run test model on alphafold

[Maryamtarazkar]

Dear AlphaFold support team: I am trying to run monomer structures on alphafold, so I first initiated with test model on the github page: python3 docker/run_docker.py --fasta_paths=T1050.fasta --max_template_date=2020-05-14 --data_dir=../DOWNLOAD_DIR

But as soon as I run this command I get following lines:

""" docker/run_docker.py: --[no]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') --data_dir: Path to directory with supporting data: AlphaFold parameters and genetic and template databases. Set to the target of download_all_databases.sh. --db_preset: <full_dbs|reduced_dbs>: Choose preset MSA database configuration - smaller genetic database config (reduced_dbs) or full genetic database config (full_dbs) (default: 'full_dbs') --docker_image_name: Name of the AlphaFold Docker image. (default: 'alphafold') --docker_user: UID:GID with which to run the Docker container. The output directories will be owned by this user:group. By default, this is the current user. Valid options are: uid or uid:gid, non- numeric values are not recognised by Docker unless that user has been created within the container. (default: '1000:1000') --[no]enable_gpu_relax: Run relax on GPU if GPU is enabled. (default: 'true') --fasta_paths: Paths to FASTA files, each containing a prediction target that will be folded one after another. If a FASTA file contains multiple sequences, then it will be folded as a multimer. Paths should be separated by commas. All FASTA paths must have a unique basename as the basename is used to name the output directories for each prediction. (a comma separated list) --gpu_devices: Comma separated list of devices to pass to NVIDIA_VISIBLE_DEVICES. (default: 'all') --max_template_date: Maximum template release date to consider (ISO-8601 format: YYYY-MM-DD). Important if folding historical test sets. --model_preset: <monomer|monomer_casp14|monomer_ptm|multimer>: Choose preset model configuration - the monomer model, the monomer model with extra ensembling, monomer model with pTM head, or multimer model (default: 'monomer') --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') (an integer) --output_dir: Path to a directory that will store the results. (default: '/tmp/alphafold') --[no]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') --[no]use_gpu: Enable NVIDIA runtime to run with GPUs. (default: 'true') --[no]use_precomputed_msas: Whether to read MSAs that have been written to disk instead of running the MSA tools. The MSA files are looked up in the output directory, so it must stay the same between multiple runs that are to reuse the MSAs. WARNING: This will not check if the sequence, database or configuration have changed. (default: 'false')

The download directory ../ should not be a subdirectory in the AlphaFold repository directory. If it is, the Docker build is slow since the large databases are copied during the image creation. """

It seems to me that the command is not running at all. I appreciate if you help me what part has not done correctly?

[aputron]

Try running the command with the absolute path to your local protein database

[Augustin-Zidek]

The error message at the end explains what is going on:

The download directory ../ should not be a subdirectory in the AlphaFold repository directory. If it is, the Docker build is slow since the large databases are copied during the image creation.

I.e. the solution is to move DOWNLOAD_DIR to some directory that is not within the AlphaFold directory, for instance like this:

/home/
  alphafold/
    docker/
      ...
    ...
  data/  <- DOWNLOAD_DIR
    ...

And then run AlphaFold:

python3 docker/run_docker.py \
  --fasta_paths=T1050.fasta \
  --max_template_date=2020-05-14 \
  --data_dir=/home/data/