Agent skill

ligandmpnn

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.

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/ligandmpnn

SKILL.md

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)

bash
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

bash
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

bash
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-qc for filtering.

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