CoExpresso

CoExpresso analyzes protein subunit abundance profiles across human cell types to assess conservation, intrinsic structure, and co-regulation of protein complexes using ProteomicsDB mass spectrometry data and statistical scoring.

Key Features:

• Abundance Profile Comparison: Compares protein subunit abundance profiles within known complexes across multiple human cell types to identify variation and consistency in expression levels.
• Randomization Methods and Statistical Scoring Algorithms: Implements and evaluates multiple randomization methods and statistical scoring algorithms to determine significance of concurrent abundance profiles within complexes.
• Insights into Composition Conservation: Analyzes protein abundance data to evaluate conservation of complex composition across different human cell types.
• Identification of Intrinsic Structures: Detects intrinsic structures within complex behavior and highlights proteins pivotal to orchestrating complex functions.
• Functional Group Analysis: Investigates common abundance profiles within arbitrary protein groups to explore potential co-regulation and the capacity to form functional groups such as protein complexes.

Scientific Applications:

• Translational and post-translational regulation studies: Enables investigation of mechanisms that cause differences between gene transcription and protein abundances, including selective degradation of complex subunits.
• Characterization of complex composition across cell states: Produces comprehensive abundance maps from mass spectrometry data to characterize complex composition, behavior, and abundance across different cell populations and states.
• Prediction of functional states and targeting strategies: Provides insights into compositional and structural diversity of protein complexes to inform prediction of functional states and strategies to manipulate or target complexes.

Methodology:

Uses ProteomicsDB mass spectrometry data to generate protein abundance profiles across multiple cell types and applies randomization methods and statistical scoring algorithms within a statistical framework to assess significance of observed patterns.