PCprophet: a framework for protein complex prediction and differential analysis using proteomic data

Andrea Fossati, Chen Li, Federico Uliana, Fabian Wendt , Fabian Frommelt, Peter Sykacek, Moritz Heusel , Mahmoud Hallal, Isabell Bludau, Tümay Capraz , Peng Xue, Jiangning Song, Bernd Wollscheid, Anthony W. Purcell, Matthias Gstaiger , Ruedi Aebersold

Research output: Contribution to journalArticleResearchpeer-review

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 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.
Original languageEnglish
Number of pages11
JournalNature Methods
DOIs
Publication statusAccepted/In press - 15 Apr 2021

Cite this