This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Enzymes are biology's tiny chemists. They help build, break and rearrange molecules quickly and precisely.
In the lab, scientists have long put enzymes to work making useful chemicals. But nature's enzymes cannot do every reaction a researcher might want. Sometimes scientists need an enzyme to take on a job it did not evolve to do.
That's where enzyme engineering comes in—researchers make changes to an enzyme, test how those changes affect what the enzyme does and look for versions that work better. The challenge is that a useful enzyme often must get several things right at once. It may need to work quickly, stay stable, produce enough of the desired product and reliably make that product into the correct shape.
But improving one feature can cause another to fall apart. That makes enzyme engineering a balancing act. "Almost all enzymes have to fulfill multiple requirements," says Yang Yang, an HHMI Freeman Hrabowski Scholar at the University of California, Santa Barbara.
SynFit, an open-source AI tool was built to help researchers navigate those competing demands. Developed by researchers in Yang's lab and the lab of his collaborator, Yunan Luo at Georgia Institute of Technology, the tool looks for patterns in experimental data and predicts enzyme variants that are more likely to perform well across several traits at once. Traditionally, scientists improved enzymes through directed evolution—a lab process inspired by natural evolution.
They make many versions of an enzyme, test them, choose the best performers and repeat the process. Computational models can help narrow that search, but many are designed to predict one trait at a time. So, making an enzyme that has to do several things well can mean screening many variants over multiple rounds.
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