Collins Mark O, Wright James C, Jones Matthew, Rayner Julian C, Choudhary Jyoti S
Proteomic Mass Spectrometry, The Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.
Malaria Programme, The Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.
J Proteomics. 2014 May 30;103(100):1-14. doi: 10.1016/j.jprot.2014.03.010. Epub 2014 Mar 21.
We present a workflow using an ETD-optimised version of Mascot Percolator and a modified version of SLoMo (turbo-SLoMo) for analysis of phosphoproteomic data. We have benchmarked this against several database searching algorithms and phosphorylation site localisation tools and show that it offers highly sensitive and confident phosphopeptide identification and site assignment with PSM-level statistics, enabling rigorous comparison of data acquisition methods. We analysed the Plasmodium falciparum schizont phosphoproteome using for the first time, a data-dependent neutral loss-triggered-ETD (DDNL) strategy and a conventional decision-tree method. At a posterior error probability threshold of 0.01, similar numbers of PSMs were identified using both methods with a 73% overlap in phosphopeptide identifications. The false discovery rate associated with spectral pairs where DDNL CID/ETD identified the same phosphopeptide was <1%. 72% of phosphorylation site assignments using turbo-SLoMo without any score filtering, were identical and 99.8% of these cases are associated with a false localisation rate of <5%. We show that DDNL acquisition is a useful approach for phosphoproteomics and results in an increased confidence in phosphopeptide identification without compromising sensitivity or duty cycle. Furthermore, the combination of Mascot Percolator and turbo-SLoMo represents a robust workflow for phosphoproteomic data analysis using CID and ETD fragmentation.
Protein phosphorylation is a ubiquitous post-translational modification that regulates protein function. Mass spectrometry-based approaches have revolutionised its analysis on a large-scale but phosphorylation sites are often identified by single phosphopeptides and therefore require more rigorous data analysis to unsure that sites are identified with high confidence for follow-up experiments to investigate their biological significance. The coverage and confidence of phosphoproteomic experiments can be enhanced by the use of multiple complementary fragmentation methods. Here we have benchmarked a data analysis pipeline for analysis of phosphoproteomic data generated using CID and ETD fragmentation and used it to demonstrate the utility of a data-dependent neutral loss triggered ETD fragmentation strategy for high confidence phosphopeptide identification and phosphorylation site localisation.
我们展示了一种工作流程，该流程使用 Mascot Percolator 的 ETD 优化版本和 SLoMo 的修改版本（turbo - SLoMo）来分析磷酸化蛋白质组数据。我们已将此方法与多种数据库搜索算法和磷酸化位点定位工具进行了基准测试，结果表明它能提供高度灵敏且可靠的磷酸肽鉴定以及具有 PSM 级统计数据的位点分配，从而能够对数据采集方法进行严格比较。我们首次使用数据依赖的中性丢失触发 ETD（DDNL）策略和传统决策树方法分析了恶性疟原虫裂殖体磷酸化蛋白质组。在 0.01 的后验错误概率阈值下，两种方法鉴定出的 PSM 数量相似，磷酸肽鉴定结果有 73%的重叠。DDNL CID/ETD 鉴定出相同磷酸肽的光谱对相关的错误发现率小于 1%。在不进行任何分数过滤的情况下，使用 turbo - SLoMo 进行的磷酸化位点分配中有 72%是相同的，其中 99.8%的情况错误定位率小于 5%。我们表明 DDNL 采集是磷酸化蛋白质组学的一种有用方法，并且在不影响灵敏度或 Duty cycle 的情况下，能提高磷酸肽鉴定的可信度。此外，Mascot Percolator 和 turbo - SLoMo 的组合代表了一种使用 CID 和 ETD 碎裂进行磷酸化蛋白质组数据分析的强大工作流程。
蛋白质磷酸化是一种普遍存在的翻译后修饰，可调节蛋白质功能。基于质谱的方法彻底改变了其大规模分析，但磷酸化位点通常由单个磷酸肽鉴定，因此需要更严格的数据分析以确保位点能被高可信度鉴定，以便后续实验研究其生物学意义。使用多种互补碎裂方法可提高磷酸化蛋白质组实验的覆盖范围和可信度。在此，我们对用于分析使用 CID 和 ETD 碎裂生成的磷酸化蛋白质组数据的数据分析流程进行了基准测试，并使用它来证明数据依赖的中性丢失触发 ETD 碎裂策略对于高可信度磷酸肽鉴定和磷酸化位点定位的实用性。
