Automated Docking Script for AutoDock-GPU

This repository contains a Bash script (adt_gpu_automate.sh) for automating molecular docking simulations using AutoDock-GPU.

Prerequisites

Before using this script, ensure the following:

1. Operating System: The script is designed to run on Linux systems.
2. Software Requirements:
   • AutoDock-GPU: Ensure it is installed and added to your PATH.
   • MGLTools: Install to access the pythonsh and preparation scripts.
   • CUDA must be installed and configured and ensure the correct version of CUDA for your GPU driver.
3. Hardware Requirements:
   • Your system should have GPU cards to use this script effectively. Examples of compatible GPUs include:
     • NVIDIA GeForce RTX 30 Series (e.g., RTX 3080, RTX 3090)
     • NVIDIA Quadro RTX Series (e.g., Quadro RTX 6000)
     • NVIDIA Tesla GPUs (e.g., Tesla V100, Tesla T4)

Preparation Steps

Follow these steps to set up your environment and prepare for running the script:

1. Create a Working Directory:

   • Create a new directory to store all the required files and results.

2. Copy Required Files:

   • Place the protein.pdb file (target protein structure) in the directory.
   • Place the ligand PDB files in the directory. These files should:
     • Be in .pdb format.
     • Be energy minimized.

3. Copy Template Files:

   • Include a template .gpf (grid parameter file) in the same directory.
   • Ensure the correct values of grid center and grid points (npts) in the .gpf file. The grid center and the number of grid points define the docking region and should be adjusted based on the protein structure and ligand to ensure proper docking results.

4. Copy Python Scripts:

   • Ensure the directory contains the Python scripts used by the script:
     • prepare_receptor4.py
     • prepare_ligand4.py
     • prepare_gpf4.py

5. Run the Script:

   • Execute the script using the command:

     ./adt_gpu_automate.sh

6. Analyze Results:

   • After the script finishes, review the generated docking results and log files in the respective ligand directories.

Output

• Each ligand will have its own directory containing the processed files and docking results, including:
  • Prepared receptor (protein.pdbqt)
  • Prepared ligand (ligand_name.pdbqt)
  • Grid map files and logs (grid.gpf, grid.glg)
  • Docking log files (dock.log)

Notes

• Ensure all input files are correctly formatted and named as expected by the script.
• Modify the script to adjust parameters (e.g., grid size, number of runs) as needed for your specific docking experiments.
• AutoDock-GPU provides faster docking simulations compared to traditional AutoDock, leveraging GPU acceleration for improved performance.

Example Run

This repository includes example files for reference:

• protein.pdb (sample protein structure)
• Ligand PDB files (sample ligands in .pdb format)
• Template .gpf file
• Python preparation scripts

You can use these files to test the script and understand its workflow.

Citation

If you are using this script for your studies, kindly acknowledge the use of AutoDock-GPU as per its citation guidelines and mention my GitHub repository:

Santos-Martins, Diogo, et al. "Accelerating AutoDock4 with GPUs and gradient-based local search." Journal of chemical theory and computation 17.2 (2021): 1060-1073. https://pubs.acs.org/doi/10.1021/acs.jctc.0c01006

For the latest version of this script and updates, visit my GitHub repository: https://github.com/Gopinath-Murugan/AutoDock-GPU-Automation

Mentioning this repository in your publications or research would be greatly appreciated.

We appreciate your support!