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] source: https://github.com/adaptyvbio/protein-design-skills

ESM2 Protein Language Model

Prerequisites

How to run

Option 1: Modal

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

Option 2: Python API

import torch, esm

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

data = [("seq1", "MKTAYIAKQRQISFVK...")]
_, _, batch_tokens = batch_converter(data)

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

embeddings = results["representations"][33]

ESM2 Models

PLL Interpretation

Typical performance

Throughput: ~100-200 sequences/minute with 650M model.

Next: Structure prediction with chai or boltz → protein-qc for filtering.