ArticleNature communications2023
Data integration across conditions improves turnover number estimates and metabolic predictions.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 24 citations in OpenAlex.
- EnzymeTuning improves enzyme-constrained metabolic modeling and proteome abundance prediction through deep learning.Nature communications · 2026Article
- Accurate prediction of flux distributions compatible with metabolite concentration effects in genome-scale metabolic networks.PLoS computational biology · 2026Article
- A geometric deep learning framework for genome-wide prediction of enzyme turnover number.Genome biology · 2026Article
- Combing Directed Enzyme Evolution with Metabolic Engineering to Develop Efficient Microbial Cell Factories.Chem & bio engineering · 2025Review
- Data-driven synthetic microbes for sustainable future.NPJ systems biology and applications · 2025Review
- Harnessing the optimization of enzyme catalytic rates in engineering of metabolic phenotypes.PLoS computational biology · 2024Article
- DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D-structures.Briefings in bioinformatics · 2024Article
- Enzyme catalytic efficiency prediction: employing convolutional neural networks and XGBoost.Frontiers in artificial intelligence · 2024Article
- DLTKcat: deep learning-based prediction of temperature-dependent enzyme turnover rates.Briefings in bioinformatics · 2023Article
- Genome-scale metabolic models reveal determinants of phenotypic differences in non-Saccharomyces yeasts.BMC bioinformatics · 2023Article
- Modeling Red Blood Cell Metabolism in the Omics Era.Metabolites · 2023Review
- Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Turnover numbers characterize a key property of enzymes, and their usage in constraint-based metabolic modeling is expected to increase the prediction accuracy of diverse cellular phenotypes. In vivo turnover numbers can be obtained by integrating reaction rate and enzyme abundance measurements from individual experiments. Yet, their contribution to improving predictions of condition-specific cellular phenotypes remains elusive. Here, we show that available in vitro and in vivo turnover numbers lead to poor prediction of condition-specific growth rates with protein-constrained models of Escherichia coli and Saccharomyces cerevisiae, particularly when protein abundances are considered. We demonstrate that correction of turnover numbers by simultaneous consideration of proteomics and physiological data leads to improved predictions of condition-specific growth rates. Moreover, the obtained estimates are more precise than corresponding in vitro turnover numbers. Therefore, our approach provides the means to correct turnover numbers and paves the way towards cataloguing kcatomes of other organisms.
Indexed as
Identifiers
What OpenQuestion holds
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.