Predicting the antigenic future of influenza A: a computational approach
Each year, novel strains of influenza A viruses arise through mutation and manage to infect about one-fifth of the human population. We remain ignorant of what specific features determine the success or failure of these "antigenic drift" variants. As a result, we are left without a sound basis for understanding which influenza vaccine strains will best protect the human population against annual epidemics.
This proposal seeks to provide a rational basis for choosing annual vaccine strains by systematically quantifying influenza's antigenic evolution through computational methods. We will combine phylogenetic sequence analysis with computational structural biology to characterize, and possibly even predict, influenza's antigenic variation. The proposed research will help allow us to characterize the relevant viral phenotype: the structure of its primary antigen, called hemagglutinin. We aim to:
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Employ computational homology modeling to estimate three-dimensional protein structures for thousands of sequenced influenza A hemagglutinin variants.
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Characterize the antigenically relevant changes in structure throughout hemagglutinin's drift evolution, and analyze which structural features predict whether a strain will cause an epidemic or not.
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Characterize epistatic interactions among sites in the hemagglutinin protein, and thereby improve our ability to predict future strain variants.