Alphafold
Alphafold
AlphaFold is an artificial intelligence (AI) program developed by Alphabets's/Google's DeepMind which performs predictions of protein structure.
How to use
use run_alphafold.sh script located at /home/alphafold_folder/alphafold_multimer_non_docker (in compute-0-300)
script reference:
Usage: run_alphafold.sh <OPTIONS> Required Parameters: -d <data_dir> Path to directory with supporting data: AlphaFold parameters and genetic and template databases. Set to the target of download_all_databases.sh. -o <output_dir> Path to a directory that will store the results. -f <fasta_path> Path to a FASTA file containing a single sequence. -t <max_template_date> Maximum template release date to consider (ISO-8601 format: YYYY-MM-DD). Important if folding historical test sets. Optional Parameters: -n <openmm_threads> OpenMM threads (default: all available cores) -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 'CUDA_VISIBLE_DEVICES' (default: 0) -m <model_preset> Choose preset model configuration - the monomer model (monomer), the monomer model with extra ensembling (monomer_casp14), monomer model with pTM head (monomer_ptm), or multimer model (multimer) (default: monomer) -p <db_preset> Choose preset MSA database configuration - smaller genetic database config (reduced_dbs) or full genetic database config (full_dbs) (default: full_dbs) -u <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) -r <remove_msas_after_use> Whether, after structure prediction(s), to delete MSAs that have been written to disk to significantly free up storage space. (default: false) -i <is_prokaryote> Optional for multimer system, not used by the single chain system. This should contain a boolean specifying true where the target complex is from a prokaryote, and false where it is not, or where the origin is unknown. These values determine the pairing method for the MSA (default: false)
Databases
We downloaded the databases to /home/alphafold_folder/alphafold_data on compute-0-300 you may use it, or copy it to your own storage and point to it with -d flag of the run script. also, you may download the databases to your own storage via the script download_all_data.sh located at /home/alphafold_folder/alphafold_multimer_non_docker/scripts/
Sample qsub script
Note:
create folder for output in your home dir mkdir ~/alphafold_output then run the script
- you may download dummy_test folder from this github as well for the output
https://github.com/kalininalab/alphafold_non_docker
- /home/alphafold_folder/alphafold_multimer_non_docker/example/query.fasta = this is sample data, please point to the data you need to query.
- The lines export CUDA_VISIBLE_DEVICES=$(python3 /powerapps/scripts/check_avail_gpu.py) and the flag -a $CUDA_VISIBLE_DEVICES make it so you can use the next free GPU on the server, please leave it as is.
#!/bin/bash #PBS -l select=1:ncpus=4:ngpus=1 #PBS -q gpu # Description: AlphaFold-Multimer (Non-Docker) with auto-gpu selection # Original Author: Lev Arie Krapivner # load miniconda module load miniconda/miniconda3-4.7.12-environmentally # activate relevant venv conda activate /powerapps/share/centos7/miniconda/miniconda3-4.7.12-environmentally/envs/alphafold_non_docker # run alphafold cd /home/alphafold_folder/alphafold_multimer_non_docker/ # call to check_available_gpu python script # returns the param for CUDA_VISIBLE_DEVICE which the run alphafold script uses export CUDA_VISIBLE_DEVICES=$(python3 /powerapps/scripts/check_avail_gpu.py) # echo "CUDA_VISIBLE_DEVICES: $CUDA_VISIBLE_DEVICES" bash run_alphafold.sh -d /home/alphafold_folder/alphafold_data -o ~/output_dir -f /home/alphafold_folder/alphafold_multimer_non_docker/example/query.fasta -t 2020-05-14 -a $CUDA_VISIBLE_DEVICES
Sample qsub script 2
Note:
create folder for output in your home dir mkdir ~/alphafold_output then run the script
#!/bin/bash #PBS -l select=1:ncpus=4:ngpus=1 #PBS -q gpu # Description: AlphaFold-Multimer (Non-Docker) with auto-gpu selection # Original Author: Lev Arie Krapivner # load conda env module load alphafold/alphafold_non_docker_2.2.0 cd /home/alphafold_folder/alphafold_multimer_non_docker/ # call to check_available_gpu python script # returns the param for CUDA_VISIBLE_DEVICE which the run alphafold script uses export CUDA_VISIBLE_DEVICES=$(python3 /powerapps/scripts/check_avail_gpu.py) # echo "CUDA_VISIBLE_DEVICES: $CUDA_VISIBLE_DEVICES" run_alphafold.sh -d PATH_TO_DATABNASE_FOLDER -o ~/output_dir -f PATH_TO_FASTA_FILE -t 2020-05-14 -a $CUDA_VISIBLE_DEVICES