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ligandmpnn

ligandmpnn is a skill for Claude Code, Codex from adaptyvbio/protein-design-skills. It costs 86 tokens per session (1,452 once invoked), scanned A, original, MIT.

A protein-sequence design tool that accounts for attached small molecules, metals, or other chemical groups in a protein structure. It proposes sequences around the ligand and its binding pocket.

In plain words
What is it for?
Designing enzyme active sites, optimizing ligand-binding pockets, creating metal-coordination sites, and designing proteins around cofactors using a PDB structure with the ligand.
Why use it?
It helps preserve or improve the geometry needed for a molecule or metal to bind. Ordinary protein-sequence design may overlook these chemical components and their coordination requirements.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument.

Needs its repository: it reads a path above its own folder, which exists only inside the repository. The line is **First time?** See [Getting started](../../docs/getting-started.md) to set up Modal and biomodals..

not rated 159repo +1 3mo ago A scan SkillSpector: pass 86 tokens original MIT

Good fit Designing enzyme active sites, optimizing ligand-binding pockets, creating metal-coordination sites, and designing proteins around cofactors using a PDB structure with the ligand.

Compare 6 skills from other repositories ↓
View source ↗ adaptyvbio/protein-design-skills
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills
agentmods
npx agentmods add skills/adaptyvbio/protein-design-skills/ligandmpnn

Made for: Claude Code, Codex.

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agentmods badge for ligandmpnn

README.md
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Per session 86 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,452 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00086 $0.01452
Opus 5 $0.00043 $0.00726
Sonnet 5 $0.00017 $0.00290
Haiku 4.5 $0.00009 $0.00145

Measured 12d ago against content hash 8d2c7ffb3ef9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

ligandmpnn scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/ligandmpnn/SKILL.md · 175 lines

How it starts

The opening of the file, as written. The whole thing — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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)

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

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...

Read the full file on GitHub · 175 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 175 lines · 86 tokens per session scan A 8d2c7ffb3ef9

Subscribe to this mod's changes

ligandmpnn is a skill published in the GitHub repository adaptyvbio/protein-design-skills (159 stars, last pushed 3mo ago), licensed MIT. It adds 86 tokens to every session and 1,452 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Claude CodeScientificagent-skillsdesign-toolsinverse-foldingligand-awareprotein-designprotein-engineeringsequence-design
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