Neural network model for predicting amino-acid sequence from a protein backbone structure
Project description
GCNdesign
A neural network model for prediction of amino-acid probability from a protein backbone structure.
Built with
- pytorch
- numpy
- pandas
- tqdm
Getting Started
Install
To install gcndesgn through pip
pip install gcndesign
Usage
Quick usage as a python module
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
Usage of scripts
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.
External Packages
- gcndesign_autodesign.py: PyRosetta
Documents
Dataset
The dataset used for training GCNdesign is available here.
Issues
Unstable behavior is observed when the input pdb contains non-natural amino acids. (e.g. selenomethionine MSE, etc.)
Lisence
Distributed under MIT license.
Acknowledgments
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.
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