State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica , Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050 , China.
State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences , Peking University , Beijing 100191 , China.
J Chem Inf Model. 2018 May 29;58(5):1104-1120. doi: 10.1021/acs.jcim.8b00004. Epub 2018 May 8.
Chemokine receptors (CRs) have long been druggable targets for the treatment of inflammatory diseases and HIV-1 infection. As a powerful technique, virtual screening (VS) has been widely applied to identifying small molecule leads for modern drug targets including CRs. For rational selection of a wide variety of VS approaches, ligand enrichment assessment based on a benchmarking data set has become an indispensable practice. However, the lack of versatile benchmarking sets for the whole CRs family that are able to unbiasedly evaluate every single approach including both structure- and ligand-based VS somewhat hinders modern drug discovery efforts. To address this issue, we constructed Maximal Unbiased Benchmarking Data sets for human Chemokine Receptors (MUBD-hCRs) using our recently developed tools of MUBD-DecoyMaker. The MUBD-hCRs encompasses 13 subtypes out of 20 chemokine receptors, composed of 404 ligands and 15756 decoys so far and is readily expandable in the future. It had been thoroughly validated that MUBD-hCRs ligands are chemically diverse while its decoys are maximal unbiased in terms of "artificial enrichment", "analogue bias". In addition, we studied the performance of MUBD-hCRs, in particular CXCR4 and CCR5 data sets, in ligand enrichment assessments of both structure- and ligand-based VS approaches in comparison with other benchmarking data sets available in the public domain and demonstrated that MUBD-hCRs is very capable of designating the optimal VS approach. MUBD-hCRs is a unique and maximal unbiased benchmarking set that covers major CRs subtypes so far.
趋化因子受体（CRs）长期以来一直是治疗炎症性疾病和 HIV-1 感染的可药物靶点。作为一种强大的技术，虚拟筛选（VS）已广泛应用于识别小分子先导物，这些先导物是包括 CRs 在内的现代药物靶点。为了合理选择各种 VS 方法，基于基准数据集的配体富集评估已成为必不可少的实践。然而，缺乏能够公正评估包括基于结构和基于配体的 VS 在内的各种方法的整个 CRs 家族的通用基准数据集，在一定程度上阻碍了现代药物发现的努力。为了解决这个问题，我们使用我们最近开发的 MUBD-DecoyMaker 工具构建了用于人类趋化因子受体（MUBD-hCRs）的最大无偏基准数据集。MUBD-hCRs 包含 20 种趋化因子受体中的 13 种亚型，迄今为止包含 404 种配体和 15756 种诱饵，并且将来可以很容易地扩展。已经彻底验证了 MUBD-hCRs 配体在化学上是多样化的，而其诱饵在“人为富集”、“类似物偏差”方面是最大无偏的。此外，我们研究了 MUBD-hCRs，特别是 CXCR4 和 CCR5 数据集，在与公共领域中可用的其他基准数据集相比，在基于结构和基于配体的 VS 方法的配体富集评估中的性能，并证明 MUBD-hCRs 非常能够指定最佳的 VS 方法。MUBD-hCRs 是迄今为止涵盖主要 CRs 亚型的独特且最大无偏的基准数据集。
