scholarly journals Uncovering Quercetin’s Effects against Influenza A Virus Using Network Pharmacology and Molecular Docking

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1627
Author(s):  
Minjee Kim ◽  
Young Bong Kim
Keyword(s):  
Molecular Docking ◽  
Influenza A ◽  
Target Genes ◽  
Network Pharmacology ◽  
Binding Affinities ◽  
Anticancer Activities ◽  
Biological Targets ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Docking Method

(1) Background: Re-emerging influenza threats continue to challenge medical and public health systems. Quercetin is a ubiquitous flavonoid found in food and is recognized to possess antioxidant, anti-inflammatory, antiviral, and anticancer activities. (2) Methods: To elucidate the targets and mechanisms underlying the action of quercetin as a therapeutic agent for influenza, network pharmacology and molecular docking were employed. Biological targets of quercetin and target genes associated with influenza were retrieved from public databases. Compound–disease target (C-D) networks were constructed, and targets were further analyzed using KEGG pathway analysis. Potent target genes were retrieved from the compound–disease–pathway (C-D-P) and protein–protein interaction (PPI) networks. The binding affinities between quercetin and the targets were identified using molecular docking. (3) Results: The pathway study revealed that quercetin-associated influenza targets were mainly involved in viral diseases, inflammation-associated pathways, and cancer. Four targets, MAPK1, NFKB1, RELA, and TP53, were identified to be involved in the inhibitory effects of quercetin on influenza. Using the molecular docking method, we evaluated the binding affinity of each ligand (quercetin)–target and discovered that quercetin and MAPK1 showed the strongest calculated binding energy among the four ligand–target complexes. (4) Conclusion: These findings identified potential targets of quercetin and suggest quercetin as a potential drug for influenza treatment.

scholarly journals Identification of the Active Constituents and Significant Pathways of Shen-qi-Yi-zhu Decoction on Antigastric Cancer: A Network Pharmacology Research and Experimental Validation

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Shuhong Zeng ◽  
Zhibao Yu ◽  
Xintian Xu ◽  
Yuanjie Liu ◽  
Jiepin Li ◽  
...  
Keyword(s):  
Experimental Validation ◽  
Target Genes ◽  
Cell Model ◽  
Network Pharmacology ◽  
Disease Network ◽  
Systematic Method ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Underlying Mechanisms

Shen-qi-Yi-zhu decoction (SQYZD) is an empirical prescription with antigastric cancer (GC) property created by Xu Jing-fan, a National Chinese Medical Master. However, its underlying mechanisms are still unclear. Based on network pharmacology and experimental verification, this study puts forward a systematic method to clarify the anti-GC mechanism of SQYZD. The active ingredients of SQYZD and their potential targets were acquired from the TCMSP database. The target genes related to GC gathered from GeneCards, DisGeNET, OMIM, TTD, and DrugBank databases were imported to establish protein-protein interaction (PPI) networks in GeneMANIA. Cytoscape was used to establish the drug-ingredients-targets-disease network. The hub target genes collected from the SQYZD and GC were parsed via GO and KEGG analysis. Our findings from network pharmacology were successfully validated using an in vitro HGC27 cell model experiment. In a word, this study proves that the combination of network pharmacology and in vitro experiments is effective in clarifying the potential molecular mechanism of traditional Chinese medicine (TCM).

