Dataset Information

Xilmass: a new approach towards the identification of cross-linked peptides

ABSTRACT: Chemical cross-linking coupled with mass spectrometry plays an important role in unravelling protein interactions, especially weak and transient interactions. Moreover, cross-linking complements several structure determination approaches such as cryo-EM. Although several computational approaches are available for the annotation of spectra obtained from cross-linked peptides, there remains room for improvement. Here, we present Xilmass, a novel algorithm to identify cross-linked peptides that introduces two new concepts: (i) the cross-linked peptides are represented in a novel way in the search database that explicitly encodes cross-linking sites and (ii) the scoring function from the Andromeda algorithm was adapted to score against a theoretical MS spectrum that contains the peaks from all the possible fragment ions of a cross-linked peptide pair. The performance of Xilmass was subsequently evaluated against the recently published Kojak and the popular pLink algorithms on a data set that contains calmodulin-plectin.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Lennart Martens

PROVIDER: MSV000080758 | MassIVE | Wed Mar 29 07:19:00 BST 2017

SECONDARY ACCESSION(S): PXD003880

REPOSITORIES: MassIVE

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Xilmass: A New Approach toward the Identification of Cross-Linked Peptides.

Yılmaz Şule Ş Drepper Friedel F Hulstaert Niels N Černič Maša M Gevaert Kris K Economou Anastassios A Warscheid Bettina B Martens Lennart L Vandermarliere Elien E

Analytical chemistry 20160928 20

Chemical cross-linking coupled with mass spectrometry plays an important role in unravelling protein interactions, especially weak and transient ones. Moreover, cross-linking complements several structural determination approaches such as cryo-EM. Although several computational approaches are available for the annotation of spectra obtained from cross-linked peptides, there remains room for improvement. Here, we present Xilmass, a novel algorithm to identify cross-linked peptides that introduces ...[more]

PMID: 27642655

Similar Datasets

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.