BAGEL Protein Design
User-configured BAGEL workflows for mini-enzyme, mimic-enzyme, and binder design.
Overview
BAGEL is a programmable protein design framework that treats protein engineering as exploration over an energy landscape. This service exposes user-driven BAGEL workflows for mini-enzyme optimization, enzyme mimicry, and binder design. Users provide the protein structure or sequence inputs directly, along with the conserved residues, hotspot positions, and optimization settings that define each run.
Run BAGEL Protein Design on Neurosnap
The BAGEL Protein Design online webserver allows anybody with a Neurosnap account to run and access BAGEL Protein Design, 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
- Supports user-driven mini-enzyme, mimic-enzyme, and binder workflows instead of fixed preset templates.
- Mini-enzyme mode accepts a user structure together with residue range, offset, and critical residue definitions.
- Mimic-enzyme mode optimizes a user-supplied sequence while preserving explicitly selected immutable residues.
- Binder mode designs a mutable binder sequence against a user-supplied target sequence and hotspot definition.
- Exports user-friendly CSV summaries of energies, sequences, and optimization logs.
- Includes configurable sampling temperature, step count, mutation rates, and mode-specific controls.
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 BAGEL Protein Design 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 BAGEL Protein Design in publications or research outputs.
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Lála, J., Al-Saffar, A., & Angioletti-Uberti, S. BAGEL: Protein engineering via exploration of an energy landscape. PLOS Computational Biology (2025). https://doi.org/10.1371/journal.pcbi.1013774 |
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Lála, J., Agrawal, H., Dong, F., Wells, J., & Angioletti-Uberti, S. An Energy Landscape Approach to Miniaturizing Enzymes using Protein Language Model Embeddings. openRxiv (2026). https://doi.org/10.64898/2026.03.04.709378 |
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Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/ |