# ZeUS

Generating symmetric protein complexes previously required a forward pass over the entire assembly, so time and memory grew with assembly size for higher-order symmetry, while training data on symmetric structures stayed sparse. ZeUS recasts symmetric assembly generation as sampling a single canonical subunit in a quotient space, using phantom subunits to preserve the model's internal operations, turning any SE(3)-equivariant backbone generative model into a symmetric assembly generator without retraining. On FrameDiff, FoldFlow and RFdiffusion, design quality matches full-complex inference while forward-pass time and memory fall with the order of the symmetry group, and it generates symmetric complexes over 10,000 residues on a single A100.

Generating symmetric protein complexes previously required a forward pass over the entire assembly, so time and memory grew with assembly size for higher-order symmetry, while training data on symmetric structures stayed sparse. ZeUS recasts symmetric assembly generation as sampling a single canonical subunit in a quotient space, using phantom subunits to preserve the model's internal operations, turning any SE(3)-equivariant backbone generative model into a symmetric assembly generator without retraining. On FrameDiff, FoldFlow and RFdiffusion, design quality matches full-complex inference while forward-pass time and memory fall with the order of the symmetry group, and it generates symmetric complexes over 10,000 residues on a single A100.