Run Boltz-2 online

Pick Boltz-2 to validate a designed binder against your antigen. Single-sequence cofold + interface confidence (ipTM), antibody-trained and orthogonal to AF2-multimer. For sequence design, use ProteinMPNN; for de novo backbones, use RFantibody, BindCraft, or BoltzGen first.

Boltz-2 (Wohlwend et al., bioRxiv 2025). An open-weights structure prediction model trained on antibody-antigen complexes with a calibrated confidence head. Single-sequence mode is orthogonal to AF2-multimer: when both agree, the predicted complex is real; when they disagree, the disagreement itself is informative. Returns a folded complex PDB + ipTM + pTM + complex_pLDDT per design.

When it fits:

• You designed binders with MPNN, RFantibody, BindCraft, BoltzGen, RFdiffusion, or PXDesign and need to score them against the intended antigen.
• You have native or near-native scFv / Fab / nanobody / peptide sequences you want a fast independent fold for.
• AF2-multimer ipTM is saturated and you want a second confidence channel from a different architecture before ordering DNA.

Use the score legend below to read results. Each tool reports a subset of these depending on whether it does design, sequence recovery, or structure prediction.

ipTM

Predicted confidence in the binder to target interface. Higher is better. Aim above roughly 0.7 on a tractable target.

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.

ProteinMPNN recovery

Fraction of native residues recovered when ProteinMPNN redesigns a known sequence on its native backbone. Higher is better; well calibrated above roughly 0.4 on diverse folds.

<1 min to ~3 min per run on a dedicated GPU. Billing is by the second of compute, so a faster preset costs less.

If you are picking between Boltz-2 and a sibling algorithm, these run on the same hub against the same target.

Run AlphaFold2 online

Pick AF2 when you need the gold-standard structure prediction with calibrated pLDDT + PAE. For faster single-sequence folds use ESMFold (D4); for affinity-aware folds use Boltz-2 (D6).

Run ColabFold online

Pick ColabFold when you need a fast no-MSA fold — 1-2 min per run, no MMseqs2 round-trip. Pair with AF2 standalone (D2) when you want full MSA + templates, or with ESMFold (D4) for single-sequence monomers on an even smaller GPU.

Run BoltzGen online

Pick BoltzGen when you want one model that can design mini-proteins, nanobodies, antibodies, or peptides against the same target, or when your target involves glycans, post-translational modifications, or non-canonical residues.

Wohlwend et al., bioRxiv 2025