Page Comparison

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Compared to ColabFold (see https://harvardmed.atlassian.net/wiki/spaces/O2/pages/2180546561/Using+Local+ColabFold+on+O2 ), AlphaFold takes fewer parameters, and uses jackhmmer as an MSA generator instead of mmseqs2, which can make it slower than ColabFold for certain inputs. It may also require more resources to run. If you are unsure about which to use, feel free to leverage try both tools and compare results accordingly.

Note: If you’re new to Slurm or O2, please see Using Slurm Basic for lots of information on submitting jobs.

How to load and use the AlphaFold module

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Note that this is for version 2.1.1. If you are running a newer version, you may want to refer to the help output (e.g. invoke alphafold.py -h) , and/or check the https://harvardmed.atlassian.net/wiki/spaces/O2/pages/1995177985/Using+AlphaFold+on+O2#Other-Important-Details section for any notable nuances with flags that are version-specific. If you are interested in a single protein sequence, you might want to set --model_preset to monomer, for example. Also check the https://harvardmed.atlassian.net/wiki/spaces/O2/pages/1995177985/Using+AlphaFold+on+O2#Other-Important-Details section for some tips.

Your output directory should contain several .pdb files, of which the “best” one should be called ranked_0.pdb. You should also find a .json file that contains the rankings, and .pkl files that contain metrics for each prediction. If one of these types of files is missing, it is possible that your run did not complete correctly.

Other Important Details

• The number of threads/core cores was hardcoded within the AlphaFold internal code, you should request ~8 CPU cores when submitting AlphaFold jobs. Requesting more cores will not improve performance, and it will make your job pend longer before it starts.

• The required database was downloaded into a centralized location (i.e., /n/shared_db/alphafold/) for the benefit of all O2 users. Please don’t download the 2 terabytes yourself, as that would be a waste of space.

• Submit your AlphaFold jobs to a GPU partition. For more about the GPU partitions, please visit our wiki page - Using O2 GPU resources.

• As of version 2.2.0, the base implementation has changed how the amber relaxation step is requested by the user. If you would like to run your analysis with the relaxed models with the 2.2.0 module, please include the --run_relax flag (as well as the --enable_gpu_relax flag if you are submitting to a GPU partition).

• For version 2.2.0, submissions will assume you are running on a GPU by default. If for some reason you desire to run explicitly on CPU, you will have to submit to a partition that does not have GPUs (e.g. short, etc.).

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