SwitchCraft: A Programmatic Framework for Designing State-Switching Proteins
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914616652595200 |
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| author | Jing, Bowen Bafna, Mihir Parsan, Anisha Ni, Heyuan Michael Kwabi-Addo, David Bryson, Bryan Klivans, Adam Berger, Bonnie |
| author_facet | Jing, Bowen Bafna, Mihir Parsan, Anisha Ni, Heyuan Michael Kwabi-Addo, David Bryson, Bryan Klivans, Adam Berger, Bonnie |
| contents | Multistate mechanisms underlie many of the complex functions observed in natural proteins. The ability to rationally design multistate proteins would have transformative implications for many areas of biotechnology, yet lies beyond the capabilities of existing deep learning frameworks for protein design. To address this gap, we introduce SwitchCraft, a versatile and programmatic framework for designing state-switching proteins based on backpropagation through compositional design constraints parameterized by structure prediction models. In silico evaluations demonstrate success on a wide range of state-switching functional primitives, from allosteric regulation of motifs to discrimination of bound ligand identities. Using these primitives, we demonstrate an in silico strategy for de novo design of fluorescent biosensors to arbitrary small molecule analytes. These results position SwitchCraft at the inception of a powerful paradigm for higher-order functional protein design. Code is available at https://github.com/bjing2016/switchcraft. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_31236 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SwitchCraft: A Programmatic Framework for Designing State-Switching Proteins Jing, Bowen Bafna, Mihir Parsan, Anisha Ni, Heyuan Michael Kwabi-Addo, David Bryson, Bryan Klivans, Adam Berger, Bonnie Biomolecules Multistate mechanisms underlie many of the complex functions observed in natural proteins. The ability to rationally design multistate proteins would have transformative implications for many areas of biotechnology, yet lies beyond the capabilities of existing deep learning frameworks for protein design. To address this gap, we introduce SwitchCraft, a versatile and programmatic framework for designing state-switching proteins based on backpropagation through compositional design constraints parameterized by structure prediction models. In silico evaluations demonstrate success on a wide range of state-switching functional primitives, from allosteric regulation of motifs to discrimination of bound ligand identities. Using these primitives, we demonstrate an in silico strategy for de novo design of fluorescent biosensors to arbitrary small molecule analytes. These results position SwitchCraft at the inception of a powerful paradigm for higher-order functional protein design. Code is available at https://github.com/bjing2016/switchcraft. |
| title | SwitchCraft: A Programmatic Framework for Designing State-Switching Proteins |
| topic | Biomolecules |
| url | https://arxiv.org/abs/2605.31236 |