wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && bash Miniconda3-latest-Linux-x86_64.shconda create --name alphafold python==3.9
conda update -n base condaconda activate alphafold- Change
cudatoolkit==11.2.2version if it is not supported in your system - Use the latest version of OpenMM to be compatible with v2.3.2
conda install -y -c conda-forge openmm cudatoolkit==11.2.2 pdbfixer
conda install -y -c bioconda hmmer hhsuite==3.3.0 kalign2- Change
jaxlib==0.3.25+cuda11.cudnn805version if this is not supported in your system
pip install absl-py==1.0.0 biopython==1.79 chex==0.0.7 dm-haiku==0.0.9 dm-tree==0.1.6 immutabledict==2.0.0 jax==0.3.25 ml-collections==0.1.0 numpy==1.21.6 pandas==1.3.4 protobuf==3.20.1 scipy==1.7.0 tensorflow-cpu==2.9.0
pip install --upgrade --no-cache-dir jax==0.3.25 jaxlib==0.3.25+cuda11.cudnn805 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.htmlwget https://github.com/deepmind/alphafold/archive/refs/tags/v2.3.2.tar.gz && tar -xzf v2.3.2.tar.gz && export alphafold_path="$(pwd)/alphafold-2.3.2"wget -q -P $alphafold_path/alphafold/common/ https://git.scicore.unibas.ch/schwede/openstructure/-/raw/7102c63615b64735c4941278d92b554ec94415f8/modules/mol/alg/src/stereo_chemical_props.txt# $alphafold_path variable is set to the alphafold git repo directory (absolute path)
cd ~/anaconda3/envs/alphafold/lib/python3.9/site-packages/ && patch -p0 < $alphafold_path/docker/openmm.patch
# or
cd ~/miniconda3/envs/alphafold/lib/python3.9/site-packages/ && patch -p0 < $alphafold_path/docker/openmm.patch- Option 1: Use our download_db.sh script which uses wget, rsync, gunzip and tar instead of aria2c
- Our script maintains the AF2 download directory structure
- Option 2: Follow https://github.com/deepmind/alphafold#genetic-databases
# To use our download_db script (download the script first)
Usage: download_db.sh <OPTIONS>
Required Parameters:
-d <download_dir> Absolute path to the AF2 download directory (example: /home/johndoe/alphafold_data)
Optional Parameters:
-m <download_mode> full_dbs or reduced_dbs mode [default: full_dbs]
# To download all data (full_dbs mode)
# The script will create the folder </home/johndoe/alphafold_data> if it does not exist
bash download_db.sh -d </home/johndoe/alphafold_data>
# To download reduced version of the databases (reduced_dbs mode)
# The script will create the folder </home/johndoe/alphafold_data> if it does not exist
bash download_db.sh -d </home/johndoe/alphafold_data> -m reduced_dbs- Use this bash script
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.
-f <fasta_paths> Path to FASTA files containing sequences. If a FASTA file contains multiple sequences, then it will be folded as a multimer. To fold more sequences one after another, write the files separated by a comma
-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:
-g <use_gpu> Enable NVIDIA runtime to run with GPUs (default: true)
-e <enable_gpu_relax> Run relax on GPU if GPU is enabled (default: true)
-n <openmm_threads> OpenMM threads (default: all available cores)
-a <gpu_devices> Comma separated list of devices to pass to 'CUDA_VISIBLE_DEVICES' (default: 0)
-m <model_preset> Choose preset model configuration - the monomer model, the monomer model with extra ensembling, monomer model with pTM head, or multimer model (default: 'monomer')
-c <db_preset> Choose preset MSA database configuration - smaller genetic database config (reduced_dbs) or full genetic database config (full_dbs) (default: 'full_dbs')
-p <use_precomputed_msas> Whether to read MSAs that have been written to disk. WARNING: This will not check if the sequence, database or configuration have changed (default: 'false')
-l <num_multimer_predictions_per_model> How many predictions (each with a different random seed) will be generated per model. E.g. if this is 2 and there are 5 models then there will be 10 predictions per input. Note: this FLAG only applies if model_preset=multimer (default: 5)
-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')
- 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 in a fasta file <filename.fasta>.
- In the below example query sequence was obtained from here
-
Running the script
# Example run (Uses the GPU with index id 0 as default)
bash run_alphafold.sh -d ./alphafold_data/ -o ./dummy_test/ -f ./example/query.fasta -t 2020-05-14
# OR for CPU only run
bash run_alphafold.sh -d ./alphafold_data/ -o ./dummy_test/ -f ./example/query.fasta -t 2020-05-14 -g False
- The results folder
dummy_testcan be found in this git repo along with the query (example/query.fasta) used - 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 terminal multiplexer (example: tmux/screen) to do multiple runs. - One can also control the number of cores used by OpenMM using the
-nargument (dafult: uses all available cores) - For further information refer here
-
All steps are the same as when running the monomer system, but you will have to
- provide an input fasta with multiple sequences,
- set -m multimer option when running run_alphafold.sh script,
# Example run (Uses the GPU with index id 0 as default) bash run_alphafold.sh -d alphafold_data/ -o dummy_test/ -f multimer_query.fasta -t 2021-11-01 -m multimer
Examples (Modified from AF2)
Below are examples on how to use AlphaFold in different scenarios.
Say we have a monomer with the sequence <SEQUENCE>. The input fasta should be:
>sequence_name
<SEQUENCE>
Then run the following command:
bash run_alphafold.sh -d alphafold_data/ -o dummy_test/ -f monomer.fasta -t 2021-11-01 -m monomerSay we have a homomer from a prokaryote with 3 copies of the same sequence
<SEQUENCE>. The input fasta should be:
>sequence_1
<SEQUENCE>
>sequence_2
<SEQUENCE>
>sequence_3
<SEQUENCE>
Then run the following command:
bash run_alphafold.sh -d alphafold_data/ -o dummy_test/ -f homomer.fasta -t 2023-11-01 -m multimerSay we have a heteromer A2B3 of unknown origin, i.e. with 2 copies of
<SEQUENCE A> and 3 copies of <SEQUENCE B>. The input fasta should be:
>sequence_1
<SEQUENCE A>
>sequence_2
<SEQUENCE A>
>sequence_3
<SEQUENCE B>
>sequence_4
<SEQUENCE B>
>sequence_5
<SEQUENCE B>
Then run the following command:
bash run_alphafold.sh -d alphafold_data/ -o dummy_test/ -f heteromer.fasta -t 2023-11-01 -m multimer- AF2 parameters link and database download links have been updated.
- Updated package requirements
- The is_prokaryote option -l is removed.
- New option -l is now used for setting the number of multimer predictions per model
- Options for relaxation -r and to enable GPU relaxation -e are added
- AF2 parameters link has been updated in the download_db.sh script (users should download this new parameters when using AF2 v2.2.0)
- The preset flag -p was split into -c (db_preset) and -m (model_preset) in our run_alphafold.sh
- Four model presets (for option -m) are now supported
- monomer
- monomer_casp14
- monomer_ptm
- multimer
- Two db preset configurations (for option -c) are supported
- full_dbs
- reduced_dbs
- Four model presets (for option -m) are now supported
- The model names to use are not specified using -m option anymore. If you want to customize model names you will have to modify the appropriate MODEL_PRESETS dictionary in alphafold/model/config.py
- 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
- Storage
- Downloaded database size: 2.2 TB (uncompressed)
- Dell server