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In silico prediction of drug resistance due to S247R mutation of Influenza H1N1 neuraminidase protein

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  • In silico prediction of drug resistance due to S247R mutation of Influenza H1N1 neuraminidase protein

    J Biomol Struct Dyn. 2017 Mar 9:1-43. doi: 10.1080/07391102.2017.1305295. [Epub ahead of print]
    In silico prediction of drug resistance due to S247R mutation of Influenza H1N1 neuraminidase protein.

    Mandal RS1, Panda S2, Das S1.
    Author information

    Abstract

    We present here in silico studies on antiviral drug resistance due to a novel mutation of influenza A/H1N1 neuraminidase (NA) protein. Influenza A/H1N1 virus was responsible for a recent pandemic and is currently circulating among the seasonal influenza strains. M2 and NA are the two major viral proteins related to pathogenesis in humans and have been targeted for drug designing. Among them, NA is preferred because the ligand-binding site of NA is highly conserved between different strains of influenza virus. Different mutations of the NA active site residues leading to drug resistance or susceptibility of the virus were studied earlier. We report here a novel mutation (S247R) in the NA protein that was sequenced earlier from the nasopharyngeal swab from Sri Lanka and Thailand in the year 2009 and 2011, respectively. Another mutation (S247N) was already known to confer resistance to oseltamivir. We did a comparative study of these two mutations vis-s-vis the drug-sensitive wild type NA to understand the mechanism of drug resistance of S247N and to predict the probability of the novel S247R mutation to become resistant to the currently available drugs, oseltamivir and zanamivir. We performed molecular docking- and molecular dynamics-based analysis of both the mutant proteins and showed that mutation of S247R affects drug binding to the protein by positional displacement due to altered active site cavity architecture, which in turn reduces the affinity of the drug molecules to the NA active site. Our analysis shows that S247R may have high probability of being resistant.


    KEYWORDS:

    H1N1; drug-resistance; homology modeling; molecular docking; molecular dynamics simulation; mutation

    PMID: 28279127 DOI: 10.1080/07391102.2017.1305295
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