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Curr Drug Metab. 2019;20(3):177-184. doi: 10.2174/1389200219666180829121038.

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

Author information

1
School of Software, Central South University, Changsha, 410075, China.
2
School of Electronics and Computer Science, Zhejiang Wanli University, Ningbo 315100, China.
3
Shanghai Key Lab of Intelligent Information Processing, Shanghai 200433, China.

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; deep learning; feature extraction; feature representation; machine learning.

[Indexed for MEDLINE]

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