ProteinWeaver: A Divide-and-Assembly Approach for Protein Backbone Design
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Abstract (arXiv)
Nature creates diverse proteins through a 'divide and assembly' strategy. Inspired by this idea, we introduce ProteinWeaver, a two-stage framework for protein backbone design. Our method first generates individual protein domains and then employs an SE(3) diffusion model to flexibly assemble these domains. A key challenge lies in the assembling step, given the complex and rugged nature of the inter-domain interaction landscape. To address this challenge, we employ preference alignment to discern complex relationships between structure and interaction landscapes through comparative analysis of generated samples. Comprehensive experiments demonstrate that ProteinWeaver: (1) generates high-quality, novel protein backbones through versatile domain assembly; (2) outperforms RFdiffusion, the current state-of-the-art in backbone design, by 13% and 39% for long-chain proteins; (3) shows the potential for cooperative function design through illustrative case studies. To sum up, by introducing a `divide-and-assembly' paradigm, ProteinWeaver advances protein engineering and opens new avenues for functional protein design.
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References · 11
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
- 2024Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Designcited by 305
- 2022Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problemcited by 236
- 2024Self-Play Preference Optimization for Language Model Alignmentcited by 232
- 2023SE(3) diffusion model with application to protein backbone generationcited by 219
- 2023SE(3)-Stochastic Flow Matching for Protein Backbone Generationcited by 150
- 2022Protein structure generation via folding diffusioncited by 115
- 2023Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Cloudscited by 54
- 2024Improved motif-scaffolding with SE(3) flow matchingcited by 43
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- 2024Out of Many, One: Designing and Scaffolding Proteins at the Scale of the Structural Universe with Genie 2
- 2026Property-driven Protein Inverse Folding With Multi-Objective Preference Alignment
- 2024Model-based reinforcement learning for protein backbone design
- 2025Joint Design of Protein Surface and Structure Using a Diffusion Bridge Model
- 2025Constrained Diffusion for Protein Design with Hard Structural Constraints
- 2026ProteinOPD: Towards Effective and Efficient Preference Alignment for Protein Design
- 2025Improving Protein Sequence Design through Designability Preference Optimization
- 2026SaDiT: Efficient Protein Backbone Design via Latent Structural Tokenization and Diffusion Transformers