DAQ-Refine

DAQ-refine: Refinement of Protein Structures Utilizing DAQ-score and ColabFold

DAQ-Refine utilizes DAQ-score and ColabFold to refine protein structures from cryo-EM maps, aiming to improve prediction accuracy and reliability through detailed residue-wise quality assessment.

Table of Contents

• Getting Started with DAQ-Refine
• DAQ-Refine Comprehensive Guide
  • Source Code Instructions
  • Colab Instructions
• References

Getting Started with DAQ-Refine

DAQ-Refine is an advanced protocol designed to evaluate and refine protein models derived from cryo-electron microscopy (cryo-EM) maps. Utilizing a modified AlphaFold2 approach, DAQ-Refine identifies and corrects potential inaccuracies in specified regions of protein models.

Available Usage Options

Depending on your specific needs and familiarity with DAQ-Refine, we offer three distinct approaches to access and utilize this powerful tool:

1. EM Server:

Ideal for those new to DAQ-Refine or seeking a straightforward method to obtain results quickly. Visit our EM Server website for easy access and guidance on submitting your protein models for evaluation and refinement.

2. Source Code:

For users with unique requirements or those interested in developing a customized version of DAQ-Refine, we provide direct access to our source code. You can download and install it from our GitHub repository. This option allows for extensive customization and integration into existing workflows.

3. Google Colab:

We also offer a Google Colab notebook for users who prefer an interactive, web-based platform. Access the DAQ-Refine notebook here, upload your protein model, and execute the notebook cells to start the refinement process and receive your results promptly.

DAQ-Refine Comprehensive Guide

Source Code Instructions

Local Environment Setup with Conda

This section guides you through creating a Conda environment for DAQ-Refine, including the installation of Python and other dependencies. Ensure DAQ-score is installed locally under the same path with DAQ-Refine for optimal performance.

Step 1: Create a Conda Environment

Create and activate a new Conda environment:

conda create -n daq_refine python=3.9
conda activate daq_refine

Step 2: Install Python Dependencies

Switch to the DAQ-Refine local branch and install the required Python packages in your Conda environment:

cd /your/path/to/DAQ-Refine
git checkout local
chmode +x install_dependency.sh
./install_dependency.sh

Note:

• Adjust the package versions according to your project's requirements.
• This script assumes you're in the DAQ-Refine directory to start with.
• Adjust paths and commands as necessary, especially if paths differ on your system or if additional configuration is needed.
• You may need to install additional dependencies like pytorch, CUDA, gcc, make, bison, flex, Julia and csh using your system's package manager (e.g., apt for Ubuntu/Debian or yum for CentOS/RedHat).
• TensorFlow and CUDA configurations may present compatibility issues within your local environment. For comprehensive guidance and resolution strategies, we recommend consulting the official documentation available on their respective websites.

Step 3: install Rosetta Relaxation

We will use Rosetta Relaxation in the DAQ-Refine final part, so please refer to the Rosetta for furture installation.

Usage

1. Command parameters

usage: python3 main.py [-h] [--log_folder_path=LOG_FOLDER_PATH] [--ip_folder_path=IP_FOLDER_PATH] [--op_folder_path=OP_FOLDER_PATH] [--root_run_dir=ROOT_RUN_DIR] [--resolution=RESOLUTION] [--job_id=JOB_ID] [--input_map=INPUT_MAP_PATH] [--pdb_file_path=PDB_FILE_PATH] [--pdb_name=PDB_NAME] [--fasta_file_path=FASTA_FILE_PATH] [--align_strategy=ALIGN_STRATEGY("Manual alignment" or "Smith Waterman")] [--rosetta_pth=ROSETTA_PATH]

required arguments:
  -h, --help               show this help message and exit
  --log_folder_path        log files folder path
  --ip_folder_path         folder path for sanitizing all input files
  --op_folder_path         folder path for output files
  --root_run_dir           root dir for both DAQ git repo and DAQ-Refine git repo
  --input_map              input map path, use the .mrc format, default: input.mrc
  --pdb_file_path          input PDB file path, default: input.pdb
  --fasta_file_path        input fasta file path, default: input.fasta
  --rosetta_path           path for the Rosetta script(/your/path/to/rosetta_bin_system_version_bundle/main)

Colab Instructions

This notebook integrates a modified ColabFold notebook with our DAQ-Refine tools. To pinpoint low-quality regions in the protein structure, please utilize our DAQ-score Colab notebook.

To commence with DAQ-Refine in Colab:

• Click here to open the Colab notebook.
• Follow the instructions within the notebook to execute DAQ-Refine.

References:

• [1] Terashi, G., Wang, X., Maddhuri Venkata Subramaniya, S. R., Tesmer, J. J., & Kihara, D. (2022). Residue-wise local quality estimation for protein models from cryo-EM maps. Nature Methods, 19(9), 1116-1125. https://doi.org/10.1038/s41592-022-01574-4

• [2] Terashi, G., Wang, X., & Kihara, D. (2023). Protein model refinement for cryo-EM maps using AlphaFold2 and the DAQ score. Acta Crystallographica Section D: Structural Biology, 79(1), 10-21. https://doi.org/10.1107/S2059798322011676