RFdiffusion — de novo binder design
Composite binder design: RFdiffusion backbones + ProteinMPNN sequences + AF2 multimer validation. Candidates carry real ipTM / pLDDT / i_pAE scores. Pilot ~15-30 min on caller targets.
What it is for
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.
RFdiffusion (Watson et al., Nature 2023). Diffusion-based backbone generator. The Ranomics composite pipeline pairs it with ProteinMPNN sequence design and AF2 multimer scoring, so every candidate carries real ipTM / pLDDT / i_pAE statistics from the AF2 re-prediction stage.
When it fits:
- You want general de novo binder design with AF2-grounded scoring.
- Your target is a standard protein epitope (no glycans, no PTMs).
- You want flexible binder length and topology rather than an antibody scaffold.
Inputs
You will need:
- Target structure (
.pdb/.cif). - Chain ID of the target.
- At least one hotspot residue.
Each run uses a preset that sets the scale and scope:
- Pilot — your target, ~30 min
- Real RFdiffusion run against your uploaded target PDB with AF2 multimer validation. Up to 200 candidates with real scores; results emailed when complete (~15-30 min on A100-40GB).
Parameters you set on the form:
- Hotspot residues
- Comma-separated target-chain residues the binder should contact during diffusion.
- Binder length (min/max)
- Residue-count window. 55–65 is a sane default for compact PD-L1-style targets; longer binders work for larger interfaces.
- Number of designs
- How many candidates to generate. Each passes ProteinMPNN sequence design and AF2 multimer scoring.
Typical runtime:
- pilot
- 15–30 min
How to read the results
Ranked candidates with ipTM, pLDDT, i_pAE, and downloadable PDBs. Aim for at least 1/5 ipTM ≥ 0.65 on a tractable target before committing to a full pilot.
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)
- Classic de novo binder design benchmark; targets the ACE2 interface on the spike RBD.
- TIGIT ectodomain (7byr chain A)
- Immuno-oncology target. Hotspots on the PVR / nectin binding interface.
References
Watson, J. L., Juergens, D., Bennett, N. R., et al. "De novo design of protein structure and function with RFdiffusion." Nature 620, 1089-1100 (2023). Composite pipeline: RFdiffusion backbones + ProteinMPNN sequences + AF2 multimer validation.