AI delivers Michaelis constants as fuel for genome-scale metabolic models

dc.contributor.authorAntolin, Albert A.
dc.contributor.authorCascante i Serratosa, Marta
dc.date.accessioned2022-03-21T18:13:02Z
dc.date.available2022-03-21T18:13:02Z
dc.date.issued2021-10-20
dc.date.updated2022-03-21T18:13:02Z
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.format.extent4 p.
dc.format.mimetypeapplication/pdf
dc.identifier.idgrec717408
dc.identifier.issn1544-9173
dc.identifier.urihttps://hdl.handle.net/2445/184294
dc.language.isoeng
dc.publisherPublic Library of Science (PLoS)
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.1371/journal.pbio.3001415
dc.relation.ispartofPLoS Biology, 2021, vol. 19, num. 10, p. e3001415
dc.relation.urihttps://doi.org/10.1371/journal.pbio.3001415
dc.rightscc-by (c) Antolin, Albert A. et al., 2021
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceArticles publicats en revistes (Bioquímica i Biomedicina Molecular)
dc.subject.classificationCinètica enzimàtica
dc.subject.classificationIntel·ligència artificial
dc.subject.otherEnzyme kinetics
dc.subject.otherArtificial intelligence
dc.titleAI delivers Michaelis constants as fuel for genome-scale metabolic models
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion

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