Protein turnover models for LC-MS data of heavy water metabolic labeling.
Rovshan G SadygovPublished in: Briefings in bioinformatics (2022)
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.
Keyphrases
- amino acid
- mass spectrometry
- liquid chromatography
- high throughput
- electronic health record
- bone mineral density
- protein protein
- big data
- oxidative stress
- optical coherence tomography
- pet imaging
- small molecule
- machine learning
- postmenopausal women
- artificial intelligence
- deep learning
- high performance liquid chromatography
- capillary electrophoresis
- dual energy