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Cross-linked peptide identification : a computational forest of algorithms

(2018) MASS SPECTROMETRY REVIEWS. 37(6). p.738-749
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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.
Keywords
algorithms, cross-linking, identification, mass spectrometry, proteomics, MASS-SPECTROMETRIC ANALYSIS, STRUCTURAL-ANALYSIS, PROTEIN-STRUCTURE, LINKING/MASS SPECTROMETRY, LARGE-SCALE, AUTOMATED ASSIGNMENT, DISULFIDE BONDS, TANDEM, VISUALIZATION, SOFTWARE

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Chicago
Yilmaz-Rumpf, Sule, Genet Abay Shiferaw, Josep Rayo, Anastassios Economou, Lennart Martens, and Elien Vandermarliere. 2018. “Cross-linked Peptide Identification : a Computational Forest of Algorithms.” Mass Spectrometry Reviews 37 (6): 738–749.
APA
Yilmaz-Rumpf, S., Shiferaw, G. A., Rayo, J., Economou, A., Martens, L., & Vandermarliere, E. (2018). Cross-linked peptide identification : a computational forest of algorithms. MASS SPECTROMETRY REVIEWS, 37(6), 738–749.
Vancouver
1.
Yilmaz-Rumpf S, Shiferaw GA, Rayo J, Economou A, Martens L, Vandermarliere E. Cross-linked peptide identification : a computational forest of algorithms. MASS SPECTROMETRY REVIEWS. 2018;37(6):738–49.
MLA
Yilmaz-Rumpf, Sule et al. “Cross-linked Peptide Identification : a Computational Forest of Algorithms.” MASS SPECTROMETRY REVIEWS 37.6 (2018): 738–749. Print.
@article{8583222,
  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.},
  author       = {Yilmaz-Rumpf, Sule and Shiferaw, Genet Abay and Rayo, Josep and Economou, Anastassios and Martens, Lennart and Vandermarliere, Elien},
  issn         = {0277-7037},
  journal      = {MASS SPECTROMETRY REVIEWS},
  keywords     = {algorithms,cross-linking,identification,mass spectrometry,proteomics,MASS-SPECTROMETRIC ANALYSIS,STRUCTURAL-ANALYSIS,PROTEIN-STRUCTURE,LINKING/MASS SPECTROMETRY,LARGE-SCALE,AUTOMATED ASSIGNMENT,DISULFIDE BONDS,TANDEM,VISUALIZATION,SOFTWARE},
  language     = {eng},
  number       = {6},
  pages        = {738--749},
  title        = {Cross-linked peptide identification : a computational forest of algorithms},
  url          = {http://dx.doi.org/10.1002/mas.21559},
  volume       = {37},
  year         = {2018},
}

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