RFantibody
Upload your target structure, mark the patch you want gripped, and get back nanobody (single-domain antibody) candidates, each refolded and scored against the target.
What it is for
You have a target structure and want nanobodies — single-domain antibodies you can carry straight into yeast display, mammalian display or a hybridoma workflow. Each candidate is refolded and scored against the target before you see it. For non-antibody mini-proteins use RFdiffusion or BindCraft; for sugar-coated targets use BoltzGen.
Designs nanobodies — single-domain antibodies, the VHH format — against a patch of your target, then refolds each one with AlphaFold2 and reports how confident it is about the contact. Nanobodies are the format that carries most easily into yeast display, mammalian display and hybridoma workflows, which is the usual reason to pick this over a de novo mini-protein. RFantibody, Bennett et al., Nature 2026.
When it fits:
- You want a nanobody scaffold rather than a de novo mini-protein.
- Your downstream work is yeast display, mammalian display or a hybridoma workflow.
- Your target is an ordinary protein epitope without heavy sugar coverage.
Inputs
You will need:
- Target structure (
.pdb/.cif). - Chain ID of the target.
- At least one hotspot residue defining the epitope face.
Each run uses a preset that sets the scale and scope:
- Your target, a first batch
- Real RFantibody design against your uploaded target PDB. Pick 1 to 1000 final VHH candidates. Start with a small batch (4 designs) to confirm your target and hotspots, then scale to 100+ once outputs look real. Results emailed when run completes; A100-40GB.
Parameters you set on the form:
- Hotspot residues
- Comma-separated target-chain residues defining the epitope the CDRs should target.
- Number of backbones (5 candidates each)
- How many backbones to generate. Each gets five ProteinMPNN sequences, and every sequence is re-folded and filtered by RoseTTAFold-2 on pAE, ipAE and pLDDT -- so 4 here returns 20 candidates.
How to read the results
Ranked VHH candidates with pAE, pLDDT, ipAE, and PDBs downloadable from a run of your own. Filter at pAE ≤ 5 / ipAE ≤ 6 for downstream wet-lab work.
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: 0.65 to 0.75 depending on the tool. Individual tools set their own pass bar a little either side of that. 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
Bennett et al., Nature 2026