Skip to content
Contact Support

AlphaFold

AlphaFold can predict protein structures with atomic accuracy even where no similar structure is known AlphaFold Homepage

Available Modules

module load AlphaFold/2.3.2

Prerequisite

An extended version of AlphaFold2 on NeSI Mahuika cluster which contains additional information such as visualisation of AlphaFold outputs, etc can be found here

Description

This package provides an implementation of the inference pipeline of AlphaFold v2.0. This is a completely new model that was entered in CASP14 and published in Nature. For simplicity, we refer to this model as AlphaFold throughout the rest of this document.

Any publication that discloses findings arising from using this source code or the model parameters should cite the AlphaFold paper. Please also refer to the Supplementary Information for a detailed description of the method.

Home page is at https://github.com/deepmind/alphafold

License and Disclaimer

This is not an officially supported Google product.

Copyright 2021 DeepMind Technologies Limited.

AlphaFold Code License

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at https://www.apache.org/licenses/LICENSE-2.0.

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Model Parameters License

The AlphaFold parameters are made available for non-commercial use only, under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You can find details at: https://creativecommons.org/licenses/by-nc/4.0/legalcode

AlphaFold Databases

AlphaFold databases are stored in /opt/nesi/db/alphafold_db/  parent directory. In order to make the database calling more convenient, we have prepared modules for each version of the database. Running module spider AlphaFold2DB will list the available versions based on when they were downloaded (Year-Month)

$ module spider AlphaFold2DB

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
AlphaFold2DB: AlphaFold2DB/2022-06
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Description:
AlphaFold2 databases

 Versions:
         AlphaFold2DB/2022-06
         AlphaFold2DB/2023-04

Loading a module will set the $AF2DB variable which is pointing to the  selected version of the database. For an example. 

$ module load AlphaFold2DB/2023-04

$ echo $AF2DB 
/opt/nesi/db/alphafold_db/2023-04

AlphaFold module ( >= 2.3.2)

As of version 2.3.2 of AlphaFold, we recommend deploying AlphaFold via the module (previous versoions were done via a Singularity container )

Example Slurm script for monomer

Input fasta used in following example  is 3RGK (https://www.rcsb.org/structure/3rgk).

#!/bin/bash -e

#SBATCH --account       nesi12345
#SBATCH --job-name      af-2.3.2-monomer
#SBATCH --mem           24G
#SBATCH --cpus-per-task 8
#SBATCH --gpus-per-node P100:1
#SBATCH --time          02:00:00
#SBATCH --output        %j.out

module purge
module load AlphaFold2DB/2023-04
module load AlphaFold/2.3.2

INPUT=/nesi/project/nesi12345/alphafold/input_data
OUTPUT=/nesi/project/nesi12345/alphafold/results

run_alphafold.py --use_gpu_relax \
--data_dir=$AF2DB \
--uniref90_database_path=$AF2DB/uniref90/uniref90.fasta \
--mgnify_database_path=$AF2DB/mgnify/mgy_clusters_2022_05.fa \
--bfd_database_path=$AF2DB/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt \
--uniref30_database_path=$AF2DB/uniref30/UniRef30_2021_03 \
--pdb70_database_path=$AF2DB/pdb70/pdb70 \
--template_mmcif_dir=$AF2DB/pdb_mmcif/mmcif_files \
--obsolete_pdbs_path=$AF2DB/pdb_mmcif/obsolete.dat \
--model_preset=monomer \
--max_template_date=2022-6-1 \
--db_preset=full_dbs \
--output_dir=$OUTPUT \
--fasta_paths=${INPUT}/rcsb_pdb_3GKI.fasta

Example Slurm script for multimer

Input fasta used in following example

    T1083
GAMGSEIEHIEEAIANAKTKADHERLVAHYEEEAKRLEKKSEEYQELAKVYKKITDVYPNIRSYMVLHYQNLTRRYKEAAEENRALAKLHHELAIVED
    T1084
MAAHKGAEHHHKAAEHHEQAAKHHHAAAEHHEKGEHEQAAHHADTAYAHHKHAEEHAAQAAKHDAEHHAPKPH

#!/bin/bash -e

#SBATCH --account       nesi12345
#SBATCH --job-name      af-2.3.2-multimer
#SBATCH --mem           30G
#SBATCH --cpus-per-task 4
#SBATCH --gpus-per-node P100:1
#SBATCH --time          01:45:00
#SBATCH --output        slurmout.%j.out

module purge
module load AlphaFold2DB/2023-04
module load AlphaFold/2.3.2

INPUT=/nesi/project/nesi12345/input_data
OUTPUT=/nesi/project/nesi12345/alphafold/2.3_multimer

run_alphafold.py \
--use_gpu_relax \
--data_dir=$AF2DB \
--model_preset=multimer \
--uniprot_database_path=$AF2DB/uniprot/uniprot.fasta \
--uniref90_database_path=$AF2DB/uniref90/uniref90.fasta \
--mgnify_database_path=$AF2DB/mgnify/mgy_clusters_2022_05.fa \
--bfd_database_path=$AF2DB/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt \
--uniref30_database_path=$AF2DB/uniref30/UniRef30_2021_03 \
--pdb_seqres_database_path=$AF2DB/pdb_seqres/pdb_seqres.txt \
--template_mmcif_dir=$AF2DB/pdb_mmcif/mmcif_files \
--obsolete_pdbs_path=$AF2DB/pdb_mmcif/obsolete.dat \
--max_template_date=2022-6-1 \
--db_preset=full_dbs \
--output_dir=${OUTPUT} \
--fasta_paths=${INPUT}/test_multimer.fasta

AlphaFold Singularity container (prior to v2.3.2)

If you would like to use a version prior to 2.3.2, It can be done via the Singularity containers.

