esm

Generate protein sequences and structures with ESM3 and embeddings with ESM C.

8|Updated Jan 13, 2026

Search Tags:#bioinformatics #genomics #structure prediction #protein design #protein language model #esm3 #esm c

Ready to run

npx skills add https://github.com/hxk622/TokenDance --skill esm-hxk622

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: esm
Source: https://github.com/hxk622/TokenDance/tree/main/backend/app/skills/builtin/scientific/bioinformatics/esm
Command: npx skills add https://github.com/hxk622/TokenDance --skill esm-hxk622

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill empowers researchers and developers to design novel proteins, predict their structures, and generate embeddings for downstream analysis, streamlining complex bioinformatics workflows.

Core Features & Use Cases

Generative Protein Design: Create new protein sequences and structures with ESM3.
Structure Prediction & Inverse Folding: Predict 3D protein structures from sequences or design sequences for given structures.
Protein Embeddings: Generate powerful representations of proteins using ESM C for tasks like classification and clustering.
Use Case: Design a novel enzyme with a specific catalytic function or predict the structure of a protein from its amino acid sequence.

Quick Start

Use the esm skill to generate a protein sequence of 100 residues with the label "fluorescent_protein".

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 with specific functions using protein language models?▼

Protein language models enable generative protein design by creating new sequences and structures. Using ESM3, you can generate novel protein sequences, such as a 100-residue sequence with a specific functional label, to solve bioinformatics and drug discovery problems.

Can I predict 3D protein structures from amino acid sequences and perform inverse folding?▼

Yes, structure prediction and inverse folding are supported. You can predict 3D protein structures from amino acid sequences or design new sequences that fold into given target structures, facilitating complex bioinformatics workflows.

How do I generate protein embeddings for downstream classification and clustering tasks?▼

Protein embeddings for classification and clustering are generated using ESM C. This creates powerful numerical representations of protein sequences, enabling representation learning for downstream bioinformatics analysis.

Does this protein design toolkit support both local model execution and cloud-based inference?▼

Yes, the toolkit supports local model execution and cloud-based Forge API. This allows you to scale protein inference workloads efficiently, from local sequence generation to cloud-based structure prediction.

What is the best way to streamline complex bioinformatics workflows for enzyme design?▼

Streamlining enzyme design workflows involves using a comprehensive protein language modeling toolkit. It combines generative design with ESM3 and efficient embeddings with ESM C to solve sequence generation, function annotation, and structure prediction problems.

Do I need specialized dependencies to run generative protein design and structure prediction?▼

No specialized dependencies are required to start. The toolkit provides scripts and references for protein language modeling, allowing you to execute generative design and structure prediction directly or via the cloud-based Forge API.