esm2-sequence-scoring

name	esm2-sequence-scoring
description	ESM2 protein language model for sequence scoring, embeddings, and plausibility checks. 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 chai1-structure-prediction or boltz-structure-prediction. For QC thresholds, use protein-design-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

Plain-language role: Use ESM when you want sequence-level scoring or embeddings rather than 3D structure prediction.

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 esm2-sequence-scoring

# Load model
model, alphabet = esm2-sequence-scoring.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	3B	Slow	Best

Output format

embeddings/
├── embeddings.npy       # (N, 1280) array
├── pll_scores.csv       # PLL for each sequence
└── metadata.json        # Sequence info

Sample output

Successful run

$ modal run modal_esm2_predict_masked.py --input-faa designs.fasta
[INFO] Loading ESM2-650M model...
[INFO] Processing 100 sequences...
[INFO] Computing pseudo-log-likelihood...

embeddings/pll_scores.csv:
sequence_id,pll,pll_normalized,length
design_0,-0.82,0.15,78
design_1,-0.95,0.08,85
design_2,-1.23,-0.12,72
...

Summary:
  Mean PLL: -0.91
  Sequences with PLL > 0: 42/100 (42%)

What good output looks like:

• PLL_normalized: > 0.0 (more natural-like)
• Embeddings shape: (N, 1280) for 650M model
• Higher PLL = more natural sequence

Decision tree

Should I use ESM2?
│
├─ What do you need?
│  ├─ Sequence plausibility score → ESM2 PLL ✓
│  ├─ Embeddings for clustering → ESM2 ✓
│  ├─ Variant effect prediction → ESM2 ✓
│  └─ Structure prediction → Use ESMFold
│
├─ What model size?
│  ├─ Fast screening → esm2_t12_35M
│  ├─ Standard use → esm2_t33_650M ✓
│  └─ Best quality → esm2_t36_3B
│
└─ Use case?
   ├─ QC filtering → PLL > 0.0 threshold
   ├─ Diversity analysis → Mean-pooled embeddings
   └─ Mutation scanning → Per-position log-odds

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 Size	Time (A10G)	Cost (Modal)	Notes
100 sequences	5-10 min	~$1	Quick screen
1000 sequences	30-60 min	~$5	Standard
5000 sequences	2-3h	~$20	Large batch

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

---

Verify

wc -l embeddings/pll_scores.csv  # Should match input + 1 (header)

---

Troubleshooting

OOM errors: Use smaller model or batch sequences Slow processing: Use esm2_t12_35M for speed Low PLL scores: May indicate unusual/designed sequences

Error interpretation

Error	Cause	Fix
RuntimeError: CUDA out of memory	Sequence too long or large batch	Reduce batch size
KeyError: representation	Wrong layer requested	Use layer 33 for 650M model
ValueError: sequence	Invalid amino acid	Check for non-standard AAs

---

Next: Structure prediction with chai1-structure-prediction or boltz-structure-prediction → protein-design-qc for filtering.

Inputs

• Protein sequences in FASTA or CSV form for scoring, embedding, or variant analysis.
• A selected ESM model size and any layer or batching preferences.
• Optional metadata linking each sequence back to a design campaign.

Outputs

• Sequence-level embeddings, pseudo-log-likelihood scores, or mutation effect estimates.
• A plausibility signal that can be merged into protein-design-qc composite ranking.
• Sequence features that support clustering, deduplication, or active-learning loops.

Next Step

Merge ESM-derived scores into protein-design-qc, then send surviving candidates to chai1-structure-prediction or boltz-structure-prediction for structure validation.