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.
...
LocalColabFold is available via our LMOD module system (Using Applications on O2 ). As development of ColabFold is relatively fast, we have labeled the last updated time stamp in the help output of the module. We cannot guarantee that this module will be updated in a timely manner, so if you are interested in keeping up to date, please refer to the Installing LocalColabFold Locally section to learn how to set up a local installation you can maintain.
Presently, the module is hidden while we work out additional dependencies of the module. Though it presently still “works”, we do not currently recommend processing a large volume of proteins, as the default behavior of the tool is to send proteins to the remote server for alignment before returning to leverage the local GPU resource. When this nuance is resolved, this page will be updated accordingly; see the Caveats section for more information.
...
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.
...