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dc.contributor.authorAntolin, AA
dc.contributor.authorCascante, M
dc.date.accessioned2021-11-15T15:08:12Z
dc.date.available2021-11-15T15:08:12Z
dc.date.issued2021-10-20
dc.identifier.citationPLoS biology, 2021, 19 (10), pp. e3001415 - ?
dc.identifier.issn1544-9173
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/4881
dc.identifier.eissn1545-7885
dc.identifier.doi10.1371/journal.pbio.3001415
dc.description.abstractMichaelis 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.formatElectronic-eCollection
dc.format.extente3001415 - ?
dc.languageeng
dc.language.isoeng
dc.publisherPublic Library of Science (PLoS)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleAI delivers Michaelis constants as fuel for genome-scale metabolic models.
dc.typeJournal Article
rioxxterms.versionAM
rioxxterms.versionofrecord10.1371/journal.pbio.3001415
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
rioxxterms.licenseref.startdate2021-10-20
rioxxterms.typeThesis
dc.relation.isPartOfPLoS biology
pubs.issue10
pubs.notesNo embargo
pubs.organisational-group/ICR
pubs.publication-statusPublished
pubs.volume19
pubs.embargo.termsNo embargo
dc.contributor.icrauthorAntolin Hernandez, Albert


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