We revealed that the redox cofactor preference of malic enzymes can be strikingly converted by applying phylogenetic analysis to machine learning, without experimental screening. This method can predict mutation positions and candidate amino acids that affect substrate specificity, which is challenging to infer solely from the crystal structures. Machine learning uses the structurally homologous but functionally distinct enzymes' amino acid sequences as input datasets to efficiently navigate toward the target function, and potentially provide new fundamental insights into enzyme–substrate specificity. Osaka, Japan – You can't expect a pharmaceutical scientist to switch labs to the facilities available in a television studio and expect the same research output. Enzymes behave exactly the same.

Researchers have demonstrated a proof-of-concept for a novel use of artificial intelligence for enzyme engineering that does not require an enzyme's crystal structure.

You can't move a pharmaceutical scientist from a lab to a kitchen and expect the same research output. Enzymes behave exactly the same: They are dependent upon a specific environment. But now, in a study recently published in ACS Synthetic Biology, researchers from Osaka University have imparted an analogous level of adaptability to enzymes, a goal that has remained elusive for over 30 years. Enzymes perform impressive functions, enabled by the unique arrangement of their constituent amino acids, but usually only within a specific cellular environment. When you change the cellular environment, the enzyme rarely functions well--if at all.