PSSNet – A new deep learning network architecture for semantic segmentation of supersecondary structures in proteins with small size, very high accuracy and performance. The PSSNet model made it possible to identify and classify more than 2.3 million supersecondary structures in whole protein structures annotated in the PDB and AlphaFold 2.0 banks and became the basis for filling the public database of supersecondary structures https://www.psskb.org
This repository contains a pretrained model for segmenting: -aa-corners -a-hairpins -b-hairpins -BaB To extract the specified structures from the folder containing the PDB, you need to run the sss_extractor.py script.
usage: sss_extractor.py [-h] -pdb PDB -out OUT [-sss SSS]
Extract SSS from PDB
options: -h, --help show this help message and exit
-pdb PDB Path to the folder with the PDB database
-out OUT Path to the output folder
-sss SSS The type of supersecondary structure to be extracted from the PDB aa-corner', 'a-hairpin', 'b-hairpin', 'bab
The output directory will contain files of pdb format with cut coordinates of supersecondary structures.
Output example (b-hairpin):
4X7F_A328_A366.pdb
To train the model, it is necessary to prepare training datasets:
- Set with positive examples
- Set with negative examples
Set with positive examples pdb format files containing the coordinates of the supersecondary structure in the file name File name format: pdb id chain code start pos chain code end pos.pdb
Example: 1a0a_A7_A60.pdb
A set with negative examples is any pdb format files that do not contain super secondary structures.
The following parameters must be defined in the config.py file:
- root_dir = root directory that contains the training datasets.
- positive_keys=name of subdirectory containing positive examples.
- negative_keys= name of subdirectory containing negative examples After specifying these parameters, you must run the command
$python preprocessor.py
The features subdirectory will be formed containing preprocessed files for model training.
To start the model training process, you need to execute the command:
$python trainer.py
The training process of the model and the metrics of each epoch of its training will be displayed in the console.
The model with the best measures based on training and validation results will be saved in the models folder.