---

name: structure-prediction description: "Predict protein structures from sequences or FASTA files with ESMFold Python APIs and the esm-fold CLI." disable-model-invocation: true metadata: disco-role: operating license: MIT

Structure Prediction

Use this sub-skill when a task needs ESMFold to convert amino-acid sequences or FASTA records into PDB strings/files, including Python infer_pdb calls, bulk esm-fold command construction, chunking, recycle counts, CPU-only execution, CPU offload, and confidence interpretation.

Start Here

• Read references/api-reference.md for esm.pretrained.esmfold_v1(), model.infer_pdb, model.infer, model.output_to_pdb, model.set_chunk_size, devices, and PDB/confidence semantics.
• Read references/cli-reference.md for esm-fold flags, FASTA-to-PDB behavior, batching, cache/model directory options, and pLDDT/pTM logs.
• Read references/workflows.md for single-sequence Python folding, bulk FASTA folding, CPU-only dry runs, CPU offload, and multimer inputs with colon-separated chains.
• Read references/troubleshooting.md for ESMFold optional dependencies, CUDA/OpenFold/dllogger install failures, CUDA OOM, slow CPU runs, model downloads, and invalid paths.
• Run python scripts/esm_fold_command_builder.py --help from this sub-skill directory to build safe printable esm-fold commands without loading models or running inference.

Routing Boundaries

• Use this sub-skill for ESMFold structure prediction, esm.pretrained.esmfold_v1(), esm.pretrained.esmfold_v0(), model.infer_pdb, model.infer, model.output_to_pdb, model.set_chunk_size, the esm-fold console script, PDB output, pLDDT, pTM, axial-attention chunking, --cpu-only, and --cpu-offload.
• Route representation extraction, esm-extract, per-token/mean/BOS/contact tensors, and language-model embeddings to ../model-embeddings/SKILL.md.
• Route fixed-backbone inverse design, coordinate-conditioned scoring, esm.inverse_folding, and ESM-IF1 workflows to ../inverse-folding/SKILL.md.
• Route zero-shot mutation scoring and DMS/variant-effect workflows to ../variant-effect-prediction/SKILL.md.
• Treat ESM Atlas web/API/bulk resources as setup alternatives only; this sub-skill does not cover Atlas API automation or bulk downloads.

Safe Default Pattern

1. Confirm the caller wants predicted structures, not embeddings or inverse design.
2. Prefer Python model.infer_pdb(sequence) for one short sequence and esm-fold -i input.fasta -o output_dir for many records.
3. Set model.eval() and use torch.no_grad() for inference; move to CUDA only when a compatible GPU stack is available.
4. For long sequences or memory pressure, lower --max-tokens-per-batch, add --chunk-size 128 or smaller, and consider fewer recycles before trying CPU offload.
5. Preserve PDB strings/files as the deliverable; use pLDDT from B-factors or output["mean_plddt"] and pTM logs as confidence signals, not as sequence embeddings.