{
 "schema_version": 1,
 "generated": "2026-10-31",
 "id": "sokrypton-colabfold--fold-a-protein-in-a-colab-notebook",
 "repo": "sokrypton/ColabFold",
 "repo_url": "https://github.com/sokrypton/ColabFold",
 "repo_slug": "sokrypton-colabfold",
 "title": "Fold a protein in a free Colab notebook",
 "page_title": "How to fold a protein in a free Colab notebook with Colab Fold",
 "summary": "Open the AlphaFold2_mmseqs2 notebook, paste a sequence and get a predicted structure without installing anything.",
 "who": "A biology student or wet-lab researcher who needs a structure model for one protein or complex today.",
 "problem": "Running AlphaFold locally needs big databases, a GPU and setup time most people do not have.",
 "outcome": "A predicted structure with pLDDT confidence values, from a notebook in your browser.",
 "steps": [
  "Open the AlphaFold2_mmseqs2 notebook from the table in the README (monomers and complexes, MMseqs2 MSAs, templates)",
  "Paste your sequence and run the cells; use ESMFold or the AlphaFold3 (OpenFold3) notebook if you want to compare",
  "Download the predicted structure files when the run finishes",
  "Color by pLDDT in PyMOL with `spectrum b, red_yellow_green_cyan_blue, minimum=50, maximum=90`",
  "For molecular replacement, remember the bfactor column holds pLDDT (higher is better), not a real B-factor"
 ],
 "agent_prompt": "I want to use sokrypton/ColabFold's AlphaFold2_mmseqs2 notebook for this sequence: <sequence>. Walk me through the settings to choose, then explain how to read the pLDDT values in the result.",
 "agents": [
  "any"
 ],
 "difficulty": "Beginner",
 "time": "30 minutes",
 "basis": "readme",
 "caveat": "Maximum length depends on the free GPU Google Colab gives you; the README suggests about 2000 residues on 16 GB GPUs.",
 "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": [],
 "best": true,
 "score": 1.0,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/sokrypton-colabfold/fold-a-protein-in-a-colab-notebook/",
 "md": "https://dedupedai.com/use-cases/sokrypton-colabfold/fold-a-protein-in-a-colab-notebook/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--fold-a-protein-in-a-colab-notebook.json",
 "api_version": "v1"
}