We prepared a Singularity container image based on the official Dockerfile with some modifications. Image (.simg) and the corresponding definition file (.def) are stored in /opt/nesi/containers/AlphaFold/

Example Slurm scripts for Singularity container based AF2 deployment

Monomer

#!/bin/bash -e

#SBATCH --account       nesi12345
#SBATCH --job-name      alphafold2_monomer_example
#SBATCH --mem           30G
#SBATCH --cpus-per-task 6
#SBATCH --gpus-per-node P100:1 
#SBATCH --time          02:00:00
#SBATCH --output        slurmout.%j.out

module purge
module load AlphaFold2DB/2022-06
module load cuDNN/8.1.1.33-CUDA-11.2.0 Singularity/3.9.8

INPUT=/path/to/input_data
OUTPUT=/path/to/results

export SINGULARITY_BIND="$INPUT,$OUTPUT,$AF2DB"

singularity exec --nv /opt/nesi/containers/AlphaFold/alphafold_2.2.0.simg python /app/alphafold/run_alphafold.py \
--use_gpu_relax \
--data_dir=$AF2DB \
--uniref90_database_path=$AF2DB/uniref90/uniref90.fasta \
--mgnify_database_path=$AF2DB/mgnify/mgy_clusters_2018_12.fa \
--bfd_database_path=$AF2DB/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt \
--uniclust30_database_path=$AF2DB/uniclust30/uniclust30_2018_08/uniclust30_2018_08 \
--pdb70_database_path=$AF2DB/pdb70/pdb70 \
--template_mmcif_dir=$AF2DB/pdb_mmcif/mmcif_files \
--obsolete_pdbs_path=$AF2DB/pdb_mmcif/obsolete.dat \
--model_preset=monomer \
--max_template_date=2022-1-1 \
--db_preset=full_dbs \
--output_dir=$OUTPUT \
--fasta_paths=$INPUT/rcsb_pdb_3GKI.fasta

Multimer

#!/bin/bash -e

#SBATCH --account       nesi12345
#SBATCH --job-name      alphafold2_monomer_example
#SBATCH --mem           30G
#SBATCH --cpus-per-task 6
#SBATCH --gpus-per-node P100:1 
#SBATCH --time          02:00:00
#SBATCH --output        slurmout.%j.out

module purge
module load AlphaFold2DB/2022-06
module load cuDNN/8.1.1.33-CUDA-11.2.0 Singularity/3.9.8

INPUT=/path/to/input_data
OUTPUT=/path/to/results


export SINGULARITY_BIND="$INPUT,$OUTPUT,$AF2DB"

singularity exec --nv /opt/nesi/containers/AlphaFold/alphafold_2.2.0.simg python /app/alphafold/run_alphafold.py \
--use_gpu_relax \
--data_dir=$AF2DB \
--uniref90_database_path=$AF2DB/uniref90/uniref90.fasta \
--mgnify_database_path=$AF2DB/mgnify/mgy_clusters_2018_12.fa \
--bfd_database_path=$AF2DB/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt \
--uniclust30_database_path=$AF2DB/uniclust30/uniclust30_2018_08/uniclust30_2018_08 \
--pdb_seqres_database_path=$AF2DB/pdb_seqres/pdb_seqres.txt \
--template_mmcif_dir=$AF2DB/pdb_mmcif/mmcif_files \
--obsolete_pdbs_path=$AF2DB/pdb_mmcif/obsolete.dat \
--uniprot_database_path=$AF2DB/uniprot/uniprot.fasta \
--model_preset=multimer \
--max_template_date=2022-1-1 \
--db_preset=full_dbs \
--output_dir=$OUTPUT \
--fasta_paths=$INPUT/rcsb_pdb_3GKI.fasta

Explanation of Slurm variables and Singularity flags

  1. Values for --mem , --cpus-per-task and --time Slurm variables are for 3RGK.fasta. Adjust them accordingly
  2. We have tested this on both P100 and A100 GPUs where the runtimes were identical. Therefore, the above example was set to former via P100:1
  3. The --nv flag enables GPU support.
  4. --pwd /app/alphafold is to workaround this existing issue

AlphaFold2 : Initial Release ( this version does not support multimer)

Input fasta used in following example and subsequent benchmarking is 3RGK (https://www.rcsb.org/structure/3rgk).

Troubleshooting

  • If you are to encounter the message "RuntimeError: Resource exhausted: Out of memory" , add the following variables to the slurm script

For module based runs

export TF_FORCE_UNIFIED_MEMORY=1
export XLA_PYTHON_CLIENT_MEM_FRACTION=4.0

For Singularity based runs

export SINGULARITYENV_TF_FORCE_UNIFIED_MEMORY=1 
export SINGULARITYENV_XLA_PYTHON_CLIENT_MEM_FRACTION=4.0