ArticleScientific reports2016
Incorporating structure context of HA protein to improve antigenicity calculation for influenza virus A/H3N2.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Alignment-free prediction of cross-reactivity in influenza A (H3N2) anticipates antigenic drift.PLoS computational biology · 2026Article
- A CNN-Based Model of Cross-Immunity to Influenza A(H3N2) Virus: Testing Under "Real-World" Conditions.Viruses · 2026Article
- PREDAC-FluB: predicting antigenic clusters of seasonal influenza B viruses with protein language model embedding based convolutional neural network.Briefings in bioinformatics · 2025Article
- MetaFluAD: meta-learning for predicting antigenic distances among influenza viruses.Briefings in bioinformatics · 2024Article
- Article
- Antigenic characterization of influenza and SARS-CoV-2 viruses.Analytical and bioanalytical chemistry · 2022Review
- Univ-flu: A structure-based model of influenza A virus hemagglutinin for universal antigenic prediction.Computational and structural biotechnology journal · 2022Article
- Antigenicity prediction and vaccine recommendation of human influenza virus A (H3N2) using convolutional neural networks.Human vaccines & immunotherapeutics · 2020Article
- A benchmark dataset of protein antigens for antigenicity measurement.Scientific data · 2020Article
- Predicting Antigenicity of Influenza A Viruses Using biophysical ideas.Scientific reports · 2019Article
- A context-free encoding scheme of protein sequences for predicting antigenicity of diverse influenza A viruses.BMC genomics · 2018Article
- Antigenic cartography of H1N1 influenza viruses using sequence-based antigenic distance calculation.BMC bioinformatics · 2018Article
- Article
- Predicting antigenic variants of H1N1 influenza virus based on epidemics and pandemics using a stacking model.PloS one · 2018Article
- Predicting influenza antigenicity from Hemagglutintin sequence data based on a joint random forest method.Scientific reports · 2017Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The rapid and consistent mutation of influenza requires frequent evaluation of antigenicity variation among newly emerged strains, during which several in-silico methods have been reported to facilitate the assays. In this paper, we designed a structure-based antigenicity scoring model instead of those sequence-based previously published. Protein structural context was adopted to derive the antigenicity-dominant positions, as well as the physic-chemical change of local micro-environment in correlation with antigenicity change. Then a position specific scoring matrix (PSSM) profile and local environmental change over above positions were integrated to predict the antigenicity variance. Independent testing showed a high accuracy of 0.875, and sensitivity of 0.986, with a significant ability to discover antigenic-escaping strains. When applying this model to the historical data, global and regional antigenic drift events can be successfully detected. Furthermore, two well-known vaccine failure events were clearly suggested. Therefore, this structure-context model may be particularly useful to identify those to-be-failed vaccine strains, in addition to suggest potential new vaccine strains.
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