  Protein diffusion models have emerged as a promising approach for protein
design. One such pioneering model is Genie, a method that asymmetrically
represents protein structures during the forward and backward processes, using
simple Gaussian noising for the former and expressive SE(3)-equivariant
attention for the latter. In this work we introduce Genie 2, extending Genie to
capture a larger and more diverse protein structure space through architectural
innovations and massive data augmentation. Genie 2 adds motif scaffolding
capabilities via a novel multi-motif framework that designs co-occurring motifs
with unspecified inter-motif positions and orientations. This makes possible
complex protein designs that engage multiple interaction partners and perform
multiple functions. On both unconditional and conditional generation, Genie 2
achieves state-of-the-art performance, outperforming all known methods on key
design metrics including designability, diversity, and novelty. Genie 2 also
solves more motif scaffolding problems than other methods and does so with more
unique and varied solutions. Taken together, these advances set a new standard
for structure-based protein design. Genie 2 inference and training code, as
well as model weights, are freely available at:
https://github.com/aqlaboratory/genie2.
