Paper citation with more information: Si, D., Moritz, S.A., Pfab, J. et al. Deep Learning to Predict Protein Backbone Structure from High-Resolution Cryo-EM Density Maps. Sci Rep 10, 4282 (2020). https://www.nature.com/articles/s41598-020-60598-y.
Deep Learning for Cα Backbone Prediction from High Resolution CryoEM Data
In order to run the backbone prediction we need to install all required Python packages (Python version of 3.5 or higher is required). This can be done by creating a virtual environment with python3 -m venv env and activating it with source ./env/bin/activate. Once the virtual Python environment is activated, the required packages can be installed with pip using pip install -r requirements.txt.
Additionally, we need to have Chimera installed on the system and a symbolic link to the chimera binary file in /usr/bin/chimera must exist.
The backbone prediction can be run by invoking the main.py script located in the root of the project. It requires two positional arguments:
- Input path
- Output path
Paths must be passed as absolute paths
In addition to the input and output path we can also provide a JSON file containing threshold values for each protein using -t THRESHOLD_FILE . If this file is not provided threshold values are determined automatically which can lead to worse prediction results.
Another optional JSON file that can be passed is the hide dusts json file using -d HIDEDUSTS_FILE. If this file is not provided results can be slightly worse due to dust noise.
An optional flag -s followed by a number n can be passed as an argument to skip the first n prediction steps.
Skipping prediction steps is only possible if the results of the skipped steps are already available in the output path
Another optional flag -c can be set if you don't want to re-predict protein maps for which all/part of the results are already available in the output path. If set only prediction steps for which the results are not there yet are executed.
Another optional flag -b can be set if you want to keep mrc files for debugging purposes. Note: This will take up a lot more memory.
Another optional flag -p can be set to change the location of the symbolic link to the chimera binary file. If it is not set, it will default to /usr/bin/chimera.
The optional flags [-t, -d, -p] need to be passed as absolute paths
An example command to execute the prediction could therefore be the following.
python main.py INPUT_PATH OUTPUT_PATH -t THRESHOLD_FILE
A sample execution of the prediction will be demonstrated for the 6272 protein density map shown in the following image.
The program will require the map as well as the fitted PDB as an input. Both files need to be stored in the same folder which is ideally named after the EMDB id. We also need to create a folder where the results will be stored. The folder structure could then look as following.
Furthermore, we want to provide a file containing the threshold value that should be used for the protein map. Therefore, we create a thresholds.json file with the following content:
{
"6272": 0.009
}
Note that the threshold value is read based on the folder name in which the density map is stored in
Now, we can start the prediction using the following command.
python main.py input output -t thresholds.json
Also, an option to apply hide dust sizes can be provided in a hidedusts.json file. Format is [contour level, hideDust size].
{
"6272": [1.0, 10.0]
}
Additionally, we can start the prediction using the following command.
python main.py input output -d hidedusts.json
During the execution all prediction steps are run and the artifacts created by each step are stored in the output folder. In the following diagram we can see the flow of the different steps and the files they create.
Note that the execution can take a significant amount of time
After the execution finished the folder structure should look as following.
The folder structure will only contain the preprocessing and cnn folders if the debug flag is set
The final prediction is stored in the 6272.pdb file. The created backbone trace is shown in the following image.
Additionally, a results.xls file is created in the output folder containing metrics about the prediction results.
The code which maps protein sequences into the predicted Cα traces can be found here. We put the paper's version in the the folder 'sequence_mapping', users can directly setup program in this folder. The latest version can be found at here.
Red Hat Enterprise Linux Server release 6.4 (Santiago), perl 5, version 16, subversion 3 (v5.16.3)
(1) Configure CaTrace2Seq (required)
cd sequence_mapping
perl setup_env.pl
(Note: since we use quality assessment tool with non-redundent sequence database, the tool requires around 33G)
cd installation/
sh P1_install_R-3.2.0.sh
(Note: check R installation: sequence_mapping/tools/R-3.2.0/bin/R )
(2) Run CaTrace2Seq (required)
sh bin/run_CaTrace2Seq.sh <path of fasta sequence> <path of Ca trace> <length threshold for fragment> <output-directory> <num of cpus>
(4) Practice the examples using fragment traces from experimental density maps
cd examples
sh run_6272.sh
*****************************************************************************
* TM-SCORE *
* A scoring function to assess the quality of protein structure predictions *
* Based on statistics: *
* 0.0 < TM-score < 0.17, Random predictions *
* 0.4 < TM-score < 1.00, Meaningful predictions *
* Reference: Yang Zhang and Jeffrey Skolnick, Proteins 2004 57: 702-710 *
* For comments, please email to: yzhang@ku.edu *
*****************************************************************************
Structure1: /home/jh7x Length= 397
Structure2: 6272/pdb3j Length= 397 (by which all scores are normalized)
Number of residues in common= 397
RMSD of the common residues= 2.150
TM-score = 0.9314 (d0= 7.20, TM10= 0.9314)
MaxSub-score= 0.7438 (d0= 3.50)
GDT-TS-score= 0.8086 %(d<1)=0.5819 %(d<2)=0.7431 %(d<4)=0.9093 %(d<8)=1.0000
GDT-HA-score= 0.6039 %(d<0.5)=0.1814 %(d<1)=0.5819 %(d<2)=0.7431 %(d<4)=0.9093
-------- rotation matrix to rotate Chain-1 to Chain-2 ------
i t(i) u(i,1) u(i,2) u(i,3)
1 -0.0638134694 0.9999870448 0.0020705458 0.0046500556
2 0.0280063468 -0.0020814379 0.9999950989 0.0023387491
3 0.1425030895 -0.0046451903 -0.0023483976 0.9999864535
(5) Practice the examples using fragment traces from simulated density maps
cd examples
sh run_2GZ4.sh
sh run_3EWG.sh
sh run_3n2t.sh
sh run_3qc7.sh
sh run_3RMU.sh
sh run_4RAJ.sh
sh run_5EBA.sh
sh run_5i68.sh
sh run_5P9V.sh
sh run_6ahv.sh