Cross-linking mass spectrometry (XL-MS) has evolved into a pivotal technique for probing protein interactions. This study describes the implementation of Parallel Accumulation-Serial Fragmentation (PASEF) on timsTOF instruments, enhancing the detection and analysis of protein interactions by XL-MS. Addressing the challenges in XL-MS, such as the interpretation of complex spectra, low abundant cross-linked peptides, and a data acquisition bias, our current study integrates a peptide-centric approach for the analysis of XL-MS data and presents the foundation for integrating data-independent acquisition (DIA) in XL-MS with a vendor-neutral and open-source platform. A novel workflow is described for processing data-dependent acquisition (DDA) of PASEF-derived information. For this, software by Bruker Daltonics is used, enabling the conversion of these data into a format that is compatible with MeroX and Skyline software tools. Our approach significantly improves the identification of cross-linked products from complex mixtures, allowing the XL-MS community to overcome current analytical limitations.

中文翻译：

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用于改进交联质谱数据分析的工作流程，将并行累积-串行碎片与 MeroX 和 Skyline 集成


交联质谱 (XL-MS) 已发展成为探测蛋白质相互作用的关键技术。本研究描述了 timsTOF 仪器上并行累积-串行片段化 (PASEF) 的实施，增强了 XL-MS 对蛋白质相互作用的检测和分析。为了解决 XL-MS 中的挑战，例如复杂光谱的解释、低丰度交联肽和数据采集偏差，我们当前的研究集成了以肽为中心的方法来分析 XL-MS 数据，并提出了基础用于将 XL-MS 中的数据独立采集 (DIA) 与供应商中立的开源平台集成。描述了一种新颖的工作流程，用于处理 PASEF 衍生信息的数据相关采集 (DDA)。为此，使用了 Bruker Daltonics 的软件，将这些数据转换为与 MeroX 和 Skyline 软件工具兼容的格式。我们的方法显着改进了复杂混合物中交联产物的识别，使 XL-MS 社区能够克服当前的分析限制。