Quantum-Assisted Motif Scaffolding inside Deep Generative Protein Design

Formulate motif placement, symmetry constraints, and long-range topology couplings as QUBO problems and solve them with QAOA/quantum annealing integrated into a generative pipeline (RFdiffusion). The quantum module proposes candidate constraint configurations; the diffusion model realizes backbones consistent with those constraints; AlphaFold2/OpenFold validates structural plausibility. This iterative classical-quantum co-optimization is inspired by quantum-assisted molecular design. While quantum computing has been explored for small-molecule design, its application to discrete, global constraints in protein backbone topology is underexplored. This hybrid approach exploits quantum computing's strength in combinatorial search paired with state-of-the-art deep models for 3D geometry. It extends diffusion-based protein design by offloading combinatorial constraint satisfaction to quantum computing, potentially escaping local minima common in symmetric oligomer or multi-motif scaffolding tasks. This method is promising even with modest qubit counts, benefiting constrained motif packing and symmetry problems, especially for metalloproteins, catalytic pocket pre-organization, or multi-epitope vaccine scaffolds. The impact is a practical, near-term quantum-classical workflow for protein design, making difficult constrained designs more tractable and diverse.

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