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Related Experiment Video

Updated: Jul 4, 2025

Quantitative Analyses of all Influenza Type A Viral Hemagglutinins and Neuraminidases using Universal Antibodies in Simple Slot Blot Assays

Quantitative Analyses of all Influenza Type A Viral Hemagglutinins and Neuraminidases using Universal Antibodies in Simple Slot Blot Assays

Published on: April 4, 2011

A sequence-based machine learning model for predicting antigenic distance for H3N2 influenza virus.

Xingyi Li^1,2, Yanyan Li^1,2, Xuequn Shang^1,2

¹School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China.

Frontiers in Microbiology

|February 5, 2024

Summary

A new computational method accurately predicts influenza A H3N2 virus antigenicity and evolution. This approach aids in identifying antigenic variants and selecting effective vaccine candidates, improving vaccine efficacy against constantly changing viruses.

Keywords:

antigenic distances antigenic drift antigenic variants influenza A H3N2 virus virus antigenicity prediction

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Last Updated: Jul 4, 2025

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Area of Science:

Virology
Immunology
Computational Biology

Background:

Influenza A H3N2 viruses evolve rapidly, necessitating frequent vaccine updates by the World Health Organization (WHO).
Traditional serological methods for assessing viral antigenicity are time-consuming and labor-intensive.
Existing computational models often lack robust quantitative links to viral sequences or focus on limited features.

Purpose of the Study:

To develop a novel computational method for predicting antigenic distances of H3N2 viruses.
To improve the accuracy and efficiency of determining viral antigenicity and antigenic drift.

Main Methods:

Integration of viral sequence attributes with four distinct feature categories influencing antigenicity.
Development of a computational model for predicting antigenic distances.

Main Results:

The proposed method demonstrates low prediction error for virus antigenicity.
Superior accuracy was achieved in identifying antigenic drift.
Analysis revealed 21 major antigenic clusters of H3N2 viruses from 1968 to 2022.
The predicted antigenic map closely matched serological data.

Conclusions:

The novel computational method is a promising tool for detecting antigenic variants.
This approach can effectively guide the selection of vaccine candidates.
The method offers an efficient alternative to traditional serological assessments for H3N2 surveillance.