/alphafold_non_docker

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AlphaFold Non-Docker setup

Install miniconda

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && bash Miniconda3-latest-Linux-x86_64.sh

Create a new conda environment and update

conda create --name alphafold python==3.8
conda update -n base conda

Activate conda environment

conda activate alphafold

Install dependencies

conda install -y -c anaconda cudnn==8.2.1
conda install -y -c bioconda hmmer hhsuite==3.3.0 kalign2
conda install -y -c conda-forge openmm==7.5.1 cudatoolkit==11.0.3 pdbfixer

Download alphafold git repo

git clone https://github.com/deepmind/alphafold.git
alphafold_path = "/path/to/alphafold/git/repo"

Download chemical properties to the common folder

wget -q -P alphafold/alphafold/common/ https://git.scicore.unibas.ch/schwede/openstructure/-/raw/7102c63615b64735c4941278d92b554ec94415f8/modules/mol/alg/src/stereo_chemical_props.txt

Install alphafold dependencies

Note: jax updgrade: cuda111 supports cuda 11.3 - jax-ml/jax#6628

pip install absl-py==0.13.0 biopython==1.79 chex==0.0.7 dm-haiku==0.0.4 dm-tree==0.1.6 immutabledict==2.0.0 jax==0.2.14 ml-collections==0.1.0 numpy==1.19.5 scipy==1.7.0 tensorflow==2.5.0

pip install --upgrade jax jaxlib==0.1.69+cuda111 -f https://storage.googleapis.com/jax-releases/jax_releases.html

Apply OpenMM patch

cd ~/anaconda3/envs/alphafold/lib/python3.8/site-packages/ && patch -p0 < $alphafold_path/docker/openmm.patch

Download all databases

Finally, running alphafold

Usage: run_alphafold.sh <OPTIONS>
Required Parameters:
-d <data_dir>     Path to directory of supporting data
-o <output_dir>   Path to a directory that will store the results.
-m <model_names>  Names of models to use (a comma separated list)
-f <fasta_path>   Path to a FASTA file containing one sequence
-t <max_template_date> Maximum template release date to consider (ISO-8601 format - i.e. YYYY-MM-DD). Important if folding historical test sets
Optional Parameters:
-b <benchmark>    Run multiple JAX model evaluations to obtain a timing that excludes the compilation time, which should be more indicative of the time required for inferencing many
    proteins (default: 'False')
-g <use_gpu>      Enable NVIDIA runtime to run with GPUs(default: 'True')
-a <gpu_devices>  Comma separated list of devices to pass to 'NVIDIA_VISIBLE_DEVICES' (default: 'all')
-p <preset>       Choose preset model configuration - no ensembling (full_dbs) or 8 model ensemblings (casp14) (default: 'full_dbs')
  • This script needs to be put into the top directory of the alphafold git repo that you have downloaded
# Directory structure
alphafold
├── alphafold
├── CONTRIBUTING.md
├── docker
├── example
├── imgs
├── LICENSE
├── README.md
├── requirements.txt
├── run_alphafold.py
├── run_alphafold.sh    <--- Copy the bash script and put it here
├── run_alphafold_test.py
├── scripts
└── setup.py
  • Put your query sequence (only one sequence per fasta file) in a fasta file <filename.fasta>. Query sequence was obtained from here
  • Run the script
# Example run
bash run_alphafold.sh -d ./alphafold_data/ -o ./dummy_test/ -m model_1 -f ./example/query.fasta -t 2020-05-14
  • The results folder dummy_test can be found in this git repo along with the query (example/query.fasta) used
  • For further information refer here.
  • The arguments to the script follows the original naming of the alphafold parameters, except for fasta_paths. This script can do only one fasta query at a time. So use a tmux session or so to do multiple runs.
  • Happy folding!

Disclaimer

  • We do not guarantee that this will work for everyone
  • The non-docker version was tested with the following system configuration
    • Dell server
      • CPU: AMD EPYC 7601 2.2 GHz
      • RAM: 1 TB
      • GPU: NVIDIA Tesla V100 16G
      • OS: CentOS 7 (kernel 3.10.0-1160.24.1.el7.x86_64)
      • Cuda: 11.3
      • NVIDIA driver version: 470.42.01