Researchers present RFdiffusion3 (RFD3), a new generative AI system that designs proteins and nucleic acids atom by atom, which could enable the ability to engineer novel biomolecules with precise, complex functions. Recent advances in generative deep learning have made it increasingly possible to design proteins from scratch with specific functions. However, most existing approaches work at the amino acid level, which provides enough detail for designing protein structures and interactions but not for precisely engineering enzyme active sites or other functional regions at the atomic scale. Newer methods have introduced atom-level modeling, but they still struggle to design detailed interactions between proteins and nonprotein molecules such as small-molecule ligands, nucleic acids, or catalytic groups. As a result, current approaches remain limited in their ability to create proteins, particularly functional RNA and DNA molecules, with precisely engineered active sites. Jasper Butcher and colleagues developed RFD3, a generative AI system that designs proteins and nucleic acids by modeling individual atoms, enabling precise control over the molecular interactions that give biomolecules their functions. Trained on a broad collection of biological structures, RFD3 can construct complex functional sites, design RNA molecules, and create enzymes that coordinate multiple catalytic components. In computational tests, Butcher et al. show that the model outperforms previous approaches in designing protein structures and organizing catalytic sites. Moreover, laboratory experiments demonstrated its ability to produce functional enzymes from scratch, including enzymes that break down paraoxon, a toxic organophosphate, and a protease engineered to cleave a peptide associated with transthyretin amyloidosis.
Science
Atomic functional site scaffolding in biopolymers with RFdiffusion3
15-Oct-2026