Bioinformatics approaches for unveiling virus-host interactions

• Published in: Computational and structural biotechnology journal Vol. 21; pp. 1774 - 1784
• Main Authors: Iuchi, Hitoshi, Kawasaki, Junna, Kubo, Kento, Fukunaga, Tsukasa, Hokao, Koki, Yokoyama, Gentaro, Ichinose, Akiko, Suga, Kanta, Hamada, Michiaki
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2023; The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology; Elsevier
• Subjects: bioinformatics; biotechnology; COVID-19 infection; data collection; host range; Host range prediction; human health; pandemic; prediction; protein-protein interactions; Protein–protein interaction prediction; Review; Virus–host interaction; Virus–host interaction; Protein–protein interaction prediction; Host range prediction
• ISSN: 2001-0370; 2001-0370
• DOI: 10.1016/j.csbj.2023.02.044

The coronavirus disease-2019 (COVID-19) pandemic has elucidated major limitations in the capacity of medical and research institutions to appropriately manage emerging infectious diseases. We can improve our understanding of infectious diseases by unveiling virus–host interactions through host range prediction and protein–protein interaction prediction. Although many algorithms have been developed to predict virus–host interactions, numerous issues remain to be solved, and the entire network remains veiled. In this review, we comprehensively surveyed algorithms used to predict virus–host interactions. We also discuss the current challenges, such as dataset biases toward highly pathogenic viruses, and the potential solutions. The complete prediction of virus–host interactions remains difficult; however, bioinformatics can contribute to progress in research on infectious diseases and human health.