HelixDesign-Binder: A Scalable Production-Grade Platform for Binder Design Built on HelixFold3
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916763017412608 |
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| author | Gao, Jie Li, Jun Hu, Jing Zhang, Shanzhuo Zhu, Kunrui Huang, Yueyang Zhang, Xiaonan Fang, Xiaomin |
| author_facet | Gao, Jie Li, Jun Hu, Jing Zhang, Shanzhuo Zhu, Kunrui Huang, Yueyang Zhang, Xiaonan Fang, Xiaomin |
| contents | Protein binder design is central to therapeutics, diagnostics, and synthetic biology, yet practical deployment remains challenging due to fragmented workflows, high computational costs, and complex tool integration. We present HelixDesign-Binder, a production-grade, high-throughput platform built on HelixFold3 that automates the full binder design pipeline, from backbone generation and sequence design to structural evaluation and multi-dimensional scoring. By unifying these stages into a scalable and user-friendly system, HelixDesign-Binder enables efficient exploration of binder candidates with favorable structural, energetic, and physicochemical properties. The platform leverages Baidu Cloud's high-performance infrastructure to support large-scale design and incorporates advanced scoring metrics, including ipTM, predicted binding free energy, and interface hydrophobicity. Benchmarking across six protein targets demonstrates that HelixDesign-Binder reliably produces diverse and high-quality binders, some of which match or exceed validated designs in predicted binding affinity. HelixDesign-Binder is accessible via an interactive web interface in PaddleHelix platform, supporting both academic research and industrial applications in antibody and protein binder development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_21873 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HelixDesign-Binder: A Scalable Production-Grade Platform for Binder Design Built on HelixFold3 Gao, Jie Li, Jun Hu, Jing Zhang, Shanzhuo Zhu, Kunrui Huang, Yueyang Zhang, Xiaonan Fang, Xiaomin Biomolecules Artificial Intelligence Quantitative Methods Protein binder design is central to therapeutics, diagnostics, and synthetic biology, yet practical deployment remains challenging due to fragmented workflows, high computational costs, and complex tool integration. We present HelixDesign-Binder, a production-grade, high-throughput platform built on HelixFold3 that automates the full binder design pipeline, from backbone generation and sequence design to structural evaluation and multi-dimensional scoring. By unifying these stages into a scalable and user-friendly system, HelixDesign-Binder enables efficient exploration of binder candidates with favorable structural, energetic, and physicochemical properties. The platform leverages Baidu Cloud's high-performance infrastructure to support large-scale design and incorporates advanced scoring metrics, including ipTM, predicted binding free energy, and interface hydrophobicity. Benchmarking across six protein targets demonstrates that HelixDesign-Binder reliably produces diverse and high-quality binders, some of which match or exceed validated designs in predicted binding affinity. HelixDesign-Binder is accessible via an interactive web interface in PaddleHelix platform, supporting both academic research and industrial applications in antibody and protein binder development. |
| title | HelixDesign-Binder: A Scalable Production-Grade Platform for Binder Design Built on HelixFold3 |
| topic | Biomolecules Artificial Intelligence Quantitative Methods |
| url | https://arxiv.org/abs/2505.21873 |