> Solubility-optimized protein sequence design using SolubleMPNN. Use this skill when: (1) Designing for E. coli expression, (2) Optimizing solubility of designed proteins, (3) Reducing aggregation propensity, (4) Need high-yield expression, (5) Avoiding inclusion body formation. For standard design, use proteinmpnn. For ligand-aware design, use ligandmpnn.
Install with the open skills CLI (global, non-interactive — available in every Claude Code session):
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill "solublempnn" -g -a claude-code -yOr manually — copy the SKILL.md below into:
~/.claude/skills/solublempnn/SKILL.md---
name: solublempnn
description: >
Solubility-optimized protein sequence design using SolubleMPNN.
Use this skill when: (1) Designing for E. coli expression,
(2) Optimizing solubility of designed proteins,
(3) Reducing aggregation propensity,
(4) Need high-yield expression,
(5) Avoiding inclusion body formation.
For standard design, use proteinmpnn.
For ligand-aware design, use ligandmpnn.
license: MIT
category: design-tools
tags: [sequence-design, inverse-folding, solubility]
biomodals_script: modal_ligandmpnn.py
---
# SolubleMPNN Solubility-Optimized Design
## Prerequisites
| Requirement | Minimum | Recommended |
|-------------|---------|-------------|
| Python | 3.8+ | 3.10 |
| CUDA | 11.0+ | 11.7+ |
| GPU VRAM | 8GB | 16GB (T4) |
| RAM | 8GB | 16GB |
## How to run
> **First time?** See [Installation Guide](../../docs/installation.md) to set up Modal and biomodals.
### Option 1: Modal (recommended)
SolubleMPNN uses the ProteinMPNN Modal wrapper with soluble model:
```bash
cd biomodals
modal run modal_proteinmpnn.py \
--pdb-path backbone.pdb \
--num-seq-per-target 16 \
--sampling-temp 0.1 \
--model-name v_48_020
```
**GPU**: T4 (16GB) | **Timeout**: 600s default
### Option 2: Local installation
```bash
git clone https://github.com/dauparas/ProteinMPNN.git
cd ProteinMPNN
# Use soluble model weights
python protein_mpnn_run.py \
--pdb_path backbone.pdb \
--out_folder output/ \
--num_seq_per_target 16 \
--sampling_temp "0.1" \
--model_name "v_48_020" # Soluble model
```
## Key parameters
| Parameter | Default | Range | Description |
|-----------|---------|-------|-------------|
| `--pdb_path` | required | path | Input structure |
| `--num_seq_per_target` | 1 | 1-1000 | Sequences per structure |
| `--sampling_temp` | "0.1" | "0.0001-1.0" | Temperature (string!) |
| `--model_name` | v_48_020 | string | Soluble model variant |
## Model Variants
| Model | Description | Use Case |
|-------|-------------|----------|
| v_48_002 | Standard | General design |
| v_48_020 | Soluble-trained | E. coli expression |
| v_48_030 | High solubility | Difficult targets |
## Output format
```
output/
├── seqs/backbone.fa
└── backbone_pdb/backbone_0001.pdb
```
## Sample output
### Successful run
```
$ python protein_mpnn_run.py --pdb_path backbone.pdb --model_name v_48_020 --num_seq_per_target 8
Loading soluble model weights (v_48_020)...
Designing sequences for backbone.pdb
Generated 8 sequences in 2.1 seconds
output/seqs/backbone.fa:
>backbone_0001, score=1.31, global_score=1.24, seq_recovery=0.78
MKTAYIAKQRQISFVKSHFSRQLE...
>backbone_0002, score=1.28, global_score=1.21, seq_recovery=0.81
MKTAYIAKQRQISFVKSQFSRQLD...
```
**What good output looks like:**
- Score: 1.0-2.0 (lower = more confident)
- Reduced hydrophobic patches compared to standard MPNN
- Improved charge distribution
## Decision tree
```
Should I use SolubleMPNN?
│
├─ What expression system?
│ ├─ E. coli → SolubleMPNN ✓
│ ├─ Mammalian → ProteinMPNN (PTMs matter more)
│ └─ Yeast → Either
│
├─ History of expression problems?
│ ├─ Yes, aggregation → SolubleMPNN ✓
│ ├─ Yes, low yield → SolubleMPNN ✓
│ └─ No → ProteinMPNN is fine
│
├─ What's in the binding site?
│ ├─ Small molecule / ligand → Use LigandMPNN
│ └─ Nothing / protein only → SolubleMPNN ✓
│
└─ Need highest solubility?
├─ Yes → Use v_48_030 model
└─ Standard → Use v_48_020 model
```
## Typical performance
| Campaign Size | Time (T4) | Cost (Modal) | Notes |
|---------------|-----------|--------------|-------|
| 100 backbones × 8 seq | 15-20 min | ~$2 | Standard |
| 500 backbones × 8 seq | 1-1.5h | ~$8 | Large campaign |
**Expected improvement**: +15-30% solubility score vs standard ProteinMPNN.
---
## Verify
```bash
grep -c "^>" output/seqs/*.fa # Should match backbone_count × num_seq_per_target
```
---
## Troubleshooting
**Still insoluble**: Try v_48_030 (higher solubility bias)
**Low diversity**: Increase temperature to 0.2
**Poor folding**: Use standard ProteinMPNN and optimize later
### Error interpretation
| Error | Cause | Fix |
|-------|-------|-----|
| `RuntimeError: CUDA out of memory` | Long protein or large batch | Reduce batch_size |
| `FileNotFoundError: v_48_020` | Missing model weights | Download soluble weights |
---
**Next**: Structure prediction for validation → `protein-qc` for filtering.
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