/ColabFold

Making Protein folding accessible to all via Google Colab!

Primary LanguageJupyter NotebookMIT LicenseMIT

ColabFold

MMseqs2 API status

  16Aug2021: WARNING - MMseqs2 API is undergoing upgrade, you may see error messages.
  17Aug2021: If you see any errors, please report them.
  17Aug2021: We are still debugging the MSA generation procedure...
  20Aug2021: WARNING - MMseqs2 API is undergoing upgrade, you may see error messages.
             To avoid Google Colab from crashing, for large MSA we did -diff 1000 to get 
             1K most diverse sequences. This caused some large MSA to degrade in quality,
             as sequences close to query were being merged to single representive.
             We are working on updating the server (today) to fix this, by making sure
             that both diverse and sequences close to query are included in the final MSA.
             We'll post update here when update is complete.
+ 21Aug2021  The MSA issues should now be resolved! Please report any errors you see.
+            In short, to reduce MSA size we filter (qsc > 0.8, id > 0.95) and take 3K
+            most diverse sequences at different qid (sequence identity to query) intervals 
+            and merge them. More specifically 3K sequences at qid at (0→0.2),(0.2→0.4),
+            (0.4→0.6),(0.6→0.8) and (0.8→1). If you submitted your sequence between
+            16Aug2021 and 20Aug2021, we recommend submitting again for best results!
  21Aug2021  The use_templates option in AlphaFold2_mmseqs2 is not properly working. We are
             working on fixing this. If you are not using templates, this does not affect the
             the results. Other notebooks that do not use_templates are unaffected.
+ 21Aug2021  The templates issue is resolved!

Making Protein folding accessible to all via Google Colab!

Notebooks monomers complexes mmseqs2 jackhmmer templates
AlphaFold2_mmseqs2 Yes No Yes No Yes
AlphaFold2_advanced Yes Yes Yes Yes No
AlphaFold2_batch Yes No Yes No Yes
RoseTTAFold Yes No Yes No No
AlphaFold2 (from Deepmind) Yes No No Yes No
OLD retired notebooks
AlphaFold2_complexes No Yes No No No
AlphaFold2_jackhmmer Yes No Yes Yes No
AlphaFold2_noTemplates_noMD
AlphaFold2_noTemplates_yesMD

FAQ

  • 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 ~1400
    • 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 here

Tutorials & Presentations

  • ColabFold Tutorial presented at the Boston Protein Design and Modeling Club. [video] [slides].

Projects based on ColabFold

Acknowledgments

  • We would like to thank the RoseTTAFold and AlphaFold team for doing an excellent job open sourcing the software.
  • Also credit to David Koes for his awesome py3Dmol plugin, without whom these notebooks would be quite boring!
  • A colab by Sergey Ovchinnikov (@sokrypton), Milot Mirdita (@milot_mirdita) and Martin Steinegger (@thesteinegger).

How do I reference this work?

  • Mirdita M, Ovchinnikov S and Steinegger M. ColabFold - Making protein folding accessible to all.
    bioRxiv (2021) doi: 10.1101/2021.08.15.456425
  • If you’re using AlphaFold, please also cite:
    Jumper et al. "Highly accurate protein structure prediction with AlphaFold."
    Nature (2021) doi: 10.1038/s41586-021-03819-2
  • If you are using RoseTTAFold, please also cite:
    Minkyung et al. "Accurate prediction of protein structures and interactions using a three-track neural network."
    Science (2021) doi: 10.1126/science.abj8754

DOI