ArticleJournal of proteome research2016
Gaussian Process Modeling of Protein Turnover.
Article in Journal of proteome research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 31 citations in OpenAlex.
- A Computational Model for Determining Labeling Duration in Protein Turnover Studies Using a Single Deuterated Water Labeled Sample.Journal of the American Society for Mass Spectrometry · 2026Article
- Duplexing metabolic deuterated water-labeled samples using dimethyl labeling to estimate protein turnover rates.Communications chemistry · 2025Article
- Article
- An Extensive Atlas of Proteome and Phosphoproteome Turnover Across Mouse Tissues and Brain Regions.bioRxiv : the preprint server for biology · 2024Article
- Enabling population protein dynamics through Bayesian modeling.Bioinformatics (Oxford, England) · 2024Article
- Metabolomic and proteomic applications to exercise biomedicine.Translational exercise biomedicine · 2024Review
- The Chemical Reactivity of Membrane Lipids.Chemical reviews · 2024Review
- A large-scale LC-MS dataset of murine liver proteome from time course of heavy water metabolic labeling.Scientific data · 2023Article
- Quantifying label enrichment from two mass isotopomers increases proteome coverage for in vivo protein turnover using heavy water metabolic labeling.Communications chemistry · 2023Article
- Determining and interpreting protein lifetimes in mammalian tissues.Trends in biochemical sciences · 2023Review
- Protein turnover models for LC-MS data of heavy water metabolic labeling.Briefings in bioinformatics · 2022Article
- Timepoint Selection Strategy for In Vivo Proteome Dynamics from Heavy Water Metabolic Labeling and LC-MS.Journal of proteome research · 2020Article
- Accumulation of "Old Proteins" and the Critical Need for MS-based Protein Turnover Measurements in Aging and Longevity.Proteomics · 2020Review
- A mass spectrometry workflow for measuring protein turnover rates in vivo.Nature protocols · 2019Article
- Proteomic Atlas of the Human Brain in Alzheimer's Disease.Journal of proteome research · 2019Article
- Hepatic Mitochondrial Defects in a Nonalcoholic Fatty Liver Disease Mouse Model Are Associated with Increased Degradation of Oxidative Phosphorylation Subunits.Molecular & cellular proteomics : MCP · 2018Article
- d2ome, Software for in Vivo Protein Turnover Analysis Using Heavy Water Labeling and LC-MS, Reveals Alterations of Hepatic Proteome Dynamics in a Mouse Model of NAFLD.Journal of proteome research · 2018Article
- Protein Turnover in Aging and Longevity.Proteomics · 2018Review
- Poisson Model To Generate Isotope Distribution for Biomolecules.Journal of proteome research · 2018Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.