SymFold: Synergizing Evolutionary and Structural Priors for Accurate Protein Inverse Folding

2026-09-01

AI Summary

该研究通过结合蛋白质语言模型和多模态蛋白质语言模型，提出了一种对称双路径架构SymFold，以提高蛋白质逆折叠的准确性。

Abstract

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.

Problem

• protein inverse folding
• amino acid sequences
• 3D protein structure
• enzyme engineering
• drug discovery

Innovation

• Symmetric Dual-Path Architecture
• Protein Inverse Folding
• Multimodal Protein Language Models
• Pretrained Structural Knowledge