Beausoleil Sean A, Villén Judit, Gerber Scott A, Rush John, Gygi Steven P
Department of Cell Biology, Harvard Medical School, 240 Longwood Ave., Boston, Massachusetts 02115, USA.
Nat Biotechnol. 2006 Oct;24(10):1285-92. doi: 10.1038/nbt1240. Epub 2006 Sep 10.
Data analysis and interpretation remain major logistical challenges when attempting to identify large numbers of protein phosphorylation sites by nanoscale reverse-phase liquid chromatography/tandem mass spectrometry (LC-MS/MS) (Supplementary Figure 1 online). In this report we address challenges that are often only addressable by laborious manual validation, including data set error, data set sensitivity and phosphorylation site localization. We provide a large-scale phosphorylation data set with a measured error rate as determined by the target-decoy approach, we demonstrate an approach to maximize data set sensitivity by efficiently distracting incorrect peptide spectral matches (PSMs), and we present a probability-based score, the Ascore, that measures the probability of correct phosphorylation site localization based on the presence and intensity of site-determining ions in MS/MS spectra. We applied our methods in a fully automated fashion to nocodazole-arrested HeLa cell lysate where we identified 1,761 nonredundant phosphorylation sites from 491 proteins with a peptide false-positive rate of 1.3%.
当试图通过纳米级反相液相色谱/串联质谱法（LC-MS/MS）鉴定大量蛋白质磷酸化位点时，数据分析和解读仍然是主要的后勤挑战（在线补充图1）。在本报告中，我们解决了通常只能通过费力的手动验证来解决的挑战，包括数据集误差、数据集灵敏度和磷酸化位点定位。我们提供了一个大规模磷酸化数据集，其测量误差率由目标-诱饵方法确定，我们展示了一种通过有效分散不正确的肽谱匹配（PSM）来最大化数据集灵敏度的方法，并且我们提出了一种基于概率的分数，即Ascore，它基于MS/MS谱中位点确定离子的存在和强度来测量正确磷酸化位点定位的概率。我们以完全自动化的方式将我们的方法应用于诺考达唑阻滞的HeLa细胞裂解物，在那里我们从491种蛋白质中鉴定出1761个非冗余磷酸化位点，肽假阳性率为1.3%。
