Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh , Edinburgh EH9 3BF, United Kingdom.
Chair of Bioanalytics, Institute of Biotechnology, Technische Universität Berlin , 13355 Berlin, Germany.
Anal Chem. 2017 Apr 4;89(7):3829-3833. doi: 10.1021/acs.analchem.6b03745. Epub 2017 Mar 24.
Cross-linking/mass spectrometry is an increasingly popular approach to obtain structural information on proteins and their complexes in solution. However, methods for error assessment are under current development. We note that false-discovery rates can be estimated at different points during data analysis, and are most relevant for residue or protein pairs. Missing this point led in our example analysis to an actual 8.4% error when 5% error was targeted. In addition, prefiltering of peptide-spectrum matches and of identified peptide pairs substantially improved results. In our example, this prefiltering increased the number of residue pairs (5% FDR) by 33% (n = 108 to n = 144). This number improvement did not come at the expense of reduced accuracy as the added data agreed with an available crystal structure. We provide an open-source tool, xiFDR ( https://github.com/rappsilberlab/xiFDR ), that implements our observations for routine application. Data are available via ProteomeXchange with identifier PXD004749.
交联/质谱联用是一种越来越受欢迎的方法,可用于获得溶液中蛋白质及其复合物的结构信息。然而,目前正在开发错误评估方法。我们注意到,在数据分析的不同阶段都可以估计假发现率,而对于残基或蛋白质对来说,假发现率最为相关。在我们的示例分析中,由于没有注意到这一点,当目标错误率为 5%时,实际错误率为 8.4%。此外,对肽-谱匹配和鉴定的肽对进行预过滤可显著改善结果。在我们的示例中,这种预过滤将残基对的数量(5% FDR)提高了 33%(n = 108 到 n = 144)。这种数量上的提高并没有以降低准确性为代价,因为添加的数据与可用的晶体结构一致。我们提供了一个开源工具 xiFDR(https://github.com/rappsilberlab/xiFDR),该工具可用于常规应用。数据可通过 ProteomeXchange 获得,标识符为 PXD004749。