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Published on: April 6, 2022
A Computational Model for Determining Labeling Duration in Protein Turnover Studies Using a Single Deuterated Water
Henock M Deberneh1, Rovshan G Sadygov1
1Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, 301 University of Blvd, Galveston, Texas 77555, United States.
This study introduces a computational method using two samples to estimate protein turnover rates in vivo, reducing the resource demands of traditional metabolic labeling experiments. The approach accurately analyzes a significant portion of proteomes, aiding in efficient protein dynamics research.
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.
