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 at an interface. Higher is better; aim above roughly 0.7 on a tractable target. On a multi-chain target it may cover the target's own chain–chain interface as well as the binder's.

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 to 60 min per run on a dedicated GPU. You pay only for the compute a job delivers, drawn from your wallet balance.

We keep a plain-English overview of RFantibody on the main Ranomics site. It covers 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 IgGM online

Pick IgGM to design or humanize an antibody / nanobody against your antigen, or to predict the antibody-antigen complex, all in one model. For VHH backbones use RFantibody; for paired scFv CDRs use ESMFold2 design; to validate a designed binder's fold use Boltz-2.

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.

Bennett et al., bioRxiv 2024