| 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
| Requirement | Minimum | Recommended |
|---|
| Python | 3.8+ | 3.10 |
| PyTorch | 1.10+ | 2.0+ |
| GPU VRAM | 8GB | 24GB (A10G) |
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
| Model | Parameters | Best For |
|---|
| esm2_t6_8M | 8M | Fast screening |
| esm2_t33_650M | 650M | Standard ✓ |
| esm2_t36_3B | 3B | Best quality |
PLL Interpretation
| Normalized PLL | Interpretation |
|---|
| > 0.2 | Very natural sequence |
| 0.0 - 0.2 | Good, natural-like |
| -0.5 - 0.0 | Acceptable |
| < -0.5 | May be unnatural |
Typical performance
| Campaign | Time (A10G) | Cost (Modal) |
|---|
| 100 sequences | 5-10 min | ~$1 |
| 1000 sequences | 30-60 min | ~$5 |
Throughput: ~100-200 sequences/minute with 650M model.
Next: Structure prediction with chai or boltz → protein-qc for filtering.
