Prediction of Molecular Targets of Cancer Preventing Flavonoid Compounds Using Computational Methods

被引:31
作者
Chen, Hanyong [1 ]
Yao, Ke [1 ]
Nadas, Janos [1 ]
Bode, Ann M. [1 ]
Malakhova, Margarita [1 ]
Oi, Naomi [1 ]
Li, Haitao [1 ]
Lubet, Ronald A. [2 ]
Dong, Zigang [1 ]
机构
[1] Univ Minnesota, Hormel Inst, Austin, MN 55912 USA
[2] NCI, Bethesda, MD 20892 USA
基金
美国国家卫生研究院;
关键词
RIBOSOMAL S6 KINASE; LIGAND DOCKING; PROTEIN; INHIBITOR; MYRICETIN; PHYTOCHEMICALS; PHYTOESTROGENS; TRANSFORMATION; PROLIFERATION; RESVERATROL;
D O I
10.1371/journal.pone.0038261
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
070301 [无机化学]; 070403 [天体物理学]; 070507 [自然资源与国土空间规划学]; 090105 [作物生产系统与生态工程];
摘要
Plant-based polyphenols (i.e., phytochemicals) have been used as treatments for human ailments for centuries. The mechanisms of action of these plant-derived compounds are now a major area of investigation. Thousands of phytochemicals have been isolated, and a large number of them have shown protective activities or effects in different disease models. Using conventional approaches to select the best single or group of best chemicals for studying the effectiveness in treating or preventing disease is extremely challenging. We have developed and used computational-based methodologies that provide efficient and inexpensive tools to gain further understanding of the anticancer and therapeutic effects exerted by phytochemicals. Computational methods involving virtual screening, shape and pharmacophore analysis and molecular docking have been used to select chemicals that target a particular protein or enzyme and to determine potential protein targets for well-characterized as well as for novel phytochemicals.
引用
收藏
页数:9
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