Joint Design of Protein Surface and Structure Using a Diffusion Bridge Model
- URL: http://arxiv.org/abs/2511.16675v1
- Date: Sat, 08 Nov 2025 12:31:07 GMT
- Title: Joint Design of Protein Surface and Structure Using a Diffusion Bridge Model
- Authors: Guanlue Li, Xufeng Zhao, Fang Wu, Sören Laue,
- Abstract summary: PepBridge is a novel framework for the joint design of protein surface and structure.<n>It seamlessly integrates receptor surface geometry and biochemical properties.<n>It generates complete protein structures through a multi-step process.
- Score: 11.265413061961128
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Protein-protein interactions (PPIs) are governed by surface complementarity and hydrophobic interactions at protein interfaces. However, designing diverse and physically realistic protein structure and surfaces that precisely complement target receptors remains a significant challenge in computational protein design. In this work, we introduce PepBridge, a novel framework for the joint design of protein surface and structure that seamlessly integrates receptor surface geometry and biochemical properties. Starting with a receptor surface represented as a 3D point cloud, PepBridge generates complete protein structures through a multi-step process. First, it employs denoising diffusion bridge models (DDBMs) to map receptor surfaces to ligand surfaces. Next, a multi-model diffusion model predicts the corresponding structure, while Shape-Frame Matching Networks ensure alignment between surface geometry and backbone architecture. This integrated approach facilitates surface complementarity, conformational stability, and chemical feasibility. Extensive validation across diverse protein design scenarios demonstrates PepBridge's efficacy in generating structurally viable proteins, representing a significant advancement in the joint design of top-down protein structure.
Related papers
- SaDiT: Efficient Protein Backbone Design via Latent Structural Tokenization and Diffusion Transformers [50.18388227899971]
We present SaDiT, a novel framework that accelerates protein backbone generation by integrating SaProt Tokenization with a Diffusion Transformer (DiT) architecture.<n>Experiments demonstrate that SaDiT outperforms state-of-the-art models, including RFDiffusion and Proteina, in both computational speed and structural viability.
arXiv Detail & Related papers (2026-02-06T13:50:13Z) - Surface-based Molecular Design with Multi-modal Flow Matching [64.00572241268597]
SurfFlow is a novel surface-based generative algorithm that enables comprehensive co-design of sequence, structure, and surface for peptides.<n> evaluated on the comprehensive PepMerge benchmark, SurfFlow consistently outperforms full-atom baselines across all metrics.
arXiv Detail & Related papers (2026-01-08T02:19:29Z) - PPDiff: Diffusing in Hybrid Sequence-Structure Space for Protein-Protein Complex Design [20.033392739225658]
PPDiff is a diffusion model to jointly design the sequence and structure of binders for arbitrary protein targets.<n> PPDiff builds upon our developed Sequence Structure Inter Network with Causal attention layers.<n>The model is pretrained on PPBench and finetuned on two real-world applications.
arXiv Detail & Related papers (2025-06-13T02:39:14Z) - DS-ProGen: A Dual-Structure Deep Language Model for Functional Protein Design [21.43301218674909]
Inverse Protein Folding is a critical subtask in the field of protein design.<n>We present DS-ProGen, a dual-structure deep language model for functional protein design.<n>By incorporating backbone coordinates as well as surface chemical and geometric descriptors into a next-amino-acid prediction paradigm, DS-ProGen is able to generate functionally relevant and structurally stable sequences.
arXiv Detail & Related papers (2025-05-18T18:08:35Z) - ProteinWeaver: A Divide-and-Assembly Approach for Protein Backbone Design [61.19456204667385]
We introduce ProteinWeaver, a two-stage framework for protein backbone design.<n>ProteinWeaver generates high-quality, novel protein backbones through versatile domain assembly.<n>By introducing a divide-and-assembly' paradigm, ProteinWeaver advances protein engineering and opens new avenues for functional protein design.
arXiv Detail & Related papers (2024-11-08T08:10:49Z) - 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) - AlphaFolding: 4D Diffusion for Dynamic Protein Structure Prediction with Reference and Motion Guidance [18.90451943620277]
This study introduces an innovative 4D diffusion model incorporating molecular dynamics (MD) simulation data to learn dynamic protein structures.<n>Our model exhibits high accuracy in predicting dynamic 3D structures of proteins containing up to 256 amino acids over 32 time steps.
arXiv Detail & Related papers (2024-08-22T14:12:50Z) - A Protein Structure Prediction Approach Leveraging Transformer and CNN
Integration [4.909112037834705]
This paper adopts a two-dimensional fusion deep neural network model, DstruCCN, which uses Convolutional Neural Networks (CCN) and a supervised Transformer protein language model for single-sequence protein structure prediction.
The training features of the two are combined to predict the protein Transformer binding site matrix, and then the three-dimensional structure is reconstructed using energy minimization.
arXiv Detail & Related papers (2024-02-29T12:24:20Z) - Joint Design of Protein Sequence and Structure based on Motifs [11.731131799546489]
We propose GeoPro, a method to design protein backbone structure and sequence jointly.
GeoPro is powered by an equivariant encoder for three-dimensional (3D) backbone structure and a protein sequence decoder guided by 3D geometry.
Our method discovers novel $beta$-lactamases and myoglobins which are not present in protein data bank (PDB) and UniProt.
arXiv Detail & Related papers (2023-10-04T03:07:03Z) - 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) - Learning Geometrically Disentangled Representations of Protein Folding
Simulations [72.03095377508856]
This work focuses on learning a generative neural network on a structural ensemble of a drug-target protein.
Model tasks involve characterizing the distinct structural fluctuations of the protein bound to various drug molecules.
Results show that our geometric learning-based method enjoys both accuracy and efficiency for generating complex structural variations.
arXiv Detail & Related papers (2022-05-20T19:38:00Z) - Independent SE(3)-Equivariant Models for End-to-End Rigid Protein
Docking [57.2037357017652]
We tackle rigid body protein-protein docking, i.e., computationally predicting the 3D structure of a protein-protein complex from the individual unbound structures.
We design a novel pairwise-independent SE(3)-equivariant graph matching network to predict the rotation and translation to place one of the proteins at the right docked position.
Our model, named EquiDock, approximates the binding pockets and predicts the docking poses using keypoint matching and alignment.
arXiv Detail & Related papers (2021-11-15T18:46:37Z)
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.