Mimicked synthetic ribosomal protein complex for benchmarking crosslinking mass spectrometry workflows

Abstract

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

Real-time library search increases cross-link identification depth across all levels of sample complexity

“…The depth of XL-MS coverage for both structural characterization and interactome profiling is highly dependent on the number of identified cross-links. Recent studies have put considerable efforts in the optimization of LC-MS parameters (e.g., charge filter, dynamic exclusion, fragmentation energy, and duty cycles) [3][4][5] , search engines [6][7][8] and cross-linker designs [9][10][11] . For instance, cross-linkers decorated with an enrichment-handle allow removal of linear peptides, which are thought to make up 95 -99% of the mixture.…”

Deep proteome profiling with reduced carry over using superficially porous microfabricated nanoLC columns

“…They were mixed into groups that were separately cross-linked, followed by quenching and pooling to a single peptide-library. In contrast to experiments where the FDR is computationally determined only by applying the known target-decoy approach, this system allows an exact FDR calculation as only interpeptide connections within a group are possible 36 . Furthermore, the maximal theoretical cross-link number is known (426 unique combinations), which allows estimating the efficiency of a detection workflow based on the reached identification numbers.…”

Cross-linking mass spectrometry discovers, evaluates, and validates the experimental and predicted structural proteome

“…The wide accessibility of different cross-linkers, alongside critical efforts to standardise XL-MS search engines and analyses (80, 81), will enable high-volume and high-confidence cross-link constraints to be generated for proteins from diverse conditions and organisms. One could envisage a (near) future in which archived XL-MS data is harvested automatically by AlphaFold or similar systems and optionally displayed on predicted structures.…”

“…The wide accessibility of different cross-linkers, alongside critical efforts to standardise XL-MS search engines and analyses (80,81), will enable high-volume and high-confidence cross-link constraints to be generated for proteins from diverse conditions and organisms.…”