AR-Decon – Deconvolution to restore cryo-EM maps with anisotropic resolution.

AR-Decon uses deconvolution to compensate for missing information in Fourier space. Running AR-Decon on COSMIC2 will generate a deconvolved output map. If you want to sharpen further/modify the map, you will need to do this next.

An example of what AR-Decon does with a published map of kinesin-binding protein (EMDB 24677):

AR-Decon improved resolvability for this reconstruction. The original map had a sphericity of 0.85 from the 3DFSC server.  You can also see that DeepEMhancer does not work as well. Importantly, AR-Decon is more transparent regarding how it modifies the reconstruction compared to the generative AI tool DeepEMhancer.

This is the original figure from our publication showing the Euler angle distribution:

And this is the output FSC curves from AR-Decon and  3DFSC:

 

Inputs:

  • Input: Unsharped 3D reconstruction
    • Do NOT use post-processed or masked maps
  • Required: Half maps
  • Optional:
    • Mask – Apply the specified mask file during dFSC calculation and deconvolution.
    • Sigma – Sigma value for generating OTF (default: auto-calculated).
    • Smooth –  Smoothing parameter for deconvolution (default: 0.5).
    • Nonlinearity – Parameter for deconvolution (default: 10000).
    • Iterations – Number of deconvolution cycles (default: 50)

Outputs

  • The following files and extensions are output from AR-Decon:
    • _Decon.log – Log file
    • _Decon.mrc  – Deconvoluted 3D reconstruction (not sharpened)
    • _HalfMapdFSC1.png/svg/txt – Image files showing directional FSC curves
    • _HalfMapdFSC3d.mrc – 3D representation of Fourier space
    • _HalfMapdFSC3d.otf – optical transfer function
    • _HalfMapdFSCAvg.txt – average FSC value
    • _HalfMapFibonacciPoints.txt – sampling points for directional FSCs
    • _Masked.mrc – Masked input (if mask provided)

Reference

Deconvolution to restore cryo-EM maps with anisotropic resolution.
Junrui Li, Yifei Chen, Shawn Zheng, Angus McDonald, John W. Sedat, David A. Agard, Yifan Cheng
bioRxiv 2025.02.23.639707; doi: https://doi.org/10.1101/2025.02.23.639707