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<a href="http://doi.org/10.1021/acs.jproteome.5b00990" target="_blank" rel="noreferrer noopener">http://doi.org/10.1021/acs.jproteome.5b00990</a>
Pages
2115–2122
Issue
7
Volume
15
Dublin Core
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Title
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Gaussian Process Modeling of Protein Turnover.
Publisher
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Journal of proteome research
Date
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2016
2016-07
Subject
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*Brain Chemistry; *dynamic proteome; *Gaussian process; *mass spectrometry; *Ornstein-Uhlenbeck process; *protein degradation rate constant; *protein turnover rate constant; *stable isotope labeling; *stochastic differential equation for protein turnover rate constant; Animals; Chromatography; Data Interpretation; Isotope Labeling; Kinetics; Liquid; Liver/*chemistry; Mass Spectrometry; Mice; Normal Distribution; Proteins/*metabolism; Proteome/*metabolism; Proteomics/methods; Statistical; Stochastic Processes
Creator
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Rahman Mahbubur; Previs Stephen F; Kasumov Takhar; Sadygov Rovshan G
Description
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We describe a stochastic model to compute in vivo protein turnover rate constants from stable-isotope labeling and high-throughput liquid chromatography-mass spectrometry experiments. We show that the often-used one- and two-compartment nonstochastic models allow explicit solutions from the corresponding stochastic differential equations. The resulting stochastic process is a Gaussian processes with Ornstein-Uhlenbeck covariance matrix. We applied the stochastic model to a large-scale data set from (15)N labeling and compared its performance metrics with those of the nonstochastic curve fitting. The comparison showed that for more than 99% of proteins, the stochastic model produced better fits to the experimental data (based on residual sum of squares). The model was used for extracting protein-decay rate constants from mouse brain (slow turnover) and liver (fast turnover) samples. We found that the most affected (compared to two-exponent curve fitting) results were those for liver proteins. The ratio of the median of degradation rate constants of liver proteins to those of brain proteins increased 4-fold in stochastic modeling compared to the two-exponent fitting. Stochastic modeling predicted stronger differences of protein turnover processes between mouse liver and brain than previously estimated. The model is independent of the labeling isotope. To show this, we also applied the model to protein turnover studied in induced heart failure in rats, in which metabolic labeling was achieved by administering heavy water. No changes in the model were necessary for adapting to heavy-water labeling. The approach has been implemented in a freely available R code.
Identifier
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<a href="http://doi.org/10.1021/acs.jproteome.5b00990" target="_blank" rel="noreferrer noopener">10.1021/acs.jproteome.5b00990</a>
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Article information provided for research and reference use only. All rights are retained by the journal listed under publisher and/or the creator(s).
*Brain Chemistry
*dynamic proteome
*Gaussian process
*Mass spectrometry
*Ornstein-Uhlenbeck process
*protein degradation rate constant
*protein turnover rate constant
*stable isotope labeling
*stochastic differential equation for protein turnover rate constant
2016
Animals
Chromatography
Data Interpretation
Department of Pharmaceutical Sciences
Isotope Labeling
Journal of proteome research
Kasumov Takhar
Kinetics
Liquid
Liver/*chemistry
Mass spectrometry
Mice
NEOMED College of Pharmacy
Normal Distribution
Previs Stephen F
Proteins/*metabolism
Proteome/*metabolism
Proteomics/methods
Rahman Mahbubur
Sadygov Rovshan G
Statistical
Stochastic Processes