Abundance-based Classifier for the Prediction of Mass Spectrometric Peptide Detectability Upon Enrichment (PPA)
Abundance-based Classifier for the Prediction of Mass Spectrometric Peptide Detectability Upon Enrichment (PPA)
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DOI：
10.1074/mcp.m114.044321
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发表时间：
2015-02-01
影响因子：
7
通讯作者：
Springer, Michael
中科院分区：
文献类型：
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作者：
Muntel, Jan;Boswell, Sarah A.;Springer, Michael
很大一部分蛋白质的功能是由翻译后修饰（PTM）调节的。目前，质谱（MS）是唯一可以识别PTM的蛋白质组技术。不幸的是，MS无法检测到PTM并不能证明不存在修改。肽的可检测性显著变化，使得MS对大部分肽可能是盲的。从通常专注于预测最可检测的肽的已发表算法中学习，我们开发了一种工具，该工具将蛋白质丰度纳入肽预测算法，旨在确定蛋白质中每个肽的可检测性。我们测试了我们的工具，“肽预测与Abstract”（PPA），在内部获得的数据集，以及从不同的仪器平台上获得的其他团体公布的数据集。将蛋白质丰度并入预测中允许我们不仅评估所有肽的可检测性，而且评估感兴趣的肽是否可能在富集后变得可检测。我们验证了我们的工具预测蛋白质可检测性变化的能力，其中31种纯化蛋白质在几种不同浓度下的稀释系列。PPA在78%的情况下正确地预测了浓度依赖性肽的可检测性，证明了其用于预测在靶向实验中观察感兴趣的肽所需的蛋白质富集的实用性。这在PTM的分析中尤其重要。PPA是一个基于网络的或可执行的软件包，可以使用一般适用的默认值或从试点MS数据集重新训练。
The function of a large percentage of proteins is modulated by post-translational modifications (PTMs). Currently, mass spectrometry (MS) is the only proteome-wide technology that can identify PTMs. Unfortunately, the inability to detect a PTM by MS is not proof that the modification is not present. The detectability of peptides varies significantly making MS potentially blind to a large fraction of peptides. Learning from published algorithms that generally focus on predicting the most detectable peptides we developed a tool that incorporates protein abundance into the peptide prediction algorithm with the aim to determine the detectability of every peptide within a protein. We tested our tool, "Peptide Prediction with Abundance" (PPA), on in-house acquired as well as published data sets from other groups acquired on different instrument platforms. Incorporation of protein abundance into the prediction allows us to assess not only the detectability of all peptides but also whether a peptide of interest is likely to become detectable upon enrichment. We validated the ability of our tool to predict changes in protein detectability with a dilution series of 31 purified proteins at several different concentrations. PPA predicted the concentration dependent peptide detectability in 78% of the cases correctly, demonstrating its utility for predicting the protein enrichment needed to observe a peptide of interest in targeted experiments. This is especially important in the analysis of PTMs. PPA is available as a web-based or executable package that can work with generally applicable defaults or retrained from a pilot MS data set.