Nature CommunicationsBioinformaticsPeer-reviewed journal article
Mimicked synthetic ribosomal protein complex for benchmarking cross-linking mass spectrometry workflows
Manuel MatzingeriD, Adrian Vasiu, Mathias Madalinski, Fränze MülleriD, Florian StanekiD, Karl MechtleriD
- Citations
- 27
- FWCI
- 2.08
- Field percentile
- top 12%
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 analysis 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, experimentally validated, 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 2, MaxLynx and xiSearch. 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 View larger version (30K): org.highwire.dtl.DTLVardef@146c751org.highwire.dtl.DTLVardef@1c7a6e1org.highwire.dtl.DTLVardef@9d2ee7org.highwire.dtl.DTLVardef@1725693_HPS_FORMAT_FIGEXP M_FIG C_FIG
- assay: proteomics
- research focus: benchmarking
- research focus: software / tool
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: benchmark study
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