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protein-language-models

Predict protein structures and mutational effects from amino acid sequences.

6|2|Updated Jun 11, 2026
Search Tags:#protein language models#mutation analysis#protein structure prediction#AI in biology#machine learning in bioinformatics
One-click install
npx skills add https://github.com/pradyumnasagar/open-research-skills --skill protein-language-models
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: protein-language-models
Source: https://github.com/pradyumnasagar/open-research-skills/tree/main/skills/machine-learning-bio/protein-language-models
Command: npx skills add https://github.com/pradyumnasagar/open-research-skills --skill protein-language-models

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires torch, transformers, fair-esm, biotite, py3Dmol, boltz, mmseqs2, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill solves the problem of predicting protein structures and mutations, allowing users to quickly assess protein variants without having to rely on time-consuming wet-lab experiments.

Core Features & Use Cases

  • Protein Structure Prediction: Generate 3D protein structures from amino acid sequences.
  • Mutation Effect Prediction: Assess the impact of mutations on protein structures and functions.
  • Use Case: Imagine you have a protein of interest and you want to understand how a specific mutation might affect its structure and function. Use this Skill to predict the structure and analyze the mutation's effects.

Quick Start

Use the protein-language-models skill to predict the structure of the protein sequence 'MKKAVINGEQIRSISDLHQTLKKELALPEYYGENLDALWDALTGWVEYPLVLEWR'.

Frequently Asked Questions about protein-language-models

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

FAQPage Schema
How do I predict protein structures from amino acid sequences?▼

To predict protein structures, you input an amino acid sequence into models like ESMFold, AlphaFold 2/3, or Boltz-1. The Skill generates 3D protein structures, bypassing time-consuming wet-lab experiments.

Can I assess mutation effects on proteins using zero-shot prediction?▼

Yes, you can assess mutation effects using zero-shot mutational-effect prediction with ESM-2 and ProtTrans. This allows you to quickly evaluate how specific mutations impact protein structure and function.

Does this protein language model environment require torch and fair-esm?▼

Yes, executing this protein language model environment requires torch, transformers, fair-esm, biotite, py3Dmol, boltz, and mmseqs2. These dependencies enable protein embeddings and 3D structure prediction.

What is the best way to analyze protein variants without wet-lab experiments?▼

The best way to analyze protein variants without wet-lab experiments is using AI in biology. This Skill uses leading protein language models to predict mutational effects and 3D structures computationally.

Are there limitations when using ESM-2 for protein embeddings?▼

While ESM-2 provides protein embeddings for zero-shot mutational-effect prediction, limitations include dependency on computational resources like torch and transformers. Complex protein interactions may still require experimental validation.

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