# Protein Design Workflow

> Protein Design Workflow Guide

- Skill: `fridrichmethod/protein-design-workflow` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add fridrichmethod/protein-design-workflow`
- Raw SKILL.md: https://api.skillmd.com/api/skills/fridrichmethod/protein-design-workflow/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: FridrichMethod (https://skillmd.com/u/fridrichmethod)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/fridrichmethod/protein-design-workflow

---


# Protein Design Workflow Guide

## Standard binder design pipeline

### Overview
```
Target Preparation --> Backbone Generation --> Sequence Design
         |                     |                     |
         v                     v                     v
    (pdb skill)          (rfdiffusion)         (proteinmpnn)
                               |                     |
                               v                     v
                        Structure Validation --> Filtering
                               |                     |
                               v                     v
                         (alphafold/chai)      (protein-qc)
```

## Phase 1: Target preparation

### 1.1 Obtain target structure
```bash
# Download from PDB
curl -o target.pdb "https://files.rcsb.org/download/XXXX.pdb"
```

### 1.2 Clean and prepare
```python
# Extract target chain
# Remove waters, ligands if needed
# Trim to binding region + 10A buffer
```

### 1.3 Select hotspots
- Choose 3-6 exposed residues
- Prefer charged/aromatic (K, R, E, D, W, Y, F)
- Check surface accessibility
- Verify residue numbering

**Output**: `target_prepared.pdb`, hotspot list

## Phase 2: Backbone generation

### Option A: RFdiffusion (diverse exploration)
```bash
# RFdiffusion runs from the official repo, not biomodals
python run_inference.py \
  inference.input_pdb=target_prepared.pdb \
  contigmap.contigs=[A1-150/0 70-100] \
  ppi.hotspot_res=[A45,A67,A89] \
  inference.num_designs=500
```

### Option B: BindCraft (end-to-end)
```bash
modal run modal_bindcraft.py \
  --input-pdb target_prepared.pdb \
  --target-hotspot-residues "45,67,89" \
  --number-of-final-designs 100
```

**Output**: 100-500 backbone PDBs

## Phase 3: Sequence design

### For RFdiffusion backbones
```bash
for backbone in backbones/*.pdb; do
  modal run modal_ligandmpnn.py \
    --input-pdb "$backbone" \
    --params-str "--number_of_batches 8 --temperature 0.1"
done
```

**Output**: 8 sequences per backbone (800-4000 total)

## Phase 4: Structure validation

### Predict complexes
```bash
# Prepare FASTA with binder + target
# binder:target format for multimer

modal run modal_alphafold.py \
  --input-fasta all_sequences.fasta \
  --out-dir predictions/
```

**Output**: AF2 predictions with pLDDT, ipTM, PAE

## Phase 5: Filtering and selection

### Apply standard thresholds
```python
import pandas as pd

# Load metrics
designs = pd.read_csv('all_metrics.csv')

# Filter
filtered = designs[
    (designs['pLDDT'] > 0.85) &
    (designs['ipTM'] > 0.50) &
    (designs['PAE_interface'] < 10) &
    (designs['scRMSD'] < 2.0) &
    (designs['esm2_pll'] > 0.0)
]

# Rank by composite score
filtered['score'] = (
    0.3 * filtered['pLDDT'] +
    0.3 * filtered['ipTM'] +
    0.2 * (1 - filtered['PAE_interface'] / 20) +
    0.2 * filtered['esm2_pll']
)

top_designs = filtered.nlargest(50, 'score')
```

**Output**: 50-200 filtered candidates

## Resource planning

### Compute requirements

| Stage | GPU | Time (100 designs) |
|-------|-----|-------------------|
| RFdiffusion | A10G | 30 min |
| ProteinMPNN | T4 | 15 min |
| Chai / AlphaFold | A100 | 4-8 hours |
| Filtering | CPU | 15 min |

### Total timeline
- Small campaign (100 designs): 8-12 hours
- Medium campaign (500 designs): 24-48 hours
- Large campaign (1000+ designs): 2-5 days

## Quality checkpoints

### After backbone generation
- [ ] Visual inspection of diverse backbones
- [ ] Secondary structure present
- [ ] No clashes with target

### After sequence design
- [ ] ESM2 PLL > 0.0 for most sequences
- [ ] No unwanted cysteines (unless intentional)
- [ ] Reasonable sequence diversity

### After validation
- [ ] pLDDT > 0.85
- [ ] ipTM > 0.50
- [ ] PAE_interface < 10
- [ ] Self-consistency RMSD < 2.0 A

### Final selection
- [ ] Diverse sequences (cluster if needed)
- [ ] Manufacturable (no problematic motifs)
- [ ] Reasonable molecular weight

## Common issues

| Problem | Solution |
|---------|----------|
| Low ipTM | Check hotspots, increase designs |
| Poor diversity | Higher temperature, more backbones |
| High scRMSD | Backbone may be unusual |
| Low pLDDT | Check design quality |

## Advanced workflows

### Multi-tool combination
1. RFdiffusion for initial backbones
2. Mosaic for gradient-based refinement
3. ProteinMPNN diversification
4. AF2 final validation

### Iterative refinement
1. Run initial campaign
2. Analyze failures
3. Adjust hotspots/parameters
4. Repeat with insights