| 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"] |
| source | https://github.com/adaptyvbio/protein-design-skills |
SolubleMPNN Solubility-Optimized Design
Prerequisites
| Requirement | Minimum | Recommended |
|---|
| Python | 3.8+ | 3.10 |
| GPU VRAM | 8GB | 16GB (T4) |
How to run
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
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 |
Decision tree
When to use SolubleMPNN?
├─ E. coli expression → SolubleMPNN ✓
├─ History of aggregation → SolubleMPNN ✓
├─ History of low yield → SolubleMPNN ✓
├─ Ligand in binding site → LigandMPNN
└─ No expression issues → ProteinMPNN is fine
Expected improvement: +15-30% solubility score vs standard ProteinMPNN.
Next: Structure prediction → protein-qc for filtering.
