Abstract

Rapid identification of influenza antigenic variants will be critical in selecting optimal vaccine candidates and thus a key to developing an effective vaccination program. Recent studies suggest that multiple simultaneous mutations at antigenic sites accumulatively enhance antigenic drift of influenza A viruses. However, pre-existing methods on antigenic variant identification are based on analyses from individual sites. Because the impacts of these co-evolved sites on influenza antigenicity may not be additive, it will be critical to quantify the impact of not only those single mutations but also multiple simultaneous mutations or co-evolved sites. Here, we developed and applied a computational method, AntigenCO, to identify and quantify both single and co-evolutionary sites driving the historical antigenic drifts. AntigenCO achieved an accuracy of up to 90.05% for antigenic variant prediction, significantly outperforming methods based on single sites. AntigenCO can be useful in antigenic variant identification in influenza surveillance.

Publisher

Public Library of Science

DOI

10.1371/journal.pone.0106660

Publication Date

9-4-2014

College

College of Veterinary Medicine

Department

Department of Basic Sciences

Keywords

Antigenic Variation, Antigenic Variation: genetics, Antigens, Computational Biology, Computational Biology: methods, Influenza A Virus, Influenza A virus: genetics, Influenza A virus: immunology, Viral, Viral: genetics

Disciplines

Veterinary Medicine

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