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Viruses with RNA Genomes01:29

Viruses with RNA Genomes

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RNA viruses are categorized into positive-strand, negative-strand, or double-stranded groups based on their genomic structure and replication mechanisms. This classification dictates how they exploit host cellular machinery for protein synthesis and replication. Some RNA viruses also utilize reverse transcription as part of their life cycle, further diversifying their replication strategies.Positive-Strand RNA VirusesPositive-strand RNA viruses have genomes that function directly as messenger...
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Viral genomes exhibit remarkable diversity in size, structure, and composition, influencing their replication strategies and interactions with host cells. These genomes consist of either DNA or RNA and may be linear or circular. Additionally, they can be single-stranded or double-stranded, with each configuration affecting how the virus propagates within a host. RNA viruses, for instance, generally have smaller genomes than DNA viruses, a factor that contributes to their high mutation rates and...
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Viruses are unique biological entities that blur the boundary between living and non-living systems. Although they lack cellular structure and metabolic processes, they can exhibit characteristics of life when infecting a host. Their defining feature is a nucleic acid core, composed of either DNA or RNA, encapsulated within a protein coat called a capsid. This simple structure allows them to invade host cells and use their machinery for replication efficiently.Viral Structure and...
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A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is prone to errors because it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, therefore accrue mutations faster than viruses with DNA-based genomes. Because mutation and recombination provide the raw material...
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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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Updated: Aug 8, 2025

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
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Bioinformatics approaches for unveiling virus-host interactions.

Hitoshi Iuchi1,2, Junna Kawasaki3, Kento Kubo2,4

  • 1Waseda Research Institute for Science and Engineering, Waseda University, Tokyo 169-8555, Japan.

Computational and Structural Biotechnology Journal
|March 6, 2023
PubMed
Summary

Understanding virus-host interactions is key to managing infectious diseases. This review surveys prediction algorithms, highlighting challenges and bioinformatics

Keywords:
Host range predictionProtein–protein interaction predictionVirus–host interaction

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Area of Science:

  • Virology
  • Bioinformatics
  • Computational Biology

Background:

  • The COVID-19 pandemic revealed critical gaps in managing emerging infectious diseases.
  • Virus-host interactions are crucial for understanding disease dynamics but remain incompletely mapped.

Purpose of the Study:

  • To comprehensively review algorithms for predicting virus-host interactions.
  • To identify current challenges and potential solutions in the field of virus-host interaction prediction.

Main Methods:

  • Systematic literature review of algorithms predicting virus-host interactions.
  • Analysis of challenges including dataset biases and limitations of current methods.

Main Results:

  • Numerous algorithms exist for predicting virus-host interactions, yet significant challenges persist.
  • Dataset biases, particularly towards highly pathogenic viruses, hinder comprehensive network prediction.

Conclusions:

  • Bioinformatics offers valuable tools for advancing infectious disease research and improving human health.
  • Further development of robust algorithms and unbiased datasets is necessary to fully elucidate virus-host interaction networks.