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Reaction-path statistical mechanics of enzymatic kinetics

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dc.contributor.authorLim, Hyuntae-
dc.contributor.authorJung, YounJoon-
dc.date.accessioned2022-06-23T04:39:54Z-
dc.date.available2022-06-23T04:39:54Z-
dc.date.created2022-05-16-
dc.date.issued2022-04-
dc.identifier.citationJournal of Chemical Physics, Vol.156 No.13, p. 134108-
dc.identifier.issn0021-9606-
dc.identifier.urihttps://hdl.handle.net/10371/183706-
dc.description.abstractWe introduce a reaction-path statistical mechanics formalism based on the principle of large deviations to quantify the kinetics of single-molecule enzymatic reaction processes under the Michaelis-Menten mechanism, which exemplifies an out-of-equilibrium process in the living system. Our theoretical approach begins with the principle of equal a priori probabilities and defines the reaction path entropy to construct a new nonequilibrium ensemble as a collection of possible chemical reaction paths. As a result, we evaluate a variety of path-based partition functions and free energies by using the formalism of statistical mechanics. They allow us to calculate the timescales of a given enzymatic reaction, even in the absence of an explicit boundary condition that is necessary for the equilibrium ensemble. We also consider the large deviation theory under a closed-boundary condition of the fixed observation time to quantify the enzyme-substrate unbinding rates. The result demonstrates the presence of a phase-separation-like, bimodal behavior in unbinding events at a finite timescale, and the behavior vanishes as its rate function converges to a single phase in the long-time limit.& nbsp;& nbsp;Published under an exclusive license by AIP Publishing.-
dc.language영어-
dc.publisherAmerican Institute of Physics-
dc.titleReaction-path statistical mechanics of enzymatic kinetics-
dc.typeArticle-
dc.identifier.doi10.1063/5.0075831-
dc.citation.journaltitleJournal of Chemical Physics-
dc.identifier.wosid000788738000007-
dc.identifier.scopusid2-s2.0-85127841956-
dc.citation.number13-
dc.citation.startpage134108-
dc.citation.volume156-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorJung, YounJoon-
dc.type.docTypeArticle-
dc.description.journalClass1-
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