New AI tool helps match enzymes to substrates

A new artificial intelligence-powered tool can help researchers determine how well an enzyme fits with a desired target, helping them find the best enzyme and substrate combination for applications from catalysis to medicine to manufacturing. Led by Huimin Zhao, a professor of chemical and biomolecular engineering at the University of Illinois Urbana-Champaign, the researchers developed EZSpecificity using new enzyme-substrate pair data and a new machine learning algorithm. They have made the tool freely available online and published their results in the journal Nature. "If we want a certain product using an enzyme, we want to use the best enzyme and substrate combination," said Zhao, who also is the director of the NSF Molecule Maker Lab Institute and of the NSF iBioFoundry at the University of Illinois "EZSpecificity is an AI model that can analyze an enzyme sequence and then predict which substrate best can fit into that enzyme. It is highly complementary to the CLEAN AI model that we developed to predict an enzyme's function from its sequence more than two years ago."