1 research outputs found
Comparative Modeling and Benchmarking Data Sets for Human Histone Deacetylases and Sirtuin Families
Histone
deacetylases (HDACs) are an important class of drug targets
for the treatment of cancers, neurodegenerative diseases, and other
types of diseases. Virtual screening (VS) has become fairly effective
approaches for drug discovery of novel and highly selective histone
deacetylase inhibitors (HDACIs). To facilitate the process, we constructed
maximal unbiased benchmarking data sets for HDACs (MUBD-HDACs) using
our recently published methods that were originally developed for
building unbiased benchmarking sets for ligand-based virtual screening
(LBVS). The MUBD-HDACs cover all four classes including Class III
(Sirtuins family) and 14 HDAC isoforms, composed of 631 inhibitors
and 24 609 unbiased decoys. Its ligand sets have been validated
extensively as chemically diverse, while the decoy sets were shown
to be property-matching with ligands and maximal unbiased in terms
of “artificial enrichment” and “analogue bias”.
We also conducted comparative studies with DUD-E and DEKOIS 2.0 sets
against HDAC2 and HDAC8 targets and demonstrate that our MUBD-HDACs
are unique in that they can be applied unbiasedly to both LBVS and
SBVS approaches. In addition, we defined a novel metric, i.e. NLBScore,
to detect the “2D bias” and “LBVS favorable”
effect within the benchmarking sets. In summary, MUBD-HDACs are the
only comprehensive and maximal-unbiased benchmark data sets for HDACs
(including Sirtuins) that are available so far. MUBD-HDACs are freely
available at http://www.xswlab.org/