Science Publishes RFdiffusion3: AI Achieves Atom-Level Protein Design with Experimental Validation

An international research team comprising the University of Washington, Oxford University, and NVIDIA published the detailed design scheme for the generative artificial intelligence system RFdiffusion3 in the journal Science on October 16 local time. The core breakthrough of this system lies in its ability to design proteins and nucleic acids based on individual atomic combinations, with the functionality of the designed proteins verified through real chemical reactions.

The corresponding author of this study is David Baker, who won the 2024 Nobel Prize in Chemistry for his contributions to predicting protein structures using computers. His landmark achievements include developing the RosettaFold protein structure prediction system. While previous versions RFdiffusion1 and 2 relied primarily on RosettaFold's structure prediction network, RFdiffusion3 references the network architecture of AlphaFold3. It can represent a single amino acid with up to 14 atoms, designing novel protein structures by directly adjusting the three-dimensional coordinates of each atom.

Researchers compared design effectiveness across 41 active sites of existing enzymes. Results showed that RFdiffusion3 outperformed RFdiffusion2 in 37 of these tests, with generation speed approximately ten times faster. Crucially, the paraoxon-degrading enzyme and peptide-cleaving enzyme designed by RFdiffusion3 successfully completed their expected chemical reactions in real experiments. This result marks a progression in AI-based protein research from 'predicting existing structures' to 'designing new molecules with target structures and functions,' confirmed at the experimental level.