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Method Article
Diogo B. Lima
Laboratory for Proteomics and Protein Engineering, Carlos Chagas Institute, Fiocruz, Paraná, Brazil
Corresponding Author
Fabio C. Gozzo
Dalton Mass Spectrometry Laboratory, University of Campinas, São Paulo, Brazil
Corresponding Author
Paulo C. Carvalho
Laboratory for Proteomics and Protein Engineering, Carlos Chagas Institute, Fiocruz, Paraná, Brazil
Corresponding Author
License:
This work is licensed under a CC BY-NC 3.0 License. Read Full License
State-of-the-art structural mass spectrometry-based proteomics is often accomplished by using cross-linkers to covalently bind two or more amino-acid groups. This strategy is complementary to classical structural biology and therefore broadens the toolset for analyzing protein and protein-protein complex structures. One of the greatest challenges in identifying cross-linked peptides in complex protein mixtures is computationally dealing with the large search space, which grows quadratically with each peptide included in the sequence database. The **Spectrum Identification Machine for Cross-Linked Peptides \(SIM-XL)** software uses an algorithm that overcomes this limitation by capitalizing on experimental features that allow it to effectively address the massive combinatorial problem at hand, and presents the results in a user-friendly manner. Thus, SIM-XL is recommended for studies dealing with protein structure and protein-protein interaction in either simple or complex protein mixtures. SIM-XL also allows the sharing of results through PRIDE by exporting them in the mzIdentML format.
Keywords
cross-linked peptide identification, cross-linked peptides, cross-linking, search engine, mass spectrometry, search tool, computational biology, structural proteomics
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The authors declare no conflicting financial interests.
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Associated Publications
10.1016/j.jprot.2015.01.013
SIM-XL: A powerful and user-friendly tool for peptide cross-linking analysis
by Diogo B. Lima, Tatiani B. de Lima, Tiago S. Balbuena, +4
Journal of Proteomics (28 January, 2015)
Method Article
Diogo B. Lima
Laboratory for Proteomics and Protein Engineering, Carlos Chagas Institute, Fiocruz, Paraná, Brazil
Corresponding Author
Fabio C. Gozzo
Dalton Mass Spectrometry Laboratory, University of Campinas, São Paulo, Brazil
Corresponding Author
Paulo C. Carvalho
Laboratory for Proteomics and Protein Engineering, Carlos Chagas Institute, Fiocruz, Paraná, Brazil
Corresponding Author
Version History
CURRENT STATUS: Posted
Version 1
Posted 13 Feb, 2015
No community comments so far
Metrics
Comments: 0
HTML Views: ...
License:
This work is licensed under a CC BY-NC 3.0 License. Read Full License
State-of-the-art structural mass spectrometry-based proteomics is often accomplished by using cross-linkers to covalently bind two or more amino-acid groups. This strategy is complementary to classical structural biology and therefore broadens the toolset for analyzing protein and protein-protein complex structures. One of the greatest challenges in identifying cross-linked peptides in complex protein mixtures is computationally dealing with the large search space, which grows quadratically with each peptide included in the sequence database. The **Spectrum Identification Machine for Cross-Linked Peptides \(SIM-XL)** software uses an algorithm that overcomes this limitation by capitalizing on experimental features that allow it to effectively address the massive combinatorial problem at hand, and presents the results in a user-friendly manner. Thus, SIM-XL is recommended for studies dealing with protein structure and protein-protein interaction in either simple or complex protein mixtures. SIM-XL also allows the sharing of results through PRIDE by exporting them in the mzIdentML format.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Loading...
Associated Publications
10.1016/j.jprot.2015.01.013
SIM-XL: A powerful and user-friendly tool for peptide cross-linking analysis
by Diogo B. Lima, Tatiani B. de Lima, Tiago S. Balbuena, +4
Journal of Proteomics (28 January, 2015)