ColabFold - v1.5.5

For details of what was changed in v1.5, see change log!

Making Protein folding accessible to all via Google Colab!

Check the wiki page old retired notebooks for unsupported notebooks.

FAQ

• Where can I chat with other ColabFold users?
  • See our Discord channel!
• Can I use the models for Molecular Replacement?
  • Yes, but be CAREFUL, the bfactor column is populated with pLDDT confidence values (higher = better). Phenix.phaser expects a "real" bfactor, where (lower = better). See post from Claudia Millán.
• What is the maximum length?
  • Limits depends on free GPU provided by Google-Colab fingers-crossed
  • For GPU: Tesla T4 or Tesla P100 with ~16G the max length is ~2000
  • For GPU: Tesla K80 with ~12G the max length is ~1000
  • To check what GPU you got, open a new code cell and type !nvidia-smi
• Is it okay to use the MMseqs2 MSA server (cf.run_mmseqs2) on a local computer?
  • You can access the server from a local computer if you queries are serial from a single IP. Please do not use multiple computers to query the server.
• Where can I download the databases used by ColabFold?
  • The databases are available at colabfold.mmseqs.com
• I want to render my own images of the predicted structures, how do I color by pLDDT?
  • In pymol for AlphaFold structures: spectrum b, red_yellow_green_cyan_blue, minimum=50, maximum=90
  • If you want to use AlphaFold Colours (credit: Konstantin Korotkov)

    set_color n0, [0.051, 0.341, 0.827]
    set_color n1, [0.416, 0.796, 0.945]
    set_color n2, [0.996, 0.851, 0.212]
    set_color n3, [0.992, 0.490, 0.302]
    color n0, b < 100; color n1, b < 90
    color n2, b < 70;  color n3, b < 50
    

  • In pymol for RoseTTAFold structures: spectrum b, red_yellow_green_cyan_blue, minimum=0.5, maximum=0.9
• What is the difference between the AlphaFold2_advanced and AlphaFold2_mmseqs2 (_batch) notebook for complex prediction?
  • We currently have two different ways to predict protein complexes: (1) using the AlphaFold2 model with residue index jump and (2) using the AlphaFold2-multimer model. AlphaFold2_advanced supports (1) and AlphaFold2_mmseqs2 (_batch) (2).
• What is the difference between localcolabfold and the pip installable colabfold_batch?
  • LocalColabFold is an installer script designed to make ColabFold functionality available on local users' machines. It supports wide range of operating systems, such as Windows 10 or later (using Windows Subsystem for Linux 2), macOS, and Linux.
• Is there a way to amber-relax structures without having to rerun alphafold/colabfold from scratch?
  • Yes, see this notebook.
• Where can I find the old notebooks that were previously developed and are now retired?
  • You can find the list of retired notebooks in the old retired notebooks wiki page.
• Where can I find the history of MSA Server Databases used in ColabFold?
  • You can view the database version history on the MSA Server Database History wiki page.

Running locally

For instructions on how to install ColabFold locally refer to localcolabfold or see our wiki on how to run ColabFold within Docker.

Generating MSAs for small scale local structure/complex predictions using the MSA server

When you pass a FASTA or CSV file containing your sequences to colabfold_batch it will automatically query the public MSA server to generate MSAs. You might want to split this into two steps for better GPU resource utilization:

# Query the MSA server and predict the structure on local GPU in one go:
colabfold_batch input_sequences.fasta out_dir

# Split querying MSA server and GPU predictions into two steps
colabfold_batch input_sequences.fasta out_dir --msa-only
colabfold_batch input_sequences.fasta out_dir

Generating MSAs for large scale structure/complex predictions

First create a directory for the databases on a disk with sufficient storage (940GB (!)). Depending on where you are, this will take a couple of hours:

Note: MMseqs2 71dd32ec43e3ac4dabf111bbc4b124f1c66a85f1 (May 28, 2023) is used to create the databases and perform sequece search in the ColabFold MSA server. Please use this version if you want to obtain the same MSAs as the server.

MMSEQS_NO_INDEX=1 ./setup_databases.sh /path/to/db_folder

If MMseqs2 is not installed in your PATH, add `--mmseqs