The Importance of Peptide Detectability for Protein Identification, Quantification, and Experiment Design in MS/MS Proteomics
The Importance of Peptide Detectability for Protein Identification, Quantification, and Experiment Design in MS/MS Proteomics
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DOI：
10.1021/pr1005586
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发表时间：
2010-12-01
影响因子：
4.4
通讯作者：
Radivojac, Predrag
中科院分区：
文献类型：
--
作者：
Li, Yong Fuga;Arnold, Randy J.;Radivojac, Predrag
肽可检测性定义为在LC-MS/MS实验中鉴定肽的概率，并且可用于提供蛋白质推断和标记的解决方案。先前提出了在标准或复杂样品上训练的肽可检测性的预测器。尽管在复杂样品上训练的模型可能受益于大的训练数据集，但不清楚它们在多大程度上受不平等的影响。鉴定的蛋白质的丰度为了解决这一挑战并改进可检测性预测，我们提出了一种用于从复杂混合物中迭代学习肽可检测性的新算法。我们提供了新方法以有用的准确度近似可检测性的证据，基于它的设计，可以用来解释其他学习策略的结果。我们将可检测性的概念从肽扩展到蛋白质，并应用该模型从单次分析预测重复LC-MS/MS实验的行为最后，我们的研究总结了肽/蛋白质鉴定和无标记定量的理论框架
Peptide detectabilay is defined as the probability that a peptide is identified in an LC-MS/MS experiment and has been useful in providing solutions to protein inference and label-free quantification Previously predictors for peptide detectability trained on standard or complex samples were proposed Although the models trained on complex samples may benefit from the large training data sets it is unclear to what extent they are affected by the unequal abundances of identified proteins To address this challenge and improve detectability prediction we present a new algorithm for the iterative learning of peptide detectability from complex mixtures We provide evidence that the new method approximates detectability with useful accuracy and, based on its design can be used to interpret the outcome of other learning strategies We studied the properties of peptides from the bacterium Deinococcus radiodurans and found that at standard quantities, its tryptic peptides can be roughly classified as either detectable or undetectable, with a relatively small fraction having medium detectability We extend the concept of detectability from peptides to proteins and apply the model to predict the behavior of a replicate LC-MS/MS experiment from a single analysis Finally, our study summarizes a theoretical framework for peptide/protein identification and label-free quantification