SymFold: Synergizing Evolutionary and Structural Priors for Accurate Protein Inverse Folding
cs.AI
Submitted: 2026-09-01
Updated: 2026-09-01
Comments: 13 pages, 5 figures
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
The gist: 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
Terminology
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.
Sources
- PiFold: Toward effective and efficient protein inverse folding
- AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB
- Knowledge-Design: Pushing the Limit of Protein Design via Knowledge Refinement
- Elucidating the Design Space of Multimodal Protein Language Models
- Learning from Protein Structure with Geometric Vector Perceptrons
- Auto-Encoding Variational Bayes
- Generative De Novo Protein Design with Global Context
- DPLM-2: A Multimodal Diffusion Protein Language Model
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