Floating Anchor Diffusion Model for Multi-motif Scaffolding
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917685199110144 |
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| author | Liu, Ke Mao, Weian Shen, Shuaike Jiao, Xiaoran Sun, Zheng Chen, Hao Shen, Chunhua |
| author_facet | Liu, Ke Mao, Weian Shen, Shuaike Jiao, Xiaoran Sun, Zheng Chen, Hao Shen, Chunhua |
| contents | Motif scaffolding seeks to design scaffold structures for constructing proteins with functions derived from the desired motif, which is crucial for the design of vaccines and enzymes. Previous works approach the problem by inpainting or conditional generation. Both of them can only scaffold motifs with fixed positions, and the conditional generation cannot guarantee the presence of motifs. However, prior knowledge of the relative motif positions in a protein is not readily available, and constructing a protein with multiple functions in one protein is more general and significant because of the synergies between functions. We propose a Floating Anchor Diffusion (FADiff) model. FADiff allows motifs to float rigidly and independently in the process of diffusion, which guarantees the presence of motifs and automates the motif position design. Our experiments demonstrate the efficacy of FADiff with high success rates and designable novel scaffolds. To the best of our knowledge, FADiff is the first work to tackle the challenge of scaffolding multiple motifs without relying on the expertise of relative motif positions in the protein. Code is available at https://github.com/aim-uofa/FADiff. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_03141 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Floating Anchor Diffusion Model for Multi-motif Scaffolding Liu, Ke Mao, Weian Shen, Shuaike Jiao, Xiaoran Sun, Zheng Chen, Hao Shen, Chunhua Biomolecules Machine Learning Motif scaffolding seeks to design scaffold structures for constructing proteins with functions derived from the desired motif, which is crucial for the design of vaccines and enzymes. Previous works approach the problem by inpainting or conditional generation. Both of them can only scaffold motifs with fixed positions, and the conditional generation cannot guarantee the presence of motifs. However, prior knowledge of the relative motif positions in a protein is not readily available, and constructing a protein with multiple functions in one protein is more general and significant because of the synergies between functions. We propose a Floating Anchor Diffusion (FADiff) model. FADiff allows motifs to float rigidly and independently in the process of diffusion, which guarantees the presence of motifs and automates the motif position design. Our experiments demonstrate the efficacy of FADiff with high success rates and designable novel scaffolds. To the best of our knowledge, FADiff is the first work to tackle the challenge of scaffolding multiple motifs without relying on the expertise of relative motif positions in the protein. Code is available at https://github.com/aim-uofa/FADiff. |
| title | Floating Anchor Diffusion Model for Multi-motif Scaffolding |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2406.03141 |