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 Elucidation of the Mechanisms and Molecular Targets of Lianhuaqingwen for Treatment of COVID-19 Based on Network Pharmacology

2021 ◽  
Vol 9 (4) ◽  
pp. 111-122
Author(s):  
Yan Luo ◽  
Si-ting Gao ◽  
Jun-xiong Cheng ◽  
Wei-jian Xiong ◽  
Wen-Fu Cao
Keyword(s):  
Target Genes ◽  
Molecular Targets ◽  
Core Gene ◽  
Mechanisms Of Action ◽  
Network Pharmacology ◽  
Pathway Network ◽  
Functional Annotations ◽  
Gene Pathway ◽  
Protein Protein Interaction ◽  
Ppi Networks

Lianhuaqingwen (LH) is the widely used in the treatment of Coronavirus disease 2019 (COVID-19). However, its mechanisms of action and molecular targets for treatment of COVID-19 are not clear. The active compounds of LH were collected and their targets were identified through the network pharmacology. The mechanism of compound multi components and multi targets and its relationship with disease are analyzed. COVID-19 targets were obtained by analyzing with TCMSP. In total, 282 active ingredients and 510 targets of LH were identified. Twenty-one target genes associated with LH and COVID-19. Protein-protein interaction (PPI) data were then obtained and PPI networks of LH putative targets and COVID-19-related targets were visualized and merged to identify the candidate targets for LH against COVID-19. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis were carried out. The gene-pathway network was constructed to screen the crucial target genes. The functional annotations of target genes were found to be related to immune regulation, host defense, inflammatory reaction and autoimmune diseases and so on. Twenty pathways including immunology, cancer, and cell processing were significantly enriched. Quercetin and luteolin might be the crucial ingredients. IL6 was the core gene and other several genes including IL1B, STAT1, IFNGR1, and NCF1 were the key genes in the gene-pathway network of LH for treatment of COVID-19. The results indicated that LH’s effects against COVID-19 might relate to regulation of immunological function through the specific biological processes and the related pathways. This study demonstrates the application of network pharmacology in evaluating mechanisms of action and molecular targets of complex herbal formulations.

scholarly journals Analysis of the Active Components and Mechanism of Three Prescriptions in the Treatment of COVID-19 Via Network Pharmacology and Molecular Docking

2021 ◽  
Vol 16 (9) ◽  
pp. 1934578X2110477
Author(s):  
Fei Wang ◽  
Jia-Hui Chen ◽  
Bo Liu ◽  
Ting Zhang
Keyword(s):  
Molecular Docking ◽  
Binding Energies ◽  
Network Pharmacology ◽  
Active Components ◽  
Chinese Medicines ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Ppi Networks ◽  
Infection And Inflammation ◽  
Analysis Platform

Purpose: Prescriptions of Han-Shi-Yu-Fei (HSYF), Han-Shi-Zu-Fei (HSZF), and Yi-Du-Bi-Fei (YDBF) were effective in treating COVID-19. Based on network pharmacology and molecular docking, overlapping Traditional Chinese medicines (TCMs), their active components, and core targets were explored in this study. Methods: First, the overlapping TCMs and their active components were collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) by evaluating Oral Bioactivity (OB) and Drug Likeness (DL). The overlapping targets of potential components and COVID-19 were collected by SwissTargetPrediction, Gene Cards, and Venn 2.1.0 databases. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were analyzed via DAVID6.8.1 database. Through comprehensive analysis of the “prescriptions-TCMs-components” (P-T-C), “components-targets-pathways” (C-T-P) and “protein–protein interaction” (PPI) networks constructed by Cytoscape 3.7.1 software, the active components and core targets were obtained. Finally, the binding energies of these components with ACE2 and SARS-CoV-2 3CL were analyzed by AutDockTools-1.5.6 and PyMOL software. Results: In all, five overlapping TCMs, 40 potential active components, and 47 candidate targets were obtained and analyzed in these prescriptions. There were 288 GO entries ( P < 0.05), including 211 biological process (BP), 40 cell composition (CC), and 37 molecular function (MF) entries. Most of the 105 KEGG pathways ( P < 0.05) were involved with viral infection and inflammation. Through “PPI” and “C-T-P” networks, the core targets (EGFR, PTGS2, CDK2, GSK3B, PIK3R1, and MAPK3) and active components (Q27134551, acanthoside B, neohesperidin, and irisolidone) with high degrees were obtained. Molecular docking results showed that the above-mentioned four components could inhibit the binding of ACE2 and SARS-COV-2 3CL to protect against COVID-19. Conclusion: In this study, the active components and core targets of three prescriptions in the treatment of COVID-19 were elaborated by network pharmacology and molecular docking, providing a reference for their applications.

