Title: Joint Design of Protein Surface and Backbone Using a Diffusion Bridge Model
Venue: NeurIPS 2025
Accepted: yes
Keywords: Diffusion Bridge model

Molecular Surfaces

Protein Design
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a top-down joint design framework for protein surface and structure based on a multi-modal diffusion approach. The key innovation lies in employing a denoising diffusion bridge model to directly map the receptor surface to the ligand surface, rather than relying on a traditional Gaussian noise prior.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. Using the receptor surface as an informative prior instead of Gaussian noise is a promising approach.\n\n2. The methodological design is well-founded, decomposing the complex protein design problem into two interrelated sub-tasks: surface generation and structure prediction. \n\n3. The topic is very innovative, although the method has certain shortcomings compared to existing methods.\n\nWeaknesses:\n1. In the introduction, the author mentions \"(i) Limited ability to generate diverse ...\" However, according to the experimental results in Table 1, it seems that the \"Div\" metric has not improved compared to \"PepFlow w/Bb\", but rather decreased.\n\n2. In the original paper of \"PepFlow w/Bb\", the effect of \"PepFlow w/Bb+Seq+Ang\" seems to be the best, why not compare it with \"PepFlow w/Bb+Seq+Ang\"? Although there have been improvements in some metrics, they do not show a breakthrough compared to \"PepFlow w/Bb\".\n\n3. The paper did not provide a detailed analysis of computational complexity and comparison of running time. The multi-modal diffusion process may bring significant computational overhead.\n\n4. The figures in the paper are beautifully drawn, but it would be clearer to see if there were more textual descriptions.\n\n5. It seems that the author may need to further compare with [1].\n\n6. The PPFLOW [2] method has been mentioned in the related work section, but why hasn't it been compared in Table 1? Can you explain it?\n\n[1] Hotspot-Driven Peptide Design via Multi-Fragment Autoregressive Extension. ICLR 2025\n\n[2] PPFLOW: Target-Aware Peptide Design with Torsional Flow Matching. ICML 2024\n\nQUESTIONS:\n1. How do different hyperparameters affect the final performance?\n\n2. Although diversity metrics have been reported, there is a lack of in-depth analysis on how to find the optimal balance between diversity and quality.\n\n3. Although visual results are provided, I cannot see if they meet the practical significance. Can it be used for practical applications? Does the author plan to conduct wet experiments for validation? How to prove the reliability of computational predictions without experimental verification?\n\n4. The multi-modal diffusion process requires simultaneous processing of surfaces, torsion angles, and residue types, which inevitably leads to computational overhead. Is the additional computational cost reasonable compared to existing uni-modal methods? Can you provide a detailed analysis of computation time and resource consumption?\n\n5. Is it applicable to receptors without obvious binding pockets?\n\n6. There is another small but important point: the introduction of surface modeling is an innovative aspect of the paper, but how can it be proven to be effective?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors introduce an approach that uses an annotated 3D surface of a receptor (with amino acid and atom position information) and generate a compatible ligand surface as well as the backbone in the form of frame geometry. They use diffusion bridge models starting from the receptor surface instead of Gaussian noise, which they demonstrate to be beneficial. As for the backbone generation, they use a surface frame matching network which is jointly trained. With their approach, they generate high quality samples and also demonstrate superior side-chain packing performance.\n\nSTRENGTHS AND WEAKNESSES:\nThe paper is well written, clearly structured and easy to follow. The authors have presented (to my knowledge) novel ideas and new models; especially the idea of incorporating the surface structure seems promising. This idea could potentially transfer to other applications and be used in a variety of different models and settings. \n\nThe authors show improved results in some metrics (such as the RMSD), but do not improve the results in all benchmarks. Due to the lack of error bars and/or standard deviations, it is hard to judge how consistent the improvements are. Especially because most models are intrinsically stochastic during inference. \n\nHowever, the authors mitigate this by comparing their approach with multiple different methods and also perform ablation studies how the different parts of their approach impact the main results. Compared to other approaches, their model support side-chain packing, making it more versatile.\n\nQUESTIONS:\n1. I would be interested in seeing how consistent the results are, as the authors did not provide any standard deviation. In the caption of table 1 they state that they draw 40 candidates, but it is hard so say if this is enough for the results to be consistent. Also, did the authors run the inference with multiple different seeds?\n\n2. How does the runtime of the different methods compare, and how does it scale with size? How many inference steps did have been used for the diffusion model and how does the number of steps impact the sample quality?