imseaton/cgan-cmap-a-protein-contact-map-predictor

CGAN_Cmap

This is the code and data for CGAN-Cmap: protein contact map prediction using deep generative adversarial neural networks paper submitted in Briefings in Bioinformatics.

Code Usage

Dependencies

• Tensorflow == 2.3.0
• H5Py == 2.10.0
• Matplotlib == 3.5.1
• Numpy == 1.21.6

Commands

• --traintest:
  • Options:
    • traintest which train the model and test that
    • test which test the saved model
• --test_data: The type of data set to use. default = initial, options: [initial, CAMEO, casp12,casp13,casp14]
• --batch_size: The batch size for the model training (default = 4)
• --n_epoch: The number of epochs for training (default = 500)
• --save_step : Save models every x epoch (default = 50)
• --lr : Learning rate (default = 2e-4)
• --SE_concat : Number of SE_Concat block used in the generator (default = 3)
• --Premodel_name : Pretrain model name (default = CGAN_Cmap.h5)

Data and Models

The data can be downloaded from this link (Folder includes training, validation and initial test sets ( ready to use for training) and Casp 11, 12, 13, 14, and CAMEO.). You have to extract that to the data folder ( it would be like data/) . To download the pretrained models, you can use this link. You have to extract the models under GANTL folder (it would be like GANTL/model/).

Using Example

To train a model you can use the following command:

python main.py --traintest traintest

The code will train the model and save the models in GANTL/model folder. It also save the images obtained during the training in GANTL/images. The final predictions will be saved in GANTL/prediction.

To test the model, one can run the following command:

python main.py --traintest test

The result will be saved in GANTL/prediction.

Citation

If you use this works please cite CGAN-Cmap: protein contact map prediction using deep generative adversarial neural networks uploaded in biorxiv:

@article {Madani2022.07.26.501607,
	author = {Madani, Mohammad and Behzadi, Mohammad Mahdi and Song, Dongjin and Ilies, Horea and Tarakanova, Anna},
	title = {CGAN-Cmap: protein contact map prediction using deep generative adversarial neural networks},
	elocation-id = {2022.07.26.501607},
	year = {2022},
	doi = {10.1101/2022.07.26.501607},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2022/07/28/2022.07.26.501607},
	eprint = {https://www.biorxiv.org/content/early/2022/07/28/2022.07.26.501607.full.pdf},
	journal = {bioRxiv}
}