(176z) A Metagenomic Search Successfully Identifies Natural Amine Dehydrogenases with High Accuracy

The discovery of enzymes from metagenomic data can be a powerful tool to meet some of the shortcomings of biocatalysis and biotechnology. By working with a small set of experimentally validated enzymes of a known function, it is possible to utilize the growing volume of metagenomic databases for genome mining to acquire new enzymes with similar function. These new enzymes may be isofunctional, but can vary greatly in specific activity, substrate scope, and stability. This can lead to a broader set of industrial applications and operating conditions for a family of enzymes.

Among the highly desired biocatalytic alternative to conventional synthesis, amine dehydrogenases are an important emerging native enzyme family for the synthesis of chiral amines, a chemical function present in many bioactive compounds. In this work, we used six validated amine dehydrogenases to generate a Hidden Markov Model used to identify distant homologs in the TARA Oceans and human gut microbiome metagenomic (IGC) databases. The sequences with highest e-values were selected from each database, and representatives were chosen to maximize sequence diversity among the candidates. Corresponding enzymes were verified for expression and activity, and all selected candidates showed amine dehydrogenase activity. A subset of these candidates was furthered characterized to demonstrate the substrate scope, temperature preferences, and structural differences between the enzymes. Stabilities studies and semi-scale preparative reactions were also conducted to demonstrate the biocatalytic applications of these novel AmDHs.