esm

To generate de novo protein sequences using ESM3, specify desired functional annotations like 'fluorescent_protein' and target residue length. The Skill leverages ESM3's multimodal generation to create novel sequences matching your design constraints.

Protein embeddings generated using ESM C provide high-quality vector representations for classification, similarity search, and clustering tasks. These embeddings encode structural and functional information to train downstream machine learning models.

You can run ESM3 inference either locally or via the cloud-based Forge API. This dual approach allows you to execute protein design and analysis tasks depending on your available computational resources.

Yes, this Skill supports inverse folding and structure prediction alongside function prediction. It utilizes state-of-the-art language models to map protein sequences to structural conformations and identify functional domains.

The best way to design a novel enzyme is to specify desired functional annotations using ESM3's multimodal generation, then analyze potential function and similarity to known proteins using ESM C embeddings.

Generative models for protein design require precise functional annotation inputs to produce viable sequences. While ESM3 facilitates novel sequence generation, predicted structures and functions must be experimentally validated.