Revisiting Peptide Identification by High-Accuracy Mass Spectrometry: Problems Associated with the Use of Narrow Mass Precursor Windows
Revisiting Peptide Identification by High-Accuracy Mass Spectrometry: Problems Associated with the Use of Narrow Mass Precursor Windows
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DOI：
10.1021/pr5007284
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发表时间：
2015-02-01
影响因子：
4.4
通讯作者：
Vazquez, Jesus
中科院分区：
文献类型：
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作者：
Bonzon-Kulichenko, Elena;Garcia-Marques, Fernando;Vazquez, Jesus
肽鉴定越来越多地通过数据库搜索来实现，其中质量前体耐受性设置在 ppm 范围内。这一趋势的推动因素是现代质谱仪的高分辨率和准确性，以及人们相信肽鉴定的质量可以通过使用诱饵目标方法估计错误发现率 (FDR) 来完全控制。然而，缩小质量容差会减少候选序列的数量，一些作者担心这些搜索条件可能会引入不准确性。在这里，我们证明，当使用仅依赖于一个候选序列的分数时，即使候选序列的平均数量仅为 200，基于诱饵的误报识别数量估计也是准确的，以至于可以在完全不同的搜索条件下做出非常准确的 FDR 预测。然而，当从其他候选序列中获取信息而构建的分数与低前体质量公差一起使用时，错误识别的肽的比例可能会显着高于目标诱饵方法估计的 FDR。我们的结果表明，对于这种分数，应通过使用宽质量窗口和后评分质量过滤算法来利用现代质谱仪的高质量精度。
Peptide identification is increasingly achieved through database searches in which mass precursor tolerance is set in the ppm range. This trend is driven by the high resolution and accuracy of modern mass spectrometers and the belief that the quality of peptide identification is fully controlled by estimating the false discovery rate (FDR) using the decoy-target approach. However, narrowing mass tolerance decreases the number of sequence candidates, and several authors have raised concerns that these search conditions can introduce inaccuracies. Here, we demonstrate that when scores that only depend on one sequence candidate are used, decoy-based estimates of the number of false positive identifications are accurate even with an average number of candidates of just 200, to the point that remarkably accurate FDR predictions can be made in completely different search conditions. However, when scores that are constructed taking information from additional sequence candidates are used together with low precursor mass tolerances, the proportion of peptides incorrectly identified may become significantly higher than the FDR estimated by the target-decoy approach. Our results suggest that with this kind of score the high mass accuracy of modern mass spectrometers should be exploited by using wide mass windows followed by postscoring mass filtering algorithms.