Esm2 Sequence Scoring Agent Skill

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.