ligandmpnn
About
This skill uses LigandMPNN for protein sequence design specifically optimized for interactions with small molecules, cofactors, or metals. It's designed for specialized tasks like enzyme active site engineering and ligand binding pocket optimization. Developers should choose this over standard ProteinMPNN when designing sequences around any bound ligand or metal ion.
Quick Install
Claude Code
Recommendednpx skills add NeverSight/skills_feed -a claude-code/plugin add https://github.com/NeverSight/skills_feedgit clone https://github.com/NeverSight/skills_feed.git ~/.claude/skills/ligandmpnnCopy and paste this command in Claude Code to install this skill
GitHub Repository
Frequently asked questions
What is the ligandmpnn skill?
ligandmpnn is a Claude Skill by NeverSight. Skills package instructions and resources that Claude loads on demand, so Claude can perform ligandmpnn-related tasks without extra prompting.
How do I install ligandmpnn?
Use the install commands on this page: add ligandmpnn to Claude Code as a plugin, or clone its repository into your skills directory, then restart Claude so it picks up the skill.
What category does ligandmpnn belong to?
ligandmpnn is in the design-tools category, tagged sequence-design, inverse-folding and ligand-aware.
Is ligandmpnn free to use?
Yes. ligandmpnn is listed on AIMCP and free to install. It runs inside Claude, so no separate service account is required to use the skill itself.
Related Skills
Boltz provides open-source biomolecular structure prediction using Boltz-1/Boltz-2 models, serving as an alternative to AlphaFold2. It specializes in predicting protein complexes, validating designed binders, and handling protein-ligand interactions. This skill is particularly useful when you need open-source structure prediction or want to leverage local GPU resources.
The alphafold skill uses AlphaFold2 to validate protein designs by predicting structures and calculating confidence metrics. It supports single-chain validation, binder-target complexes, and multi-chain predictions with AlphaFold-Multimer. For faster single-chain predictions, developers should use the esm skill instead.
BoltzGen is an all-atom diffusion model for protein design that generates both backbone and side-chain coordinates simultaneously. It is particularly suited for designing proteins around small molecules or ligands where precise binding geometries are required. Use this skill when you need side-chain-aware design from the start and are working with a YAML-based configuration.
BindCraft provides end-to-end protein binder design with joint backbone and sequence optimization and built-in AlphaFold2 validation. It's ideal for production-quality binder campaigns offering different speed protocols to balance design quality and computational cost. Use this skill when you need high experimental success rates for binder design rather than just backbone generation.
