寄主(生物学)
计算机科学
人工智能
机器学习
鉴定(生物学)
计算生物学
计算模型
蛋白质-蛋白质相互作用
病毒
生物
病毒学
生态学
遗传学
植物
作者
Nantao Zheng,Kairou Wang,Wenfeng Zhan,Lei Deng
出处
期刊:Current Drug Metabolism
[Bentham Science]
日期:2019-05-22
卷期号:20 (3): 177-184
被引量:27
标识
DOI:10.2174/1389200219666180829121038
摘要
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.
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