ColabFold (https://github.com/sokrypton/ColabFold ) is an emerging protein folding prediction tool based on Google DeepMind’s Alphafold (see Using AlphaFold on O2 ). LocalColabFold (https://github.com/YoshitakaMo/localcolabfold )is a packaging of ColabFold for use on local machines; we provide instructions on how to leverage LocalColabFold on O2 below. LocalColabFold uses MMseqs2 (conditionally faster than jackhmmer), and runs AlphaFold2 for single protein modeling and AlphaFold-Multimer for protein complex modeling. If you are unsure about which to use, feel free to try both tools and compare results.
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$ module help localcolabfold/latest
--------------------------- Module Specific Help for "localcolabfold/.latest" ----------------------------For detailed instructions, go to:
https://github.com/YoshitakaMo/localcolabfold
This module was last installed on February 9, 2022 using the latest colabfold commit as of ~4:30pm ET.
Due to frequency in development updates, this module may be reinstalled any time.
Please refer to the timestamp above for most recent installation time.
This module currently requires gcc/9.2.0 to be loaded due to requiring external cuda libraries.
If you are working under a different compiler stack (e.g. gcc/6.2.0), you may want to install this yourself
until we offer an updated version of the cuda module under a different compiler. Visit the repository website
for more information about how to install this yourself. |
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LocalColabFold is a repackaging of ColabFold for local use. This means that LocalColabFold requires all the same local hardware resources and connections that ColabFold would require (but without the Google Colab soft dependency). This includes the allowing of shipping the protein sequence to a remote server maintained by the ColabFold developers for processing during the alignment step. This server is shared by all users of ColabFold, and is not an HPC environment to our knowledge. This means that SUBMITTING LARGE BATCHES OF PROTEINS IS NOT RECOMMENDED AT THIS TIME, regardless of whether you are using the O2 module or your own installation on O2. Do note that we are unable to quantify “large”, as this is to the discretion of the system administrators maintaining the remote server.
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