Biocluster Alphafold
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About[edit]
- Alphafold is a Highly accurate protein structure prediction program
- More information at https://github.com/deepmind/alphafold/
How to Run[edit]
- Load alphafold module. This loads alphafold, singularity, and the alphafold databases.
module load alphafold/2.3.2
- Create scratch folder. This is important so the temporary data will go to the local scratch disk instead of the system's /tmp folder. The /tmp has limited space which if it gets filled up, the node will become unresponsive and cause jobs to fail.
mkdir /scratch/$SLURM_JOB_ID export TMPDIR=/scratch/$SLURM_JOB_ID
- Run run_singularity.py to run alphafold. This is a wrapper script for the alphafold singularity container to make things easier to run.
run_singularity.py --data-dir $BIODB --cpus $SLURM_NTASKS --use-gpu --output-dir example_output --fasta-paths example.fasta
- --data-dir parameter should be set to $BIODB. $BIODB points to the location of the alphafold databases
- --cpus parameter should be set to $SLURM_NTASKS. $SLURM_NTASKS is a variable which is equal to the number of processors you reserved
- --use-gpu enables the use of GPUS. Singularity will automatically use the number of the GPUs you have reserved.
- --output-dir parameter specifies where the output files should go. Change this parameter to an folder in your home folder
- --fasta-paths parameter specifies your input fasta files. Only one fasta sequence per a file is allowed. If you want to run on multiple sequences, each sequence needs to be in its own file. Then you can specify multiple files like below
--fasta-paths example.fasta,example2.fasta,example3.fasta
Example Job Script[edit]
#!/bin/bash # ----------------SLURM Parameters---------------- #SBATCH -n 4 #SBATCH -N 1 #SBATCH -p gpu #SBATCH --gres=gpu:1 #SBATCH --mem 70G # ----------------Load Modules-------------------- module load alphafold/2.3.2 # ----------------Commands------------------------ mkdir /scratch/$SLURM_JOB_ID export TMPDIR=/scratch/$SLURM_JOB_ID run_singularity.py --data-dir $BIODB --cpus $SLURM_NTASKS --use-gpu --db-preset full_dbs --output-dir output \ --fasta-paths example.fasta rm -fr /scratch/$SLURM_JOB_ID
Submit Job[edit]
- Submit job to the cluster
sbatch example.sh
Parameters[edit]
- These are all the parameters for run_singularity.py. This can be accessed by running run_singularity.py --help
-h, --help show this help message and exit
--fasta-paths FASTA_PATHS [FASTA_PATHS ...], -f FASTA_PATHS [FASTA_PATHS ...]
Paths to FASTA files, each containing one sequence.
All FASTA paths must have a unique basename as the
basename is used to name the output directories for
each prediction.
--max-template-date MAX_TEMPLATE_DATE, -t MAX_TEMPLATE_DATE
Maximum template release date to consider (ISO-8601
format - i.e. YYYY-MM-DD). Important if folding
historical test sets.
--db-preset {reduced_dbs,full_dbs}
Choose preset model configuration - no ensembling with
uniref90 + bfd + uniclust30 (full_dbs), or 8 model
ensemblings with uniref90 + bfd + uniclust30 (casp14).
--model-preset {monomer,monomer_casp14,monomer_ptm,multimer}
Choose preset model configuration - the monomer model,
the monomer model with extra ensembling, monomer model
with pTM head, or multimer model
--num-multimer-predictions-per-model NUM_MULTIMER_PREDICTIONS_PER_MODEL
How many predictions (each with a different random
seed) will be generated per model. E.g. if this is 2
and there are 5 models then there will be 10
predictions per input. Note: this FLAG only applies if
model_preset=multimer
--benchmark, -b Run multiple JAX model evaluations to obtain a timing
that excludes the compilation time, which should be
more indicative of the time required for inferencing
many proteins.
--use-precomputed-msas
Whether to read MSAs that have been written to disk
instead of running the MSA tools. The MSA files are
looked up in the output directory, so it must stay the
same between multiple runs that are to reuse the MSAs.
WARNING: This will not check if the sequence, database
or configuration have changed.
--data-dir DATA_DIR, -d DATA_DIR
Path to directory with supporting data: AlphaFold
parameters and genetic and template databases. Set to
the target of download_all_databases.sh.
--docker-image DOCKER_IMAGE
Alphafold docker image.
--output-dir OUTPUT_DIR, -o OUTPUT_DIR
Output directory for results.
--use-gpu Enable NVIDIA runtime to run with GPUs.
--models-to-relax MODELS_TO_RELAX
Whether to run the final relaxation step on the
predicted models. Turning relax off might result in
predictions with distracting stereochemical violations
but might help in case you are having issues with the
relaxation stage.
--enable-gpu-relax Run relax on GPU if GPU is enabled.
--gpu-devices GPU_DEVICES
Comma separated list of devices to pass to
NVIDIA_VISIBLE_DEVICES.
--cpus CPUS, -c CPUS Number of CPUs to use.
Issues[edit]
- If you receive an error like
RuntimeError: HHSearch failed
Most likely you need to increase the amount of memory you are reserving in your job script.
