# Novel computational methods for in vivo proteome dynamics estimation using heavy water metabolic labeling and LC-MS

> **NIH NIH R01** · UNIVERSITY OF TEXAS MED BR GALVESTON · 2021 · $355,500

## Abstract

SUMMARY
 There is a fundamental need for computational methods that can increase proteome coverage for in vivo
studies of proteome dynamics using metabolic labeling with heavy water and LC-MS. Currently, only 30-40% of
all quantified peptides are utilized to determine proteome dynamics, as the rest are filtered due to poor goodness-
of-fit (Pearson correlation, residual sum of squares) between experimental data and its theoretical fit. The long-
term goal is to develop methods for inferring the causative effects of protein turnover changes on the
development of diseases. The objectives of this application are to develop computational methods to estimate
protein turnover using abundances of only two mass isotopomers, estimate the number of exchangeable
hydrogens in a peptide from three mass isotopomers, and use chromatogram alignment to quantify label
incorporation into peptides whose elution profiles have not been sampled in MS2. Current methods for estimation
of protein turnover use time-course of relative abundance (RA) depletion of the monoisotopic peak of a peptide,
as determined from a normalization of the complete isotope profile of the peptide in MS1. Thus, only peptides
identified in MS2 are used in quantification of label incorporation. Determination of the RA requires accurate
quantification of all isotopomers (≤ six) of a peptide. In complex samples, contamination of at least one of the
isotopomers by a co-eluting species is high. The model of deuterium incorporation into a peptide is dependent
on the number of exchangeable hydrogens, NEH. NEH values have been accurately determined only for a mouse.
 Based on preliminary data, three specific aims will be pursued to resolve the methodological issues: 1)
Develop, test, and validate bioinformatics solutions to determine degradation rate constant using two mass
isotopomers; 2) Develop, test, and validate computational methods to estimate the number of exchangeable
hydrogens from three mass isotopomers; and 3) Develop bioinformatics solutions to address the missing data
problem in the presence of metabolic labeling. We derived two new equations relating the time-course of raw
abundances of three mass isotopomers in metabolic labeling. The rationale for Aim 1 is that the equation relating
the raw abundances of two mass isotopomers can be used to estimate label quantification from the raw
abundances of only two mass isotopomers. The rationale for Aim 2 is that the two equations for three mass
isotopomers can be used to estimate the NEH values. Aim 3 uses mutual information between the
chromatographic profiles to obtain a time-warping function. The rationale is that mutual information is a better-
suited criterium for estimation of the non-linear relationship between profiles of a peptide at different timepoints
of metabolic labeling.
Aims 1 and 3 will provide bioinformatics solutions that increase proteome coverage in heavy water labeling and
LC-MS experiments. The implementation of Aim 2 wil...

## Key facts

- **NIH application ID:** 10264154
- **Project number:** 5R01GM112044-06
- **Recipient organization:** UNIVERSITY OF TEXAS MED BR GALVESTON
- **Principal Investigator:** Rovshan G Sadygov
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $355,500
- **Award type:** 5
- **Project period:** 2015-04-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10264154

## Citation

> US National Institutes of Health, RePORTER application 10264154, Novel computational methods for in vivo proteome dynamics estimation using heavy water metabolic labeling and LC-MS (5R01GM112044-06). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10264154. Licensed CC0.

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