Run RFantibody online

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.

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.

15-60 min per run on a dedicated GPU. Billing is by the second of compute, so a faster preset costs less.

We keep a plain-English overview of RFantibody on the main Ranomics site — what the model does under the hood, the kinds of targets it works on, and where it fits inside a full design-to-wet-lab campaign.

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

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.

Run RFdiffusion online

Pick RFdiffusion when you want general de novo binder design scored by AF2 multimer (ipTM / pLDDT / i_pAE). For antibody and nanobody scaffolds use RFantibody, for AF2-IG initial-guess scoring use PXDesign, and for hallucination-driven binders without AF2 filtering use BindCraft.

Run BindCraft online

Pick BindCraft when you have a target PDB plus known hotspot residues and want de novo 60-150 aa protein binders.

Bennett et al., bioRxiv 2024