Title here
Summary here
Reference data for AlphaFold are stored at a central storage point to avoid overhead.
The path is /lustre/project/alphafold_users
.
alphafold_users -> alphafold_users_v2.3.0
. Older version will be kept for a period of time (which is not yet set).Generally, software is provided per modulefile.
#!/bin/bash
#SBATCH -J <name of your job>
#SBATCH -o <desired name for log file>.%j.log
#SBATCH -A <account>
#SBATCH -p <m2_gpu|deeplearning>
#SBATCH --gres=gpu:1 # NOTE: AlphaFold is multi gpu capable, but
# apparently not stable.
#SBATCH -c 8 # NOTE: Non-GPU components of AlphaFold are
# hardly able to use more than 8 CPUs.
#SBATCH --mem=20G # NOTE: For really large protein complexes more
# memory might be needed.
#SBATCH -t 300 # NOTE: This is plenty of time for small and medium
# sized problems. Increase the time value in case
# of bigger simulations.
################################################################################
# load environment
module purge
module load bio/AlphaFold
################################################################################
# variables
INFILE=<path to input FASTA file>
# NOTE: AlphaFold per default creates an output file using the input file name.
# In order to avoid overriding old runs, you can indicate an own, holding
# the unique jobid.
OUTDIR=$PWD/alphafold_test_$SLURM_JOB_ID
mkdir OUTDIR
# NOTE: As the environment variable $ALPHA_FOLD_DATA is set by the module,
# no further data indicators are required upon starting the programm.
srun alphafold \
--output_dir=$OUTDIR \
--fasta_paths=$INFILE \
--max_template_date=<max_template_date, e.g. '2020-05-14'> \
--db_preset=<full_dbs|reduced_dbs> \
--model_preset=<monomer|multimer>
The container location is /lustre/project/alphafold_users/container
. Select your container version there and enter the appropriate name in the template script below (under <alphafold_container_version>.sif
).
#!/bin/bash
#SBATCH -J <name of your job>
#SBATCH -o <desired name for log file>.%j.log
#SBATCH -A <account>
#SBATCH -p <m2_gpu|deeplearning>
#SBATCH --gres=gpu:1 # NOTE: AlphaFold is multi gpu capable, but
# apparently not stable.
#SBATCH -c 8 # NOTE: Non-GPU components of AlphaFold are
# hardly able to use more than 8 CPUs.
#SBATCH --mem=20G # NOTE: For really large protein complexes more
# memory might be needed.
#SBATCH -t 300 # NOTE: This is plenty of time for small and medium
# sized problems. Increase the time value in case
# of bigger simulations.
################################################################################
module purge
module load tools/AppTainer # NOTE: The AppTainer module provides support for
# a Singularity container.
################################################################################
INFILE=<path to input FASTA file>
# NOTE: AlphaFold per default creates an output file using the input file name.
# In order to avoid overriding old runs, you can indicate an own, holding
# the unique jobid.
OUTDIR=$PWD/alphafold_test_$SLURM_JOB_ID
mkdir -p $OUTDIR
ALPHAFOLD_DATA_DIR=/lustre/project/alphafold_users
CONTAINERPATH=${ALPHAFOLD_DATA_DIR}/container
# NOTE: The $ALPHAFOLD_DATA_DIR is not accepted by the current container version, therefore
# the individual flags need to be defined.
srun singularity run --env TF_FORCE_UNIFIED_MEMORY=1,XLA_PYTHON_CLIENT_MEM_FRACTION4.0,OPENMM_CPU_THREADS=8
-B .:/etc --nv ${CONTAINERPATH}/<alphafold_container_version>.sif
--fasta_paths=$INFILE
--output_dir=$OUTDIR
--max_template_date=<max_template_date, e.g. '2020-05-14'>
--data_dir=$ALPHAFOLD_DATA_DIR
--uniref90_database_path=$ALPHAFOLD_DATA_DIR/uniref90/uniref90.fasta
--mgnify_database_path=$ALPHAFOLD_DATA_DIR/mgnify/mgy_clusters_2018_12.fa
--small_bfd_database_path=$ALPHAFOLD_DATA_DIR/small_bfd/bfd-first_non_consensus_sequences.fasta
--pdb70_database_path=$ALPHAFOLD_DATA_DIR/pdb70/pdb70
--template_mmcif_dir=$ALPHAFOLD_DATA_DIR/pdb_mmcif/mmcif_files
--obsolete_pdbs_path=$ALPHAFOLD_DATA_DIR/pdb_mmcif/obsolete.dat
--use_gpu_relax
--db_preset=<full_dbs|reduced_dbs>
--model_preset=<monomer|multimer>