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Mass Spectrom Rev. 2018 Nov;37(6):738-749. doi: 10.1002/mas.21559. Epub 2018 Mar 12.

Cross-linked peptide identification: A computational forest of algorithms.

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VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium.
Department of Biochemistry, Ghent University, Ghent, Belgium.
Department of Microbiology and Immunology, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, KULeuven-University of Leuven, Leuven, Belgium.


Chemical cross-linking analyzed by mass spectrometry (XL-MS) has become an important tool in unravelling protein structure, dynamics, and complex formation. Because the analysis of cross-linked proteins with mass spectrometry results in specific computational challenges, many computational tools have been developed to identify cross-linked peptides from mass spectra and subsequently interpret the identified cross-links within their structural context. In this review, we will provide an overview of the different tools that are currently available to tackle the computational part of an XL-MS experiment. First, we give an introduction on the computational challenges encountered when processing data from a cross-linking experiment. We then discuss available tools to identify peptides that are linked by intact or MS-cleavable cross-linkers, and we provide an overview of tools to interpret cross-linked peptides in the context of protein structure. Finally, we give an outlook on data management and dissemination challenges and opportunities for cross-linking experiments.


algorithms; cross-linking; identification; mass spectrometry; proteomics


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