Improved validation of peptide MS/MS assignments using spectral intensity prediction
Improved validation of peptide MS/MS assignments using spectral intensity prediction
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DOI：
10.1074/mcp.m600320-mcp200
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发表时间：
2007-01-01
影响因子：
7
通讯作者：
Resing, Katheryn A.
中科院分区：
文献类型：
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作者：
Sun, Shaojun;Meyer-Arendt, Karen;Resing, Katheryn A.
通过鸟枪蛋白质组学从复杂混合物中鉴定肽的主要限制是搜索程序使用质谱裂解谱（MS/MS谱）准确分配肽序列的能力。人工分析用于评估边界识别;然而，它容易出错且耗时，并且接受或拒绝的标准没有很好的定义。在这里，我们报告了一个手动分析模拟器（MAE）程序，该程序通过执行两个常用标准来评估搜索程序的结果：1）碎片离子强度与预测气相化学的一致性，以及2）MS/MS光谱中的高比例离子强度（离子电流比例（PIC））是否可以源自肽序列。为了评估化学可亲合性，MAE利用相似性（Sim）评分来对抗由Mas-sAnalyzer软件模拟的理论光谱（Zhang，Z.（2004）肽的低能碰撞诱导解离光谱的预测。Anal. 76，3908-3922）。结果表明，Sim分数提供了显着更大的区分正确和不正确的搜索结果比实现Sequest XCorr评分或Mascot Mowse评分，允许可靠的自动验证边界情况。为了评价PIC，MAE简化了总结MS/MS光谱的DTA文本文件，并应用启发式规则对碎片离子进行分类。MAE输出还提供数据挖掘功能，通过使用PIC识别光谱嵌合体来说明，其中两个或更多个肽离子一起测序，以及片段化化学不能很好预测的情况。
A major limitation in identifying peptides from complex mixtures by shotgun proteomics is the ability of search programs to accurately assign peptide sequences using mass spectrometric fragmentation spectra (MS/MS spectra). Manual analysis is used to assess borderline identifications; however, it is error-prone and time-consuming, and criteria for acceptance or rejection are not well defined. Here we report a Manual Analysis Emulator (MAE) program that evaluates results from search programs by implementing two commonly used criteria: 1) consistency of fragment ion intensities with predicted gas phase chemistry and 2) whether a high proportion of the ion intensity ( proportion of ion current (PIC)) in the MS/MS spectra can be derived from the peptide sequence. To evaluate chemical plausibility, MAE utilizes similarity (Sim) scoring against theoretical spectra simulated by Mas-sAnalyzer software (Zhang, Z. (2004) Prediction of low-energy collision-induced dissociation spectra of peptides. Anal. Chem. 76, 3908-3922) using known gas phase chemical mechanisms. The results show that Sim scores provide significantly greater discrimination between correct and incorrect search results than achieved by Sequest XCorr scoring or Mascot Mowse scoring, allowing reliable automated validation of borderline cases. To evaluate PIC, MAE simplifies the DTA text files summarizing the MS/MS spectra and applies heuristic rules to classify the fragment ions. MAE output also provides data mining functions, which are illustrated by using PIC to identify spectral chimeras, where two or more peptide ions were sequenced together, as well as cases where fragmentation chemistry is not well predicted.