DAQ-refine is a protocol using DAQ score to evaluate protein models from cryo-EM maps (up to 5 A resolution) and employs a modified AlphaFold2 to refine regions with potential errors.
* Drag to the right: Refined model.
* Drag to the left: Original model.
- If the image does not fit in the box, please reload the page.
3D model produced after refining regions with potential errors. The model is colored by DAQ(AA) score scaled from red (-1.0) to blue (1.0) with a 19 residues sliding window.
Collect 3D cryo-EM map from microscope in .mrc and .map format.
Example map
You can also find many maps in EMDataResource as testing
examples.
Upload your structure model in pdb format
Example pdb
Once you collected the input files, please submit your job here. For each input field, please input the files/info collected before.
Once you finished input, simply click the upload button to submit jobs. After submission, you will be redirected to the “view job“ page. If you are not registered, please bookmark the link. Once the job is done, you can view jobs from this link. If you are registered, you will receive email notifications once job is done and you can also check job status from my jobs list under job manager.
Once job is done, you can check the modeled structure from the link bookmarked before. Here you can also download the modeled structure in .pdb format by clicking the “Download Output” button. You can also visualize the 3D cryo-EM map online to check its consistency with the modeled structure. For more detailed instructions, please see the “Instructions” in the same page.
If you noticed any strange outputs or job failure on your side, please submit a backend review by using the field in the bottom of the “View Job” page. We will get back to you as soon as possible.
This github contains a modified ColabFold notebook and our tools.
Step-by-step instructions are available.
Genki Terashi, Xiao Wang, Daisuke Kihara. Protein model refinement for cryo-EM maps using AlphaFold2 and the DAQ score. Acta Crystallographica Section D Structural Biology, 79, 10–21, (2023). https://doi.org/10.1107/s2059798322011676