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Upload one antibody-antigen complex in mmCIF or CIF format with the chains and residue IDs needed for the masked CDR design task.
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What is Proteo-R1?
Proteo-R1 is a reasoning-guided antibody CDR design method for antibody-antigen complexes. It uses a language-model reasoning stage to identify CDR design decisions, then a conditional diffusion model to generate designed antibody structures and sequence artifacts.
The online tool is intended for antibody-antigen complexes where the antigen residue positions of interest are already known. It accepts one mmCIF or CIF structure file and a set of antigen design-point tuples such as [C,4], [C,1], [C,71].
How to use Proteo-R1 online
Upload one antibody-antigen mmCIF or CIF file, enter the antigen residue tuples that should guide CDR redesign, and run the job. Proteo-R1 returns ranked designed PDB structures, designed sequence tables, CDR prediction JSON, confidence artifacts, and intermediate files used during the reasoning and generation stages.
Inputs
| Input | Description |
|---|---|
Antibody-antigen structure | One .cif or .mmcif file containing the antibody-antigen complex. Chain IDs and residue IDs must match the design-point tuples. |
Antigen design points | Comma-separated chain/residue tuples, for example [C,4], [C,1], [C,71]. These positions guide the CDR reasoning step toward the antigen surface of interest. |
Record ID | Optional output record name. If blank, the uploaded file stem is used. |
PDB input is not accepted for this workflow. Proteo-R1's CDR preparation step expects mmCIF/CIF structure metadata, so the tool requires a CIF-family upload instead of converting PDB files automatically.
Settings
| Setting | Description |
|---|---|
Design samples | Number of diffusion samples to generate. More samples broaden the search but increase runtime. |
Random seed | Seed used by the reasoning and generation stages. The default is 2025. |
Maximum reasoning tokens | Token budget for the CDR reasoning step. The default is 2048. Very small values can truncate the generated CDR JSON. |
Reasoning temperature, Reasoning top-p, Reasoning top-k | Sampling controls for the reasoning model. Defaults follow the Proteo-R1 command-line settings. |
Recycling steps | Structure refinement recycling steps. The default is 3. |
Sampling steps | Diffusion sampling steps. The default discrete diffusion mode requires 200. |
Noise type | Diffusion noise mode. The default is discrete_absorb. |
Return full PAE matrix | Returns full predicted aligned error NPZ artifacts when produced. |
Return full PDE matrix | Returns full predicted distance error NPZ artifacts when produced. |
Skip CDR prediction JSON | Disables the CDR prediction JSON artifact. Leave off for normal runs. |
Outputs
| Output | Description |
|---|---|
| Ranked PDB structures | Designed antibody-antigen structures named like record_model_0.pdb. |
| Sequence table | .seq table with designed sequence and CDR amino acid recovery fields when available. |
| CDR prediction JSON | Chain-separated CDR predictions, CDR regions, sequence metrics, and per-CDR amino acid recovery where available. |
| Confidence JSON | Confidence metrics such as confidence_score, ptm, iptm, complex_plddt, complex_pde, and chain-pair metrics. Some values may be blank when the model does not return finite scores for a run. |
| pLDDT NPZ | Per-residue pLDDT arrays for generated structures. |
| Optional PAE/PDE NPZ | Full error matrices when requested. |
| Preparation artifacts | YAML, masked JSON, x-mask CIF, hidden-state safetensors, and Protenix feature dump files used by the run. |
How Proteo-R1 works
Proteo-R1 runs in two main stages.
First, the CDR preparation and reasoning stage identifies the CDR design task from the uploaded complex and design-point tuples. It creates the masked design representation, emits CDR hidden states, and records the intermediate files used by the generation model.
Second, the generation stage runs conditional diffusion to produce designed structures and sequences. Results are ranked by the confidence score when finite confidence values are returned. The output files preserve the model's native structure, sequence, CDR, confidence, and error artifacts so they can be inspected or downloaded together.
Advanced structure inpainting assets
The standard online workflow only requires a CIF-family structure file and design-point tuples. Proteo-R1 also has an advanced structure inpainting mode used in the research pipeline, but that mode depends on a prepared asset bundle rather than a few independent files.
Full inpainting needs matching assets with the same record IDs and the directory layout expected by Proteo-R1:
- Spec-mask YAML: A canonical YAML file describing the design case and masked residues.
- Processed MSA NPZ files: Precomputed MSA assets in Proteo-R1's processed format.
- Ground-truth structure NPZ files: Structure-format NPZ files prepared for the same records.
These assets are folder-shaped because the YAML, MSA files, and structure files must agree on record IDs and paths. Separate uploaded files can only work if the application reconstructs that directory layout before running Proteo-R1.
The current online tool focuses on the CIF plus design-point workflow. Advanced inpainting asset upload can be added later as a separate expert workflow after validating it against a real prepared Proteo-R1 asset bundle.
Interpreting results
Proteo-R1 outputs should be treated as candidate designs for downstream triage. The ranked PDB files are the primary structures to inspect, while the .seq and CDR JSON files make it easier to compare how the model changed CDR regions.
Useful follow-up checks include:
- Structure inspection: Review the antibody-antigen interface for clashes, buried unsatisfied groups, or unrealistic loop placement.
- Sequence review: Compare redesigned CDR sequences against the starting antibody and flag unusual motifs before synthesis.
- Confidence artifacts: Use pLDDT and confidence JSON as screening signals, not experimental validation.
- Downstream scoring: Consider additional structure validation, binding assessment, and developability checks before selecting designs for experiments.