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msa-templates

msa-templates

Prepare and validate Chai MSA and template inputs from aligned parquet, A3M, ColabFold server, m8, and staged ColabFold outputs.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill-Dokumentation

GitHub

name: msa-templates description: "Prepare and validate Chai MSA and template inputs from aligned parquet, A3M, ColabFold server, m8, and staged ColabFold outputs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MSA Templates

Use this sub-skill when a Chai task involves evolutionary alignments, .aligned.pqt files, A3M conversion, ColabFold MSA/template servers, template hit .m8 files, or reusing staged ColabFold outputs.

Route

  • For local MSAs, read references/msa-template-workflows.md and validate each .aligned.pqt with scripts/validate_aligned_pqt.py before folding.
  • For A3M inputs, use chai-lab a3m-to-pqt for one query sequence per directory, then validate the generated filename and query row.
  • For server MSAs/templates, prefer explicit --use-msa-server and --use-templates-server only when network calls to the ColabFold service are acceptable.
  • For local templates, prepare a BLAST m8-style table and use --template-hits-path; use CHAI_TEMPLATE_CIF_FOLDER when template CIFs are custom or pre-cached.
  • For existing ColabFold output trees, run scripts/stage_colabfold_outputs.py to create Chai-ready FASTA, msas/*.aligned.pqt, and all_template_hits.m8 files.

Primary References

  • references/msa-template-workflows.md covers local MSA preparation, A3M conversion, server-side generation, template inputs, and ColabFold staging.
  • references/api-reference.md lists CLI/API entry points, .aligned.pqt schema details, m8 columns, and mutual-exclusion rules.
  • references/troubleshooting.md maps common MSA/template symptoms to concrete validation and recovery steps.

Bundled Scripts

  • scripts/validate_aligned_pqt.py --help checks required parquet columns, strict source values, query-row placement, aligned lengths, and Chai filename hashes without running inference.
  • scripts/stage_colabfold_outputs.py --help adapts ColabFold output folders into a Chai-ready local input tree without calling any network service.

Boundaries

  • Use ../cli-inference/SKILL.md for full chai-lab fold execution, GPU/runtime settings, ranking outputs, and run_inference result handling.
  • Use ../input-data-formats/SKILL.md for FASTA entity syntax, entity names, and chain naming before matching local m8 query IDs.
  • Use ../restraints-glycans/SKILL.md for contact, pocket, covalent, or glycan restraint files.
Gespeicherte Skills