Metabolic enzyme clustering by coiled coils improves the biosynthesis of resveratrol and mevalonate
The clustering of biosynthetic enzymes is used in nature to channel reaction products and increase the yield of compounds produced by multiple reaction steps. The coupling of multiple enzymes has been shown to increase the biosynthetic product yield. Different clustering strategies have particular a...
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Published in: | AMB Express Vol. 10; no. 1; p. 97 |
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Main Authors: | , , , , , , , |
Format: | Journal Article |
Language: | English |
Published: |
Berlin/Heidelberg
Springer Berlin Heidelberg
24-05-2020
Springer Nature B.V SpringerOpen |
Subjects: | |
Online Access: | Get full text |
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Summary: | The clustering of biosynthetic enzymes is used in nature to channel reaction products and increase the yield of compounds produced by multiple reaction steps. The coupling of multiple enzymes has been shown to increase the biosynthetic product yield. Different clustering strategies have particular advantages as the spatial organization of multiple enzymes creates biocatalytic cascades with a higher efficiency of biochemical reaction. However, there are also some drawbacks, such as misfolding and the variable stability of interaction domains, which may differ between particular biosynthetic reactions and the host organism. Here, we compared different protein-based clustering strategies, including direct fusion, fusion mediated by intein, and noncovalent interactions mediated through small coiled-coil dimer-forming domains. The clustering of enzymes through orthogonally designed coiled-coil interaction domains increased the production of resveratrol in
Escherichia coli
more than the intein-mediated fusion of biosynthetic enzymes. The improvement of resveratrol production correlated with the stability of the coiled-coil dimers. The coiled-coil fusion-based approach also increased mevalonate production in
Saccharomyces cerevisiae
, thus demonstrating the wider applicability of this strategy. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 2191-0855 2191-0855 |
DOI: | 10.1186/s13568-020-01031-5 |