A Computational Model for Determining Labeling Duration in Protein Turnover Studies Using a Single Deuterated Water...

Area of Science:

• Proteomics
• Biochemistry
• Computational Biology

Background:

• Metabolic labeling with deuterated water is crucial for in vivo protein turnover rate determination.
• Current methods are resource-intensive, requiring extensive labeling time and multiple samples.
• Developing efficient computational approaches is vital to reduce experimental demands.

Purpose of the Study:

• To develop and evaluate a computational method for estimating protein turnover rates using only two samples (one unlabeled, one labeled).
• To define the optimal labeling duration range for a two-sample approach.
• To create a user-friendly tool for applying this method.

Main Methods:

• Evaluation of linear and logarithmic models for protein turnover rate estimation.
• Analysis of key factors influencing labeling duration (exchangeable hydrogens, deuterium enrichment, turnover rate).
• Integration of derived inequalities into an R Shiny App for practical application.

Main Results:

• A two-sample approach was developed to estimate protein turnover rates from limited labeling data.
• Mathematical inequalities were established to define the appropriate labeling duration for peptides.
• The method was successfully applied to four murine tissues, analyzing over 60% of the liver proteome.

Conclusions:

• The two-sample computational method significantly reduces the resource intensity of protein turnover studies.
• Adjusting labeling duration based on tissue proteome turnover enhances analytical efficiency.
• This approach provides a valuable tool for large-scale proteomic analysis in vivo.