SuperEM is a computational tool, which enables capturing protein structure information from cryo-EM maps more effectively than raw maps. It is based on 3D deep learning. It is aimed to help protein structure modeling from cryo-EM maps.


SuperEM uses a 3D deep neural network architecture, Generative Adversarial Networks (GAN), to modify an input cryo-EM map of 3 Å to 6 Å to enable more efficient extraction of protein structure information from the input map. In our benchmark, protein structure modeling tools were able to build more accurate protein structure models by using output from SuperEM. SuperEM takes local regions (patches) of experimental EM map and processed patches are then combined together to output a whole EM map.

GAN architecture of SuperEM

Figure 1. The GAN architecture of SuperEM. The detailed architecture of the generator and discriminator networks are shown. The blue arrows that connect the input of a ResNet block and a plus sign, which is the operator that simply add two matrices, is a skip connection.



The architecture of SuperEM is summarized in the flowchart on the right.

This document will provide a detailed explanation of each step of the SuperEM architecture and programs needed to run those steps. It concludes by giving a step-by-step application walk-through on an example EM map.

Flow Chart of SuperEM

Input and pre-processing of your EM map

The input file for SuperEM is generated in two-steps. In the first step, a program named HLmapData_new takes an EM map and relevant details such as the map's author recommended contour level and pixels/Angstrom values as input and generates an intermediate readable text file called [your_map_id]_trimmap. A trimmap contains normalized electron density values of voxels. In the second step, a program named dataset uses this trimmap to generate an input file called [your_map_id]_dataset, which contains rows of density values that we get by scanning the input map in all the 3 directions using a 25*25*25 cube.

Input cryo-EM Map (EMD-2788)

SuperEM super-resolution map generation

The test dataset generated in the previous step is fed to the SuperEM architecture that contains a Generative Adversarial Network (GAN). This step outputs 25*25*25 Å^3 super-resolution cube for every corresponding input cube of similar dimensions.

Super-Resolution (SR) map produced by SuperEM

Usage guide

For detailed step by step usage guide, please visit here.


Experimental map example (EMD-2788)

You can download the EM map for protein structure with EMID 2788 here. Use this map file and follow the instructions in step 1 of usage guide to generate input dataset file.The trimmap file is generated as

data_prep/HLmapData_new 2788.mrc -c 0.16 > 2788_trimmap

The author recommended contour level for the map EMD-2788 is 0.16 which has been provided as one of the options above.
You can generate the input dataset file as follows,

python data_prep/ 2788_trimmap 2788_dataset 2788

If the generated input file is 2788_dataset, write the file location to a dataset location file as follows

echo ./2788_dataset > test_dataset_location

Density Map, 2788.mrc

SuperEM super-resolution map generation

You can then run the SuperEM program to generate super-resolution EM map as follows

python --input=test_dataset_location --G_res_blocks=15
--D_res_blocks=3 --G_path=model/Generator --D_path=model/Generator

An example of generated super-resolution map of EMD-2788 is shown on the right.
Super-Resolution (SR) Map, 2788_SR.mrc


You can refer to github for all the necessary instructions to download and install SuperEM github link


Copyright © 2020 Sai Raghavendra Maddhuri Venkata Subramaniya, Genki Terashi, Daisuke Kihara, and Purdue University.

SuperEM is a free software for academic and non-commercial users.
It is released under the terms of the GNU General Public License Ver.3 (
Commercial users please contact for alternate licensing.


Citation of the following reference should be included in any publication that uses data or results generated by SuperEM program.

Sai Raghavendra Maddhuri Venkata Subramaniya, Genki Terashi & Daisuke Kihara. Super Resolution Cryo-EM Maps With 3D Deep Generative Networks.
In submission, BioRxiv, (2021).