OpenDDE

Overview

Preview status: OpenDDE is an upstream preview release. Its CLI, JSON schema, checkpoints, and numerical behavior may change, and predictions are not guaranteed to be reproducible across releases.

• Protein, DNA, RNA, ligand, ion, and mixed-complex structure prediction.
• Typed assembly building for common systems.
• Native OpenDDE JSON for prepared or advanced inputs.
• Batch prediction from native JSON or SubSeq sequence artifacts.
• The general-purpose opendde.pt and antibody-antigen opendde_abag.pt checkpoints.

Workflows

Canonical Job Configuration

The current typed fields are returned by GET /api/v1/program/params?program=opendde and submitted as the params JSON object to POST /api/v1/job/submit.

Local Feature Policy

• SubSeq runs OpenDDE without hosted preprocessing services.
• Protein and RNA MSA are off unless a prepared input explicitly references a local A3M for every applicable entity.
• Template inference is disabled because the preview fetches template structures from a third-party service.
• Native JSON may reference prepared A3M files mounted under the selected input source.
• SubSeq seq JSON with a precomputed MSA keeps its relative MSA reference during conversion.
• When RNA MSA is enabled without protein MSA in a mixed system, SubSeq supplies query-only protein alignments so OpenDDE can activate RNA features without a hosted protein search.
• Do not submit paths outside the mounted input and service-managed reference trees.

Outputs And Results

/outputs/<job_name>/seed_<seed>/predictions/
  <job_name>_sample_<rank>.cif
  <job_name>_summary_confidence_sample_<rank>.json

• Native mmCIF structures and confidence JSON remain available in the raw output manifest.
• Rank 0 is the highest-ranked sample for a job and seed.
• Final structures are also published as standardized SubSeq structure artifacts for preview and pipeline handoff.
• plddt is local confidence on a 0-100 scale; higher is better.
• gpde is predicted global distance error in angstroms; lower is better.
• ptm estimates global topology confidence; iptm focuses on interfaces. Higher is better.
• ranking_score orders samples; detected clashes can apply a large penalty.
• The result reader recognizes optional protein-binding ranking and van der Waals clash fields if a compatible preview emits them.

The Results view pairs each predicted structure with its recognized metrics. Use the raw manifest for full confidence matrices, per-chain values, and other detailed sidecars.

Common Examples

• Protein monomer: Build Assembly with one protein entity, the general checkpoint, one sample, and local feature searches off.
• Antibody-antigen complex: Build Assembly or Custom JSON with the antibody-antigen checkpoint.
• Protein-ligand complex: Build Assembly with protein and ligand entities; optionally enable training-free guidance.
• Pipeline prediction: place OpenDDE after a sequence-producing step and use Batch Input to consume canonical SubSeq seq JSON artifacts.

Minimal native OpenDDE JSON

[
  {
    "name": "tiny",
    "modelSeeds": [101],
    "sequences": [
      {
        "proteinChain": {
          "sequence": "ACDEFGHIK",
          "count": 1
        }
      }
    ]
  }
]

Caveats

• Preview releases may change output fields, checkpoint behavior, or reproducibility.
• Each job is limited to 700 polymer residues or bases, five model seeds, and 64 total entity instances; batch sources are limited to 64 jobs.
• Single-sequence prediction may be less accurate than prediction with an informative prepared MSA.
• High local confidence does not guarantee a correct interface, ligand pose, or biological assembly.
• Ranking and confidence values are computational prioritization signals, not measured affinity, activity, or experimental validation.

Advanced Submit

Advanced submit is available for direct OpenDDE arguments through POST /api/v1/job/submit-advanced. Prefer canonical configuration for normal jobs and batch pipelines.

curl -X POST https://subseq.bio/api/v1/job/submit-advanced \
  -H "Authorization: Bearer <api_key>" \
  -F program=opendde \
  -F 'args=--input=/inputs/input.json' \
  -F 'args=--model=general' \
  -F 'args=--samples=1' \
  -F 'args=--steps=200' \
  -F 'args=--cycles=10' \
  -F 'args=--use-msa=false' \
  -F 'args=--use-templates=false' \
  -F 'args=--use-rna-msa=false' \
  -F 'folder=@input.json;filename=input.json'

Further Reading

• OpenDDE GitHub
• OpenDDE inference instructions
• OpenDDE input JSON format