Description of files
list_train: list of pdb entries used as training dataset
list_test: list of pdb entries used as test dataset
list_validate: list of pdb entries used as validation dataset

ss2.py: the python script used to build,  train, and test the LocalNet as described in the manuscript

train.sh:  Linux shell script for running the training program ss2.py

ss3.train: training dataset for window of 7 residues extracted from list_train
ss3.test: training dataset for window of 7 residues extracted from list_test
ss3.validate: training dataset for window of 7 residues extracted from list_validate

for other datasets
ss4: for window of 9 residues
ss5: for window of 11 residues
ss6: for window of 13 residues
ss7: for window of 15 residues
ss8: for window of 17 residues
ss9: for window of 19 residues
ssa: for window of 21 residues

out00.model-16*:  saved model with best accuracy for dataset of window of 7 residues
out01.model-10*:  saved model with best accuracy for dataset of window of 9 residues
out02.model-17*:  saved model with best accuracy for dataset of window of 11 residues
out03.model-19*:  saved model with best accuracy for dataset of window of 13 residues
out04.model-7*:  saved model with best accuracy for dataset of window of 15 residues
out05.model-6*:  saved model with best accuracy for dataset of window of 17 residues
out06.model-7*:  saved model with best accuracy for dataset of window of 19 residues
out07.model-12*:  saved model with best accuracy for dataset of window of 21 residues