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.
Public Library of Science
College of Veterinary Medicine
Department of Basic Sciences
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
Yang, Jialiang; Zhang, Tong; and Wan, Xiu-Feng, "Sequence-based antigenic change prediction by a sparse learning method incorporating co-evolutionary information." (2014). College of Veterinary Medicine Publications and Scholarship. 20.