Struct-NB: predicting protein-RNA binding sites using structural features
International Journal of Data Mining and Bioinformatics
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A promising method for flu treatment is small interfering RNAs siRNAs designed against conserved sequences in influenza virus. In this study, we developed a computational method for designing therapeutics siRNA and applied in the recent H1N1 influenza virus based on its biological characteristics that are substantially different from seasonal influenza virus. We first compared the PA fragments between the H1N1 virus in 2009 and the seasonal influenza virus genes in 2008, and the comparison found significant differences between them not only in sequence features but also in RNA secondary structures. In particular, the RNA secondary structures only share 76.8% identity, which suggests major changes in biological characteristics. Furthermore, we designed a metric of 'structure coefficient' to measure the effectiveness of siRNA based on the secondary structure of its target. Our study provides an improved approach for siRNA design and it may help developing siRNA-based therapeutics for seasonal epidemics and reoccurring pandemics of flu.