Cross-linking mass spectrometry has matured to a frequently used tool for the investigation of protein structures as well as interactome studies up to a system-wide level. The growing community generated a broad spectrum of applications, linker types, acquisition strategies and specialized data analysis tools, which makes it challenging to decide for an appropriate analysis workflow. Here, we report a large and flexible synthetic peptide library as reliable instrument to benchmark crosslink workflows. Additionally, we provide a tool, IMP-X-FDR, that calculates the real, experimentally validated, FDR, compares results across search engine platforms and analyses crosslink properties in an automated manner. We apply the library with 6 commonly used linker reagents and analyse the data with 6 established search engines. We thereby show that the correct algorithm and search setting choice is highly important to improve identification rate and reliability. We reach identification rates of up to ~70 % of the theoretical maximum (i.e. 700 unique lysine-lysine cross-links) while maintaining a real false-discovery-rate of <3 % at cross-link level with high reproducibility, representatively showing that our test system delivers valuable and statistically solid results.

The field of cross-linking mass spectrometry has matured to a frequently used tool for the investigation of protein structures as well as interactome studies up to a system wide level. The growing community generated a broad spectrum of applications, linker types, acquisition strategies and specialized data anal-ysis tools, which makes it challenging, especially for newcomers, to decide for an appropriate analysis workflow. Therefore, we here present a large and flexible synthetic peptide library as reliable instrument to benchmark crosslinkers with different reactive sites as well as acquisition techniques and data analysis algorithms. Additionally, we provide a tool, IMP-X-FDR, that calculates the real FDR, compares results across search engine platforms and analyses crosslink properties in an automated manner. The library was used with the reagents DSSO, DSBU, CDI, ADH, DHSO and azide-a-DSBSO and data were analysed using the algorithms MeroX, MS Annika, XlinkX, pLink and MaxLynx. We thereby show that the correct algorithm and search setting choice is highly important to improve ID rate and FDR in combination with software and sample-complexity specific score cut-offs. When analysing DSSO data with MS Annika, we reach high identification rates of up to ∼70 % of the theoretical maximum (i.e. 700 unique lysine-lysine cross-links) while maintaining a low real FDR of < 3 % at cross-link level and with extraordinary high reproducibility, representatively showing that our test system delivers valuable and statistically solid results.Graphical abstract