Rational assignment of key motifs for function guides in silico enzyme identification

The identification or development of enzymes with new functions remains a significant challenge. A new strategy uses rationally selected sequences anticipated to serve as functional motifs to search the wealth of available genomic data, successfully yielding 17 ( R )-selective amine transaminases. Biocatalysis has emerged as a powerful alternative to traditional chemistry, especially for asymmetric synthesis. One key requirement during process development is the discovery of a biocatalyst with an appropriate enantiopreference and enantioselectivity, which can be achieved, for instance, by protein engineering or screening of metagenome libraries. We have developed an in silico strategy for a sequence-based prediction of substrate specificity and enantiopreference. First, we used rational protein design to predict key amino acid substitutions that indicate the desired activity. Then, we searched protein databases for proteins already carrying these mutations instead of constructing the corresponding mutants in the laboratory. This methodology exploits the fact that naturally evolved proteins have undergone selection over millions of years, which has resulted in highly optimized catalysts. Using this in silico approach, we have discovered 17 ( R )-selective amine transaminases, which catalyzed the synthesis of several ( R )-amines with excellent optical purity up to >99% enantiomeric excess.