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 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 (Zero-shot Unified Symmetrization), a framework that converts any pretrained $\mathrm{SE}(3)$-equivariant backbone generative model into an efficient generator of symmetric assemblies with no retraining. operates on a single canonical subunit and utilize 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 for IPA-based models (FrameDiff, FoldFlow) and axial-attention-based RFdiffusion, showing high generation efficiency with minimal degradation to generation quality. We then apply to generate symmetric complexes of over $10,000$ residues with exact, untruncated attention, a scale previously intractable with any model.