Screening of selective histone deacetylase inhibitors by proteochemometric modeling

BMC Bioinformatics

Research Article

Zhiwei Cao¹  Dingfeng Wu¹  Yida Zhang¹  Qingchen Zhang¹  Qi Liu¹  Qi Huang¹  Ruixin Zhu²  Jun Gao³ 

[1] School of Life Sciences and Technology, Tongji University, 200092, Shanghai, P.R. China;School of Life Sciences and Technology, Tongji University, 200092, Shanghai, P.R. China;Institute for Advanced Study of Translational Medicine, Tongji University, 200092, Shanghai, P.R. China;School of Pharmacy, Liaoning University of Traditional Chinese Medicine, 116600, Dalian, Liaoning, P.R. China;School of Life Sciences and Technology, Tongji University, 200092, Shanghai, P.R. China;School of Information Engineering, Shanghai Maritime University, 201306, Shanghai, P.R. China;

关键词: Histone deacetylases inhibitors;    Proteochemometric;    Selective inhibitors;   

DOI  :  10.1186/1471-2105-13-212

 received in 2012-03-22, accepted in 2012-08-16,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundHistone deacetylase (HDAC) is a novel target for the treatment of cancer and it can be classified into three classes, i.e., classes I, II, and IV. The inhibitors selectively targeting individual HDAC have been proved to be the better candidate antitumor drugs. To screen selective HDAC inhibitors, several proteochemometric (PCM) models based on different combinations of three kinds of protein descriptors, two kinds of ligand descriptors and multiplication cross-terms were constructed in our study.ResultsThe results show that structure similarity descriptors are better than sequence similarity descriptors and geometry descriptors in the leftacterization of HDACs. Furthermore, the predictive ability was not improved by introducing the cross-terms in our models. Finally, a best PCM model based on protein structure similarity descriptors and 32-dimensional general descriptors was derived (R2 = 0.9897, Qtest2 = 0.7542), which shows a powerful ability to screen selective HDAC inhibitors.ConclusionsOur best model not only predict the activities of inhibitors for each HDAC isoform, but also screen and distinguish class-selective inhibitors and even more isoform-selective inhibitors, thus it provides a potential way to discover or design novel candidate antitumor drugs with reduced side effect.

【 授权许可】

Unknown   
© Wu et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. 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 cited.