Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models
oleh: David Heckmann, Colton J. Lloyd, Nathan Mih, Yuanchi Ha, Daniel C. Zielinski, Zachary B. Haiman, Abdelmoneim Amer Desouki, Martin J. Lercher, Bernhard O. Palsson
| Format: | Article |
|---|---|
| Diterbitkan: | Nature Portfolio 2018-12-01 |
Deskripsi
Experimental data on enzyme turnover numbers is sparse and noisy. Here, the authors use machine learning to successfully predict enzyme turnover numbers for E. coli, and show that using these to parameterize mechanistic genome-scale models enhances their predictive accuracy.