Koes David Ryan, Pabon Nicolas A, Deng Xiaoyi, Phillips Margaret A, Camacho Carlos J
Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, United States of America.
Department of Pharmacology, University of Texas Southwestern Medical Center at Dallas, 6001 Forest Park Blvd, Dallas, TX, United States of America.
PLoS One. 2015 Aug 10;10(8):e0134697. doi: 10.1371/journal.pone.0134697. eCollection 2015.
The 2012 Teach-Discover-Treat (TDT) community-wide experiment provided a unique opportunity to test prospective virtual screening protocols targeting the anti-malarial target dihydroorotate dehydrogenase (DHODH). Facilitated by ZincPharmer, an open access online interactive pharmacophore search of the ZINC database, the experience resulted in the development of a novel classification scheme that successfully predicted the bound structure of a non-triazolopyrimidine inhibitor, as well as an overall hit rate of 27% of tested active compounds from multiple novel chemical scaffolds. The general approach entailed exhaustively building and screening sparse pharmacophore models comprising of a minimum of three features for each bound ligand in all available DHODH co-crystals and iteratively adding features that increased the number of known binders returned by the query. Collectively, the TDT experiment provided a unique opportunity to teach computational methods of drug discovery, develop innovative methodologies and prospectively discover new compounds active against DHODH.
2012年开展的“教学-发现-治疗”(TDT)全社区范围实验提供了一个独特的机会,用以测试针对抗疟靶点二氢乳清酸脱氢酶(DHODH)的前瞻性虚拟筛选方案。在ZincPharmer的协助下,对ZINC数据库进行了开放获取的在线交互式药效团搜索,该实验促成了一种新型分类方案的开发,该方案成功预测了一种非三唑并嘧啶抑制剂的结合结构,以及来自多个新型化学支架的测试活性化合物的总体命中率为27%。一般方法包括详尽构建和筛选稀疏药效团模型,这些模型由所有可用的DHODH共晶体中每个结合配体的至少三个特征组成,并迭代添加能够增加查询返回的已知结合剂数量的特征。总体而言,TDT实验提供了一个独特的机会,用于传授药物发现的计算方法、开发创新方法并前瞻性地发现对DHODH有活性的新化合物。