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Welcome to
Huang Lab

About EMRNA

Deep learning based automated RNA modeling from cryo-EM maps

Copyright © 2022 Tao Li, Jiahua He, Sheng-You Huang and Huazhong University of Science and Technology
Released under GNU General Public License Version 3

EMRNA is freely available for academic or commercial users. If you have any questions regarding EMRNA, please don't hesitate to contact us via huangsy@hust.edu.cn

Reference:
Li T, He J, Cao H, Zhang Y, Chen J, Xiao Y, Huang SY. All-atom RNA structure determination from cryo-EM maps. Nat Biotechnol. 2024 Feb 23. doi: 10.1038/s41587-024-02149-8.

Release Notes:

2024-06-03: We have noticed EMRNA may be run failed on Ubuntu system due to the use of non-POSIX shell syntax. So we have updated EMRNA to v1.5 that should work on Ubuntu (and other Linux systems) now.

2024-02-29: We have updated EMRNA to v1.4 in which gcc-6.2.0 is no longer required as we have compiled the program statically. EMRNA_v1.4 should work on CentOS 7.x or similar systems now.
In addition, per users' requests, we have also added 10 initial models named "output_*.pdb" that contains only those parts of structures with good confidences in the output directory.

2024-02-23: EMRNA v1.3 was released.

Download EMRNA

The download link below contains the trained EMRNA models and Python script for applying EMRNA.

Click here to download EMRNA (v1.5)^new

Click here to download EMRNA (v1.4)

List part of files
     EMRNA.sh: The main program of EMRNA
     preds.py: Python script for deep learning prediction
     frn.py: Pytorch implementation of Filter Response Normalization Layer used in EMRNA
     interp3d.f90: Fortran source code for the interpolation of EM grid
     scunet.py: Pytorch implementation of 3D Swin-Conv-UNet used in EMRNA
     utils.py: Python utilities used in EMRNA
     models/: The trained EMRNA models
     environment.yml: Required packages for Python virtual environment of EMRNA
     lib/: Library for full-atom construction and helix rebuilding
     bin/: Binary executables

Install EMRNA

Software requirements

System

CentOS Linux 7.x (or other unix-based systems)

External programs

EternaFold (1.3.1) (https://github.com/eternagame/EternaFold)
LKH-3 (3.0.6) (http://webhotel4.ruc.dk/~keld/research/LKH-3)
These programs can be easily installed according to the documentation on the websites. We already included program "CSSR" under GNU General Public License V3.

Quick installation of python and required online packages

$ conda env create -f environment.yml

This command will create a Python conda virtual environment named "emrna" and automatically install all the required packages. However, if you encounter 'No module named einops/timm/or other packages' when running the EMRNA.sh, please download the needed packages manually by command $ pip install missing-packages in your EMRNA environment.

Details of required online packages

Python (3.8.8) (https://www.python.org)
     Python package requirements:
         sklearn (https://scikit-learn.org/stable/install.html)
         pytorch (1.9.0+cuda11.1) (https://pytorch.org)
         torchvision (0.10.0+cuda11.1) (https://pytorch.org)
         cudatoolkit (11.1) (https://developer.nvidia.com/cuda-toolkit)
         numpy (1.20.1) (https://www.numpy.org)
         einops (0.3.2) (https://einops.rocks/)
         mrcfile (1.3.0) (https://github.com/ccpem/mrcfile)
         timm(0.4.12) (https://github.com/rwightman/pytorch-image-models)
         tqdm (4.60.0) (https://github.com/tqdm/tqdm)

NOTE: In order to run Python scripts and EMRNA properly, users should properly set the variables in EMRNA.sh :

1. Set "activate" to path of conda activator, for example

activate="/home/taoli/anaconda3/bin/activate"

2. Set "EMRNA_env" to name of the python conda virtual environment that have all the required packages installed. An conda environment named "emrna" will be created using the quick installation command, so EMRNA_env="emrna". If the environment is built with a different name, users should modify "EMRNA_env" accordingly

3. Set "LKH_dir" to the path of LKH-3, for example

LKH_dir="/home/taoli/LKH-3.0.6"

4. Set "EMRNA_home" to the path of EMRNA, for example

EMRNA_home="/home/taoli/EMRNA_v1.5/"

Please do not use path like "~/...", the Tilde "~" in the path may not be properly recongnized in EMRNA.sh

((((((..((((.........)))).(((((.......))))).....(((((.......))))))))))).....

The only line of "input_ss.txt" is the predicted secondary structure (in dot-bracket representation) of the input sequence. By default, EMRNA reads it as the input secondary structure.

Step 2. Run EMRNA with input sequence and predicted secondary structure Please reduce the BATCH_SIZE if CUDA runs out of memory.

Examples

Finally, use phenix to conduct model refinement by running the following command

$ phenix.real_space_refine 7p7s_D.mrc output/ranked_0.pdb resolution=3.0

This command will generate an output model named "ranked_0_real_space_refined_000.pdb".
Note that the final refinement can be done by other software and is optional for users.

Input density map (named 7p7s_D.mrc)

Output model (top-1 ranked, unrefined, named 'ranked_0.pdb' in output/)

Try command

$ /path/to/EMRNA_v1.5/EMRNA.sh emd_20755.map input_seq.fasta input_ss.txt output -g 0 -b 40

Input density map (named emd_20755.map, shown at contour level of 3.0)

Output model (top-1 ranked, unrefined, named 'ranked_0.pdb' in output/)