Esm
Comprehensive toolkit for protein language models including ESM3 (generative multimodal...
Install
npx skills add synthetic-sciences/openscience --skill esm -g -a claude-code -y- Repository
- synthetic-sciences/openscience4.3k
- Category
- Design
- Updated
- 5d ago
- License
- MIT license
- Context cost
- Mediumabout 2,573 tokens
- Runs scripts
- No, instructions only
What it does
Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.
ESM: Evolutionary Scale Modeling
Overview
ESM provides state-of-the-art protein language models for understanding, generating, and designing proteins. This skill enables working with two model families: ESM3 for generative protein design across sequence, structure, and function, and ESM C for efficient protein representation learning and embeddings.
Core Capabilities
1. Protein Sequence Generation with ESM3
Generate novel protein sequences with desired properties using multimodal generative modeling.
When to use:
- Designing proteins with specific functional properties
- Completing partial protein sequences
- Generating variants of existing proteins
- Creating proteins with desired structural characteristics
Basic usage:
from esm.models.esm3 import ESM3
from esm.sdk.api import ESM3InferenceClient, ESMProtein, GenerationConfig
# Load model locally
model: ESM3InferenceClient = ESM3.from_pretrained("esm3-sm-open-v1").to("cuda")
# Create protein prompt
protein = ESMProtein(sequence="MPRT___KEND") # '_' represents masked positions
# Generate completion
protein = model.generate(protein, GenerationConfig(track="sequence", num_steps=8))
print(protein.sequence)
For remote/cloud usage via Forge API:
from esm.sdk.forge import ESM3ForgeInferenceClient
from esm.sdk.api import ESMProtein, GenerationConfig
# Connect to Forge
model = ESM3ForgeInferenceClient(model="esm3-medium-2024-08", url="https://forge.evolutionaryscale.ai", token="<token>")
# Generate
protein = model.generate(protein, GenerationConfig(track="sequence", num_steps=8))
See references/esm3-api.md for detailed ESM3 model specifications, advanced generation configurations, and multimodal prompting examples.
2. Structure Prediction and Inverse Folding
Use ESM3's structure track for structure prediction from sequence or inverse folding (sequence design from structure).
Structure prediction:
