Escherichia coli and Sf9 Contaminant Databases to Increase Efficiency of Tandem Mass Spectrometry Peptide Identification in Structural Mass Spectrometry Experiments
Escherichia coli and Sf9 Contaminant Databases to Increase Efficiency of Tandem Mass Spectrometry Peptide Identification in Structural Mass Spectrometry Experiments
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DOI：
10.1021/jasms.0c00283
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发表时间：
2020-10-07
影响因子：
3.2
通讯作者：
Burke, John E.
中科院分区：
文献类型：
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作者：
Dobbs, Joseph M.;Jenkins, Meredith L.;Burke, John E.
从亲和纯化质谱（AP-MS）数据中过滤非特异性结合污染蛋白是一种完善的策略，可提高鉴定蛋白的统计置信度。CRAPome（亲和纯化污染物库）描述了许多纯化策略中存在的污染背景含量。然而，镍-次氮基三乙酸（NiNTA）和谷胱甘肽S-转移酶（GST）亲和基质的完整污染物列表缺乏。同样，没有草地贪夜蛾（Sf 9）污染物可用，并且仅描述了大肠杆菌的FLAG纯化污染物。对于使用重组蛋白的MS实验，如结构质谱实验（氢氘交换质谱（HDX-MS）、化学交联和自由基足迹），在初始串联MS（MS/MS）鉴定阶段，如果搜索数据库中不包括这些污染物，可能会导致肽鉴定复杂化。我们已经建立了Sf 9和E的污染物FASTA数据库。coli NiNTA或GST纯化策略，并表明使用这些数据库可以有效地提高HDX-MS蛋白质覆盖率，片段计数和肽鉴定的置信度。这种方法为任何纯化方法的污染物数据库设计提供了一种稳健的策略，这将扩大能够通过HDX-MS查询的系统的复杂性。
Filtering of nonspecifically binding contaminant proteins from affinity purification mass spectrometry (AP-MS) data is a well-established strategy to improve statistical confidence in identified proteins. The CRAPome (contaminant repository for affinity purification) describes the contaminating background content present in many purification strategies. However, full contaminant lists for nickel-nitrilotriacetic acid (NiNTA) and glutathione S-transferase (GST) affinity matrices are lacking. Similarly, no Spodoptera frugiperda (Sf9) contaminants are available, and only the FLAG-purified contaminants are described for Escherichia coli. For MS experiments that use recombinant protein, such as structural mass spectrometry experiments (hydrogendeuterium exchange mass spectrometry (HDX-MS), chemical cross-linking, and radical foot-printing), failing to include these contaminants in the search database during the initial tandem MS (MS/MS) identification stage can result in complications in peptide identification. We have created contaminant FASTA databases for Sf9 and E. coli NiNTA or GST purification strategies and show that the use of these databases can effectively improve HDX-MS protein coverage, fragment count, and confidence in peptide identification. This approach provides a robust strategy toward the design of contaminant databases for any purification approach that will expand the complexity of systems able to be interrogated by HDX-MS.