HVIDB: a comprehensive database for human–virus protein–protein interactions. Issue 2 (30th January 2021)

• Record Type: Journal Article
• Authors: Yang, Xiaodi; Lian, Xianyi; Fu, Chen; Wuchty, Stefan; Yang, Shiping; Zhang, Ziding
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 2(2021)
• Page Start: 832
• Page End: 844
• Publication Date: 2021-01-30
• Subjects: human–virus interaction -- database -- protein–protein interaction -- annotation -- prediction; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbaa425
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: While leading to millions of people's deaths every year the treatment of viral infectious diseases remains a huge public health challenge.Therefore, an in-depth understanding of human– virus protein–protein interactions (PPIs) as the molecular interface between a virus and its host cell is of paramount importance to obtain new insights into the pathogenesis of viral infections and development of antiviral therapeutic treatments. However, current human– virus PPI database resources are incomplete, lack annotation and usually do not provide the opportunity to computationally predict human– virus PPIs. Here, we present the Human–Virus Interaction DataBase (HVIDB, http://zzdlab.com/hvidb/ ) that provides comprehensively annotated human– virus PPI data as well as seamlessly integrates online PPI prediction tools. Currently, HVIDB highlights 48 643 experimentally verified human– virus PPIs covering 35 virus families, 6633 virally targeted host complexes, 3572 host dependency/restriction factors as well as 911 experimentally verified/predicted 3D complex structures of human– virus PPIs. Furthermore, our database resource provides tissue-specific expression profiles of 6790 human genes that are targeted by viruses and 129 Gene Expression Omnibus series of differentially expressed genes post-viral infections. Based on these multifaceted and annotated data, our database allows the users to easily obtain reliable information about PPIs of various human viruses and conduct an in-depth analysis of their inherent biological significance. In particular, HVIDB also integrates well-performing machine learning models to predict interactions between the human host and viral proteins that are based on (i) sequence embedding techniques, (ii) interolog mapping and (iii) domain–domain interaction inference. We anticipate that HVIDB will serve as a one-stop knowledge base to further guide hypothesis-driven experimental efforts to investigate human–virus relationships.