binder-design-campaign-manager

name	binder-design-campaign-manager
description	Binder design campaign planning, monitoring, and troubleshooting. 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. For individual tool parameters, use the specific tool skills.
license	MIT
category	orchestration
tags	["planning","campaign","coordination"]

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

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	ColabDesign	AF2 gradient optimization
Need fast iteration	ESMFold + 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
ColabFold	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
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.