ProteinWeaver: A Divide-and-Assembly Approach for Protein Backbone Design
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913588180942848 |
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| author | Ma, Yiming Ye, Fei Zhou, Yi Zheng, Zaixiang Xue, Dongyu Gu, Quanquan |
| author_facet | Ma, Yiming Ye, Fei Zhou, Yi Zheng, Zaixiang Xue, Dongyu Gu, Quanquan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_16686 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ProteinWeaver: A Divide-and-Assembly Approach for Protein Backbone Design Ma, Yiming Ye, Fei Zhou, Yi Zheng, Zaixiang Xue, Dongyu Gu, Quanquan Biomolecules Machine Learning 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. |
| title | ProteinWeaver: A Divide-and-Assembly Approach for Protein Backbone Design |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2411.16686 |