Joint Design of Protein Surface and Structure Using a Diffusion Bridge Model
📝 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.
💡 Analysis
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.
📄 Content
Proteins are fundamental biological macromolecules that perform their functions through intricate interactions with other biomolecules, particularly through protein-protein interactions (PPIs) [22]. PPIs are primarily determined by surface complementarity and hydrophobic interactions at the interface regions, which facilitate specific and stable binding [36]. Understanding and designing PPIs is a central challenge in computational protein design, which seeks to predict sequences, generate structures, and design proteins with tailored properties while adhering to biochemical and geometric constraints [9]. These constraints are crucial for engineering proteins with desired binding characteristics and functional properties. Recent studies underscore that a protein’s surface features, such as geometry and biochemical properties, have a more direct influence on its biological function than its sequence or backbone structure alone [20,44,53]. This insight is particularly relevant to PPIs, where interacting protein complexes exhibit geometric complementarity in the 3D space.
The interacting surfaces conform to their ligands’ shapes and chemical properties, highlighting the importance of surface characteristics in protein design.
Protein design methods can generally be categorized into three approaches: sequence-based methods [15,30,49], structure-based methods [50,55,58], and sequence-structure co-design approaches [21,25]. Sequence-based and structure-based methods focus on isolated aspects, which simplifies modeling but limits their ability to explore interactions at interface regions. Co-design approaches aim to holistically model both sequence and structure to capture their interdependence, yet they still struggle to accurately represent interface interactions. Providing the crucial role of protein surface analysis in predicting interaction sites and inferring PPIs [35,46,47], more efforts have considered comprising surface geometry and biochemical properties for protein discovery in parallel. For instance, Gainza et al. [14] built a surface-centric de novo design framework to capture the physical and chemical determinants of molecular recognition for new protein binders. Subsequent works [31] extract surface fingerprints from protein-ligand complexes for innovative drug-controlled cell-based therapies. Another line of works [44,48] incorporates surface point clouds augmented with biochemical properties for protein engineering. Despite these advancements, existing methodologies face several limitations: (i) Limited ability to generate diverse yet receptor-compatible surface configurations. (ii) Lack of explicit modeling to establish robust correspondences between molecular shapes and backbone structures. (iii) Absence of a comprehensive strategy for top-down protein design, where coherent protein structures are generated based on receptor surface features.
To address these challenges, we introduce PepBridge, a novel framework for top-down protein design based on a multi-modal diffusion approach [17,[41][42][43]. As shown in Figure 1, given a receptor represented as a surface point cloud and structure annotated with geometric and biochemical properties, PepBridge generates a complete protein structure, including both the upper surface and the underlying residue structure. Notably, PepBridge leverages denoising diffusion bridge models (DDBMs) [62,63], which interpolate between paired distributions, enabling the direct mapping of receptor surfaces to ligand surfaces while preserving physical and biochemical relevance. For structure generation, PepBridge employs an SE(3) diffusion model for backbone prediction, a torus diffusion model for torsion angle generation, and a logit-normal diffusion model for residue identity prediction. To ensure alignment and consistency, we introduce a Shape-Frame Matching Network that learns correspondences between generated ligand surfaces and backbone structures. Our main contributions are as follows:
• Unified Protein Design Framework: We present PepBridge, a novel framework that jointly designs protein surfaces and structures by integrating receptor surface geometry and biochemical properties-tackling core challenges in top-down protein design.
• Methodological Advances: PepBridge incorporates DDBMs to generate receptor-compatible ligand surfaces and a multimodal diffusion model for peptide structure prediction. Additionally, a Shape-Frame Matching Network is introduced to align generated surfaces and backbone structures, improving geometric and biochemical consistency.
• Effective Validation: We demonstrate the efficacy of Pep-Bridge through extensive validation on peptide design tasks, showcasing its ability to generate diverse, structurally viable proteins with receptor-specific binding characteristics.
Diffusion Models. Let q 0 (x 0 ) be a d-dimensional data distribution. The forward diffusion process [17,40,42] is defined by the following stochastic differential equat
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