Screening of selective histone deacetylase inhibitors by proteochemometric modeling
- Equal contributors
1 School of Life Sciences and Technology, Tongji University, Shanghai, 200092, P.R. China
2 School of Information Engineering, Shanghai Maritime University, Shanghai, 201306, P.R. China
3 Institute for Advanced Study of Translational Medicine, Tongji University, Shanghai, 200092, P.R. China
4 School of Pharmacy, Liaoning University of Traditional Chinese Medicine, Dalian, Liaoning, 116600, P.R. China
BMC Bioinformatics 2012, 13:212 doi:10.1186/1471-2105-13-212Published: 22 August 2012
Histone 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.
The 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.
Our 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.