OpenDDE co-folding

OpenDDE co-folding is a no-install online all-atom complex prediction tool you can run through tools.ranomics.com on a dedicated GPU. DNA, RNA, cofactors and small molecules are first-class parts of the input rather than something to strip out before folding, so the complex is modelled as it actually exists.

Co-folding inputs

Checkpoint

General co-folding Any mix of protein, DNA, RNA, and ligand. The default. Antibody-antigen (ABAG) The antibody-antigen specialised checkpoint. Use for antibody or nanobody complexes.

Input mode

Guided (fill in each entity) One box per entity type. We assemble the OpenDDE JSON for you. Paste an OpenDDE JSON spec Power users. Validated against the real schema and the same size limits.

Sampler

Samples / seed

Diffusion steps

Recycles

Starting seed

Seeds

Predictions returned = seeds × samples.

Campaign

Group related runs on your jobs page. Leave blank to file under Uncategorized.

Estimated cost $0.00 based on parameters and historical compute time

Billed by actual GPU consumed, up to the estimate. Surplus returns to your wallet automatically. Cancellation does not refund GPU time already used.

About OpenDDE co-folding

What it is

Folds a whole complex at once when the complex is not all protein — protein with DNA, protein with RNA, protein with a bound small molecule, or any mix of those written into a single specification. Every atom is modelled, not just the protein backbone. It is the multi-molecule counterpart to Boltz-2, which is faster but protein-only. OpenDDE, Aureka AI Research, Apache-2.0.

When to use

Your complex has something in it other than protein: DNA, RNA, or a bound small molecule.
You are modelling an antibody or nanobody together with its antigen (use the ABAG checkpoint).
You want an AlphaFold3-style all-atom prediction without standing up the pipeline yourself.

Prerequisites

Sequences for each polymer chain (protein / DNA / RNA).
For ligands: a CCD code (e.g. CCD_ATP), a bare SMILES string, or a bundled FILE_*.sdf reference.

Input glossary

Checkpoint. General for any entity mix, or ABAG for antibody-antigen complexes.
Entities (guided). One textarea per entity type. Proteins / DNA / RNA are entered as FASTA (>id headers optional); ligands one per line. This adapter assembles the OpenDDE JSON for you.
Entities (JSON). Or paste an exact OpenDDE spec. It is validated against the real schema and re-checked against the same size limits — the JSON mode is not a way around the ceilings.
Samples / steps / recycles. Sampler settings. Samples is how many structures per seed; steps and recycles trade compute for quality.
Seeds. How many seeds to run from the starting seed. Total predictions returned is seeds × samples.

Runtime

Preset	Typical min
general	~2 to 8 min
abag	~2 to 8 min

A ranked set of predicted complexes (mmCIF/PDB) with the model's own confidence ranking score per prediction. Download each structure or view it in the browser.

References

What good looks like

Each tool reports a subset of these depending on whether it does design, sequence recovery, or structure prediction.

ipTM: Predicted confidence in the contact between two chains, on a 0 to 1 scale. Higher is better: 0.65 to 0.75 depending on the tool. Individual tools set their own pass bar a little either side of that — the guide for the tool you ran states its one. On a multi-chain target the number 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 — the folded shape on its own, before a sequence is chosen for it. Higher is better: > 0.4 usable; > 0.6 excellent.