HVIDB: a comprehensive database for human-virus protein-protein interactions

Brief Bioinform. 2021 Mar 22;22(2):832-844. doi: 10.1093/bib/bbaa425.

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.

Keywords: annotation; database; human–virus interaction; prediction; protein–protein interaction.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Databases, Protein*
  • Gene Expression Profiling
  • Humans
  • Machine Learning
  • Protein Array Analysis
  • Protein Conformation
  • Protein Interaction Mapping / methods*
  • Proteins / chemistry
  • Proteins / genetics
  • Proteins / metabolism*
  • Viral Proteins / chemistry
  • Viral Proteins / genetics
  • Viral Proteins / metabolism*

Substances

  • Proteins
  • Viral Proteins