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https://hdl.handle.net/2445/184294| Title: | AI delivers Michaelis constants as fuel for genome-scale metabolic models |
| Author: | Antolin, Albert A. Cascante i Serratosa, Marta |
| Keywords: | Cinètica enzimàtica Intel·ligència artificial Enzyme kinetics Artificial intelligence |
| Issue Date: | 20-Oct-2021 |
| Publisher: | Public Library of Science (PLoS) |
| Abstract: | Michaelis constants (Km) are essential to predict the catalytic rate of enzymes, but are not widely available. A new study in PLOS Biology uses artificial intelligence (AI) to accurately predict Km on a proteome-wide scale, paving the way for dynamic, genome-wide modeling of metabolism. |
| Note: | Reproducció del document publicat a: https://doi.org/10.1371/journal.pbio.3001415 |
| It is part of: | PLoS Biology, 2021, vol. 19, num. 10, p. e3001415 |
| URI: | https://hdl.handle.net/2445/184294 |
| Related resource: | https://doi.org/10.1371/journal.pbio.3001415 |
| ISSN: | 1544-9173 |
| Appears in Collections: | Articles publicats en revistes (Bioquímica i Biomedicina Molecular) |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| 717408.pdf | 612.4 kB | Adobe PDF | View/Open |
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