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AlphaFold

Note: currently (Dec/2022) this documentation is in testing phase.

What is AlphaFold?

AlphaFold

AlphaFold in the FASRC Cannon cluster

Alphafold runs within a Docker container. However, Docker containers are not
allowed in high performance computing (HPC) systems such as Cannon because
Docker requires root/sudo privileges, which poses a security concern in HPC
systems.

Instead, we use Singularity
containers

which was specifically designed for HPC systems. We build a Singularity
container based on the Singularity definition file from
https://github.com/prehensilecode/alphafold_singularity, At the time of the
first deployment (Dec 2022), this refers to version 2.2.4 of AlphaFold. The
Singularity image is tagged with the AlphaFold version.

The AlphaFold database is stored in a cluster-wide location, meaning that
individual users do not have to download the AlphaFold database to run their
simulations. The database is stored in SSD as recommended by the developers.

Running AlphaFold

We recommend running AlphaFold on GPU partitions because it runs much faster
than solely using CPUs — due to AlphaFold’s GPU optimization. See slurm
partitions

for the specifics of each partition.

Below you will find a slurm script example
run_alphafold.sh that uses the fasta file
5ZE6_1.fasta.
This example assumes that run_alphafold.sh and my_fasta are located in the
same directory. If they are located in different directories, you will have to
edit my_fasta_path.

You will have to edit in the run_alphafold.sh script:

  • SBATCH directives to suit your needs (e.g. time -t, number of cores -c,
    amount of memory --mem)
  • my_fasta to suit to your own fasta file
  • (optional) my_output_dir if you would like your output to go somewhere else
  • (optional) my_fasta_path

Note: AlphaFold screen output goes to the stderr file (.err) rather than the
stdout file (.out).

Slurm script

#!/bin/bash
#SBATCH -J alphafold # Job name
#SBATCH -p gpu # Partition(s) (separate with commas if using multiple)
#SBATCH –gres=gpu:1 # number of GPUs
#SBATCH -c 8 # Number of cores
#SBATCH -t 03:00:00 # Time (D-HH:MM:SS)
#SBATCH –mem=60G # Memory
#SBATCH -o alphafold_%j.out # Name of standard output file
#SBATCH -e alphafold_%j.err # Name of standard error file
# create and set path of output directory
my_output_dir=alphafold_output # name of output directory
mkdir $my_output_dir # create output directory
my_output_dir_path=$PWD/$my_output_dir # entire path of output directory
# set fasta file name and path
my_fasta=5ZE6_1.fasta
my_fasta_path=$PWD/$my_fasta
# run AlphaFold using Singularity
singularity run –env TF_FORCE_UNIFIED_MEMORY=1,XLA_PYTHON_CLIENT_MEM_FRACTION=4.0,OPENMM_CPU_THREADS=$SLURM_CPUS_PER_TASK,LD_LIBRARY_PATH=/usr/local/cuda-11.1/targets/x86_64-linux/lib/ -B /n/holylfs04-ssd2/LABS/FAS/alphafold_database:/data -B .:/etc –pwd /app/alphafold –nv /n/singularity_images/FAS/alphafold/alphafold_v2.2.4.sif \
–data_dir=/data/ \
–bfd_database_path=/data/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt \
–db_preset=full_dbs \
–fasta_paths=$my_fasta_path \
–max_template_date=2021-07-28 \
–mgnify_database_path=/data/mgnify/mgy_clusters_2018_12.fa \
–model_preset=monomer \
–obsolete_pdbs_path=/data/pdb_mmcif/obsolete.dat \
–output_dir=$my_output_dir_path/ \
–pdb70_database_path=/data/pdb70/pdb70 \
–template_mmcif_dir=/data/pdb_mmcif/mmcif_files \
–uniclust30_database_path=/data/uniclust30/uniclust30_2018_08/uniclust30_2018_08 \
–uniref90_database_path=/data/uniref90/uniref90.fasta \
–use_gpu_relax=True

Fasta file

>5ZE6_1
MNLEKINELTAQDMAGVNAAILEQLNSDVQLINQLGYYIVSGGGKRIRPMIAVLAARAVGYEGNAHVTIAALIEFIHTATLLHDDVVDESDMRRGKATANAAFGNAASVLVGDFIYTRAFQMMTSLGSLKVLEVMSEAVNVIAEGEVLQLMNVNDPDITEENYMRVIYSKTARLFEAAAQCSGILAGCTPEEEKGLQDYGRYLGTAFQLIDDLLDYNADGEQLGKNVGDDLNEGKPTLPLLHAMHHGTPEQAQMIRTAIEQGNGRHLLEPVLEAMNACGSLEWTRQRAEEEADKAIAALQVLPDTPWREALIGLAHIAVQRDR

Submitting a slurm batch job that runs AlphaFold

Log in to Cannon (see login
instructions
). Go to the
directory where run_alphafold.sh is located. Then submit a slurm batch job
with the command:

sbatch run_alphafold.sh

This example takes about 1-2 hours to run on Cannon in the gpu partition with
8 cores (-c 8).

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