Steady State Kinetics for Enzymes with Multiple Binding Sites Upstream of the Catalytic Site

dc.contributor.affiliationUniversidad Andres Bello
dc.contributor.affiliationUniversity Diego Portales
dc.contributor.affiliationPontificia Universidad Catolica de Chile
dc.contributor.affiliationUniversidad de Las Americas - Chile
dc.contributor.authorOsorio, Manuel I.
dc.contributor.authorPetrache, Mircea
dc.contributor.authorSalinas, Dino G.
dc.contributor.authorValenzuela-Ibaceta, Felipe
dc.contributor.authorGonzalez-Nilo, Fernando
dc.contributor.authorTiznado, William
dc.contributor.authorPérez-Donoso, José M.
dc.contributor.authorBravo, Denisse
dc.contributor.authorYáñez, Osvaldo
dc.date.accessioned2024-09-03T19:17:36Z
dc.date.available2024-09-03T19:17:36Z
dc.date.issued2023-12-08
dc.description.abstractThe Michaelis–Menten mechanism, which describes the binding of a substrate to an enzyme, is a simplification of the process on a molecular scale. A more detailed model should include the binding of the substrate to precatalytic binding sites (PCBSs) prior to the transition to the catalytic site. Our work shows that the incorporation of PCBSs, in steady-state conditions, generates a Michaelis–Menten-type expression, in which the kinetic parameters KM and Vmax adopt more complex expressions than in the model without PCBSs. The equations governing reaction kinetics can be seen as generalized symmetries, relative to time translation actions over the state space of the underlying chemical system. The study of their structure and defining parameters can be interpreted as looking for invariants associated with these time evolution actions. The expression of KM decreases as the number of PCBSs increases, while Vmax reaches a minimum when the first PCBSs are incorporated into the model. To evaluate the trend of the dynamic behavior of the system, numerical simulations were performed based on schemes with different numbers of PCBSs and six conditions of kinetic constants. From these simulations, with equal kinetic constants for the formation of the Substrate/PCBS complex, it is observed that KM and Vmax are lower than those obtained with the Michaelis–Menten model. For the model with PCBSs, the Vmax reaches a minimum at one PCBS and that value is maintained for all of the systems evaluated. Since KM decreases with the number of PCBSs, the catalytic efficiency increases for enzymes fitting this model. All of these observations are consistent with the general equation obtained. This study allows us to explain, on the basis of the PCBS to KM and Vmax ratios, the effect on enzyme parameters due to mutations far from the catalytic site, at sites involved in the first enzyme/substrate interaction. In addition, it incorporates a new mechanism of enzyme activity regulation that could be fundamental to search for new activity-modulating sites or for the design of mutants with modified enzyme parameters.
dc.description.sponsorshipFondecyt; Center for Bioinformatics and Integrative Biology; We acknowledge the Center for Bioinformatics and Integrative Biology and by CenIA (Centro Nacional de Inteligencia Artificial).
dc.format.mimetypeapplication/pdf
dc.identifier.citationSymmetry, 15(12), 2176. https://doi.org/10.3390/sym15122176
dc.identifier.doihttps://doi.org/10.3390/sym15122176
dc.identifier.folio3201013
dc.identifier.issn2073-8994
dc.identifier.orcidhttps://orcid.org/0000-0002-1297-8351
dc.identifier.orcidhttps://orcid.org/0000-0003-2181-169X
dc.identifier.orcidhttps://orcid.org/0000-0002-6061-8879
dc.identifier.orcidhttps://orcid.org/0000-0002-9506-1716
dc.identifier.orcidhttps://orcid.org/0000-0001-8993-9353
dc.identifier.orcidhttps://orcid.org/0000-0001-6857-3575
dc.identifier.orcidhttps://orcid.org/0000-0001-8152-1622
dc.identifier.researcheridM-5671-2016
dc.identifier.researcheridG-3668-2013
dc.identifier.researcheridF-6583-2018
dc.identifier.researcheridG-7479-2014
dc.identifier.rorhttps://ror.org/01qq57711
dc.identifier.rorhttps://ror.org/03gtdcg60
dc.identifier.rorhttps://ror.org/04teye511
dc.identifier.rorhttps://ror.org/0166e9x11
dc.identifier.scopusauthorid57201481176
dc.identifier.scopusauthorid35797406500
dc.identifier.scopusauthorid22964033400
dc.identifier.scopusauthorid58187322900
dc.identifier.scopusauthorid6603635113
dc.identifier.scopusauthorid6507292367
dc.identifier.scopusauthorid36244308800
dc.identifier.scopusauthorid12766524900
dc.identifier.scopusauthorid55794064800
dc.identifier.urihttps://repositorio.udla.cl/handle/udla/1331
dc.language.isoeng
dc.publisherMDPI AG
dc.relation.fundingFondo Nacional de Desarrollo Científico y Tecnológico, FONDECYT, (3201013)
dc.relation.fundingFondo Nacional de Desarrollo Científico y Tecnológico, FONDECYT
dc.relation.fundingFondecyt
dc.relation.fundingCenter for Bioinformatics and Integrative Biology
dc.relation.isindexedbyWeb of Science
dc.relation.issn2073-8994
dc.rightsCreative Commons Attribution 4.0 International
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceSymmetry
dc.source.urihttps://doi.org/10.3390/sym15122176
dc.subjectsteady-state enzyme kinetics
dc.subjectmulti-precatalytic binding sites
dc.subjectnumerical simulation
dc.titleSteady State Kinetics for Enzymes with Multiple Binding Sites Upstream of the Catalytic Site
dc.typejournal article
dc.type.coarhttp://purl.org/coar/resource_type/c_6501
dc.type.driverinfo:eu-repo/semantics/article
dc.udla.catalogadorCBM
oaire.citation.issue12
oaire.citation.titleSymmetry
oaire.citation.volume15
oaire.fundingReference.awardNumber3201013
oaire.fundingReference.funderNameAgencia Nacional de Investigación y Desarrollo (ANID)
udla.curacion.controljmvg
udla.oecd.area1 Ciencias Naturales
udla.oecd.discipline1.4.3 Fisicoquímica
udla.oecd.subarea1.4 Ciencias Químicas

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