“De novo design of protein structure and function with RFdiffusion,” by Joseph Watson, David Baker, and colleagues, was published in Nature on July 11, 2023. Where AlphaFold predicts the shape of a protein that already exists, RFdiffusion runs the problem backward: it designs entirely new protein structures to meet a specification.
RFdiffusion was built by fine-tuning RoseTTAFold, the Baker lab’s structure-prediction network, on a denoising task, turning it into a generative diffusion model of the same kind that powers AI image generation. Starting from noise, it iteratively refines a protein backbone toward a valid, designable structure.
The paper demonstrates strong results across many design tasks: unconditional generation of new monomers up to 600 residues, symmetric oligomers, scaffolding of functional motifs and enzyme active sites, and design of proteins that bind a chosen target. The authors validated designs experimentally, including a binder whose cryo-EM structure matched the design model to sub-Angstrom accuracy. Earlier methods often required testing tens of thousands of candidates to find one that worked; RFdiffusion produced far higher success rates.
David Baker shared the 2024 Nobel Prize in Chemistry for computational protein design, the field that RFdiffusion advanced; the other half went to DeepMind’s Demis Hassabis and John Jumper for structure prediction.