Accelerating Enzyme Engineering with AI and Cell-Free Systems

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Enzyme engineering is an important field in biotechnology and traditionally has faced challenges in efficiently exploring relationship between protein sequences and their functions. A new machine learning (ML)-guided platform has lately come up and it is said to revolutionize the process by combining with cell-free technologies.

The platform integrates DNA assembly, gene expression and functional testing in a cell-free environment. The setup helps researchers in mapping protein fitness space and how changes in enzyme sequences impact the performance. Scientists can therefore design and optimize enzymes for specific tasks such as creating pharmaceuticals or other high-value chemicals.

Key Achievements

The researchers engineered enzymes called amide synthetases using this approach by studying more than 1,200 variants in nearly 11,000 chemical reactions. They were able to predict and produce enzyme variants with significantly improved performance by analyzing the resulting data with advanced ML models.

Breaking Traditional Barriers

Traditional methods for enzyme engineering often rely on directed evolution and it involves testing many variants to identify improvements. However, the methods can be slow and limited in scope. It often missed complex interactions between mutations.

The new ML-guided framework overcomes such hurdles by rapidly building datasets and predicting outcomes with higher accuracy. The use of cell-free systems enables quicker protein synthesis and testing without the need for time-consuming cloning or transformation steps.

Sustainable Future for Biocatalysis

The team demonstrated potential of the platform by converting a generalist enzyme into specialists and that was tailored for specific reactions.

The team identified mutations that improved the ability of enzyme to work with complex acids, amines and more such challenging substrates.

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