Abstract
Metabolic labeling with deuterated water is used in combination with liquid-chromatography coupled with mass spectrometry to study the turnover rates of individual proteins in vivo. This technique and bioinformatics tools for data analysis quantify the turnover rates of thousands of proteins. Turnover rates change during organismal growth and respond to alterations in the environment and diet. The accurate and statistically significant determination of the turnover rate changes of a protein depend on the variations in the turnover rates of the peptides of the protein. One of the systematic factors contributing to this variability is the dependence of the turnover rates on the number of exchangeable hydrogens of the peptides. This variability (by reducing the statistical power) reduces biological interpretability. Here, we propose a computational approach to eliminate the dependence of the turnover rates on the number of exchangeable hydrogens. This approach enhances the accuracy of turnover rate estimation and may help to support more accurate assessments of biological dynamics and disease mechanisms.