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 2.3.1 py3 generic
alphafold 2.3.0 py3 generic
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 databases. A copy of the databases is available in /cvmfs/bio.data.computecanada.ca/content/databases/Core/alphafold2_dbs/2023_01/.
(alphafold_env) [name@server ~] export DOWNLOAD_DIR=/cvmfs/bio.data.computecanada.ca/content/databases/Core/alphafold2_dbs/2023_01/
The user can also choose to download the database locally into their $SCRATCH directory.
Important: The database must live in the $SCRATCH.
1. From a DTN or login node, create the data folder.
(alphafold_env) [name@server ~] export DOWNLOAD_DIR=$SCRATCH/alphafold/data
(alphafold_env) [name@server ~] mkdir -p $DOWNLOAD_DIR
2. With your modules loaded and 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. It should be done on a data transfer node (DTN) on clusters that have them (see Transferring data). On clusters that have no DTN, use a login node instead. Since the download can take up to a full day, 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/ # ~ 2.6 TB (total)
bfd/ # ~ 1.8 TB
# 6 files
mgnify/ # ~ 120 GB
mgy_clusters.fa
params/ # ~ 5.3 GB
# LICENSE
# 15 models
# 16 files (total)
pdb70/ # ~ 56 GB
# 9 files
pdb_mmcif/ # ~ 246 GB
mmcif_files/
# 202,764 files
obsolete.dat
pdb_seqres/ # ~ 237 MB
pdb_seqres.txt
uniprot/ # ~ 111 GB
uniprot.fasta
uniref30/ # ~ 206 GB
# 7 files
uniref90/ # ~ 73 GB
uniref90.fasta
(alphafold_env) [name@server ~] tree -d $DOWNLOAD_DIR
$DOWNLOAD_DIR/ # Total: ~ 2.2 TB (download: 428 GB)
bfd/ # ~ 1.8 TB (download: 271.6 GB)
# 6 files.
mgnify/ # ~ 64 GB (download: 32.9 GB)
mgy_clusters.fa
params/ # ~ 3.5 GB (download: 3.5 GB)
# 5 CASP14 models,
# 5 pTM models,
# LICENSE,
# = 11 files.
pdb70/ # ~ 56 GB (download: 19.5 GB)
# 9 files.
pdb_mmcif/ # ~ 206 GB (download: 46 GB)
mmcif_files/
# About 180,000 .cif files.
obsolete.dat
uniclust30/ # ~ 87 GB (download: 24.9 GB)
uniclust30_2018_08/
# 13 files.
uniref90/ # ~ 59 GB (download: 29.7 GB)
uniref90.fasta
Running AlphaFold
Performance
You can request at most 8 CPU cores when running AlphaFold because it is hardcoded to not use more and does not benefit from using more.
Edit one of following submission scripts according to your needs.
File : alphafold-2.3-cpu.sh
#!/bin/bash
#SBATCH --job-name=alphafold_run
#SBATCH --account=def-someprof # adjust this to match the accounting group you are using to submit jobs
#SBATCH --time=08:00:00 # adjust this to match the walltime of your job
#SBATCH --cpus-per-task=8 # a MAXIMUM of 8 core, Alpafold has no benefit to use more
#SBATCH --mem=20G # adjust this according to the memory you need
# Load modules dependencies
module load gcc/9.3.0 openmpi/4.0.3 cuda/11.4 cudnn/8.2.0 kalign/2.03 hmmer/3.2.1 openmm-alphafold/7.5.1 hh-suite/3.3.0 python/3.8
DOWNLOAD_DIR=$SCRATCH/alphafold/data # set the appropriate path to your downloaded data
INPUT_DIR=$SCRATCH/alphafold/input # set the appropriate path to your input data
OUTPUT_DIR=${SCRATCH}/alphafold/output # set the appropriate path to your output data
# Generate your virtual environment in $SLURM_TMPDIR
virtualenv --no-download ${SLURM_TMPDIR}/env
source ${SLURM_TMPDIR}/env/bin/activate
# Install alphafold and its dependencies
pip install --no-index --upgrade pip
pip install --no-index --requirement ~/alphafold-requirements.txt
# Edit with the proper arguments, run your commands.
# run_alphafold.py --help
run_alphafold.py \
--fasta_paths=${INPUT_DIR}/YourSequence.fasta,${INPUT_DIR}/AnotherSequence.fasta \
--output_dir=${OUTPUT_DIR} \
--data_dir=${DOWNLOAD_DIR} \
--db_preset=full_dbs \
--model_preset=multimer \
--bfd_database_path=${DOWNLOAD_DIR}/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt \
--mgnify_database_path=${DOWNLOAD_DIR}/mgnify/mgy_clusters_2022_05.fa \
--pdb70_database_path=${DOWNLOAD_DIR}/pdb70/pdb70 \
--template_mmcif_dir=${DOWNLOAD_DIR}/pdb_mmcif/mmcif_files \
--obsolete_pdbs_path=${DOWNLOAD_DIR}/pdb_mmcif/obsolete.dat \
--pdb_seqres_database_path=${DOWNLOAD_DIR}/pdb_seqres/pdb_seqres.txt \
--uniprot_database_path=${DOWNLOAD_DIR}/uniprot/uniprot.fasta \
--uniref30_database_path=${DOWNLOAD_DIR}/uniref30/UniRef30_2021_03 \
--uniref90_database_path=${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 \
--max_template_date=2022-01-01 \
--use_gpu_relax=False
File : alphafold-2.3-gpu.sh
#!/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 --gres=gpu:1 # a GPU helps to accelerate the inference part only
#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 input data
OUTPUT_DIR=${SCRATCH}/alphafold/output # set the appropriate path to your output data
