RFantibody — VHH (nanobody) design
Structure-based VHH (nanobody) binder design. Generates single-domain antibody candidates against a target epitope, then validates the fold with RoseTTAFold-2.
What it is for
Pick RFantibody when you need a VHH (nanobody) scaffold against a target PDB. For de novo non-antibody binders, use BindCraft. For designs involving modified residues or glycans, use BoltzGen.
RFantibody (Bennett et al., bioRxiv 2024). RoseTTAFold-derived diffusion model that generates VHH (single-domain heavy-chain antibody) scaffolds against a target. Outputs are scored with AF2 re-prediction (pAE, pLDDT, ipAE).
When it fits:
- You want a VHH (nanobody) scaffold rather than a de novo mini-protein.
- Your downstream validation uses yeast display, mammalian display, or hybridoma workflows.
- Your target is a standard protein epitope without heavy glycosylation.
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:
- Pilot — your target, ~30 min
- Real RFantibody design against your uploaded target PDB. Up to 24 final VHH candidates (your choice); results emailed when run completes (~15-60 min on A100-40GB).
Parameters you set on the form:
- Hotspot residues
- Comma-separated target-chain residues defining the epitope the CDRs should target.
- Number of designs
- How many candidates to generate. Each passes AF2 re-prediction filtering on pAE and pLDDT.
Typical runtime:
- pilot
- 15–60 min
How to read the results
Ranked VHH candidates with pAE, pLDDT, ipAE, and downloadable PDBs. Filter at pAE ≤ 5 / ipAE ≤ 6 for downstream wet-lab work.
Where a tool reports them, the scores mean:
- 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.
Try these examples
One-click sample inputs that load straight into the run form. Edit any field before submitting.
- SARS-CoV-2 RBD (6m0j chain E)
- Nanobody design against the spike RBD. Same target as the canonical RFantibody paper benchmark.
References
Bennett et al., bioRxiv 2024