Sequence tagging reveals unexpected modifications in toxicoproteomics.
Sequence tagging reveals unexpected modifications in toxicoproteomics.
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DOI：
10.1021/tx100275t
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发表时间：
2011-02-18
影响因子：
4.1
通讯作者：
Tabb DL
中科院分区：
文献类型：
--
作者：
Dasari S;Chambers MC;Codreanu SG;Liebler DC;Collins BC;Pennington SR;Gallagher WM;Tabb DL
毒性蛋白质组学样品富含蛋白质的翻译后修饰（PTM）。通过标准的数据库搜索来识别这些修改可能会导致显著的性能损失。在这里，我们描述了TagRecon的最新进展，该算法利用推断的序列标签来识别toxicoproteomic数据集中的修饰肽。TagRecon比MyriMatch数据库搜索引擎更有效地识别已知修改。TagRecon在识别LTQ、Orbitrap和QTOF数据集的非预期修改方面优于最先进的软件。我们开发了用户友好的软件，用于检测样品的持续质量变化。我们遵循三步策略来检测样品中意外的PTM。首先，我们用标准的数据库搜索来识别样品中存在的蛋白质。接下来，用基于序列标签的搜索来询问所鉴定的蛋白质的意外PTM。最后，收集额外的证据，检测到的质量变化与细化搜索。该技术在toxicoproteomic数据集上的应用揭示了蛋白质和样品处理试剂之间的意外交叉反应。当暴露于潜在毒性药物时，大鼠肝脏中的25种蛋白质显示出氧化应激的迹象。这些结果表明，挖掘toxicoproteomic数据集的修改的价值。
Toxicoproteomic samples are rich in posttranslational modifications (PTMs) of proteins. Identifying these modifications via standard database searching can incur significant performance penalties. Here we describe the latest developments in TagRecon, an algorithm that leverages inferred sequence tags to identify modified peptides in toxicoproteomic data sets. TagRecon identifies known modifications more effectively than the MyriMatch database search engine. TagRecon outperformed state of the art software in recognizing unanticipated modifications from LTQ, Orbitrap, and QTOF data sets. We developed user-friendly software for detecting persistent mass shifts from samples. We follow a three-step strategy for detecting unanticipated PTMs in samples. First, we identify the proteins present in the sample with a standard database search. Next, identified proteins are interrogated for unexpected PTMs with a sequence tag-based search. Finally, additional evidence is gathered for the detected mass shifts with a refinement search. Application of this technology on toxicoproteomic data sets revealed unintended cross-reactions between proteins and sample processing reagents. Twenty five proteins in rat liver showed signs of oxidative stress when exposed to potentially toxic drugs. These results demonstrate the value of mining toxicoproteomic data sets for modifications.