Literature DB >> 35062023

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

Rovshan G Sadygov1.   

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.
© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

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)

Mesh:

Substances:

Year:  2022        PMID: 35062023      PMCID: PMC8921656          DOI: 10.1093/bib/bbab598

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  84 in total

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