Prediction, dynamics, and visualization of antigenic phenotypes of seasonal 
influenza viruses

Neher, RA; Bedford, T; Daniels, RS; Russell, CA; Shraiman, BI

doi:10.1073/pnas.1525578113

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Journal Article

Prediction, dynamics, and visualization of antigenic phenotypes of seasonal influenza viruses

MPS-Authors

/persons/resource/persons272322

Neher, RA
Research Group Evolutionary Dynamics and Biophysics, Max Planck Institute for Developmental Biology, Max Planck Society;

External Resource

No external resources are shared

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Neher, R., Bedford, T., Daniels, R., Russell, C., & Shraiman, B. (2016). Prediction, dynamics, and visualization of antigenic phenotypes of seasonal influenza viruses. Proceedings of the National Academy of Sciences of the United States of America, 113(12), E1701-E1709. doi:10.1073/pnas.1525578113.

Cite as: https://hdl.handle.net/21.11116/0000-000A-9411-B

Abstract

Human seasonal influenza viruses evolve rapidly, enabling the virus population to evade immunity and reinfect previously infected individuals. Antigenic properties are largely determined by the surface glycoprotein hemagglutinin (HA), and amino acid substitutions at exposed epitope sites in HA mediate loss of recognition by antibodies. Here, we show that antigenic differences measured through serological assay data are well described by a sum of antigenic changes along the path connecting viruses in a phylogenetic tree. This mapping onto the tree allows prediction of antigenicity from HA sequence data alone. The mapping can further be used to make predictions about the makeup of the future A(H3N2) seasonal influenza virus population, and we compare predictions between models with serological and sequence data. To make timely model output readily available, we developed a web browser-based application that visualizes antigenic data on a continuously updated phylogeny.