How to run AlphaFold locally?

Prepare Your System

• Ensure your system has a compatible version of Linux, with Ubuntu being highly recommended for seamless installation.

 

• Install necessary dependencies like Docker, NVIDIA container toolkit, and CUDA drivers, since AlphaFold requires GPU support for optimal performance.

 

• Verify sufficient system resources: ideally, more than 16 GB RAM and a modern NVIDIA GPU with at least 8 GB of VRAM.

Install Docker and NVIDIA Container Toolkit

• Download and install Docker from their official website or use your Linux distribution's package manager.

 

• Install the NVIDIA container toolkit. This can typically be done via package managers, ensuring compatibility with your system's CUDA drivers.

 

• Restart your system to ensure all components are properly initialized.

Download AlphaFold Source Code

• Clone the AlphaFold GitHub repository to your local machine using a terminal with the command: `git clone https://github.com/deepmind/alphafold.git`.

 

• Navigate into the cloned repository folder using `cd alphafold`.

 

• Review the documentation provided in the repository for any updates or changes in installation steps.

Configure AlphaFold Database

• Download the necessary protein sequence and structure databases. You'll find links and scripts to automate this process in the AlphaFold GitHub repository.

 

• Ensure you have enough disk space as these databases require several terabytes of storage.

 

• Configure the downloaded database paths in the script files as described in the repository's installation guide.

Build and Run AlphaFold Docker Image

• Set up the Docker environment by building the AlphaFold Docker image using the provided Dockerfile with the command: `docker build -f docker/Dockerfile -t alphafold .`.

 

• Run the Docker container with the required runtime arguments that specify the paths to the input data, model parameters, and output directories.

 

• Leverage flags for GPU use, ensuring that the container has adequate permissions to access the GPU hardware.

Launch AlphaFold Job

• Execute AlphaFold via a terminal within the Docker container, specifying the input protein sequence file, and output path.

 

• Monitor the job progress either through terminal logs or configured logging outputs, noting error messages for troubleshooting.

 

• Post-run, check the output directory for prediction result files containing protein structure models.

Analyze Results

• Visualize predicted protein structures using molecular visualization tools like PyMOL or Chimera for further analysis.

 

• Cross-reference these models with existing structures in databases to validate and refine your predictions.

 

• Take notes on result accuracy, identifying any anomalies or unexpected results for future optimizations.