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Protein turnover estimation from fragment ions and precursor enrichment in heavy water labeled LC-MS experiments

Award Number: R01GM149762
ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/10/2024
PERIOD OF PERFORMANCE END DATE: 08/31/2028

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Protein turnover estimation from fragment ions and precursor enrichment in heavy water labeled LC-MS experiments - SUMMARY Metabolic labeling with heavy water followed by LC-MS quantifies the turnover of thousands of proteins in vivo. As a cost-effective and easy-to-use stable isotope labeling tracer, heavy water has been used to study the turnover of proteins in the heart, liver, kidney, blood, and brain of various model animals such as mice, rats, dogs, and fish. Since the deuterium in heavy water is incorporated into all non-essential amino acids, virtually all peptides are labeled. However, despite the ubiquitous nature of heavy water labeling, only 30-40% of all quantified peptides are useful for protein turnover rate estimation. Most of the mass spectral data is filtered out due to the low goodness-of-fit characteristics (such as the coefficient of determination, R2). Our long-term goal is to develop a unified dynamics of biomolecules (proteins, lipids, and metabolites) using heavy water labeling and LC-MS-MS/MS to study changes in diseases. The objective of this project is to develop techniques to increase the throughput, statistical power, and accuracy of protein turnover estimation. To achieve our objectives, we will pursue two Aims. In the first Aim, we employ a new approach to estimate protein turnover from isotope labeling of fragment ions in tandem mass spectra. Unlike the intact peptide ions (used in current bioinformatics techniques), the fragment ions are less affected by space-charging, limited dynamic range, and overlapping profiles caused by co-eluting contaminants. However, new techniques need to be developed and applied to estimate label enrichment from truncated isotope distributions observed in tandem mass spectra. In addition, a statistical normalization modeling will be required to account for the limited isolation width of peptides chosen for fragmentation. In the second Aim, we develop a model of label incorporation which uses two pools of amino acids and accounts for the individual labeling kinetics of each amino acid. The traditional methods of estimation of label incorporation assume a single amino acid pool – newly synthesized, labeled amino acids. However, it has been observed that peptide rates are dependent on the amino acid composition. The labeling kinetics of each amino acid is dependent on its biogenesis pathway. The proposed computational tools will improve the accuracy of protein turnover estimations. They will increase the depth of proteome coverage and statistical power in the studies of in vivo protein turnover using heavy water metabolic labeling and LC-MS.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $389,921 )
2026	2026	UNIVERSITY OF TEXAS MEDICAL BRANCH AT GALVESTON	301 UNIVERSITY BLVD	GALVESTON	TX	77555	GALVESTON	USA	Biomedical Research and Research Training	000	3	8/25/2026	NON-COMPETING CONTINUATION	$389,921
														Subtotal = $389,921

Issue Date FY: 2025 ( Subtotal = $372,285 )
2025	2025	UNIVERSITY OF TEXAS MEDICAL BRANCH AT GALVESTON	301 UNIVERSITY BLVD	GALVESTON	TX	77555	GALVESTON	USA	Biomedical Research and Research Training	000	2	8/21/2025	NON-COMPETING CONTINUATION	$372,285
														Subtotal = $372,285

Issue Date FY: 2024 ( Subtotal = $387,375 )
2024	2024	UNIVERSITY OF TEXAS MEDICAL BRANCH AT GALVESTON	301 UNIVERSITY BLVD	GALVESTON	TX	77555	GALVESTON	USA	Biomedical Research and Research Training	000	1	9/9/2024	NEW	$387,375
														Subtotal = $387,375

Grand Total All Awards = $1,149,581

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Protein turnover estimation from fragment ions and precursor enrichment in heavy water labeled LC-MS experiments

Award Number: R01GM149762

ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 09/10/2024

PERIOD OF PERFORMANCE END DATE: 08/31/2028

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