Accelerated enzyme engineering by machine-learning guided cell-free expression

Landwehr, Grant M.; Bogart, Jonathan W.; Magalhaes, Carol; Hammarlund, Eric; Karim, Ashty S.; Jewett, Michael C.

doi:10.1038/s41467-024-55399-0

articleNature CommunicationsJan 20, 2025GOLD OA

Accelerated enzyme engineering by machine-learning guided cell-free expression

GMGrant M. Landwehr JWJonathan W. Bogart CMCarol Magalhaes EHEric Hammarlund ASAshty S. Karim

Northwestern University · Stanford University

PubMed

Indexed incrossrefdoajpubmed

Abstract

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 develop 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 apply this platform to engineer amide synthetases by evaluating substrate preference for 1217 enzyme variants in 10,953 unique reactions. We use these data to build augmented ridge regression ML models for predicting amide synthetase variants capable of making 9 small…

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Topics & keywords

Topics

Keywords

Protein engineering
Sequence space
Computer science
Sequence (biology)
Function (biology)
Machine learning
Computational biology
Artificial intelligence

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