Paper Info
Title
Screening of selective histone deacetylase inhibitors by proteochemometric modeling.
Abstract
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.
Year
DOI
Venue
2012
10.1186/1471-2105-13-212
BMC Bioinformatics
Keywords
Field
DocType
histone deacetylases inhibitors,proteochemometric,selective inhibitors,microarrays,bioinformatics,algorithms,ligands
Biology,Histone deacetylase,Bioinformatics,Genetics,Cancer,DNA microarray
Journal
Volume
Issue
ISSN
13
1
1471-2105
Citations 
PageRank 
References 
11
0.47
14
Authors
8
Name
Order
Citations
PageRank
Ding-Feng Wu1202.67
Qi Huang2110.47
Yida Zhang3131.65
Qingchen Zhang4304.97
Qi Liu556849.57
Jun Gao6211.32
Zhi-Wei Cao731921.14
Ruixin Zhu81438.66