用检索增强扩散模型设计蛋白质序列，准确率提升19%
RadDiff: Retrieval-Augmented Denoising Diffusion for Protein Inverse Folding
- 引入检索增强机制获取最新蛋白知识
- 扩散过程融合外部知识，序列恢复率最高提升19%
- 适合需要高可折叠性序列的设计场景
蛋白质逆折叠——根据目标结构设计氨基酸序列——是计算蛋白质工程的核心问题。现有方法或不利用外部知识，或依赖蛋白语言模型（PLMs），前者忽略自然蛋白数据中的知识，后者参数效率低且难以适应不断增长的蛋白数据。为此，本文提出一种新方法RadDiff（检索增强去噪扩散），设计了新颖的检索增强机制以捕获最新蛋白知识，并构建轻量级知识感知扩散模型，将知识融入扩散过程。在CATH、TS50和PDB2022数据集上的实验表明，RadDiff持续优于现有方法，序列恢复率最高提升19%。结果还显示，RadDiff生成的序列具有高可折叠性，且能随数据库规模有效扩展。
原文摘要 · Abstract (English)
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.
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