AlphaFold
AlphaFold is a machine-learning model for the prediction of protein folding.
This page discusses how to use AlphaFold v2.0, the version that was entered in CASP14 and published in Nature.
Source code and documentation for AlphaFold can be found at their GitHub page. Any publication that discloses findings arising from using this source code or the model parameters should cite the AlphaFold paper.
Available versions
AlphaFold is available on our clusters as prebuilt Python packages (wheels). You can list available versions with avail_wheels.
[name@server ~]$ avail_wheels alphafold --all-versions
name version python arch
--------- --------- -------- -------
alphafold 2.2.4 py3 generic
alphafold 2.2.3 py3 generic
alphafold 2.2.2 py3 generic
alphafold 2.2.1 py3 generic
alphafold 2.1.1 py3 generic
alphafold 2.0.0 py3 generic
Installing AlphaFold in a Python virtual environment
1. Load AlphaFold dependencies.
[name@server ~]$ module load gcc/9.3.0 openmpi/4.0.3 cuda/11.4 cudnn/8.2.0 kalign/2.03 hmmer/3.2.1 openmm-alphafold/7.5.1 hh-suite/3.3.0 python/3.8
As of July 2022, only Python 3.7 and 3.8 are supported.
2. Create and activate a Python virtual environment.
[name@server ~]$ virtualenv --no-download ~/alphafold_env
[name@server ~]$ source ~/alphafold_env/bin/activate
3. Install a specific version of AlphaFold and its Python dependencies.
(alphafold_env) [name@server ~] pip install --no-index --upgrade pip
(alphafold_env) [name@server ~] pip install --no-index alphafold==X.Y.Z
where X.Y.Z is the exact desired version, for instance 2.2.4.
You can omit to specify the version in order to install the latest one available from the wheelhouse.
4. Validate it.
(alphafold_env) [name@server ~] run_alphafold.py --help
5. Freeze the environment and requirements set.
(alphafold_env) [name@server ~] pip freeze > ~/alphafold-requirements.txt
Databases
Note that AlphaFold requires a set of datasets/databases to be downloaded into the $SCRATCH.
Important: The database must live in the $SCRATCH.
1. From a login node, create the data folder.
(alphafold_env) [name@server ~] export DOWNLOAD_DIR=$SCRATCH/alphafold/data
(alphafold_env) [name@server ~] mkdir -p $DOWNLOAD_DIR
2. With your virtual environment activated, you can download the data.
(alphafold_env) [name@server ~] download_all_data.sh $DOWNLOAD_DIR
Note that this step cannot be done from a compute node but rather from a login node. Since the download might take a while, we suggest using a terminal multiplexer.
1. Set DOWNLOAD_DIR.
(alphafold_env) [name@server ~] export DOWNLOAD_DIR=/datashare/alphafold
Afterwards, the structure of your data should be similar to
(alphafold_env) [name@server ~] tree -d $DOWNLOAD_DIR
$DOWNLOAD_DIR/ # Total: ~ 2.2 TB (download: 428 GB)
bfd/ # ~ 1.8 TB (download: 271.6 GB)
# 6 files.
mgnify/ # ~ 64 GB (download: 32.9 GB)
mgy_clusters.fa
params/ # ~ 3.5 GB (download: 3.5 GB)
# 5 CASP14 models,
# 5 pTM models,
# LICENSE,
# = 11 files.
pdb70/ # ~ 56 GB (download: 19.5 GB)
# 9 files.
pdb_mmcif/ # ~ 206 GB (download: 46 GB)
mmcif_files/
# About 180,000 .cif files.
obsolete.dat
uniclust30/ # ~ 87 GB (download: 24.9 GB)
uniclust30_2018_08/
# 13 files.
uniref90/ # ~ 59 GB (download: 29.7 GB)
uniref90.fasta
Running AlphaFold
You can request at most 8 CPU cores when running AlphaFold because it is hardcoded to not use more and does not benefit from using more.
Edit the following submission script according to your needs.
#!/bin/bash
#SBATCH --job-name=alphafold_run
#SBATCH --account=def-someprof # adjust this to match the accounting group you are using to submit jobs
#SBATCH --time=08:00:00 # adjust this to match the walltime of your job
#SBATCH --cpus-per-task=8 # a MAXIMUM of 8 core, Alpafold has no benefit to use more
#SBATCH --mem=20G # adjust this according to the memory you need
# Load modules dependencies
module load gcc/9.3.0 openmpi/4.0.3 cuda/11.4 cudnn/8.2.0 kalign/2.03 hmmer/3.2.1 openmm-alphafold/7.5.1 hh-suite/3.3.0 python/3.8
DOWNLOAD_DIR=$SCRATCH/alphafold/data # set the appropriate path to your downloaded data
INPUT_DIR=$SCRATCH/alphafold/input # set the appropriate path to your supporting data
OUTPUT_DIR=${SCRATCH}/alphafold/output # set the appropriate path to your supporting data
