Reverse engineering: transaminase biocatalyst development using ancestral sequence reconstruction

Matthew Wilding; Thomas S. Peat; Subha Kalyaanamoorthy; Janet Newman; Colin Scott; Lars S. Jermiin

doi:10.1039/c7gc02343j

Reverse engineering: transaminase biocatalyst development using ancestral sequence reconstruction

Matthew Wilding^*, Thomas S. Peat, Subha Kalyaanamoorthy, Janet Newman, Colin Scott, Lars S. Jermiin

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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Abstract

The development of new biocatalysts using ancestral sequence reconstruction is reported. When applied to an ω-transaminase, the ancestral proteins demonstrated novel and superior activities with eighty percent of the forty compounds tested compared to the modern day protein, and improvements in activity of up to twenty fold. These included a range of compounds pertinent as feedstocks in polyamide manufacture.

Original language	English
Pages (from-to)	5375-5380
Number of pages	6
Journal	Green Chemistry
Volume	19
Issue number	22
DOIs	https://doi.org/10.1039/c7gc02343j
Publication status	Published - 2017
Externally published	Yes

Bibliographical note

Copyright The Royal Society of Chemistry 2017. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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Publisher version (open access)Final published version, 1.04 MBLicence: CC BY-NC

Cite this

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AU - Newman, Janet

AU - Scott, Colin

AU - Jermiin, Lars S.

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AB - The development of new biocatalysts using ancestral sequence reconstruction is reported. When applied to an ω-transaminase, the ancestral proteins demonstrated novel and superior activities with eighty percent of the forty compounds tested compared to the modern day protein, and improvements in activity of up to twenty fold. These included a range of compounds pertinent as feedstocks in polyamide manufacture.

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Reverse engineering: transaminase biocatalyst development using ancestral sequence reconstruction

Abstract

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