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

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

Published on: July 18, 2013

CHERRY: a Computational metHod for accuratE pRediction of virus-pRokarYotic interactions using a graph

Jiayu Shang¹, Yanni Sun¹

¹Department of Electrical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China SAR.

Briefings in Bioinformatics

|May 20, 2022

Summary

Predicting prokaryotic virus hosts is crucial for understanding microbial communities. A new computational method, CHERRY, significantly improves host prediction accuracy using integrated sequence features, outperforming existing tools.

Keywords:

deep learning graph convolutional network link prediction phage host prediction

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Area of Science:

Microbiology
Bioinformatics
Computational Biology

Background:

Prokaryotic viruses (phages) are vital components of microbial ecosystems.
Accurate prediction of phage hosts is essential for understanding microbe-virus dynamics.
Experimental host prediction methods lag behind rapid phage sequencing.

Purpose of the Study:

To develop a computational method for accurate prokaryotic virus host prediction.
To address the limitations of existing host prediction tools.

Main Methods:

Formulated host prediction as a link prediction problem within a knowledge graph.
Integrated multiple protein and DNA-based sequence features.
Developed and evaluated a computational implementation named CHERRY.

Main Results:

CHERRY achieved the highest accuracy in identifying virus-prokaryote interactions to date.
Demonstrated a 37% accuracy increase at the species level compared to state-of-the-art methods.
Showed more stable performance on short sequence contigs.

Conclusions:

CHERRY represents a significant advancement in computational phage host prediction.
The method is effective for predicting hosts of new viruses and identifying viruses infecting specific bacteria.
CHERRY offers a more accurate and robust solution for analyzing large-scale viral genomic data.