza by GeneCard, Durgbank, OMIM, TTD and PharmGkb databases. Then, constructed the “component-durg-target” network and PPI network were by Cytoscape3.8.0 software. Morethan, analyzed and the biological function and pathway, verified the molecular docking by AutoDock Vina software. Results The 12 drugs in the recommended scheme (XBCQ) for severe influenza contain 192 active components and involve 31 key antiviral targets, which may play an antiviral role through biological processes such as lipopolysaccharide, pathogen molecular reaction and regulate signaling pathway via the IL-17, influenza A, TNF, Toll-like receptors. Conclusion TCM play critical therapeutic roles through “multi-components, multi-targets and multi-pathways” mechanisms in influenza infection. The antiviral pharmacological mechanism of Xuanbai Chengqi decoction, which was analyzed by network pharmacology and molecular docking, provide a new idea for further exploring the diagnosis and treatment of severe influenza.

scholarly journals Network pharmacology study of vaccarin for the prevention and treatment of postmenopausal osteoporosis

2020 ◽  
Author(s):  
Mengying Bao ◽  
Yan Dai ◽  
Xiaojun Chen ◽  
Shijie Liao ◽  
Wenyu Feng ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Postmenopausal Osteoporosis ◽  
Inflammatory Responses ◽  
Enrichment Analysis ◽  
Osteoclast Formation ◽  
Flavonoid Glycoside ◽  
Network Pharmacology ◽  
Biological Processes ◽  
Ppi Network ◽  
Prevention And Treatment

Abstract Background: As the main active ingredient of Semen Vaccariae, vaccarin is a flavonoid glycoside useful for the prevention and treatment of numerous diseases. Our previous study found that vaccarin can reduce osteolysis-induced titanium by inhibiting osteoclast formation. However, the issue of whether vaccarin can prevent and treat postmenopausal osteoporosis remains unclear.Method: In this study, we explored the mechanism of action of vaccarin for the prevention of postmenopausal osteoporosis via a network pharmacological approach. We identified the intersecting targets of osteoporosis-related genes retrieved from multiple disease target databases, as well as targets of potential action of vaccarin retrieved from drug-related databases. We then used the intersectional targets to establish a protein-protein interaction (PPI) network. Finally, we performed bioinformatics analysis to enrich Gene Ontology (GO) biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.Results：A total of 28 cross targets of vaccarin and osteoporosis were identified. PPI network analysis identified six target proteins, namely, IL-6, TNF, VEGFA, HSP90AA1, CREB1, and IL-2, which may be the key targets of vaccarin against osteoporosis. The 28 intersectional targets were mainly involved in 23 biological processes, such as regulation of apoptosis, positive regulation of neovascularization, and angiogenesis, whereas KEGG enrichment analysis revealed that they were primarily related to 22 different signaling pathways, such as PI3K/Akt pathway, cancer pathway, hepatitis B pathway, and tuberculosis pathway.Conclusion: We used a network pharmacology approach to predict the key targets of vaccarin for the prevention of osteoporosis from a systems perspective. We determined that the signaling pathways were chiefly engaged in different pathological processes affecting differentiation and apoptosis of bone rebuilding cells, endocrine metabolic disorders, inflammatory responses, and other disease interactions. This study provides a theoretical basis and therapeutic ideas for the treatment of postmenopausal osteoporosis and offers promising directions for further research on the regulatory mechanism of vaccarin.

scholarly journals Uncovering the Pharmacological of Xiaochaihu Decoction in the Treatment of Pancreatitis Based on the Network Pharmacology

2020 ◽  
Author(s):  
Lianghui Zhan ◽  
Jinbao Pu ◽  
Yijuan Hu ◽  
Pan Xu ◽  
Weiqing Liang ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Mapk Signaling ◽  
Network Pharmacology ◽  
Ppi Network ◽  
Rt Pcr ◽  
Elisa Kit ◽  
Kegg Analysis ◽  
Tnf Α

Abstract BackgroundXiaochaihu Decoction (XD) has been demonstrated the pharmacodynamic on pancreatitis. This study was aimed to investigate the material and molecular basis of Xiaochaihu Decoction.MethodsFirstly, compounds of seven herbs containing XD were collected from TCMSP Database and the putative targets of Pancreatitis were obtained from OMIM, TTD, Genecards Database. Then PPI network was constructed according to the matching results between XD potential targets and pancreatic neoplasms targets. Furthermore, enrichment analysis on GO and KEGG by DAVID utilized bioinformatics resources. Finally, Molecular Docking was performed to simulate the interaction between the active compound of XD and putative targets. In vitro experiment, AR42J cells were induced by LPS and then treated with Quercetin (25, 50, 100 μM). The IL-6, TNF-α, IL-1β levels were detected by Elisa kit and MAPK3, TP53 mRNA expressions were measured by RT-PCR.ResultsA total of 196 active ingredients and 91 putative targets were selected out. The PPI interaction network analysis demonstrated that Quercetin was the candidate agents and MAPK3, IL-6 and TP53 were the potential targets for the XD treatment of pancreatitis. The KEGG analysis revealed that pathways in cancers, TNF signaling way, MAPK signaling way might play an important role in pancreatitis therapy. And Molecular Docking results showed that Quercetin combined well with MAPK3, IL-6 and TP53. In vitro experiment indicated that, Quercetin inhibited the IL-6, TNF-α, IL-1β levels and MAPK3, TP53 mRNA. ConclusionThis study illustrated that Quercetin containing in XD might played an important role in pancreatitis therapy by acting the key genes of MPAK3, IL-6 and TP53 which were associating with inflammation and apoptosis.

