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Related Concept Videos

Leaky Scanning02:28

Leaky Scanning

During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R stands for...
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Influenza

Influenza is an acute, highly communicable viral disease that affects the respiratory tract and is responsible for seasonal epidemics worldwide. Influenza A is the most prevalent type associated with widespread outbreaks and is subtyped based on two surface glycoproteins: hemagglutinin (H) and neuraminidase (N), as in H1N1. These glycoproteins are essential for viral infectivity, transmission, and immune recognition. Transmission occurs primarily through respiratory droplets and contaminated...

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Related Experiment Video

Updated: Jun 29, 2026

Subnanometer-Resolution Structural Determination of Hemagglutinin from Cryo-Electron Tomography of Influenza Viruses
08:19

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Published on: November 7, 2025

CIST: A clade-informed sequence transformer framework for predicting influenza virus antigenicity.

Kang Hu1, Yongshan Zhu2, Qingchuan Zhang3

  • 1Information Center, National Institutes for Food and Drug Control, Beijing, China.

Scientific Reports
|June 27, 2026
PubMed
Summary
This summary is machine-generated.

A new deep learning framework, CIST, accurately predicts influenza virus antigenicity from hemagglutinin sequences. This computational approach aids in vaccine strain selection and antigenic surveillance, outperforming existing methods.

Keywords:
AntigenicityDeep learningHemagglutination inhibitionInfluenza virusNeutralizing antibody

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Area of Science:

  • Virology
  • Computational Biology
  • Immunology

Background:

  • Influenza viruses evolve rapidly, complicating vaccine development and interpretation of serological assays like neutralization (NT) and hemagglutination inhibition (HI).
  • Predicting influenza antigenicity from hemagglutinin (HA) sequences is crucial for timely interventions.

Purpose of the Study:

  • To develop and validate CIST, a clade-informed deep learning framework for predicting influenza antigenicity from HA sequences.
  • To assess CIST's performance against traditional methods in predicting NT and HI titers.

Main Methods:

  • CIST utilizes masked-language-model pre-training on extensive influenza A HA sequences.
  • It incorporates evolutionary clade embeddings and a dual-attention encoder for sequence representation.
  • The framework predicts both NT values and HI titers.

Main Results:

  • CIST significantly outperformed baseline machine learning and Transformer models in predicting NT and HI titers, with error reductions up to 51.7%.
  • High correlation coefficients (0.97 for NT, 0.82 for HI) indicate strong agreement between predicted and observed titers.
  • External validation on emerging strains demonstrated CIST's utility for antigenic surveillance.

Conclusions:

  • Clade-informed deep learning, as implemented in CIST, provides an accurate and interpretable method for assessing influenza antigenicity.
  • CIST offers a practical computational complement to laboratory assays for surveillance and vaccine evaluation.
  • The framework shows promise for predicting the antigenicity of emerging influenza strains.