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Exploring the mechanism of Ginkgo biloba L. leaves in the treatment of vascular dementia based on network pharmacology, molecular docking, and molecular dynamics simulation
Ginkgo biloba L. leaves (GBLs) play a substantial role in the treatment of vascular dementia (VD); however, the underlying mechanisms of action are unclear. OBJECTIVE: This study was conducted to investigate the mechanisms of action of GBLs in the treatment of VD through network pharmacology, molecu...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10219709/ https://www.ncbi.nlm.nih.gov/pubmed/37233418 http://dx.doi.org/10.1097/MD.0000000000033877 |
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author | Pan, Jienuo Tang, Jiqin Gai, Jialin Jin, Yilan Tang, Bingshun Fan, Xiaohua |
author_facet | Pan, Jienuo Tang, Jiqin Gai, Jialin Jin, Yilan Tang, Bingshun Fan, Xiaohua |
author_sort | Pan, Jienuo |
collection | PubMed |
description | Ginkgo biloba L. leaves (GBLs) play a substantial role in the treatment of vascular dementia (VD); however, the underlying mechanisms of action are unclear. OBJECTIVE: This study was conducted to investigate the mechanisms of action of GBLs in the treatment of VD through network pharmacology, molecular docking, and molecular dynamics simulations. METHODS: The active ingredients and related targets of GBLs were screened using the traditional Chinese medicine systems pharmacology, Swiss Target Prediction and GeneCards databases, and the VD-related targets were screened using the OMIM, DrugBank, GeneCards, and DisGeNET databases, and the potential targets were identified using a Venn diagram. We used Cytoscape 3.8.0 software and the STRING platform to construct traditional Chinese medicine–active ingredient–potential target and protein–protein interaction networks, respectively. After gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis of potential targets using the DAVID platform, the binding affinity between key active ingredients and targets was analyzed by molecular docking, and finally, the top 3 proteins–ligand pairs with the best binding were simulated by molecular dynamics to verify the molecular docking results. RESULTS: A total of 27 active ingredients of GBLs were screened and 274 potential targets involved in the treatment of VD were identified. Quercetin, luteolin, kaempferol, and ginkgolide B were the core ingredients for treatment, and AKT1, TNF, IL6, VEGFA, IL1B, TP53, CASP3, SRC, EGFR, JUN, and EGFR were the main targets of action. The main biological processes involved apoptosis, inflammatory response, cell migration, lipopolysaccharide response, hypoxia response, and aging. PI3K/Akt appeared to be a key signaling pathway for GBLs in the treatment of VD. Molecular docking displayed strong binding affinity between the active ingredients and the targets. Molecular dynamics simulation results further verified the stability of their interactions. CONCLUSION SUBSECTIONS: This study revealed the potential molecular mechanisms involved in the treatment of VD by GBLs using multi-ingredient, multi-target, and multi-pathway interactions, providing a theoretical basis for the clinical treatment and lead drug development of VD. |
format | Online Article Text |
id | pubmed-10219709 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-102197092023-05-27 Exploring the mechanism of Ginkgo biloba L. leaves in the treatment of vascular dementia based on network pharmacology, molecular docking, and molecular dynamics simulation Pan, Jienuo Tang, Jiqin Gai, Jialin Jin, Yilan Tang, Bingshun Fan, Xiaohua Medicine (Baltimore) 3800 Ginkgo biloba L. leaves (GBLs) play a substantial role in the treatment of vascular dementia (VD); however, the underlying mechanisms of action are unclear. OBJECTIVE: This study was conducted to investigate the mechanisms of action of GBLs in the treatment of VD through network pharmacology, molecular docking, and molecular dynamics simulations. METHODS: The active ingredients and related targets of GBLs were screened using the traditional Chinese medicine systems pharmacology, Swiss Target Prediction and GeneCards databases, and the VD-related targets were screened using the OMIM, DrugBank, GeneCards, and DisGeNET databases, and the potential targets were identified using a Venn diagram. We used Cytoscape 3.8.0 software and the STRING platform to construct traditional Chinese medicine–active ingredient–potential target and protein–protein interaction networks, respectively. After gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis of potential targets using the DAVID platform, the binding affinity between key active ingredients and targets was analyzed by molecular docking, and finally, the top 3 proteins–ligand pairs with the best binding were simulated by molecular dynamics to verify the molecular docking results. RESULTS: A total of 27 active ingredients of GBLs were screened and 274 potential targets involved in the treatment of VD were identified. Quercetin, luteolin, kaempferol, and ginkgolide B were the core ingredients for treatment, and AKT1, TNF, IL6, VEGFA, IL1B, TP53, CASP3, SRC, EGFR, JUN, and EGFR were the main targets of action. The main biological processes involved apoptosis, inflammatory response, cell migration, lipopolysaccharide response, hypoxia response, and aging. PI3K/Akt appeared to be a key signaling pathway for GBLs in the treatment of VD. Molecular docking displayed strong binding affinity between the active ingredients and the targets. Molecular dynamics simulation results further verified the stability of their interactions. CONCLUSION SUBSECTIONS: This study revealed the potential molecular mechanisms involved in the treatment of VD by GBLs using multi-ingredient, multi-target, and multi-pathway interactions, providing a theoretical basis for the clinical treatment and lead drug development of VD. Lippincott Williams & Wilkins 2023-05-26 /pmc/articles/PMC10219709/ /pubmed/37233418 http://dx.doi.org/10.1097/MD.0000000000033877 Text en Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY) (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | 3800 Pan, Jienuo Tang, Jiqin Gai, Jialin Jin, Yilan Tang, Bingshun Fan, Xiaohua Exploring the mechanism of Ginkgo biloba L. leaves in the treatment of vascular dementia based on network pharmacology, molecular docking, and molecular dynamics simulation |
title | Exploring the mechanism of Ginkgo biloba L. leaves in the treatment of vascular dementia based on network pharmacology, molecular docking, and molecular dynamics simulation |
title_full | Exploring the mechanism of Ginkgo biloba L. leaves in the treatment of vascular dementia based on network pharmacology, molecular docking, and molecular dynamics simulation |
title_fullStr | Exploring the mechanism of Ginkgo biloba L. leaves in the treatment of vascular dementia based on network pharmacology, molecular docking, and molecular dynamics simulation |
title_full_unstemmed | Exploring the mechanism of Ginkgo biloba L. leaves in the treatment of vascular dementia based on network pharmacology, molecular docking, and molecular dynamics simulation |
title_short | Exploring the mechanism of Ginkgo biloba L. leaves in the treatment of vascular dementia based on network pharmacology, molecular docking, and molecular dynamics simulation |
title_sort | exploring the mechanism of ginkgo biloba l. leaves in the treatment of vascular dementia based on network pharmacology, molecular docking, and molecular dynamics simulation |
topic | 3800 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10219709/ https://www.ncbi.nlm.nih.gov/pubmed/37233418 http://dx.doi.org/10.1097/MD.0000000000033877 |
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