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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#Version 14-7e284 (loaded with localcolabfold/1.5.2)
colabfold_search \
--db-load-mode 2 \
--mmseqs mmseqs \
--use-env 1 \
--use-templates 0 \
--threads 3 \
/PATH/TO/INPUT.fasta /n/shared_db/misc/mmseqs2/14-7e284 /PATH/TO/OUTPUT/DIRECTORY
#Previous Versions
colabfold_search --db1 uniref30/uniref30_2103_db \
--db3 colabfold/colabfold_envdb_202108_db \
--mmseqs mmseqs \
--use-env 1 \
--use-templates 0 \
--threads 3 \
/PATH/TO/INPUT.fasta /n/shared_db/misc/mmseqs2 /PATH/TO/OUTPUT/DIRECTORY |
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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 LARGE BATCHES OF PROTEIN ALIGNMENTS MUST BE GENERATED LOCALLY USING MMSEQS2, regardless of whether you are using the O2 module or your own installation on O2. At this time, the developers define large as “a few thousand” sequences. This could change, and is at the discretion of the system administrators maintaining the remote server. Please be considerate of other ongoing analysis on O2 when submitting large queries.
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