PCprophet

PCprophet predicts protein complex composition and performs differential analysis of protein complexes and protein-protein interactions from cofractionation mass spectrometry data, particularly size exclusion chromatography-sequential window acquisition of all theoretical mass spectrometry (SEC-SWATH-MS), to identify altered interactions across experimental conditions.

Key Features:

• Protein complex prediction: Predicts protein complex composition from cofractionation mass spectrometry datasets.
• Differential analysis: Detects changes in complex abundance and composition between experimental conditions.
• SEC-SWATH-MS support: Leverages size exclusion chromatography-sequential window acquisition of all theoretical mass spectrometry (SEC-SWATH-MS) data.
• Bayesian framework: Implements a Bayesian framework to analyze altered protein-protein interactions between samples.
• Quantitative tracking: Enables quantitative tracking of protein complexes and their dynamic interactions across experiments.
• Dataset versatility: Applicable to any cofractionation MS dataset irrespective of the separation or quantitative liquid chromatography-MS workflow.
• Improved accuracy: Provides more accurate predictions of complex alterations compared to state-of-the-art approaches.

Scientific Applications:

• Complex composition inference: Infers protein complex membership from SEC-SWATH-MS cofractionation profiles.
• Interaction perturbation detection: Identifies altered protein-protein interactions between conditions.
• Dynamic complex analysis: Studies temporal or condition-specific dynamics of protein complexes.
• Comparative cofractionation proteomics: Applies across diverse cofractionation MS datasets and LC-MS workflows for comparative analyses.

Methodology:

Operates on cofractionation mass spectrometry data, notably SEC-SWATH-MS, using a Bayesian computational framework to predict protein complex composition and perform differential analysis of protein-protein interactions.