scholarly journals Based on Network Pharmacology and Molecular Docking to Explore the Underlying Mechanism of Huangqi Gegen Decoction for Treating Diabetic Nephropathy

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Shanshan Ding ◽  
Weihao Wang ◽  
Xujiao Song ◽  
Hao Ma
Keyword(s):  
Diabetic Nephropathy ◽  
Molecular Docking ◽  
Target Genes ◽  
Chinese Herb ◽  
Underlying Mechanism ◽  
Binding Activity ◽  
Network Pharmacology ◽  
Active Ingredients ◽  
Protein Protein Interaction ◽  
Core Components

Background. Huangqi Gegen decoction (HGD), a Chinese herb formula, has been widely used to treat diabetic nephropathy in China, while the pharmacological mechanisms are still unclear. Therefore, the present study aims to explore the underlying mechanism of HGD for treating diabetic nephropathy (DN). Materials and Methods. Traditional Chinese Medicine Systems Pharmacology Database (TCMSP), UniProt, and SwissTargetPrediction databases were used to search the active ingredients and potential targets of HGD. In addition, multiple disease-related databases were used to collect DN-related targets. Common targets of the protein-protein interaction (PPI) network were established using the STRING database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the DAVID database. At last, AutoDockVina was used to conduct molecular docking verification for the core components and targets. Results. A total of 27 active ingredients and 354 putative identified target genes were screened from HGD, of which 99 overlapped with the targets of DN and were considered potential therapeutic targets. Further analysis showed that the HGD activity of quercetin, formononetin, kaempferol, isorhamnetin, and beta-sitosterol ingredients is possible through VEGFA, IL6, TNF, AKT1, and TP53 targets involved in TNF, toll-like receptors, and MAPK-related pathways, which have anti-inflammatory, antiapoptosis, antioxidation, and autophagy effects, relieve renal fibrosis and renal cortex injury, and improve renal function, thus delaying the development of DN. The molecular docking results showed that quercetin, formononetin, kaempferol, isorhamnetin, beta-sitosterol had a good binding activity with VEGFA, IL6, TNF, AKT1, and TP53. Conclusion. This study demonstrated that HGD might take part in the treatment of DN through multicomponent, multitarget, and multichannel combined action.

Study on the Mechanism of Bushen Jianpi Decoction in the Treatment of T2DM Based on Network Pharmacology

2021 ◽  
Author(s):  
Dongqiang Luo ◽  
Ying Shao ◽  
Yong Sun ◽  
Shuntang Du ◽  
Feng Liu
Keyword(s):  
Molecular Docking ◽  
Signaling Pathway ◽  
Target Genes ◽  
Fluid Shear Stress ◽  
Interaction Network ◽  
Binding Potential ◽  
Network Pharmacology ◽  
Signal Pathways ◽  
Protein Protein Interaction ◽  
Target Disease

Abstract Through the preliminary clinical observation, we had found that Bushen Jianpi decoction (BJD) combined with had better efficacy and less side effects, but its mechanism was not clear. The purpose of this study was to determine its molecular targets and mechanism in T2DM therapy by means of network pharmacology and molecular docking.Results: A total of 144 candidate compounds and 1103 differentially expressed genes were screened, and 43 common targets related to T2DM in BJD were identified. The "TCM-compound-target-disease" network suggested that quercetin, luteolin and kaempferol were the top three compounds. Through protein-protein interaction network, 45 core target genes were identified, including ITGA4, TP53, MYC and so on. GO enrichment showed that genes were significantly enriched in biological processes such as response to oxidative stress, response to lipopolysaccharide, response to molecule of bacterial origin and response to reactive oxygen species. KEGG enrichment showed that there was significant gene enrichment in Fluid shear stress and atherosclerosis, TNF signaling pathway, P13K-Akt signaling pathway, IL-17 signaling pathway and AGE-RAGE signaling pathway in diabetic complications signal pathways. The results of molecular docking showed that the key components of BJD had good binding potential with target genes. Conclusions: BJD may play a role in the treatment of T2DM through anti-inflammation, antioxidation and regulating metabolism, but it still needs to be further confirmed by experiments.Keywords: Network pharmacology, GEO database, Molecular docking, Bushen Jianpi decoction; T2DM

