PROMISed

PROMISed analyzes co-fractionation mass spectrometry (CF-MS) data from untargeted proteomics or metabolomics to identify and characterize protein-protein and protein-metabolite complexes and their altered elution profiles across conditions.

Key Features:

• Data Pre-processing and Normalization: Pre-processes raw fractionation profiles and applies normalization procedures to standardize datasets for comparative analyses.
• Deconvolution of Fractionation Profiles: Deconvolutes complex fractionation profiles into distinct peaks for identification of individual molecular components.
• Identification of Co-eluting Pairs: Detects co-eluting protein-protein and protein-metabolite pairs using user-defined correlation methods.
• Co-fractionation Network Analysis: Constructs and analyzes co-fractionation networks to explore molecular interaction relationships and detect altered elution profiles indicative of interaction changes.
• Comparative Dataset Analysis: Compares multiple CF-MS datasets (e.g., different environmental conditions) to identify consistent co-eluting pairs or condition-specific interactions.
• Data Visualization: Provides multiple visualization options for interpreting CF-MS-derived interaction and elution profile data.

Scientific Applications:

• Discovery of molecular complexes: Identification of novel protein-protein and protein-metabolite complexes from CF-MS data.
• Comparative interactomics: Detection of condition-specific changes in interaction status through altered elution profiles across datasets.
• Support for experimental follow-up: Prioritization of candidate complexes for targeted experimental validation and mechanistic studies.

Methodology:

Computational steps explicitly include data pre-processing and normalization of fractionation profiles, deconvolution of profiles into distinct peaks, identification of co-eluting pairs via user-defined correlation methods, co-fractionation network analysis, and comparative analysis of multiple CF-MS datasets.