Efficient and Accurate Zero Shot Generation of Symmetric Protein Complexes

Symmetric organization is a fundamental principle of biomolecular complex assembly. The same principle underlies engineered applications, from vaccine platforms to nanomaterials, making symmetric \textit{de novo} generation a core capability for computational protein design. We establish a theoretical foundation equating generation of an assembly with underlying symmetry group $G$ to generation within a quotient space over $G$. This directly allows $\mathrm{SE}(3)$-equivariant models to generate symmetric structures with minimal distribution shift. With this insight we introduce \textbf{\ours{}} (\textbf{Ze}ro-shot \textbf{U}nified \textbf{S}ymmetrization), a framework that converts any pretrained $\mathrm{SE}(3)$-equivariant backbone generative model into an efficient generator of symmetric assemblies with no retraining. \ours{} operates on a single \emph{canonical subunit} and utilize \emph{phantom subunits} to preserve internal model operations, reducing memory and runtime cost by $\mathcal{O}(|G|)$ for finite groups $G$, compared to a full-complex forward pass. We implement \ours{} for IPA-based models (FrameDiff, FoldFlow) and axial-attention-based RFdiffusion, showing high generation efficiency with minimal degradation to generation quality. We then apply \ours{} to generate symmetric complexes of over $10{,}000$ residues with exact, untruncated attention, a scale previously intractable with any model.