Identification of CK2 inhibitors with new scaffolds by a hybrid virtual screening approach based on Bayesian model; pharmacophore hypothesis and molecular docking

详细信息	查看全文 | 推荐本文 |

• 作者：Lei Di-wu^a ; Lin-Li Li^b ; ^{ysylilinli@sina.com.cn} ; Wen-Jing Wang^a ; Huan-Zhang Xie^b ; Jiao Yang^b ; Chun-Hui Zhang^a ; Qi Huang^a ; Lei Zhong^b ; Shan Feng^a ; Sheng-Yong Yang^a ; ^{yangsy@scu.edu.cn}
• 关键词：CK2 ; Bayesian categorization model ; Pharmacophore model ; Molecular docking ; Virtual screening
• 刊名：Journal of Molecular Graphics and Modelling
• 出版年：2012
• 期刊代码：77_10933263
• 类别：cp
• 出版时间：June, 2012
• 卷：36
• 期：Complete
• 页码：42-47
• 文件大小：749 K

摘要

Protein kinase casein kinase 2 (CK2), a member of the serine/threonine kinase family, has been established as one of the most attractive targets for molecularly targeted cancer therapy. The discovery of CK2 inhibitors has thus attracted much attention in recent years. In this investigation, a hybrid virtual screening approach based on Bayesian classification model, pharmacophore hypothesis and molecular docking was proposed and employed to identify CK2 inhibitors. We first established a na茂ve Bayes classification model of CK2 inhibitors/non-inhibitors and pharmacophore hypotheses of CK2 inhibitors. The docking parameters and scoring functions were also optimized in advance. The three virtual screening methods were sequentially used to screen two large chemical libraries, Specs and Enamine, for retrieving new CK2 inhibitors. Finally 30 compounds were selected from the final hits for in vitro CK2 kinase inhibitory assays. Five compounds with completely novel scaffolds showed a good inhibitory potency against CK2, which have good potentials for a future hit-to-lead optimization.