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

Total estimated time: 8-12 hours Total estimated cost: ~$60-70

Campaign size recommendations

Rule of thumb: Generate 50x more designs than you need (10-15% pass rate × clustering).

Tool selection guide

When to use each tool

Target difficulty assessment

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

Cost estimation

Per-tool costs (Modal)

Campaign cost estimates

Pipeline variants

High-throughput (maximize diversity)

# More backbones, fewer sequences each
modal run modal_rfdiffusion.py --num-designs 2000
modal run modal_proteinmpnn.py --num-seq-per-target 4 --sampling-temp 0.2

High-quality (maximize per-design quality)

# Fewer backbones, more sequences each, lower temperature
modal run modal_rfdiffusion.py --num-designs 200
modal run modal_proteinmpnn.py --num-seq-per-target 32 --sampling-temp 0.1

Quick exploration (fast iteration)

# Small batch, ESMFold instead of ColabFold
modal run modal_rfdiffusion.py --num-designs 50
modal run modal_proteinmpnn.py --num-seq-per-target 8
modal run modal_esmfold.py --fasta all_seqs.fa  # Faster than ColabFold

See also

• Tool-specific parameters: rfdiffusion, proteinmpnn, colabfold, chai1-structure-prediction, boltz-structure-prediction
• QC thresholds and filtering: protein-design-qc
• Tool selection guidance: binder-design-tool-selection

Inputs

• A campaign goal expressed in experimental terms such as binder count, timeline, or target class.
• Upstream context including target structure quality, hotspot knowledge, and compute budget.
• Observed campaign metrics if the design effort is already in progress.

Outputs

• A concrete campaign plan with tool selection, design counts, and expected funnel sizes.
• Health checks on failure modes such as poor pLDDT, weak interfaces, or low diversity.
• Prioritized next actions that map into the relevant generation, validation, or QC skills.

Next Step

Execute the planned campaign through binder-design-tool-selection and end-to-end-protein-design-workflow, then revisit this skill when pass rates drift.