SymFold：融合进化与结构先验实现精准蛋白质逆折叠
SymFold: Synergizing Evolutionary and Structural Priors for Accurate Protein Inverse Folding
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中文总结 AI 辅助
本文提出SymFold对称双路径架构，融合PLMs序列进化知识与MPLMs结构知识，在蛋白质逆折叠基准上实现SOTA性能，为该领域提供新方向。
中文摘要 AI 辅助
蛋白质逆折叠旨在为给定的3D蛋白质结构恢复氨基酸序列，支撑酶工程、药物发现等广泛应用。现有方法通常遵循串行流程：结构编码器预测粗序列，再由蛋白质语言模型（PLMs）优化。但PLMs仅执行事后序列编辑，优化效果受上游结构编码器质量限制。近期多模态蛋白质语言模型（MPLMs）可直接编码结构以生成具备预训练结构知识的序列，但实验发现其对逆折叠任务效果不佳。因此，本文提出一种对称双路径架构，同时利用PLMs的预训练序列进化知识与MPLMs的预训练结构知识，迭代指导蛋白质序列生成。在标准蛋白质逆折叠基准上的大量实验表明，本文方法达到了SOTA性能，超越了现有方法； ablation研究验证了对称设计的合理性，为该领域研究指明了有前景的方向。
英文摘要
Protein inverse folding aims to recover amino acid sequences for a given 3D protein structure, underpinning broad applications such as enzyme engineering and drug discovery.Current methods often follow a serial pipeline, in which a structure encoder predicts a coarse sequence, which is then refined by protein language models (PLMs). However, because PLMs only perform post-hoc sequence edits, the refinement is bounded by the quality of upstream predictions.Thanks to recent multimodal protein language models (MPLMs), we could directly encode structure to generate sequences with pretrained structural knowledge, but we observe that they are not effective for inverse folding. Therefore, we introduce a symmetric dual-path architecture that both leverages PLMs for pretrained sequence evolution knowledge and MPLMs for pretrained structural knowledge to iteratively guide protein sequence generation.Through extensive experiments across standard protein inverse folding benchmarks, our method achieves state-of-the-art performance, surpassing prior approaches, and ablation studies validate the rationale of our symmetric design, revealing a promising direction for the community.
发表机构
- Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）
- University of Chinese Academy of Sciences（中国科学院大学）
机构由 AI 辅助整理，请以论文原文为准。
