A Workflow for Improved Analysis of Cross-Linking Mass Spectrometry Data Integrating Parallel Accumulation-Serial Fragmentation with MeroX and Skyline.
Juan Camilo Rojas EcheverriFrank HauseClaudio IacobucciChristian H IhlingDirk TänzlerNicholas ShulmanMichael RiffleBrendan X MacLeanAndrea SinzPublished in: Analytical chemistry (2024)
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.
Keyphrases
- mass spectrometry
- electronic health record
- multiple sclerosis
- liquid chromatography
- ms ms
- big data
- data analysis
- gas chromatography
- high performance liquid chromatography
- high resolution
- machine learning
- high throughput
- quality improvement
- amino acid
- artificial intelligence
- protein protein
- ionic liquid
- loop mediated isothermal amplification
- sensitive detection
- molecular dynamics