BoltzGen

You have a target and have not settled on what shape the binder should be. One model here aims mini-proteins, nanobodies, antibodies or peptides at the same site, so you can compare formats instead of guessing. The only design tool here that handles sugars and modified residues on the target natively.

Designs a binder against your target, then refolds each candidate from its own sequence so you can see whether it holds the shape it was designed as. The interface score is the generator's own read, not a second opinion, so rank on it and re-fold a shortlist before you trust it. Four formats share one target — a small de novo protein, a nanobody, an antibody, or a short peptide — so you can compare formats on the same epitope rather than guessing which to commit to. It is the only design tool here that handles sugars, post-translational modifications and non-standard residues on the target natively. BoltzGen, Stark et al., bioRxiv 2025.

When it fits:

• You have not settled on the binder format and want to aim mini-proteins, nanobodies, antibodies and peptides at the same site.
• Your target carries sugars, modified residues or other chemistry a protein-only model would silently drop.
• You want each design refolded on its own, so you can see whether it folds back to the shape it was designed as.
• You can leave the run to finish rather than needing an answer immediately.

You will need:

• Target structure (.pdb / .cif).
• Chain ID of the target.
• Optionally, hotspot residues — target-chain residues the binder should contact. Leave the field empty and the run starts with no hotspot constraint.

Each run uses a preset that sets the scale and scope:

Your target, a guided first run

Real BoltzGen run against your uploaded target. Pick 1 to 50 final candidates with refolding RMSD and ipTM scores. 4 is enough to read through while you confirm your target and binder length, and raising it on a later run returns more candidates out of the same search. Results emailed when complete; A100-40GB.

Ranked candidate binders with ipTM, pLDDT, refolding RMSD, and structures downloadable from a run of your own — mmCIF or PDB, whichever the container wrote. Refolding RMSD at or under 1.5 Å clears the RMSD leg of the pass bar, which also needs pLDDT at or above 80; the number is the design measured against its own refold. That says the binder folds as designed, not that it binds — re-fold a shortlist against your target to check that.

Where a tool reports them, the scores mean:

ipTM

Predicted confidence in the contact between two chains, on a 0 to 1 scale. Higher is better, but BoltzGen's number is not on the shared scale the other tools are read against, so there is no cross-tool band to compare it to — rank on it, then re-fold a shortlist to confirm. On a multi-chain target the number may cover the target's own chain–chain interface as well as the binder's, so read the per-tool note on the results table before comparing designs on it.

pLDDT

Per-residue confidence in the predicted fold. Higher means the model is more sure of that part of the structure.

i_pAE and pAE

Predicted alignment error, at the interface (i_pAE) or across the whole structure (pAE). Lower is better.

Stark et al., bioRxiv 2025