esm

Generate protein sequences, predict structures, and analyze embeddings.

Updated May 10, 2026
One-click install
npx skills add https://github.com/Imad-Oute/ResearchForge --skill esm-imad-oute
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: esm
Source: https://github.com/Imad-Oute/ResearchForge/tree/main/OpenSource-Projects/claude-scientific-skills/scientific-skills/esm
Command: npx skills add https://github.com/Imad-Oute/ResearchForge --skill esm-imad-oute

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires esm, pandas, numpy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides comprehensive tools for understanding, designing, and analyzing proteins using state-of-the-art language models.

Core Features & Use Cases

  • Protein Sequence Generation: Create novel or variant protein sequences with desired properties.
  • Structure Prediction and Inverse Folding: Predict 3D structures from sequences and design sequences to fit target structures.
  • Embedding Extraction and Analysis: Obtain high-quality protein embeddings for similarity, classification, and clustering.
  • Function Prediction and Conditioning: Generate or predict protein functions and annotations based on sequence and structure.
  • Workflow Integration: Supports multi-modal prompt conditioning and iterative chain-of-thought reasoning for complex protein engineering tasks.
  • Use Case: Design a new enzyme by generating sequences conditioned on specific functions, predict their structures, and analyze diversity through embeddings.

Quick Start

Load the protein modeling tools and generate a new sequence for an enzyme function, then predict its structure automatically, and analyze the results by visualizing embeddings.

Frequently Asked Questions about esm

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

FAQPage Schema
How do I predict a protein structure from an amino acid sequence?

Protein structure prediction is handled by generating 3D models from input amino acid sequences. This Skill uses language models to map sequence data to structural coordinates for analysis.

Can I generate novel protein sequences conditioned on specific functions?

Yes, protein sequence generation supports conditioning on specific functions. The Skill enables multi-modal prompt conditioning to create novel variants tailored to desired biological properties.

How do I extract embeddings for protein similarity and clustering analysis?

Protein embeddings are extracted by processing sequences through integrated language models. The resulting vectors enable similarity comparisons, sequence classification, and clustering analysis.

Does protein inverse folding work for designing sequences to fit target structures?

Inverse folding is supported for designing sequences that fold into target 3D structures. This enables engineering amino acid sequences to match specific structural scaffolds for protein design.

Do I need specific Python libraries installed for protein modeling workflows?

Yes, the esm, pandas, and numpy libraries are required. These dependencies provide the foundational environment for sequence processing, structural modeling, and embedding data manipulation.

What is the best way to design a new enzyme and analyze its diversity?

The best approach is generating sequences conditioned on enzyme function, predicting their structures, and visualizing embeddings. This workflow integrates sequence design, structural prediction, and diversity analysis.