# Generate your virtual environment in $SLURM_TMPDIR
virtualenv --no-download ${SLURM_TMPDIR}/env
source ${SLURM_TMPDIR}/env/bin/activate
# Install alphafold and its dependencies
pip install --no-index --upgrade pip
pip install --no-index --requirement ~/alphafold-requirements.txt
# Edit with the proper arguments, run your commands
# v2.3.0 `uniclust30_database_path` argument was renamed to `uniref30_database_path`.
# run_alphafold.py --help
run_alphafold.py \
--data_dir=${DOWNLOAD_DIR} \
--fasta_paths=${INPUT_DIR}/YourSequence.fasta,${INPUT_DIR}/AnotherSequence.fasta \
--bfd_database_path=${DOWNLOAD_DIR}/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt \
--pdb70_database_path=${DOWNLOAD_DIR}/pdb70/pdb70 \
--template_mmcif_dir=${DOWNLOAD_DIR}/pdb_mmcif/mmcif_files \
--uniref30_database_path=${DOWNLOAD_DIR}/uniclust30/uniclust30_2018_08/uniclust30_2018_08 \
--uniref90_database_path=${DOWNLOAD_DIR}/uniref90/uniref90.fasta \
--hhblits_binary_path=${EBROOTHHMINSUITE}/bin/hhblits \
--hhsearch_binary_path=${EBROOTHHMINSUITE}/bin/hhsearch \
--jackhmmer_binary_path=${EBROOTHMMER}/bin/jackhmmer \
--kalign_binary_path=${EBROOTKALIGN}/bin/kalign \
--mgnify_database_path=${DOWNLOAD_DIR}/mgnify/mgy_clusters_2018_12.fa \
--output_dir=${OUTPUT_DIR} \
--obsolete_pdbs_path=${DOWNLOAD_DIR}/pdb_mmcif/obsolete.dat \
--max_template_date=2020-05-14 \
--model_preset=monomer_casp14 \
--use_gpu_relax=False
#!/bin/bash
#SBATCH --job-name=alphafold_run
#SBATCH --account=def-someprof # adjust this to match the accounting group you are using to submit jobs
#SBATCH --time=08:00:00 # adjust this to match the walltime of your job
#SBATCH --gres=gpu:1 # a GPU helps to accelerate the inference part only
#SBATCH --cpus-per-task=8 # a MAXIMUM of 8 core, Alpafold has no benefit to use more
#SBATCH --mem=20G # adjust this according to the memory you need
# Load modules dependencies
module load gcc/9.3.0 openmpi/4.0.3 cuda/11.4 cudnn/8.2.0 kalign/2.03 hmmer/3.2.1 openmm-alphafold/7.5.1 hh-suite/3.3.0 python/3.8
DOWNLOAD_DIR=$SCRATCH/alphafold/data # set the appropriate path to your downloaded data
INPUT_DIR=$SCRATCH/alphafold/input # set the appropriate path to your supporting data
OUTPUT_DIR=${SCRATCH}/alphafold/output # set the appropriate path to your supporting data
# Generate your virtual environment in $SLURM_TMPDIR
virtualenv --no-download ${SLURM_TMPDIR}/env
source ${SLURM_TMPDIR}/env/bin/activate
# Install alphafold and its dependencies
pip install --no-index --upgrade pip
pip install --no-index --requirement ~/alphafold-requirements.txt
# Edit with the proper arguments, run your commands
# v2.3.0 `uniclust30_database_path` argument was renamed to `uniref30_database_path`.
# run_alphafold.py --help
run_alphafold.py \
--data_dir=${DOWNLOAD_DIR} \
--fasta_paths=${INPUT_DIR}/YourSequence.fasta,${INPUT_DIR}/AnotherSequence.fasta \
--bfd_database_path=${DOWNLOAD_DIR}/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt \
--pdb70_database_path=${DOWNLOAD_DIR}/pdb70/pdb70 \
--template_mmcif_dir=${DOWNLOAD_DIR}/pdb_mmcif/mmcif_files \
--uniref30_database_path=${DOWNLOAD_DIR}/uniclust30/uniclust30_2018_08/uniclust30_2018_08 \
--uniref90_database_path=${DOWNLOAD_DIR}/uniref90/uniref90.fasta \
--hhblits_binary_path=${EBROOTHHMINSUITE}/bin/hhblits \
--hhsearch_binary_path=${EBROOTHHMINSUITE}/bin/hhsearch \
--jackhmmer_binary_path=${EBROOTHMMER}/bin/jackhmmer \
--kalign_binary_path=${EBROOTKALIGN}/bin/kalign \
--mgnify_database_path=${DOWNLOAD_DIR}/mgnify/mgy_clusters_2018_12.fa \
--output_dir=${OUTPUT_DIR} \
--obsolete_pdbs_path=${DOWNLOAD_DIR}/pdb_mmcif/obsolete.dat \
--max_template_date=2020-05-14 \
--model_preset=monomer_casp14 \
--use_gpu_relax=True
Then, submit the job to the scheduler.
(alphafold_env) [name@server ~] sbatch --job-name alphafold-X alphafold-gpu.sh