Targeting Virus-host Protein Interactions: Feature Extraction and Machine Learning Approaches

Author(s): Nantao Zheng, Kairou Wang, Weihua Zhan, Lei Deng*.

Journal Name: Current Drug Metabolism

Volume 20 , Issue 3 , 2019

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Graphical Abstract:


Background: Targeting critical viral-host Protein-Protein Interactions (PPIs) has enormous application prospects for therapeutics. Using experimental methods to evaluate all possible virus-host PPIs is labor-intensive and time-consuming. Recent growth in computational identification of virus-host PPIs provides new opportunities for gaining biological insights, including applications in disease control. We provide an overview of recent computational approaches for studying virus-host PPI interactions.

Methods: In this review, a variety of computational methods for virus-host PPIs prediction have been surveyed. These methods are categorized based on the features they utilize and different machine learning algorithms including classical and novel methods.

Results: We describe the pivotal and representative features extracted from relevant sources of biological data, mainly include sequence signatures, known domain interactions, protein motifs and protein structure information. We focus on state-of-the-art machine learning algorithms that are used to build binary prediction models for the classification of virus-host protein pairs and discuss their abilities, weakness and future directions.

Conclusion: The findings of this review confirm the importance of computational methods for finding the potential protein-protein interactions between virus and host. Although there has been significant progress in the prediction of virus-host PPIs in recent years, there is a lot of room for improvement in virus-host PPI prediction.

Keywords: Virus-host protein-protein interactions, computational methods, feature extraction, feature representation, machine learning, deep learning.

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Year: 2019
Page: [177 - 184]
Pages: 8
DOI: 10.2174/1389200219666180829121038
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