DLTKcat: deep learning-based prediction of temperature-dependent enzyme turnover rates
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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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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
UniProt: the Universal Protein Knowledgebase in 2023
The UniProt Consortium, Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., Bye-A-Jee, H., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac1052 · Cited by 7,210 · see citing works
What is flux balance analysis?
Orth, J. D., Thiele, I., & Palsson, B. Ø. (2010) · Nature Biotechnology · doi:10.1038/nbt.1614 · Cited by 3,903 · see citing works
Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac956 · Cited by 3,443 · see citing works
Genome‐scale models of metabolism and gene expression extend and refine growth phenotype prediction
O'Brien, E. J., Lerman, J. A., Chang, R. L., Hyduke, D. R., & Palsson, B. Ø. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.52 · Cited by 471 · see citing works
Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction
Li, F., Yuan, L., Lu, H., Li, G., Chen, Y., Engqvist, M. K. M., Kerkhoven, E. J., & Nielsen, J. (2022) · Nature Catalysis · doi:10.1038/s41929-022-00798-z · Cited by 466 · cited 4× · see citing works
“DLKcat [16], the first CPI deep learning model for k cat prediction, can predict log 10 (cid:2) k cat (cid”
BackgroundInfluentialUniKP: a unified framework for the prediction of enzyme kinetic parameters
Yu, H., Deng, H., He, J., Keasling, J. D., & Luo, X. (2023) · Nature Communications · doi:10.1038/s41467-023-44113-1 · Cited by 226 · see citing works
The BRENDA enzyme information system–From a database to an expert system
Schomburg, I., Jeske, L., Ulbrich, M., Placzek, S., Chang, A., & Schomburg, D. (2017) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2017.04.020 · Cited by 222 · see citing works
Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning
Kroll, A., Rousset, Y., Hu, X.-P., Liebrand, N. A., & Lercher, M. J. (2023) · Nature Communications · doi:10.1038/s41467-023-39840-4 · Cited by 186 · cited 2× · see citing works
“…additional features, such as enzyme molar mass or the octanol– water partition coefficient of substrate, could not improve model performance [15, 21], the finalized data set of this study only contained SMILES strings of substrates, amino acid sequences of enzyme proteins and temperature values.”
BackgroundAdadi, R., Volkmer, B., Milo, R., Heinemann, M., & Shlomi, T. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002575 · Cited by 179 · see citing works
Deep learning allows genome-scale prediction of Michaelis constants from structural features
Kroll, A., Engqvist, M. K. M., Heckmann, D., & Lercher, M. J. (2021) · PLOS Biology · doi:10.1371/journal.pbio.3001402 · Cited by 132 · see citing works
Data integration across conditions improves turnover number estimates and metabolic predictions
Wendering, P., Arend, M., Razaghi-Moghadam, Z., & Nikoloski, Z. (2023) · Nature Communications · doi:10.1038/s41467-023-37151-2 · Cited by 23 · see citing works
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- Authors
- Qiu, S., Zhao, S., & Yang, A.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Yang, Aidong
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-11-22
- Volume
- 25
- Issue
- 1
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- PMC
- PMC10772988
- OpenAlex
- W4390678097
- Cited by
- 43
- References
- 12
- Citations by year
- 2026: 18 · 2025: 19 · 2024: 6
- FWCI
- 3.83
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Briefings in Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- University of Oxford
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Enzyme Catalysis and Immobilization
- Keywords
- enzyme turnover rate, temperature dependence, enzyme kinetics, deep learning, temperature effects on metabolism
- MeSH terms
- Deep Learning, Amino Acid Sequence, Catalysis, Temperature, Databases, Factual
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