Protein turnover models for LC-MS data of heavy water metabolic labeling

BRIEFINGS IN BIOINFORMATICS(2022)

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摘要
Protein turnover is vital for cellular functioning and is often associated with the pathophysiology of a variety of diseases. Metabolic labeling with heavy water followed by liquid chromatography coupled to mass spectrometry is a powerful tool to study in vivo protein turnover in high throughput and large scale. Heavy water is a cost-effective and easy to use labeling agent. It labels all nonessential amino acids. Due to its toxicity in high concentrations (20% or higher), small enrichments (8% or smaller) of heavy water are used with most organisms. The low concentration results in incomplete labeling of peptides/proteins. Therefore, the data processing is more challenging and requires accurate quantification of labeled and unlabeled forms of a peptide from overlapping mass isotopomer distributions. The work describes the bioinformatics aspects of the analysis of heavy water labeled mass spectral data, available software tools and current challenges and opportunities.
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关键词
protein turnover, evolution of deuterium-enriched mass isotopomers, nonlinear models of time course data, rate constant estimation from metabolic labeling with heavy water followed by liquid chromatography, mass spectrometry (LC-MS)
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