State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China.
State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China.
Brief Bioinform. 2021 Sep 2;22(5). doi: 10.1093/bib/bbab029.
The protein-protein interactions (PPIs) between human and viruses mediate viral infection and host immunity processes. Therefore, the study of human-virus PPIs can help us understand the principles of human-virus relationships and can thus guide the development of highly effective drugs to break the transmission of viral infectious diseases. Recent years have witnessed the rapid accumulation of experimentally identified human-virus PPI data, which provides an unprecedented opportunity for bioinformatics studies revolving around human-virus PPIs. In this article, we provide a comprehensive overview of computational studies on human-virus PPIs, especially focusing on the method development for human-virus PPI predictions. We briefly introduce the experimental detection methods and existing database resources of human-virus PPIs, and then discuss the research progress in the development of computational prediction methods. In particular, we elaborate the machine learning-based prediction methods and highlight the need to embrace state-of-the-art deep-learning algorithms and new feature engineering techniques (e.g. the protein embedding technique derived from natural language processing). To further advance the understanding in this research topic, we also outline the practical applications of the human-virus interactome in fundamental biological discovery and new antiviral therapy development.
蛋白质-蛋白质相互作用 (PPIs) 是介导病毒感染和宿主免疫过程的关键因素。因此，研究人类与病毒的蛋白质-蛋白质相互作用有助于我们理解人类与病毒相互关系的原理，从而指导开发高效药物以阻断病毒传染病的传播。近年来，实验鉴定的人类与病毒蛋白质-蛋白质相互作用数据迅速积累，为围绕人类与病毒蛋白质-蛋白质相互作用的生物信息学研究提供了前所未有的机会。本文全面概述了人类与病毒蛋白质-蛋白质相互作用的计算研究，特别是重点介绍了人类与病毒蛋白质-蛋白质相互作用预测方法的发展。我们简要介绍了人类与病毒蛋白质-蛋白质相互作用的实验检测方法和现有数据库资源，然后讨论了计算预测方法开发方面的研究进展。特别是，我们详细阐述了基于机器学习的预测方法，并强调需要采用最新的深度学习算法和新的特征工程技术（例如源自自然语言处理的蛋白质嵌入技术）。为了进一步推动该研究主题的理解，我们还概述了人类与病毒相互作用组在基础生物学发现和新抗病毒治疗开发中的实际应用。
