ArticleNature communications2018
Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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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Who cites it
8 citing papers in PubMed, 22 citations in OpenAlex.
- AUKAT: Conditional VAE-Driven Augmentation and Neural Modeling of Enzyme Turnover Numbers.Biomolecules · 2026Article
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- Weakest link epistasis and the geometry of genetic load.bioRxiv : the preprint server for biology · 2026Article
- Phosphate ions modulate enzyme activity and epistatic effects in two clavulanic acid-resistant β-lactamase mutants.Protein science : a publication of the Protein Society · 2025Article
- Principles of metabolome conservation in animals.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Optimal enzyme utilization suggests that concentrations and thermodynamics determine binding mechanisms and enzyme saturations.Nature communications · 2023Article
- Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models.Nature communications · 2018Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Systems biology describes cellular phenotypes as properties that emerge from the complex interactions of individual system components. Little is known about how these interactions have affected the evolution of metabolic enzymes. Here, we combine genome-scale metabolic modeling with population genetics models to simulate the evolution of enzyme turnover numbers (k
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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.