Software Tool for Visualization and Validation of Protein Turnover Rates Using Heavy Water Metabolic Labeling and LC-MS.
Henock M DebernehRovshan G SadygovPublished in: International journal of molecular sciences (2022)
Metabolic stable isotope labeling followed by liquid chromatography coupled with mass spectrometry (LC-MS) is a powerful tool for in vivo protein turnover studies of individual proteins on a large scale and with high throughput. Turnover rates of thousands of proteins from dozens of time course experiments are determined by data processing tools, which are essential components of the workflows for automated extraction of turnover rates. The development of sophisticated algorithms for estimating protein turnover has been emphasized. However, the visualization and annotation of the time series data are no less important. The visualization tools help to validate the quality of the model fits, their goodness-of-fit characteristics, mass spectral features of peptides, and consistency of peptide identifications, among others. Here, we describe a graphical user interface (GUI) to visualize the results from the protein turnover analysis tool, d2ome, which determines protein turnover rates from metabolic D 2 O labeling followed by LC-MS. We emphasize the specific features of the time series data and their visualization in the GUI. The time series data visualized by the GUI can be saved in JPEG format for storage and further dissemination.
Keyphrases
- bone mineral density
- mass spectrometry
- high throughput
- liquid chromatography
- protein protein
- electronic health record
- amino acid
- machine learning
- big data
- binding protein
- deep learning
- postmenopausal women
- body composition
- data analysis
- magnetic resonance
- small molecule
- magnetic resonance imaging
- single cell
- artificial intelligence
- rna seq
- high resolution
- high resolution mass spectrometry
- ms ms
- electron microscopy