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Three‐level hybrid modeling for systematic optimization of biocatalytic synthesis: α‐glucosyl glycerol production by enzymatic trans‐glycosylation from sucrose

Mechanism‐based kinetic models are rigorous tools to analyze enzymatic reactions, but their extension to actual conditions of the biocatalytic synthesis can be difficult. Here, we demonstrate (mechanistic‐empirical) hybrid modeling for systematic optimization of the sucrose phosphorylase‐catalyzed g...

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Autores principales: Sigg, Alexander, Klimacek, Mario, Nidetzky, Bernd
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8518044/
https://www.ncbi.nlm.nih.gov/pubmed/34232503
http://dx.doi.org/10.1002/bit.27878
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author Sigg, Alexander
Klimacek, Mario
Nidetzky, Bernd
author_facet Sigg, Alexander
Klimacek, Mario
Nidetzky, Bernd
author_sort Sigg, Alexander
collection PubMed
description Mechanism‐based kinetic models are rigorous tools to analyze enzymatic reactions, but their extension to actual conditions of the biocatalytic synthesis can be difficult. Here, we demonstrate (mechanistic‐empirical) hybrid modeling for systematic optimization of the sucrose phosphorylase‐catalyzed glycosylation of glycerol from sucrose, to synthesize the cosmetic ingredient α‐glucosyl glycerol (GG). The empirical model part was developed to capture nonspecific effects of high sucrose concentrations (up to 1.5 M) on microscopic steps of the enzymatic trans‐glycosylation mechanism. Based on verified predictions of the enzyme performance under initial rate conditions (Level 1), the hybrid model was expanded by microscopic terms of the reverse reaction to account for the full‐time course of GG synthesis (Level 2). Lastly (Level 3), the application of the hybrid model for comprehensive window‐of‐operation analysis and constrained optimization of the GG production (~250 g/L) was demonstrated. Using two candidate sucrose phosphorylases (from Leuconostoc mesenteroides and Bifidobacterium adolescentis), we reveal the hybrid model as a powerful tool of “process decision making” to guide rational selection of the best‐suited enzyme catalyst. Our study exemplifies a closing of the gap between enzyme kinetic models considered for mechanistic research and applicable in technologically relevant reaction conditions; and it highlights the important benefit thus realizable for biocatalytic process development.
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spelling pubmed-85180442021-10-21 Three‐level hybrid modeling for systematic optimization of biocatalytic synthesis: α‐glucosyl glycerol production by enzymatic trans‐glycosylation from sucrose Sigg, Alexander Klimacek, Mario Nidetzky, Bernd Biotechnol Bioeng ARTICLES Mechanism‐based kinetic models are rigorous tools to analyze enzymatic reactions, but their extension to actual conditions of the biocatalytic synthesis can be difficult. Here, we demonstrate (mechanistic‐empirical) hybrid modeling for systematic optimization of the sucrose phosphorylase‐catalyzed glycosylation of glycerol from sucrose, to synthesize the cosmetic ingredient α‐glucosyl glycerol (GG). The empirical model part was developed to capture nonspecific effects of high sucrose concentrations (up to 1.5 M) on microscopic steps of the enzymatic trans‐glycosylation mechanism. Based on verified predictions of the enzyme performance under initial rate conditions (Level 1), the hybrid model was expanded by microscopic terms of the reverse reaction to account for the full‐time course of GG synthesis (Level 2). Lastly (Level 3), the application of the hybrid model for comprehensive window‐of‐operation analysis and constrained optimization of the GG production (~250 g/L) was demonstrated. Using two candidate sucrose phosphorylases (from Leuconostoc mesenteroides and Bifidobacterium adolescentis), we reveal the hybrid model as a powerful tool of “process decision making” to guide rational selection of the best‐suited enzyme catalyst. Our study exemplifies a closing of the gap between enzyme kinetic models considered for mechanistic research and applicable in technologically relevant reaction conditions; and it highlights the important benefit thus realizable for biocatalytic process development. John Wiley and Sons Inc. 2021-07-28 2021-10 /pmc/articles/PMC8518044/ /pubmed/34232503 http://dx.doi.org/10.1002/bit.27878 Text en © 2021 The Authors. Biotechnology and Bioengineering published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle ARTICLES
Sigg, Alexander
Klimacek, Mario
Nidetzky, Bernd
Three‐level hybrid modeling for systematic optimization of biocatalytic synthesis: α‐glucosyl glycerol production by enzymatic trans‐glycosylation from sucrose
title Three‐level hybrid modeling for systematic optimization of biocatalytic synthesis: α‐glucosyl glycerol production by enzymatic trans‐glycosylation from sucrose
title_full Three‐level hybrid modeling for systematic optimization of biocatalytic synthesis: α‐glucosyl glycerol production by enzymatic trans‐glycosylation from sucrose
title_fullStr Three‐level hybrid modeling for systematic optimization of biocatalytic synthesis: α‐glucosyl glycerol production by enzymatic trans‐glycosylation from sucrose
title_full_unstemmed Three‐level hybrid modeling for systematic optimization of biocatalytic synthesis: α‐glucosyl glycerol production by enzymatic trans‐glycosylation from sucrose
title_short Three‐level hybrid modeling for systematic optimization of biocatalytic synthesis: α‐glucosyl glycerol production by enzymatic trans‐glycosylation from sucrose
title_sort three‐level hybrid modeling for systematic optimization of biocatalytic synthesis: α‐glucosyl glycerol production by enzymatic trans‐glycosylation from sucrose
topic ARTICLES
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8518044/
https://www.ncbi.nlm.nih.gov/pubmed/34232503
http://dx.doi.org/10.1002/bit.27878
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