# Ligandmpnn

> >

- **Type:** Skill
- **Install:** `agentstack add skill-adaptyvbio-protein-design-skills-ligandmpnn`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [adaptyvbio](https://agentstack.voostack.com/s/adaptyvbio)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [adaptyvbio](https://github.com/adaptyvbio)
- **Source:** https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/ligandmpnn

## Install

```sh
agentstack add skill-adaptyvbio-protein-design-skills-ligandmpnn
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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](../../docs/getting-started.md) 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.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [adaptyvbio](https://github.com/adaptyvbio)
- **Source:** [adaptyvbio/protein-design-skills](https://github.com/adaptyvbio/protein-design-skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-adaptyvbio-protein-design-skills-ligandmpnn
- Seller: https://agentstack.voostack.com/s/adaptyvbio
- Browse the marketplace: https://agentstack.voostack.com/browse

---
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