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

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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.

Original languageEnglish (US)
Article numberbbab598
JournalBriefings in Bioinformatics
Volume23
Issue number2
DOIs
StatePublished - Mar 1 2022

Keywords

  • evolution of deuterium-enriched mass isotopomers
  • nonlinear models of time course data
  • protein turnover
  • rate constant estimation from metabolic labeling with heavy water followed by liquid chromatography - mass spectrometry (LC-MS)

ASJC Scopus subject areas

  • Information Systems
  • Molecular Biology

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