ArticleNature communications2025
Robust enzyme discovery and engineering with deep learning using CataPro.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Predicting Enzyme Turnover Numbers and Enabling Rational Enzyme Evolution.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Enhancing biocatalytic retrosynthesis with a graph-to-graph model.Chemical science · 2026Article
- Article
- Integrative machine learning approaches for enzyme kinetic parameter prediction.Briefings in bioinformatics · 2026Review
- Rhobot-Screen: an integrated robotic platform for functional screening of rhodopsin variants.BMC biology · 2026Article
- WILDkCAT: extract, retrieve, and predict enzyme turnover numbers of constraint-based metabolic models.Bioinformatics (Oxford, England) · 2026Article
- Kinetic parameter prediction using neural networks identifies limitations to CThe New phytologist · 2026Article
- De novo L-(+)-tartaric acid biosynthesis in multi-modular engineered yeasts.Nature communications · 2026Article
- An enzyme-specific protein language model for catalytic property prediction.Nature communications · 2026Article
- EnzymeMiner 2.0: advancing automated enzyme discovery with expansive sequence mining and smart property analysis.Nucleic acids research · 2026Article
- EnzymeHunter: Achieving fine-grained enzyme function prediction with a hierarchically aware contrastive learning framework.Patterns (New York, N.Y.) · 2026Article
- Artificial intelligence and automation in enzyme engineering: evolution, advances, and future perspectives.Bioresources and bioprocessing · 2026Review
- Overcoming cellular secretion bottlenecks: advanced secretion engineering and molecular tailoring for next-generation microbial α-amylases with enhanced industrial performance.World journal of microbiology & biotechnology · 2026Review
- Chemical neighborhood exploration for substrate discovery in biocatalysis.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- Integrating multimodal features with deep learning for protein solubility prediction.Journal of cheminformatics · 2026Article
- Emerging enzymatic modifications and AI-driven strategies for smart tailoring of taste characteristics in food-derived peptides: A review.Food chemistry: X · 2026Review
- Protein foundation models: a comprehensive survey.Science China. Life sciences · 2026Review
- Integrating Protein Language Models with Multimodal Embeddings to Accelerate Function Prediction of Uncharacterized Proteins.International journal of molecular sciences · 2026Review
- Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate prediction of enzyme kinetic parameters is crucial for enzyme exploration and modification. Existing models face the problem of either low accuracy or poor generalization ability due to overfitting. In this work, we first developed unbiased datasets to evaluate the actual performance of these methods and proposed a deep learning model, CataPro, based on pre-trained models and molecular fingerprints to predict turnover number (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.