Dataset Information

Proteome-wide cross-linking mass spectrometry of human K562 cells

ABSTRACT: Proteome-wide cross-linking mass spectrometry was performed on human K562 cell lysates. MS2-MS3 fragmentation strategy was used to aquire the XL-MS data. The raw files in this dataset are a subset of PXD013928.

INSTRUMENT(S): Orbitrap Fusion Lumos

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Erythrocyte

SUBMITTER: Kumar Yugandhar

LAB HEAD: Haiyuan Yu

PROVIDER: PXD018771 | Pride | 2020-07-01

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	HaiyuanYu10378672_DSSO_XL_MS2-PD-MS3_Fr_23.msf	Msf
	HaiyuanYu10378672_DSSO_XL_MS2-PD-MS3_Fr_23.raw	Raw
	HaiyuanYu10378672_DSSO_XL_MS2-PD-MS3_Fr_24.msf	Msf
	HaiyuanYu10378672_DSSO_XL_MS2-PD-MS3_Fr_24.raw	Raw
	HaiyuanYu10378672_DSSO_XL_MS2-PD-MS3_Fr_25.msf	Msf

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Publications

Structure-based validation can drastically underestimate error rate in proteome-wide cross-linking mass spectrometry studies.

Yugandhar Kumar K Wang Ting-Yi TY Wierbowski Shayne D SD Shayhidin Elnur Elyar EE Yu Haiyuan H

Nature methods 20200929 10

Thorough quality assessment of novel interactions identified by proteome-wide cross-linking mass spectrometry (XL-MS) studies is critical. Almost all current XL-MS studies have validated cross-links against known three-dimensional structures of representative protein complexes. Here, we provide theoretical and experimental evidence demonstrating that this approach can drastically underestimate error rates for proteome-wide XL-MS datasets, and propose a comprehensive set of four data-quality metr ...[more]

PMID: 32994567

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Project description:Over the past few decades cross-linking mass spectrometry (XLMS) has become a powerful tool for identification of protein-protein interactions and for gaining insight into the structures of proteins in living cells, tissues, and organelles. The development of new crosslinkers, enrichment strategies and data acquisition methods led to the establishment of numerous new software tools specifically for the analysis and interpretation of cross-linking data. We previously published one of these tools called MS Annika, a cross-linking search engine which can accurately identify cross-linked peptides in MS2 spectra from a variety of different MS-cleavable crosslinkers. In this publication we present an updated MS Annika and a new search algorithm that additionally supports processing of data from MS2-MS3-based approaches and identification of peptides from MS3 spectra. In the new MS2-MS3 search algorithm, MS3 spectra are matched to their corresponding precursor doublet peak in the MS2 scan to identify the crosslink modification and the monoisotopic peptide mass. This information is then used to adjust the MS3 spectra for search with MS Amanda, our in-house developed peptide search engine, to identify the cross-linked peptides. Peptides that are identified in the MS2 scan and one or more of the associated product MS3 scans are re-scored with a novel scoring function to reflect the increased confidence. Finally, the detected cross-links are validated by estimating the false discovery rate (FDR) using a target-decoy approach. We evaluated the MS3-search-capabilities of MS Annika on five different datasets covering a variety of experimental approaches and compared it to XlinkX and MaXLinker, two other cross-linking search engines that support MS3 crosslink identification. Three of the datasets were benchmark datasets of synthetic peptides that allow calculation of an experimentally validated FDR, and we show that MS Annika detects up to 4 times more true unique crosslinks than MaXLinker and up to 35% more than XlinkX while simultaneously yielding less false positive hits and therefore a more accurate FDR than the other two search engines. Additionally, for the other two datasets we could show that MS Annika finds between 74% to 2.5 times more crosslinks at 1% estimated FDR and reveals protein-protein interactions that are not detected by either XlinkX or MaXLinker.