scholarly journals Molecular Mechanism of the Effect of Huanglian Jiedu Decoction on Type 2 Diabetes Mellitus Based on Network Pharmacology and Molecular Docking

2020 ◽  
Vol 2020 ◽  
pp. 1-24
Author(s):  
Bei Yin ◽  
Yi-Ming Bi ◽  
Guan-Jie Fan ◽  
Ya-Qing Xia
Keyword(s):  
Molecular Docking ◽  
Molecular Mechanism ◽  
Target Genes ◽  
Binding Capacity ◽  
Topological Analysis ◽  
Herbal Medicines ◽  
Differentially Expressed ◽  
Network Pharmacology ◽  
Active Components ◽  
Ppi Networks

Background. Huanglian Jiedu Decoction (HLJDD) is a Traditional Chinese Medicine (TCM) formula comprising four herbal medicines. This decoction has long been used in China for clinically treating T2DM. However, the molecular mechanism of HLJDD treat for T2DM is still not fully known. Hence, this study was designed to reveal the synergistic mechanism of HLJDD formula in the treatment of T2DM by using network pharmacology method and molecular docking. Methods. Retrieving and screening of active components of different herbs in HLJDD and corresponding T2DM-related target genes across multiple databases. Subsequently, STRING and Cytoscape were applied to analysis and construct PPI network. In addition, cluster and topological analysis were employed for the analysis of PPI networks. Then, the GO and KEGG enrichment analysis were performed by using ClueGO tool. Finally, the differentially expressed analysis was used to verify whether the expression of key target genes in T2DM and non-T2DM samples was statistically significant, and the binding capacity between active components and key targets was validated by molecular docking using AutoDock. Results. There are 65 active components involved in 197 T2DM-related targets that are identified in HLJDD formula. What is more, 39 key targets (AKT1, IL-6, FOS, VEGFA, CASP3, etc.) and 3 clusters were obtained after topological and cluster analysis. Further, GO and KEGG analysis showed that HLJDD may play an important role in treating T2DM and its complications by synergistically regulating many biological processes and pathways which participated in signaling transduction, inflammatory response, apoptotic process, and vascular processes. Differentially expressed analysis showed that AKT1, IL-6, and FOS were upregulated in T2DM samples and a significant between sample differential expression. These results were validated by molecular docking, which identified 5 high-affinity active components in HLJDD, including quercetin, wogonin, baicalein, kaempferol, and oroxylin A. Conclusion. Our research firstly revealed the basic pharmacological effects and relevant mechanisms of the HLJDD in the treatment of T2DM and its complications. The prediction results might facilitate the development of HLJDD or its active compounds as alternative therapy for T2DM. However, more pharmacological experiments should be performed for verification.

