Boltz-2
Paste a designed binder, upload the target it should hit, and get back the predicted complex plus a 0-to-1 confidence score for the contact between them. About 15 s per design in single-sequence mode.
What it is for
You already have a binder sequence and the target it should hit, and you want to know whether they actually stick together before you order DNA. Returns the predicted complex and a 0-to-1 interface confidence score. Trained on antibody-antigen complexes, so it is a genuinely second opinion next to AlphaFold2.
Folds a binder and its target together and tells you how confident it is that they touch. It was trained on antibody-antigen complexes and works from sequence alone, which makes it a genuinely independent second opinion next to AlphaFold2 multimer: when the two agree the complex is probably real, and when they disagree that is worth knowing before you order DNA. Returns the folded complex plus ipTM (confidence in the interface), pTM (confidence in the whole complex) and per-residue confidence for every design. Boltz-2, Passaro et al., bioRxiv 2025.
When it fits:
- You designed binders with another tool here and need them scored against the antigen you actually care about.
- You have natural or near-natural antibody fragments (scFv or Fab), nanobodies or peptides and want a fast independent fold of each.
- AlphaFold2 interface confidence has topped out across your candidates and you want a second, differently trained opinion before committing to synthesis.
Inputs
You will need:
- Antigen PDB or mmCIF (single chain; the binder is added separately).
- One or more binder sequences (scFv, nanobody, peptide, anything that folds as a single protein chain), 20 to 400 aa each.
- Optional: a list of antigen hotspot residue numbers to count contacts against (1-indexed on the chosen antigen chain).
Each run uses a preset that sets the scale and scope:
- Single-sequence (fast)
- YAML ``msa: empty`` per chain. The right choice for designed sequences (MPNN, RFantibody, BindCraft, BoltzGen, RFdiffusion, PXDesign outputs) where no informative MSA exists. ~15 s/design on A100-40GB.
- With MSA (slower, natural sequences)
- Boltz fetches MSAs from the public ColabFold MMseqs2 endpoint at runtime. Better for natural / near-native sequences; ~3 min/design including MSA fetch.
Parameters you set on the form:
- Antigen PDB
- Upload the target structure as .pdb, .cif, or .mmcif. CIF inputs are converted to PDB server-side.
- Antigen chain
- Single chain ID (e.g.
A) that Boltz-2 should treat as the antigen. The binder folds as a separate chain. - Hotspot residues
- Optional. Comma-separated 1-indexed positions on the antigen chain (e.g.
55,56,57,71,72,73,74). The pipeline reports how many of these residues the binder contacts (heavy atom within 5 Å). - Binder sequences
- Paste one sequence per line, or upload as FASTA (
>nameheaders). Each sequence folds independently against the antigen. 20 to 400 aa per binder, up to 50 binders per run. - Preset
- Single-sequence (default) folds in
msa: emptymode, the right choice for designed sequences. With MSA fetches MSAs from the public ColabFold MMseqs2 endpoint and is slower but more accurate on natural sequences.
Typical runtime:
- standalone
- <1 min/design
- msa_server
- ~3 min/design
How to read the results
Per-design folded complex PDB + ipTM, pTM, complex_pLDDT, complex_iplddt, and hotspot contact count. Strict-pass classification (complex_pLDDT > 85, ipTM > 0.7, n_hotspot_contacts > 4) surfaces which designs are worth ordering.
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: > 0.75 strong; > 0.65 acceptable. Individual tools set their own pass bar a little either side of that — this guide's own results summary above states this tool's. 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.
References
Passaro et al., bioRxiv 2025