Translating chemical reactions into enzyme sequences enables formate dehydrogenase engineering

Abstract

Enzyme engineering has broad applications across chemistry and biotechnology but remains costly and susceptible to low success rates. Generative artificial intelligence can substantially expand the sequence space accessible to enzyme engineering; however, directing generation towards enzymes that catalyse a user-defined chemical reaction remains challenging. Inspired by machine translation, we formulate enzyme generation as translation between two molecular languages: chemical reactions and amino acid sequences. Here, we introduce REXzyme, a translation model that generates enzymes conditioned on user-defined reactions. First, we evaluate its designs using multiple in silico metrics and characterize the strengths and limitations of its generalisation capacity. Then, we steer REXzyme towards the generation of enhanced formate dehydrogenases for their reverse natural reaction, i.e., the reduction of carbon dioxide. All 20 generated enzymes were expressed and purified as soluble proteins and exhibited CO2-reduction activity, with the most active design reaching 2.5-fold the specific activity of the reference enzyme. Notably, all substitutions in the designed variants were performed on distal positions outside the catalytic site. Our results show that reaction-to-sequence translation provides a route for efficiently engineering enhanced biocatalysts.