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 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.

~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