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How to batch-fold many proteins on your own machines with Colab Fold

Run colabfold_search for alignments and colabfold_batch for predictions over a whole FASTA file.

Short answer: Set up a local Colab Fold install, MMseqs2 and the Colab Fold databases with setup_databases.sh; then 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. All steps.

platform. 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.

Who it is forA lab with a list of hundreds of sequences that would take days to paste into notebooks.

The problemNotebooks are one sequence at a time and the public MSA server asks you not to hammer it from many machines.

The outcomeAn msas folder of a3m alignments and a predictions folder with pdb, json and png files for every sequence.

What you need first

  • 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

Steps

  1. Set up a local Colab Fold install, MMseqs2 and the Colab Fold databases with setup_databases.sh
  2. Build alignments (CPU heavy) with colabfold_search --mmseqs /path/to/bin/mmseqs input_sequences.fasta /path/to/db_folder msas
  3. Predict structures (needs a GPU) with colabfold_batch msas predictions
  4. Review the predicted pdb, json and png files in the predictions folder
  5. Keep MMSEQS_NO_INDEX=1 for most uses; build the precomputed index only for a server or very fast storage

Prompt for your agent

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.

Caveat. Local installation steps are not in the README text itself; use the project's linked install instructions.

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Using the Deduped MCP, get the setup steps for sokrypton/ColabFold and "Batch-fold many proteins on your own machines"

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