ArticlePLoS pathogens2016
Identification of Low- and High-Impact Hemagglutinin Amino Acid Substitutions That Drive Antigenic Drift of Influenza A(H1N1) Viruses.
Article in PLoS pathogens, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The molecular basis of antigenic variation among A(H9N2) avian influenza viruses.Emerging microbes & infections · 2018Pooled it
- Heterologous prime-boost vaccination against H5 avian influenza: Safety and immunogenicity of a MF59-adjuvanted, cell-culture derived H5N6 vaccine.Human vaccines & immunotherapeutics · 2026Trial
- Convergent evolution of the N156K mutation in A(H1N1)pdm09 hemagglutinin contributes to antigenic drift and cluster transition.Emerging microbes & infections · 2026Article
- CAPYBARA: A generalizable framework for predicting serological measurements across human cohorts.PLoS computational biology · 2026Article
- High-throughput pseudovirus neutralisation maps the antigenic landscape of influenza A/H1N1 viruses.EBioMedicine · 2025Article
- Immunodominant antibodies against hemagglutinin in ferrets following infection with 2018 to 2019 influenza vaccine strains.NPJ vaccines · 2025Article
- Different antigenic distance metrics generate similar predictions of influenza vaccine response breadth despite moderate correlation.PLoS computational biology · 2025Article
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- Article
- Topolow: a mapping algorithm for antigenic cross-reactivity and binding affinity assays.Bioinformatics (Oxford, England) · 2025Article
- Antigenic drift expands influenza viral escape pathways from recalled humoral immunity.Immunity · 2025Article
- Leveraging large language models to predict antibody biological activity against influenza A hemagglutinin.Computational and structural biotechnology journal · 2025Article
- Forecasting framework for dominant SARS-CoV-2 strains before clade replacement using phylogeny-informed genetic distances.Frontiers in microbiology · 2025Article
- Seasonal antigenic prediction of influenza A H3N2 using machine learning.Nature communications · 2024Article
- An atlas of continuous adaptive evolution in endemic human viruses.Cell host & microbe · 2023Article
- Development of PREDAC-H1pdm to model the antigenic evolution of influenza A/(H1N1) pdm09 viruses.Virologica Sinica · 2023Article
- A Bayesian approach to incorporate structural data into the mapping of genotype to antigenic phenotype of influenza A(H3N2) viruses.PLoS computational biology · 2023Article
- Antigenic characterization of influenza and SARS-CoV-2 viruses.Analytical and bioanalytical chemistry · 2022Review
- Antigenic Evolution Characteristics and Immunological Evaluation of H9N2 Avian Influenza Viruses from 1994-2019 in China.Viruses · 2022Article
- Librator: a platform for the optimized analysis, design, and expression of mutable influenza viral antigens.Briefings in bioinformatics · 2022Article
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Authors and funding
10 authors.
Funding
Abstract
Determining phenotype from genetic data is a fundamental challenge. Identification of emerging antigenic variants among circulating influenza viruses is critical to the vaccine virus selection process, with vaccine effectiveness maximized when constituents are antigenically similar to circulating viruses. Hemagglutination inhibition (HI) assay data are commonly used to assess influenza antigenicity. Here, sequence and 3-D structural information of hemagglutinin (HA) glycoproteins were analyzed together with corresponding HI assay data for former seasonal influenza A(H1N1) virus isolates (1997-2009) and reference viruses. The models developed identify and quantify the impact of eighteen amino acid substitutions on the antigenicity of HA, two of which were responsible for major transitions in antigenic phenotype. We used reverse genetics to demonstrate the causal effect on antigenicity for a subset of these substitutions. Information on the impact of substitutions allowed us to predict antigenic phenotypes of emerging viruses directly from HA gene sequence data and accuracy was doubled by including all substitutions causing antigenic changes over a model incorporating only the substitutions with the largest impact. The ability to quantify the phenotypic impact of specific amino acid substitutions should help refine emerging techniques that predict the evolution of virus populations from one year to the next, leading to stronger theoretical foundations for selection of candidate vaccine viruses. These techniques have great potential to be extended to other antigenically variable pathogens.
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