College of Veterinary Medicine (CVM) Publications
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
Recommended Citation
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 (CVM) Publications. 20.
https://scholarsjunction.msstate.edu/cvm-publications/20
