esm

name	esm
description	ESM2 protein language model for embeddings and sequence scoring. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) scores, (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai or boltz. For QC thresholds, use protein-qc.
license	MIT
category	design-tools
tags	["sequence-design","embeddings","scoring"]
proteinbase_slug	esm2-optimization
proteinbase_url	https://proteinbase.com/design-methods/esm2-optimization
biomodals_script	modal_esm2_predict_masked.py

ESM2 Protein Language Model

Prerequisites

Requirement	Minimum	Recommended
Python	3.8+	3.10
PyTorch	1.10+	2.0+
CUDA	11.0+	11.7+
GPU VRAM	8GB	24GB (A10G)
RAM	16GB	32GB

How to run

First time? See Installation Guide to set up Modal and biomodals.

Option 1: Modal

cd biomodals
modal run modal_esm2_predict_masked.py \
  --input-faa sequences.fasta \
  --out-dir embeddings/

GPU: A10G (24GB) | Timeout: 300s default

Option 2: Python API (recommended)

import torch
import esm

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

# Process sequences
data = [("seq1", "MKTAYIAKQRQISFVK...")]
batch_labels, batch_strs, batch_tokens = batch_converter(data)

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

# Get embeddings
embeddings = results["representations"][33]

Key parameters

ESM2 Models

Model	Parameters	Speed	Quality
esm2_t6_8M	8M	Fastest	Fast screening
esm2_t12_35M	35M	Fast	Good
esm2_t33_650M	650M	Medium	Better
esm2_t36_3B