Rovshan Sadygov
University of Texas Medical Branch at Galveston, TX·Biochemistry
Proteins in the body are constantly being built and broken down. The speed of that cycle, called protein turnover, is a basic feature of healthy cells and goes wrong in many diseases.
- Fields
Biochemistry
Biophysics
Mathematics & Statistics
- Topics
protein turnover
proteomics
heavy water labeling
- Grants
- 1 known grant, through Aug 2028
What they work on
- Develop methods to estimate protein turnover from the deuterium labeling of fragment ions in tandem mass spectra, which are less distorted by space-charging, limited dynamic range and co-eluting contaminants than intact peptide ions.
- Create mathematical techniques to recover label enrichment from the truncated isotope distributions seen in tandem mass spectra, plus statistical normalization that accounts for the instrument's limited peptide isolation width.
- Build a label-incorporation model with two amino acid pools that accounts for the distinct labeling kinetics of each amino acid, explaining why peptide turnover rates depend on amino acid composition.
- Deliver computational tools that increase proteome coverage and statistical power for in vivo protein turnover studies using heavy water metabolic labeling and LC-MS.
More about the research
One way to measure it in a living animal is to give heavy water, water containing deuterium (a heavier form of hydrogen). The deuterium gets built into newly made amino acids, and therefore into newly made proteins. Mass spectrometers can then weigh peptides, short protein fragments, and tell new protein from old. Heavy water is cheap and easy to administer, and it has been used to track turnover in heart, liver, kidney, blood and brain tissue in mice, rats, dogs and fish. The problem is data loss: although nearly every peptide gets labeled, only about 30 to 40 percent of measured peptides end up usable, because most are discarded for fitting the expected labeling pattern poorly.
Why it matters
Protein turnover is disrupted in many diseases, so measuring it accurately matters for understanding disease mechanisms. Tools that rescue the majority of mass spectral data currently thrown away could let researchers measure more proteins per experiment with greater confidence, and could reduce the dose of heavy water, the sampling frequency and the duration of labeling needed in animal studies.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Matthew O'Reilly
Villanova University, PA·Chemistry·Early-career
Design arrays of cyclization reagents and screen many reaction conditions in parallel, using liquid chromatography-mass spectrometry for rapid analysis, to find reliable ways to close strained cyclic tetrapeptides.
Arthur Salomon
University of California-San Francisco, CA·Biochemistry
Identify the binding partners of signaling proteins in T cell lines and primary mouse T cells, using TurboID, a labeling enzyme that tags nearby proteins in living cells, followed by mass spectrometry.
James Papin
University of Oklahoma-Health Sciences Center, OK·Biochemistry
Provide next-generation DNA and RNA sequencing, and bioinformatics analysis of combined genomic, proteomic and metabolomic data, to cancer center members and other investigators.
The school
University of Texas Medical Branch at Galveston: 14 labs on PI Pages, $241M a year on research, 141 research doctorates a year.
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