Phosphorylation-Specific MS/MS Scoring for Rapid and Accurate Phosphoproteome Analysis

The promise of mass spectrometry as a tool for probing signal-transduction is predicated on reliable identification of post-translational modifications. Phosphorylations are key mediators of cellular signaling, yet are hard to detect, partly because of unusual fragmentation patterns of phosphopeptides. In addition to being accurate, MS/MS identification software must be robust and efficient to deal with increasingly large spectral data sets. Here, we present a new scoring function for the Inspect software for phosphorylated peptide tandem mass spectra for ion-trap instruments, without the need for manual validation. The scoring function was modeled by learning fragmentation patterns from 7677 validated phosphopeptide spectra. We compare our algorithm against SEQUEST and X!Tandem on testing and training data sets. At a 1% false positive rate, Inspect identified the greatest total number of phosphorylated spectra, 13% more than SEQUEST and 39% more than X!Tandem. Spectra identified by Inspect tended to score better in several spectral quality measures. Furthermore, Inspect runs much faster than either SEQUEST or X!Tandem, making desktop phosphoproteomics feasible. Finally, we used our new models to reanalyze a corpus of 423 000 LTQ spectra acquired for a phosphoproteome analysis of Saccharomyces cerevisiae DNA damage and repair pathways and discovered 43% more phosphopeptides than the previous study.

质谱（mass spectrometry）作为探究信号转导的工具，其应用前景有赖于对翻译后修饰（post-translational modifications）的可靠鉴定。磷酸化（Phosphorylations）是细胞信号传导的关键介导因子，但因其对应的磷酸肽（phosphopeptides）存在特殊的碎裂模式，故而难以被检测。除需具备准确性外，串联质谱（MS/MS）鉴定软件还需具备鲁棒性与高效性，以应对日益增长的谱图数据集。本文针对离子阱仪器（ion-trap instruments）下的磷酸肽串联质谱谱图，为Inspect软件开发了一款全新的评分函数，无需进行人工验证。该评分函数通过从7677条已验证的磷酸肽谱图中学习碎裂模式构建而成。我们将本算法与SEQUEST、X!Tandem在训练集与测试集上进行了对比。在1%假阳性率（false positive rate）条件下，Inspect鉴定得到的磷酸化谱图总数最多，较SEQUEST多出13%，较X!Tandem多出39%。经Inspect鉴定的谱图，在多项谱图质量指标（spectral quality measures）上的评分表现更优。此外，Inspect的运行速度远快于SEQUEST与X!Tandem，使得桌面磷酸蛋白质组学（desktop phosphoproteomics）成为可行方案。最后，我们利用新模型重新分析了一组包含423000条LTQ谱图的数据集，该数据集源自酿酒酵母（Saccharomyces cerevisiae）DNA损伤与修复通路的磷酸蛋白质组学分析，最终鉴定得到的磷酸肽数量较此前的研究多出43%。