A trio of AI methods tackles enzyme design

Naturally occurring enzymes, while powerful, catalyze only a fraction of the reactions chemists care about. That’s why scientists are eager to design new-to-nature versions that could manufacture drugs more efficiently, break down pollutants, capture carbon, or carry out entirely new forms of chemistry that biology never evolved. But doing this requires placing catalytic residues with a lot of precision—something that has been extremely hard to achieve computationally.Three new papers now report improvements to enzyme design using diffusion models, a class of generative artificial intelligence algorithms that add and then subtract noise. The models—RFdiffusion2, RFdiffusion3, and Riff-Diff—focus on different barriers in enzyme design but all have the same outcome: computationally generated proteins that successfully carry out reactions.Researchers led by 2024 Nobel laureate in chemistry David Baker at the Institute for Protein Design at University of Washington have developed RFdiffusion2, which addresses a long-standing problem in enzyme design: determining where in the sequence the catalytic residues should go, and how the rest of the protein can lock them into place (Nature 2025, DOI: 10.1038/s41586-025-09746-w).“The challenge in enzyme design is placing the groups that are going to carry out the catalysis really precisely in 3-dimensional space,” Baker says. Earlier methods required researchers to manually specify both the identity and the sequence position of catalytic residues before the rest of the protein could be built. This manual step restricted how broadly designers could search for scaffold solutions, limiting both efficiency and creativity in the final designs, says Seth Woodbury, a graduate student in Baker’s