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

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Abstract:

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.





Keywords: Amino acid; RNA; hemagglutinin; influenza; logistic regression; modelling; mutation; prediction; virus protein

Document Type: Research Article

DOI: http://dx.doi.org/10.2174/092986607779816032

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

Publication date: February 1, 2007

More about this publication?
  • Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.
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