ArticleJournal of proteome research2016
Proteome Scale-Protein Turnover Analysis Using High Resolution Mass Spectrometric Data from Stable-Isotope Labeled Plants.
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 17 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 42 citations in OpenAlex.
- Protein turnover in plants-cost of synthesis, repair, and crop engineering opportunities.Plant physiology · 2026Review
- High-Grade Thalamic Glioma: Case Report with Literature Review.Medicina (Kaunas, Lithuania) · 2024Review
- Elevated Temperature Effects on Protein Turnover Dynamics inInternational journal of molecular sciences · 2024Article
- Analytical methods for stable isotope labeling to elucidate rapid auxin kinetics in Arabidopsis thaliana.PloS one · 2024Article
- Tracking nitrogen allocation to proteome biosynthesis in a marine microbial community.Nature microbiology · 2023Article
- TurnoveR: A Skyline External Tool for Analysis of Protein Turnover in Metabolic Labeling Studies.Journal of proteome research · 2023Article
- Protein turnover in the developing Triticum aestivum grain.The New phytologist · 2022Article
- Understanding the Phytoremediation Mechanisms of Potentially Toxic Elements: A Proteomic Overview of Recent Advances.Frontiers in plant science · 2022Review
- Protocol: analytical methods for visualizing the indolic precursor network leading to auxin biosynthesis.Plant methods · 2021Article
- Diurnal dynamics of the Arabidopsis rosette proteome and phosphoproteome.Plant, cell & environment · 2021Article
- Tracing metabolic flux through time and space with isotope labeling experiments.Current opinion in biotechnology · 2020Review
- Bayesian Confidence Intervals for Multiplexed Proteomics Integrate Ion-statistics with Peptide Quantification Concordance.Molecular & cellular proteomics : MCP · 2019Article
- Protein Turnover in Aging and Longevity.Proteomics · 2018Review
- Delaying chloroplast turnover increases water-deficit stress tolerance through the enhancement of nitrogen assimilation in rice.Journal of experimental botany · 2018Article
- Metabolic Patterns inFrontiers in chemistry · 2018Article
- A reference-based protein degradation assay without global translation inhibitors.The Journal of biological chemistry · 2017Article
- Protein Degradation Rate inThe Plant cell · 2017Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Protein turnover is an important aspect of the regulation of cellular processes for organisms when responding to developmental or environmental cues. The measurement of protein turnover in plants, in contrast to that of rapidly growing unicellular organismal cultures, is made more complicated by the high degree of amino acid recycling, resulting in significant transient isotope incorporation distributions that must be dealt with computationally for high throughput analysis to be practical. An algorithm in R, ProteinTurnover, was developed to calculate protein turnover with transient stable isotope incorporation distributions in a high throughput automated manner using high resolution MS and MS/MS proteomic analysis of stable isotopically labeled plant material. ProteinTurnover extracts isotopic distribution information from raw MS data for peptides identified by MS/MS from data sets of either isotopic label dilution or incorporation experiments. Variable isotopic incorporation distributions were modeled using binomial and beta-binomial distributions to deconvolute the natural abundance, newly synthesized/partial-labeled, and fully labeled peptide distributions. Maximum likelihood estimation was performed to calculate the distribution abundance proportion of old and newly synthesized peptides. The half-life or turnover rate of each peptide was calculated from changes in the distribution abundance proportions using nonlinear regression. We applied ProteinTurnover to obtain half-lives of proteins from enriched soluble and membrane fractions from Arabidopsis roots.
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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.