Alphafold2

Alphafold2 (https://alphafold.ebi.ac.uk/) is available on the IGBMC cluster from the command line by loading the alphafold/2.01 module or in Jupyter using the Alphafold 2.0.1 kernel.

Alphafold requires GPU to run its prediction algorithm. Please make sure to follow our GPU allocation guide to create the proper allocation for Alphafold.

Alphafold databases are made available on every GPU node on NVMe storage in /mnt/alphafold

Usage

To run Alphafold2 from the command line :

We assume you have an amino acid sequences called my.fasta

Connect to the cluster login node through SSH :

 ssh <login>@hpc.igbmc.fr

Load the Alphafold2 module :

module load alphafold/2.0.1

Start an interactive session or run a batch job on the GPU node :

Interactive approach

Create an allocation for GPU ressources

salloc -p gpu --gres=gpu:a3g.20gb:1 --cpus-per-tasks=10 --mem=50G

Create a folder to store alphafold output on the allocated node

srun mkdir -p /tmp/$USER_alphafold

Run alphafold

srun run_alphafold.sh \
     --fasta_paths=my.fasta \
     --output_dir=/tmp/$USER_alphafold \
     --model_names='model_1','model_2','model_3','model_4','model_5' \
     --data_dir=/mnt/alphafold \
     --preset=casp14 \
     --uniref90_database_path=/mnt/alphafold/uniref90/uniref90.fasta \
     --mgnify_database_path=/mnt/alphafold/mgnify/mgy_clusters_2018_12.fa \
     --pdb70_database_path=/mnt/alphafold/pdb70/pdb70 \
     --template_mmcif_dir=/mnt/alphafold/pdb_mmcif/mmcif_files \
     --max_template_date=2020-05-14 \
     --obsolete_pdbs_path=/mnt/alphafold/pdb_mmcif/obsolete.dat \
     --bfd_database_path=/mnt/alphafold/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt \
     --uniclust30_database_path=/mnt/alphafold/uniclust30/uniclust30_2018_08/uniclust30_2018_08

Get your outputs back to your team space

srun mv /tmp/$USER_alphafold /shared/space2/my_project

Do not forget to relinquish the allocation

exit

Batch job approach :

Create a my_fold.sh script based on the following example :

#!/bin/bash

#SBATCH -p gpu
#SBATCH --gres=gpu:a3g.20gb:1
#SBATCH --cpus-per-tasks=10
#SBATCH --mem=50G

module load alphafold/2.0.1

mkdir -p /tmp/$USER_alphafold

srun run_alphafold.sh --fasta_paths=my.fasta \
	--output_dir=/tmp/$USER_alphafold \
	--model_names='model_1','model_2','model_3','model_4','model_5' \
	--data_dir=/mnt/alphafold --preset=casp14 \
	--uniref90_database_path=/mnt/alphafold/uniref90/uniref90.fasta \
	--mgnify_database_path=/mnt/alphafold/mgnify/mgy_clusters_2018_12.fa \
	--pdb70_database_path=/mnt/alphafold/pdb70/pdb70 \
	--template_mmcif_dir=/mnt/alphafold/pdb_mmcif/mmcif_files \
	--max_template_date=2020-05-14 \
	--obsolete_pdbs_path=/mnt/alphafold/pdb_mmcif/obsolete.dat \
	--bfd_database_path=/mnt/alphafold/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt \
	--uniclust30_database_path=/mnt/alphafold/uniclust30/uniclust30_2018_08/uniclust30_2018_08

mv /tmp/$USER_alphafold /shared/space2/my_project

Start your batch job

sbatch my_fold.sh