Title: RadDiff: Retrieval-Augmented Denoising Diffusion for Protein Inverse Folding
Primary area: learning on graphs and other geometries & topologies
Keywords: Protein Design, Retrieval Augmentation, Graph

Abstract: Protein inverse folding, the design of an amino acid sequence based on a target 3D structure, is a fundamental problem of computational protein engineering. Existing methods either generate sequences without leveraging external knowledge or relying on protein language models(PLMs). The former omits the evolutionary information stored in protein databases, while the latter is parameter-inefficient and inflexible to adapt to ever-growing protein data.
To overcome the above drawbacks, in this paper we propose a novel method, called $\underline{\text{r}}$etrieval-$\underline{\text{a}}$ugmented $\underline{\text{d}}$enoising $\underline{\text{diff}}$usion ($\mbox{RadDiff}$), for protein inverse folding. Given the target protein backbone, RadDiff uses a hierarchical search strategy to efficiently retrieve structurally similar proteins from large protein databases. The retrieved structures are then aligned residue-by-residue to the target to construct a position-specific amino acid profile, which serves as an evolutionary-informed prior that conditions the denoising process. A lightweight integration module is further designed to incorporate this prior effectively.
Experimental results on the CATH, PDB, and TS50 datasets show that $\mbox{RadDiff}$ consistently outperforms existing methods, improving sequence recovery rate by up to 19\%.
Experimental results also demonstrate that RadDiff generates highly foldable sequences and scales effectively with database size.

TL;DR: A retrieval-augmented denoising diffusion method for protein inverse folding.