Nantao Zheng, Kairou Wang, Weihua Zhan and Lei Deng* Pages 177 - 184 ( 8 )
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.
Virus-host protein-protein interactions, computational methods, feature extraction, feature representation, machine learning, deep learning.
School of Software, Central South University, Changsha, 410075, School of Software, Central South University, Changsha, 410075, School of Electronics and Computer Science, Zhejiang Wanli University, Ningbo 315100, School of Software, Central South University, Changsha, 410075