Xia Jie, Tilahun Ermias Lemma, Kebede Eyob Hailu, Reid Terry-Elinor, Zhang Liangren, Wang Xiang Simon
State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University , 38 Xueyuan Road, Beijing 100191, China.
J Chem Inf Model. 2015 Feb 23;55(2):374-88. doi: 10.1021/ci5005515. Epub 2015 Feb 9.
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 24609 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/ .
组蛋白去乙酰化酶（HDACs）是治疗癌症、神经退行性疾病和其他类型疾病的一类重要药物靶点。虚拟筛选（VS）已成为发现新型高选择性组蛋白去乙酰化酶抑制剂（HDACIs）的相当有效的药物发现方法。为了推动这一过程，我们使用我们最近发表的方法构建了用于HDACs的最大无偏基准数据集（MUBD-HDACs），这些方法最初是为基于配体的虚拟筛选（LBVS）构建无偏基准集而开发的。MUBD-HDACs涵盖了包括III类（沉默调节蛋白家族）在内的所有四类以及14种HDAC亚型，由631种抑制剂和24609个无偏诱饵组成。其配体集已被广泛验证为具有化学多样性，而诱饵集在“人工富集”和“类似物偏差”方面与配体性质匹配且最大程度无偏。我们还针对HDAC2和HDAC8靶点与DUD-E和DEKOIS 2.0数据集进行了比较研究，并证明我们的MUBD-HDACs的独特之处在于它们可以无偏地应用于LBVS和SBVS方法。此外，我们定义了一种新的指标，即NLBScore，以检测基准数据集中的“二维偏差”和“LBVS有利”效应。总之，MUBD-HDACs是目前可用的唯一针对HDACs（包括沉默调节蛋白）的全面且最大无偏的基准数据集。MUBD-HDACs可在http://www.xswlab.org/免费获取。
