SeedProteo: Accurate De Novo All-Atom Design of Protein Binders
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
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| _version_ | 1866908849444749312 |
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| author | Qu, Wei Ma, Yiming Ye, Fei Lu, Chan Zhou, Yi Zhang, Kexin Wang, Lan Gui, Minrui Gu, Quanquan |
| author_facet | Qu, Wei Ma, Yiming Ye, Fei Lu, Chan Zhou, Yi Zhang, Kexin Wang, Lan Gui, Minrui Gu, Quanquan |
| contents | We present SeedProteo, a diffusion-based model for de novo all-atom protein design. We demonstrate how to repurpose a cutting-edge folding architecture into a powerful generative design framework by effectively integrating self-conditioning features. Extensive benchmarks highlight the model's capabilities across two distinct tasks: in unconditional generation, SeedProteo exhibits superior length generalization and structural diversity, maintaining robustness for long sequences and complex topologies; in binder design, it achieves state-of-the-art performance among open-source methods, attaining the highest in-silico design success rates, structural diversity and novelty. Finally, we validate SeedProteo through wet-lab assays on two therapeutic targets, achieving hit rates of 70%-80% and picomolar-level binding affinities, establishing leading results. To facilitate community adoption, we provide public access to SeedProteo via a webserver (https://seedfold.io/proteinDesign). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_24192 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SeedProteo: Accurate De Novo All-Atom Design of Protein Binders Qu, Wei Ma, Yiming Ye, Fei Lu, Chan Zhou, Yi Zhang, Kexin Wang, Lan Gui, Minrui Gu, Quanquan Biomolecules We present SeedProteo, a diffusion-based model for de novo all-atom protein design. We demonstrate how to repurpose a cutting-edge folding architecture into a powerful generative design framework by effectively integrating self-conditioning features. Extensive benchmarks highlight the model's capabilities across two distinct tasks: in unconditional generation, SeedProteo exhibits superior length generalization and structural diversity, maintaining robustness for long sequences and complex topologies; in binder design, it achieves state-of-the-art performance among open-source methods, attaining the highest in-silico design success rates, structural diversity and novelty. Finally, we validate SeedProteo through wet-lab assays on two therapeutic targets, achieving hit rates of 70%-80% and picomolar-level binding affinities, establishing leading results. To facilitate community adoption, we provide public access to SeedProteo via a webserver (https://seedfold.io/proteinDesign). |
| title | SeedProteo: Accurate De Novo All-Atom Design of Protein Binders |
| topic | Biomolecules |
| url | https://arxiv.org/abs/2512.24192 |