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使用多重定量质谱进行无偏热蛋白质组谱分析,以鉴定直接和间接药物靶点。

Thermal proteome profiling for unbiased identification of direct and indirect drug targets using multiplexed quantitative mass spectrometry.

机构信息

Cellzome, Molecular Discovery Research, GlaxoSmithKline, Heidelberg, Germany.

Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.

出版信息

Nat Protoc. 2015 Oct;10(10):1567-93. doi: 10.1038/nprot.2015.101. Epub 2015 Sep 17.

Abstract

The direct detection of drug-protein interactions in living cells is a major challenge in drug discovery research. Recently, we introduced an approach termed thermal proteome profiling (TPP), which enables the monitoring of changes in protein thermal stability across the proteome using quantitative mass spectrometry. We determined the intracellular thermal profiles for up to 7,000 proteins, and by comparing profiles derived from cultured mammalian cells in the presence or absence of a drug we showed that it was possible to identify direct and indirect targets of drugs in living cells in an unbiased manner. Here we demonstrate the complete workflow using the histone deacetylase inhibitor panobinostat. The key to this approach is the use of isobaric tandem mass tag 10-plex (TMT10) reagents to label digested protein samples corresponding to each temperature point in the melting curve so that the samples can be analyzed by multiplexed quantitative mass spectrometry. Important steps in the bioinformatic analysis include data normalization, melting curve fitting and statistical significance determination of compound concentration-dependent changes in protein stability. All analysis tools are made freely available as R and Python packages. The workflow can be completed in 2 weeks.

摘要

在活细胞中直接检测药物-蛋白质相互作用是药物发现研究中的一个主要挑战。最近,我们引入了一种称为热蛋白质组谱分析(TPP)的方法,该方法能够使用定量质谱法监测整个蛋白质组中蛋白质热稳定性的变化。我们确定了多达 7000 种蛋白质的细胞内热谱,并且通过比较来自培养的哺乳动物细胞在存在或不存在药物的情况下得出的图谱,我们表明可以在活细胞中以无偏倚的方式鉴定药物的直接和间接靶标。在这里,我们使用组蛋白去乙酰化酶抑制剂帕比司他(panobinostat)来展示完整的工作流程。该方法的关键是使用等重串联质量标签 10 plex(TMT10)试剂标记对应于熔化曲线中每个温度点的消化蛋白质样品,以便可以通过多重定量质谱法分析样品。生物信息学分析中的重要步骤包括数据归一化、熔化曲线拟合以及化合物浓度依赖性蛋白质稳定性变化的统计学显著性确定。所有分析工具都以 R 和 Python 包的形式免费提供。该工作流程可以在 2 周内完成。

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