DeepDom

DeepDom is an ab-initio method for protein domain boundary prediction

Installation

• Installation has been tested in Linux and Mac OS X with Python 3.5.
• Since the package is written in python 3.5, python 3.5 with the pip tool must be installed first. DeepDom uses the following dependencies: numpy, scipy, pandas, h5py, keras version==2.3.0, tensorflow==1.13.1 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==2.1.2
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.

Training data

Since the data size is larger than the Github limitation, the training data for DeepDom is stored at Google Drive that can be accessed through the link https://drive.google.com/drive/folders/1HsHEbk1gjtEdivr9vvOpXPK7MsoBZp3h?usp=sharing

Usage:

1. Train a model by your own data

To train a model, users should have .fasta file for protein sequences and domain boundary annotations on corresponding protein sequences. Please refer to the sample_data_seq.txt and sample_data_label.txt as examples. Then use the "dataprocess.pl" to transformat the data. (run "perl dataprocess.pl -h" to see the helps) After that, the processed data can be used as input for "train.py" to train a model (run "python train.py -h" to see the helps). Note: the requirement of packages that imported in the code need to be met.

Examples (using our provided example data):

perl dataprocess.pl -input_seq sample_data_seq.txt -input_label sample_data_label.txt -output_seq processed_seq.txt -output_label processed_label.txt

python train.py -seqfil processed_seq.txt -labelfile processed_label.txt -model-prefix custom_model.h5

custom_model.h5 is the model generated, users can use this file to predict and can also use our pre-trained model that mentioned in our paper. The pre-trained model was saved in file "foldmodel_bilstmwrapper_4sum200_80_40nr_sliwin.h5".

2. Predict

To predict domain boundary for protein sequences, firstly, users need to transformat the .fasta sequence using "dataprocess.pl" (run "perl dataprocess.pl -h" to see the helps) and using "predict.py" to predict for protein sequences (run "python predict.py -h" to see the helps). Either users' own model or the model we provided can be used for prediction.

Examples (using our provided example data):

For GPU users, predict.py will use our pre-trained GPU model "foldmodel_bilstmwrapper_4sum200_80_40nr_sliwin.h5":

perl dataprocess.pl -input_seq sample_data_seq.txt -output_seq processed_seq.txt

python predict.py -input processed_seq.txt -output predict_result.txt

For CPU users, be sure to add -model-prefix cpu_model.h5 when call predict.py:

perl dataprocess.pl -input_seq sample_data_seq.txt -output_seq processed_seq.txt

python predict.py -input processed_seq.txt -output predict_result.txt -model-prefix cpu_model.h5

Or users can use the custom model trained (as shown in 1) by their own data to predict.

python predict.py -input processed_seq.txt -output predict_result.txt -model-prefix custom_model.h5

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If you find the codes or our method is useful, please cite our paper "DeepDom: Predicting protein domain boundary from sequence alone using stacked bidirectional LSTM".