Joint Design of Protein Surface and Structure Using a Diffusion Bridge Model
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Abstract (arXiv)
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.
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References · 26
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- 2022Classifier-Free Diffusion Guidancecited by 5,717
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- 2023SE(3) diffusion model with application to protein backbone generationcited by 214
14 more
- 2023Denoising Diffusion Bridge Modelscited by 171
- 2023SE(3)-Stochastic Flow Matching for Protein Backbone Generationcited by 147
- 2022Concrete Score Matching: Generalized Score Matching for Discrete Datacited by 144
- 2022Riemannian Diffusion Modelscited by 120
- 2022Protein structure generation via folding diffusioncited by 113
- 2024Diffusion Bridge Implicit Modelscited by 47
- 2024Full-Atom Peptide Design based on Multi-modal Flow Matchingcited by 34
- 2024PPFlow: Target-aware Peptide Design with Torsional Flow Matchingcited by 26
- 2023DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packingcited by 24
- 2020DeepSurf: A surface-based deep learning approach for the prediction of ligand binding sites on proteinscited by 17
- 2022Pre-training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Bindingcited by 15
- 2024SurfPro: Functional Protein Design Based on Continuous Surfacecited by 14
- 2024ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug designcited by 12
- 2024Hotspot-Driven Peptide Design via Multi-Fragment Autoregressive Extensioncited by 9
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