\n\n3. Although I am aware that diffusion bridges can be seen as a generalization of flow matching, I am curious to hear why the authors decided opted to use diffusion bridges instead of flow matching? \n\n4. It is not entirely clear to me how the authors encoded the surface structure. As I understood it, they use a point cloud for the surface, but I am not sure how many points do they use (one for each atom?) and if it could benefit from a more precise parameterization (i.e., more points).\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces **PepBridge**, a diffusion-based framework for joint peptide surface and structure generation conditioned on a receptor. It uses a **Surface Diffusion Bridge** to map receptor surface point clouds to complementary peptide surfaces, followed by multi-modal diffusion for backbone and side-chain modeling. A shape-frame matching module aligns surface and structure. The method shows solid performance on peptide design tasks, with the key contribution being the integration of surface geometry as a generative prior.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n- Clarity/Quality: The paper is well-written and easy to follow. Figures and illustrations clearly convey the architecture and main ideas. The paper includes thorough ablation studies, multiple baselines, and clear evidence that each component contributes to the final performance.\n\n- Originality: The proposed **Surface Diffusion Bridge** is a novel and elegant approach to modeling receptor-conditioned surface generation.\n\n- Significance: The task of receptor-aware peptide generation is important and underexplored. The method highlighs the advantages of shape-driven generation in target protein design.\n\n**Weaknesses**\n\n- Quality: The performance improvement over strong baselines like PepFlow is relatively modest. While statistically sound, the gain may not fully justify the increased model complexity\n\n- Significance: When surface context is removed, performance drops sharply. This raises the question that could other peptide design models, such as PepFlow, also benefit from adding surface? The core contribution may be this **surface + bridge** formulation.\n\nQUESTIONS:\n**Questions**\n\n1. As I understand, the model does not use receptor backbone or sequence information. Would adding these information further help?\n\n2. Could PepFlow benefit from adding surface context similarly? Also, can you elaborate on why the performance gain is modest compared to PepFlow despite richer modeling\n\n3. Could you also discuss the cost of surface computation, which could affect real-world applicability.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose a joint framework to jointly design peptide sequence and structure while ensuring the surface complementarity in peptide design task. The proposed method is first use a diffusion bridge model to predict the ligand surfaces with receptor surfaces, and then jointly predict the residue type and frame structure with a multi-modal diffusion model. The authors provide a good illustration of their method and extensive experiments to show its superiority.\n\nSTRENGTHS AND WEAKNESSES:\nThe paper is well written and easy to understand. The experiments are extensive with sufficient ablations and benchmarks. The result seems promising and proves the authors' point that the bidirectional information flow between surface and designed structure can help this task. I think a major concern is that such joint models with multiple losses and components could introduce potential problems for efficiency and robustness. However, considering the task formulation and authors' intention to jointly design surface and peptide structure, the proposed method is a reasonably sophisticated method. More ablation studies by further decomposing the model could be helpful to address this issue.\n\nQUESTIONS:\n1.There has been a long history to use surface complementarity to design ligands in traditional protein or small molecule design previous to deep learning era. I think authors should include this part of related works, and provide a discussion whether PepBridge can perform better than these methods.\n\n2.Following the previous context, there are two questions for surface-based design:\n1)How is the robustness of surface complementary methods against imperfect structure? The error of crystal structure is not significant in whole protein structure, but can be harmful when describing very local surface. If we use AlphaFold-like models to predict the receptor  structure, the error could be even larger.\n2)Another problem is that the surface complementarity condition can be misleading sometimes. I think this could probably be a major advantage for learning-based methods, because they don't need to assume a very strict complementarity but can learn from data. Could authors help me understand how is PepBridge designed to address this issue?\n\n3.I think there should be more ablation study contributed to understand the performance of separate components. For example, how is the DDBM surface diffusion part and structure design part influence each other? I notice that the authors already provide some ablation study on the change of joint performance, but I wonder if it is possible to decompose the model totally.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}