Generate protein sequences and embeddings using ESM3 and ESM C.
npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill esm-lord1egypt
Please help me install this Agent Skill. Skill: esm Source: https://github.com/Lord1Egypt/scientific-agent-toolkit/tree/main/scientific-skills/esm Command: npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill esm-lord1egypt
This skill addresses the complexity of protein engineering and bioinformatics by providing a unified interface for generative protein design and representation learning, removing the need for manual model orchestration.
Use the esm skill to generate a novel protein sequence with a specified functional annotation for a 200 residue protein.
High-intent search queries and answers about installing and using this skill.
You can generate novel protein sequences using ESM3 by specifying a functional annotation and target length, leveraging the esm SDK for iterative multimodal sequence design without manual model orchestration.
To extract protein embeddings for clustering, use ESM C within the esm SDK to generate high-quality representations that enable similarity analysis and classification of protein variants.
Flash-attention is an optional dependency for running ESM3 inference, providing optimized performance, but the core generative protein design and embedding tasks function using the standard esm SDK and torch.
The best way to design fluorescent protein variants is using ESM3 for generative sequence design, followed by applying ESM C to cluster the resulting embeddings and identify promising candidates for validation.
Yes, ESM3 supports inverse folding and protein structure prediction alongside generative sequence design, providing a comprehensive bioinformatics toolkit through the esm SDK for representation learning.
ESM C is designed specifically for efficient representation learning to generate high-quality protein embeddings, whereas ESM3 focuses on iterative multimodal generative protein design, structure prediction, and inverse folding.
tachyon-beepSelect attention mechanisms to reduce memory use for long sequences.
silversteinGenerate protein sequences, predict structures, and compute embeddings with ESM3 and ESM C.
robotlearning123Reduce high-dimensional data to lower dimensions for visualization and clustering.
jimmc414Generate protein sequences and extract embeddings using ESM3 and ESM C.
