Alphafold

Software Summary

Mesabi

Default Module:

2.3.1-multimer

Other Modules Available:

2.0, 2.1.1-multimer, 2.3.1-multimer

Last Updated On:

Tuesday, August 29, 2023

Mesabi K40

Default Module:

Other Modules Available:

Last Updated On:

Mangi

Default Module:

2.3.1-multimer

Other Modules Available:

2.0, 2.1.1-multimer, 2.3.1-multimer

Last Updated On:

Tuesday, August 29, 2023

Mangi v100

Default Module:

Other Modules Available:

Last Updated On:

NICE

Default Module:

Other Versions Available:

Last Updated On:

Tuesday, August 29, 2023

Support Level:

Secondary Support

Software Access Level:

Open Access

Home Page:

Link

Software Description

Alphafold is an AI-based protein structure inference pipeline developed by DeepMind, which can predict a protein's 3D structure from its amino acid sequence.

Software Documentation

Software Documentation Tabs

General Linux

You can load alphafold for use in a Linux environment via:

module load alphafold

Running alphafold at MSI is a little different than what is indicated in DeepMind's original documentation. Since we are using a non-docker implementation based on https://github.com/kalininalab/alphafold_non_docker , we need to adapt our workflow accordingly. A workflow might look like:

module load alphafold

# We need a symlink to this directory wherever we are running alphafold, since several reference files are
# supposed to be located in ./alphafold
cd project_dir
ln -s $alphafold_path alphafold 2>/dev/null || true

# Use a provided wrapper script to invoke the pipeline
run_alphafold.sh -o ./output_directory -m model_1 -f ./input/my_fasta.fasta -t 2020-05-14

Slurm Example

Example scripts for submission to k40 and v100 partitions are provided below. Note that you will need to customize these scripts with your email (replacing sample_email@umn.edu), specific project directory (replacing project_dir), and input/output file locations (replacing ./output and ./input/my_fasta.fasta).

k40:

#!/bin/bash -l        
#SBATCH --time=24:00:00
#SBATCH --ntasks=24
#SBATCH --mem=20g
#SBATCH --tmp=100g
#SBATCH -p k40
#SBATCH --gres=gpu:k40:2
#SBATCH --mail-type=ALL  
#SBATCH --mail-user=sample_email@umn.edu

module load alphafold/2.0

cd project_dir

ln -s $alphafold_path alphafold  2>/dev/null || true

run_alphafold.sh -o ./output -m model_1 -f ./input/my_fasta.fasta -t 2020-05-14

v100:

#!/bin/bash -l        
#SBATCH --time=24:00:00
#SBATCH --ntasks=24
#SBATCH --mem=20g
#SBATCH --tmp=100g
#SBATCH -p v100
#SBATCH --gres=gpu:v100:2
#SBATCH --mail-type=ALL  
#SBATCH --mail-user=sample_email@umn.edu

module load alphafold/2.0

cd project_dir

ln -s $alphafold_path alphafold  2>/dev/null || true

run_alphafold.sh -o ./output -m model_1 -f ./input/my_fasta.fasta -t 2020-05-14

v100 multimer workflow:

#!/bin/bash -l        
#SBATCH --time=24:00:00
#SBATCH --ntasks=24
#SBATCH --mem=20g
#SBATCH --tmp=100g
#SBATCH -p v100
#SBATCH --gres=gpu:v100:2
#SBATCH --mail-type=ALL  
#SBATCH --mail-user=sample_email@umn.edu

module load alphafold/2.1.1-multimer

cd project_dir

ln -s $alphafold_path alphafold  2>/dev/null || true

run_alphafold.sh -o ./output -m multimer -f ./input/my_multimer_fasta.fasta -t 2021-11-01