Repo: [BAGEL](https://github.com/softnanolab/bagel)

Paper: [BAGEL: Protein engineering via exploration of an energy landscape](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013774)

Came across BAGEL, a new open-source framework for programmable protein engineering, and thought it was worth discussing here.

The basic idea is pretty intuitive: instead of treating protein design as a fixed pipeline, BAGEL lets you define a protein engineering problem as an “energy landscape” and then search through sequence space using modular objectives.

What I like about BAGEL is that it does not seem to be trying to be one model that solves everything. It is more like a framework where you can combine different design pressures:

* “Make this region structurally confident”
* “Bring these two protein regions into contact”
* “Avoid exposed hydrophobic patches”
* “Preserve this catalytic motif”
* “Bind this target but not that off-target”
* “Generate diverse variants while keeping the functional site intact”

That makes it quite interesting for protein engineering problems, because many useful designs are not single-objective. You rarely just want “a folded protein.” You want something that folds, binds, avoids a close homolog, keeps a motif, stays soluble, and gives you enough sequence diversity to test.

Some examples where BAGEL could be useful:

**Peptide binder design** Designing short binders against a target protein, especially when you want to bias the search toward confident interfaces.

**Targeting disordered regions** This is a cool one. BAGEL was shown on intrinsically disordered epitopes, where a designed binder can potentially stabilize a region that is otherwise flexible or poorly structured. That could be relevant for targets that do not present a nice rigid pocket.

**Species-selective or state-selective binders** The multi-state setup is probably one of the strongest parts. You can optimize one sequence to bind one target while avoiding another similar target. In principle, this could be useful for cross-reactivity design, species selectivity, off-target avoidance, or model-organism-to-human translation.

**Enzyme variant generation** Another useful application is exploring sequence diversity while preserving catalytic residues or an active-site geometry. That is interesting as a way to seed directed evolution with more focused libraries instead of mutating blindly.

**Custom design campaigns** Since the framework is modular, you could imagine plugging in future folding models, embedding models, interface predictors, solubility predictors, or custom lab-derived scoring functions.

Curious what people here think: is this kind of modular energy-landscape approach the right direction for practical AI protein engineering, or do you think end-to-end generative models will dominate?





BAGEL: a modular energy-landscape framework for protein design