BAGEL: Protein engineering via exploration of an energy landscape
File(s)
Published version
Author(s)
Lála, Jakub
Al-Saffar, Ayham
Angioletti-Uberti, Stefano
Type
Journal Article
Abstract
Despite recent breakthroughs in deep learning methods for protein design, existing computational pipelines remain rigid, highly specific, and ill-suited for tasks requiring non-differentiable or multi-objective design goals. In this report, we introduce BAGEL, a modular, open-source framework for programmable protein engineering, enabling flexible exploration of sequence space through model-agnostic and gradient-free exploration of an energy landscape. BAGEL formalizes protein design as the sampling of an energy function, either to optimize (find a global optimum) or to explore a basin of interest (generate diverse candidates). This energy function is composed of user-defined terms capturing geometric constraints, sequence embedding similarities, or structural confidence metrics. BAGEL also natively supports multi-state optimization and advanced Monte Carlo techniques, providing researchers with a flexible alternative to fixed-backbone and inverse-folding paradigms common in current design workflows. Moreover, the package seamlessly integrates a wide range of publicly available deep learning protein models, allowing users to rapidly take full advantage of any future improvements in model accuracy and speed. We illustrate the versatility of BAGEL on four archetypal applications: designing de novo peptide binders, targeting intrinsically disordered epitopes, selectively binding to species-specific variants, and generating enzyme variants with conserved catalytic sites. By offering a modular, easy-to-use platform to define custom protein design objectives and optimization strategies, BAGEL aims to speed up the design of new proteins. Our goal with its release is to democratize protein design, abstracting the process as much as possible from technical implementation details and thereby making it more accessible to the broader scientific community, unlocking untapped potential for innovation in biotechnology and therapeutics.
Editor(s)
Singh, Amar
Date Issued
2025-12-03
Date Acceptance
2025-11-20
Citation
PLoS Computational Biology, 2025, 21 (12)
ISSN
1553-734X
Publisher
Public Library of Science (PLoS)
Journal / Book Title
PLoS Computational Biology
Volume
21
Issue
12
Copyright Statement
© 2025 Lála et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
License URL
Identifier
10.1371/journal.pcbi.1013774
Publication Status
Published
Article Number
e1013774
Date Publish Online
2025-12-03