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关于评估用于构象预测和富集因子的分子对接方法。

On evaluating molecular-docking methods for pose prediction and enrichment factors.

作者信息

Chen Hongming, Lyne Paul D, Giordanetto Fabrizio, Lovell Timothy, Li Jin

机构信息

GDECS Computational Chemistry, AstraZeneca R&D, Mölndal, Sweden.

出版信息

J Chem Inf Model. 2006 Jan-Feb;46(1):401-15. doi: 10.1021/ci0503255.

Abstract

Four of the most well-known, commercially available docking programs, FlexX, GOLD, GLIDE, and ICM, have been examined for their ligand-docking and virtual-screening capabilities. The relative performance of the programs in reproducing the native ligand conformation from starting SMILES strings for 164 high-resolution protein-ligand complexes is presented and compared. Applying only the native scoring functions, the latest versions of these four docking programs were also used to conduct virtual screening for 12 protein targets of therapeutic interest, involving both publicly available structures and AstraZeneca in-house structures. The capability of the four programs to correctly rank-order target-specific active compounds over alternative binders and nonbinders (decoys plus randomly selected compounds) and thereby enrich a small subset of a screening library is compared. Enrichments from the virtual-screening experiments are contrasted with those obtained with alternative 3D shape-matching and 2D similarity database-search methods.

摘要

对四款最知名的商用对接程序FlexX、GOLD、GLIDE和ICM的配体对接和虚拟筛选能力进行了研究。展示并比较了这些程序从164个高分辨率蛋白质-配体复合物的起始SMILES字符串重现天然配体构象的相对性能。仅应用天然评分函数,还使用这四款对接程序的最新版本对12个具有治疗意义的蛋白质靶点进行虚拟筛选,这些靶点涉及公开可用结构和阿斯利康内部结构。比较了这四款程序将靶点特异性活性化合物相对于替代结合剂和非结合剂(诱饵加上随机选择的化合物)正确排序从而富集筛选库小子集的能力。将虚拟筛选实验的富集结果与通过替代的3D形状匹配和2D相似性数据库搜索方法获得的结果进行对比。

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