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Related Experiment Video

Updated: Sep 29, 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

Sequence Matching between Hemagglutinin and Neuraminidase through Sequence Analysis Using Machine Learning.

He Wang¹, Yongjian Zang¹, Yizhen Zhao¹

¹MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, School of Physics, Xi'an Jiaotong University, Xi'an 710049, China.

|March 26, 2022

Summary

This summary is machine-generated.

Hemagglutinin (HA) and neuraminidase (NA) proteins coevolve at the sequence level, influencing influenza virus (IAV) evolution. A novel sequence-to-sequence model reveals this relationship, aiding in understanding viral transmission.

Keywords:

hemagglutinin influenza A viruses machine learning neuraminidase sequence analysis viral evolution

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Expression of Functional Recombinant Hemagglutinin and Neuraminidase Proteins from the Novel H7N9 Influenza Virus Using the Baculovirus Expression System

Expression of Functional Recombinant Hemagglutinin and Neuraminidase Proteins from the Novel H7N9 Influenza Virus Using the Baculovirus Expression System

Published on: November 6, 2013

Area of Science:

Virology
Bioinformatics
Machine Learning

Background:

The functional balance between hemagglutinin (HA) and neuraminidase (NA) is vital for influenza A virus (IAV) mobility, production, and transmission.
Understanding the sequence-level mechanisms governing HA-NA balance is crucial but remains under-investigated.

Purpose of the Study:

To investigate the coevolutionary relationship between HA and NA sequences in IAVs.
To develop a machine learning model for predicting HA-NA sequence relationships.

Main Methods:

Analysis of thousands of HA and NA sequences from A/H1N1 and A/H3N2 using principal component analysis and hierarchical clustering.
Development of a sequence-to-sequence transformer model (S2STM) with an encoder-decoder architecture and multi-head attention.

Main Results:

Significant coevolution between HA and NA sequences was identified, linked to host species and epidemic year.
The S2STM model successfully demonstrated the ability to "translate" between HA and NA sequences, establishing a relationship network.
The study integrated unsupervised and supervised machine learning for sequence matching analysis.

Conclusions:

Coevolution at the sequence level is a key factor in HA-NA functional balance and IAV evolution.
The S2STM offers a novel approach for analyzing sequence relationships and understanding viral evolution.
This work provides insights into IAV evolution and proposes new sequence analysis methodologies.