esm

name	esm
description	ESM protein language models for sequence embeddings and structure prediction

ESM Protein Language Models

Overview

ESM (Evolutionary Scale Modeling) is Meta AI's family of protein language models trained on millions of protein sequences. ESM-2 provides per-residue and per-sequence embeddings. ESMFold predicts 3D structure from sequence alone.

Key Models

• ESM-2: Embedding model (8M to 15B params). Use esm2_t33_650M_UR50D as default.
• ESMFold: Single-sequence structure prediction (no MSA needed).

Usage Patterns

import torch, esm

model, alphabet = esm.pretrained.esm2_t33_650M_UR50D()
batch_converter = alphabet.get_batch_converter()
model.eval()

data = [("protein1", "MKTLLILAVL")]
batch_labels, batch_strs, batch_tokens = batch_converter(data)

with torch.no_grad():
    results = model(batch_tokens, repr_layers=[33], return_contacts=True)

embeddings = results["representations"][33]  # (batch, seq_len, 1280)
contact_map = results["contacts"]            # predicted contacts

ESMFold Structure Prediction

model = esm.pretrained.esmfold_v1()
model.eval()
with torch.no_grad():
    output = model.infer_pdb("MKTLLILAVL")
# output is a PDB-format string

Key Details

• Input sequences use standard single-letter amino acid codes.
• Maximum sequence length ~1024 residues for ESMFold; ESM-2 handles longer.
• Embeddings from final layer are most informative for downstream tasks.
• Contact prediction uses attention maps; symmetric and valid for i-j where |i-j| >= 6.
• Install: pip install fair-esm.