AI method advances customized enzyme design (opens in new tab)

New AI method revolutionizes the design of enzymes Riff-Diff scaffolds de novo enzymes starting from catalytic arrays. Credit: Nature (2025). DOI: 10.1038/s41586-025-09747-9

Enzymes with specific functions are becoming increasingly important in industry, medicine and environmental protection. For example, they make it possible to synthesize chemicals in a more environmentally friendly way, produce active ingredients in a targeted manner or break down environmentally harmful substances.

Researchers from Gustav Oberdorfer’s working group at the Institute of Biochemistry at Graz University of Technology (TU Graz), together with colleagues from the University of Graz, have now published a study in Nature describing a new method for the design of customized enzymes.

The technology called Riff-Diff (Rotamer Inverted Fragment Finder–Diffusion) makes it possible to accurately and efficiently build the protein structure specifically around the active center instead of searching for a suitable structure from existing databases. The resulting enzymes are not only significantly more active than previous artificial enzymes, but also more stable.

Highly efficient biocatalysts

"Instead of putting the cart before the horse and searching databases to see which structure matches an active center, we can now design enzymes for chemical reactions efficiently and precisely from scratch using a one-shot process," says Gustav Oberdorfer, whose ERC project HELIXMOLD was a key basis for this breakthrough.

New AI method revolutionizes the design of enzymes From left: Adrian Tripp, Markus Braun and Gustav Oberdorfer from the Institute of Biochemistry at TU Graz as well as Mélanie Hall from the Institute of Chemistry at Uni Graz. Credit: IBC—TU Graz

Lead author Markus Braun from the Institute of Biochemistry at TU Graz adds, "The enzymes that can now be produced are highly efficient biocatalysts that can also be used in industrial environments thanks to their stability. This drastically reduces the screening and optimization effort previously required and makes enzyme design more accessible to the wider biotechnology community."

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