AlphaFold2

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AlphaFold2 predicts a protein's 3D folding structure by its amino acid sequence with the accuracy that is competitive with experimental results. This AI-powered structure prediction of AlphaFold2 has been recognized as the scientific breakthrough of the year 2021. The AlphaFold package is now installed in the new software stack on Euler.

Load modules

The AlphaFold module can be loaded as following.

$ env2lmod
$ module load gcc/6.3.0 openmpi/4.0.2 alphafold/2.1.1
Now run 'alphafold_init' to initialize the virtual environment

The following have been reloaded with a version change:
  1) gcc/4.8.5 => gcc/6.3.0

$ alphafold_init
(venv_alphafold) [jarunanp@eu-login-18 ~]$ 

Databases

The AlphaFold databases are available for all cluster users at /cluster/project/alphafold.

If you wish to download databases separately, you can see the instruction here.

Submit a job

Here is an example of a job submission script (run_alphafold.bsub) which requests 12 CPU cores, in total 120GB of memory, in total 120GB of local scratch space and one GPU. This job is to fold a monomeric protein Ubiquitin (76aa).

#!/usr/bin/bash
#BSUB -n 12
#BSUB -W 4:00
#BSUB -R "rusage[mem=10000, scratch=10000, ngpus_excl_p=1]"
#BSUB -J alphafold

source /cluster/apps/local/env2lmod.sh
module load gcc/6.3.0 openmpi/4.0.2 alphafold/2.1.1
source /cluster/apps/nss/alphafold/venv_alphafold/bin/activate

# Define paths to databases, fasta file and output directory
DATA_DIR="/cluster/project/alphafold"
FASTA_DIR="/cluster/home/jarunanp/fastafiles"
OUTPUT_DIR=$TMPDIR/output

python /cluster/apps/nss/alphafold/alphafold-2.1.1/run_alphafold.py \
--data_dir=$DATA_DIR \
--output_dir=$OUTPUT_DIR \
--max_template_date="2021-12-06" \
--bfd_database_path=$DATA_DIR/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt \
--uniref90_database_path=$DATA_DIR/uniref90/uniref90.fasta \
--uniclust30_database_path=$DATA_DIR/uniclust30/uniclust30_2018_08/uniclust30_2018_08 \
--mgnify_database_path=$DATA_DIR/mgnify/mgy_clusters_2018_12.fa \
--pdb70_database_path=$DATA_DIR/pdb70/pdb70 \
--template_mmcif_dir=$DATA_DIR/pdb_mmcif/mmcif_files \
--obsolete_pdbs_path=$DATA_DIR/pdb_mmcif/obsolete.dat \
--fasta_paths=$FASTA_DIR/ubiquitin.fasta

# Copy the results from the compute node
mkdir -p output
cp -r $OUTPUT_DIR/* output

To fold a multimeric protein, the option --model_preset=multimer has to be called, and --pdb_seqres_database_path and --uniprot_database_path have to be set. The command to run AlphaFold becomes:

python /cluster/apps/nss/alphafold/alphafold-2.1.1/run_alphafold.py \
--data_dir=$DATA_DIR \
--output_dir=$OUTPUT_DIR \
--max_template_date="2021-12-06" \
--bfd_database_path=$DATA_DIR/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt \
--uniref90_database_path=$DATA_DIR/uniref90/uniref90.fasta \
--uniclust30_database_path=$DATA_DIR/uniclust30/uniclust30_2018_08/uniclust30_2018_08 \
--mgnify_database_path=$DATA_DIR/mgnify/mgy_clusters_2018_12.fa \
--pdb_seqres_database_path=$DATA_DIR/pdb_seqres/pdb_seqres.txt \
--uniprot_database_path=$DATA_DIR/uniprot/uniprot.fasta \
--template_mmcif_dir=$DATA_DIR/pdb_mmcif/mmcif_files \
--obsolete_pdbs_path=$DATA_DIR/pdb_mmcif/obsolete.dat \
--model_preset=multimer \
--fasta_paths=$FASTA_DIR/IFGSC_6mer.fasta

Submit a job with the command

$ bsub < run_alphafold.bsub

The screen output is saved in the output file named starting with lsf.o followed by the JobID, e.g., lsf.o195525946. Please see this page for how to read the output file.

From our benchmark, it took around 40 minutes to fold Ubiquitin[76aa] and 2.5 hours to fold T1050[779aa].

Further readings

• DeepMind Blog post: "AlphaFold: a solution to a 50-year-old grand challenge in biology"
• ETH News: "Computer algorithms are currently revolutionising biology"

< Examples