By combining artificial intelligence with automated robotics and synthetic biology, researchers at the University of Illinois Urbana-Champaign have dramatically improved performance of two important industrial enzymes and created a user-friendly, fast process to improve many more. Enzymes have been increasingly used in energy production, in therapeutics, even in consumer products like laundry detergent. But they are not as widely used as they could be, because they still have limitations.

Our technology can help address those limitations efficiently, said Zhao, who also is affiliated with the Carl R. Woese Institute for Genomic Biology at the U. of I. Enzymes are proteins that carry out specific catalytic functions that drive many biological processes. Those seeking to harness enzymes to advance medicine, technology, energy or sustainability often run into roadblocks involving an enzyme’s efficiency or its ability to single out a desired target amidst a complex chemical environment, Zhao said.

Improving protein function, particularly enzyme function, is challenging because we don’t know exactly what kinds of mutations we should introduce and it’s usually not just a single mutation; it’s a lot of synergistic mutations, Zhao said. With our model of integrating AI and automated synthetic biology, we offer an efficient way to solve that problem. Zhao’s group previously reported an AI model to predict an enzyme’s function based on its sequence.

In the new paper, the researchers take their AI a step farther: predicting what changes to a known protein would improve its function. In a typically sized enzyme, the possible number of variations is larger than the number of atoms in the universe, said Nilmani Singh, the co-first author of the paper. So we use the AI method to help us create a relatively small library of potentially useful variant combinations, instead of randomly searching the whole protein sequence.

Source: https://news.illinois.edu/self-driving-lab-ai-and-automated-biology-combine-to-improve-enzymes/