A chemoinformatics analysis of hit lists obtained from high-throughput affinity-selection screening
A chemoinformatics analysis of hit lists obtained from high-throughput affinity-selection screening
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DOI：
10.1177/1087057105283579
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发表时间：
2006-03-01
影响因子：
--
通讯作者：
Jacoby, E
中科院分区：
文献类型：
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作者：
Brown, N;Zehender, H;Jacoby, E
诺华生物医学研究所最近报道了高通量亲和选择筛选平台SpeedScreen，该平台是一种具有质谱读数的均质无标记筛选技术。SpeedScreen依赖于筛选具有各种靶蛋白的化合物混合物，并使用快速尺寸排阻色谱法将靶结合物质与未结合物质分离。靶-结合剂复合物分解后，使用液相色谱/质谱法通过其分子量鉴定结合剂分子。作者报告了对使用SpeedScreen在过去几年中在诺华使用该技术筛选的26个靶点上获得的命中的分子特性的分析。基于亲和力的SpeedScreen是一种强大的高通量筛选技术，不会积累频繁的命中物或潜在的共价结合物。命中率代表了已知药物观察到的最常见的支架类别。与整个文库相比，经验证的SpeedScreen命中倾向于富集更多亲脂性和更大分子量的化合物。评估了在只有有限蛋白质量可用的情况下使用简化SpeedScreen筛选集的可能性。这样一个减少的化合物集也应该最大限度地提高化学性质和类空间的高性能区域的覆盖率;化学信息学方法，包括遗传算法和分裂的K-均值聚类用于这一目的。
The high-throughput affinity-selection screening platform SpeedScreen was recently reported by the Novartis Institutes for BioMedical Research as a homogeneous, label-free screening technology with mass-spectrometry readout. SpeedScreen relies on the screening of compound mixtures with various target proteins and uses fast size-exclusion chromatography to separate target-bound from unbound substances. After disintegration of the target-binder complex, the binder molecules are identified by their molecular masses using liquid chromatography/mass spectrometry. The authors report an analysis of the molecular properties of hits obtained with SpeedScreen on 26 targets screened within the past few years at Novartis using this technology. Affinity-based SpeedScreen is a robust high-throughput screening technology that does not accumulate frequent hitters or potential covalent binders. The hits are representative of the most commonly identified scaffold classes observed for known drugs. Validated SpeedScreen hits tend to be enriched on more lipophilic and larger-molecular-weight compounds compared to the whole library. The potential for a reduced SpeedScreen screening set to be used in case only limited protein quantities are available is evaluated. Such a reduced compound set should also maximize the coverage of the high-performing regions of the chemical property and class spaces; chemoinformatics methods including genetic algorithms and divisive K-means clustering are used for this aim.