Graph Convolutional Network based Virus-Human Protein-Protein Interaction Prediction for Novel Viruses

This repository contains the code of a three-stage machine learning pipeline that generates and uses hybrid embeddings for PHI prediction. The details of this study and the experimental results are available in its paper. Please click here to access the paper.

System requirements

The code is tested on Python version >= 3.6. Following Python libraries are required:

• Pip3
• Pandas
• Numpy
• Scikit-learn
• NetworkX
• Gensim
• TensorFlow / Keras
• Pickle
• Joblib

The libraries shown below are installed automatically by the code using Pip3:

• StellarGraph
• Interpret
• SentencePiece
• Bio

Usage

Data format

The interactions are given in Excel (.xlsx) file. The file is in a simple format:

HOST	VIRUS	HOST_SEQ	VIRUS_SEQ
P13861	P03259	MSHIQ...	MRTEM...

where the HOST is the unique id of a host protein and VIRUS is the unique id of the interacting virus protein. HOST_SEQ and VIRUS_SEQ are the aminoacid sequences of the host and virus proteins, respectively. Please check Data/Adenoviridae.xlsx for an example file.

Demo

Run the main.py file in the command line without passing no argument for testing the Adenovirus dataset with all the proposed embedding methods. The PHISTO dataset is used for training in demo.

Quick start

The model can be tested by running main.py file in command line or calling DeepPHI() function in the code block. Use the following command for calling in Terminal:

$ python3 main.py train_dataset_path test_dataset_path experiment_type classifier_type fts, lpp, lem, lpc

or just calling as a function:

DeePHI(train_dataset_path, test_dataset_path, experiment_type, classifier_type, fts, lpp, lem, lpc)

Parameter	Description
train_dataset_path	The path of the interaction dataset used for training GraphSAGE, preliminary classifier and final classifier (in holdout experiments).
test_dataset_path	The path of the test interaction dataset.
experiment_type	Type of the experiment. 5_fold and Holdout are valid types.
classifier_type	Type of the final interaction classifier. SVM, NN, RF, LR, and GA2M are the available methods.
fts	(optional, default = False) Pass True for freezing sampled negative edges between experiments.
lpp	(optional, default = False) Pass True for loading pre-trained preliminary classifier model instead of training a new one.
lem	(optional, default = False) Pass True for loading pre-trained biological feature embedding model instead of training a new one.
lpc	(optional, default = False) Pass True for loading pre-trained interaction classifier model instead of training a new one.

Citation

Koca, M. B., Nourani, E., Abbasoğlu, F., Karadeniz, İ., & Sevilgen, F. E. (2022). Graph convolutional network based virus-human protein-protein interaction prediction for novel viruses. Computational Biology and Chemistry, 101, 107755.