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Cheetah-MS: a web server to model protein complexes using tandem cross-linking mass spectrometry data

Khakzad, Hamed ; Happonen, Lotta LU ; Malmström, Johan LU orcid and Malmström, Lars LU (2021) In Bioinformatics 37(24). p.4871-4872
Abstract

SUMMARY: Protein-protein interactions (PPI) are central in many biological processes but difficult to characterize, especially in complex, unfractionated samples. Chemical cross-linking combined with mass spectrometry (MS) and computational modeling is gaining recognition as a viable tool in protein interaction studies. Here, we introduce Cheetah-MS, a web server for predicting the PPIs in a complex mixture of samples. It combines the capability and sensitivity of MS to analyze complex samples with the power and resolution of protein-protein docking. It produces the quaternary structure of the PPI of interest by analyzing tandem MS/MS data (also called MS2). Combining MS analysis and modeling increases the sensitivity and, importantly,... (More)

SUMMARY: Protein-protein interactions (PPI) are central in many biological processes but difficult to characterize, especially in complex, unfractionated samples. Chemical cross-linking combined with mass spectrometry (MS) and computational modeling is gaining recognition as a viable tool in protein interaction studies. Here, we introduce Cheetah-MS, a web server for predicting the PPIs in a complex mixture of samples. It combines the capability and sensitivity of MS to analyze complex samples with the power and resolution of protein-protein docking. It produces the quaternary structure of the PPI of interest by analyzing tandem MS/MS data (also called MS2). Combining MS analysis and modeling increases the sensitivity and, importantly, facilitates the interpretation of the results.

AVAILABILITY: Cheetah-MS is freely available as a web server at https://www.txms.org.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Bioinformatics
volume
37
issue
24
pages
2 pages
publisher
Oxford University Press
external identifiers
  • pmid:34128979
  • scopus:85121988793
ISSN
1367-4803
DOI
10.1093/bioinformatics/btab449
language
English
LU publication?
yes
id
39eb0d0b-5026-46d2-b062-17b1c2defdf9
date added to LUP
2021-06-21 08:25:21
date last changed
2025-10-14 13:19:54
@article{39eb0d0b-5026-46d2-b062-17b1c2defdf9,
  abstract     = {{<p>SUMMARY: Protein-protein interactions (PPI) are central in many biological processes but difficult to characterize, especially in complex, unfractionated samples. Chemical cross-linking combined with mass spectrometry (MS) and computational modeling is gaining recognition as a viable tool in protein interaction studies. Here, we introduce Cheetah-MS, a web server for predicting the PPIs in a complex mixture of samples. It combines the capability and sensitivity of MS to analyze complex samples with the power and resolution of protein-protein docking. It produces the quaternary structure of the PPI of interest by analyzing tandem MS/MS data (also called MS2). Combining MS analysis and modeling increases the sensitivity and, importantly, facilitates the interpretation of the results.</p><p>AVAILABILITY: Cheetah-MS is freely available as a web server at https://www.txms.org.</p><p>SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.</p>}},
  author       = {{Khakzad, Hamed and Happonen, Lotta and Malmström, Johan and Malmström, Lars}},
  issn         = {{1367-4803}},
  language     = {{eng}},
  month        = {{06}},
  number       = {{24}},
  pages        = {{4871--4872}},
  publisher    = {{Oxford University Press}},
  series       = {{Bioinformatics}},
  title        = {{Cheetah-MS: a web server to model protein complexes using tandem cross-linking mass spectrometry data}},
  url          = {{http://dx.doi.org/10.1093/bioinformatics/btab449}},
  doi          = {{10.1093/bioinformatics/btab449}},
  volume       = {{37}},
  year         = {{2021}},
}