Software Tool for Visualization and Validation of Protein Turnover Rates Using Heavy Water Metabolic Labeling and LC-MS

Deberneh, Henock M.; Sadygov, Rovshan G.

doi:10.3390/ijms232314620

Cited by 5 publications

(6 citation statements)

References 21 publications

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“…The choice stems from the fact that the isotope profiles of peptides change because of stable isotope incorporation. The alignment algorithm is incorporated into d2ome via the graphical user interface . It is also available as a standalone tool from GitHub, .…”

Section: Methodsmentioning

confidence: 99%

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Retention Time Alignment for Protein Turnover Studies Using Heavy Water Metabolic Labeling

Deberneh

Sadygov

2023

J. Proteome Res.

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Retention time (RT) alignment has been important for robust protein identification and quantification in proteomics. In data-dependent acquisition mode, whereby the precursor ions are semistochastically chosen for fragmentation in MS/MS, the alignment is used in an approach termed matched between runs (MBR). MBR transfers peptides, which were fragmented and identified in one experiment, to a replicate experiment where they were not identified. Before the MBR transfer, the RTs of experiments are aligned to reduce the chance of erroneous transfers. Despite its widespread use in other areas of quantitative proteomics, RT alignment has not been applied in data analyses for protein turnover using an atom-based stable isotope-labeling agent such as metabolic labeling with deuterium oxide, D2O. Deuterium incorporation changes isotope profiles of intact peptides in full scans and their fragment ions in tandem mass spectra. It reduces the peptide identification rates in current database search engines. Therefore, the MBR becomes more important. Here, we report on an approach to incorporate RT alignment with peptide quantification in studies of proteome turnover using heavy water metabolic labeling and LC-MS. The RT alignment uses correlation-optimized time warping. The alignment, followed by the MBR, improves labeling time point coverage, especially for long labeling durations.

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Section: Methodsmentioning

confidence: 99%

“…The alignment algorithm is incorporated into d2ome via the graphical user interface. 36 It is also available as a standalone tool from GitHub, https://github. com/rgsadygov/ChromatographicAlignment.…”

Section: Approach To Rt Alignmentmentioning

confidence: 99%

Retention Time Alignment for Protein Turnover Studies Using Heavy Water Metabolic Labeling

Deberneh

Sadygov

2023

J. Proteome Res.

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“…Proteins are continuously synthesized and degraded to maintain the proteome (i.e., proteostasis) appropriate for the cellular environment . Mass spectrometry-based proteomics of metabolically stable-isotope labeled samples is a relatively high-throughput approach to studying turnover rates of individual proteins in vivo . − Among the stable-isotope labeling agents, − deuterated water (D 2 O) offers several advantageous properties such as (1) easy administration of D 2 O, as it can be given orally as drinking water, (2) a low dose of D 2 O is safe for consumption, (3) it is relatively inexpensive, and (4) it freely and rapidly equilibrates with total body water in all organs, organelles, and cells within an organism . For in vivo labeling, the initial bolus of deuterated water (also referred to as heavy water) is followed by the provision of deuterium-enriched (typically around 8%) drinking water for a predetermined period.…”

Section: Introductionmentioning

confidence: 99%

Numbers of Exchangeable Hydrogens from LC–MS Data of Heavy Water Metabolically Labeled Samples

Deberneh,

Taylor,

Borowik

et al. 2024

J. Am. Soc. Mass Spectrom.

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Labeling with deuterium oxide (D 2 O) has emerged as one of the preferred approaches for measuring the synthesis of individual proteins in vivo. In these experiments, the synthesis rates of proteins are determined by modeling mass shifts in peptides during the labeling period. This modeling depends on a theoretical maximum enrichment determined by the number of labeling sites (N EH ) of each amino acid in the peptide sequence. Currently, N EH is determined from one set of published values. However, it has been demonstrated that N EH can differ between species and potentially tissues. The goal of this work was to determine the number of N EH for each amino acid within a given experiment to capture the conditions unique to that experiment. We used four methods to compute the N EH values. To test these approaches, we used two publicly available data sets. In a de novo approach, we compute N EH values and the label enrichment from the abundances of three mass isotopomers. The other three methods use the complete isotope profiles and body water enrichment in deuterium as an input parameter. They determine the N EH values by (1) minimizing the residual sum of squares, (2) from the mole percent excess of labeling, and (3) the time course profile of the depletion of the relative isotope abundance of monoisotope. In the test samples, the method using residual sum of squares performed the best. The methods are implemented in a tool for determining the N EH for each amino acid within a given experiment to use in the determination of protein synthesis rates using D 2 O.

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“…Several software tools [1,8,10,11] have been developed to automate the estimation of protein turnover rate from LC-MS experiments, including d2ome [9,12]. d2ome is a powerful tool for protein turnover estimation from deuterium-labeled LC-MS experiments.…”

Section: Introductionmentioning

confidence: 99%

Flexible Quality Control for Protein Turnover Rates Using d2ome

Deberneh,

Sadygov

2023

IJMS

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Bioinformatics tools are used to estimate in vivo protein turnover rates from the LC-MS data of heavy water labeled samples in high throughput. The quantification includes peak detection and integration in the LC-MS domain of complex input data of the mammalian proteome, which requires the integration of results from different experiments. The existing software tools for the estimation of turnover rate use predefined, built-in, stringent filtering criteria to select well-fitted peptides and determine turnover rates for proteins. The flexible control of filtering and quality measures will help to reduce the effects of fluctuations and interferences to the signals from target peptides while retaining an adequate number of peptides. This work describes an approach for flexible error control and filtering measures implemented in the computational tool d2ome for automating protein turnover rates. The error control measures (based on spectral properties and signal features) reduced the standard deviation and tightened the confidence intervals of the estimated turnover rates.

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Software Tool for Visualization and Validation of Protein Turnover Rates Using Heavy Water Metabolic Labeling and LC-MS

Cited by 5 publications

References 21 publications

Retention Time Alignment for Protein Turnover Studies Using Heavy Water Metabolic Labeling

Retention Time Alignment for Protein Turnover Studies Using Heavy Water Metabolic Labeling

Numbers of Exchangeable Hydrogens from LC–MS Data of Heavy Water Metabolically Labeled Samples

Flexible Quality Control for Protein Turnover Rates Using d2ome

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