2023
DOI: 10.3390/ijms242115553
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Flexible Quality Control for Protein Turnover Rates Using d2ome

Henock M. Deberneh,
Rovshan G. Sadygov

Abstract: 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 … Show more

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“…The two-parameter data modeling simultaneously determines the turnover rate ( k ) and asymptotic RIA ( I 0 asymp ) for a peptide by fitting the time series of the monoisotopic RIA to the exponential decaying model, eq , using nonlinear least-squares regression . The N EH for a peptide at the plateau of labeling is then determined from eq , as N EH = ln false( italicI 0 normalasymp false) ln false( italicI 0 ( 0 ) false) ln false( 1 italicp normalW / ( 1 p H ) false) …”
Section: Methodsmentioning
confidence: 99%
“…The two-parameter data modeling simultaneously determines the turnover rate ( k ) and asymptotic RIA ( I 0 asymp ) for a peptide by fitting the time series of the monoisotopic RIA to the exponential decaying model, eq , using nonlinear least-squares regression . The N EH for a peptide at the plateau of labeling is then determined from eq , as N EH = ln false( italicI 0 normalasymp false) ln false( italicI 0 ( 0 ) false) ln false( 1 italicp normalW / ( 1 p H ) false) …”
Section: Methodsmentioning
confidence: 99%