Function-guided design of active enzymes

Authors

Hu, M., Wu, L., Yang, Y., Li, F., Zhu, L.

Abstract

Designing enzymes from functional descriptions remains challenging because catalytic activity is governed by sequence-structure-function relationships. Here we present EnzymeArt, a function-conditioned enzyme-design framework centred on a generative sequence model. EnzymeArt couples function-conditioned sequence generation with structure-guided refinement, annotation checks and substrate-aware computational prioritization to select candidates for synthesis and biochemical testing. Across alcohol dehydrogenase (ADH), malate dehydrogenase (MDH) and triacylglycerol lipase design campaigns, 57 of 60 synthesized designs showed crude-lysate activity above matched background controls. Purified representatives further showed quantitative steady-state catalytic activity. The best designed ADH reached kcat = 223.7/s and exceeded a wild-type reference under matched conditions, an MDH reached kcat = 267.57/s despite having only 33% sequence identity to its closest BLASTP hit, and a designed lipase hydrolysed both short- and long-chain triglycerides with apparent activity modestly above that of a commercial lipase reference. Together, these results establish a route for converting functional descriptions into experimentally validated enzyme designs with quantitative steady-state kinetic activity.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 01 Jul 2026.

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