af3-on-hpc

AlphaFold3 on HPC

Colby T. Ford, Ph.D.

Steps for running AlphaFold3 in a container on HPC cluster environments.

Blog Post: https://blog.colbyford.com/running-alphafold3-at-scale-on-high-performance-computing-clusters-82764a6b809e

Setup

Create an alphafold3_resources folder on the cluster with the following subfolders: code, databases, image weights.

mkdir code databases image weights

Placeholder folders

Filling in the Folders

  1. For the code/ folder, clone the AlphaFold3 repository. This will give you the scripts to actually run the AlphaFold3 process.
git clone https://github.com/google-deepmind/alphafold3.git ./code/alphafold3
  1. In the databases/ folder, run the fetch_databases.py script that was cloned from the GitHub repository.
python ./code/alphafold3/fetch_databases.py --download_destination ./databases
  1. In the image/ folder, you will need to generate a Singularity/Apptainer image from the prebuilt Docker image hosted on DockerHub.
module load singularity

singularity pull ./image/alphafold3.sif docker://cford38/alphafold3
  1. In the weights/ folder, you’ll need to request the model parameters using this form. Once they send you the link to the weights, you can download them using the following command. Not that there are restrictions as to how the model can be used.
wget -P ./weights/ https://storage.googleapis.com/alphafold3/models/64/<your_key>/af3.bin.zst

Running AlphaFold3

  1. Next, create a script to run your folding process in SLURM. Using environment variables, you will point to the various resources (the database, weights, etc.) that you previously set up. An example submit.sh script is below (but you’ll need to modify the header and paths for your HPC system).
#!/bin/bash
#SBATCH --job-name="af3_test"
#SBATCH --partition=GPU
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=32
#SBATCH --mem=256GB
#SBATCH --gres=gpu:L40S:2
#SBATCH --time=1:00:00

module load singularity

export AF3_RESOURCES_DIR=/users/cford/alphafold3_resources

export AF3_IMAGE=${AF3_RESOURCES_DIR}/image/alphafold3.sif
export AF3_CODE_DIR=${AF3_RESOURCES_DIR}/code
export AF3_INPUT_DIR=${AF3_RESOURCES_DIR}/examples/fold_protein_2PV7
export AF3_OUTPUT_DIR=${AF3_RESOURCES_DIR}/examples/fold_protein_2PV7/output
export AF3_MODEL_PARAMETERS_DIR=${AF3_RESOURCES_DIR}/weights
export AF3_DATABASES_DIR=${AF3_RESOURCES_DIR}/databases

singularity exec \
     --nv \
     --bind $AF3_INPUT_DIR:/root/af_input \
     --bind $AF3_OUTPUT_DIR:/root/af_output \
     --bind $AF3_MODEL_PARAMETERS_DIR:/root/models \
     --bind $AF3_DATABASES_DIR:/root/public_databases \
     $AF3_IMAGE \
     python ${AF3_CODE_DIR}/alphafold3/run_alphafold.py \
     --json_path=/root/af_input/alphafold_input.json \
     --model_dir=/root/models \
     --db_dir=/root/public_databases \
     --output_dir=/root/af_output
  1. Lastly, you can submit the job using the following command.
sbatch submit.sh

AlphaFold3 JSON Files

AlphaFold3 runs using JSON files that are a specific format. An example alphafold_input.json file is shown below.

{
  "name": "5tgy",
  "modelSeeds": [
    1234
  ],
  "sequences": [
    {
      "protein": {
        "id": "A",
        "sequence": "SEFEKLRQTGDELVQAFQRLREIFDKGDDDSLEQVLEEIEELIQKHRQLFDNRQEAADTEAAKQGDQWVQLFQRFREAIDKGDKDSLEQLLEELEQALQKIRELAEKKN"
      }
    },
    {
      "ligand": {
        "id": "B",
        "ccdCodes": [
          "7BU"
        ]
      }
    }
  ],
  "dialect": "alphafold3",
  "version": 1
}

Other example files are in the examples/ folder in this repository.

Resources

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