| name | proteinmpnn |
| description | Design protein sequences using ProteinMPNN inverse folding. Use this skill when: (1) Designing sequences for generated backbones (e.g. from BindCraft), (2) Redesigning existing protein sequences, (3) Fixing specific residues while designing others, (4) Optimizing sequences for expression or stability, (5) Multi-state or negative design.
For backbone generation, use boltzgen (recommended) or bindcraft. For ligand-aware design, use ligandmpnn. For solubility optimization, use solublempnn.
|
| license | MIT |
| category | design-tools |
| tags | ["sequence-design","inverse-folding"] |
| source | https://github.com/adaptyvbio/protein-design-skills |
ProteinMPNN Sequence 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
Option 1: Local installation (recommended)
git clone https://github.com/dauparas/ProteinMPNN.git
cd ProteinMPNN
python protein_mpnn_run.py \
--pdb_path backbone.pdb \
--out_folder output/ \
--num_seq_per_target 16 \
--sampling_temp "0.1"
Option 2: Modal (via LigandMPNN wrapper)
cd biomodals
modal run modal_ligandmpnn.py \
--pdb-path backbone.pdb \
--num-seq-per-target 16
Key parameters
| Parameter | Default | Range | Description |
|---|
--pdb_path | required | path | Single PDB input |
--num_seq_per_target | 1 | 1-1000 | Sequences per structure |
--sampling_temp | "0.1" | "0.0001-1.0" | Temperature (must be string!) |
--pdb_path_chains | all | A,B | Chains to design |
Temperature guide
| Temp | Effect |
|---|
| 0.1 | Low diversity, high recovery (production) |
| 0.2 | Moderate diversity (default) |
| 0.3 | Higher diversity (exploration) |
| 0.5+ | Very diverse, lower quality |
Common mistakes
✅ --sampling_temp "0.1" — String with quotes
❌ --sampling_temp 0.1 — Float without quotes may cause errors
✅ --pdb_path_chains A,B — No spaces
❌ --pdb_path_chains A, B — Space after comma
Variants Comparison
| Variant | Use Case |
|---|
| ProteinMPNN | General |
| SolubleMPNN | Expression (E. coli) |
| LigandMPNN | Small molecules/metals |
Output format
output/
├── seqs/backbone.fa # FASTA sequences
└── backbone_pdb/backbone_0001.pdb
Typical performance
| Campaign | Time (T4) | Cost (Modal) |
|---|
| 100 × 8 seq | 15-20 min | ~$2 |
| 500 × 8 seq | 1-1.5h | ~$8 |
Throughput: ~50-100 sequences/minute.
Troubleshooting
| Error | Cause | Fix |
|---|
CUDA out of memory | Long protein | Reduce batch_size |
KeyError: 'A' | Chain not in PDB | Check chain IDs |
JSONDecodeError | Invalid JSONL | Validate JSON syntax |
Next: Structure prediction → protein-qc for filtering.
