RFdiffusion generates protein backbones. ProteinMPNN designs the amino acid sequences that fold into those backbones. Together, the two tools constitute the first genuinely useful pipeline for de novo protein design at scale. Published in Nature in 2022 and 2023 respectively by teams at the Baker Lab and University of Washington, both tools are open-source and have been applied to drug discovery, enzyme engineering, and vaccine antigen design.
How RFdiffusion Works
RFdiffusion adapts the diffusion process used in image generation to protein backbone geometry. Starting from random atomic noise, the model iteratively denoises a cloud of Calpha coordinates toward a physically reasonable protein backbone, conditioned on any structural or functional constraints specified by the user. Conditioning can specify binding partners, active site geometries, symmetry requirements for multimers, or target binding interfaces. The model was trained on protein structures from the PDB using a denoising score matching objective.
How ProteinMPNN Works
Given a backbone geometry from RFdiffusion or any other source, ProteinMPNN performs inverse folding: it predicts the amino acid sequence most likely to adopt that backbone conformation when folded. The model was trained to predict the sequence of a protein given its backbone coordinates and a masked or alternative sequence context, using a graph neural network architecture that encodes backbone geometry as a set of distance and angle features.
The Influenza Binding Interface Result
A 2023 Science paper from the Baker Lab designed binders to the influenza hemagglutinin stem region using RFdiffusion plus ProteinMPNN. The designed binders achieved sub-Angstrom backbone RMSD to the computational design at crystal structure determination. The binding affinity was in the nanomolar range. This was the first demonstration of fully computational protein design achieving functional binders to a validated drug target without any experimental optimization cycles after the initial computational design.
Limitations and Dual-Use Concerns
RFdiffusion and ProteinMPNN together achieve roughly 1.5% success rates on novel target binder design from scratch, meaning 98.5% of designed sequences fail experimental characterization. They are not a replacement for experimental protein engineering. The dual-use concern is real: the same pipeline that designs therapeutic proteins can design novel proteins with other functions, including potential toxins with novel sequences that evade biosecurity screening.
Related coverage: ESM3: The Protein Language Model That Unifies Sequence, Structure and Function | AlphaFold 3 in Drug Discovery: Where It Works and Where It Fails | How Protein Language Models Learned to Design Dangerous Proteins
Primary sources: Watson JL et al., Science 2023 (RFdiffusion); Dauparas J et al., Science 2022 (ProteinMPNN).