Cross‐linked peptide identification: A computational forest of algorithms. Issue 6 (12th March 2018)

• Record Type: Journal Article
• Authors: Yılmaz, Şule; Shiferaw, Genet A.; Rayo, Josep; Economou, Anastassios; Martens, Lennart; Vandermarliere, Elien
• Is Part Of: Mass spectrometry reviews. Volume 37:Issue 6(2018)
• Page Start: 738
• Page End: 749
• Publication Date: 2018-03-12
• Subjects: algorithms -- cross‐linking -- identification -- mass spectrometry -- proteomics; Mass spectrometry -- Periodicals; 543
• DOI: 10.1002/mas.21559
• Languages: English
• ISSNs: 0277-7037

Abstract

Abstract : 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.