Campaign Manager
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 → chai → protein-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)
# RFdiffusion runs from the official repo, not biomodals
python run_inference.py \
inference.input_pdb=target.pdb \
contigmap.contigs=[A1-150/0 70-100] \
ppi.hotspot_res=[A45,A67,A89] \
inference.num_designs=500
# Checkpoint: ls output/*.pdb | wc -l # Should be 500
# Step 3: Design sequences (1-2h, ~$10)
for f in output/*.pdb; do
modal run modal_ligandmpnn.py \
--input-pdb "$f" \
--params-str "--number_of_batches 8 --temperature 0.1"
done
# Checkpoint: grep -c "^>" output/seqs/*.fa # Should be ~4000
# Step 4: Quick ESM2 filter (30 min, ~$5, optional)
modal run modal_esm2_predict_masked.py --input-faa output/all_seqs.fa
# Filter sequences with PLL < 0.0
# Step 5: Structure validation (3-4h, ~$35)
modal run modal_alphafold.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-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
Total estimated time: 8-12 hours
Total estimated cost: ~$60-70
Campaign size recommendations
| Goal | Backbones | Sequences/BB | Total Seq | Expected Passing |
|---|
| 5 binders | 200 | 8 | 1,600 | 160-240 |
| 10 binders | 500 | 8 | 4,000 | 400-600 |
| 20 binders | 1,000 | 8 | 8,000 | 800-1,200 |
| 50 binders | 2,500 | 8 | 20,000 | 2,000-3,000 |
Rule of thumb: Generate 50x more designs than you need (10-15% pass rate × clustering).
Tool selection guide
When to use each tool
| Scenario | Recommended Tool | Reason |
|---|
| Standard miniprotein | RFdiffusion + ProteinMPNN | High diversity, proven |
| Need higher success rate | BindCraft | Integrated design loop |
| All-atom precision needed | BoltzGen | Side-chain aware |
| Difficult target | Mosaic | Gradient, multi-model objective |
| Need fast iteration | ESMFold2 + ESM2 | Quick screening |
Target difficulty assessment
| Indicator | Easy Target | Difficult Target |
|---|
| Surface type | Concave pocket | Flat or convex |
| Conservation | High | Low |
| Known binders | Yes | No |
| Flexibility | Rigid | Flexible |
| Expected pass rate | 15-20% | 5-10% |
Campaign health assessment
Quick metrics check
import pandas as pd
def assess_campaign(csv_path):
df = pd.read_csv(csv_path)
# Calculate pass rates
plddt_pass = (df['pLDDT'] > 0.85).mean()
iptm_pass = (df['ipTM'] > 0.50).mean()
scrmsd_pass = (df['scRMSD'] < 2.0).mean()
all_pass = ((df['pLDDT'] > 0.85) & (df['ipTM'] > 0.5) & (df['scRMSD'] < 2.0)).mean()
# Determine health
if all_pass > 0.15:
health = "EXCELLENT"
elif all_pass > 0.10:
health = "GOOD"
elif all_pass > 0.05:
health = "MARGINAL"
else:
health = "POOR"
# Identify top issue
issues = []
if plddt_pass < 0.20:
issues.append("Low pLDDT - backbone or sequence issue")
if iptm_pass < 0.20:
issues.append("Low ipTM - hotspot or interface issue")
if scrmsd_pass < 0.50:
issues.append("High scRMSD - sequence doesn't specify backbone")
return {
"health": health,
"overall_pass_rate": all_pass,
"plddt_pass_rate": plddt_pass,
"iptm_pass_rate": iptm_pass,
"scrmsd_pass_rate": scrmsd_pass,
"top_issues": issues
}
Interpreting results
| Health | Pass Rate | Action |
|---|
| EXCELLENT | > 15% | Proceed to selection |
| GOOD | 10-15% | Proceed, normal yield |
| MARGINAL | 5-10% | Review failure tree |
| POOR | < 5% | Diagnose and restart |
Cost estimation
Per-tool costs (Modal)
| Tool | GPU | $/hour | Typical Job | Cost |
|---|
| RFdiffusion | A10G | ~$1.20 | 500 designs/2h | ~$2.50 |
| ProteinMPNN | T4 | ~$0.60 | 4000 seq/1.5h | ~$1.00 |
| ESM2 (PLL) | A10G | ~$1.20 | 4000 seq/30min | ~$0.60 |
| AlphaFold | A100 | ~$4.50 | 4000 preds/4h | ~$18.00 |
| Chai | A100 | ~$4.50 | 500 preds/1h | ~$4.50 |
Campaign cost estimates
| Campaign Size | Total Cost | Notes |
|---|
| Small (100 bb) | ~$15 | Quick exploration |
| Standard (500 bb) | ~$60 | Most campaigns |
| Large (1000 bb) | ~$120 | Comprehensive |
| XL (5000 bb) | ~$600 | Very thorough |
Pipeline variants
High-throughput (maximize diversity)
# More backbones, fewer sequences each (RFdiffusion from the official repo)
python run_inference.py inference.num_designs=2000
modal run modal_ligandmpnn.py --input-pdb bb.pdb --params-str "--number_of_batches 4 --temperature 0.2"
High-quality (maximize per-design quality)
# Fewer backbones, more sequences each, lower temperature
python run_inference.py inference.num_designs=200
modal run modal_ligandmpnn.py --input-pdb bb.pdb --params-str "--number_of_batches 32 --temperature 0.1"
Quick exploration (fast iteration)
# Small batch, ESMFold2 for fast single-sequence folding
# RFdiffusion runs from the official repo (not biomodals); see the rfdiffusion skill
modal run modal_ligandmpnn.py --input-pdb bb.pdb --params-str "--number_of_batches 8"
modal run modal_esmfold2.py --input-faa all_seqs.fa
See also
- Tool-specific parameters:
rfdiffusion, proteinmpnn, mosaic, chai, boltz, alphafold
- QC thresholds and filtering:
protein-qc
- Tool selection guidance:
binder-design
