Na Seungjin, Paek Eunok, Lee Cheolju
Department of Mechanical and Information Engineering, University of Seoul, 90 Jeonnong-dong, Dongdaemun-gu, Seoul, Korea.
Anal Chem. 2008 Mar 1;80(5):1520-8. doi: 10.1021/ac702038q. Epub 2008 Feb 2.
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. This algorithm can be used to prefilter spectra so that only reasonably good spectra are submitted to database search programs, thereby saving considerable time. The software for MS/MS charge-state determination, which we named "CIFTER", is available at a website http://prix.uos.ac.kr/sifter/cifter.
串联质谱法（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光谱中鉴定肽段最常用的方法。然而，从低分辨率质谱仪获得的肽段MS/MS光谱的电荷状态估计并不可靠。它们需要重复进行数据库搜索并对搜索结果进行额外分析。我们在此提出一种算法，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分析。我们利用分析中发现的显著特征，并使用机器学习方法开发了一种用于多电荷光谱的分类器。对各种数据集的测试表明，我们的方法可以成功应用，而与实验设置和质谱仪无关。该算法可用于对光谱进行预筛选，以便仅将质量较好的光谱提交给数据库搜索程序，从而节省大量时间。我们将用于MS/MS电荷状态测定的软件命名为“CIFTER”，可在网站http://prix.uos.ac.kr/sifter/cifter上获取。
