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

• URL: http://arxiv.org/abs/2609.01353v1
• Date: Tue, 01 Sep 2026 14:57:24 GMT
• Title: SymFold: Synergizing Evolutionary and Structural Priors for Accurate Protein Inverse Folding
• Abstract summary: Protein inverse folding aims to recover amino acid sequences for a given 3D protein structure.<n>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)<n>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.<n>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.
• Score: 30.025725962157498
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• Scaling Atomistic Protein Binder Design with Generative Pretraining and Test-Time Compute [64.59043351161004]
  We propose Proteina-Complexa, a novel fully atomistic binder generation method unifying both paradigms.<n>Proteina-Complexa sets a new state of the art in computational binder design benchmarks.<n>We also demonstrate interface hydrogen bond optimization, fold class-guided binder generation, and extensions to small molecule targets and enzyme design tasks.
  arXiv  Detail & Related papers  (2026-03-30T01:54:03Z)
• PRISM: Enhancing Protein Inverse Folding through Fine-Grained Retrieval on Structure-Sequence Multimodal Representations [42.870409939729974]
  We present PRISM, a multimodal retrieval-augmented generation framework for inverse folding.<n>It retrieves fine-grained representations of potential motifs from known proteins and integrates them with a hybrid self-cross attention decoder.<n> PRISM establishes new state of the art in both perplexity and amino acid recovery, while also improving foldability metrics.
  arXiv  Detail & Related papers  (2025-10-12T00:45:22Z)
• SpecMER: Fast Protein Generation with K-mer Guided Speculative Decoding [0.0]
  SpecMER (Speculative Decoding via k-mer Guidance) is a novel framework that incorporates biological, structural, and functional priors.<n>It achieves 24-32% speedup over standard autoregressive decoding, along with higher acceptance rates and improved sequence likelihoods.
  arXiv  Detail & Related papers  (2025-09-25T23:26:04Z)
• Protein Inverse Folding From Structure Feedback [78.27854221882572]
  We introduce a novel approach to fine-tune an inverse folding model using feedback from a protein folding model.<n>Our results on the CATH 4.2 test set demonstrate that DPO fine-tuning leads to a significant improvement in average TM-Score.
  arXiv  Detail & Related papers  (2025-06-03T16:02:12Z)
• SFM-Protein: Integrative Co-evolutionary Pre-training for Advanced Protein Sequence Representation [97.99658944212675]
  We introduce a novel pre-training strategy for protein foundation models. It emphasizes the interactions among amino acid residues to enhance the extraction of both short-range and long-range co-evolutionary features. Trained on a large-scale protein sequence dataset, our model demonstrates superior generalization ability.
  arXiv  Detail & Related papers  (2024-10-31T15:22:03Z)
• Structure-informed Language Models Are Protein Designers [69.70134899296912]
  We present LM-Design, a generic approach to reprogramming sequence-based protein language models (pLMs) We conduct a structural surgery on pLMs, where a lightweight structural adapter is implanted into pLMs and endows it with structural awareness. Experiments show that our approach outperforms the state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2023-02-03T10:49:52Z)
• Protein Sequence and Structure Co-Design with Equivariant Translation [19.816174223173494]
  Existing approaches generate both protein sequence and structure using either autoregressive models or diffusion models. We propose a new approach capable of protein sequence and structure co-design, which iteratively translates both protein sequence and structure into the desired state. Our model consists of a trigonometry-aware encoder that reasons geometrical constraints and interactions from context features. All protein amino acids are updated in one shot in each translation step, which significantly accelerates the inference process.
  arXiv  Detail & Related papers  (2022-10-17T06:00:12Z)
• AlphaFold Distillation for Protein Design [25.190210443632825]
  Inverse protein folding is crucial in bio-engineering and drug discovery. Forward folding models like AlphaFold offer a potential solution by accurately predicting structures from sequences. We propose using knowledge distillation on folding model confidence metrics to create a faster and end-to-end differentiable distilled model.
  arXiv  Detail & Related papers  (2022-10-05T19:43:06Z)
• State-specific protein-ligand complex structure prediction with a multi-scale deep generative model [68.28309982199902]
  We present NeuralPLexer, a computational approach that can directly predict protein-ligand complex structures. Our study suggests that a data-driven approach can capture the structural cooperativity between proteins and small molecules, showing promise in accelerating the design of enzymes, drug molecules, and beyond.
  arXiv  Detail & Related papers  (2022-09-30T01:46:38Z)
• Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure Prediction [28.630603355510324]
  We present EvoGen, a meta generative model, to remedy the underperformance of AlphaFold2 for poor MSA targets. By prompting the model with calibrated or virtually generated homologue sequences, EvoGen helps AlphaFold2 fold accurately in low-data regime.
  arXiv  Detail & Related papers  (2022-08-20T10:23:17Z)
• EBM-Fold: Fully-Differentiable Protein Folding Powered by Energy-based Models [53.17320541056843]
  We propose a fully-differentiable approach for protein structure optimization, guided by a data-driven generative network. Our EBM-Fold approach can efficiently produce high-quality decoys, compared against traditional Rosetta-based structure optimization routines.
  arXiv  Detail & Related papers  (2021-05-11T03:40:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.