Cargando…
In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database
There is a constant demand to develop new, effective, and affordable anti-cancer drugs. The traditional Chinese medicine (TCM) is a valuable and alternative resource for identifying novel anti-cancer agents. In this study, we aim to identify the anti-cancer compounds and plants from the TCM database...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4857115/ https://www.ncbi.nlm.nih.gov/pubmed/27145869 http://dx.doi.org/10.1038/srep25462 |
_version_ | 1782430601865854976 |
---|---|
author | Dai, Shao-Xing Li, Wen-Xing Han, Fei-Fei Guo, Yi-Cheng Zheng, Jun-Juan Liu, Jia-Qian Wang, Qian Gao, Yue-Dong Li, Gong-Hua Huang, Jing-Fei |
author_facet | Dai, Shao-Xing Li, Wen-Xing Han, Fei-Fei Guo, Yi-Cheng Zheng, Jun-Juan Liu, Jia-Qian Wang, Qian Gao, Yue-Dong Li, Gong-Hua Huang, Jing-Fei |
author_sort | Dai, Shao-Xing |
collection | PubMed |
description | There is a constant demand to develop new, effective, and affordable anti-cancer drugs. The traditional Chinese medicine (TCM) is a valuable and alternative resource for identifying novel anti-cancer agents. In this study, we aim to identify the anti-cancer compounds and plants from the TCM database by using cheminformatics. We first predicted 5278 anti-cancer compounds from TCM database. The top 346 compounds were highly potent active in the 60 cell lines test. Similarity analysis revealed that 75% of the 5278 compounds are highly similar to the approved anti-cancer drugs. Based on the predicted anti-cancer compounds, we identified 57 anti-cancer plants by activity enrichment. The identified plants are widely distributed in 46 genera and 28 families, which broadens the scope of the anti-cancer drug screening. Finally, we constructed a network of predicted anti-cancer plants and approved drugs based on the above results. The network highlighted the supportive role of the predicted plant in the development of anti-cancer drug and suggested different molecular anti-cancer mechanisms of the plants. Our study suggests that the predicted compounds and plants from TCM database offer an attractive starting point and a broader scope to mine for potential anti-cancer agents. |
format | Online Article Text |
id | pubmed-4857115 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-48571152016-05-19 In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database Dai, Shao-Xing Li, Wen-Xing Han, Fei-Fei Guo, Yi-Cheng Zheng, Jun-Juan Liu, Jia-Qian Wang, Qian Gao, Yue-Dong Li, Gong-Hua Huang, Jing-Fei Sci Rep Article There is a constant demand to develop new, effective, and affordable anti-cancer drugs. The traditional Chinese medicine (TCM) is a valuable and alternative resource for identifying novel anti-cancer agents. In this study, we aim to identify the anti-cancer compounds and plants from the TCM database by using cheminformatics. We first predicted 5278 anti-cancer compounds from TCM database. The top 346 compounds were highly potent active in the 60 cell lines test. Similarity analysis revealed that 75% of the 5278 compounds are highly similar to the approved anti-cancer drugs. Based on the predicted anti-cancer compounds, we identified 57 anti-cancer plants by activity enrichment. The identified plants are widely distributed in 46 genera and 28 families, which broadens the scope of the anti-cancer drug screening. Finally, we constructed a network of predicted anti-cancer plants and approved drugs based on the above results. The network highlighted the supportive role of the predicted plant in the development of anti-cancer drug and suggested different molecular anti-cancer mechanisms of the plants. Our study suggests that the predicted compounds and plants from TCM database offer an attractive starting point and a broader scope to mine for potential anti-cancer agents. Nature Publishing Group 2016-05-05 /pmc/articles/PMC4857115/ /pubmed/27145869 http://dx.doi.org/10.1038/srep25462 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Dai, Shao-Xing Li, Wen-Xing Han, Fei-Fei Guo, Yi-Cheng Zheng, Jun-Juan Liu, Jia-Qian Wang, Qian Gao, Yue-Dong Li, Gong-Hua Huang, Jing-Fei In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database |
title | In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database |
title_full | In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database |
title_fullStr | In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database |
title_full_unstemmed | In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database |
title_short | In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database |
title_sort | in silico identification of anti-cancer compounds and plants from traditional chinese medicine database |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4857115/ https://www.ncbi.nlm.nih.gov/pubmed/27145869 http://dx.doi.org/10.1038/srep25462 |
work_keys_str_mv | AT daishaoxing insilicoidentificationofanticancercompoundsandplantsfromtraditionalchinesemedicinedatabase AT liwenxing insilicoidentificationofanticancercompoundsandplantsfromtraditionalchinesemedicinedatabase AT hanfeifei insilicoidentificationofanticancercompoundsandplantsfromtraditionalchinesemedicinedatabase AT guoyicheng insilicoidentificationofanticancercompoundsandplantsfromtraditionalchinesemedicinedatabase AT zhengjunjuan insilicoidentificationofanticancercompoundsandplantsfromtraditionalchinesemedicinedatabase AT liujiaqian insilicoidentificationofanticancercompoundsandplantsfromtraditionalchinesemedicinedatabase AT wangqian insilicoidentificationofanticancercompoundsandplantsfromtraditionalchinesemedicinedatabase AT gaoyuedong insilicoidentificationofanticancercompoundsandplantsfromtraditionalchinesemedicinedatabase AT ligonghua insilicoidentificationofanticancercompoundsandplantsfromtraditionalchinesemedicinedatabase AT huangjingfei insilicoidentificationofanticancercompoundsandplantsfromtraditionalchinesemedicinedatabase |