DAQ is a computational tool employing deep learning to estimate residue-wise local quality for protein models derived from cryo-EM maps (target resolution: 0-5 A). It generates a file in .pdb format documenting the scored structure, with scores stored in the B-factor column

Our result webpage comprises three tabs: Results Visualization, Output Logs, and Job Configuration.

The 'Result Visualization' panel showcases the protein structure scored by DAQ-Score, with the DAQ(AA) score stored in the b-factor column.
The 3D model is color-coded based on the DAQ(AA) score, ranging from red (-1.0) to blue (1.0). Blue represents a good score, while red signifies a lower score from DAQ.
On the right-hand side, you'll find the 'Download Outputs' button to acquire the modeled structure in .pdb format. You can utilize 'spectrum b, red_white_blue, all, -1,1' in PyMol to visualize the score.
Additionally, you can visualize the map online by clicking the "Show map" button. Once loaded, the default contour level matches your input; however, you can make adjustments by clicking the "..." button beside "isosurface." Within the "Type: Isosurface" option, you can modify the iso-surface value and opacity by scrolling through the bar for precise adjustments. This feature allows you to assess the alignment between the modeled structure and the map.

Output Logs:
The 'Output Logs' panel compiles all outputs generated by the scripts.
If you're interested in monitoring the job's progress during execution, this section provides a comprehensive overview.

Job Configuration:
In the 'Job Configuration' panel, you'll find the input parameters used for this specific job. These records serve to maintain a log of your submitted input for reference.

Problem Debugging:
For any troubleshooting needs: Should you encounter any issues, please don't hesitate to contact us via email to report the problems. When sending an email, kindly use the subject line format 'DAQ-score problem: [jobid]', where [jobid] corresponds to the job displayed in the title. This specific identification helps us efficiently locate and debug jobs in the backend, ensuring a prompt response to your concerns.

Contact:
dkihara@purdue.edu, gterashi@purdue.edu, xiaowang20140001@gmail.com.