# Generate your virtual environment in $SLURM_TMPDIR
virtualenv --no-download ${SLURM_TMPDIR}/env
source ${SLURM_TMPDIR}/env/bin/activate
# Install alphafold and its dependencies
pip install --no-index --upgrade pip
pip install --no-index --requirement ~/alphafold-requirements.txt
# Edit with the proper arguments, run your commands.
# run_alphafold.py --help
run_alphafold.py \
--fasta_paths=${INPUT_DIR}/YourSequence.fasta,${INPUT_DIR}/AnotherSequence.fasta \
--output_dir=${OUTPUT_DIR} \
--data_dir=${DOWNLOAD_DIR} \
--db_preset=full_dbs \
--model_preset=multimer \
--bfd_database_path=${DOWNLOAD_DIR}/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt \
--mgnify_database_path=${DOWNLOAD_DIR}/mgnify/mgy_clusters_2022_05.fa \
--pdb70_database_path=${DOWNLOAD_DIR}/pdb70/pdb70 \
--template_mmcif_dir=${DOWNLOAD_DIR}/pdb_mmcif/mmcif_files \
--obsolete_pdbs_path=${DOWNLOAD_DIR}/pdb_mmcif/obsolete.dat \
--pdb_seqres_database_path=${DOWNLOAD_DIR}/pdb_seqres/pdb_seqres.txt \
--uniprot_database_path=${DOWNLOAD_DIR}/uniprot/uniprot.fasta \
--uniref30_database_path=${DOWNLOAD_DIR}/uniref30/UniRef30_2021_03 \
--uniref90_database_path=${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 \
--max_template_date=2022-01-01 \
--use_gpu_relax=True
File : alphafold-cpu.sh
#!/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 input data
OUTPUT_DIR=${SCRATCH}/alphafold/output # set the appropriate path to your output data
# Generate your virtual environment in $SLURM_TMPDIR
virtualenv --no-download ${SLURM_TMPDIR}/env
source ${SLURM_TMPDIR}/env/bin/activate
# Install alphafold and its dependencies
pip install --no-index --upgrade pip
pip install --no-index --requirement ~/alphafold-requirements.txt
# Edit with the proper arguments, run your commands.
# Note that the `--uniclust30_database_path` option below was renamed to
# `--uniref30_database_path` in 2.3.
# run_alphafold.py --help
run_alphafold.py \
--fasta_paths=${INPUT_DIR}/YourSequence.fasta,${INPUT_DIR}/AnotherSequence.fasta \
--output_dir=${OUTPUT_DIR} \
--data_dir=${DOWNLOAD_DIR} \
--model_preset=monomer_casp14 \
--bfd_database_path=${DOWNLOAD_DIR}/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt \
--mgnify_database_path=${DOWNLOAD_DIR}/mgnify/mgy_clusters_2018_12.fa \
--pdb70_database_path=${DOWNLOAD_DIR}/pdb70/pdb70 \
--template_mmcif_dir=${DOWNLOAD_DIR}/pdb_mmcif/mmcif_files \
--obsolete_pdbs_path=${DOWNLOAD_DIR}/pdb_mmcif/obsolete.dat \
--uniclust30_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 \
--max_template_date=2020-05-14 \
--use_gpu_relax=False
File : alphafold-gpu.sh
#!/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 input data
OUTPUT_DIR=${SCRATCH}/alphafold/output # set the appropriate path to your output data
# Generate your virtual environment in $SLURM_TMPDIR
virtualenv --no-download ${SLURM_TMPDIR}/env
source ${SLURM_TMPDIR}/env/bin/activate
# Install alphafold and its dependencies
pip install --no-index --upgrade pip
pip install --no-index --requirement ~/alphafold-requirements.txt
# Edit with the proper arguments, run your commands.
# Note that the `--uniclust30_database_path` option below was renamed to
# `--uniref30_database_path` in 2.3.
# run_alphafold.py --help
run_alphafold.py \
--fasta_paths=${INPUT_DIR}/YourSequence.fasta,${INPUT_DIR}/AnotherSequence.fasta \
--output_dir=${OUTPUT_DIR} \
--data_dir=${DOWNLOAD_DIR} \
--model_preset=monomer_casp14 \
--bfd_database_path=${DOWNLOAD_DIR}/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt \
--mgnify_database_path=${DOWNLOAD_DIR}/mgnify/mgy_clusters_2018_12.fa \
--pdb70_database_path=${DOWNLOAD_DIR}/pdb70/pdb70 \
--template_mmcif_dir=${DOWNLOAD_DIR}/pdb_mmcif/mmcif_files \
--obsolete_pdbs_path=${DOWNLOAD_DIR}/pdb_mmcif/obsolete.dat \
--uniclust30_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 \
--max_template_date=2020-05-14 \
--use_gpu_relax=True
Then, submit the job to the scheduler.
(alphafold_env) [name@server ~] sbatch --job-name alphafold-X alphafold-gpu.sh