protenix

Protenix — Structure Prediction Skill

Protenix v1 is an AF3-class structure prediction model (368M parameters) for proteins, complexes, and protein-ligand systems. This skill wraps the protenix CLI with a documented input spec, an input-validating Python entry point, and a multi-seed ensemble aggregator so that callers can drive predictions through scripts instead of ad-hoc bash invocations.

The default compute target is the local GPU. HPC (RunPod) is the second choice and Tamarind cloud is the fallback — set the target via --target (see scripts).

When to Use This Skill

Use Protenix when you have:

• ✅ A sequence (or set of sequences) and need a 3D structure — single chain, complex, homo-oligomer, or protein-ligand.
• ✅ A designed binder to validate by refolding — predict the binder + target complex and inspect ipTM / interface pLDDT.
• ✅ A need for explicit confidence metrics — ipTM, pTM, pLDDT, ranking_score.
• ✅ A need for multi-seed ensemble stability — 3-25 seeds with variance reported.
• ✅ A protein-ligand complex — SMILES + protein chain via the ligand entity type.
• ✅ A local GPU available (CUDA, bf16 capable) or an approved HPC / Tamarind path.

Do NOT use Protenix when:

• ❌ You need to design a new binder → use pxdesign (de novo binder) or boltzgen (antibody / nanobody).
• ❌ You only need to score an existing prediction (ipSAE from PAE matrices) → use by-scoring.
• ❌ You need the full liability + developability battery → use by-screening.
• ❌ You need to fetch sequences from PDB/UniProt → use by-database first, then return here.
• ❌ You are running on CPU only → Protenix requires a CUDA-capable GPU; no CPU fallback.
• ❌ You want pipeline orchestration across research → design → screen → use by-design-workflow.

Quick Start

Local GPU (default). Write an input JSON, then run the wrapper script:

# 1. Write input spec (one prediction object, JSON array form)
cat > /tmp/fold_run/input.json <<'JSON'
[
  {
    "name": "lysozyme_pred",
    "sequences": [
      {"proteinChain": {"sequence": "KVFGRCELAA...", "count": 1}}
    ],
    "modelSeeds": [42],
    "sampleCount": 1
  }
]
JSON

# 2. Run via the wrapper (validates input, invokes local GPU CLI)
python scripts/protenix_fold.py \
  --input /tmp/fold_run/input.json \
  --output-dir /tmp/fold_run/output \
  --model protenix_base_default_v1.0.0 \
  --target local

# 3. Read confidence
ls /tmp/fold_run/output/lysozyme_pred/seed_42/*_summary_confidence_sample_*.json