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name: ligandmpnn description: > Ligand-aware protein sequence design using LigandMPNN. Use this skill when: (1) Designing sequences around small molecules, (2) Enzyme active site design, (3) Ligand binding pocket optimization, (4) Metal coordination site design, (5) Cofactor binding proteins.

For standard protein design, use proteinmpnn. For solubility optimization, use solublempnn. license: MIT category: design-tools tags: [sequence-design, inverse-folding, ligand-aware] biomodals_script: modal_ligandmpnn.py

LigandMPNN Ligand-Aware Design

Prerequisites

How to run

First time? See Installation Guide to set up Modal and biomodals.

Option 1: Modal (recommended)

Copycd 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

Copygit 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

Ligand Specification

In PDB File

Ligand must be present as HETATM records:

CopyATOM    ...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

Copyoutput/
├── seqs/
│   └── protein.fa          # FASTA sequences
└── protein_pdb/
    └── protein_0001.pdb    # PDBs with designed sequence

Sample output

Successful run

Copy$ 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

CopyShould 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

Throughput: ~50-100 sequences/minute on T4 GPU.

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Verify

Copygrep -c "^>" output/seqs/*.fa  # Should match backbone_count × num_seq_per_target

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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

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Next: Structure prediction for validation → protein-qc for filtering.