Radley, E.; Andrews, A.; Kalvet, I.; Deng, Y.; Levy, C.; Ortmayer, M.; Heyes, D.; Megarity, C.; Nunez-Franco, R.; Hutton, A.; Lu, Y.; Baker, D.; Green, A.

Modern protein design methods based on deep learning allow generation of customized protein scaffolds with diverse geometries and functionalities. Here, we capitalize on these recent advances to develop hyper-thermostable de novo CO2 reductases featuring a cobalt porphyrin IX cofactor (CoPPIX). CoPPIX containing enzymes were assembled in vivo through media supplementation with cobalt salts and assessed for photocatalytic CO2 reductase activity. We identified two cysteine-ligated designs that exhibit high activity (>1000 turnovers at rates of up to 25 min-1) while suppressing competing hydrogen evolution pathways. A 2.1 [A] crystal structure shows close agreement to the design model with the Co-Cys bond programmed as intended. This study showcases the power of computational protein design in developing artificial enzymes to activate challenging molecules such as CO2.