campaign-manager

name	campaign-manager
description	Goal-oriented binder design campaign planning and health assessment. Use this skill when: (1) Planning a complete binder design campaign, (2) Converting high-level goals into runnable pipelines, (3) Assessing campaign health and pass rates, (4) Diagnosing why designs are failing QC, (5) Estimating time, cost, and expected yields, (6) Selecting between design tools for a specific target. This skill orchestrates the other protein design tools.
license	MIT
category	orchestration
tags	["planning","campaign","coordination"]
source	https://github.com/adaptyvbio/protein-design-skills

Campaign Manager

Goal-Oriented Planning

When user says "I need 10 good binders for EGFR":

Goal: 10 high-quality binders
├── Recommended: boltzgen → chai → protein-qc
├── 100-200 BoltzGen designs → Chai validation → ~10-20% pass QC
├── Estimated time: 4-8 hours
└── Estimated cost: ~$30-60 (Modal GPU compute)

Recommended Pipeline (BoltzGen)

# Step 1: Prepare target (5 min)
curl -o target.pdb "https://files.rcsb.org/download/{PDB_ID}.pdb"

# Step 2: Create YAML config
cat > binder.yaml << 'EOF'
entities:
  - protein:
      id: B
      sequence: 70..100
  - file:
      path: target.cif
      include:
        - chain:
            id: A
      binding_types:
        - chain:
            id: A
            binding: 45,67,89
EOF

# Step 3: Generate all-atom designs (2-4h, ~$20)
GPU=L40S modal run modal_boltzgen.py \
  --input-yaml binder.yaml \
  --protocol protein-anything \
  --num-designs 100

# Step 4: Validate (1-2h, ~$10)
modal run modal_chai1.py --input-faa designs.fasta --out-dir predictions/

# Step 5: Filter and rank (protein-qc skill)

Alternative Pipeline (BindCraft — integrated)

# Single command, end-to-end
GPU=A100 modal run modal_bindcraft.py \
  --input-pdb target.pdb \
  --hotspots "A45,A67,A89" \
  --number-of-final-designs 50

Campaign Size Recommendations

Goal	BoltzGen designs	Expected Passing	Final candidates
5 binders	50	~5-10	5
10 binders	100	~10-20	10
20 binders	200	~20-40	20
50 binders	500	~50-100	50

Campaign Health Assessment

def assess_campaign(csv_path):
    df = pd.read_csv(csv_path)
    plddt_pass = (df['pLDDT'] > 0.85).mean()
    iptm_pass = (df['ipTM'] > 0.50).mean()
    all_pass = ((df['pLDDT'] > 0.85) & (df['ipTM'] > 0.5) & (df['scRMSD'] < 2.0)).mean()

    health = "EXCELLENT" if all_pass > 0.15 else \
             "GOOD" if all_pass > 0.10 else \
             "MARGINAL" if all_pass > 0.05 else "POOR"

    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")

    return {"health": health, "pass_rate": all_pass, "issues": issues}

Health	Pass Rate	Action
EXCELLENT	> 15%	Proceed to selection
GOOD	10-15%	Normal yield
MARGINAL	5-10%	Review failure tree
POOR	< 5%	Diagnose and restart

Cost Estimates (Modal)

Tool	GPU	Typical Job	Cost
BoltzGen	L40S	100 designs/2h	~$20
BindCraft	A100	50 designs/4h	~$18
Chai	A100	200 preds/1h	~$4.50
Boltz	L40S	200 preds/1h	~$4

Standard campaign (100 designs): ~$25-30 total