Bioinformatics models for predicting antigenic variants of influenza A/H3N2 virus.

Abstract

Continual and accumulated mutations in hemagglutinin (HA) protein of influenza A virus generate novel antigenic strains that cause annual epidemics. We propose a model by incorporating scoring and regression methods to predict antigenic variants. Based on collected sequences of influenza A/H3N2 viruses isolated between 1971 and 2002, our model can be used to accurately predict the antigenic variants in 1999-2004 (agreement rate = 91.67%). Twenty amino acid positions identified in our model contribute significantly to antigenic difference and are potential immunodominant positions.