LigandMPNN Ligand-Aware 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 to set up Modal and biomodals.
Option 1: Modal (recommended)
bashcd biomodals modal run modal_ligandmpnn.py \ --pdb-path protein_ligand.pdb \ --num-seq-per-target 16 \ --sampling-temp 0.1
GPU: T4 (16GB) | Timeout: 600s default
Option 2: Local installation
bashgit clone https://github.com/dauparas/LigandMPNN.git cd LigandMPNN python run.py \ --pdb_path protein_ligand.pdb \ --out_folder output/ \ --num_seq_per_target 16
Key parameters
| Parameter | Default | Range | Description |
|---|---|---|---|
--pdb_path | required | path | PDB with ligand |
--num_seq_per_target | 1 | 1-1000 | Sequences per structure |
--sampling_temp | "0.1" | "0.0001-1.0" | Temperature (string!) |
--ligand_mpnn_use_side_chain_context | true | bool | Use ligand context |
Ligand Specification
In PDB File
Ligand must be present as HETATM records:
ATOM ...protein atoms... HETATM 1 C1 LIG A 999 x.xxx y.yyy z.zzz 1.00 0.00 C
Supported Ligand Types
- Small molecules (HETATM)
- Metals (Zn, Fe, Mg, Ca, etc.)
- Cofactors (NAD, FAD, ATP)
- DNA/RNA
Output format
output/ ├── seqs/ │ └── protein.fa # FASTA sequences └── protein_pdb/ └── protein_0001.pdb # PDBs with designed sequence
Sample output
Successful run
$ python run.py --pdb_path enzyme_substrate.pdb --out_folder output/ --num_seq_per_target 8 Loading LigandMPNN model weights... Processing enzyme_substrate.pdb Found ligand: LIG (12 atoms) Generated 8 sequences in 3.1 seconds output/seqs/enzyme_substrate.fa: >enzyme_substrate_0001, score=1.45, global_score=1.38 MKTAYIAKQRQISFVKSHFSRQLE... >enzyme_substrate_0002, score=1.52, global_score=1.41 MKTAYIAKQRQISFVKSQFSRQLD...
What good output looks like:
- Score: 1.0-2.0 (lower = more confident)
- Ligand detected and incorporated in context
- Active site residues preserved or optimized
Decision tree
Should I use LigandMPNN? │ ├─ What's in your binding site? │ ├─ Small molecule / ligand → LigandMPNN ✓ │ ├─ Metal ion (Zn, Fe, etc.) → LigandMPNN ✓ │ ├─ Cofactor (NAD, FAD, ATP) → LigandMPNN ✓ │ ├─ DNA/RNA → LigandMPNN ✓ │ └─ Nothing / protein only → Use ProteinMPNN │ ├─ What type of design? │ ├─ Enzyme active site → LigandMPNN ✓ │ ├─ Metal binding site → LigandMPNN ✓ │ ├─ Protein-protein binder → Use ProteinMPNN │ └─ De novo scaffold → Use ProteinMPNN │ └─ Priority? ├─ Solubility/expression → Consider SolubleMPNN └─ Ligand context accuracy → LigandMPNN ✓
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 |
Throughput: ~50-100 sequences/minute on T4 GPU.
Verify
bashgrep -c "^>" output/seqs/*.fa # Should match backbone_count × num_seq_per_target
Troubleshooting
Ligand not recognized: Check HETATM format, verify ligand residue name Poor binding residues: Increase sampling around active site Missing contacts: Verify ligand coordinates in PDB
Error interpretation
| Error | Cause | Fix |
|---|---|---|
RuntimeError: CUDA out of memory | Long protein or large batch | Reduce batch_size |
KeyError: 'LIG' | Ligand not found in PDB | Check HETATM records |
ValueError: no ligand atoms | Empty ligand | Verify ligand has atoms in PDB |
Next: Structure prediction for validation → protein-qc for filtering.
