adaptyvbio/protein-design-skills/ligandmpnn

Name: ligandmpnn
Author: adaptyvbio

Claude Code and agent skill by adaptyvbio/protein-design-skills

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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.

Install

npx vskill@latest install adaptyvbio/protein-design-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 Getting started to set up Modal and biomodals.

Option 1: Modal (recommended)

bash

cd biomodals
# modal_ligandmpnn.py takes --input-pdb; LigandMPNN run.py args go in --params-str
modal run modal_ligandmpnn.py \
  --input-pdb protein_ligand.pdb \
  --params-str "--model_type ligand_mpnn --number_of_batches 16 --temperature 0.1"

GPU: A10G default | Timeout: 900s default

Option 2: Local installation

bash

git clone https://github.com/dauparas/LigandMPNN.git
cd LigandMPNN
 
python run.py \
  --model_type ligand_mpnn \
  --pdb_path protein_ligand.pdb \
  --out_folder output/ \
  --number_of_batches 16 \
  --temperature 0.1

Key parameters (LigandMPNN run.py)

Parameter	Default	Description
`--pdb_path`	required	PDB with ligand
`--model_type`	`protein_mpnn`	`ligand_mpnn`, `soluble_mpnn`, etc.
`--temperature`	0.1	Sampling temperature
`--number_of_batches`	1	Batches (sequences = batch_size x batches)
`--batch_size`	1	Sequences per batch
`--ligand_mpnn_use_side_chain_context`	0	Use ligand side-chain 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.

Security

Security scanPassed, 100 of 100Pattern checks on the skill's files.
Model reviewPassed

See the full security report

Quality evaluation

Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

See evaluation results

Source

SKILL.md in the repositoryCheckingskills/ligandmpnn/SKILL.md

Popularity

Repository stars: 122
Repository forks: 14
7-day trend score: 24

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 7 Apr 2026, 7 months ago.

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