RadDiff: Retrieval-Augmented Denoising Diffusion for Protein Inverse Folding

TL;DR

RadDiff is a novel retrieval-augmented diffusion model for protein inverse folding that leverages up-to-date protein knowledge, outperforming existing methods in sequence recovery and foldability across multiple datasets.

Contribution

Introduces RadDiff, a retrieval-augmented diffusion approach that incorporates current protein knowledge into inverse folding, addressing limitations of prior methods.

Findings

RadDiff outperforms existing methods with up to 19% higher sequence recovery.

RadDiff generates highly foldable sequences.

RadDiff scales effectively with database size.

Abstract

Protein inverse folding, the design of an amino acid sequence based on a target protein 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 knowledge stored in natural protein data, 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. In RadDiff, a novel retrieval-augmentation mechanism is designed to capture the up-to-date protein knowledge. We further design a knowledge-aware diffusion model that integrates this protein knowledge into the diffusion process via a lightweight module. Experimental results on the CATH, TS50, and PDB2022 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.