A neural network model for prediction of amino-acid probability from a protein backbone structure.

• pytorch
• numpy
• pandas
• tqdm

To install gcndesgn through pip

pip install gcndesign

from gcndesign.prediction import Predictor

gcndes = Predictor(device='cpu') # 'cuda' can also be applied
gcndes.pred(pdb='pdb-file-path') # returns list of amino-acid probabilities

gcndesign_predict.py

To predict amino-acid probabilities for each residue-site

gcndesign_predict.py  YOUR_BACKBONE_STR.pdb

gcndesign_autodesign.py

To design 20 sequences in a completely automatic fashion

gcndesign_autodesign.py  YOUR_BACKBONE_STR.pdb  -n 20

For more detailed usage, please run the following command

gcndesign_autodesign.py -h

Note

The gcndesign_autodesign script requires pyrosetta software. Installation & use of pyrosetta must be in accordance with their license.

• gcndesign_autodesign.py: PyRosetta

This code is not completely compatible with an input of a protein complex structure.

Distributed under MIT license.

The author was supported by Grant-in-Aid for JSPS Research Fellows (PD, 17J02339). Koga Laboratory of Institutes for Molecular Science (NINS, Japan) has provided a part of the computational resources. Koya Sakuma (yakomaxa) gave a critical idea for neural net architecture design in a lot of deep discussions. Naoya Kobayashi (naokob) created excellent applications to help broader needs, ColabGCNdesign and FolditStandalone_Sequence_Design.