dc.contributor.author | Antolin, AA | |
dc.contributor.author | Cascante, M | |
dc.date.accessioned | 2021-11-15T15:08:12Z | |
dc.date.available | 2021-11-15T15:08:12Z | |
dc.date.issued | 2021-10-20 | |
dc.identifier.citation | PLoS biology, 2021, 19 (10), pp. e3001415 - ? | |
dc.identifier.issn | 1544-9173 | |
dc.identifier.uri | https://repository.icr.ac.uk/handle/internal/4881 | |
dc.identifier.eissn | 1545-7885 | |
dc.identifier.doi | 10.1371/journal.pbio.3001415 | |
dc.description.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. | |
dc.format | Electronic-eCollection | |
dc.format.extent | e3001415 - ? | |
dc.language | eng | |
dc.language.iso | eng | |
dc.publisher | Public Library of Science (PLoS) | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | |
dc.title | AI delivers Michaelis constants as fuel for genome-scale metabolic models. | |
dc.type | Journal Article | |
rioxxterms.version | AM | |
rioxxterms.versionofrecord | 10.1371/journal.pbio.3001415 | |
rioxxterms.licenseref.uri | https://creativecommons.org/licenses/by/4.0 | |
rioxxterms.licenseref.startdate | 2021-10-20 | |
rioxxterms.type | Thesis | |
dc.relation.isPartOf | PLoS biology | |
pubs.issue | 10 | |
pubs.notes | No embargo | |
pubs.organisational-group | /ICR | |
pubs.publication-status | Published | |
pubs.volume | 19 | |
pubs.embargo.terms | No embargo | |
dc.contributor.icrauthor | Antolin Hernandez, Albert | |