Sequence Tagging Reveals Unexpected Modifications in Toxicoproteomics

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.毒理蛋白质组学样本富含蛋白质的翻译后修饰（posttranslational modifications, PTMs）。通过标准数据库检索识别这类修饰往往会带来显著的性能损耗。本文介绍了TagRecon的最新进展——这是一种利用推断序列标签来识别毒理蛋白质组学数据集中修饰肽段的算法。相较于MyriMatch数据库搜索引擎，TagRecon对已知修饰的识别效率更高。在针对LTQ、Orbitrap及QTOF数据集的非预期修饰识别任务中，TagRecon的表现优于当前最先进的软件。我们开发了用于检测样本中持续质量偏移的用户友好型软件。我们采用三步策略来识别样本中的非预期PTMs：首先，通过标准数据库检索鉴定样本中存在的蛋白质；其次，利用基于序列标签的检索对已鉴定蛋白质进行非预期PTMs分析；最后，通过精修检索为检测到的质量偏移收集额外佐证证据。将该技术应用于毒理蛋白质组学数据集后，发现了蛋白质与样品处理试剂之间的非预期交叉反应。在暴露于潜在毒性药物的大鼠肝脏样本中，有25种蛋白质表现出氧化应激特征。上述结果证实了从毒理蛋白质组学数据集中挖掘修饰信息的重要价值。