Jiang Lingling, Rizzo Robert C
Department of Applied Mathematics & Statistics, ‡Institute of Chemical Biology & Drug Discovery, §Laufer Center for Physical & Quantitative Biology, Stony Brook University , Stony Brook, New York 11794-3600, United States.
J Phys Chem B. 2015 Jan 22;119(3):1083-102. doi: 10.1021/jp506555w. Epub 2014 Oct 10.
Pharmacophore modeling incorporates geometric and chemical features of known inhibitors and/or targeted binding sites to rationally identify and design new drug leads. In this study, we have encoded a three-dimensional pharmacophore matching similarity (FMS) scoring function into the structure-based design program DOCK. Validation and characterization of the method are presented through pose reproduction, crossdocking, and enrichment studies. When used alone, FMS scoring dramatically improves pose reproduction success to 93.5% (∼20% increase) and reduces sampling failures to 3.7% (∼6% drop) compared to the standard energy score (SGE) across 1043 protein-ligand complexes. The combined FMS+SGE function further improves success to 98.3%. Crossdocking experiments using FMS and FMS+SGE scoring, for six diverse protein families, similarly showed improvements in success, provided proper pharmacophore references are employed. For enrichment, incorporating pharmacophores during sampling and scoring, in most cases, also yield improved outcomes when docking and rank-ordering libraries of known actives and decoys to 15 systems. Retrospective analyses of virtual screenings to three clinical drug targets (EGFR, IGF-1R, and HIVgp41) using X-ray structures of known inhibitors as pharmacophore references are also reported, including a customized FMS scoring protocol to bias on selected regions in the reference. Overall, the results and fundamental insights gained from this study should benefit the docking community in general, particularly researchers using the new FMS method to guide computational drug discovery with DOCK.
药效团建模结合了已知抑制剂和/或靶向结合位点的几何和化学特征，以合理地识别和设计新的药物先导物。在本研究中，我们将三维药效团匹配相似性（FMS）评分函数编码到基于结构的设计程序DOCK中。通过构象重现、交叉对接和富集研究对该方法进行了验证和表征。单独使用时，与标准能量评分（SGE）相比，FMS评分在1043个蛋白质-配体复合物中显著提高了构象重现成功率至93.5%（增加约20%），并将采样失败率降低至3.7%（下降约6%）。FMS+SGE组合函数进一步将成功率提高到98.3%。对于六个不同的蛋白质家族，使用FMS和FMS+SGE评分进行的交叉对接实验同样显示成功率有所提高，前提是采用合适的药效团参考。对于富集，在对15个系统的已知活性化合物和诱饵库进行对接和排序时，在采样和评分过程中纳入药效团在大多数情况下也能产生更好的结果。还报告了使用已知抑制剂的X射线结构作为药效团参考对三个临床药物靶点（EGFR、IGF-1R和HIVgp41）进行虚拟筛选的回顾性分析，包括一种定制的FMS评分协议，以偏向参考中的选定区域。总体而言，本研究获得的结果和基本见解应使整个对接领域受益，特别是使用新的FMS方法指导使用DOCK进行计算药物发现的研究人员。
