Run OpenDDE co-folding online

Pick OpenDDE to co-fold a mixed complex — protein with DNA, RNA, or a bound ligand in one prediction. For a plain protein-protein or protein-peptide cofold, Boltz-2 is faster and cheaper.

OpenDDE is an AlphaFold3-class, all-atom co-folding foundation model (Aureka AI Research, Apache-2.0). It predicts the joint structure of an arbitrary mix of biomolecular entities — protein, DNA, RNA, and small molecules (ligands) — from a single specification.

When it fits:

• You need a complex with more than just protein: protein plus DNA / RNA, or a bound small molecule.
• You are modelling an antibody or nanobody with its antigen (use the ABAG checkpoint).
• You want an AlphaFold3-style multi-modal prediction without standing up the pipeline yourself.

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.

~2 to 8 min per run on a dedicated GPU. You pay only for the compute a job delivers, drawn from your wallet balance.

Aureka AI Research, OpenDDE-Preview, arXiv 2026