Computational approach for predicting the conserved B-cell epitopes of hemagglutinin H7 subtype influenza virus

An avian-origin influenza H7N9 virus epidemic occurred in China in 2013-2014, in which >422 infected people suffered from pneumonia, respiratory distress syndrome and septic shock. H7N9 viruses belong to the H7 subtype of avian-origin influenza viruses (AIV-H7). Hemagglutinin (HA) is a vital membrane protein of AIV that has an important role in host recognition and infection. The epitopes of HA are significant determinants of the regularity of epidemic and viral mutation and recombination mechanisms. The present study aimed to predict the conserved B-cell epitopes of AIV-H7 HA using a bioinformatics approach, including the three most effective epitope prediction softwares available online: Artificial Neural Network based B-cell Epitope Prediction (ABCpred), B-cell Epitope Prediction (BepiPred) and Linear B-cell Epitope Prediction (LBtope). A total of 24 strains of Euro-Asiatic AIV-H7 that had been associated with a serious poultry pandemic or had infected humans in the past 30 years were selected to identify the conserved regions of HA. Sequences were obtained from the National Center for Biotechnology Information and Global Initiative on Sharing Avian Influenza Data databases. Using a combination of software prediction and sequence comparisons, the conserved epitopes of AIV-H7 were predicted and clarified. A total of five conserved epitopes [amino acids (aa) 37-52, 131-142, 215-234, 465-484 and 487-505] with a suitable length, high antigenicity and minimal variation were predicted and confirmed. Each obtained a score of >0.80 in ABCpred, 60% in LBtope and a level of 0.35 in Bepipred. In addition, a representative amino acid change (glutamine(235)-to-leucine(235)) in the HA protein of the 2013 AIV-H7N9 was discovered. The strategy adopted in the present study may have profound implications on the rapid diagnosis and control of infectious disease caused by H7N9 viruses, as well as by other virulent viruses, such as the Ebola virus.