skills.rest
Sologa
SologaCOMMUNITY
Explore all skills by this author →
GitHub

esm

Generate protein sequences and embeddings with ESM3 and ESM C.

1|Updated Jan 14, 2026
Search Tags:#embeddings#bioinformatics#protein engineering#esm#protein design#generative models
One-click install
npx skills add https://github.com/Sologa/codex-pipeline --skill esm-sologa
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: esm
Source: https://github.com/Sologa/codex-pipeline/tree/main/.codex/skills/esm
Command: npx skills add https://github.com/Sologa/codex-pipeline --skill esm-sologa

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a comprehensive toolkit for working with advanced protein language models, enabling complex tasks like protein design, sequence generation, structure prediction, and function annotation.

Core Features & Use Cases

  • Generative Protein Design (ESM3): Design novel protein sequences and structures, predict protein functions, and perform inverse folding.
  • Protein Embeddings (ESM C): Generate high-quality embeddings for downstream machine learning tasks like classification and similarity analysis.
  • Use Case: Design a novel enzyme with a specific catalytic activity by providing functional constraints and desired structural properties.

Quick Start

Use the esm skill to generate a protein sequence based on a partial input sequence.

Frequently Asked Questions about esm

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I design a novel protein sequence using a partial input?▼

Design a novel protein sequence by inputting partial sequences and functional constraints into the ESM3 generative model, which outputs complete sequences and predicted structures.

Can I generate protein embeddings for downstream machine learning tasks?▼

Generate high-quality protein embeddings using ESM C for downstream machine learning tasks such as sequence classification and similarity analysis.

What is inverse folding in protein engineering and how does this toolkit support it?▼

Inverse folding predicts an amino acid sequence that folds into a given 3D structure. This toolkit supports inverse folding alongside structure prediction and function annotation via the ESM3 model.

Does the protein design toolkit support cloud-based API inference for large-scale tasks?▼

Yes, the toolkit supports cloud-based Forge API for scalable inference alongside local model usage, accommodating large-scale protein engineering tasks.

What's the best way to predict protein functions and structures from a novel sequence?▼

Use ESM3 for structure prediction and function annotation, enabling you to evaluate novel sequences against desired catalytic activities and structural properties.

Do I need local computational resources to run generative protein models?▼

No, local resources are not strictly required because the toolkit supports cloud-based Forge API for scalable inference, though local model usage is also supported.

Related Skills

Explore More Education & Research Skills→
NVIDIA-BioNeMoNVIDIA-BioNeMo
413

proteinmpnn-nim

Design protein sequences for PDB backbones using ProteinMPNN via NVIDIA NIM.

Official
Advanced
crazymsncrazymsn
22

molecular-dynamics

Automate OpenMM molecular dynamics simulations and analyze trajectories with MDAnalysis.

Community
Advanced
Estrella-231Estrella-231

esm

Generate protein sequences, predict structures, and extract embeddings with ESM3 and ESM C.

Community
Advanced
robinbarvaagrobinbarvaag

esm

Generate protein sequences, predict structures, and extract embeddings with ESM3 and ESM C.

Community
Advanced