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Anal Chem. 2016 Oct 18;88(20):9949-9957. Epub 2016 Sep 28.

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

Author information

Medical Biotechnology Center, VIB , 9000 Ghent, Belgium.
Department of Biochemistry, Ghent University , 9000 Ghent, Belgium.
Bioinformatics Institute Ghent, Ghent University , 9000 Ghent, Belgium.
Department of Biochemistry and Functional Proteomics, Institute of Biology II, Faculty of Biology, University of Freiburg , 79104 Freiburg, Germany.
BIOSS Centre for Biological Signaling Studies, University of Freiburg , 79104 Freiburg, Germany.
Centre of Excellence for Integrated Approaches in Chemistry and Biology of Proteins , Jamova Cesta 39, 1000 Ljubljana, Slovenia.
Faculty of Medicine, University of Ljubljana , 1000 Ljubljana, Slovenia.
KU Leuven-University of Leuven , Department of Microbiology and Immunology, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, 3000 Leuven, Belgium.
Institute of Molecular Biology and Biotechnology-FoRTH and Department of Biology, University of Crete , Iraklio, 71100 Crete, Greece.


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 two new concepts: (i) the cross-linked peptides are represented in the search database such that the cross-linking sites are explicitly encoded, and (ii) the scoring function derived from the Andromeda algorithm was adapted to score against a theoretical tandem mass spectrometry (MS/MS) spectrum that contains the peaks from all possible fragment ions of a cross-linked peptide pair. The performance of Xilmass was evaluated against the recently published Kojak and the popular pLink algorithms on a calmodulin-plectin complex data set, as well as three additional, published data sets. The results show that Xilmass typically had the highest number of identified distinct cross-linked sites and also the highest number of predicted cross-linked sites.

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