Protein engineering requires specialized AI models to analyze sequence-structure-function relationships. ESM provides state-of-the-art protein language models for generating novel sequences, predicting structures, and designing functional proteins through multimodal AI.
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正在使用「esm」。 Design a novel enzyme with catalytic activity using ESM3
預期結果:
- Generated sequence with 238 amino acids
- Predicted structure shows typical enzyme fold
- Chromophore region designed with catalytic residues
- Saved to novel_enzyme.pdb
- Confidence score: 0.84
正在使用「esm」。 Find similar proteins to this sequence using embeddings
預期結果:
- Generated 512-dim embedding for query sequence
- Compared against 100 protein database
- Found 3 high-similarity matches (>0.85 cosine similarity)
- Top match: membrane_protein_variant_A
正在使用「esm」。 Inverse fold this PDB structure
預期結果:
- Loaded structure with 342 residues
- Generated novel sequence matching the fold
- Sequence identity: 42%
- Output saved to designed_sequence.fasta
安全審計
安全This is a legitimate scientific documentation skill for ESM protein language models. All findings are false positives: backticks are markdown formatting, eval() is PyTorch's model.eval() method, and the network+credentials+code pattern is expected for an API client. No malicious code, no credential harvesting, no suspicious system operations.
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Design novel proteins
Generate new protein sequences with desired properties using ESM3 multimodal generation across sequence, structure, and function tracks.
Screen protein variants
Create and analyze libraries of protein variants using embeddings for clustering and similarity analysis.
Predict structures
Predict 3D structures from sequences or design sequences for known structures using inverse folding.
試試這些提示
Generate a 200-amino acid protein sequence for a fluorescent protein using ESM3. Start with a masked sequence and use function conditioning for green fluorescent protein properties.
Predict the 3D structure for this protein sequence using ESM3 structure track. Output the coordinates and save as PDB format.
Load this PDB structure and use inverse folding to generate a sequence that folds to the target structure. Use temperature 0.7 for diversity.
Generate ESM C embeddings for these protein sequences and compute pairwise similarity using cosine similarity. Cluster similar sequences together.
最佳實務
- Start with smaller models (esm3-sm-open-v1) for prototyping before production
- Use temperature scheduling (high to low) for better generation quality
- Validate generated sequences with structure prediction before experimental work
- Cache embeddings for repeated analyses to reduce compute costs
避免
- Using generated proteins directly in clinical applications without validation
- Ignoring biosafety considerations when designing novel proteins
- Processing extremely long sequences without chunking strategies
- Skipping error handling for GPU memory errors and API rate limits
常見問題
What models are available locally vs via API?
What are typical sequence length limits?
Can this integrate with my existing ML pipeline?
Is my protein data sent to external servers?
Why is my GPU running out of memory?
How does this compare to AlphaFold?
開發者詳情
作者
davila7授權
MIT
儲存庫
https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/scientific/esm引用
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