This repo is under major reconstruction, new version is coming soon!
run
pip install -e .
run
python utils/train_richdbd2_fixed6mmgk.py -dataset [your dataset] \
for example here \
python utils/train_richdbd2_fixed6mmgk.py -dataset dbd
In python with torch imported
classifier.load_state_dict(torch.load('model/seg_model_protein_15.pth'))
Dbd 5
dataset folder should follow dbd folder structure
input data should be a n-by-5 matrix, with columns' order [x,y,z,electrostatic,hydrophobicity]
dbd data are now avalable at https://www.dropbox.com/sh/qqi9op061mfxbmo/AADibYuDdMF4n2bDS3uqiEVha?dl=0
EpiPred
dataset folder should follow dbd folder structure
input data should be a n-by-5 matrix, with columns' order [x,y,z,electrostatic,hydrophobicity]
EpiPred data are now avalable at https://www.dropbox.com/sh/wnrn65y4vfs2m84/AABSFL8IeWh_U7gQO2__ZFEHa?dl=0
utils/pdb2wrlpymol2_pub.py
change pdb files folder in the script and for a given complex ABCD.pdb, split the ligand and receptor as ABCD_l.pdb and ABCD_r.pdb.
utils/pdb2pqrall_pub.py
compute apbs for all pdb files. Need to change path in script for your pdb2pqr and apbs binary exe file.
utils/transdata2.m
set your path to data folder which will be your train script --dataset input. It will create coordinate data and interface label file.
utils/RichFeatureApbsCon_pub.py
modify matlab output 3 dims coordinate feature data file to 5 dims coordinate+electrostatic+hydrophobicity feature.