ArticleBriefings in bioinformatics2023
DLTKcat: deep learning-based prediction of temperature-dependent enzyme turnover rates.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Enhancing kCommunications biology · 2026Article
- Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026Review
- Machine learning for enzyme catalytic activity: current progress and future horizons.Briefings in bioinformatics · 2026Review
- GotEnzymes2: expanding coverage of enzyme kinetics and thermal properties.Nucleic acids research · 2026Article
- Deep Learning-Based Prediction of Enzyme Optimal pH and Design of Point Mutations to Improve Acid Resistance.ACS synthetic biology · 2025Article
- Rubisco is slow across the tree of life.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- IECata: interpretable bilinear attention network and evidential deep learning improve the catalytic efficiency prediction of enzymes.Briefings in bioinformatics · 2025Article
- NNKcat: deep neural network to predict catalytic constants (Kcat) by integrating protein sequence and substrate structure with enhanced data imbalance handling.Briefings in bioinformatics · 2025Article
- Robust enzyme discovery and engineering with deep learning using CataPro.Nature communications · 2025Article
- Seq2Topt: a sequence-based deep learning predictor of enzyme optimal temperature.Briefings in bioinformatics · 2025Article
- DEKP: a deep learning model for enzyme kinetic parameter prediction based on pretrained models and graph neural networks.Briefings in bioinformatics · 2025Article
- RBC-GEM: A genome-scale metabolic model for systems biology of the human red blood cell.PLoS computational biology · 2025Article
- CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters.Nature communications · 2025Article
- EnzyACT: A Novel Deep Learning Method to Predict the Impacts of Single and Multiple Mutations on Enzyme Activity.Journal of chemical information and modeling · 2024Article
- DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D-structures.Briefings in bioinformatics · 2024Article
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3 authors.
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Abstract
The enzyme turnover rate, ${k}_{cat}$, quantifies enzyme kinetics by indicating the maximum efficiency of enzyme catalysis. Despite its importance, ${k}_{cat}$ values remain scarce in databases for most organisms, primarily because of the cost of experimental measurements. To predict ${k}_{cat}$ and account for its strong temperature dependence, DLTKcat was developed in this study and demonstrated superior performance (log10-scale root mean squared error = 0.88, R-squared = 0.66) than previously published models. Through two case studies, DLTKcat showed its ability to predict the effects of protein sequence mutations and temperature changes on ${k}_{cat}$ values. Although its quantitative accuracy is not high enough yet to model the responses of cellular metabolism to temperature changes, DLTKcat has the potential to eventually become a computational tool to describe the temperature dependence of biological systems.
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