A recent study has combined machine learning with cell-free gene expression systems to accelerate the optimization of enzymes for various chemical reactions. The framework, called DBTL (design-build-test-learn), enables the simultaneous evaluation of multiple enzyme variants, greatly increasing the speed and efficiency of enzyme engineering. The researchers used a dataset of over 1,200 enzyme variants and more than 10,900 reactions to train their machine learning models, which accurately predicted variants likely to excel at producing specific small molecules. In tests, ML-optimized mutants showed improvements ranging from 1.6 to 42-fold for the synthesis of nine different pharmaceuticals. The ML-guided framework has the potential to revolutionize the field of enzyme engineering, allowing for the rapid creation of specialized biocatalysts across various domains. This approach could particularly benefit pharmaceutical applications, where cost-effective and sustainable production methods are crucial. Overall, the integration of machine learning and cell-free gene expression has the potential to accelerate the discovery of novel enzymes and their applications in various industries.
Enhancing Enzyme Efficiency with AI-Driven Design
