SymFold: Synergizing Evolutionary and Structural Priors for Accurate Protein Inverse Folding· SymFold: 结合进化和结构先验的蛋白质逆折叠
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 kn
结合进化和结构先验，提出SymFold模型改善蛋白质逆折叠预测。
- 核心方法
- SymFold采用对称双路径架构，直接利用多模态蛋白质语言模型编码结构信息，同时结合预训练序列进化知识进行氨基酸序列恢复。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 现有的蛋白质逆折叠方法往往依赖于上下游的串行流程，导致序列精炼受限于初始预测质量。
- 关键实验
- 通过一系列基准测试验证了SymFold模型在不同条件下的优越性，包括与现有方法的对比实验。
- 主要贡献
- 提高了蛋白质逆折叠任务的预测准确性，为酶工程和药物发现提供了更有效的方法。
- 意义与局限
- SymFold的提出不仅推进了蛋白质逆折叠技术的发展，也为相关应用领域带来了新的可能性；局限性在于多模态模型的训练复杂度较高。