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虚拟筛选以丰富高通量筛选的命中列表:血管生成素小分子抑制剂的案例研究

Virtual screening to enrich hit lists from high-throughput screening: a case study on small-molecule inhibitors of angiogenin.

作者信息

Jenkins Jeremy L, Kao Richard Y T, Shapiro Robert

机构信息

Center for Biochemical and Biophysical Sciences and Medicine, Harvard Medical School, Cambridge, Massachusetts 02139, USA.

出版信息

Proteins. 2003 Jan 1;50(1):81-93. doi: 10.1002/prot.10270.

Abstract

"Hit lists" generated by high-throughput screening (HTS) typically contain a large percentage of false positives, making follow-up assays necessary to distinguish active from inactive substances. Here we present a method for improving the accuracy of HTS hit lists by computationally based virtual screening (VS) of the corresponding chemical libraries and selecting hits by HTS/VS consensus. This approach was applied in a case study on the target-enzyme angiogenin, a potent inducer of angiogenesis. In conjunction with HTS of the National Cancer Institute Diversity Set and ChemBridge DIVERSet E (approximately 18,000 compounds total), VS was performed with two flexible library docking/scoring methods, DockVision/Ludi and GOLD. Analysis of the results reveals that dramatic enrichment of the HTS hit rate can be achieved by selecting compounds in consensus with one or both of the VS functions. For example, HTS hits ranked in the top 2% by GOLD included 42% of the true hits, but only 8% of the false positives; this represents a sixfold enrichment over the HTS hit rate. Notably, the HTS/VS method was effective in selecting out inhibitors with midmicromolar dissociation constants typical of leads commonly obtained in primary screens.

摘要

高通量筛选(HTS)生成的“命中列表”通常包含很大比例的假阳性,因此需要进行后续检测以区分活性物质和非活性物质。在此,我们提出一种方法,通过对相应化学文库进行基于计算的虚拟筛选(VS)并通过HTS/VS共识选择命中物,来提高HTS命中列表的准确性。这种方法应用于针对目标酶血管生成素(一种强大的血管生成诱导剂)的案例研究中。结合美国国立癌症研究所多样性集和ChemBridge DIVERSet E(总共约18,000种化合物)的HTS,使用两种灵活的文库对接/评分方法DockVision/Ludi和GOLD进行VS。结果分析表明,通过选择与一种或两种VS功能达成共识的化合物,可以显著提高HTS命中率。例如,GOLD排名前2%的HTS命中物包含42%的真实命中物,但仅8%的假阳性;这比HTS命中率提高了六倍。值得注意的是,HTS/VS方法有效地筛选出了具有中微摩尔解离常数的抑制剂,这种解离常数是在初次筛选中通常获得的先导化合物所具有的典型特征。

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