PCprophet : a framework for protein complex prediction and differential analysis using proteomic data
(2021) In Nature Methods 18(5). p.520-527- Abstract
Despite the availability of methods for analyzing protein complexes, systematic analysis of complexes under multiple conditions remains challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise. However, most approaches for interpreting cofractionation datasets to yield complex composition and rearrangements between samples depend considerably on protein–protein interaction inference. We introduce PCprophet, a toolkit built on size exclusion chromatography–sequential window acquisition of all theoretical mass spectrometry (SEC-SWATH-MS) data to predict protein complexes and characterize their changes across experimental conditions. We demonstrate... (More)
Despite the availability of methods for analyzing protein complexes, systematic analysis of complexes under multiple conditions remains challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise. However, most approaches for interpreting cofractionation datasets to yield complex composition and rearrangements between samples depend considerably on protein–protein interaction inference. We introduce PCprophet, a toolkit built on size exclusion chromatography–sequential window acquisition of all theoretical mass spectrometry (SEC-SWATH-MS) data to predict protein complexes and characterize their changes across experimental conditions. We demonstrate improved performance of PCprophet over state-of-the-art approaches and introduce a Bayesian approach to analyze altered protein–protein interactions across conditions. We provide both command-line and graphical interfaces to support the application of PCprophet to any cofractionation MS dataset, independent of separation or quantitative liquid chromatography–MS workflow, for the detection and quantitative tracking of protein complexes and their physiological dynamics.
(Less)
- author
- organization
- publishing date
- 2021
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Nature Methods
- volume
- 18
- issue
- 5
- pages
- 8 pages
- publisher
- Nature Publishing Group
- external identifiers
-
- scopus:85104600643
- pmid:33859439
- ISSN
- 1548-7091
- DOI
- 10.1038/s41592-021-01107-5
- language
- English
- LU publication?
- yes
- id
- c497e6d9-3b8e-447a-a326-0b5af020ceea
- date added to LUP
- 2022-01-13 12:46:12
- date last changed
- 2024-09-22 08:56:02
@article{c497e6d9-3b8e-447a-a326-0b5af020ceea, abstract = {{<p>Despite the availability of methods for analyzing protein complexes, systematic analysis of complexes under multiple conditions remains challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise. However, most approaches for interpreting cofractionation datasets to yield complex composition and rearrangements between samples depend considerably on protein–protein interaction inference. We introduce PCprophet, a toolkit built on size exclusion chromatography–sequential window acquisition of all theoretical mass spectrometry (SEC-SWATH-MS) data to predict protein complexes and characterize their changes across experimental conditions. We demonstrate improved performance of PCprophet over state-of-the-art approaches and introduce a Bayesian approach to analyze altered protein–protein interactions across conditions. We provide both command-line and graphical interfaces to support the application of PCprophet to any cofractionation MS dataset, independent of separation or quantitative liquid chromatography–MS workflow, for the detection and quantitative tracking of protein complexes and their physiological dynamics.</p>}}, author = {{Fossati, Andrea and Li, Chen and Uliana, Federico and Wendt, Fabian and Frommelt, Fabian and Sykacek, Peter and Heusel, Moritz and Hallal, Mahmoud and Bludau, Isabell and Capraz, Tümay and Xue, Peng and Song, Jiangning and Wollscheid, Bernd and Purcell, Anthony W. and Gstaiger, Matthias and Aebersold, Ruedi}}, issn = {{1548-7091}}, language = {{eng}}, number = {{5}}, pages = {{520--527}}, publisher = {{Nature Publishing Group}}, series = {{Nature Methods}}, title = {{PCprophet : a framework for protein complex prediction and differential analysis using proteomic data}}, url = {{http://dx.doi.org/10.1038/s41592-021-01107-5}}, doi = {{10.1038/s41592-021-01107-5}}, volume = {{18}}, year = {{2021}}, }