binder-design-campaign-manager

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

# 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-temp 0.1
done

# Checkpoint: grep -c "^>" output/seqs/*.fa  # Should be ~4000

# Step 4: Quick ESM2 filter (30 min, ~$5, optional)
modal run modal_esm.py --fasta output/all_seqs.fa --mode pll
# Filter sequences with PLL < 0.0

# Step 5: Structure validation (3-4h, ~$35)
modal run modal_colabfold.py \
  --input-faa output/filtered_seqs.fa \
  --out-dir predictions/

# Checkpoint: find predictions -name "*rank_001.pdb" | wc -l

# Step 6: Filter and rank (protein-design-qc skill)
# Apply thresholds: pLDDT > 0.85, ipTM > 0.5, scRMSD < 2.0
# Compute composite score
# Cluster at 70% identity, select top from each cluster