Network Pharmacology and Molecular Docking Study of Jinwei Decoction for Enhancement of Glucocorticoid Anti-Inflammatory Effect in COPD through miR-21

2021 ◽  
Author(s):  
jianjun wu ◽  
Ping-an Zhang ◽  
Ming-zhe Chen ◽  
Yi-xuan Li ◽  
Ying-xue Zhang ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Signaling Pathways ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Active Ingredients ◽  
Ppi Networks ◽  
Core Proteins ◽  
Anti Inflammatory ◽  
Inflammatory Effect

Abstract PurposeJinwei decoction can enhance the anti-inflammatory effect of glucocorticoid (GC) on chronic obstructive pulmonary disease (COPD) by restoring the activity of HDAC2. But the upstream mechanism of Jinwei decoction on HDAC2 expression is not clear. ObjectiveTo explore whether Jinwei decoction can enhance the anti-inflammatory effect of GC on COPD through microRNA21 (miR-21) by network pharmacology. MethodsThe TCMSP database was used to screen active ingredients and target genes of Jinwei decoction, and miRWalk2.0 was used to predict downstream target genes of miR-21. COPD-related genes were identified by searching GeneCards and OMIM databases; Venny 2.1 was used to screen intersection genes; Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of intersection genes were analyzed by R software. Protein-protein interactions (PPIs) were analyzed by Cytoscape 3.7.2 software to identify core genes. Finally, interactions between main compounds and potential targets were verified by molecular docking. ResultsTwo hundred ninety-two active ingredients, 316 Jinwei drug targets, 10170 miR-21 target genes, 6617 COPD target genes, and 184 intersection gene were identified. Eleven core proteins of PPI networks may be involved. GO enrichment analysis showed that oxidative stress, regulation of inflammatory response, hormone transport, and histone modification were involved; KEGG pathway enrichment analysis concentrated in the PI3K-Akt, mitogen-activated protein kinase (MAPK), HIF-1, neutrophil extracellular bactericidal network, and other signaling pathways. ConclusionJinwei decoction can regulate histone deacetylase-2 activity and enhance the anti-inflammatory effect of GC on COPD by modulating miR-21. Its mechanism of action may be related to its effect on the PI3K Akt, MAPK, and TNF signaling pathways and neutrophil extracellular trap formation through miR-21.

scholarly journals Integrating Network Pharmacology and Molecular Docking to Analyse the Potential Mechanism of action of Macleaya cordata (Willd.) R. Br. in the Treatment of Bovine Hoof Disease

2021 ◽  
Vol 9 (1) ◽  
pp. 11
Author(s):  
Zhen Dong ◽  
Mengting Liu ◽  
Xianglin Zou ◽  
Wenqing Sun ◽  
Xiubin Liu ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Signaling Pathways ◽  
Mechanism Of Action ◽  
Target Genes ◽  
Network Pharmacology ◽  
Pharmacokinetic Parameters ◽  
Active Compounds ◽  
Ppi Networks ◽  
Bovine Hoof ◽  
Compound Target

Based on network pharmacological analysis and molecular docking techniques, the main components of M. cordata for the treatment of bovine relevant active compounds in M. cordata were searched for through previous research bases and literature databases, and then screened to identify candidate compounds based on physicochemical properties, pharmacokinetic parameters, bioavailability, and drug-like criteria. Target genes associated with hoof disease were obtained from the GeneCards database. Compound−target, compound−target−pathway−disease visualization networks, and protein−protein interaction (PPI) networks were constructed by Cytoscape. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed in R language. Molecular docking analysis was done using AutoDockTools. The visual network analysis showed that four active compounds, sanguinarine, chelerythrine, allocryptopine and protopine, were associated with the 10 target genes/proteins (SRC, MAPK3, MTOR, ESR1, PIK3CA, BCL2L1, JAK2, GSK3B, MAPK1, and AR) obtained from the screen. The enrichment analysis indicated that the cAMP, PI3K-Akt, and ErbB signaling pathways may be key signaling pathways in network pharmacology. The molecular docking results showed that sanguinarine, chelerythrine, allocryptopine, and protopine bound well to MAPK3 and JAK2. A comprehensive bioinformatics-based network topology strategy and molecular docking study has elucidated the multi-component synergistic mechanism of action of M. cordata in the treatment of bovine hoof disease, offering the possibility of developing M. cordata as a new source of drugs for hoof disease treatment.

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