CIFTER: Automated charge-state determination for peptide tandem mass spectra
CIFTER: Automated charge-state determination for peptide tandem mass spectra
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DOI：
10.1021/ac702038q
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发表时间：
2008-03-01
影响因子：
7.4
通讯作者：
Lee, Cheolju
中科院分区：
文献类型：
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作者：
Na, Seungjin;Paek, Eunok;Lee, Cheolju
串联质谱 (MS/MS) 已成为分析复杂蛋白质混合物的常用且有用的工具。数据库搜索程序是从 MS/MS 谱图中鉴定肽的最流行的方法。然而，从低分辨率质谱仪获得的肽 MS/MS 谱的电荷状态估计并不可靠。它们需要重复的数据库搜索和对搜索结果的额外分析。我们在这里提出了一种算法，旨在可靠地区分双电荷光谱和三电荷光谱。我们对各种光谱特征及其影响进行了严格的分析。我们利用分析中发现的显着特征，并使用机器学习方法开发了多电荷光谱分类器。对各种数据集的测试表明，我们的方法可以成功应用，独立于实验设置和质量仪器。 Ibis 算法可用于预过滤光谱，以便仅将相当好的光谱提交给数据库搜索程序，从而节省大量时间。
Tandem mass spectrometry (MS/MS) has become a common and useful tool for analyzing complex protein mixtures. Database search programs are the most popular means for peptide identification from MS/MS spectra. However, estimations of charge states of peptide MS/MS spectra obtained from low-resolution mass spectrometers have not been reliable. They require repetitive database searches and additional analyses of the search results. We propose here an algorithm designed to reliably differentiate doubly charged spectra from triply charged ones. We conducted a rigorous analysis of various spectral features and their effects. We employed the distinguishing features found in our analysis and developed a classifier for multiply charged spectra using a machine learning approach. The test on various data sets showed that our method could be successfully applied independent of experimental setup and mass instrument. Ibis algorithm can be used to prefilter spectra so that only reasonably good spectra are submitted to database search programs, thereby saving considerable time.