proteina-complexa

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.