JUMPt

JUMPt estimates protein turnover rates from mass spectrometry (MS) data using an ordinary differential equation (ODE)-based model that accounts for amino acid recycling during pulse stable isotope labeling by amino acids in cell culture (pSILAC) experiments.

Key Features:

• Novel ODE-Based Model: Employs an ODE-based model that simultaneously fits the kinetics of unlabeled free Lys and protein-bound Lys from individual proteins to calculate protein half-lives.
• Amino Acid Recycling Consideration: Incorporates native amino acids recycled from protein degradation to correct delayed labeling effects in multicellular animals during pSILAC.
• Flexible Data Input Settings: Supports analysis across datasets of varying completeness through multiple input settings.
• Large-Scale Proteome Analysis: Analyzes turnover rates for thousands of proteins and has been validated on brain and liver tissues from pSILAC-labeled mice.
• Identification of Long-Lived Proteins: Detects long-lived proteins and their enrichment in cellular components such as integral membrane, myelin sheath, and mitochondrion in brain tissue.

Scientific Applications:

• Neuroscience: Quantifies protein turnover in brain tissue to study protein dynamics and longevity in neural systems.
• Metabolism Studies: Measures protein half-lives in liver tissue to investigate metabolic protein stability and turnover.
• Cell Biology: Examines protein stability and degradation dynamics across different cellular compartments and environments.

Methodology:

Fits an ordinary differential equation (ODE)-based model to MS-derived kinetics of unlabeled free Lys and protein-bound Lys from individual proteins, simultaneously estimating amino acid recycling effects and protein half-lives.