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Highly accurate enzyme turnover number prediction and enzyme engineering with PreKcat
Open accessPreprint — published as 10.1038/s41467-023-44113-1 · Paper page
Abstract
Prediction of enzyme turnover number (kcat) is essential for designing and optimizing enzymes for various biotechnological and industrial applications, but the limited performance of current prediction tools on diverse tasks hinders their practical applications. Here, we introduce PreKcat, a method based on pretrained language models for highly accurate kcat prediction from protein sequences and substrate structures. A two-layer framework derived from PreKcat was proposed to allow robust kcat prediction in considering environmental factors, including pH and temperature. In addition, four representative re-weighting methods were systematically explored to successfully reduce the prediction error in high kcat values prediction tasks. Furthermore, PreKcat showed strong generalizability in two extended tasks, Km and kcat / Km prediction. Using PreKcat, we achieve significant increases kcat and kcat / Km (approximately 7-fold and 3.5-fold, respectively) on one enzyme, the highest reported values to date. PreKcat is a valuable tool for deciphering the mechanisms of enzyme kinetics and enables novel insights into enzyme engineering and their industrial applications.
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Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction
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Metadata
- Authors
- Yu, H., Deng, H., He, J., Keasling, J., & Luo, X.
- Affiliations
- Chinese Academy of Sciences (CN)
- Lawrence Berkeley National Laboratory (US)
- Corresponding author
- Luo, Xiaozhou
- Venue
- Nature Communications
- Published
- 2023-05-18
- Volume
- 14
- Issue
- 1
- Pages
- 8211-8211
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC10713628
- OpenAlex
- W4389546039
- Cited by
- 226
- References
- 1
- Citations by year
- 2026: 96 · 2025: 88 · 2024: 39 · 2023: 3
- FWCI
- 20.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion, cc-by)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (32071421, 2018YFA0903200)
- Basic and Applied Basic Research Foundation of Guangdong Province (2021B1515020049)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Enzyme Catalysis and Immobilization
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- Michaelis constant, catalytic efficiency, protein sequences, substrate structure, pre-trained language models, environmental factors, pH and temperature effects, enzyme discovery, directed evolution, enzyme engineering
- MeSH terms
- Biotechnology, Catalysis, Kinetics, Temperature
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