SwitchCraft
Design proteins with ligand-dependent changes in conformation or motif geometry.
Overview
Design small-molecule-responsive, state-switching proteins by optimizing a shared sequence across multiple predicted states. SwitchCraft supports positive and negative allostery, induced binding, ligand discrimination, and motif switching. Results are computational design hypotheses and require independent structural and experimental validation.
Run SwitchCraft on Neurosnap
The SwitchCraft online webserver allows anybody with a Neurosnap account to run and access SwitchCraft, no downloads required. Information submitted through this webserver is kept confidential and never sold to third parties as detailed by our strong Terms of Use and Privacy Policy.
Features
- Five multistate design tasks with one sequence shared across all states.
- User-supplied motif geometry and explicit positions in the designed scaffold.
- Small molecules supplied as SMILES, CCD codes, or structure files.
- Five final structure predictions per state with confidence scores and aligned-error matrices.
- Sequence tables, optimization trajectories, motif RMSDs, and comparisons between predicted states.
Statistics
Neurosnap periodically calculates runtime statistics based on job execution data. These estimates provide a general guideline for how long your job may take, but actual runtimes can vary significantly depending on factors like input size or settings used.
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API Request
Access SwitchCraft using the Neurosnap API by sending a request using any programming language with HTTP support. To safely generate an API key, visit the API tab of your overview page.
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Citations
Please cite the original work when using SwitchCraft in publications or research outputs.
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Jing, B., Bafna, M., Parsan, A., Ni, H. M., Kwabi-Addo, D., Bryson, B., Klivans, A., and Berger, B. SwitchCraft: A Programmatic Framework for Designing State-Switching Proteins. ICML (2026). https://arxiv.org/abs/2605.31236 |
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Wohlwend, J., et al. Boltz-1: Democratizing Biomolecular Interaction Modeling. bioRxiv (2024). https://doi.org/10.1101/2024.11.19.624167 |
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Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/ |