Department of Computer Science , University of Waterloo , Waterloo , Ontario N2L 3G1 , Canada.
J Proteome Res. 2018 Sep 7;17(9):3325-3331. doi: 10.1021/acs.jproteome.8b00594. Epub 2018 Aug 16.
Tandem mass tag (TMT)-based liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a proven approach for large-scale multiplexed protein quantification. However, the identification of TMT-labeled peptides is compromised by the labeling during traditional sequence database searches. In this study, we aim to use a spectral library search to increase the sensitivity and specificity of peptide identification for TMT-based MS data. Compared to MS/MS spectra of unlabeled peptides, the spectra of TMT-labeled counterparts usually display intensified b ions, suggesting that TMT labeling can alter product ion patterns during MS/MS fragementation. We compiled a human TMT spectral library of 401,168 unique peptides of high quality from millions of peptide-spectrum matches in tens of profiling projects, matching to 14,048 nonredundant proteins (13,953 genes). A mouse TMT spectral library of similar size was also constructed. The libraries were subsequently appended with decoy spectra to evaluate the false discovery rate, which was validated by a simulated null TMT data set. The performance of the library search was further optimized by removing TMT reporter ions and selecting an appropriate library construction method. Finally, we searched a human TMT data set against the spectral library to demonstrate that the spectral library outperformed the sequence database. Both human and mouse TMT libraries were made publicly available to the research community.
串联质量标签（TMT）-基于液相色谱-串联质谱（LC-MS/MS）是一种用于大规模多重蛋白质定量的成熟方法。然而，在传统的序列数据库搜索中，TMT 标记肽的鉴定受到标记的影响。在这项研究中，我们旨在使用谱库搜索来提高基于 TMT 的 MS 数据中肽鉴定的灵敏度和特异性。与未标记肽的 MS/MS 谱相比，TMT 标记对应物的谱通常显示出增强的 b 离子，表明 TMT 标记可以在 MS/MS 片段化过程中改变产物离子模式。我们从数十个分析项目中的数百万个肽-谱匹配中编译了一个高质量的人类 TMT 光谱库，其中包含 401,168 个独特肽，对应于 14,048 个非冗余蛋白质（13,953 个基因）。还构建了一个大小相似的小鼠 TMT 光谱库。随后，将诱饵谱添加到库中以评估假发现率，并用模拟的空 TMT 数据集进行验证。通过去除 TMT 报告离子和选择适当的库构建方法进一步优化了库搜索的性能。最后，我们搜索了人类 TMT 数据集以证明该谱库优于序列数据库。人类和小鼠 TMT 库都可供研究界使用。
