pxdesign

PXDesign — De Novo Protein Binder Design

You are an expert at designing de novo protein binders using PXDesign. This skill covers YAML config construction, CLI invocation, output parsing, and result interpretation. PXDesign achieves 17–82% pass rate for de novo binder design depending on target difficulty (see references/de-novo-strategy.md).

PXDesign is invoked from the command line via the pxdesign binary. This skill uses a strict Write → Bash → Read pattern: write a YAML config, run the CLI, read the summary.csv. Do not call internal Python wrappers.

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When to Use This Skill

Use PXDesign when you have:

• ✅ A non-antibody, non-nanobody binder target — a small globular protein binder is acceptable
• ✅ A target structure file (CIF preferred, PDB acceptable)
• ✅ A defined epitope or hotspot list (or willingness to let PXDesign explore)
• ✅ Local GPU available (A100/H100/RTX PRO 6000) OR HPC/Tamarind fallback configured
• ✅ A clear quality bar — preview for exploration, extended for production designs

Do NOT use this skill when:

• ❌ The user wants an antibody or nanobody → use the boltzgen skill
• ❌ The user wants structure prediction only → use the protenix skill
• ❌ The user wants to score or screen existing designs → use by-scoring / by-screening
• ❌ No target structure exists → predict it with Protenix first
• ❌ The target is membrane-embedded with no soluble construct → restate the problem; PXDesign requires a soluble target

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Quick Start

Design a 100-residue binder against chain A of IL6R.cif, preview preset, 10 samples:

# Step 1 — Write config.yaml (see references/yaml-config-spec.md)
cat > /tmp/il6r/config.yaml <<'EOF'
target:
  file: "/data/targets/IL6R.cif"
  chains:
    A: "all"
binder_length: 100
EOF

# Step 2 — Run PXDesign on local GPU
PATH=/path/to/conda/envs/protenix/bin:$PATH \
PROTENIX_DATA_ROOT_DIR=$PROTEUS_PROT_DIR/release_data/ccd_cache \
TOOL_WEIGHTS_ROOT=$PROTEUS_PROT_DIR/tool_weights \
CUTLASS_PATH=$HOME/cutlass \
CUDA_HOME=$CUDA_HOME \
pxdesign pipeline \
  --preset preview \
  -i /tmp/il6r/config.yaml \
  --N_sample 10 \
  --dtype bf16 \
  --use_fast_ln True \
  -o /tmp/il6r/output

# Step 3 — Parse outputs
python scripts/parse_pxdesign_output.py \
  --output-dir /tmp/il6r/output \
  --out /tmp/il6r/designs.csv