wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && bash Miniconda3-latest-Linux-x86_64.shconda create --name alphafold python==3.8
conda update -n base condaconda activate alphafold- Change
cudatoolkit==11.2.2version if it is not supported in your system
conda install -y -c conda-forge openmm==7.5.1 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/v2.3.1.tar.gz && tar -xzf v2.3.1.tar.gz && export alphafold_path="$(pwd)/alphafold-2.3.1"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/python3.8/site-packages/ && patch -p0 < $alphafold_path/docker/openmm.patch
# or
cd ~/miniconda3/envs/alphafold/lib/python3.8/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)
-r <run_relax> Whether to run the final relaxation step on the predicted models. Turning relax off might result in predictions with distracting stereochemical violations but might help in case you are having issues with the relaxation stage (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 2021-11-01 -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 2021-11-01 -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
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