Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models
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DOI: 10.1038/s41467-018-07652-6
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Cited by:
- Alexander Kroll & Yvan Rousset & Xiao-Pan Hu & Nina A. Liebrand & Martin J. Lercher, 2023. "Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
- Gi Bae Kim & Ji Yeon Kim & Jong An Lee & Charles J. Norsigian & Bernhard O. Palsson & Sang Yup Lee, 2023. "Functional annotation of enzyme-encoding genes using deep learning with transformer layers," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
- Han Yu & Huaxiang Deng & Jiahui He & Jay D. Keasling & Xiaozhou Luo, 2023. "UniKP: a unified framework for the prediction of enzyme kinetic parameters," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
- Philipp Wendering & Marius Arend & Zahra Razaghi-Moghadam & Zoran Nikoloski, 2023. "Data integration across conditions improves turnover number estimates and metabolic predictions," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
- Guido Zampieri & Supreeta Vijayakumar & Elisabeth Yaneske & Claudio Angione, 2019. "Machine and deep learning meet genome-scale metabolic modeling," PLOS Computational Biology, Public Library of Science, vol. 15(7), pages 1-24, July.
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