CapsNet_PTM

CapsNet for Protein Post-translational Modification site prediction. It is implemented by deep learning library Keras2.1.1 and Tensorflow backend

Training and testing data

The 10-fold cross-validation training and tesing data for each PTM used in the paper are in folder all_PTM_raw_data. Each subfolder contains 10-fold annotated training sequences (metazoa_sequence_annotated_training_X.fasta, sequences that have more than 30% sequence identity with the testing set were removed) and corresponding annotated testing sequences (metazoa_sequences_cross_testing_annotated_X.fasta) and testing sequences without annotation (metazoa_sequences_cross_testing_X.fasta)

Installation

Download codes by

git clone https://github.com/duolinwang/CapsNet_PTM

Installation has been tested in Linux and Mac OS X with Python 2.7.
Since the package is written in python 2.7, python 2.7 with the pip tool must be installed first. It uses the following dependencies: numpy, scipy, pandas, h5py, keras version=2.1.1 You can install these packages first, by the following commands:

pip install pandas
pip install numpy
pip install scipy
pip install h5py
pip install -v keras==2.1.1
pip install tensorflow (or GPU supported tensorflow, refer to https://www.tensorflow.org/install/ for instructions)

This is the Tensorflow version, you must change the backend to TensorFlow. If you have run Keras at least once, you will find the Keras configuration file at: $HOME/.keras/keras.json If it isn’t there, you can create it. Change the default configuration file into:

{	
    "image_dim_ordering": "th",
    "epsilon": 1e-07,
    "floatx": "float32",
    "backend": "tensorflow"
}

Running on GPU or CPU

If you want to use GPU, you also need to install CUDA and cuDNN; refer to their websites for instructions. CPU is only suitable for prediction not training.

For custom training:

python train_models.py -input [custom training data in fasta format] -output-prefix [prefix of pre-trained model] -residue-types [custom specified residue types]

For details of other parameters, run:

python train_models.py --help

python train_models.py -h

####Example

python train_models.py -input train_example_data.fasta -output-prefix example_models -residue-types S,T

Custom prediction from custom general models and custom kinase-specific models:

python predict.py -input [custom predicting data in fasta format] -model-prefix [prefix of pre-trained model] -output [custom specified file for predicting results]

####Example

python predict.py -input test_example_data.fasta -model-prefix example_models -output test_example_result

Citation：

Please cite the following paper for using MusiteDeep: Duolin Wang, Yanchun Liang, Dong Xu*, Capsule Network for Protein Post-translational Modification Site Prediction.

License

GNU v2.0

tilii/CapsNet_PTM