ligandmpnn

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

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)

cd 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

git 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

grep -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-design-qc for filtering.

Inputs

• A protein structure containing the ligand, cofactor, or metal context to design around.
• Design masks or residue constraints describing which positions may change.
• Sampling settings such as temperature, sequence count, and batch size.

Outputs

• Ligand-aware designed sequences consistent with the provided backbone and pocket context.
• Per-design metadata that can be paired with structure validation results.
• Sequence candidates ready for chai1-structure-prediction or boltz-structure-prediction prediction and later protein-design-qc filtering.

Next Step

Validate designed sequences structurally with chai1-structure-prediction or boltz-structure-prediction, then filter them using protein-design-qc.