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In-silico prediction of drug targets, biological activities, signal pathways and regulating networks of dioscin based on bioinformatics
BACKGROUND: Inverse docking technology has been a trend of drug discovery, and bioinformatics approaches have been used to predict target proteins, biological activities, signal pathways and molecular regulating networks affected by drugs for further pharmacodynamic and mechanism studies. METHODS: I...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4354738/ https://www.ncbi.nlm.nih.gov/pubmed/25879470 http://dx.doi.org/10.1186/s12906-015-0579-6 |
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author | Yin, Lianhong Zheng, Lingli Xu, Lina Dong, Deshi Han, Xu Qi, Yan Zhao, Yanyan Xu, Youwei Peng, Jinyong |
author_facet | Yin, Lianhong Zheng, Lingli Xu, Lina Dong, Deshi Han, Xu Qi, Yan Zhao, Yanyan Xu, Youwei Peng, Jinyong |
author_sort | Yin, Lianhong |
collection | PubMed |
description | BACKGROUND: Inverse docking technology has been a trend of drug discovery, and bioinformatics approaches have been used to predict target proteins, biological activities, signal pathways and molecular regulating networks affected by drugs for further pharmacodynamic and mechanism studies. METHODS: In the present paper, inverse docking technology was applied to screen potential targets from potential drug target database (PDTD). Then, the corresponding gene information of the obtained drug-targets was applied to predict the related biological activities, signal pathways and processes networks of the compound by using MetaCore platform. After that, some most relevant regulating networks were considered, which included the nodes and relevant pathways of dioscin. RESULTS: 71 potential targets of dioscin from humans, 7 from rats and 8 from mice were screened, and the prediction results showed that the most likely targets of dioscin were cyclin A2, calmodulin, hemoglobin subunit beta, DNA topoisomerase I, DNA polymerase lambda, nitric oxide synthase and UDP-N-acetylhexosamine pyrophosphorylase, etc. Many diseases including experimental autoimmune encephalomyelitis of human, temporal lobe epilepsy of rat and ankylosing spondylitis of mouse, may be inhibited by dioscin through regulating immune response alternative complement pathway, G-protein signaling RhoB regulation pathway and immune response antiviral actions of interferons, etc. The most relevant networks (5 from human, 3 from rat and 5 from mouse) indicated that dioscin may be a TOP1 inhibitor, which can treat cancer though the cell cycle– transition and termination of DNA replication pathway. Dioscin can down regulate EGFR and EGF to inhibit cancer, and also has anti-inflammation activity by regulating JNK signaling pathway. CONCLUSIONS: The predictions of the possible targets, biological activities, signal pathways and relevant regulating networks of dioscin provide valuable information to guide further investigation of dioscin on pharmacodynamics and molecular mechanisms, which also suggests a practical and effective method for studies on the mechanism of other chemicals. |
format | Online Article Text |
id | pubmed-4354738 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-43547382015-03-11 In-silico prediction of drug targets, biological activities, signal pathways and regulating networks of dioscin based on bioinformatics Yin, Lianhong Zheng, Lingli Xu, Lina Dong, Deshi Han, Xu Qi, Yan Zhao, Yanyan Xu, Youwei Peng, Jinyong BMC Complement Altern Med Research Article BACKGROUND: Inverse docking technology has been a trend of drug discovery, and bioinformatics approaches have been used to predict target proteins, biological activities, signal pathways and molecular regulating networks affected by drugs for further pharmacodynamic and mechanism studies. METHODS: In the present paper, inverse docking technology was applied to screen potential targets from potential drug target database (PDTD). Then, the corresponding gene information of the obtained drug-targets was applied to predict the related biological activities, signal pathways and processes networks of the compound by using MetaCore platform. After that, some most relevant regulating networks were considered, which included the nodes and relevant pathways of dioscin. RESULTS: 71 potential targets of dioscin from humans, 7 from rats and 8 from mice were screened, and the prediction results showed that the most likely targets of dioscin were cyclin A2, calmodulin, hemoglobin subunit beta, DNA topoisomerase I, DNA polymerase lambda, nitric oxide synthase and UDP-N-acetylhexosamine pyrophosphorylase, etc. Many diseases including experimental autoimmune encephalomyelitis of human, temporal lobe epilepsy of rat and ankylosing spondylitis of mouse, may be inhibited by dioscin through regulating immune response alternative complement pathway, G-protein signaling RhoB regulation pathway and immune response antiviral actions of interferons, etc. The most relevant networks (5 from human, 3 from rat and 5 from mouse) indicated that dioscin may be a TOP1 inhibitor, which can treat cancer though the cell cycle– transition and termination of DNA replication pathway. Dioscin can down regulate EGFR and EGF to inhibit cancer, and also has anti-inflammation activity by regulating JNK signaling pathway. CONCLUSIONS: The predictions of the possible targets, biological activities, signal pathways and relevant regulating networks of dioscin provide valuable information to guide further investigation of dioscin on pharmacodynamics and molecular mechanisms, which also suggests a practical and effective method for studies on the mechanism of other chemicals. BioMed Central 2015-03-05 /pmc/articles/PMC4354738/ /pubmed/25879470 http://dx.doi.org/10.1186/s12906-015-0579-6 Text en © Yin et al.; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Yin, Lianhong Zheng, Lingli Xu, Lina Dong, Deshi Han, Xu Qi, Yan Zhao, Yanyan Xu, Youwei Peng, Jinyong In-silico prediction of drug targets, biological activities, signal pathways and regulating networks of dioscin based on bioinformatics |
title | In-silico prediction of drug targets, biological activities, signal pathways and regulating networks of dioscin based on bioinformatics |
title_full | In-silico prediction of drug targets, biological activities, signal pathways and regulating networks of dioscin based on bioinformatics |
title_fullStr | In-silico prediction of drug targets, biological activities, signal pathways and regulating networks of dioscin based on bioinformatics |
title_full_unstemmed | In-silico prediction of drug targets, biological activities, signal pathways and regulating networks of dioscin based on bioinformatics |
title_short | In-silico prediction of drug targets, biological activities, signal pathways and regulating networks of dioscin based on bioinformatics |
title_sort | in-silico prediction of drug targets, biological activities, signal pathways and regulating networks of dioscin based on bioinformatics |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4354738/ https://www.ncbi.nlm.nih.gov/pubmed/25879470 http://dx.doi.org/10.1186/s12906-015-0579-6 |
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