RadDiff: Retrieval-Augmented Denoising Diffusion for Protein Inverse Folding

• URL: http://arxiv.org/abs/2512.00126v1
• Date: Fri, 28 Nov 2025 07:32:15 GMT
• Title: RadDiff: Retrieval-Augmented Denoising Diffusion for Protein Inverse Folding
• Abstract summary: Protein inverse folding is a fundamental problem of computational protein engineering.<n>Existing methods either generate sequences without leveraging external knowledge or relying on protein language models.<n>We propose retrieval-augmented denoising diffusion (RadDiff) for protein inverse folding.
• Score: 32.214133513502205
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Protein inverse folding, the design of an amino acid sequence based on a target 3D structure, is a fundamental problem of computational protein engineering. Existing methods either generate sequences without leveraging external knowledge or relying on protein language models (PLMs). The former omits the evolutionary information stored in protein databases, while the latter is parameter-inefficient and inflexible to adapt to ever-growing protein data. To overcome the above drawbacks, in this paper we propose a novel method, called retrieval-augmented denoising diffusion (RadDiff), for protein inverse folding. Given the target protein backbone, RadDiff uses a hierarchical search strategy to efficiently retrieve structurally similar proteins from large protein databases. The retrieved structures are then aligned residue-by-residue to the target to construct a position-specific amino acid profile, which serves as an evolutionary-informed prior that conditions the denoising process. A lightweight integration module is further designed to incorporate this prior effectively. Experimental results on the CATH, PDB, and TS50 datasets show that RadDiff consistently outperforms existing methods, improving sequence recovery rate by up to 19%. Experimental results also demonstrate that RadDiff generates highly foldable sequences and scales effectively with database size.

Related papers

• SymFold: Synergizing Evolutionary and Structural Priors for Accurate Protein Inverse Folding [30.025725962157498]
  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.
  arXiv  Detail & Related papers  (2026-09-01T14:57:24Z)
• S$^2$Drug: Bridging Protein Sequence and 3D Structure in Contrastive Representation Learning for Virtual Screening [72.89086338778098]
  We propose a two-stage framework for protein-ligand contrastive representation learning.<n>In the first stage, we perform protein sequence pretraining on ChemBL using an ESM2-based backbone.<n>In the second stage, we fine-tune on PDBBind by fusing sequence and structure information through a residue-level gating module.<n>This auxiliary task guides the model to accurately localize binding residues within the protein sequence and capture their 3D spatial arrangement.
  arXiv  Detail & Related papers  (2025-11-10T11:57:47Z)
• ProteinAE: Protein Diffusion Autoencoders for Structure Encoding [64.77182442408254]
  We introduce ProteinAE, a novel and streamlined protein diffusion autoencoder.<n>ProteinAE directly maps protein backbone coordinates from E(3) into a continuous, compact latent space.<n>We demonstrate that ProteinAE achieves state-of-the-art reconstruction quality, outperforming existing autoencoders.
  arXiv  Detail & Related papers  (2025-10-12T14:30:32Z)
• 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)
• Mask prior-guided denoising diffusion improves inverse protein folding [3.1373465343833704]
  Inverse protein folding generates valid amino acid sequences that can fold into a desired protein structure.<n>To tackle such low-confidence residue prediction, we propose a Mask-prior-guided denoising Diffusion framework.<n>MapDiff is a discrete diffusion probabilistic model that iteratively generates amino acid sequences with reduced noise.
  arXiv  Detail & Related papers  (2024-12-10T09:10:28Z)
• 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)
• A PLMs based protein retrieval framework [3.878994100846403]
  We propose a novel protein retrieval framework that mitigates the bias towards sequence similarity.<n>Our framework initiatively harnesses protein language models (PLMs) to embed protein sequences within a high-dimensional feature space.<n>Extensive experiments demonstrate that our framework can equally retrieve both similar and dissimilar proteins.
  arXiv  Detail & Related papers  (2024-07-16T09:52:42Z)
• Efficiently Predicting Protein Stability Changes Upon Single-point Mutation with Large Language Models [51.57843608615827]
  The ability to precisely predict protein thermostability is pivotal for various subfields and applications in biochemistry. We introduce an ESM-assisted efficient approach that integrates protein sequence and structural features to predict the thermostability changes in protein upon single-point mutations.
  arXiv  Detail & Related papers  (2023-12-07T03:25:49Z)
• Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model [14.949807579474781]
  We propose SidechainDiff, a representation learning-based approach that leverages unlabelled experimental protein structures. SidechainDiff is the first diffusion-based generative model for side-chains, distinguishing it from prior efforts that have predominantly focused on generating protein backbone structures.
  arXiv  Detail & Related papers  (2023-10-30T15:23:42Z)
• AbDiffuser: Full-Atom Generation of in vitro Functioning Antibodies [44.149969082612486]
  AbDiffuser is an equivariant and physics-informed diffusion model for antibody 3D structures and sequences. Our approach improves protein diffusion by taking advantage of domain knowledge and physics-based constraints. Numerical experiments showcase the ability of AbDiffuser to generate antibodies that closely track the sequence and structural properties of a reference set.
  arXiv  Detail & Related papers  (2023-07-28T11:57:44Z)
• Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory Prediction [29.375830561817047]
  Self-supervised pre-training methods on proteins have recently gained attention, with most approaches focusing on either protein sequences or structures. We propose the DiffPreT approach to pre-train a protein encoder by sequence-structure joint diffusion modeling. We enhance DiffPreT by a method called Siamese Diffusion Trajectory Prediction (SiamDiff) to capture the correlation between different conformers of a protein.
  arXiv  Detail & Related papers  (2023-01-28T02:48:20Z)
• 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.