binder-design-campaign-managerlisted

# Binder Design Campaign Manager **Plain-language role**: Use this skill to plan design batches, estimate yields, and diagnose why a campaign is underperforming. ## Goal-oriented design ### From goal to pipeline When user says: "I need 10 good binders for EGFR" **Campaign Planning:** ``` Goal: 10 high-quality binders for EGFR ├── Achievable: Yes (standard target) ├── Recommended pipeline: rfdiffusion → proteinmpnn → colabfold → protein-design-qc ├── Estimated designs needed: 500 backbones (to get ~50 passing QC) ├── Estimated time: 8-12 hours total ├── Estimated cost: ~$60 (Modal GPU compute) └── Expected yield: ├── After backbone (500): 500 structures ├── After sequence (×8): 4,000 sequences ├── After validation: 4,000 predictions ├── After QC (~10-15%): 400-600 candidates └── After clustering: 10-20 diverse final designs ``` --- ## Complete pipeline generator ### Standard miniprotein binder campaign ```bash # Step 1: Fetch and prepare target (5 min) curl -o target.pdb "https://files.rcsb.org/download/{PDB_ID}.pdb" # Trim to binding region if needed # Step 2: Generate backbones (2-3h, ~$15) modal run modal_rfdiffusion.py \ --pdb target.pdb \ --contigs "A1-150/0 70-100" \ --hotspot "A45,A67,A89" \ --num-designs 500 # Checkpoint: ls output/*.pdb | wc -l # Should be 500 # Step 3: Design sequences (1-2h, ~$10) for f in output/*.pdb; do modal run modal_proteinmpnn.py \ --pdb-path "$f" \ --num-seq-per-target 8 \ --sampling-tem