Graph Denoising Diffusion for Inverse Protein Folding

TL;DR

This paper introduces a graph denoising diffusion model for inverse protein folding that effectively generates diverse amino acid sequences conditioned on a given backbone, outperforming existing methods in sequence recovery.

Contribution

The paper presents a novel diffusion probabilistic model that incorporates biological priors and graph structures to improve diversity and accuracy in inverse protein folding.

Findings

Achieves state-of-the-art sequence recovery performance.

Generates diverse protein sequences for fixed backbones.

Utilizes amino acid replacement matrices to encode biological priors.

Abstract

Inverse protein folding is challenging due to its inherent one-to-many mapping characteristic, where numerous possible amino acid sequences can fold into a single, identical protein backbone. This task involves not only identifying viable sequences but also representing the sheer diversity of potential solutions. However, existing discriminative models, such as transformer-based auto-regressive models, struggle to encapsulate the diverse range of plausible solutions. In contrast, diffusion probabilistic models, as an emerging genre of generative approaches, offer the potential to generate a diverse set of sequence candidates for determined protein backbones. We propose a novel graph denoising diffusion model for inverse protein folding, where a given protein backbone guides the diffusion process on the corresponding amino acid residue types. The model infers the joint distribution of amino acids conditioned on the nodes' physiochemical properties and local environment. Moreover, we utilize amino acid replacement matrices for the diffusion forward process, encoding the biologically-meaningful prior knowledge of amino acids from their spatial and sequential neighbors as well as themselves, which reduces the sampling space of the generative process. Our model achieves state-of-the-art performance over a set of popular baseline methods in sequence recovery and exhibits great potential in generating diverse protein sequences for a determined protein backbone structure.