esm2-sequence-scoring
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.
npx skills add BioTender-max/awesome-bio-agent-skills --skill esm2-sequence-scoring --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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](../../docs/installation.md) to set up Modal and biomodals. ### Option 1: Modal ```bash 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) ```python 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] `
- Prerequisites
- How to run
- Option 1: Modal
- Option 2: Python API (recommended)
- Key parameters
- ESM2 Models
- Output format
- Sample output
- Successful run
- Decision tree
- PLL interpretation
- Typical performance
- Verify
- Troubleshooting
cd biomodals modal run modal_esm2_predict_masked.py \ wc -l embeddings/pll_scores.csv # Should match input + 1 (header)
What does the esm2-sequence-scoring skill do?
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.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill esm2-sequence-scoring --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.