How to batch-process sequences in AlphaFold?

Setup Your Environment

• Ensure that you have a working installation of AlphaFold. This includes the necessary software dependencies and data downloads as outlined in the official AlphaFold repository.

 

• Create a dedicated directory to store your input protein sequences and corresponding output data. This organization helps manage large batch processing efficiently.

Prepare Input Sequences

• Gather your protein sequences in FASTA format. You can store multiple sequences in a single FASTA file, or have separate files for each sequence.

 

• Ensure the headers of your FASTA files are unique to avoid confusion during batch processing. A descriptive header is helpful for tracking proteins in the output.

Configure the Batch Processing Script

• Use the command-line interface (CLI) or write a custom script to automate your batch processing. If writing a script, ensure it loops through a list of input sequences.

 

• Define the output directory structure within your script, making sure it saves results in an organized manner for each input sequence.

Run AlphaFold on Batch Sequences

• Execute your batch processing script. The script should call AlphaFold for each protein sequence, passing the necessary arguments such as model configuration and data files location.

 

• Monitor the processing to handle any errors or issues that may arise, modifying the job scripts as needed for smoother execution.

Post-process Results

• Once all sequences have been processed, organize the output data. This may include renaming output files or aggregating results into a summary report or database.

 

• Inspect the generated structural models and associated prediction metrics, using visualization tools if needed for deeper analysis.

Optimize Batch Workflow

• Review the performance of the batch processing to identify bottlenecks. Adjust compute resources or refine the input sequence batches for efficiency.

 

• Consider setting up automated notifications or logging for completed jobs or errors, maintaining productivity and workflow management.