multiFLEX-LF

multiFLEX-LF quantifies modification stoichiometries of peptide precursors in label-free proteomics datasets to analyze protein modification dynamics.

Key Features:

• Extension of FLEXIQuant: Extends the FLEXIQuant approach to operate on label-free discovery proteomics with a focus on modified peptide precursors.
• Precursor-Centric Analysis: Operates in a precursor-centric manner to enable comprehensive analysis across entire label-free datasets without preselection of proteins.
• Quantification Methodology: Quantifies modification extents by comparing observed intensities against expected values of unmodified precursors and reporting extents relative to within-study references.
• Robust Linear Regression: Uses robust linear regression to derive accurate modification extents.
• Hierarchical Clustering for Dynamics Analysis: Hierarchically clusters peptide precursors by relative modification scores to identify coregulated modifications and sequence modification events.
• DIA and Time-Series Compatibility: Handles large-scale time-series investigations and has been applied to data-independent acquisition (DIA) datasets, including analysis of the anaphase-promoting complex/cyclosome (APC/C) during mitosis.

Scientific Applications:

• Time-series dynamics analysis: Analyzing temporal dynamics of protein modifications in time-series proteomics experiments.
• Case-control differential modification: Detecting and quantifying differentially modified peptide precursors in case-control studies.
• Cell cycle regulation: Investigating modification dynamics of cell cycle regulators, exemplified by APC/C during mitosis.
• Signal transduction and disease mechanisms: Studying modification-mediated regulation in signal transduction and disease-associated processes.
• Regulatory network and biomarker discovery: Supporting identification of coregulated modifications for regulatory network mapping and modification-based biomarker discovery.

Methodology:

Focuses on modified peptide precursors, compares observed precursor intensities to expected unmodified precursor values, calculates modification extents relative to within-study references, applies robust linear regression to estimate extents, and performs hierarchical clustering on relative modification scores.