Comparative Modeling and Benchmarking Data Sets for Human Histone Deacetylases and Sirtuin Families

Abstract

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 Deacetylases Inhibitors (HDACIs). To facilitate the process, we constructed the 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 covers all 4 Classes including Class III (Sirtuins family) and 14 HDACs 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 is unique in that it 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 is the only comprehensive and maximal-unbiased benchmark data sets for HDACs (including Sirtuins) that is available so far. MUBD-HDACs is freely available at http://www.xswlab.org/.

The Roles of the NLRP3 Inflammasome in Neurodegenerative and Metabolic Diseases and in Relevant Advanced Therapeutic Interventions

“…The maximal unbiased benchmarking datasets were constructed to facilitate the discovery of their associated inhibitors. The relevant database is composed of 14 datasets: five of them (HDAC5, HDAC6, HDAC9, HDAC10, and HDAC11) were used to assess ligand-based virtual screening as no experimentally-resolved structures are available for these protein targets [186,187]. More recently, the DeepScreening web server, designed with an integration of deep learning-based algorithms, uses either the user-provided or publically available datasets to assist virtual screening of drugs against the targets of interest [173].…”

An Improved Protocol for the Virtual Screening Discovery of Novel Histone Deacetylase Inhibitors

“…We used pharmacophore modeling methods to extract the steric and electronic features of ligand-receptor interactions and then used those features for rapid screening of the Life Chemicals database. 10) Seven known HDACis from the literature were used to generate 20 pharmacophore models with GALAHAD, and the top 5 models were chosen for database screening 17) (Supplementary Table 1). All models derived from more than six ligands (N_NITS ≥ 6 and FEATS ≥ 6), representing a different trade-off among the conflicting demands of maximizing steric consensus (STERICS), maximizing pharmacophore consensus (HBOND), and minimizing energy (ENERGY) (Supplementary Table 2).…”

“…Molecular Docking-Based Virtual Screening of Novel HDAC8 Inhibitors In order to estimate the rationality of the 98122 candidates hit by pharmacophore-based screening and confirm whether they could bind to the active pocket of HDACs, all the candidates were docked into the active site of HDAC8 using a semi-flexible docking method reported in a previously validated protocol. 17) When the GA value is 30, the average RMSD value is 1.08 Å and the docking scores derived from Chemscore and ChemPLP were used for docking. According to the data, 463 small candidates were screened out when we set the cutoff values of the ChemPLP score at >68.5 and the Chemscore at >21.4.…”

Screening of cytochrome P450 3A4 inhibitors via in silico and in vitro approaches

“…Mubddecoymaker was originally developed for building unbiased benchmarking sets for ligand-based virtual screening (LBVS). 28 We also collected 1306 CYP3A4 non-inhibitors from Bind-ingDB and ChEMBL 29 database. Totally, 2830 CYP3A4 inhibitors and 2710 non-inhibitors/decoys were divided into training set and test set randomly.…”