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Updated: Feb 6, 2026

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

Targeting Virus-host Protein Interactions: Feature Extraction and Machine Learning Approaches.

Nantao Zheng¹, Kairou Wang¹, Weihua Zhan²

¹School of Software, Central South University, Changsha, 410075, China.

Current Drug Metabolism

|August 30, 2018

Summary

Computational methods accelerate the identification of viral-host Protein-Protein Interactions (PPIs), crucial for developing new therapeutics. This review surveys these computational approaches for predicting virus-host PPIs.

Keywords:

Virus-host protein-protein interactions computational methods deep learning feature extraction feature representation machine learning.

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Last Updated: Feb 6, 2026

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

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Detecting Virus and Salivary Proteins of a Leafhopper Vector in the Plant Host

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Dissecting Host-virus Interaction in Lytic Replication of a Model Herpesvirus

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Published on: October 7, 2011

Area of Science:

Computational Biology
Bioinformatics
Drug Discovery

Background:

Viral-host Protein-Protein Interactions (PPIs) are critical therapeutic targets.
Experimental PPI identification is laborious and time-intensive.
Computational approaches offer efficient alternatives for studying virus-host PPIs and disease control.

Purpose of the Study:

To provide an overview of computational methods for predicting virus-host PPIs.
To categorize these methods based on features and machine learning algorithms.
To highlight the potential of computational approaches in understanding viral infections.

Main Methods:

Surveying diverse computational methods for virus-host PPI prediction.
Categorizing methods by utilized features (sequence, domain, motif, structure).
Analyzing classical and novel machine learning algorithms for binary classification models.

Main Results:

Key features for PPI prediction include sequence signatures, domain interactions, motifs, and protein structure.
State-of-the-art machine learning algorithms are employed for classifying virus-host protein pairs.
Discussion of the strengths, weaknesses, and future directions of these prediction models.

Conclusions:

Computational methods are vital for identifying potential virus-host PPIs.
Significant progress has been made in predicting virus-host PPIs.
Further improvements in computational prediction methods are needed.