Modeling and characterization of multi-charge mass spectra for peptide sequencing
Modeling and characterization of multi-charge mass spectra for peptide sequencing
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DOI：
10.1142/s021972000600248x
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发表时间：
2006-12-01
影响因子：
1
通讯作者：
Pevzner, Pavel
中科院分区：
文献类型：
--
作者：
Chong, Ket Fah;Ning, Kang;Pevzner, Pavel
使用串联质谱数据进行肽测序是蛋白质组学中一个重要且具有挑战性的问题。我们解决了多电荷光谱的肽测序问题。目前大多数肽测序算法仅考虑对一或两个离子充电，即使对于更高电荷的光谱也是如此。我们通过推广现有模型来描述多电荷光谱的特征。使用我们的模型，我们分析了来自 Global Proteome Machine (GPM) [Craig R, Cortens JP, Beavis RC, J Proteome Res 3:1234-1242, 2004.]（收费 1-5）、系统生物学研究所 (ISB) [Keller A、Purvine S、Nesvizhskii Al、Stolyar S、Goodlett DR、Kolker E、OMICS 的光谱6:207-212, 2002.] 和 Orbitrap（均带有电荷 1-3）。我们对 GPM 数据集的分析表明，较高的电荷峰对完整肽的预测有显着贡献。它们还有助于解释为什么现有算法在多电荷光谱上表现不佳。基于这些分析，我们声称，如果肽测序算法也考虑更高电荷的离子，则可以实现更高的灵敏度结果。我们通过提出一种称为贪婪最佳强标签（GBST）算法的从头测序算法来验证这一说法，该算法很简单，但基于我们的新模型考虑了更高的电荷离子。对多电荷谱的评估表明，我们的简单 GBST 算法优于 Lutefisk 和 PepNovo，特别是对于三个或更多电荷的 GPM 谱。
Peptide sequencing using tandem mass spectrometry data is an important and challenging problem in proteomics. We address the problem of peptide sequencing for multi-charge spectra. Most peptide sequencing algorithms currently consider only charge one or two ions even for higher-charge spectra. We give a characterization of multi-charge spectra by generalizing existing models. Using our models, we analyzed spectra from Global Proteome Machine (GPM) [Craig R, Cortens JP, Beavis RC, J Proteome Res 3:1234-1242, 2004.] (with charges 1-5), Institute for Systems Biology (ISB) [Keller A, Purvine S, Nesvizhskii Al, Stolyar S, Goodlett DR, Kolker E, OMICS 6:207-212, 2002.] and Orbitrap (both with charges 1-3). Our analysis for the GPM dataset shows that higher charge peaks contribute significantly to prediction of the complete peptide. They also help to explain why existing algorithms do not perform well on multi-charge spectra. Based on these analyses, we claim that peptide sequencing algorithms can achieve higher sensitivity results if they also consider higher charge ions. We verify this claim by proposing a de novo sequencing algorithm called the greedy best strong tag (GBST) algorithm that is simple but considers higher charge ions based on our new model. Evaluation on multi-charge spectra shows that our simple GBST algorithm outperforms Lutefisk and PepNovo, especially for the GPM spectra of charge three or more.