scholarly journals Study on the Pharmacological Mechanism of Phthalazinone Derivatives as Potential Drugs for Alzheimer’s Disease Treatment by Network Pharmacology Analysis and Molecular Docking

2020 ◽  
Author(s):  
Huimin Ye ◽  
Zhong Li
Keyword(s):  
Molecular Docking ◽  
Network Analysis ◽  
Homo Sapiens ◽  
In Silico Analysis ◽  
Enrichment Analysis ◽  
Docking Studies ◽  
Network Pharmacology ◽  
Receptor Interaction ◽  
Pharmacological Mechanism ◽  
Approved Drugs

Abstract Objective: To screen the bioactivity of phthalazinone derivatives for AD treatment and investigate the potential pharmacological mechanism, the network pharmacology analysis and molecular docking were adopted in this study.Methods: Those phthalazinone derivatives with certain structures and physical properties were screened out by Pubchem database in this study. Besides, to explore the potential activity of these phthalazinone derivatives as drugs for AD treatment, network pharmacology study was employed, including targets prediction, gene enrichment analysis and network analysis. Network analysis of AD approved drugs and molecular docking studies were also adopted to further investigate drug-likeness of phthalazinone derivatives for AD treatment.Results: Five compounds and 57 common targets were recognized and adopted to the construction of compounds-targets network. 15 approved drugs with clear indication for AD were figured out, with 57 associated targets that originated from Homo sapiens. The KEGG enrichment analysis showed that phthalazinone derivatives and approved drugs shared the same essential pathway (neuroactive ligand-receptor interaction) and other important pathways with associated targets. The result of molecular docking indicated that these phthalazinone derivatives could interact with essential targets stably.Conclusion: In silico analysis suggested that these derivatives are probable to be effective for the treatment of AD by interacting with the essential targets and initiating ATP binding, signal transduction, finally regulating neuroactive ligand-receptor interaction pathway、calcium signaling pathway , and so on.

scholarly journals Utilizing network pharmacology and molecular docking to explore the underlying mechanism of Guizhi Fuling Wan in treating endometriosis

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11087
Author(s):  
Haoxian Wang ◽  
Gang Zhou ◽  
Mingyan Zhuang ◽  
Wei Wang ◽  
Xianyun Fu
Keyword(s):  
Molecular Docking ◽  
Mapk Pathway ◽  
Colorimetric Assay ◽  
Topological Analysis ◽  
Docking Simulation ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Control Group ◽  
Ppi Network ◽  
Active Compounds

Background Guizhi Fuling Wan (GZFLW) is a widely used classical Chinese herbal formulae prescribed for the treatment of endometriosis (EMs). This study aimed to predict the key targets and mechanisms of GZFLW in the treatment of EMs by network pharmacology and molecular docking. Methods Firstly, related compounds and targets of GZFLW were identified through the TCMSP, BATMAN-TCM and CASC database. Then, the EMs target database was built by GeneCards. The overlapping targets between GZFLW and EMs were screened out, and then data of the PPI network was obtained by the STRING Database to analyze the interrelationship of these targets. Furthermore, a topological analysis was performed to screen the hub targets. After that, molecular docking technology was used to confirm the binding degree of the main active compounds and hub targets. Finally, the DAVID database and Metascape database were used for GO and KEGG enrichment analysis. Results A total of 89 GZFLW compounds and 284 targets were collected. One hundred one matching targets were picked out as the correlative targets of GZFLW in treating EMs. Among these, 25 significant hub targets were recognized by the PPI network. Coincidently, molecular docking simulation indicated that the hub targets had a good bonding activity with most active compounds (69.71%). Furthermore, 116 items, including the inflammatory reaction, RNA polymerase, DNA transcription, growth factor activity, and steroid-binding, were selected by GO enrichment analysis. Moreover, the KEGG enrichment analysis results included 100 pathways focused on the AGE-RAGE pathway, HIF pathway, PI3K Akt pathway, MAPK pathway, and TP53 pathway, which exposed the potential mechanisms of GZFLW in treating EMs. Also, the MTT colorimetric assay indicated that the cell proliferation could be inhibited by GZFLW. Compared with the control group, the protein levels of P53, BAX, and caspase3 in the drug groups were all increased in Western blotting results. The results of flow cytometry showed that the percentage of apoptotic cells in the GZFLW group was significantly higher than that in the control group. Conclusion Through the exploration of network pharmacology and molecular docking technology, GZFLW has a therapeutic effect on EMs through multi-target mechanism. This study provided a good foundation for further experimental research.

Sign in / Sign up

Export Citation Format

Share Document