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name: msa-search description: "Generate ColabFold MSAs using the public server, local MMseqs2 databases, GPU search, split/merge helpers, AlphaFold3 JSON export, and local MSA server planning." disable-model-invocation: true metadata: disco-role: operating license: MIT

MSA Search

Use this sub-skill when the task is to create, validate, stage, or troubleshoot multiple sequence alignments for ColabFold jobs. It owns public-server MSA generation, local colabfold_search database searches, MMseqs2 GPU/gpuserver routes, MSA-only staging, split/merge helpers, AlphaFold3 JSON export from MSAs, and local MSA server caveats.

Route away from this sub-skill when the user primarily needs:

• Input syntax, FASTA/CSV/A3M validation, AF3 ligand syntax, or filename normalization: use ../inputs-and-formats/.
• Running structure prediction after MSAs already exist: use ../batch-prediction/.
• Amber relaxation, PDB/mmCIF confidence interpretation, or post-processing outputs: use ../relaxation-and-outputs/.

Fast route

1. Decide the MSA source:
   • Small serial local jobs: public MSA server through colabfold_batch --msa-only or implicit colabfold_batch input out.
   • Large batches or private/high-throughput work: local colabfold_search with a prepared MMseqs2 database directory.
   • Repeated low-latency local searches: local databases plus MMseqs2 GPU search or gpuserver, if hardware and database layout support it.
2. Validate inputs before searching. Protein sequences receive MMseqs2 MSAs; non-protein AF3 components are represented in JSON but do not receive MMseqs2 unpaired MSAs.
3. For local database work, run the bundled read-only checker before giving search commands:

python scripts/check_mmseqs_databases.py /path/to/db_folder --check-mmseqs --mode cpu
python scripts/check_mmseqs_databases.py /path/to/db_folder --mode gpu --require-index

4. Build the workflow from the references:
   • CLI options and examples: references/cli-reference.md
   • Local database, GPU, split/merge, and AF3 JSON workflows: references/local-mmseqs-workflows.md
   • Local API/MSA server deployment caveats: references/msa-server.md
   • Failure diagnosis: references/troubleshooting.md

Common task patterns

• Public server MSA-only staging:

colabfold_batch input_sequences.fasta out_dir --msa-only
colabfold_batch input_sequences.fasta out_dir

• Local database MSA generation:

colabfold_search input_sequences.fasta /path/to/db_folder msas
colabfold_batch msas predictions

• Local GPU search with explicit devices:

CUDA_VISIBLE_DEVICES=0,1 colabfold_search input_sequences.fasta /path/to/db_folder msas --gpu 1

• AlphaFold3-compatible MSA JSON from local search:

colabfold_search input_sequences.fasta /path/to/db_folder msas --af3-json

Safety notes

• Database setup is a heavy network/storage operation. This sub-skill describes the required layout and safe validation checks but does not bundle or run database download scripts.
• Local MSA server setup mutates system state, downloads binaries/databases, and may install services; treat it as planning guidance unless the user explicitly approves those operations.
• Do not run public MSA server jobs in parallel from multiple machines against the same service/IP. Prefer serial small jobs or a local database/server for high-throughput workloads.