A model for random sampling and estimation of relative protein abundance in shotgun proteomics
A model for random sampling and estimation of relative protein abundance in shotgun proteomics
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DOI：
10.1021/ac0498563
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发表时间：
2004-07-15
影响因子：
7.4
通讯作者：
Yates, JR
中科院分区：
文献类型：
--
作者：
Liu, HB;Sadygov, RG;Yates, JR
使用蛋白水解消化和液相色谱与串联质谱法结合使用蛋白质混合物的蛋白质组学分析是生物学研究中的标准方法。数据依赖性的采集用于自动获取流洗到质谱仪中的肽的串联质谱。在更复杂的混合物中，例如，全细胞裂解物，数据依赖性的采集不完全是存在的肽离子中的样品，而不是为所有可用离子获取串联质谱。我们分析了采样过程并开发了一个统计模型，以准确预测特定复杂性混合物预期的采样水平。该模型还预测了复杂蛋白混合物的饱和采样需要多少分析。对于酵母溶细胞裂解物，需要10个分析才能基于我们的模型达到蛋白质鉴定的95％饱和水平。统计模型还表明，观察到蛋白质的采样水平与混合物中蛋白质的相对丰度之间的关系。我们通过使用为每种蛋白质获得的光谱（光谱采样）数量（光谱采样）数量来证明了超过2个数量级的线性动态范围。
Proteomic analysis of complex protein mixtures using proteolytic digestion and liquid chromatography in combination with tandem mass spectrometry is a standard approach in biological studies. Data-dependent acquisition is used to automatically acquire tandem mass spectra of peptides eluting into the mass spectrometer. In more complicated mixtures, for example, whole cell lysates, data-dependent acquisition incompletely samples among the peptide ions present rather than acquiring tandem mass spectra for all ions available. We analyzed the sampling process and developed a statistical model to accurately predict the level of sampling expected for mixtures of a specific complexity. The model also predicts how many analyses are required for saturated sampling of a complex protein mixture. For a yeast-soluble cell lysate 10 analyses are required to reach a 95% saturation level on protein identifications based on our model. The statistical model also suggests a relationship between the level of sampling observed for a protein and the relative abundance of the protein in the mixture. We demonstrate a linear dynamic range over 2 orders of magnitude by using the number of spectra (spectral sampling) acquired for each protein.