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Human Virome

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The human body harbors a vast and diverse viral community known as the human virome. The virome includes bacteriophages that infect bacteria, and eukaryotic viruses that infect human cells. Transient dietary and environmental viruses also contribute to this dynamic ecosystem. Estimates suggest the human body may contain on the order of 10¹³ viral particles, though abundance varies widely by body site and detection method.Comprehensive characterization of the virome has become possible only with...

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...

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Related Experiment Video

Updated: May 26, 2026

Subnanometer-Resolution Structural Determination of Hemagglutinin from Cryo-Electron Tomography of Influenza Viruses

Subnanometer-Resolution Structural Determination of Hemagglutinin from Cryo-Electron Tomography of Influenza Viruses

Published on: November 7, 2025

HVIface: sequence-based deep learning for decoding human-virus protein-protein interfaces.

Krishna Kant Gupta^1,2, Monty Vijayvargiya^1,3, Geetha Paul⁴

¹Laboratory of Macromolecular Assemblies and Dynamics, Lab 02 New Building, National Center for Cell Science, Pune, India.

Frontiers in Bioinformatics

|May 25, 2026

Summary

This summary is machine-generated.

A new deep learning framework, HVIface, accurately predicts human-virus protein-protein interaction sites. It reveals that electrostatic complementarity and residue clustering drive viral targeting strategies for therapeutic development.

Keywords:

ANN Nup62 deep-learning integrase network analysis protein-protein interactions

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Published on: November 7, 2025

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Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

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Published on: July 2, 2016

Area of Science:

Virology
Structural Biology
Computational Biology

Background:

Human-virus protein-protein interactions (PPIs) are crucial for viral pathogenesis.
Understanding the physicochemical basis of these interactions is key for therapeutic development.
Current methods for predicting viral-host interfaces have limitations.

Purpose of the Study:

To develop a deep learning framework, HVIface, for predicting human-virus PPI interfaces at the residue level.
To identify key physicochemical determinants governing viral-host recognition.
To provide mechanistic insights into viral hijacking strategies.

Main Methods:

Trained HVIface on 73 structurally resolved human-virus complexes.
Integrated 18 sequence-derived features, including contact potentials and co-evolutionary signals.
Evaluated performance against existing methods (PIPENN, PeSTo) on an independent test set.

Main Results:

HVIface achieved 0.85 accuracy, outperforming existing methods.
Environmentally weighted charge compatibility and co-evolution-derived contact potentials were identified as key features.
Case studies showed viruses target solvent-accessible, electrostatically favorable host regions.

Conclusions:

Electrostatic complementarity and residue clustering are central to viral-host interface formation.
HVIface offers a robust predictive framework and mechanistic insights.
Findings support the development of targeted antiviral therapies.