Fig. 1: RFdiffusion2 overview.

a, De novo enzyme design starts from a configuration of catalytic groups around the reaction transition state(s) (a theozyme) generated using quantum chemistry, protein structural analysis and/or chemical reasoning. b, RFdiffusion2 generates protein structures that support the theozyme. In row 1, the backbone trajectory shows the amino acid residue frames (pastel) as they transform from a sample drawn from the noise distribution into a protein backbone. Row 2—a zoom-in of row 1—shows the non-motif side-chain atoms (slate gray) connecting the atomic motif (teal) with the protein backbone. At t = 1 the intra-residue bonds are shown for the atomized residues. Right, The distances between the Cα coordinates of the unindexed, atomized residues and the backbone residues they superimpose at t = 1. Over the course of the trajectory, the model matches these unindexed residues to indexed residues of the protein backbone, such that by the end of the trajectory the unindexed residue’s Cα occupies the same location as the Cα of the protein backbone in Euclidean space. c, The design pipeline starts from the input theozyme, followed by RFdiffusion2 to generate the structure, and LigandMPNN to generate amino acid sequences that encode the structure and stabilize the transition state. Designs are evaluated by all-atom structure prediction (for example, using Chai-1 and AF3) and are considered an in silico success if the design (pastel) and prediction (light gray) align to a sufficient degree. Two representative examples of consistency between design model and predicted structure at the level we take to constitute a success are shown in the right panels. The two cases pictured are the creatinase and taurine dioxygenase motifs from the AME benchmark described in ‘AME benchmark’ in the Results (AME IDs: M0096_1chm AND M0129_1os7).