| name | proteina-complexa |
| description | Proteina-Complexa flow-based protein backbone generation with fold-conditioned sampling guidance. Use this skill when: (1) Generating de novo protein backbones with hierarchical fold conditioning, (2) Exploring long-chain backbone generation beyond standard diffusion baselines, (3) Using NVIDIA Proteina-style flow matching workflows for controllable backbone design, (4) Comparing flow-based backbone generation against RFdiffusion or BoltzGen, (5) Prototyping fold-guided backbone campaigns before sequence design.
This skill is based on the public NVIDIA Digital Bio Proteina project and uses "Proteina-Complexa" as the BioClaw-facing skill label. For sequence design after backbone generation, use proteinmpnn or solublempnn. For QC thresholds, use protein-design-qc.
|
| license | MIT |
| category | design-tools |
| tags | ["structure-design","backbone-generation","flow-matching","fold-conditioning"] |
Proteina-Complexa Backbone Generation
Plain-language role: Use this skill when you want a flow-based backbone generator with fold-class conditioning, especially for exploratory de novo design.
Source Notes
- Public upstream reference:
NVIDIA-Digital-Bio/proteina
- Publicly described as a large-scale flow-based protein backbone generator with hierarchical fold class conditioning
- Upstream setup and weights may change over time, so verify the current README and license before running
- Check the upstream NVIDIA license before commercial use or redistribution of model artifacts
Prerequisites
| Requirement | Minimum | Recommended |
|---|
| Python | 3.10+ | 3.11 |
| CUDA | 12.0+ | 12.1+ |
| GPU VRAM | 24GB | 40GB+ |
| Environment manager | conda | mamba or micromamba |
How to Run
Option 1: Upstream Proteina environment
git clone https://github.com/NVIDIA-Digital-Bio/proteina.git
cd proteina
mamba env create -f environment.yaml
conda activate proteina_env
pip install -e .
Create a .env file in the repository root:
echo "DATA_PATH=/path/to/proteina-data" > .env
Additional files
The upstream project documents extra data and weight bundles that must live under DATA_PATH.
At minimum, verify:
- metric feature files
- model weights
- CATH label mapping files
- dataset index files if you plan to train or evaluate
Recommended Use Pattern
1. Start from backbone generation
Use Proteina-Complexa when the main task is generating diverse backbones, not sequence optimization.
2. Prefer fold-conditioned exploration
The upstream model is especially useful when you want:
- hierarchical fold control
- long-chain generation
- comparison against diffusion-based backbone generators
3. Hand off to sequence design
After generating promising backbones:
- use
proteinmpnn for general inverse folding
- use
solublempnn when expression robustness matters more
4. Validate and filter
After sequence design:
- use
chai1-structure-prediction, boltz-structure-prediction, or alphafold2-multimer
- use
protein-design-qc for filtering and ranking
Typical Workflow
Target goal
-> Proteina-Complexa backbone generation
-> ProteinMPNN / SolubleMPNN sequence design
-> Chai / Boltz / AlphaFold validation
-> Protein Design QC
When to Prefer This Over Other Tools
| Need | Prefer |
|---|
| Maximum backbone diversity with established community recipes | rfdiffusion |
| All-atom generation with side-chain awareness | boltzgen |
| Flow-based backbone generation with fold conditioning | proteina-complexa |
| End-to-end integrated binder pipeline | bindcraft |
Key Ideas to Preserve
- Keep fold-conditioning choices explicit
- Record which checkpoint and config produced each backbone batch
- Separate backbone-generation artifacts from downstream sequence-design artifacts
- Treat generated backbones as candidates that still require validation and QC
Common Mistakes
- Treating Proteina-Complexa as a sequence-design tool
- Skipping required upstream weight and data bundles
- Comparing outputs against RFdiffusion or BoltzGen without matching length and conditioning settings
- Moving generated backbones directly to experiments without refolding validation
Troubleshooting
| Error | Likely cause | Fix |
|---|
Missing DATA_PATH files | Required upstream bundles not downloaded | Re-check upstream setup and place files under the documented directory tree |
| CUDA OOM | Backbone length or batch too large | Reduce batch size or use a larger GPU |
| Config mismatch | Wrong checkpoint/config pair | Keep checkpoint, config, and conditioning mode aligned |
| Weak downstream foldability | Backbone exploration too unconstrained | Tighten fold conditioning and validate more aggressively |
Inputs
- A backbone-generation objective such as fold-conditioned sampling, long-chain exploration, or de novo backbone discovery.
- A configured Proteina-style environment with checkpoints, configs, and required data bundles available under the configured data path.
- Optional fold-class or topology guidance for controlled generation.
Outputs
- Generated protein backbone candidates suitable for downstream inverse folding.
- Run metadata describing checkpoint choice, conditioning mode, and generation settings.
- Backbone batches ready for sequence design with
proteinmpnn or solublempnn.
Next Step
Send promising backbones to proteinmpnn or solublempnn, then validate them structurally and filter with protein-design-qc.
