Landwehr, G. M.; Bogart, J. W.; Magalhaes, C.; Hammarlund, E.; Karim, A. S.; Jewett, M. C.

Enzyme engineering is limited by the challenge of rapidly generating and using large datasets of sequence-function relationships for predictive design. To address this challenge, we developed a machine learning (ML)-guided platform that integrates cell-free DNA assembly, cell-free gene expression, and functional assays to rapidly map fitness landscapes across protein sequence space and optimize enzymes for multiple, distinct chemical reactions. We applied this platform to engineer amide synthetases by evaluating substrate preference for 1,217 enzyme variants in 10,953 unique reactions. We used these data to build augmented ridge regression ML models for predicting amide synthetase variants capable of making 9 small molecule pharmaceuticals. Our ML-guided, cell-free framework promises to accelerate enzyme engineering by enabling iterative exploration of protein sequence space to build specialized biocatalysts in parallel.