---

name: msa-template-and-prep description: "Plan and validate Protenix protein MSA, template search, RNA MSA, ColabFold-compatible MSA, and prep workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Protenix MSA, Template, and Prep

When To Use

Use this sub-skill when a Protenix task needs planning or validation for protein MSA generation, template search, RNA MSA search, ColabFold-compatible MSA conversion, database/tool readiness, or MSA/template/RNA path layouts before inference or training.

Start here for:

• Choosing among protenix msa, protenix mt, and protenix prep.
• Validating existing pairing.a3m, non_pairing.a3m, hmmsearch.a3m, and RNA A3M paths before rerunning expensive search.
• Deciding whether --msa_server_mode protenix or --msa_server_mode colabfold is appropriate.
• Checking taxonomy-like paired MSA headers for multimer pairing.
• Planning HMMER, kalign, ColabFold/MMseqs, template database, and RNA database requirements.
• Understanding the training MSA layout involving common/seq_to_pdb_index.json and mmcif_msa_template/<id>/ directories.

Route elsewhere for:

• Prediction command construction once MSA/template files already exist: ../cli-and-inference/SKILL.md.
• Full JSON entity schema, ligand fields, bonds, modifications, and pairedMsaPath/unpairedMsaPath field placement details: ../input-data-and-features/SKILL.md.
• Dataset-scale training data generation and training loops: ../training-and-data-pipeline/SKILL.md.

Safe First Actions

1. Identify what the user already has: input JSON, protein FASTA, protein MSA directory, RNA MSA file/root, template output, released data root, or custom training MSA root.
2. Treat MSA search, template search, RNA MSA search, ColabFold/MMseqs, database downloads, and bulk training MSA generation as expensive or side-effecting until the user confirms local tools, databases, storage, and permission.
3. Run the bundled read-only checker before rerunning search: python sub-skills/msa-template-and-prep/scripts/check_msa_template_layout.py <path>.
4. If existing files are valid, reuse them and route prediction to ../cli-and-inference/SKILL.md.
5. If files are stale or missing, choose the smallest preprocessing command: msa for protein MSA, mt for protein MSA plus templates, or prep for protein MSA plus templates plus RNA MSA.

Command Choice

User need	Recommended action
Protein JSON/FASTA lacks MSA only	Plan protenix msa --input INPUT --out_dir OUT --msa_server_mode protenix or colabfold.
Protein JSON lacks templates too	Plan protenix mt --input INPUT.json --out_dir OUT with HMMER and seqres checks.
Mixed protein/RNA JSON lacks RNA MSA	Plan protenix prep --input INPUT.json --out_dir OUT with both template and RNA HMMER/database checks.
Existing JSON paths may be stale	Run the checker and fix paths before any search.
Raw ColabFold result needs Protenix layout	Validate/split into per-chain pairing.a3m and non_pairing.a3m; do not use raw combined A3M directly.
Training MSA layout is failing	Validate common/seq_to_pdb_index.json and mmcif_msa_template/<id>/; route full dataset prep to training.

Read Next

• references/workflows.md for CLI behavior, msa_server_mode, JSON update suffixes, live APIs, ColabFold-compatible MSA handling, and training MSA stages.
• references/data-layouts.md for protein, template, RNA, released-data, custom-training, and search-database layouts.
• references/database-preparation.md for safe external tool and database readiness decisions.
• references/troubleshooting.md for missing binaries/databases, stale paths, taxonomy header problems, missing MSA files, RNA database choices, ColabFold limitations, large outputs, network requirements, and template parser failures.