A refined picture of the native amine dehydrogenase family revealed by extensive biodiversity screening

Eddy Elisée, Laurine Ducrot, Raphaël Méheust, Karine Bastard, Aurélie Fossey-Jouenne, Gideon Grogan, Eric Pelletier, Jean-Louis Petit, Mark Stam, Véronique Berardinis, Anne Zaparucha, David Vallenet () and Carine Vergne-Vaxelaire ()
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Eddy Elisée: Université Paris-Saclay
Laurine Ducrot: Université Paris-Saclay
Raphaël Méheust: Université Paris-Saclay
Karine Bastard: University of Sydney
Aurélie Fossey-Jouenne: Université Paris-Saclay
Gideon Grogan: University of York
Eric Pelletier: Université Paris-Saclay
Jean-Louis Petit: Université Paris-Saclay
Mark Stam: Université Paris-Saclay
Véronique Berardinis: Université Paris-Saclay
Anne Zaparucha: Université Paris-Saclay
David Vallenet: Université Paris-Saclay
Carine Vergne-Vaxelaire: Université Paris-Saclay

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: Abstract Native amine dehydrogenases offer sustainable access to chiral amines, so the search for scaffolds capable of converting more diverse carbonyl compounds is required to reach the full potential of this alternative to conventional synthetic reductive aminations. Here we report a multidisciplinary strategy combining bioinformatics, chemoinformatics and biocatalysis to extensively screen billions of sequences in silico and to efficiently find native amine dehydrogenases features using computational approaches. In this way, we achieve a comprehensive overview of the initial native amine dehydrogenase family, extending it from 2,011 to 17,959 sequences, and identify native amine dehydrogenases with non-reported substrate spectra, including hindered carbonyls and ethyl ketones, and accepting methylamine and cyclopropylamine as amine donor. We also present preliminary model-based structural information to inform the design of potential (R)-selective amine dehydrogenases, as native amine dehydrogenases are mostly (S)-selective. This integrated strategy paves the way for expanding the resource of other enzyme families and in highlighting enzymes with original features.

Date: 2024
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DOI: 10.1038/s41467-024-49009-2

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