Is it possible to design sequences for specific structures?

Yes, the skill supports inverse folding, which allows you to generate protein sequences that are predicted to fold into a specific target PDB structure.

Does this skill support the ESM3 model?

Yes, it supports the ESM3 generative multimodal protein design model, including sequence, structure, and function tracks.

What is the ESM skill used for?

The ESM skill is a toolkit for protein language models, allowing users to generate novel protein sequences, predict structures, and create embeddings for analysis.

What is the difference between ESM3 and ESM C in this skill?

ESM3 is primarily used for generative tasks and design, whereas ESM C is optimized for generating efficient protein embeddings and representations for ML tasks.

Can I run ESM models locally with this skill?

Yes, it supports local model usage (like esm3-sm-open-v1) as well as cloud-based inference via the Forge API for larger models.

ESM: Protein Modeling Toolkit

Name: ESM: Protein Modeling Toolkit
Author: Activer007

byActiver007

Data Science & ML

Provides a comprehensive toolkit for protein language models to design, predict, and analyze protein sequences and structures.

About

The ESM skill empowers researchers and developers to leverage state-of-the-art protein language models directly within Claude. It provides a comprehensive suite of tools for designing novel protein sequences, predicting 3D structures through inverse folding, and generating high-quality embeddings for machine learning tasks. Supporting both local inference and the scalable cloud-based Forge API, this skill streamlines complex bioinformatics workflows, including function-conditioned generation and chain-of-thought protein refinement, making it an essential tool for modern protein engineering and drug discovery.

Key Features

Multimodal generative design across sequence, structure, and function tracks
Structure prediction and inverse folding for protein design
Function-conditioned protein generation and functional annotation
Support for both local execution and scalable Forge API cloud inference
High-efficiency protein embeddings using the ESM C model family
0 GitHub stars

Use Cases

Automating complex bioinformatics pipelines for variant generation and screening
Designing novel proteins with specific functional properties or structural characteristics
Extracting high-quality protein representations for downstream classification and ML tasks

About

Key Features

Multimodal generative design across sequence, structure, and function tracks
Structure prediction and inverse folding for protein design
Function-conditioned protein generation and functional annotation
Support for both local execution and scalable Forge API cloud inference
High-efficiency protein embeddings using the ESM C model family
0 GitHub stars

Use Cases

Automating complex bioinformatics pipelines for variant generation and screening
Designing novel proteins with specific functional properties or structural characteristics
Extracting high-quality protein representations for downstream classification and ML tasks