Optimization of the Predicting of the Influenza Vaccine Strains

Abstract

 Relevance. Vaccination is still the most effective way to reduce the incidence and mortality from influenza and the complications it causes. WHO recommends the composition of the vaccine strain for each influenza season. Unfortunately, the relevance of vaccines and strains of influenza virus circulating during the epidemic season cannot always coincide. The cause is flu variability.Aim is to develop a new computational method for predicting an optimal hemagglutinin (HA) structure in H1N1 and H3N2 human influenza vaccine strains for coming epidemic seasons and to compare its results with WHO recommendations.Materials and method. For this study HA sequences were used from data bases available in INTERNET and the modified hidden Markov model was used to construct the HA primary structures.Results. It was indicated that the new bioinformatics approach allowed to construct an optimal structure of HA for vaccine strains. It was at most close to HA of circulating virus strains in coming epidemic seasons, spreaded over them and was superior to WHO recommendations. Conclusion: HA sequences should be considered as reliable background for predicting vaccine strains to decrease risks of not optimal and even mistakable choices. Bioinformatics approach allows to continually monitor HA changes after epidemics and to estimate adequacy of manufacturing vaccines to the future epidemic season.