{
 "schema_version": 1,
 "generated": "2026-10-31",
 "id": "sokrypton-colabfold--batch-fold-many-proteins-locally-with-colabfold",
 "repo": "sokrypton/ColabFold",
 "repo_url": "https://github.com/sokrypton/ColabFold",
 "repo_slug": "sokrypton-colabfold",
 "title": "Batch-fold many proteins on your own machines",
 "page_title": "How to batch-fold many proteins on your own machines with Colab Fold",
 "summary": "Run colabfold_search for alignments and colabfold_batch for predictions over a whole FASTA file.",
 "who": "A lab with a list of hundreds of sequences that would take days to paste into notebooks.",
 "problem": "Notebooks are one sequence at a time and the public MSA server asks you not to hammer it from many machines.",
 "outcome": "An msas folder of a3m alignments and a predictions folder with pdb, json and png files for every sequence.",
 "steps": [
  "Set up a local Colab Fold install, MMseqs2 and the Colab Fold databases with setup_databases.sh",
  "Build alignments (CPU heavy) with `colabfold_search --mmseqs /path/to/bin/mmseqs input_sequences.fasta /path/to/db_folder msas`",
  "Predict structures (needs a GPU) with `colabfold_batch msas predictions`",
  "Review the predicted pdb, json and png files in the predictions folder",
  "Keep MMSEQS_NO_INDEX=1 for most uses; build the precomputed index only for a server or very fast storage"
 ],
 "agent_prompt": "Using sokrypton/ColabFold locally, run colabfold_search on <fasta file> against my databases at <db folder>, then colabfold_batch on the msas folder. Summarise the mean pLDDT per sequence from the outputs.",
 "agents": [
  "claude-code",
  "codex",
  "cursor",
  "gemini-cli"
 ],
 "difficulty": "Advanced",
 "time": "half a day",
 "basis": "readme",
 "caveat": "Local installation steps are not in the README text itself; use the project's linked install instructions.",
 "category": "Security and science",
 "tags": [
  "Security and science"
 ],
 "lang": "jupyter notebook",
 "requirements": [
  "A Google account for the Colab notebooks, or a local ColabFold install for colabfold_search and colabfold_batch",
  "For local searches: MMseqs2 and the ColabFold databases (large downloads, a lot of CPU)",
  "A GPU for structure prediction; about 16 GB of GPU memory handles sequences up to roughly 2000 residues",
  "For GPU search: GPU databases set up with GPU=1"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [
  {
   "tag": "platform",
   "notice": "The databases are very large and the search needs a lot of CPU; fast single-query searches with a full in-memory index need 768GB to 1TB of RAM."
  }
 ],
 "best": false,
 "score": 0.9,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/sokrypton-colabfold/batch-fold-many-proteins-locally-with-colabfold/",
 "md": "https://dedupedai.com/use-cases/sokrypton-colabfold/batch-fold-many-proteins-locally-with-colabfold/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/sokrypton-colabfold--batch-fold-many-proteins-locally-with-colabfold.json",
 "api_version": "v1"
}