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Improvement of Model for Prediction of Hemagglutinin Mutations in H5N1 Influenza Viruses with Distinguishing of Arginine, Leucine and Serine

[ Vol. 14 , Issue. 2 ]


Guang Wu and Shaomin Yan   Pages 191 - 196 ( 6 )


In a continuation of our attempt to predict mutations in proteins from influenza A virus, this study attempted to answer the question of whether distinguishing between arginine, leucine and serine can improve the predictability as these residues are governed by different probabilistic mechanism translating from RNA codons to amino acids. In this study, we made the prediction based on the mutation relation among 299 H5N1 hemagglutinins of influenza A virus. Then, we compared the results based on the distinguishing of arginine, leucine and serine with the results without distinguishing of arginine, leucine and serine. The results show that the prediction together with distinguishing between arginine, leucine and serine is better than prediction without distinguishing between these residues.


Amino acid, logistic regression, hemagglutinin, influenza, modelling, mutation, prediction, RNA, virus protein


Computational Mutation Project, DreamSciTech Consulting, 301, Building 12, Nanyou A-zone,Jiannan Road, Shenzhen, Guangdong Province CN-518054, China.

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