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AlphaFold is a protein structure prediction system developed by DeepMind. It uses machine learning techniques to accurately predict protein structures, which is an important task in computational biology.

How to use

  1. Prepare Directories

    Create two new directory for AlphaFold, as well as input and output directories.

    mkdir alphafoldTest
    mkdir alphafold_output
    cd alphafoldTest
    mkdir input
    mkdir output

  2. Prepare Input Files

    You need to create a query.fasta file in the input directory and put your sequences in it. The content should be in FASTA format, as shown below:


  3. Submit the Job

    Below is the content of an example job submission script. You can use this as a template to create your own job submission script.
    #SBATCH -J Alphafold      # job name
    #SBATCH -o log_slurm.o%j  # output and error file name (%j expands to jobID)
    #SBATCH -N 1              # number of nodes you want to run on
    #SBATCH --gres=gpu:2
    #SBATCH -p gpu            # queue (partition) -- bsudfq, eduq, gpuq, shortq
    #SBATCH -t 12:00:00       # run time (hh:mm:ss) - 12.0 hours in this example.
    # Load the necessary modules
    module load alphafold
    # Set the environment variables
    export OUTPUT_DIR=/bsuscratch/${USER}/alphafold_output
    export DATA_DIR=/bsuscratch/alphafold_data
    # Execute the program: -d $DATA_DIR -o $OUTPUT_DIR -m model_1 -f ./input/query.fasta -t 2020-05-14

    Submit the job using:


  4. Check the Output

    After the job is finished, you can find the predicted protein structures in the OUTPUT_DIR directory.

Further Learning

  1. Official Documentation: The official AlphaFold Documentation is a comprehensive resource that covers all aspects of AlphaFold.
  2. GitHub Repository: The AlphaFold GitHub Repository provides the latest version of AlphaFold, including source code, release notes, and additional documentation.
  3. Research Papers: The original paper introducing AlphaFold, "Highly accurate protein structure prediction for the human proteome", was published in 2021 and provides detailed information about the techniques used in AlphaFold. Many other papers have since cited and expanded upon this work. These can be found in scientific databases like PubMed.
  4. Online Forums and Communities: Online resources such as Bioinformatics Stack Exchange and Biostars often have threads discussing issues, solutions, and best practices related to AlphaFold and other bioinformatics tools.