Dataset Information

Monolinks Matter: Rational Modification of a Cross-Linker for Improved Flexible Protein Structure Modeling

ABSTRACT: Chemical cross-linking/mass spectrometry (XL-MS) has emerged as a complementary tool for mapping interaction sites within protein networks as well as gaining moderate-resolution native structurale insight with minimal interference. XL-MS technology mostly relies on chemoselective reactions (crosslinking) between protein residues and a linker. Although complementary to crosslinks, monolinks are less commonly recognised as adjuvants for protein structure elucidation. Herein we report the rational design and synthesis of DSSO-carbamate, a novel cross-linker with increased selectivity for monolinks. Monolinks represent useful information for the elucidation of protein conformational changes and recent work has exploited their application in experimental crosslinking MS integrated protein structure prediction algorithms such as XL-MS-AlphaFold2 . its application supported by AlphaFold2 . Herein we report the rational design and synthesis of DSSO-carbamate, a novel cross-linker with increased selectivity for monolinks. Further, we demonstrate DSSO-carbamate’s capacity to facilitate improved protein structure prediction on account of its superior monolink generation.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Kish Adoni

LAB HEAD: Prof Konstantinos Thalassinos

PROVIDER: PXD051742 | Pride | 2025-05-06

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	120124_QBP_CaCl2_DSSO-carbamate_rep1.raw	Raw
	120124_QBP_CaCl2_DSSO-carbamate_rep1t2.raw	Raw
	120124_QBP_CaCl2_DSSO-carbamate_rep2.raw	Raw
	120124_QBP_CaCl2_DSSO-carbamate_rep2t2.raw	Raw
	120124_QBP_CaCl2_DSSO_rep1.raw	Raw

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Publications

Rational Modification of a Cross-Linker for Improved Flexible Protein Structure Modeling.

Saridakis Iakovos I Adoni Kish R KR Leischner Thomas T Brutiu Bogdan R BR Shaaban Saad S Ferrari Giammarco G Thalassinos Konstantinos K Maulide Nuno N

Analytical chemistry 20250109 2

Chemical cross-linking/mass spectrometry (XL-MS) has emerged as a complementary tool for mapping interaction sites within protein networks as well as gaining moderate-resolution native structural insight with minimal interference. XL-MS technology mostly relies on chemoselective reactions (cross-linking) between protein residues and a linker. DSSO represents a versatile cross-linker for protein structure investigation and in-cell XL-MS. However, our assessment of its shelf life and batch purity ...[more]

PMID: 39785238

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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.