ArticleBriefings in bioinformatics2022
Protein turnover models for LC-MS data of heavy water metabolic labeling.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A Computational Model for Determining Labeling Duration in Protein Turnover Studies Using a Single Deuterated Water Labeled Sample.Journal of the American Society for Mass Spectrometry · 2026Article
- Duplexing metabolic deuterated water-labeled samples using dimethyl labeling to estimate protein turnover rates.Communications chemistry · 2025Article
- Numbers of Exchangeable Hydrogens from LC-MS Data of Heavy Water Metabolically Labeled Samples.Journal of the American Society for Mass Spectrometry · 2024Article
- Enabling population protein dynamics through Bayesian modeling.Bioinformatics (Oxford, England) · 2024Article
- Flexible Quality Control for Protein Turnover Rates Using d2ome.International journal of molecular sciences · 2023Article
- A large-scale LC-MS dataset of murine liver proteome from time course of heavy water metabolic labeling.Scientific data · 2023Article
- Quantifying label enrichment from two mass isotopomers increases proteome coverage for in vivo protein turnover using heavy water metabolic labeling.Communications chemistry · 2023Article
- Software Tool for Visualization and Validation of Protein Turnover Rates Using Heavy Water Metabolic Labeling and LC-MS.International journal of molecular sciences · 2022Article
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Abstract
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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