esm

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:

import os
import esm
from esm.sdk.api import ESMProtein, GenerationConfig

# Same interface as local ESM3 — token from ESM_API_KEY (see Authentication)
model = esm.sdk.client("esm3-medium-2024-08", token=os.environ["ESM_API_KEY"])

# 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:

from esm.sdk.api import ESM3InferenceClient, ESMProtein, GenerationConfig

# Predict structure from sequence
protein = ESMProtein(sequence="MPRTKEINDAGLIVHSP...")
protein_with_structure = model.generate(
    protein,
    GenerationConfig(track="structure", num_steps=protein.sequence.count("_"))
)

# Access predicted structure
coordinates = protein_with_structure.coordinates  # 3D coordinates
pdb_string = protein_with_structure.to_pdb()