Supported Applications


  • Description

    an implementation of the inference pipeline of AlphaFold v2.0 using a completely new model that was entered in CASP14.

  • Usage Notes

    AlphaFold requires a set of parameters and genetic databases that must be downloaded separately. See https://github.com/deepmind/alphafold#genetic-databases for more information.

    These parameters and databases can be downloaded with the included download script and the aria2c program, both of which are available in the SBGrid collection. Note that these databases are large in size (> 2Tb) and may require a significant amount of time to download.

    Note that while the AlphaFold code is licensed under the open source Apache 2.0 License, the AlphaFold parameters are made available for non-commercial use only under the terms of the CC BY-NC 4.0 license. Please see the Disclaimer at https://github.com/deepmind/alphafold#license-and-disclaimer.

    To download parameters and dabatases:

    /programs/x86_64-linux/alphafold/2.0.0/alphafold/scripts/download_all_data.sh <destination path>

    Once the databases are in place, AlphaFold can be run with the wrapper script run_alphafold.sh :

    run_alphafold.sh <path to fasta file> <path to an output directory>

    run_alphafold.py is also available which requires all parameters to be set explicitly, but provides greater flexibility. Pass --helpshort or --helpfull to see help on flags.

    Please see https://sbgrid.org//wiki/examples/alphafold2 for more information about using alphafold in SBGrid.

  • Installation

    Use the following command to install this title with the CLI client: $ sbgrid-cli install alphafold Copy to clipboard
  • Primary Citation*

    J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. Žídek, A. Potapenko, A. Bridgland, C. Meyer, S. A. A. Kohl, A. J. Ballard, A. Cowie, B. Romera-Paredes, S. Nikolov, R. Jain, J. Adler, T. Back, S. Petersen, D. Reiman, E. Clancy, M. Zielinski, M. Steinegger, M. Pacholska, T. Berghammer, S. Bodenstein, D. Silver, O. Vinyals, A. W. Senior, K. Kavukcuoglu, P. Kohli, and D. Hassabis. 2021. Highly accurate protein structure prediction with AlphaFold. Nature.

    • *Full citation information available through

  • Default Versions

    Linux 64:  2.1.1

  • Other Versions

      Linux 64:

      2.1.0, 2.0.0
  • Developers

    Tom Ward, Augustin Zidek, Saran Tunyasuvunakool, John Jumper, Demis Hassabis