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ProteinGCN: Protein model quality assessment using Graph Convolutional Networks

Source code for the paper: ProteinGCN: Protein model quality assessment using Graph Convolutional Networks

Overview of ProteinGCN: Given a protein structure, it first generates a protein graph and uses GCN to learn the atom embeddings. Then, it pools the atom embeddings to generate residue-level embeddings. The residue embeddings are passed through a non-linear fully connected layer to predict the local scores. Further, the residue embeddings are pooled to generate a global protein embedding. Similar to residue embeddings, this is used to predict the global score.

Dependencies

  • Compatible with PyTorch 1.0 and Python 3.x.
  • Dependencies can be installed using the requirements.txt file.

Dataset:

  • We use Rosetta-300k to train the ProteinGCN model and test it on both Rosetta-300k and CASP13 dataset for local(residue) and global Quality Assessment predictions.

Training model:

  • Install all the requirements by executing pip install -r requirements.txt.

  • Install required protein .pdb processing library by executing sh preprocess.sh which clones and installs this github repository.

  • Next execute python preprocess_pdb_to_pkl.py script which creates the required .pkl files from the dataset to be used for model training. It defaults to a sample dataset provided with the code at ./data/. To use the original datasets, please change the paths accordingly.

  • To start a training run:

    python train.py trial_run --epochs 10

    Once successfully run, this creates a folder by the name trial_run under the path ./data/pkl/results/ which contains the test results test_results.csv and best model checkpoint model_best.pth.tar. Rest of the training arguments and the defaults can be found in arguments.py. We support multi-gpu training using PyTorch DataParallel on a single server by default. To enable multi-gpu training, just set the required number of gpus in CUDA_VISIBLE_DEVICES environment.

  • To get the final pearson correlation scores, run:

    python correlation.py -file ./data/pkl/results/trial_run/test_results.csv

  • We have published our best ProteinGCN model that was trained on Rosetta-300k dataset. To run the pretrained model on the sample data, execute:

    python train.py trial_testrun --pretrained ./pretrained/pretrained.pth.tar --epochs 0 --train 0 --val 0 --test 1

Please cite the following paper if you use this code in your work.

@article {Sanyal2020.04.06.028266,
	author = {Sanyal, Soumya and Anishchenko, Ivan and Dagar, Anirudh and Baker, David and Talukdar, Partha},
	title = {ProteinGCN: Protein model quality assessment using Graph Convolutional Networks},
	year = {2020},
	doi = {10.1101/2020.04.06.028266},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2020/04/07/2020.04.06.028266},
	journal = {bioRxiv}
}

For any clarification, comments, or suggestions please create an issue or contact Soumya.