Hoopmann Michael R, Merrihew Gennifer E, von Haller Priska D, MacCoss Michael J
University of Washington, Department of Genome Sciences, Seattle, Washington 98195, USA.
J Proteome Res. 2009 Apr;8(4):1870-5. doi: 10.1021/pr800828p.
The identification of peptides by microcapillary liquid chromatography-tandem mass spectrometry (microLC-MS/MS) has become routine because of the development of fast scanning mass spectrometers, data-dependent acquisition, and database searching algorithms. However, many peptides within the detection limit of the mass spectrometer remain unidentified because of limitations in MS/MS sampling speed despite the dynamic range and peak capacity of the instrument. We have developed an automated approach that uses the mass spectra from high resolution microLC-MS data to define the molecular species present in the mixture and directs the acquisition of MS/MS spectra to precursors that were missed in prior analyses. This approach increases the coverage of the molecular species sampled by MS/MS and consequently the number of peptides and proteins identified during the acquisition of technical or biological replicates using a simple one-dimensional chromatographic separation. The combination of a unique workflow and custom software contribute to the improved identification of molecular features detected in proteomics experiments of complex protein mixtures.
由于快速扫描质谱仪、数据依赖型采集和数据库搜索算法的发展，通过微毛细管液相色谱-串联质谱法（microLC-MS/MS）鉴定肽段已成为常规操作。然而，尽管质谱仪具有动态范围和峰容量，但由于MS/MS采样速度的限制，许多在质谱仪检测限内的肽段仍未被鉴定出来。我们开发了一种自动化方法，该方法利用高分辨率微LC-MS数据的质谱图来定义混合物中存在的分子种类，并将MS/MS谱图的采集导向先前分析中遗漏的前体。这种方法增加了MS/MS采样的分子种类覆盖率，从而增加了在使用简单一维色谱分离进行技术或生物学重复采集过程中鉴定出的肽段和蛋白质的数量。独特的工作流程和定制软件相结合，有助于改进在复杂蛋白质混合物的蛋白质组学实验中检测到的分子特征的鉴定。
