In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database

被引：56

作者：

Dai, Shao-Xing ^{[1
,2
]}

Li, Wen-Xing ^{[1
,3
]}

Han, Fei-Fei ^{[1
,2
]}

Guo, Yi-Cheng ^{[1
,4
]}

Zheng, Jun-Juan ^{[1
,2
]}

Liu, Jia-Qian ^{[1
,2
]}

Wang, Qian ^{[1
,2
]}

Gao, Yue-Dong ^{[5
]}

Li, Gong-Hua ^{[1
,2
]}

Huang, Jing-Fei ^{[1
,2
,6
,7
]}

机构：

[1] Chinese Acad Sci, Kunming Inst Zool, State Key Lab Genet Resources & Evolut, Kunming 650223, Yunnan, Peoples R China

[2] Univ Chinese Acad Sci, Kunming Coll Life Sci, Beijing 100049, Peoples R China

[3] Anhui Univ, Inst Hlth Sci, Hefei 230601, Anhui, Peoples R China

[4] Univ Sci & Technol China, Sch Life Sci, Hefei 230027, Anhui, Peoples R China

[5] Chinese Acad Sci, Kunming Inst Zool, Kunming Biol Divers Reg Ctr Instruments, Kunming 650223, Peoples R China

[6] Soochow Univ, Coll Pharmaceut Sci, KIZ SU Joint Lab Anim Models & Drug Dev, Kunming 650223, Yunnan, Peoples R China

[7] Collaborat Innovat Ctr Nat Prod & Biol Drugs Yunn, Kunming 650223, Yunnan, Peoples R China

来源：

SCIENTIFIC REPORTS | 2016年 / 6卷

基金：

中国国家自然科学基金;

关键词：

COLON-CANCER CELLS; NATURAL-PRODUCTS; SALVIA-MILTIORRHIZA; PARIS-POLYPHYLLA; INFORMATION-SYSTEM; HERBAL MEDICINES; PROSTATE-CANCER; APOPTOSIS; GROWTH; VITRO;

D O I：

10.1038/srep25462

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页数：9

共 59 条

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