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.

提出了一种新颖的图去噪扩散模型，使用氨基酸替代矩阵对扩散过程进行编码，并在多种基准方法中实现了最先进的序列恢复性能，为特定的蛋白质骨架结构生成多样性的蛋白序列具有极大的潜力。