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

DOI: https://doi.org/10.1038/s41467-024-55399-0

Central Idea: This paper presents an ML-guided platform for accelerating enzyme engineering by combining cell-free protein synthesis, functional screening, and machine learning to rapidly optimize enzymes for multiple distinct chemical reactions. The approach is demonstrated by engineering variants of an amide synthetase (McbA) to improve synthesis of various pharmaceutical compounds.

Key Concepts:

Cell-Free Platform Integration:

- Combines cell-free DNA assembly, protein expression, and activity screening

- Enables rapid testing of enzyme variants without time-consuming cloning steps

- Complete process from DNA design to activity testing takes hours instead of weeks

Machine Learning Strategy:

- Uses single mutation data to  ...