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A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

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Published on: July 18, 2013

Virus-host interactions predictor (VHIP): Machine learning approach to resolve microbial virus-host interaction

G Eric Bastien¹, Rachel N Cable¹, Cecelia Batterbee¹

¹Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America.

Plos Computational Biology

|September 18, 2024

Summary

A new machine learning model, the Virus-Host Interaction Predictor (VHIP), accurately predicts which viruses infect which microbes. This tool helps understand virus-host interactions and their ecological impacts.

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Arbovirus Infections As Screening Tools for the Identification of Viral Immunomodulators and Host Antiviral Factors

Arbovirus Infections As Screening Tools for the Identification of Viral Immunomodulators and Host Antiviral Factors

Published on: September 13, 2018

Area of Science:

Microbiology
Virology
Bioinformatics

Background:

Microbial viruses are crucial for microbiome evolution and health.
Genome sequencing reveals vast viral diversity, but host identification remains a challenge.
Understanding virus-host interactions is key to inferring microbial ecosystem impacts.

Purpose of the Study:

To develop a machine learning model for predicting virus-host interactions.
To enable a deeper understanding of virus-host networks in natural systems.
To move beyond identifying viral presence to understanding infection dynamics.

Main Methods:

Developed the Virus-Host Interaction Predictor (VHIP) machine learning model.
Trained and tested VHIP on the manually curated Virus Host Range network (VHRnet) dataset of 8849 virus-host pairs.
Computed viral adaptation signals from genomic sequences of lab-tested virus-host pairs.

Main Results:

VHIP achieves 87.8% accuracy in predicting virus-host interactions at the species level.
The model can predict multiple potential hosts for a single virus, reflecting network complexity.
VHIP can analyze novel viral and host genomes from metagenomic data.

Conclusions:

VHIP provides a powerful tool for predicting virus-host interactions from genomic data.
The model enhances our ability to study the ecological and evolutionary roles of viruses in microbiomes.
Accurate prediction of virus-host interactions is essential for understanding microbial community dynamics and their implications for health and the environment.