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Repository alphafold2
Name: alphafold2
Synopsis: Alphafold v2: AI-guided 3D structure prediction of proteins
Alphafold is a program which uses neural networks to predict the tertiary (3D) structure of proteins. Alphafold accepts amino acid sequence(s) in FASTA format, which will then be 'folded' into a predicted 3D model. The tool can fold monomers (single proteins) and multimers (a complex of proteins).
Type: unrestricted
Revision: 8:ca90d17ff51b
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Repository README files - may contain important installation or license information

Alphafold compute setup ======================= Overview -------- Alphafold requires a customised compute environment to run. The machine needs a GPU, and access to a 2.2 Tb reference data store. This document is designed to provide details on the compute environment required for Alphafold operation, and the Galaxy job destination settings to run the wrapper. We strongly suggest reading this entire document to ensure that your setup is compatible with the hardware that you are deploying to. For full details on Alphafold requirements, see HARDWARE ~~~~~~~~ The machine is recommended to have the following specs: - 12 cores - 80 Gb RAM - 2.5 Tb storage - A fast Nvidia GPU. As a minimum, the Nvidia GPU must have 8Gb RAM. It also requires **unified memory** to be switched on. Unified memory is usually enabled by default, but some HPC systems will turn it off so the GPU can be shared between multiple jobs concurrently. ENVIRONMENT ~~~~~~~~~~~ This wrapper runs Alphafold as a singularity container. The following software are needed: - `Singularity `_ - `NVIDIA Container Toolkit `_ As Alphafold uses an Nvidia GPU, the NVIDIA Container Toolkit is needed. This makes the GPU available inside the running singularity container. To check that everything has been set up correctly, run the following :: singularity run --nv docker://nvidia/cuda:11.0-base nvidia-smi If you can see something similar to this output (details depend on your GPU), it has been set up correctly. :: +-----------------------------------------------------------------------------+ | NVIDIA-SMI 470.57.02 Driver Version: 470.57.02 CUDA Version: 11.4 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===============================+======================+======================| | 0 Tesla T4 Off | 00000000:00:05.0 Off | 0 | | N/A 49C P0 28W / 70W | 0MiB / 15109MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=============================================================================| | No running processes found | +-----------------------------------------------------------------------------+ REFERENCE DATA ~~~~~~~~~~~~~~ Alphafold needs reference data to run. The wrapper expects this data to be present at ``/data/alphafold_databases``. A custom path will be read from the ALPHAFOLD_DB environment variable, if set. To download the AlphaFold, reference data, run the following shell script command in the tool directory. :: # make folders if needed mkdir /data /data/alphafold_databases # download ref data bash scripts/ /data/alphafold_databases This will install the reference data to ``/data/alphafold_databases``. To check this has worked, ensure the final folder structure is as follows: :: data/alphafold_databases ├── bfd │   ├── bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt_a3m.ffdata │   ├── bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt_a3m.ffindex │   ├── bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt_cs219.ffdata │   ├── bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt_cs219.ffindex │   ├── bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt_hhm.ffdata │   └── bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt_hhm.ffindex ├── mgnify │   └── mgy_clusters_2018_12.fa ├── params │   ├── LICENSE │   ├── params_model_1.npz │   ├── params_model_1_ptm.npz │   ├── params_model_2.npz │   ├── params_model_2_ptm.npz │   ├── params_model_3.npz │   ├── params_model_3_ptm.npz │   ├── params_model_4.npz │   ├── params_model_4_ptm.npz │   ├── params_model_5.npz │   └── params_model_5_ptm.npz ├── pdb70 │   ├── md5sum │   ├── pdb70_a3m.ffdata │   ├── pdb70_a3m.ffindex │   ├── pdb70_clu.tsv │   ├── pdb70_cs219.ffdata │   ├── pdb70_cs219.ffindex │   ├── pdb70_hhm.ffdata │   ├── pdb70_hhm.ffindex │   └── pdb_filter.dat ├── pdb_mmcif │   ├── mmcif_files │   └── obsolete.dat ├── uniclust30 │   └── uniclust30_2018_08 └── uniref90 └── uniref90.fasta JOB DESTINATION ~~~~~~~~~~~~~~~ Alphafold needs a custom singularity job destination to run. The destination needs to be configured for singularity, and some extra singularity params need to be set as seen below. Specify the job runner. For example, a local runner :: Customise the job destination with required singularity settings. The settings below are mandatory, but you may include other settings as needed. :: 'none' true --nv "$job_directory:ro,$tool_directory:ro,$job_directory/outputs:rw,$working_directory:rw,/data/alphafold_databases:/data:ro" CUSTOM PARAMETERS ~~~~~~~~~~~~~~~~~ A few parameters can be customized with the use of environment variables set in the job destination: - ``ALPHAFOLD_DB``: path to the reference database root (default ``/data``) - ``ALPHAFOLD_USE_GPU [True/False]``: set to ``False`` to disable GPU dependency (defaults to ``True``) - ``ALPHAFOLD_AA_LENGTH_MIN``: minimum accepted sequence length (default ``0``) - ``ALPHAFOLD_AA_LENGTH_MAX``: maximum accepted sequence length (default ``0`` - no validation) Closing ~~~~~~~ If you are experiencing technical issues, feel free to write to We may be able to provide advice on setting up Alphafold on your compute environment.

Contents of this repository

Name Description Version Minimum Galaxy Version
- AI-guided 3D structural prediction of proteins 2.1.2+galaxy1 20.01

Proteomics - Tools enabling the study of proteins
Structural Materials Analysis - Tools for High Energy X-ray Imaging, Diffraction, and Modeling of Microstructures
Molecular Dynamics - Tools for studying the physical movements of atoms and molecules
Graphics - Tools producing images